johndpope · March 11, 2024 21:07
diff --git a/diffuser wizard prompt b/diffuser wizard prompt
 As the PyTorch , your role is to provide expert assistance on all things related to PyTorch, the open-source machine learning library. You are equipped to handle a wide range of queries, from basic introductory questions about PyTorch's functionalities to more complex topics like model optimization, troubleshooting, and implementation of advanced features. Your responses should always be clear, concise, and accurate, tailored to the user's level of expertise. When faced with unclear or incomplete queries, politely request additional information to ensure you provide the most helpful guidance. Your focus should remain strictly on PyTorch-related topics, avoiding advice on unrelated subjects. Maintain a professional yet accessible tone, simplifying complex concepts for users with different levels of understanding in PyTorch and machine learning. you recognize the related / relevant code building blocks and provide clarity to end user when relevant. any pytorch model should include assertions where relevant. any code created from white - add comments - describe intention of neural network block. the attached models.txt maybe used in code to subclass from. the pipelines can be used / referenced to implement diffusion pipelines. suggest reviewing diffusers https://github.com/huggingface/diffusers to look at appropriate models.

 when generating training code in - use slurm - refer to train.md sample
diff --git a/models.txt b/models.txt

 from ..utils import deprecate
 from .transformers.transformer_temporal import (
    TransformerSpatioTemporalModel,
    TransformerTemporalModel,
    TransformerTemporalModelOutput,
 )


 class TransformerTemporalModelOutput(TransformerTemporalModelOutput):
    deprecation_message = "Importing `TransformerTemporalModelOutput` from `diffusers.models.transformer_temporal` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.tranformer_temporal import TransformerTemporalModelOutput`, instead."
    deprecate("TransformerTemporalModelOutput", "0.29", deprecation_message)


 class TransformerTemporalModel(TransformerTemporalModel):
    deprecation_message = "Importing `TransformerTemporalModel` from `diffusers.models.transformer_temporal` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.tranformer_temporal import TransformerTemporalModel`, instead."
    deprecate("TransformerTemporalModel", "0.29", deprecation_message)


 class TransformerSpatioTemporalModel(TransformerSpatioTemporalModel):
    deprecation_message = "Importing `TransformerSpatioTemporalModel` from `diffusers.models.transformer_temporal` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.tranformer_temporal import TransformerSpatioTemporalModel`, instead."
    deprecate("TransformerTemporalModelOutput", "0.29", deprecation_message)
 from dataclasses import dataclass

 from ..utils import BaseOutput


 @dataclass
 class AutoencoderKLOutput(BaseOutput):
    """
    Output of AutoencoderKL encoding method.

    Args:
        latent_dist (`DiagonalGaussianDistribution`):
            Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
            `DiagonalGaussianDistribution` allows for sampling latents from the distribution.
    """

    latent_dist: "DiagonalGaussianDistribution"  # noqa: F821
 # coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ PyTorch - Flax general utilities."""
 import re

 import jax.numpy as jnp
 from flax.traverse_util import flatten_dict, unflatten_dict
 from jax.random import PRNGKey

 from ..utils import logging


 logger = logging.get_logger(__name__)


 def rename_key(key):
    regex = r"\w+[.]\d+"
    pats = re.findall(regex, key)
    for pat in pats:
        key = key.replace(pat, "_".join(pat.split(".")))
    return key


 #####################
 # PyTorch => Flax #
 #####################


 # Adapted from https://github.com/huggingface/transformers/blob/c603c80f46881ae18b2ca50770ef65fa4033eacd/src/transformers/modeling_flax_pytorch_utils.py#L69
 # and https://github.com/patil-suraj/stable-diffusion-jax/blob/main/stable_diffusion_jax/convert_diffusers_to_jax.py
 def rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict):
    """Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary"""
    # conv norm or layer norm
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)

    # rename attention layers
    if len(pt_tuple_key) > 1:
        for rename_from, rename_to in (
            ("to_out_0", "proj_attn"),
            ("to_k", "key"),
            ("to_v", "value"),
            ("to_q", "query"),
        ):
            if pt_tuple_key[-2] == rename_from:
                weight_name = pt_tuple_key[-1]
                weight_name = "kernel" if weight_name == "weight" else weight_name
                renamed_pt_tuple_key = pt_tuple_key[:-2] + (rename_to, weight_name)
                if renamed_pt_tuple_key in random_flax_state_dict:
                    assert random_flax_state_dict[renamed_pt_tuple_key].shape == pt_tensor.T.shape
                    return renamed_pt_tuple_key, pt_tensor.T

    if (
        any("norm" in str_ for str_ in pt_tuple_key)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        pt_tuple_key = pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        pt_tensor = pt_tensor.transpose(2, 3, 1, 0)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight":
        pt_tensor = pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor


 def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model, init_key=42):
    # Step 1: Convert pytorch tensor to numpy
    pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    random_flax_params = flax_model.init_weights(PRNGKey(init_key))

    random_flax_state_dict = flatten_dict(random_flax_params)
    flax_state_dict = {}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        renamed_pt_key = rename_key(pt_key)
        pt_tuple_key = tuple(renamed_pt_key.split("."))

        # Correctly rename weight parameters
        flax_key, flax_tensor = rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                    f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
                )

        # also add unexpected weight so that warning is thrown
        flax_state_dict[flax_key] = jnp.asarray(flax_tensor)

    return unflatten_dict(flax_state_dict)


 from typing import TYPE_CHECKING

 from ..utils import (
    DIFFUSERS_SLOW_IMPORT,
    _LazyModule,
    is_flax_available,
    is_torch_available,
 )


 _import_structure = {}

 if is_torch_available():
    _import_structure["adapter"] = ["MultiAdapter", "T2IAdapter"]
    _import_structure["autoencoders.autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
    _import_structure["autoencoders.autoencoder_kl"] = ["AutoencoderKL"]
    _import_structure["autoencoders.autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
    _import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
    _import_structure["autoencoders.consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
    _import_structure["controlnet"] = ["ControlNetModel"]
    _import_structure["dual_transformer_2d"] = ["DualTransformer2DModel"]
    _import_structure["embeddings"] = ["ImageProjection"]
    _import_structure["modeling_utils"] = ["ModelMixin"]
    _import_structure["transformers.prior_transformer"] = ["PriorTransformer"]
    _import_structure["transformers.t5_film_transformer"] = ["T5FilmDecoder"]
    _import_structure["transformers.transformer_2d"] = ["Transformer2DModel"]
    _import_structure["transformers.transformer_temporal"] = ["TransformerTemporalModel"]
    _import_structure["unets.unet_1d"] = ["UNet1DModel"]
    _import_structure["unets.unet_2d"] = ["UNet2DModel"]
    _import_structure["unets.unet_2d_condition"] = ["UNet2DConditionModel"]
    _import_structure["unets.unet_3d_condition"] = ["UNet3DConditionModel"]
    _import_structure["unets.unet_i2vgen_xl"] = ["I2VGenXLUNet"]
    _import_structure["unets.unet_kandinsky3"] = ["Kandinsky3UNet"]
    _import_structure["unets.unet_motion_model"] = ["MotionAdapter", "UNetMotionModel"]
    _import_structure["unets.unet_spatio_temporal_condition"] = ["UNetSpatioTemporalConditionModel"]
    _import_structure["unets.unet_stable_cascade"] = ["StableCascadeUNet"]
    _import_structure["unets.uvit_2d"] = ["UVit2DModel"]
    _import_structure["vq_model"] = ["VQModel"]

 if is_flax_available():
    _import_structure["controlnet_flax"] = ["FlaxControlNetModel"]
    _import_structure["unets.unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
    _import_structure["vae_flax"] = ["FlaxAutoencoderKL"]


 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
    if is_torch_available():
        from .adapter import MultiAdapter, T2IAdapter
        from .autoencoders import (
            AsymmetricAutoencoderKL,
            AutoencoderKL,
            AutoencoderKLTemporalDecoder,
            AutoencoderTiny,
            ConsistencyDecoderVAE,
        )
        from .controlnet import ControlNetModel
        from .embeddings import ImageProjection
        from .modeling_utils import ModelMixin
        from .transformers import (
            DualTransformer2DModel,
            PriorTransformer,
            T5FilmDecoder,
            Transformer2DModel,
            TransformerTemporalModel,
        )
        from .unets import (
            I2VGenXLUNet,
            Kandinsky3UNet,
            MotionAdapter,
            StableCascadeUNet,
            UNet1DModel,
            UNet2DConditionModel,
            UNet2DModel,
            UNet3DConditionModel,
            UNetMotionModel,
            UNetSpatioTemporalConditionModel,
            UVit2DModel,
        )
        from .vq_model import VQModel

    if is_flax_available():
        from .controlnet_flax import FlaxControlNetModel
        from .unets import FlaxUNet2DConditionModel
        from .vae_flax import FlaxAutoencoderKL

 else:
    import sys

    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

 from ..utils import deprecate
 from .transformers.transformer_2d import Transformer2DModel, Transformer2DModelOutput


 class Transformer2DModelOutput(Transformer2DModelOutput):
    deprecation_message = "Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.transformer_2d import Transformer2DModelOutput`, instead."
    deprecate("Transformer2DModelOutput", "0.29", deprecation_message)


 class Transformer2DModel(Transformer2DModel):
    deprecation_message = "Importing `Transformer2DModel` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.transformer_2d import Transformer2DModel`, instead."
    deprecate("Transformer2DModel", "0.29", deprecation_message)

 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Tuple, Union

 import torch
 from torch import nn
 from torch.nn import functional as F

 from ..configuration_utils import ConfigMixin, register_to_config
 from ..loaders import FromOriginalControlNetMixin
 from ..utils import BaseOutput, logging
 from .attention_processor import (
    ADDED_KV_ATTENTION_PROCESSORS,
    CROSS_ATTENTION_PROCESSORS,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor,
 )
 from .embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
 from .modeling_utils import ModelMixin
 from .unets.unet_2d_blocks import (
    CrossAttnDownBlock2D,
    DownBlock2D,
    UNetMidBlock2D,
    UNetMidBlock2DCrossAttn,
    get_down_block,
 )
 from .unets.unet_2d_condition import UNet2DConditionModel


 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


 @dataclass
 class ControlNetOutput(BaseOutput):
    """
    The output of [`ControlNetModel`].

    Args:
        down_block_res_samples (`tuple[torch.Tensor]`):
            A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
            be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
            used to condition the original UNet's downsampling activations.
        mid_down_block_re_sample (`torch.Tensor`):
            The activation of the midde block (the lowest sample resolution). Each tensor should be of shape
            `(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
            Output can be used to condition the original UNet's middle block activation.
    """

    down_block_res_samples: Tuple[torch.Tensor]
    mid_block_res_sample: torch.Tensor


 class ControlNetConditioningEmbedding(nn.Module):
    """
    Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
    [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
    training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
    convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
    (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
    model) to encode image-space conditions ... into feature maps ..."
    """

    def __init__(
        self,
        conditioning_embedding_channels: int,
        conditioning_channels: int = 3,
        block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
    ):
        super().__init__()

        self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)

        self.blocks = nn.ModuleList([])

        for i in range(len(block_out_channels) - 1):
            channel_in = block_out_channels[i]
            channel_out = block_out_channels[i + 1]
            self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
            self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))

        self.conv_out = zero_module(
            nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
        )

    def forward(self, conditioning):
        embedding = self.conv_in(conditioning)
        embedding = F.silu(embedding)

        for block in self.blocks:
            embedding = block(embedding)
            embedding = F.silu(embedding)

        embedding = self.conv_out(embedding)

        return embedding


 class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlNetMixin):
    """
    A ControlNet model.

    Args:
        in_channels (`int`, defaults to 4):
            The number of channels in the input sample.
        flip_sin_to_cos (`bool`, defaults to `True`):
            Whether to flip the sin to cos in the time embedding.
        freq_shift (`int`, defaults to 0):
            The frequency shift to apply to the time embedding.
        down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
            The tuple of downsample blocks to use.
        only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
        block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each block.
        layers_per_block (`int`, defaults to 2):
            The number of layers per block.
        downsample_padding (`int`, defaults to 1):
            The padding to use for the downsampling convolution.
        mid_block_scale_factor (`float`, defaults to 1):
            The scale factor to use for the mid block.
        act_fn (`str`, defaults to "silu"):
            The activation function to use.
        norm_num_groups (`int`, *optional*, defaults to 32):
            The number of groups to use for the normalization. If None, normalization and activation layers is skipped
            in post-processing.
        norm_eps (`float`, defaults to 1e-5):
            The epsilon to use for the normalization.
        cross_attention_dim (`int`, defaults to 1280):
            The dimension of the cross attention features.
        transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
        encoder_hid_dim (`int`, *optional*, defaults to None):
            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
            dimension to `cross_attention_dim`.
        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
        attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
            The dimension of the attention heads.
        use_linear_projection (`bool`, defaults to `False`):
        class_embed_type (`str`, *optional*, defaults to `None`):
            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
        addition_embed_type (`str`, *optional*, defaults to `None`):
            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
            "text". "text" will use the `TextTimeEmbedding` layer.
        num_class_embeds (`int`, *optional*, defaults to 0):
            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
            class conditioning with `class_embed_type` equal to `None`.
        upcast_attention (`bool`, defaults to `False`):
        resnet_time_scale_shift (`str`, defaults to `"default"`):
            Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
        projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
            The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
            `class_embed_type="projection"`.
        controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
            The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
        conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
            The tuple of output channel for each block in the `conditioning_embedding` layer.
        global_pool_conditions (`bool`, defaults to `False`):
            TODO(Patrick) - unused parameter.
        addition_embed_type_num_heads (`int`, defaults to 64):
            The number of heads to use for the `TextTimeEmbedding` layer.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        in_channels: int = 4,
        conditioning_channels: int = 3,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        down_block_types: Tuple[str, ...] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
        only_cross_attention: Union[bool, Tuple[bool]] = False,
        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
        layers_per_block: int = 2,
        downsample_padding: int = 1,
        mid_block_scale_factor: float = 1,
        act_fn: str = "silu",
        norm_num_groups: Optional[int] = 32,
        norm_eps: float = 1e-5,
        cross_attention_dim: int = 1280,
        transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
        encoder_hid_dim: Optional[int] = None,
        encoder_hid_dim_type: Optional[str] = None,
        attention_head_dim: Union[int, Tuple[int, ...]] = 8,
        num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
        use_linear_projection: bool = False,
        class_embed_type: Optional[str] = None,
        addition_embed_type: Optional[str] = None,
        addition_time_embed_dim: Optional[int] = None,
        num_class_embeds: Optional[int] = None,
        upcast_attention: bool = False,
        resnet_time_scale_shift: str = "default",
        projection_class_embeddings_input_dim: Optional[int] = None,
        controlnet_conditioning_channel_order: str = "rgb",
        conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
        global_pool_conditions: bool = False,
        addition_embed_type_num_heads: int = 64,
    ):
        super().__init__()

        # If `num_attention_heads` is not defined (which is the case for most models)
        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
        # The reason for this behavior is to correct for incorrectly named variables that were introduced
        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
        # which is why we correct for the naming here.
        num_attention_heads = num_attention_heads or attention_head_dim

        # Check inputs
        if len(block_out_channels) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
            )

        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)

        # input
        conv_in_kernel = 3
        conv_in_padding = (conv_in_kernel - 1) // 2
        self.conv_in = nn.Conv2d(
            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
        )

        # time
        time_embed_dim = block_out_channels[0] * 4
        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
        timestep_input_dim = block_out_channels[0]
        self.time_embedding = TimestepEmbedding(
            timestep_input_dim,
            time_embed_dim,
            act_fn=act_fn,
        )

        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
            encoder_hid_dim_type = "text_proj"
            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")

        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
            raise ValueError(
                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
            )

        if encoder_hid_dim_type == "text_proj":
            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
        elif encoder_hid_dim_type == "text_image_proj":
            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
            self.encoder_hid_proj = TextImageProjection(
                text_embed_dim=encoder_hid_dim,
                image_embed_dim=cross_attention_dim,
                cross_attention_dim=cross_attention_dim,
            )

        elif encoder_hid_dim_type is not None:
            raise ValueError(
                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
            )
        else:
            self.encoder_hid_proj = None

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
        elif class_embed_type == "timestep":
            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
        elif class_embed_type == "identity":
            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
        elif class_embed_type == "projection":
            if projection_class_embeddings_input_dim is None:
                raise ValueError(
                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
                )
            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
            # 2. it projects from an arbitrary input dimension.
            #
            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
        else:
            self.class_embedding = None

        if addition_embed_type == "text":
            if encoder_hid_dim is not None:
                text_time_embedding_from_dim = encoder_hid_dim
            else:
                text_time_embedding_from_dim = cross_attention_dim

            self.add_embedding = TextTimeEmbedding(
                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
            )
        elif addition_embed_type == "text_image":
            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
            self.add_embedding = TextImageTimeEmbedding(
                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
            )
        elif addition_embed_type == "text_time":
            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)

        elif addition_embed_type is not None:
            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")

        # control net conditioning embedding
        self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
            conditioning_embedding_channels=block_out_channels[0],
            block_out_channels=conditioning_embedding_out_channels,
            conditioning_channels=conditioning_channels,
        )

        self.down_blocks = nn.ModuleList([])
        self.controlnet_down_blocks = nn.ModuleList([])

        if isinstance(only_cross_attention, bool):
            only_cross_attention = [only_cross_attention] * len(down_block_types)

        if isinstance(attention_head_dim, int):
            attention_head_dim = (attention_head_dim,) * len(down_block_types)

        if isinstance(num_attention_heads, int):
            num_attention_heads = (num_attention_heads,) * len(down_block_types)

        # down
        output_channel = block_out_channels[0]

        controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
        controlnet_block = zero_module(controlnet_block)
        self.controlnet_down_blocks.append(controlnet_block)

        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block,
                transformer_layers_per_block=transformer_layers_per_block[i],
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=time_embed_dim,
                add_downsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=cross_attention_dim,
                num_attention_heads=num_attention_heads[i],
                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
                downsample_padding=downsample_padding,
                use_linear_projection=use_linear_projection,
                only_cross_attention=only_cross_attention[i],
                upcast_attention=upcast_attention,
                resnet_time_scale_shift=resnet_time_scale_shift,
            )
            self.down_blocks.append(down_block)

            for _ in range(layers_per_block):
                controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
                controlnet_block = zero_module(controlnet_block)
                self.controlnet_down_blocks.append(controlnet_block)

            if not is_final_block:
                controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
                controlnet_block = zero_module(controlnet_block)
                self.controlnet_down_blocks.append(controlnet_block)

        # mid
        mid_block_channel = block_out_channels[-1]

        controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
        controlnet_block = zero_module(controlnet_block)
        self.controlnet_mid_block = controlnet_block

        if mid_block_type == "UNetMidBlock2DCrossAttn":
            self.mid_block = UNetMidBlock2DCrossAttn(
                transformer_layers_per_block=transformer_layers_per_block[-1],
                in_channels=mid_block_channel,
                temb_channels=time_embed_dim,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                output_scale_factor=mid_block_scale_factor,
                resnet_time_scale_shift=resnet_time_scale_shift,
                cross_attention_dim=cross_attention_dim,
                num_attention_heads=num_attention_heads[-1],
                resnet_groups=norm_num_groups,
                use_linear_projection=use_linear_projection,
                upcast_attention=upcast_attention,
            )
        elif mid_block_type == "UNetMidBlock2D":
            self.mid_block = UNetMidBlock2D(
                in_channels=block_out_channels[-1],
                temb_channels=time_embed_dim,
                num_layers=0,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                output_scale_factor=mid_block_scale_factor,
                resnet_groups=norm_num_groups,
                resnet_time_scale_shift=resnet_time_scale_shift,
                add_attention=False,
            )
        else:
            raise ValueError(f"unknown mid_block_type : {mid_block_type}")

    @classmethod
    def from_unet(
        cls,
        unet: UNet2DConditionModel,
        controlnet_conditioning_channel_order: str = "rgb",
        conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
        load_weights_from_unet: bool = True,
        conditioning_channels: int = 3,
    ):
        r"""
        Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`].

        Parameters:
            unet (`UNet2DConditionModel`):
                The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied
                where applicable.
        """
        transformer_layers_per_block = (
            unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
        )
        encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None
        encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None
        addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None
        addition_time_embed_dim = (
            unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None
        )

        controlnet = cls(
            encoder_hid_dim=encoder_hid_dim,
            encoder_hid_dim_type=encoder_hid_dim_type,
            addition_embed_type=addition_embed_type,
            addition_time_embed_dim=addition_time_embed_dim,
            transformer_layers_per_block=transformer_layers_per_block,
            in_channels=unet.config.in_channels,
            flip_sin_to_cos=unet.config.flip_sin_to_cos,
            freq_shift=unet.config.freq_shift,
            down_block_types=unet.config.down_block_types,
            only_cross_attention=unet.config.only_cross_attention,
            block_out_channels=unet.config.block_out_channels,
            layers_per_block=unet.config.layers_per_block,
            downsample_padding=unet.config.downsample_padding,
            mid_block_scale_factor=unet.config.mid_block_scale_factor,
            act_fn=unet.config.act_fn,
            norm_num_groups=unet.config.norm_num_groups,
            norm_eps=unet.config.norm_eps,
            cross_attention_dim=unet.config.cross_attention_dim,
            attention_head_dim=unet.config.attention_head_dim,
            num_attention_heads=unet.config.num_attention_heads,
            use_linear_projection=unet.config.use_linear_projection,
            class_embed_type=unet.config.class_embed_type,
            num_class_embeds=unet.config.num_class_embeds,
            upcast_attention=unet.config.upcast_attention,
            resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
            projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
            mid_block_type=unet.config.mid_block_type,
            controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
            conditioning_embedding_out_channels=conditioning_embedding_out_channels,
            conditioning_channels=conditioning_channels,
        )

        if load_weights_from_unet:
            controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
            controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
            controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())

            if controlnet.class_embedding:
                controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())

            controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
            controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())

        return controlnet

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnAddedKVProcessor()
        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnProcessor()
        else:
            raise ValueError(
                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
            )

        self.set_attn_processor(processor)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
    def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
        r"""
        Enable sliced attention computation.

        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
        several steps. This is useful for saving some memory in exchange for a small decrease in speed.

        Args:
            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                must be a multiple of `slice_size`.
        """
        sliceable_head_dims = []

        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
            if hasattr(module, "set_attention_slice"):
                sliceable_head_dims.append(module.sliceable_head_dim)

            for child in module.children():
                fn_recursive_retrieve_sliceable_dims(child)

        # retrieve number of attention layers
        for module in self.children():
            fn_recursive_retrieve_sliceable_dims(module)

        num_sliceable_layers = len(sliceable_head_dims)

        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = [dim // 2 for dim in sliceable_head_dims]
        elif slice_size == "max":
            # make smallest slice possible
            slice_size = num_sliceable_layers * [1]

        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size

        if len(slice_size) != len(sliceable_head_dims):
            raise ValueError(
                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
            )

        for i in range(len(slice_size)):
            size = slice_size[i]
            dim = sliceable_head_dims[i]
            if size is not None and size > dim:
                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")

        # Recursively walk through all the children.
        # Any children which exposes the set_attention_slice method
        # gets the message
        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
            if hasattr(module, "set_attention_slice"):
                module.set_attention_slice(slice_size.pop())

            for child in module.children():
                fn_recursive_set_attention_slice(child, slice_size)

        reversed_slice_size = list(reversed(slice_size))
        for module in self.children():
            fn_recursive_set_attention_slice(module, reversed_slice_size)

    def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
        if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
            module.gradient_checkpointing = value

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        controlnet_cond: torch.FloatTensor,
        conditioning_scale: float = 1.0,
        class_labels: Optional[torch.Tensor] = None,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guess_mode: bool = False,
        return_dict: bool = True,
    ) -> Union[ControlNetOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]:
        """
        The [`ControlNetModel`] forward method.

        Args:
            sample (`torch.FloatTensor`):
                The noisy input tensor.
            timestep (`Union[torch.Tensor, float, int]`):
                The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.Tensor`):
                The encoder hidden states.
            controlnet_cond (`torch.FloatTensor`):
                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
            conditioning_scale (`float`, defaults to `1.0`):
                The scale factor for ControlNet outputs.
            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
            timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
                Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
                timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
                embeddings.
            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            added_cond_kwargs (`dict`):
                Additional conditions for the Stable Diffusion XL UNet.
            cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
            guess_mode (`bool`, defaults to `False`):
                In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
                you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
            return_dict (`bool`, defaults to `True`):
                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.

        Returns:
            [`~models.controlnet.ControlNetOutput`] **or** `tuple`:
                If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
                returned where the first element is the sample tensor.
        """
        # check channel order
        channel_order = self.config.controlnet_conditioning_channel_order

        if channel_order == "rgb":
            # in rgb order by default
            ...
        elif channel_order == "bgr":
            controlnet_cond = torch.flip(controlnet_cond, dims=[1])
        else:
            raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")

        # prepare attention_mask
        if attention_mask is not None:
            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            is_mps = sample.device.type == "mps"
            if isinstance(timestep, float):
                dtype = torch.float32 if is_mps else torch.float64
            else:
                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps.expand(sample.shape[0])

        t_emb = self.time_proj(timesteps)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=sample.dtype)

        emb = self.time_embedding(t_emb, timestep_cond)
        aug_emb = None

        if self.class_embedding is not None:
            if class_labels is None:
                raise ValueError("class_labels should be provided when num_class_embeds > 0")

            if self.config.class_embed_type == "timestep":
                class_labels = self.time_proj(class_labels)

            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
            emb = emb + class_emb

        if self.config.addition_embed_type is not None:
            if self.config.addition_embed_type == "text":
                aug_emb = self.add_embedding(encoder_hidden_states)

            elif self.config.addition_embed_type == "text_time":
                if "text_embeds" not in added_cond_kwargs:
                    raise ValueError(
                        f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
                    )
                text_embeds = added_cond_kwargs.get("text_embeds")
                if "time_ids" not in added_cond_kwargs:
                    raise ValueError(
                        f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
                    )
                time_ids = added_cond_kwargs.get("time_ids")
                time_embeds = self.add_time_proj(time_ids.flatten())
                time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))

                add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
                add_embeds = add_embeds.to(emb.dtype)
                aug_emb = self.add_embedding(add_embeds)

        emb = emb + aug_emb if aug_emb is not None else emb

        # 2. pre-process
        sample = self.conv_in(sample)

        controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
        sample = sample + controlnet_cond

        # 3. down
        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)

            down_block_res_samples += res_samples

        # 4. mid
        if self.mid_block is not None:
            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
                sample = self.mid_block(
                    sample,
                    emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                )
            else:
                sample = self.mid_block(sample, emb)

        # 5. Control net blocks

        controlnet_down_block_res_samples = ()

        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
            down_block_res_sample = controlnet_block(down_block_res_sample)
            controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)

        down_block_res_samples = controlnet_down_block_res_samples

        mid_block_res_sample = self.controlnet_mid_block(sample)

        # 6. scaling
        if guess_mode and not self.config.global_pool_conditions:
            scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0
            scales = scales * conditioning_scale
            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
            mid_block_res_sample = mid_block_res_sample * scales[-1]  # last one
        else:
            down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
            mid_block_res_sample = mid_block_res_sample * conditioning_scale

        if self.config.global_pool_conditions:
            down_block_res_samples = [
                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
            ]
            mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)

        if not return_dict:
            return (down_block_res_samples, mid_block_res_sample)

        return ControlNetOutput(
            down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
        )


 def zero_module(module):
    for p in module.parameters():
        nn.init.zeros_(p)
    return module

 import math

 import flax.linen as nn
 import jax.numpy as jnp


 def get_sinusoidal_embeddings(
    timesteps: jnp.ndarray,
    embedding_dim: int,
    freq_shift: float = 1,
    min_timescale: float = 1,
    max_timescale: float = 1.0e4,
    flip_sin_to_cos: bool = False,
    scale: float = 1.0,
 ) -> jnp.ndarray:
    """Returns the positional encoding (same as Tensor2Tensor).

    Args:
        timesteps: a 1-D Tensor of N indices, one per batch element.
        These may be fractional.
        embedding_dim: The number of output channels.
        min_timescale: The smallest time unit (should probably be 0.0).
        max_timescale: The largest time unit.
    Returns:
        a Tensor of timing signals [N, num_channels]
    """
    assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
    assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
    num_timescales = float(embedding_dim // 2)
    log_timescale_increment = math.log(max_timescale / min_timescale) / (num_timescales - freq_shift)
    inv_timescales = min_timescale * jnp.exp(jnp.arange(num_timescales, dtype=jnp.float32) * -log_timescale_increment)
    emb = jnp.expand_dims(timesteps, 1) * jnp.expand_dims(inv_timescales, 0)

    # scale embeddings
    scaled_time = scale * emb

    if flip_sin_to_cos:
        signal = jnp.concatenate([jnp.cos(scaled_time), jnp.sin(scaled_time)], axis=1)
    else:
        signal = jnp.concatenate([jnp.sin(scaled_time), jnp.cos(scaled_time)], axis=1)
    signal = jnp.reshape(signal, [jnp.shape(timesteps)[0], embedding_dim])
    return signal


 class FlaxTimestepEmbedding(nn.Module):
    r"""
    Time step Embedding Module. Learns embeddings for input time steps.

    Args:
        time_embed_dim (`int`, *optional*, defaults to `32`):
                Time step embedding dimension
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
                Parameters `dtype`
    """

    time_embed_dim: int = 32
    dtype: jnp.dtype = jnp.float32

    @nn.compact
    def __call__(self, temb):
        temb = nn.Dense(self.time_embed_dim, dtype=self.dtype, name="linear_1")(temb)
        temb = nn.silu(temb)
        temb = nn.Dense(self.time_embed_dim, dtype=self.dtype, name="linear_2")(temb)
        return temb


 class FlaxTimesteps(nn.Module):
    r"""
    Wrapper Module for sinusoidal Time step Embeddings as described in https://arxiv.org/abs/2006.11239

    Args:
        dim (`int`, *optional*, defaults to `32`):
                Time step embedding dimension
    """

    dim: int = 32
    flip_sin_to_cos: bool = False
    freq_shift: float = 1

    @nn.compact
    def __call__(self, timesteps):
        return get_sinusoidal_embeddings(
            timesteps, embedding_dim=self.dim, flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.freq_shift
        )

 from dataclasses import dataclass
 from typing import Any, Dict, Optional

 import torch
 from torch import nn

 from ...configuration_utils import ConfigMixin, register_to_config
 from ...utils import BaseOutput
 from ..attention import BasicTransformerBlock, TemporalBasicTransformerBlock
 from ..embeddings import TimestepEmbedding, Timesteps
 from ..modeling_utils import ModelMixin
 from ..resnet import AlphaBlender


 @dataclass
 class TransformerTemporalModelOutput(BaseOutput):
    """
    The output of [`TransformerTemporalModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`):
            The hidden states output conditioned on `encoder_hidden_states` input.
    """

    sample: torch.FloatTensor


 class TransformerTemporalModel(ModelMixin, ConfigMixin):
    """
    A Transformer model for video-like data.

    Parameters:
        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
        in_channels (`int`, *optional*):
            The number of channels in the input and output (specify if the input is **continuous**).
        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        attention_bias (`bool`, *optional*):
            Configure if the `TransformerBlock` attention should contain a bias parameter.
        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
            This is fixed during training since it is used to learn a number of position embeddings.
        activation_fn (`str`, *optional*, defaults to `"geglu"`):
            Activation function to use in feed-forward. See `diffusers.models.activations.get_activation` for supported
            activation functions.
        norm_elementwise_affine (`bool`, *optional*):
            Configure if the `TransformerBlock` should use learnable elementwise affine parameters for normalization.
        double_self_attention (`bool`, *optional*):
            Configure if each `TransformerBlock` should contain two self-attention layers.
        positional_embeddings: (`str`, *optional*):
            The type of positional embeddings to apply to the sequence input before passing use.
        num_positional_embeddings: (`int`, *optional*):
            The maximum length of the sequence over which to apply positional embeddings.
    """

    @register_to_config
    def __init__(
        self,
        num_attention_heads: int = 16,
        attention_head_dim: int = 88,
        in_channels: Optional[int] = None,
        out_channels: Optional[int] = None,
        num_layers: int = 1,
        dropout: float = 0.0,
        norm_num_groups: int = 32,
        cross_attention_dim: Optional[int] = None,
        attention_bias: bool = False,
        sample_size: Optional[int] = None,
        activation_fn: str = "geglu",
        norm_elementwise_affine: bool = True,
        double_self_attention: bool = True,
        positional_embeddings: Optional[str] = None,
        num_positional_embeddings: Optional[int] = None,
    ):
        super().__init__()
        self.num_attention_heads = num_attention_heads
        self.attention_head_dim = attention_head_dim
        inner_dim = num_attention_heads * attention_head_dim

        self.in_channels = in_channels

        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
        self.proj_in = nn.Linear(in_channels, inner_dim)

        # 3. Define transformers blocks
        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim,
                    num_attention_heads,
                    attention_head_dim,
                    dropout=dropout,
                    cross_attention_dim=cross_attention_dim,
                    activation_fn=activation_fn,
                    attention_bias=attention_bias,
                    double_self_attention=double_self_attention,
                    norm_elementwise_affine=norm_elementwise_affine,
                    positional_embeddings=positional_embeddings,
                    num_positional_embeddings=num_positional_embeddings,
                )
                for d in range(num_layers)
            ]
        )

        self.proj_out = nn.Linear(inner_dim, in_channels)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.LongTensor] = None,
        timestep: Optional[torch.LongTensor] = None,
        class_labels: torch.LongTensor = None,
        num_frames: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        return_dict: bool = True,
    ) -> TransformerTemporalModelOutput:
        """
        The [`TransformerTemporal`] forward method.

        Args:
            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
                Input hidden_states.
            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            timestep ( `torch.LongTensor`, *optional*):
                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
                `AdaLayerZeroNorm`.
            num_frames (`int`, *optional*, defaults to 1):
                The number of frames to be processed per batch. This is used to reshape the hidden states.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.

        Returns:
            [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
                returned, otherwise a `tuple` where the first element is the sample tensor.
        """
        # 1. Input
        batch_frames, channel, height, width = hidden_states.shape
        batch_size = batch_frames // num_frames

        residual = hidden_states

        hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, channel, height, width)
        hidden_states = hidden_states.permute(0, 2, 1, 3, 4)

        hidden_states = self.norm(hidden_states)
        hidden_states = hidden_states.permute(0, 3, 4, 2, 1).reshape(batch_size * height * width, num_frames, channel)

        hidden_states = self.proj_in(hidden_states)

        # 2. Blocks
        for block in self.transformer_blocks:
            hidden_states = block(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                timestep=timestep,
                cross_attention_kwargs=cross_attention_kwargs,
                class_labels=class_labels,
            )

        # 3. Output
        hidden_states = self.proj_out(hidden_states)
        hidden_states = (
            hidden_states[None, None, :]
            .reshape(batch_size, height, width, num_frames, channel)
            .permute(0, 3, 4, 1, 2)
            .contiguous()
        )
        hidden_states = hidden_states.reshape(batch_frames, channel, height, width)

        output = hidden_states + residual

        if not return_dict:
            return (output,)

        return TransformerTemporalModelOutput(sample=output)


 class TransformerSpatioTemporalModel(nn.Module):
    """
    A Transformer model for video-like data.

    Parameters:
        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
        in_channels (`int`, *optional*):
            The number of channels in the input and output (specify if the input is **continuous**).
        out_channels (`int`, *optional*):
            The number of channels in the output (specify if the input is **continuous**).
        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
    """

    def __init__(
        self,
        num_attention_heads: int = 16,
        attention_head_dim: int = 88,
        in_channels: int = 320,
        out_channels: Optional[int] = None,
        num_layers: int = 1,
        cross_attention_dim: Optional[int] = None,
    ):
        super().__init__()
        self.num_attention_heads = num_attention_heads
        self.attention_head_dim = attention_head_dim

        inner_dim = num_attention_heads * attention_head_dim
        self.inner_dim = inner_dim

        # 2. Define input layers
        self.in_channels = in_channels
        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6)
        self.proj_in = nn.Linear(in_channels, inner_dim)

        # 3. Define transformers blocks
        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim,
                    num_attention_heads,
                    attention_head_dim,
                    cross_attention_dim=cross_attention_dim,
                )
                for d in range(num_layers)
            ]
        )

        time_mix_inner_dim = inner_dim
        self.temporal_transformer_blocks = nn.ModuleList(
            [
                TemporalBasicTransformerBlock(
                    inner_dim,
                    time_mix_inner_dim,
                    num_attention_heads,
                    attention_head_dim,
                    cross_attention_dim=cross_attention_dim,
                )
                for _ in range(num_layers)
            ]
        )

        time_embed_dim = in_channels * 4
        self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels)
        self.time_proj = Timesteps(in_channels, True, 0)
        self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy="learned_with_images")

        # 4. Define output layers
        self.out_channels = in_channels if out_channels is None else out_channels
        # TODO: should use out_channels for continuous projections
        self.proj_out = nn.Linear(inner_dim, in_channels)

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        image_only_indicator: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
                Input hidden_states.
            num_frames (`int`):
                The number of frames to be processed per batch. This is used to reshape the hidden states.
            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            image_only_indicator (`torch.LongTensor` of shape `(batch size, num_frames)`, *optional*):
                A tensor indicating whether the input contains only images. 1 indicates that the input contains only
                images, 0 indicates that the input contains video frames.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.transformer_temporal.TransformerTemporalModelOutput`] instead of a plain
                tuple.

        Returns:
            [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
                returned, otherwise a `tuple` where the first element is the sample tensor.
        """
        # 1. Input
        batch_frames, _, height, width = hidden_states.shape
        num_frames = image_only_indicator.shape[-1]
        batch_size = batch_frames // num_frames

        time_context = encoder_hidden_states
        time_context_first_timestep = time_context[None, :].reshape(
            batch_size, num_frames, -1, time_context.shape[-1]
        )[:, 0]
        time_context = time_context_first_timestep[None, :].broadcast_to(
            height * width, batch_size, 1, time_context.shape[-1]
        )
        time_context = time_context.reshape(height * width * batch_size, 1, time_context.shape[-1])

        residual = hidden_states

        hidden_states = self.norm(hidden_states)
        inner_dim = hidden_states.shape[1]
        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_frames, height * width, inner_dim)
        hidden_states = self.proj_in(hidden_states)

        num_frames_emb = torch.arange(num_frames, device=hidden_states.device)
        num_frames_emb = num_frames_emb.repeat(batch_size, 1)
        num_frames_emb = num_frames_emb.reshape(-1)
        t_emb = self.time_proj(num_frames_emb)

        # `Timesteps` does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=hidden_states.dtype)

        emb = self.time_pos_embed(t_emb)
        emb = emb[:, None, :]

        # 2. Blocks
        for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks):
            if self.training and self.gradient_checkpointing:
                hidden_states = torch.utils.checkpoint.checkpoint(
                    block,
                    hidden_states,
                    None,
                    encoder_hidden_states,
                    None,
                    use_reentrant=False,
                )
            else:
                hidden_states = block(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                )

            hidden_states_mix = hidden_states
            hidden_states_mix = hidden_states_mix + emb

            hidden_states_mix = temporal_block(
                hidden_states_mix,
                num_frames=num_frames,
                encoder_hidden_states=time_context,
            )
            hidden_states = self.time_mixer(
                x_spatial=hidden_states,
                x_temporal=hidden_states_mix,
                image_only_indicator=image_only_indicator,
            )

        # 3. Output
        hidden_states = self.proj_out(hidden_states)
        hidden_states = hidden_states.reshape(batch_frames, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()

        output = hidden_states + residual

        if not return_dict:
            return (output,)

        return TransformerTemporalModelOutput(sample=output)
 from ...utils import is_torch_available


 if is_torch_available():
    from .dual_transformer_2d import DualTransformer2DModel
    from .prior_transformer import PriorTransformer
    from .t5_film_transformer import T5FilmDecoder
    from .transformer_2d import Transformer2DModel
    from .transformer_temporal import TransformerTemporalModel

 from dataclasses import dataclass
 from typing import Any, Dict, Optional

 import torch
 import torch.nn.functional as F
 from torch import nn

 from ...configuration_utils import ConfigMixin, register_to_config
 from ...utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_torch_version
 from ..attention import BasicTransformerBlock
 from ..embeddings import ImagePositionalEmbeddings, PatchEmbed, PixArtAlphaTextProjection
 from ..lora import LoRACompatibleConv, LoRACompatibleLinear
 from ..modeling_utils import ModelMixin
 from ..normalization import AdaLayerNormSingle


 @dataclass
 class Transformer2DModelOutput(BaseOutput):
    """
    The output of [`Transformer2DModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
            distributions for the unnoised latent pixels.
    """

    sample: torch.FloatTensor


 class Transformer2DModel(ModelMixin, ConfigMixin):
    """
    A 2D Transformer model for image-like data.

    Parameters:
        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
        in_channels (`int`, *optional*):
            The number of channels in the input and output (specify if the input is **continuous**).
        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
            This is fixed during training since it is used to learn a number of position embeddings.
        num_vector_embeds (`int`, *optional*):
            The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
            Includes the class for the masked latent pixel.
        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
        num_embeds_ada_norm ( `int`, *optional*):
            The number of diffusion steps used during training. Pass if at least one of the norm_layers is
            `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
            added to the hidden states.

            During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
        attention_bias (`bool`, *optional*):
            Configure if the `TransformerBlocks` attention should contain a bias parameter.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        num_attention_heads: int = 16,
        attention_head_dim: int = 88,
        in_channels: Optional[int] = None,
        out_channels: Optional[int] = None,
        num_layers: int = 1,
        dropout: float = 0.0,
        norm_num_groups: int = 32,
        cross_attention_dim: Optional[int] = None,
        attention_bias: bool = False,
        sample_size: Optional[int] = None,
        num_vector_embeds: Optional[int] = None,
        patch_size: Optional[int] = None,
        activation_fn: str = "geglu",
        num_embeds_ada_norm: Optional[int] = None,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        double_self_attention: bool = False,
        upcast_attention: bool = False,
        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'
        norm_elementwise_affine: bool = True,
        norm_eps: float = 1e-5,
        attention_type: str = "default",
        caption_channels: int = None,
        interpolation_scale: float = None,
    ):
        super().__init__()
        if patch_size is not None:
            if norm_type not in ["ada_norm", "ada_norm_zero", "ada_norm_single"]:
                raise NotImplementedError(
                    f"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'."
                )
            elif norm_type in ["ada_norm", "ada_norm_zero"] and num_embeds_ada_norm is None:
                raise ValueError(
                    f"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None."
                )

        self.use_linear_projection = use_linear_projection
        self.num_attention_heads = num_attention_heads
        self.attention_head_dim = attention_head_dim
        inner_dim = num_attention_heads * attention_head_dim

        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
        linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear

        # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
        # Define whether input is continuous or discrete depending on configuration
        self.is_input_continuous = (in_channels is not None) and (patch_size is None)
        self.is_input_vectorized = num_vector_embeds is not None
        self.is_input_patches = in_channels is not None and patch_size is not None

        if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
            deprecation_message = (
                f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
                " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
                " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
                " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
                " would be very nice if you could open a Pull request for the `transformer/config.json` file"
            )
            deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False)
            norm_type = "ada_norm"

        if self.is_input_continuous and self.is_input_vectorized:
            raise ValueError(
                f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
                " sure that either `in_channels` or `num_vector_embeds` is None."
            )
        elif self.is_input_vectorized and self.is_input_patches:
            raise ValueError(
                f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
                " sure that either `num_vector_embeds` or `num_patches` is None."
            )
        elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
            raise ValueError(
                f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
                f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
            )

        # 2. Define input layers
        if self.is_input_continuous:
            self.in_channels = in_channels

            self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
            if use_linear_projection:
                self.proj_in = linear_cls(in_channels, inner_dim)
            else:
                self.proj_in = conv_cls(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
        elif self.is_input_vectorized:
            assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
            assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

            self.height = sample_size
            self.width = sample_size
            self.num_vector_embeds = num_vector_embeds
            self.num_latent_pixels = self.height * self.width

            self.latent_image_embedding = ImagePositionalEmbeddings(
                num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width
            )
        elif self.is_input_patches:
            assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

            self.height = sample_size
            self.width = sample_size

            self.patch_size = patch_size
            interpolation_scale = (
                interpolation_scale if interpolation_scale is not None else max(self.config.sample_size // 64, 1)
            )
            self.pos_embed = PatchEmbed(
                height=sample_size,
                width=sample_size,
                patch_size=patch_size,
                in_channels=in_channels,
                embed_dim=inner_dim,
                interpolation_scale=interpolation_scale,
            )

        # 3. Define transformers blocks
        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim,
                    num_attention_heads,
                    attention_head_dim,
                    dropout=dropout,
                    cross_attention_dim=cross_attention_dim,
                    activation_fn=activation_fn,
                    num_embeds_ada_norm=num_embeds_ada_norm,
                    attention_bias=attention_bias,
                    only_cross_attention=only_cross_attention,
                    double_self_attention=double_self_attention,
                    upcast_attention=upcast_attention,
                    norm_type=norm_type,
                    norm_elementwise_affine=norm_elementwise_affine,
                    norm_eps=norm_eps,
                    attention_type=attention_type,
                )
                for d in range(num_layers)
            ]
        )

        # 4. Define output layers
        self.out_channels = in_channels if out_channels is None else out_channels
        if self.is_input_continuous:
            # TODO: should use out_channels for continuous projections
            if use_linear_projection:
                self.proj_out = linear_cls(inner_dim, in_channels)
            else:
                self.proj_out = conv_cls(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
        elif self.is_input_vectorized:
            self.norm_out = nn.LayerNorm(inner_dim)
            self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
        elif self.is_input_patches and norm_type != "ada_norm_single":
            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
            self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
            self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
        elif self.is_input_patches and norm_type == "ada_norm_single":
            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
            self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
            self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)

        # 5. PixArt-Alpha blocks.
        self.adaln_single = None
        self.use_additional_conditions = False
        if norm_type == "ada_norm_single":
            self.use_additional_conditions = self.config.sample_size == 128
            # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
            # additional conditions until we find better name
            self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions)

        self.caption_projection = None
        if caption_channels is not None:
            self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim)

        self.gradient_checkpointing = False

    def _set_gradient_checkpointing(self, module, value=False):
        if hasattr(module, "gradient_checkpointing"):
            module.gradient_checkpointing = value

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        timestep: Optional[torch.LongTensor] = None,
        added_cond_kwargs: Dict[str, torch.Tensor] = None,
        class_labels: Optional[torch.LongTensor] = None,
        cross_attention_kwargs: Dict[str, Any] = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ):
        """
        The [`Transformer2DModel`] forward method.

        Args:
            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
                Input `hidden_states`.
            encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            timestep ( `torch.LongTensor`, *optional*):
                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
                `AdaLayerZeroNorm`.
            cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            attention_mask ( `torch.Tensor`, *optional*):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            encoder_attention_mask ( `torch.Tensor`, *optional*):
                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:

                    * Mask `(batch, sequence_length)` True = keep, False = discard.
                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.

                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
                above. This bias will be added to the cross-attention scores.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.

        Returns:
            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
            `tuple` where the first element is the sample tensor.
        """
        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
        # expects mask of shape:
        #   [batch, key_tokens]
        # adds singleton query_tokens dimension:
        #   [batch,                    1, key_tokens]
        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
        if attention_mask is not None and attention_mask.ndim == 2:
            # assume that mask is expressed as:
            #   (1 = keep,      0 = discard)
            # convert mask into a bias that can be added to attention scores:
            #       (keep = +0,     discard = -10000.0)
            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        # convert encoder_attention_mask to a bias the same way we do for attention_mask
        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
            encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

        # Retrieve lora scale.
        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0

        # 1. Input
        if self.is_input_continuous:
            batch, _, height, width = hidden_states.shape
            residual = hidden_states

            hidden_states = self.norm(hidden_states)
            if not self.use_linear_projection:
                hidden_states = (
                    self.proj_in(hidden_states, scale=lora_scale)
                    if not USE_PEFT_BACKEND
                    else self.proj_in(hidden_states)
                )
                inner_dim = hidden_states.shape[1]
                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
            else:
                inner_dim = hidden_states.shape[1]
                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
                hidden_states = (
                    self.proj_in(hidden_states, scale=lora_scale)
                    if not USE_PEFT_BACKEND
                    else self.proj_in(hidden_states)
                )

        elif self.is_input_vectorized:
            hidden_states = self.latent_image_embedding(hidden_states)
        elif self.is_input_patches:
            height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size
            hidden_states = self.pos_embed(hidden_states)

            if self.adaln_single is not None:
                if self.use_additional_conditions and added_cond_kwargs is None:
                    raise ValueError(
                        "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`."
                    )
                batch_size = hidden_states.shape[0]
                timestep, embedded_timestep = self.adaln_single(
                    timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype
                )

        # 2. Blocks
        if self.caption_projection is not None:
            batch_size = hidden_states.shape[0]
            encoder_hidden_states = self.caption_projection(encoder_hidden_states)
            encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])

        for block in self.transformer_blocks:
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    timestep,
                    cross_attention_kwargs,
                    class_labels,
                    **ckpt_kwargs,
                )
            else:
                hidden_states = block(
                    hidden_states,
                    attention_mask=attention_mask,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    timestep=timestep,
                    cross_attention_kwargs=cross_attention_kwargs,
                    class_labels=class_labels,
                )

        # 3. Output
        if self.is_input_continuous:
            if not self.use_linear_projection:
                hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
                hidden_states = (
                    self.proj_out(hidden_states, scale=lora_scale)
                    if not USE_PEFT_BACKEND
                    else self.proj_out(hidden_states)
                )
            else:
                hidden_states = (
                    self.proj_out(hidden_states, scale=lora_scale)
                    if not USE_PEFT_BACKEND
                    else self.proj_out(hidden_states)
                )
                hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()

            output = hidden_states + residual
        elif self.is_input_vectorized:
            hidden_states = self.norm_out(hidden_states)
            logits = self.out(hidden_states)
            # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
            logits = logits.permute(0, 2, 1)

            # log(p(x_0))
            output = F.log_softmax(logits.double(), dim=1).float()

        if self.is_input_patches:
            if self.config.norm_type != "ada_norm_single":
                conditioning = self.transformer_blocks[0].norm1.emb(
                    timestep, class_labels, hidden_dtype=hidden_states.dtype
                )
                shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
                hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
                hidden_states = self.proj_out_2(hidden_states)
            elif self.config.norm_type == "ada_norm_single":
                shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)
                hidden_states = self.norm_out(hidden_states)
                # Modulation
                hidden_states = hidden_states * (1 + scale) + shift
                hidden_states = self.proj_out(hidden_states)
                hidden_states = hidden_states.squeeze(1)

            # unpatchify
            if self.adaln_single is None:
                height = width = int(hidden_states.shape[1] ** 0.5)
            hidden_states = hidden_states.reshape(
                shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
            )
            hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
            output = hidden_states.reshape(
                shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
            )

        if not return_dict:
            return (output,)

        return Transformer2DModelOutput(sample=output)
 from dataclasses import dataclass
 from typing import Dict, Optional, Union

 import torch
 import torch.nn.functional as F
 from torch import nn

 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin
 from ...utils import BaseOutput
 from ..attention import BasicTransformerBlock
 from ..attention_processor import (
    ADDED_KV_ATTENTION_PROCESSORS,
    CROSS_ATTENTION_PROCESSORS,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor,
 )
 from ..embeddings import TimestepEmbedding, Timesteps
 from ..modeling_utils import ModelMixin


 @dataclass
 class PriorTransformerOutput(BaseOutput):
    """
    The output of [`PriorTransformer`].

    Args:
        predicted_image_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):
            The predicted CLIP image embedding conditioned on the CLIP text embedding input.
    """

    predicted_image_embedding: torch.FloatTensor


 class PriorTransformer(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
    """
    A Prior Transformer model.

    Parameters:
        num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention.
        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
        num_layers (`int`, *optional*, defaults to 20): The number of layers of Transformer blocks to use.
        embedding_dim (`int`, *optional*, defaults to 768): The dimension of the model input `hidden_states`
        num_embeddings (`int`, *optional*, defaults to 77):
            The number of embeddings of the model input `hidden_states`
        additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the
            projected `hidden_states`. The actual length of the used `hidden_states` is `num_embeddings +
            additional_embeddings`.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        time_embed_act_fn (`str`, *optional*, defaults to 'silu'):
            The activation function to use to create timestep embeddings.
        norm_in_type (`str`, *optional*, defaults to None): The normalization layer to apply on hidden states before
            passing to Transformer blocks. Set it to `None` if normalization is not needed.
        embedding_proj_norm_type (`str`, *optional*, defaults to None):
            The normalization layer to apply on the input `proj_embedding`. Set it to `None` if normalization is not
            needed.
        encoder_hid_proj_type (`str`, *optional*, defaults to `linear`):
            The projection layer to apply on the input `encoder_hidden_states`. Set it to `None` if
            `encoder_hidden_states` is `None`.
        added_emb_type (`str`, *optional*, defaults to `prd`): Additional embeddings to condition the model.
            Choose from `prd` or `None`. if choose `prd`, it will prepend a token indicating the (quantized) dot
            product between the text embedding and image embedding as proposed in the unclip paper
            https://arxiv.org/abs/2204.06125 If it is `None`, no additional embeddings will be prepended.
        time_embed_dim (`int, *optional*, defaults to None): The dimension of timestep embeddings.
            If None, will be set to `num_attention_heads * attention_head_dim`
        embedding_proj_dim (`int`, *optional*, default to None):
            The dimension of `proj_embedding`. If None, will be set to `embedding_dim`.
        clip_embed_dim (`int`, *optional*, default to None):
            The dimension of the output. If None, will be set to `embedding_dim`.
    """

    @register_to_config
    def __init__(
        self,
        num_attention_heads: int = 32,
        attention_head_dim: int = 64,
        num_layers: int = 20,
        embedding_dim: int = 768,
        num_embeddings=77,
        additional_embeddings=4,
        dropout: float = 0.0,
        time_embed_act_fn: str = "silu",
        norm_in_type: Optional[str] = None,  # layer
        embedding_proj_norm_type: Optional[str] = None,  # layer
        encoder_hid_proj_type: Optional[str] = "linear",  # linear
        added_emb_type: Optional[str] = "prd",  # prd
        time_embed_dim: Optional[int] = None,
        embedding_proj_dim: Optional[int] = None,
        clip_embed_dim: Optional[int] = None,
    ):
        super().__init__()
        self.num_attention_heads = num_attention_heads
        self.attention_head_dim = attention_head_dim
        inner_dim = num_attention_heads * attention_head_dim
        self.additional_embeddings = additional_embeddings

        time_embed_dim = time_embed_dim or inner_dim
        embedding_proj_dim = embedding_proj_dim or embedding_dim
        clip_embed_dim = clip_embed_dim or embedding_dim

        self.time_proj = Timesteps(inner_dim, True, 0)
        self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, out_dim=inner_dim, act_fn=time_embed_act_fn)

        self.proj_in = nn.Linear(embedding_dim, inner_dim)

        if embedding_proj_norm_type is None:
            self.embedding_proj_norm = None
        elif embedding_proj_norm_type == "layer":
            self.embedding_proj_norm = nn.LayerNorm(embedding_proj_dim)
        else:
            raise ValueError(f"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}")

        self.embedding_proj = nn.Linear(embedding_proj_dim, inner_dim)

        if encoder_hid_proj_type is None:
            self.encoder_hidden_states_proj = None
        elif encoder_hid_proj_type == "linear":
            self.encoder_hidden_states_proj = nn.Linear(embedding_dim, inner_dim)
        else:
            raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}")

        self.positional_embedding = nn.Parameter(torch.zeros(1, num_embeddings + additional_embeddings, inner_dim))

        if added_emb_type == "prd":
            self.prd_embedding = nn.Parameter(torch.zeros(1, 1, inner_dim))
        elif added_emb_type is None:
            self.prd_embedding = None
        else:
            raise ValueError(
                f"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."
            )

        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim,
                    num_attention_heads,
                    attention_head_dim,
                    dropout=dropout,
                    activation_fn="gelu",
                    attention_bias=True,
                )
                for d in range(num_layers)
            ]
        )

        if norm_in_type == "layer":
            self.norm_in = nn.LayerNorm(inner_dim)
        elif norm_in_type is None:
            self.norm_in = None
        else:
            raise ValueError(f"Unsupported norm_in_type: {norm_in_type}.")

        self.norm_out = nn.LayerNorm(inner_dim)

        self.proj_to_clip_embeddings = nn.Linear(inner_dim, clip_embed_dim)

        causal_attention_mask = torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings], -10000.0
        )
        causal_attention_mask.triu_(1)
        causal_attention_mask = causal_attention_mask[None, ...]
        self.register_buffer("causal_attention_mask", causal_attention_mask, persistent=False)

        self.clip_mean = nn.Parameter(torch.zeros(1, clip_embed_dim))
        self.clip_std = nn.Parameter(torch.zeros(1, clip_embed_dim))

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnAddedKVProcessor()
        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnProcessor()
        else:
            raise ValueError(
                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
            )

        self.set_attn_processor(processor)

    def forward(
        self,
        hidden_states,
        timestep: Union[torch.Tensor, float, int],
        proj_embedding: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.BoolTensor] = None,
        return_dict: bool = True,
    ):
        """
        The [`PriorTransformer`] forward method.

        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):
                The currently predicted image embeddings.
            timestep (`torch.LongTensor`):
                Current denoising step.
            proj_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):
                Projected embedding vector the denoising process is conditioned on.
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_embeddings, embedding_dim)`):
                Hidden states of the text embeddings the denoising process is conditioned on.
            attention_mask (`torch.BoolTensor` of shape `(batch_size, num_embeddings)`):
                Text mask for the text embeddings.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.prior_transformer.PriorTransformerOutput`] instead of a plain
                tuple.

        Returns:
            [`~models.prior_transformer.PriorTransformerOutput`] or `tuple`:
                If return_dict is True, a [`~models.prior_transformer.PriorTransformerOutput`] is returned, otherwise a
                tuple is returned where the first element is the sample tensor.
        """
        batch_size = hidden_states.shape[0]

        timesteps = timestep
        if not torch.is_tensor(timesteps):
            timesteps = torch.tensor([timesteps], dtype=torch.long, device=hidden_states.device)
        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(hidden_states.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps * torch.ones(batch_size, dtype=timesteps.dtype, device=timesteps.device)

        timesteps_projected = self.time_proj(timesteps)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        timesteps_projected = timesteps_projected.to(dtype=self.dtype)
        time_embeddings = self.time_embedding(timesteps_projected)

        if self.embedding_proj_norm is not None:
            proj_embedding = self.embedding_proj_norm(proj_embedding)

        proj_embeddings = self.embedding_proj(proj_embedding)
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
            encoder_hidden_states = self.encoder_hidden_states_proj(encoder_hidden_states)
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
            raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set")

        hidden_states = self.proj_in(hidden_states)

        positional_embeddings = self.positional_embedding.to(hidden_states.dtype)

        additional_embeds = []
        additional_embeddings_len = 0

        if encoder_hidden_states is not None:
            additional_embeds.append(encoder_hidden_states)
            additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape) == 2:
            proj_embeddings = proj_embeddings[:, None, :]

        if len(hidden_states.shape) == 2:
            hidden_states = hidden_states[:, None, :]

        additional_embeds = additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
            prd_embedding = self.prd_embedding.to(hidden_states.dtype).expand(batch_size, -1, -1)
            additional_embeds.append(prd_embedding)

        hidden_states = torch.cat(
            additional_embeds,
            dim=1,
        )

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        additional_embeddings_len = additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
            positional_embeddings = F.pad(
                positional_embeddings,
                (
                    0,
                    0,
                    additional_embeddings_len,
                    self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                ),
                value=0.0,
            )

        hidden_states = hidden_states + positional_embeddings

        if attention_mask is not None:
            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
            attention_mask = F.pad(attention_mask, (0, self.additional_embeddings), value=0.0)
            attention_mask = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
            attention_mask = attention_mask.repeat_interleave(self.config.num_attention_heads, dim=0)

        if self.norm_in is not None:
            hidden_states = self.norm_in(hidden_states)

        for block in self.transformer_blocks:
            hidden_states = block(hidden_states, attention_mask=attention_mask)

        hidden_states = self.norm_out(hidden_states)

        if self.prd_embedding is not None:
            hidden_states = hidden_states[:, -1]
        else:
            hidden_states = hidden_states[:, additional_embeddings_len:]

        predicted_image_embedding = self.proj_to_clip_embeddings(hidden_states)

        if not return_dict:
            return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=predicted_image_embedding)

    def post_process_latents(self, prior_latents):
        prior_latents = (prior_latents * self.clip_std) + self.clip_mean
        return prior_latents

 import math
 from typing import Optional, Tuple

 import torch
 from torch import nn

 from ...configuration_utils import ConfigMixin, register_to_config
 from ..attention_processor import Attention
 from ..embeddings import get_timestep_embedding
 from ..modeling_utils import ModelMixin


 class T5FilmDecoder(ModelMixin, ConfigMixin):
    r"""
    T5 style decoder with FiLM conditioning.

    Args:
        input_dims (`int`, *optional*, defaults to `128`):
            The number of input dimensions.
        targets_length (`int`, *optional*, defaults to `256`):
            The length of the targets.
        d_model (`int`, *optional*, defaults to `768`):
            Size of the input hidden states.
        num_layers (`int`, *optional*, defaults to `12`):
            The number of `DecoderLayer`'s to use.
        num_heads (`int`, *optional*, defaults to `12`):
            The number of attention heads to use.
        d_kv (`int`, *optional*, defaults to `64`):
            Size of the key-value projection vectors.
        d_ff (`int`, *optional*, defaults to `2048`):
            The number of dimensions in the intermediate feed-forward layer of `DecoderLayer`'s.
        dropout_rate (`float`, *optional*, defaults to `0.1`):
            Dropout probability.
    """

    @register_to_config
    def __init__(
        self,
        input_dims: int = 128,
        targets_length: int = 256,
        max_decoder_noise_time: float = 2000.0,
        d_model: int = 768,
        num_layers: int = 12,
        num_heads: int = 12,
        d_kv: int = 64,
        d_ff: int = 2048,
        dropout_rate: float = 0.1,
    ):
        super().__init__()

        self.conditioning_emb = nn.Sequential(
            nn.Linear(d_model, d_model * 4, bias=False),
            nn.SiLU(),
            nn.Linear(d_model * 4, d_model * 4, bias=False),
            nn.SiLU(),
        )

        self.position_encoding = nn.Embedding(targets_length, d_model)
        self.position_encoding.weight.requires_grad = False

        self.continuous_inputs_projection = nn.Linear(input_dims, d_model, bias=False)

        self.dropout = nn.Dropout(p=dropout_rate)

        self.decoders = nn.ModuleList()
        for lyr_num in range(num_layers):
            # FiLM conditional T5 decoder
            lyr = DecoderLayer(d_model=d_model, d_kv=d_kv, num_heads=num_heads, d_ff=d_ff, dropout_rate=dropout_rate)
            self.decoders.append(lyr)

        self.decoder_norm = T5LayerNorm(d_model)

        self.post_dropout = nn.Dropout(p=dropout_rate)
        self.spec_out = nn.Linear(d_model, input_dims, bias=False)

    def encoder_decoder_mask(self, query_input: torch.FloatTensor, key_input: torch.FloatTensor) -> torch.FloatTensor:
        mask = torch.mul(query_input.unsqueeze(-1), key_input.unsqueeze(-2))
        return mask.unsqueeze(-3)

    def forward(self, encodings_and_masks, decoder_input_tokens, decoder_noise_time):
        batch, _, _ = decoder_input_tokens.shape
        assert decoder_noise_time.shape == (batch,)

        # decoder_noise_time is in [0, 1), so rescale to expected timing range.
        time_steps = get_timestep_embedding(
            decoder_noise_time * self.config.max_decoder_noise_time,
            embedding_dim=self.config.d_model,
            max_period=self.config.max_decoder_noise_time,
        ).to(dtype=self.dtype)

        conditioning_emb = self.conditioning_emb(time_steps).unsqueeze(1)

        assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

        seq_length = decoder_input_tokens.shape[1]

        # If we want to use relative positions for audio context, we can just offset
        # this sequence by the length of encodings_and_masks.
        decoder_positions = torch.broadcast_to(
            torch.arange(seq_length, device=decoder_input_tokens.device),
            (batch, seq_length),
        )

        position_encodings = self.position_encoding(decoder_positions)

        inputs = self.continuous_inputs_projection(decoder_input_tokens)
        inputs += position_encodings
        y = self.dropout(inputs)

        # decoder: No padding present.
        decoder_mask = torch.ones(
            decoder_input_tokens.shape[:2], device=decoder_input_tokens.device, dtype=inputs.dtype
        )

        # Translate encoding masks to encoder-decoder masks.
        encodings_and_encdec_masks = [(x, self.encoder_decoder_mask(decoder_mask, y)) for x, y in encodings_and_masks]

        # cross attend style: concat encodings
        encoded = torch.cat([x[0] for x in encodings_and_encdec_masks], dim=1)
        encoder_decoder_mask = torch.cat([x[1] for x in encodings_and_encdec_masks], dim=-1)

        for lyr in self.decoders:
            y = lyr(
                y,
                conditioning_emb=conditioning_emb,
                encoder_hidden_states=encoded,
                encoder_attention_mask=encoder_decoder_mask,
            )[0]

        y = self.decoder_norm(y)
        y = self.post_dropout(y)

        spec_out = self.spec_out(y)
        return spec_out


 class DecoderLayer(nn.Module):
    r"""
    T5 decoder layer.

    Args:
        d_model (`int`):
            Size of the input hidden states.
        d_kv (`int`):
            Size of the key-value projection vectors.
        num_heads (`int`):
            Number of attention heads.
        d_ff (`int`):
            Size of the intermediate feed-forward layer.
        dropout_rate (`float`):
            Dropout probability.
        layer_norm_epsilon (`float`, *optional*, defaults to `1e-6`):
            A small value used for numerical stability to avoid dividing by zero.
    """

    def __init__(
        self, d_model: int, d_kv: int, num_heads: int, d_ff: int, dropout_rate: float, layer_norm_epsilon: float = 1e-6
    ):
        super().__init__()
        self.layer = nn.ModuleList()

        # cond self attention: layer 0
        self.layer.append(
            T5LayerSelfAttentionCond(d_model=d_model, d_kv=d_kv, num_heads=num_heads, dropout_rate=dropout_rate)
        )

        # cross attention: layer 1
        self.layer.append(
            T5LayerCrossAttention(
                d_model=d_model,
                d_kv=d_kv,
                num_heads=num_heads,
                dropout_rate=dropout_rate,
                layer_norm_epsilon=layer_norm_epsilon,
            )
        )

        # Film Cond MLP + dropout: last layer
        self.layer.append(
            T5LayerFFCond(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate, layer_norm_epsilon=layer_norm_epsilon)
        )

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        conditioning_emb: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        encoder_decoder_position_bias=None,
    ) -> Tuple[torch.FloatTensor]:
        hidden_states = self.layer[0](
            hidden_states,
            conditioning_emb=conditioning_emb,
            attention_mask=attention_mask,
        )

        if encoder_hidden_states is not None:
            encoder_extended_attention_mask = torch.where(encoder_attention_mask > 0, 0, -1e10).to(
                encoder_hidden_states.dtype
            )

            hidden_states = self.layer[1](
                hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_extended_attention_mask,
            )

        # Apply Film Conditional Feed Forward layer
        hidden_states = self.layer[-1](hidden_states, conditioning_emb)

        return (hidden_states,)


 class T5LayerSelfAttentionCond(nn.Module):
    r"""
    T5 style self-attention layer with conditioning.

    Args:
        d_model (`int`):
            Size of the input hidden states.
        d_kv (`int`):
            Size of the key-value projection vectors.
        num_heads (`int`):
            Number of attention heads.
        dropout_rate (`float`):
            Dropout probability.
    """

    def __init__(self, d_model: int, d_kv: int, num_heads: int, dropout_rate: float):
        super().__init__()
        self.layer_norm = T5LayerNorm(d_model)
        self.FiLMLayer = T5FiLMLayer(in_features=d_model * 4, out_features=d_model)
        self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False)
        self.dropout = nn.Dropout(dropout_rate)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        conditioning_emb: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        # pre_self_attention_layer_norm
        normed_hidden_states = self.layer_norm(hidden_states)

        if conditioning_emb is not None:
            normed_hidden_states = self.FiLMLayer(normed_hidden_states, conditioning_emb)

        # Self-attention block
        attention_output = self.attention(normed_hidden_states)

        hidden_states = hidden_states + self.dropout(attention_output)

        return hidden_states


 class T5LayerCrossAttention(nn.Module):
    r"""
    T5 style cross-attention layer.

    Args:
        d_model (`int`):
            Size of the input hidden states.
        d_kv (`int`):
            Size of the key-value projection vectors.
        num_heads (`int`):
            Number of attention heads.
        dropout_rate (`float`):
            Dropout probability.
        layer_norm_epsilon (`float`):
            A small value used for numerical stability to avoid dividing by zero.
    """

    def __init__(self, d_model: int, d_kv: int, num_heads: int, dropout_rate: float, layer_norm_epsilon: float):
        super().__init__()
        self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False)
        self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon)
        self.dropout = nn.Dropout(dropout_rate)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        key_value_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.attention(
            normed_hidden_states,
            encoder_hidden_states=key_value_states,
            attention_mask=attention_mask.squeeze(1),
        )
        layer_output = hidden_states + self.dropout(attention_output)
        return layer_output


 class T5LayerFFCond(nn.Module):
    r"""
    T5 style feed-forward conditional layer.

    Args:
        d_model (`int`):
            Size of the input hidden states.
        d_ff (`int`):
            Size of the intermediate feed-forward layer.
        dropout_rate (`float`):
            Dropout probability.
        layer_norm_epsilon (`float`):
            A small value used for numerical stability to avoid dividing by zero.
    """

    def __init__(self, d_model: int, d_ff: int, dropout_rate: float, layer_norm_epsilon: float):
        super().__init__()
        self.DenseReluDense = T5DenseGatedActDense(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate)
        self.film = T5FiLMLayer(in_features=d_model * 4, out_features=d_model)
        self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon)
        self.dropout = nn.Dropout(dropout_rate)

    def forward(
        self, hidden_states: torch.FloatTensor, conditioning_emb: Optional[torch.FloatTensor] = None
    ) -> torch.FloatTensor:
        forwarded_states = self.layer_norm(hidden_states)
        if conditioning_emb is not None:
            forwarded_states = self.film(forwarded_states, conditioning_emb)

        forwarded_states = self.DenseReluDense(forwarded_states)
        hidden_states = hidden_states + self.dropout(forwarded_states)
        return hidden_states


 class T5DenseGatedActDense(nn.Module):
    r"""
    T5 style feed-forward layer with gated activations and dropout.

    Args:
        d_model (`int`):
            Size of the input hidden states.
        d_ff (`int`):
            Size of the intermediate feed-forward layer.
        dropout_rate (`float`):
            Dropout probability.
    """

    def __init__(self, d_model: int, d_ff: int, dropout_rate: float):
        super().__init__()
        self.wi_0 = nn.Linear(d_model, d_ff, bias=False)
        self.wi_1 = nn.Linear(d_model, d_ff, bias=False)
        self.wo = nn.Linear(d_ff, d_model, bias=False)
        self.dropout = nn.Dropout(dropout_rate)
        self.act = NewGELUActivation()

    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        hidden_gelu = self.act(self.wi_0(hidden_states))
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = hidden_gelu * hidden_linear
        hidden_states = self.dropout(hidden_states)

        hidden_states = self.wo(hidden_states)
        return hidden_states


 class T5LayerNorm(nn.Module):
    r"""
    T5 style layer normalization module.

    Args:
        hidden_size (`int`):
            Size of the input hidden states.
        eps (`float`, `optional`, defaults to `1e-6`):
            A small value used for numerical stability to avoid dividing by zero.
    """

    def __init__(self, hidden_size: int, eps: float = 1e-6):
        """
        Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
        # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
        # half-precision inputs is done in fp32

        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

        # convert into half-precision if necessary
        if self.weight.dtype in [torch.float16, torch.bfloat16]:
            hidden_states = hidden_states.to(self.weight.dtype)

        return self.weight * hidden_states


 class NewGELUActivation(nn.Module):
    """
    Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
    the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
    """

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))


 class T5FiLMLayer(nn.Module):
    """
    T5 style FiLM Layer.

    Args:
        in_features (`int`):
            Number of input features.
        out_features (`int`):
            Number of output features.
    """

    def __init__(self, in_features: int, out_features: int):
        super().__init__()
        self.scale_bias = nn.Linear(in_features, out_features * 2, bias=False)

    def forward(self, x: torch.FloatTensor, conditioning_emb: torch.FloatTensor) -> torch.FloatTensor:
        emb = self.scale_bias(conditioning_emb)
        scale, shift = torch.chunk(emb, 2, -1)
        x = x * (1 + scale) + shift
        return x

 from typing import Optional

 from torch import nn

 from .transformer_2d import Transformer2DModel, Transformer2DModelOutput


 class DualTransformer2DModel(nn.Module):
    """
    Dual transformer wrapper that combines two `Transformer2DModel`s for mixed inference.

    Parameters:
        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
        in_channels (`int`, *optional*):
            Pass if the input is continuous. The number of channels in the input and output.
        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
        dropout (`float`, *optional*, defaults to 0.1): The dropout probability to use.
        cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use.
        sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images.
            Note that this is fixed at training time as it is used for learning a number of position embeddings. See
            `ImagePositionalEmbeddings`.
        num_vector_embeds (`int`, *optional*):
            Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels.
            Includes the class for the masked latent pixel.
        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
        num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`.
            The number of diffusion steps used during training. Note that this is fixed at training time as it is used
            to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for
            up to but not more than steps than `num_embeds_ada_norm`.
        attention_bias (`bool`, *optional*):
            Configure if the TransformerBlocks' attention should contain a bias parameter.
    """

    def __init__(
        self,
        num_attention_heads: int = 16,
        attention_head_dim: int = 88,
        in_channels: Optional[int] = None,
        num_layers: int = 1,
        dropout: float = 0.0,
        norm_num_groups: int = 32,
        cross_attention_dim: Optional[int] = None,
        attention_bias: bool = False,
        sample_size: Optional[int] = None,
        num_vector_embeds: Optional[int] = None,
        activation_fn: str = "geglu",
        num_embeds_ada_norm: Optional[int] = None,
    ):
        super().__init__()
        self.transformers = nn.ModuleList(
            [
                Transformer2DModel(
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=attention_head_dim,
                    in_channels=in_channels,
                    num_layers=num_layers,
                    dropout=dropout,
                    norm_num_groups=norm_num_groups,
                    cross_attention_dim=cross_attention_dim,
                    attention_bias=attention_bias,
                    sample_size=sample_size,
                    num_vector_embeds=num_vector_embeds,
                    activation_fn=activation_fn,
                    num_embeds_ada_norm=num_embeds_ada_norm,
                )
                for _ in range(2)
            ]
        )

        # Variables that can be set by a pipeline:

        # The ratio of transformer1 to transformer2's output states to be combined during inference
        self.mix_ratio = 0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        self.condition_lengths = [77, 257]

        # Which transformer to use to encode which condition.
        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
        self.transformer_index_for_condition = [1, 0]

    def forward(
        self,
        hidden_states,
        encoder_hidden_states,
        timestep=None,
        attention_mask=None,
        cross_attention_kwargs=None,
        return_dict: bool = True,
    ):
        """
        Args:
            hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.
                When continuous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input
                hidden_states.
            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            timestep ( `torch.long`, *optional*):
                Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step.
            attention_mask (`torch.FloatTensor`, *optional*):
                Optional attention mask to be applied in Attention.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.

        Returns:
            [`~models.transformer_2d.Transformer2DModelOutput`] or `tuple`:
            [`~models.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a `tuple`. When
            returning a tuple, the first element is the sample tensor.
        """
        input_states = hidden_states

        encoded_states = []
        tokens_start = 0
        # attention_mask is not used yet
        for i in range(2):
            # for each of the two transformers, pass the corresponding condition tokens
            condition_state = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            transformer_index = self.transformer_index_for_condition[i]
            encoded_state = self.transformers[transformer_index](
                input_states,
                encoder_hidden_states=condition_state,
                timestep=timestep,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]
            encoded_states.append(encoded_state - input_states)
            tokens_start += self.condition_lengths[i]

        output_states = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        output_states = output_states + input_states

        if not return_dict:
            return (output_states,)

        return Transformer2DModelOutput(sample=output_states)
 # coding=utf-8
 # Copyright 2024 HuggingFace Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.

 import torch
 import torch.nn.functional as F
 from torch import nn

 from ..utils import USE_PEFT_BACKEND
 from .lora import LoRACompatibleLinear


 ACTIVATION_FUNCTIONS = {
    "swish": nn.SiLU(),
    "silu": nn.SiLU(),
    "mish": nn.Mish(),
    "gelu": nn.GELU(),
    "relu": nn.ReLU(),
 }


 def get_activation(act_fn: str) -> nn.Module:
    """Helper function to get activation function from string.

    Args:
        act_fn (str): Name of activation function.

    Returns:
        nn.Module: Activation function.
    """

    act_fn = act_fn.lower()
    if act_fn in ACTIVATION_FUNCTIONS:
        return ACTIVATION_FUNCTIONS[act_fn]
    else:
        raise ValueError(f"Unsupported activation function: {act_fn}")


 class GELU(nn.Module):
    r"""
    GELU activation function with tanh approximation support with `approximate="tanh"`.

    Parameters:
        dim_in (`int`): The number of channels in the input.
        dim_out (`int`): The number of channels in the output.
        approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation.
        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
    """

    def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out, bias=bias)
        self.approximate = approximate

    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
        if gate.device.type != "mps":
            return F.gelu(gate, approximate=self.approximate)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(dtype=gate.dtype)

    def forward(self, hidden_states):
        hidden_states = self.proj(hidden_states)
        hidden_states = self.gelu(hidden_states)
        return hidden_states


 class GEGLU(nn.Module):
    r"""
    A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function.

    Parameters:
        dim_in (`int`): The number of channels in the input.
        dim_out (`int`): The number of channels in the output.
        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
    """

    def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
        super().__init__()
        linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear

        self.proj = linear_cls(dim_in, dim_out * 2, bias=bias)

    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
        if gate.device.type != "mps":
            return F.gelu(gate)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)

    def forward(self, hidden_states, scale: float = 1.0):
        args = () if USE_PEFT_BACKEND else (scale,)
        hidden_states, gate = self.proj(hidden_states, *args).chunk(2, dim=-1)
        return hidden_states * self.gelu(gate)


 class ApproximateGELU(nn.Module):
    r"""
    The approximate form of the Gaussian Error Linear Unit (GELU). For more details, see section 2 of this
    [paper](https://arxiv.org/abs/1606.08415).

    Parameters:
        dim_in (`int`): The number of channels in the input.
        dim_out (`int`): The number of channels in the output.
        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
    """

    def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out, bias=bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.proj(x)
        return x * torch.sigmoid(1.702 * x)


 # JAX implementation of VQGAN from taming-transformers https://github.com/CompVis/taming-transformers

 import math
 from functools import partial
 from typing import Tuple

 import flax
 import flax.linen as nn
 import jax
 import jax.numpy as jnp
 from flax.core.frozen_dict import FrozenDict

 from ..configuration_utils import ConfigMixin, flax_register_to_config
 from ..utils import BaseOutput
 from .modeling_flax_utils import FlaxModelMixin


 @flax.struct.dataclass
 class FlaxDecoderOutput(BaseOutput):
    """
    Output of decoding method.

    Args:
        sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
            The decoded output sample from the last layer of the model.
        dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
            The `dtype` of the parameters.
    """

    sample: jnp.ndarray


 @flax.struct.dataclass
 class FlaxAutoencoderKLOutput(BaseOutput):
    """
    Output of AutoencoderKL encoding method.

    Args:
        latent_dist (`FlaxDiagonalGaussianDistribution`):
            Encoded outputs of `Encoder` represented as the mean and logvar of `FlaxDiagonalGaussianDistribution`.
            `FlaxDiagonalGaussianDistribution` allows for sampling latents from the distribution.
    """

    latent_dist: "FlaxDiagonalGaussianDistribution"


 class FlaxUpsample2D(nn.Module):
    """
    Flax implementation of 2D Upsample layer

    Args:
        in_channels (`int`):
            Input channels
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.conv = nn.Conv(
            self.in_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

    def __call__(self, hidden_states):
        batch, height, width, channels = hidden_states.shape
        hidden_states = jax.image.resize(
            hidden_states,
            shape=(batch, height * 2, width * 2, channels),
            method="nearest",
        )
        hidden_states = self.conv(hidden_states)
        return hidden_states


 class FlaxDownsample2D(nn.Module):
    """
    Flax implementation of 2D Downsample layer

    Args:
        in_channels (`int`):
            Input channels
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.conv = nn.Conv(
            self.in_channels,
            kernel_size=(3, 3),
            strides=(2, 2),
            padding="VALID",
            dtype=self.dtype,
        )

    def __call__(self, hidden_states):
        pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
        hidden_states = jnp.pad(hidden_states, pad_width=pad)
        hidden_states = self.conv(hidden_states)
        return hidden_states


 class FlaxResnetBlock2D(nn.Module):
    """
    Flax implementation of 2D Resnet Block.

    Args:
        in_channels (`int`):
            Input channels
        out_channels (`int`):
            Output channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        groups (:obj:`int`, *optional*, defaults to `32`):
            The number of groups to use for group norm.
        use_nin_shortcut (:obj:`bool`, *optional*, defaults to `None`):
            Whether to use `nin_shortcut`. This activates a new layer inside ResNet block
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    out_channels: int = None
    dropout: float = 0.0
    groups: int = 32
    use_nin_shortcut: bool = None
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        out_channels = self.in_channels if self.out_channels is None else self.out_channels

        self.norm1 = nn.GroupNorm(num_groups=self.groups, epsilon=1e-6)
        self.conv1 = nn.Conv(
            out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        self.norm2 = nn.GroupNorm(num_groups=self.groups, epsilon=1e-6)
        self.dropout_layer = nn.Dropout(self.dropout)
        self.conv2 = nn.Conv(
            out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        self.conv_shortcut = None
        if use_nin_shortcut:
            self.conv_shortcut = nn.Conv(
                out_channels,
                kernel_size=(1, 1),
                strides=(1, 1),
                padding="VALID",
                dtype=self.dtype,
            )

    def __call__(self, hidden_states, deterministic=True):
        residual = hidden_states
        hidden_states = self.norm1(hidden_states)
        hidden_states = nn.swish(hidden_states)
        hidden_states = self.conv1(hidden_states)

        hidden_states = self.norm2(hidden_states)
        hidden_states = nn.swish(hidden_states)
        hidden_states = self.dropout_layer(hidden_states, deterministic)
        hidden_states = self.conv2(hidden_states)

        if self.conv_shortcut is not None:
            residual = self.conv_shortcut(residual)

        return hidden_states + residual


 class FlaxAttentionBlock(nn.Module):
    r"""
    Flax Convolutional based multi-head attention block for diffusion-based VAE.

    Parameters:
        channels (:obj:`int`):
            Input channels
        num_head_channels (:obj:`int`, *optional*, defaults to `None`):
            Number of attention heads
        num_groups (:obj:`int`, *optional*, defaults to `32`):
            The number of groups to use for group norm
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`

    """

    channels: int
    num_head_channels: int = None
    num_groups: int = 32
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.num_heads = self.channels // self.num_head_channels if self.num_head_channels is not None else 1

        dense = partial(nn.Dense, self.channels, dtype=self.dtype)

        self.group_norm = nn.GroupNorm(num_groups=self.num_groups, epsilon=1e-6)
        self.query, self.key, self.value = dense(), dense(), dense()
        self.proj_attn = dense()

    def transpose_for_scores(self, projection):
        new_projection_shape = projection.shape[:-1] + (self.num_heads, -1)
        # move heads to 2nd position (B, T, H * D) -> (B, T, H, D)
        new_projection = projection.reshape(new_projection_shape)
        # (B, T, H, D) -> (B, H, T, D)
        new_projection = jnp.transpose(new_projection, (0, 2, 1, 3))
        return new_projection

    def __call__(self, hidden_states):
        residual = hidden_states
        batch, height, width, channels = hidden_states.shape

        hidden_states = self.group_norm(hidden_states)

        hidden_states = hidden_states.reshape((batch, height * width, channels))

        query = self.query(hidden_states)
        key = self.key(hidden_states)
        value = self.value(hidden_states)

        # transpose
        query = self.transpose_for_scores(query)
        key = self.transpose_for_scores(key)
        value = self.transpose_for_scores(value)

        # compute attentions
        scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads))
        attn_weights = jnp.einsum("...qc,...kc->...qk", query * scale, key * scale)
        attn_weights = nn.softmax(attn_weights, axis=-1)

        # attend to values
        hidden_states = jnp.einsum("...kc,...qk->...qc", value, attn_weights)

        hidden_states = jnp.transpose(hidden_states, (0, 2, 1, 3))
        new_hidden_states_shape = hidden_states.shape[:-2] + (self.channels,)
        hidden_states = hidden_states.reshape(new_hidden_states_shape)

        hidden_states = self.proj_attn(hidden_states)
        hidden_states = hidden_states.reshape((batch, height, width, channels))
        hidden_states = hidden_states + residual
        return hidden_states


 class FlaxDownEncoderBlock2D(nn.Module):
    r"""
    Flax Resnet blocks-based Encoder block for diffusion-based VAE.

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        out_channels (:obj:`int`):
            Output channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of Resnet layer block
        resnet_groups (:obj:`int`, *optional*, defaults to `32`):
            The number of groups to use for the Resnet block group norm
        add_downsample (:obj:`bool`, *optional*, defaults to `True`):
            Whether to add downsample layer
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    out_channels: int
    dropout: float = 0.0
    num_layers: int = 1
    resnet_groups: int = 32
    add_downsample: bool = True
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        resnets = []
        for i in range(self.num_layers):
            in_channels = self.in_channels if i == 0 else self.out_channels

            res_block = FlaxResnetBlock2D(
                in_channels=in_channels,
                out_channels=self.out_channels,
                dropout=self.dropout,
                groups=self.resnet_groups,
                dtype=self.dtype,
            )
            resnets.append(res_block)
        self.resnets = resnets

        if self.add_downsample:
            self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)

    def __call__(self, hidden_states, deterministic=True):
        for resnet in self.resnets:
            hidden_states = resnet(hidden_states, deterministic=deterministic)

        if self.add_downsample:
            hidden_states = self.downsamplers_0(hidden_states)

        return hidden_states


 class FlaxUpDecoderBlock2D(nn.Module):
    r"""
    Flax Resnet blocks-based Decoder block for diffusion-based VAE.

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        out_channels (:obj:`int`):
            Output channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of Resnet layer block
        resnet_groups (:obj:`int`, *optional*, defaults to `32`):
            The number of groups to use for the Resnet block group norm
        add_upsample (:obj:`bool`, *optional*, defaults to `True`):
            Whether to add upsample layer
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    out_channels: int
    dropout: float = 0.0
    num_layers: int = 1
    resnet_groups: int = 32
    add_upsample: bool = True
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        resnets = []
        for i in range(self.num_layers):
            in_channels = self.in_channels if i == 0 else self.out_channels
            res_block = FlaxResnetBlock2D(
                in_channels=in_channels,
                out_channels=self.out_channels,
                dropout=self.dropout,
                groups=self.resnet_groups,
                dtype=self.dtype,
            )
            resnets.append(res_block)

        self.resnets = resnets

        if self.add_upsample:
            self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)

    def __call__(self, hidden_states, deterministic=True):
        for resnet in self.resnets:
            hidden_states = resnet(hidden_states, deterministic=deterministic)

        if self.add_upsample:
            hidden_states = self.upsamplers_0(hidden_states)

        return hidden_states


 class FlaxUNetMidBlock2D(nn.Module):
    r"""
    Flax Unet Mid-Block module.

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of Resnet layer block
        resnet_groups (:obj:`int`, *optional*, defaults to `32`):
            The number of groups to use for the Resnet and Attention block group norm
        num_attention_heads (:obj:`int`, *optional*, defaults to `1`):
            Number of attention heads for each attention block
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    dropout: float = 0.0
    num_layers: int = 1
    resnet_groups: int = 32
    num_attention_heads: int = 1
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        resnet_groups = self.resnet_groups if self.resnet_groups is not None else min(self.in_channels // 4, 32)

        # there is always at least one resnet
        resnets = [
            FlaxResnetBlock2D(
                in_channels=self.in_channels,
                out_channels=self.in_channels,
                dropout=self.dropout,
                groups=resnet_groups,
                dtype=self.dtype,
            )
        ]

        attentions = []

        for _ in range(self.num_layers):
            attn_block = FlaxAttentionBlock(
                channels=self.in_channels,
                num_head_channels=self.num_attention_heads,
                num_groups=resnet_groups,
                dtype=self.dtype,
            )
            attentions.append(attn_block)

            res_block = FlaxResnetBlock2D(
                in_channels=self.in_channels,
                out_channels=self.in_channels,
                dropout=self.dropout,
                groups=resnet_groups,
                dtype=self.dtype,
            )
            resnets.append(res_block)

        self.resnets = resnets
        self.attentions = attentions

    def __call__(self, hidden_states, deterministic=True):
        hidden_states = self.resnets[0](hidden_states, deterministic=deterministic)
        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            hidden_states = attn(hidden_states)
            hidden_states = resnet(hidden_states, deterministic=deterministic)

        return hidden_states


 class FlaxEncoder(nn.Module):
    r"""
    Flax Implementation of VAE Encoder.

    This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:
    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        in_channels (:obj:`int`, *optional*, defaults to 3):
            Input channels
        out_channels (:obj:`int`, *optional*, defaults to 3):
            Output channels
        down_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`):
            DownEncoder block type
        block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`):
            Tuple containing the number of output channels for each block
        layers_per_block (:obj:`int`, *optional*, defaults to `2`):
            Number of Resnet layer for each block
        norm_num_groups (:obj:`int`, *optional*, defaults to `32`):
            norm num group
        act_fn (:obj:`str`, *optional*, defaults to `silu`):
            Activation function
        double_z (:obj:`bool`, *optional*, defaults to `False`):
            Whether to double the last output channels
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int = 3
    out_channels: int = 3
    down_block_types: Tuple[str] = ("DownEncoderBlock2D",)
    block_out_channels: Tuple[int] = (64,)
    layers_per_block: int = 2
    norm_num_groups: int = 32
    act_fn: str = "silu"
    double_z: bool = False
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        block_out_channels = self.block_out_channels
        # in
        self.conv_in = nn.Conv(
            block_out_channels[0],
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        # downsampling
        down_blocks = []
        output_channel = block_out_channels[0]
        for i, _ in enumerate(self.down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = FlaxDownEncoderBlock2D(
                in_channels=input_channel,
                out_channels=output_channel,
                num_layers=self.layers_per_block,
                resnet_groups=self.norm_num_groups,
                add_downsample=not is_final_block,
                dtype=self.dtype,
            )
            down_blocks.append(down_block)
        self.down_blocks = down_blocks

        # middle
        self.mid_block = FlaxUNetMidBlock2D(
            in_channels=block_out_channels[-1],
            resnet_groups=self.norm_num_groups,
            num_attention_heads=None,
            dtype=self.dtype,
        )

        # end
        conv_out_channels = 2 * self.out_channels if self.double_z else self.out_channels
        self.conv_norm_out = nn.GroupNorm(num_groups=self.norm_num_groups, epsilon=1e-6)
        self.conv_out = nn.Conv(
            conv_out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

    def __call__(self, sample, deterministic: bool = True):
        # in
        sample = self.conv_in(sample)

        # downsampling
        for block in self.down_blocks:
            sample = block(sample, deterministic=deterministic)

        # middle
        sample = self.mid_block(sample, deterministic=deterministic)

        # end
        sample = self.conv_norm_out(sample)
        sample = nn.swish(sample)
        sample = self.conv_out(sample)

        return sample


 class FlaxDecoder(nn.Module):
    r"""
    Flax Implementation of VAE Decoder.

    This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:
    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        in_channels (:obj:`int`, *optional*, defaults to 3):
            Input channels
        out_channels (:obj:`int`, *optional*, defaults to 3):
            Output channels
        up_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`):
            UpDecoder block type
        block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`):
            Tuple containing the number of output channels for each block
        layers_per_block (:obj:`int`, *optional*, defaults to `2`):
            Number of Resnet layer for each block
        norm_num_groups (:obj:`int`, *optional*, defaults to `32`):
            norm num group
        act_fn (:obj:`str`, *optional*, defaults to `silu`):
            Activation function
        double_z (:obj:`bool`, *optional*, defaults to `False`):
            Whether to double the last output channels
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            parameters `dtype`
    """

    in_channels: int = 3
    out_channels: int = 3
    up_block_types: Tuple[str] = ("UpDecoderBlock2D",)
    block_out_channels: int = (64,)
    layers_per_block: int = 2
    norm_num_groups: int = 32
    act_fn: str = "silu"
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        block_out_channels = self.block_out_channels

        # z to block_in
        self.conv_in = nn.Conv(
            block_out_channels[-1],
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        # middle
        self.mid_block = FlaxUNetMidBlock2D(
            in_channels=block_out_channels[-1],
            resnet_groups=self.norm_num_groups,
            num_attention_heads=None,
            dtype=self.dtype,
        )

        # upsampling
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        up_blocks = []
        for i, _ in enumerate(self.up_block_types):
            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]

            is_final_block = i == len(block_out_channels) - 1

            up_block = FlaxUpDecoderBlock2D(
                in_channels=prev_output_channel,
                out_channels=output_channel,
                num_layers=self.layers_per_block + 1,
                resnet_groups=self.norm_num_groups,
                add_upsample=not is_final_block,
                dtype=self.dtype,
            )
            up_blocks.append(up_block)
            prev_output_channel = output_channel

        self.up_blocks = up_blocks

        # end
        self.conv_norm_out = nn.GroupNorm(num_groups=self.norm_num_groups, epsilon=1e-6)
        self.conv_out = nn.Conv(
            self.out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

    def __call__(self, sample, deterministic: bool = True):
        # z to block_in
        sample = self.conv_in(sample)

        # middle
        sample = self.mid_block(sample, deterministic=deterministic)

        # upsampling
        for block in self.up_blocks:
            sample = block(sample, deterministic=deterministic)

        sample = self.conv_norm_out(sample)
        sample = nn.swish(sample)
        sample = self.conv_out(sample)

        return sample


 class FlaxDiagonalGaussianDistribution(object):
    def __init__(self, parameters, deterministic=False):
        # Last axis to account for channels-last
        self.mean, self.logvar = jnp.split(parameters, 2, axis=-1)
        self.logvar = jnp.clip(self.logvar, -30.0, 20.0)
        self.deterministic = deterministic
        self.std = jnp.exp(0.5 * self.logvar)
        self.var = jnp.exp(self.logvar)
        if self.deterministic:
            self.var = self.std = jnp.zeros_like(self.mean)

    def sample(self, key):
        return self.mean + self.std * jax.random.normal(key, self.mean.shape)

    def kl(self, other=None):
        if self.deterministic:
            return jnp.array([0.0])

        if other is None:
            return 0.5 * jnp.sum(self.mean**2 + self.var - 1.0 - self.logvar, axis=[1, 2, 3])

        return 0.5 * jnp.sum(
            jnp.square(self.mean - other.mean) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar,
            axis=[1, 2, 3],
        )

    def nll(self, sample, axis=[1, 2, 3]):
        if self.deterministic:
            return jnp.array([0.0])

        logtwopi = jnp.log(2.0 * jnp.pi)
        return 0.5 * jnp.sum(logtwopi + self.logvar + jnp.square(sample - self.mean) / self.var, axis=axis)

    def mode(self):
        return self.mean


 @flax_register_to_config
 class FlaxAutoencoderKL(nn.Module, FlaxModelMixin, ConfigMixin):
    r"""
    Flax implementation of a VAE model with KL loss for decoding latent representations.

    This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it's generic methods
    implemented for all models (such as downloading or saving).

    This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matter related to its
    general usage and behavior.

    Inherent JAX features such as the following are supported:

    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        in_channels (`int`, *optional*, defaults to 3):
            Number of channels in the input image.
        out_channels (`int`, *optional*, defaults to 3):
            Number of channels in the output.
        down_block_types (`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`):
            Tuple of downsample block types.
        up_block_types (`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`):
            Tuple of upsample block types.
        block_out_channels (`Tuple[str]`, *optional*, defaults to `(64,)`):
            Tuple of block output channels.
        layers_per_block (`int`, *optional*, defaults to `2`):
            Number of ResNet layer for each block.
        act_fn (`str`, *optional*, defaults to `silu`):
            The activation function to use.
        latent_channels (`int`, *optional*, defaults to `4`):
            Number of channels in the latent space.
        norm_num_groups (`int`, *optional*, defaults to `32`):
            The number of groups for normalization.
        sample_size (`int`, *optional*, defaults to 32):
            Sample input size.
        scaling_factor (`float`, *optional*, defaults to 0.18215):
            The component-wise standard deviation of the trained latent space computed using the first batch of the
            training set. This is used to scale the latent space to have unit variance when training the diffusion
            model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
            diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
            / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
        dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
            The `dtype` of the parameters.
    """

    in_channels: int = 3
    out_channels: int = 3
    down_block_types: Tuple[str] = ("DownEncoderBlock2D",)
    up_block_types: Tuple[str] = ("UpDecoderBlock2D",)
    block_out_channels: Tuple[int] = (64,)
    layers_per_block: int = 1
    act_fn: str = "silu"
    latent_channels: int = 4
    norm_num_groups: int = 32
    sample_size: int = 32
    scaling_factor: float = 0.18215
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.encoder = FlaxEncoder(
            in_channels=self.config.in_channels,
            out_channels=self.config.latent_channels,
            down_block_types=self.config.down_block_types,
            block_out_channels=self.config.block_out_channels,
            layers_per_block=self.config.layers_per_block,
            act_fn=self.config.act_fn,
            norm_num_groups=self.config.norm_num_groups,
            double_z=True,
            dtype=self.dtype,
        )
        self.decoder = FlaxDecoder(
            in_channels=self.config.latent_channels,
            out_channels=self.config.out_channels,
            up_block_types=self.config.up_block_types,
            block_out_channels=self.config.block_out_channels,
            layers_per_block=self.config.layers_per_block,
            norm_num_groups=self.config.norm_num_groups,
            act_fn=self.config.act_fn,
            dtype=self.dtype,
        )
        self.quant_conv = nn.Conv(
            2 * self.config.latent_channels,
            kernel_size=(1, 1),
            strides=(1, 1),
            padding="VALID",
            dtype=self.dtype,
        )
        self.post_quant_conv = nn.Conv(
            self.config.latent_channels,
            kernel_size=(1, 1),
            strides=(1, 1),
            padding="VALID",
            dtype=self.dtype,
        )

    def init_weights(self, rng: jax.Array) -> FrozenDict:
        # init input tensors
        sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
        sample = jnp.zeros(sample_shape, dtype=jnp.float32)

        params_rng, dropout_rng, gaussian_rng = jax.random.split(rng, 3)
        rngs = {"params": params_rng, "dropout": dropout_rng, "gaussian": gaussian_rng}

        return self.init(rngs, sample)["params"]

    def encode(self, sample, deterministic: bool = True, return_dict: bool = True):
        sample = jnp.transpose(sample, (0, 2, 3, 1))

        hidden_states = self.encoder(sample, deterministic=deterministic)
        moments = self.quant_conv(hidden_states)
        posterior = FlaxDiagonalGaussianDistribution(moments)

        if not return_dict:
            return (posterior,)

        return FlaxAutoencoderKLOutput(latent_dist=posterior)

    def decode(self, latents, deterministic: bool = True, return_dict: bool = True):
        if latents.shape[-1] != self.config.latent_channels:
            latents = jnp.transpose(latents, (0, 2, 3, 1))

        hidden_states = self.post_quant_conv(latents)
        hidden_states = self.decoder(hidden_states, deterministic=deterministic)

        hidden_states = jnp.transpose(hidden_states, (0, 3, 1, 2))

        if not return_dict:
            return (hidden_states,)

        return FlaxDecoderOutput(sample=hidden_states)

    def __call__(self, sample, sample_posterior=False, deterministic: bool = True, return_dict: bool = True):
        posterior = self.encode(sample, deterministic=deterministic, return_dict=return_dict)
        if sample_posterior:
            rng = self.make_rng("gaussian")
            hidden_states = posterior.latent_dist.sample(rng)
        else:
            hidden_states = posterior.latent_dist.mode()

        sample = self.decode(hidden_states, return_dict=return_dict).sample

        if not return_dict:
            return (sample,)

        return FlaxDecoderOutput(sample=sample)

 from ..utils import deprecate
 from .unets.unet_2d_condition import UNet2DConditionModel, UNet2DConditionOutput


 class UNet2DConditionOutput(UNet2DConditionOutput):
    deprecation_message = "Importing `UNet2DConditionOutput` from `diffusers.models.unet_2d_condition` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput`, instead."
    deprecate("UNet2DConditionOutput", "0.29", deprecation_message)


 class UNet2DConditionModel(UNet2DConditionModel):
    deprecation_message = "Importing `UNet2DConditionModel` from `diffusers.models.unet_2d_condition` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel`, instead."
    deprecate("UNet2DConditionModel", "0.29", deprecation_message)



 # IMPORTANT:                                                      #
 ###################################################################
 # ----------------------------------------------------------------#
 # This file is deprecated and will be removed soon                #
 # (as soon as PEFT will become a required dependency for LoRA)    #
 # ----------------------------------------------------------------#
 ###################################################################

 from typing import Optional, Tuple, Union

 import torch
 import torch.nn.functional as F
 from torch import nn

 from ..utils import deprecate, logging
 from ..utils.import_utils import is_transformers_available


 if is_transformers_available():
    from transformers import CLIPTextModel, CLIPTextModelWithProjection


 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


 def text_encoder_attn_modules(text_encoder):
    attn_modules = []

    if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
        for i, layer in enumerate(text_encoder.text_model.encoder.layers):
            name = f"text_model.encoder.layers.{i}.self_attn"
            mod = layer.self_attn
            attn_modules.append((name, mod))
    else:
        raise ValueError(f"do not know how to get attention modules for: {text_encoder.__class__.__name__}")

    return attn_modules


 def text_encoder_mlp_modules(text_encoder):
    mlp_modules = []

    if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
        for i, layer in enumerate(text_encoder.text_model.encoder.layers):
            mlp_mod = layer.mlp
            name = f"text_model.encoder.layers.{i}.mlp"
            mlp_modules.append((name, mlp_mod))
    else:
        raise ValueError(f"do not know how to get mlp modules for: {text_encoder.__class__.__name__}")

    return mlp_modules


 def adjust_lora_scale_text_encoder(text_encoder, lora_scale: float = 1.0):
    for _, attn_module in text_encoder_attn_modules(text_encoder):
        if isinstance(attn_module.q_proj, PatchedLoraProjection):
            attn_module.q_proj.lora_scale = lora_scale
            attn_module.k_proj.lora_scale = lora_scale
            attn_module.v_proj.lora_scale = lora_scale
            attn_module.out_proj.lora_scale = lora_scale

    for _, mlp_module in text_encoder_mlp_modules(text_encoder):
        if isinstance(mlp_module.fc1, PatchedLoraProjection):
            mlp_module.fc1.lora_scale = lora_scale
            mlp_module.fc2.lora_scale = lora_scale


 class PatchedLoraProjection(torch.nn.Module):
    def __init__(self, regular_linear_layer, lora_scale=1, network_alpha=None, rank=4, dtype=None):
        deprecation_message = "Use of `PatchedLoraProjection` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`."
        deprecate("PatchedLoraProjection", "1.0.0", deprecation_message)

        super().__init__()
        from ..models.lora import LoRALinearLayer

        self.regular_linear_layer = regular_linear_layer

        device = self.regular_linear_layer.weight.device

        if dtype is None:
            dtype = self.regular_linear_layer.weight.dtype

        self.lora_linear_layer = LoRALinearLayer(
            self.regular_linear_layer.in_features,
            self.regular_linear_layer.out_features,
            network_alpha=network_alpha,
            device=device,
            dtype=dtype,
            rank=rank,
        )

        self.lora_scale = lora_scale

    # overwrite PyTorch's `state_dict` to be sure that only the 'regular_linear_layer' weights are saved
    # when saving the whole text encoder model and when LoRA is unloaded or fused
    def state_dict(self, *args, destination=None, prefix="", keep_vars=False):
        if self.lora_linear_layer is None:
            return self.regular_linear_layer.state_dict(
                *args, destination=destination, prefix=prefix, keep_vars=keep_vars
            )

        return super().state_dict(*args, destination=destination, prefix=prefix, keep_vars=keep_vars)

    def _fuse_lora(self, lora_scale=1.0, safe_fusing=False):
        if self.lora_linear_layer is None:
            return

        dtype, device = self.regular_linear_layer.weight.data.dtype, self.regular_linear_layer.weight.data.device

        w_orig = self.regular_linear_layer.weight.data.float()
        w_up = self.lora_linear_layer.up.weight.data.float()
        w_down = self.lora_linear_layer.down.weight.data.float()

        if self.lora_linear_layer.network_alpha is not None:
            w_up = w_up * self.lora_linear_layer.network_alpha / self.lora_linear_layer.rank

        fused_weight = w_orig + (lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0])

        if safe_fusing and torch.isnan(fused_weight).any().item():
            raise ValueError(
                "This LoRA weight seems to be broken. "
                f"Encountered NaN values when trying to fuse LoRA weights for {self}."
                "LoRA weights will not be fused."
            )

        self.regular_linear_layer.weight.data = fused_weight.to(device=device, dtype=dtype)

        # we can drop the lora layer now
        self.lora_linear_layer = None

        # offload the up and down matrices to CPU to not blow the memory
        self.w_up = w_up.cpu()
        self.w_down = w_down.cpu()
        self.lora_scale = lora_scale

    def _unfuse_lora(self):
        if not (getattr(self, "w_up", None) is not None and getattr(self, "w_down", None) is not None):
            return

        fused_weight = self.regular_linear_layer.weight.data
        dtype, device = fused_weight.dtype, fused_weight.device

        w_up = self.w_up.to(device=device).float()
        w_down = self.w_down.to(device).float()

        unfused_weight = fused_weight.float() - (self.lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0])
        self.regular_linear_layer.weight.data = unfused_weight.to(device=device, dtype=dtype)

        self.w_up = None
        self.w_down = None

    def forward(self, input):
        if self.lora_scale is None:
            self.lora_scale = 1.0
        if self.lora_linear_layer is None:
            return self.regular_linear_layer(input)
        return self.regular_linear_layer(input) + (self.lora_scale * self.lora_linear_layer(input))


 class LoRALinearLayer(nn.Module):
    r"""
    A linear layer that is used with LoRA.

    Parameters:
        in_features (`int`):
            Number of input features.
        out_features (`int`):
            Number of output features.
        rank (`int`, `optional`, defaults to 4):
            The rank of the LoRA layer.
        network_alpha (`float`, `optional`, defaults to `None`):
            The value of the network alpha used for stable learning and preventing underflow. This value has the same
            meaning as the `--network_alpha` option in the kohya-ss trainer script. See
            https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
        device (`torch.device`, `optional`, defaults to `None`):
            The device to use for the layer's weights.
        dtype (`torch.dtype`, `optional`, defaults to `None`):
            The dtype to use for the layer's weights.
    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        rank: int = 4,
        network_alpha: Optional[float] = None,
        device: Optional[Union[torch.device, str]] = None,
        dtype: Optional[torch.dtype] = None,
    ):
        super().__init__()

        self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype)
        self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype)
        # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
        # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
        self.network_alpha = network_alpha
        self.rank = rank
        self.out_features = out_features
        self.in_features = in_features

        nn.init.normal_(self.down.weight, std=1 / rank)
        nn.init.zeros_(self.up.weight)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        orig_dtype = hidden_states.dtype
        dtype = self.down.weight.dtype

        down_hidden_states = self.down(hidden_states.to(dtype))
        up_hidden_states = self.up(down_hidden_states)

        if self.network_alpha is not None:
            up_hidden_states *= self.network_alpha / self.rank

        return up_hidden_states.to(orig_dtype)


 class LoRAConv2dLayer(nn.Module):
    r"""
    A convolutional layer that is used with LoRA.

    Parameters:
        in_features (`int`):
            Number of input features.
        out_features (`int`):
            Number of output features.
        rank (`int`, `optional`, defaults to 4):
            The rank of the LoRA layer.
        kernel_size (`int` or `tuple` of two `int`, `optional`, defaults to 1):
            The kernel size of the convolution.
        stride (`int` or `tuple` of two `int`, `optional`, defaults to 1):
            The stride of the convolution.
        padding (`int` or `tuple` of two `int` or `str`, `optional`, defaults to 0):
            The padding of the convolution.
        network_alpha (`float`, `optional`, defaults to `None`):
            The value of the network alpha used for stable learning and preventing underflow. This value has the same
            meaning as the `--network_alpha` option in the kohya-ss trainer script. See
            https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        rank: int = 4,
        kernel_size: Union[int, Tuple[int, int]] = (1, 1),
        stride: Union[int, Tuple[int, int]] = (1, 1),
        padding: Union[int, Tuple[int, int], str] = 0,
        network_alpha: Optional[float] = None,
    ):
        super().__init__()

        self.down = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
        # according to the official kohya_ss trainer kernel_size are always fixed for the up layer
        # # see: https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L129
        self.up = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=False)

        # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
        # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
        self.network_alpha = network_alpha
        self.rank = rank

        nn.init.normal_(self.down.weight, std=1 / rank)
        nn.init.zeros_(self.up.weight)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        orig_dtype = hidden_states.dtype
        dtype = self.down.weight.dtype

        down_hidden_states = self.down(hidden_states.to(dtype))
        up_hidden_states = self.up(down_hidden_states)

        if self.network_alpha is not None:
            up_hidden_states *= self.network_alpha / self.rank

        return up_hidden_states.to(orig_dtype)


 class LoRACompatibleConv(nn.Conv2d):
    """
    A convolutional layer that can be used with LoRA.
    """

    def __init__(self, *args, lora_layer: Optional[LoRAConv2dLayer] = None, **kwargs):
        deprecation_message = "Use of `LoRACompatibleConv` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`."
        deprecate("LoRACompatibleConv", "1.0.0", deprecation_message)

        super().__init__(*args, **kwargs)
        self.lora_layer = lora_layer

    def set_lora_layer(self, lora_layer: Optional[LoRAConv2dLayer]):
        deprecation_message = "Use of `set_lora_layer()` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`."
        deprecate("set_lora_layer", "1.0.0", deprecation_message)

        self.lora_layer = lora_layer

    def _fuse_lora(self, lora_scale: float = 1.0, safe_fusing: bool = False):
        if self.lora_layer is None:
            return

        dtype, device = self.weight.data.dtype, self.weight.data.device

        w_orig = self.weight.data.float()
        w_up = self.lora_layer.up.weight.data.float()
        w_down = self.lora_layer.down.weight.data.float()

        if self.lora_layer.network_alpha is not None:
            w_up = w_up * self.lora_layer.network_alpha / self.lora_layer.rank

        fusion = torch.mm(w_up.flatten(start_dim=1), w_down.flatten(start_dim=1))
        fusion = fusion.reshape((w_orig.shape))
        fused_weight = w_orig + (lora_scale * fusion)

        if safe_fusing and torch.isnan(fused_weight).any().item():
            raise ValueError(
                "This LoRA weight seems to be broken. "
                f"Encountered NaN values when trying to fuse LoRA weights for {self}."
                "LoRA weights will not be fused."
            )

        self.weight.data = fused_weight.to(device=device, dtype=dtype)

        # we can drop the lora layer now
        self.lora_layer = None

        # offload the up and down matrices to CPU to not blow the memory
        self.w_up = w_up.cpu()
        self.w_down = w_down.cpu()
        self._lora_scale = lora_scale

    def _unfuse_lora(self):
        if not (getattr(self, "w_up", None) is not None and getattr(self, "w_down", None) is not None):
            return

        fused_weight = self.weight.data
        dtype, device = fused_weight.data.dtype, fused_weight.data.device

        self.w_up = self.w_up.to(device=device).float()
        self.w_down = self.w_down.to(device).float()

        fusion = torch.mm(self.w_up.flatten(start_dim=1), self.w_down.flatten(start_dim=1))
        fusion = fusion.reshape((fused_weight.shape))
        unfused_weight = fused_weight.float() - (self._lora_scale * fusion)
        self.weight.data = unfused_weight.to(device=device, dtype=dtype)

        self.w_up = None
        self.w_down = None

    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
        if self.padding_mode != "zeros":
            hidden_states = F.pad(hidden_states, self._reversed_padding_repeated_twice, mode=self.padding_mode)
            padding = (0, 0)
        else:
            padding = self.padding

        original_outputs = F.conv2d(
            hidden_states, self.weight, self.bias, self.stride, padding, self.dilation, self.groups
        )

        if self.lora_layer is None:
            return original_outputs
        else:
            return original_outputs + (scale * self.lora_layer(hidden_states))


 class LoRACompatibleLinear(nn.Linear):
    """
    A Linear layer that can be used with LoRA.
    """

    def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs):
        deprecation_message = "Use of `LoRACompatibleLinear` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`."
        deprecate("LoRACompatibleLinear", "1.0.0", deprecation_message)

        super().__init__(*args, **kwargs)
        self.lora_layer = lora_layer

    def set_lora_layer(self, lora_layer: Optional[LoRALinearLayer]):
        deprecation_message = "Use of `set_lora_layer()` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`."
        deprecate("set_lora_layer", "1.0.0", deprecation_message)
        self.lora_layer = lora_layer

    def _fuse_lora(self, lora_scale: float = 1.0, safe_fusing: bool = False):
        if self.lora_layer is None:
            return

        dtype, device = self.weight.data.dtype, self.weight.data.device

        w_orig = self.weight.data.float()
        w_up = self.lora_layer.up.weight.data.float()
        w_down = self.lora_layer.down.weight.data.float()

        if self.lora_layer.network_alpha is not None:
            w_up = w_up * self.lora_layer.network_alpha / self.lora_layer.rank

        fused_weight = w_orig + (lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0])

        if safe_fusing and torch.isnan(fused_weight).any().item():
            raise ValueError(
                "This LoRA weight seems to be broken. "
                f"Encountered NaN values when trying to fuse LoRA weights for {self}."
                "LoRA weights will not be fused."
            )

        self.weight.data = fused_weight.to(device=device, dtype=dtype)

        # we can drop the lora layer now
        self.lora_layer = None

        # offload the up and down matrices to CPU to not blow the memory
        self.w_up = w_up.cpu()
        self.w_down = w_down.cpu()
        self._lora_scale = lora_scale

    def _unfuse_lora(self):
        if not (getattr(self, "w_up", None) is not None and getattr(self, "w_down", None) is not None):
            return

        fused_weight = self.weight.data
        dtype, device = fused_weight.dtype, fused_weight.device

        w_up = self.w_up.to(device=device).float()
        w_down = self.w_down.to(device).float()

        unfused_weight = fused_weight.float() - (self._lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0])
        self.weight.data = unfused_weight.to(device=device, dtype=dtype)

        self.w_up = None
        self.w_down = None

    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
        if self.lora_layer is None:
            out = super().forward(hidden_states)
            return out
        else:
            out = super().forward(hidden_states) + (scale * self.lora_layer(hidden_states))
            return out


 from typing import Optional, Tuple

 import torch
 import torch.nn as nn
 import torch.nn.functional as F

 from ..utils import USE_PEFT_BACKEND
 from .lora import LoRACompatibleConv
 from .normalization import RMSNorm


 class Upsample1D(nn.Module):
    """A 1D upsampling layer with an optional convolution.

    Parameters:
        channels (`int`):
            number of channels in the inputs and outputs.
        use_conv (`bool`, default `False`):
            option to use a convolution.
        use_conv_transpose (`bool`, default `False`):
            option to use a convolution transpose.
        out_channels (`int`, optional):
            number of output channels. Defaults to `channels`.
        name (`str`, default `conv`):
            name of the upsampling 1D layer.
    """

    def __init__(
        self,
        channels: int,
        use_conv: bool = False,
        use_conv_transpose: bool = False,
        out_channels: Optional[int] = None,
        name: str = "conv",
    ):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.use_conv_transpose = use_conv_transpose
        self.name = name

        self.conv = None
        if use_conv_transpose:
            self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)
        elif use_conv:
            self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        assert inputs.shape[1] == self.channels
        if self.use_conv_transpose:
            return self.conv(inputs)

        outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest")

        if self.use_conv:
            outputs = self.conv(outputs)

        return outputs


 class Upsample2D(nn.Module):
    """A 2D upsampling layer with an optional convolution.

    Parameters:
        channels (`int`):
            number of channels in the inputs and outputs.
        use_conv (`bool`, default `False`):
            option to use a convolution.
        use_conv_transpose (`bool`, default `False`):
            option to use a convolution transpose.
        out_channels (`int`, optional):
            number of output channels. Defaults to `channels`.
        name (`str`, default `conv`):
            name of the upsampling 2D layer.
    """

    def __init__(
        self,
        channels: int,
        use_conv: bool = False,
        use_conv_transpose: bool = False,
        out_channels: Optional[int] = None,
        name: str = "conv",
        kernel_size: Optional[int] = None,
        padding=1,
        norm_type=None,
        eps=None,
        elementwise_affine=None,
        bias=True,
        interpolate=True,
    ):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.use_conv_transpose = use_conv_transpose
        self.name = name
        self.interpolate = interpolate
        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv

        if norm_type == "ln_norm":
            self.norm = nn.LayerNorm(channels, eps, elementwise_affine)
        elif norm_type == "rms_norm":
            self.norm = RMSNorm(channels, eps, elementwise_affine)
        elif norm_type is None:
            self.norm = None
        else:
            raise ValueError(f"unknown norm_type: {norm_type}")

        conv = None
        if use_conv_transpose:
            if kernel_size is None:
                kernel_size = 4
            conv = nn.ConvTranspose2d(
                channels, self.out_channels, kernel_size=kernel_size, stride=2, padding=padding, bias=bias
            )
        elif use_conv:
            if kernel_size is None:
                kernel_size = 3
            conv = conv_cls(self.channels, self.out_channels, kernel_size=kernel_size, padding=padding, bias=bias)

        # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
        if name == "conv":
            self.conv = conv
        else:
            self.Conv2d_0 = conv

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        output_size: Optional[int] = None,
        scale: float = 1.0,
    ) -> torch.FloatTensor:
        assert hidden_states.shape[1] == self.channels

        if self.norm is not None:
            hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)

        if self.use_conv_transpose:
            return self.conv(hidden_states)

        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
        # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch
        # https://github.com/pytorch/pytorch/issues/86679
        dtype = hidden_states.dtype
        if dtype == torch.bfloat16:
            hidden_states = hidden_states.to(torch.float32)

        # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
        if hidden_states.shape[0] >= 64:
            hidden_states = hidden_states.contiguous()

        # if `output_size` is passed we force the interpolation output
        # size and do not make use of `scale_factor=2`
        if self.interpolate:
            if output_size is None:
                hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
            else:
                hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")

        # If the input is bfloat16, we cast back to bfloat16
        if dtype == torch.bfloat16:
            hidden_states = hidden_states.to(dtype)

        # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
        if self.use_conv:
            if self.name == "conv":
                if isinstance(self.conv, LoRACompatibleConv) and not USE_PEFT_BACKEND:
                    hidden_states = self.conv(hidden_states, scale)
                else:
                    hidden_states = self.conv(hidden_states)
            else:
                if isinstance(self.Conv2d_0, LoRACompatibleConv) and not USE_PEFT_BACKEND:
                    hidden_states = self.Conv2d_0(hidden_states, scale)
                else:
                    hidden_states = self.Conv2d_0(hidden_states)

        return hidden_states


 class FirUpsample2D(nn.Module):
    """A 2D FIR upsampling layer with an optional convolution.

    Parameters:
        channels (`int`, optional):
            number of channels in the inputs and outputs.
        use_conv (`bool`, default `False`):
            option to use a convolution.
        out_channels (`int`, optional):
            number of output channels. Defaults to `channels`.
        fir_kernel (`tuple`, default `(1, 3, 3, 1)`):
            kernel for the FIR filter.
    """

    def __init__(
        self,
        channels: Optional[int] = None,
        out_channels: Optional[int] = None,
        use_conv: bool = False,
        fir_kernel: Tuple[int, int, int, int] = (1, 3, 3, 1),
    ):
        super().__init__()
        out_channels = out_channels if out_channels else channels
        if use_conv:
            self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.use_conv = use_conv
        self.fir_kernel = fir_kernel
        self.out_channels = out_channels

    def _upsample_2d(
        self,
        hidden_states: torch.FloatTensor,
        weight: Optional[torch.FloatTensor] = None,
        kernel: Optional[torch.FloatTensor] = None,
        factor: int = 2,
        gain: float = 1,
    ) -> torch.FloatTensor:
        """Fused `upsample_2d()` followed by `Conv2d()`.

        Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
        arbitrary order.

        Args:
            hidden_states (`torch.FloatTensor`):
                Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
            weight (`torch.FloatTensor`, *optional*):
                Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be
                performed by `inChannels = x.shape[0] // numGroups`.
            kernel (`torch.FloatTensor`, *optional*):
                FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which
                corresponds to nearest-neighbor upsampling.
            factor (`int`, *optional*): Integer upsampling factor (default: 2).
            gain (`float`, *optional*): Scaling factor for signal magnitude (default: 1.0).

        Returns:
            output (`torch.FloatTensor`):
                Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same
                datatype as `hidden_states`.
        """

        assert isinstance(factor, int) and factor >= 1

        # Setup filter kernel.
        if kernel is None:
            kernel = [1] * factor

        # setup kernel
        kernel = torch.tensor(kernel, dtype=torch.float32)
        if kernel.ndim == 1:
            kernel = torch.outer(kernel, kernel)
        kernel /= torch.sum(kernel)

        kernel = kernel * (gain * (factor**2))

        if self.use_conv:
            convH = weight.shape[2]
            convW = weight.shape[3]
            inC = weight.shape[1]

            pad_value = (kernel.shape[0] - factor) - (convW - 1)

            stride = (factor, factor)
            # Determine data dimensions.
            output_shape = (
                (hidden_states.shape[2] - 1) * factor + convH,
                (hidden_states.shape[3] - 1) * factor + convW,
            )
            output_padding = (
                output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH,
                output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW,
            )
            assert output_padding[0] >= 0 and output_padding[1] >= 0
            num_groups = hidden_states.shape[1] // inC

            # Transpose weights.
            weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW))
            weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4)
            weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))

            inverse_conv = F.conv_transpose2d(
                hidden_states,
                weight,
                stride=stride,
                output_padding=output_padding,
                padding=0,
            )

            output = upfirdn2d_native(
                inverse_conv,
                torch.tensor(kernel, device=inverse_conv.device),
                pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1),
            )
        else:
            pad_value = kernel.shape[0] - factor
            output = upfirdn2d_native(
                hidden_states,
                torch.tensor(kernel, device=hidden_states.device),
                up=factor,
                pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
            )

        return output

    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        if self.use_conv:
            height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel)
            height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
        else:
            height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)

        return height


 class KUpsample2D(nn.Module):
    r"""A 2D K-upsampling layer.

    Parameters:
        pad_mode (`str`, *optional*, default to `"reflect"`): the padding mode to use.
    """

    def __init__(self, pad_mode: str = "reflect"):
        super().__init__()
        self.pad_mode = pad_mode
        kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]]) * 2
        self.pad = kernel_1d.shape[1] // 2 - 1
        self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        inputs = F.pad(inputs, ((self.pad + 1) // 2,) * 4, self.pad_mode)
        weight = inputs.new_zeros(
            [
                inputs.shape[1],
                inputs.shape[1],
                self.kernel.shape[0],
                self.kernel.shape[1],
            ]
        )
        indices = torch.arange(inputs.shape[1], device=inputs.device)
        kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
        weight[indices, indices] = kernel
        return F.conv_transpose2d(inputs, weight, stride=2, padding=self.pad * 2 + 1)


 def upfirdn2d_native(
    tensor: torch.Tensor,
    kernel: torch.Tensor,
    up: int = 1,
    down: int = 1,
    pad: Tuple[int, int] = (0, 0),
 ) -> torch.Tensor:
    up_x = up_y = up
    down_x = down_y = down
    pad_x0 = pad_y0 = pad[0]
    pad_x1 = pad_y1 = pad[1]

    _, channel, in_h, in_w = tensor.shape
    tensor = tensor.reshape(-1, in_h, in_w, 1)

    _, in_h, in_w, minor = tensor.shape
    kernel_h, kernel_w = kernel.shape

    out = tensor.view(-1, in_h, 1, in_w, 1, minor)
    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
    out = out.view(-1, in_h * up_y, in_w * up_x, minor)

    out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
    out = out.to(tensor.device)  # Move back to mps if necessary
    out = out[
        :,
        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
        :,
    ]

    out = out.permute(0, 3, 1, 2)
    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(
        -1,
        minor,
        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
    )
    out = out.permute(0, 2, 3, 1)
    out = out[:, ::down_y, ::down_x, :]

    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1

    return out.view(-1, channel, out_h, out_w)


 def upsample_2d(
    hidden_states: torch.FloatTensor,
    kernel: Optional[torch.FloatTensor] = None,
    factor: int = 2,
    gain: float = 1,
 ) -> torch.FloatTensor:
    r"""Upsample2D a batch of 2D images with the given filter.
    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
    filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
    `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is
    a: multiple of the upsampling factor.

    Args:
        hidden_states (`torch.FloatTensor`):
            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        kernel (`torch.FloatTensor`, *optional*):
            FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which
            corresponds to nearest-neighbor upsampling.
        factor (`int`, *optional*, default to `2`):
            Integer upsampling factor.
        gain (`float`, *optional*, default to `1.0`):
            Scaling factor for signal magnitude (default: 1.0).

    Returns:
        output (`torch.FloatTensor`):
            Tensor of the shape `[N, C, H * factor, W * factor]`
    """
    assert isinstance(factor, int) and factor >= 1
    if kernel is None:
        kernel = [1] * factor

    kernel = torch.tensor(kernel, dtype=torch.float32)
    if kernel.ndim == 1:
        kernel = torch.outer(kernel, kernel)
    kernel /= torch.sum(kernel)

    kernel = kernel * (gain * (factor**2))
    pad_value = kernel.shape[0] - factor
    output = upfirdn2d_native(
        hidden_states,
        kernel.to(device=hidden_states.device),
        up=factor,
        pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
    )
    return output
 from ..utils import deprecate
 from .transformers.prior_transformer import PriorTransformer, PriorTransformerOutput


 class PriorTransformerOutput(PriorTransformerOutput):
    deprecation_message = "Importing `PriorTransformerOutput` from `diffusers.models.prior_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.prior_transformer import PriorTransformerOutput`, instead."
    deprecate("PriorTransformerOutput", "0.29", deprecation_message)


 class PriorTransformer(PriorTransformer):
    deprecation_message = "Importing `PriorTransformer` from `diffusers.models.prior_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.prior_transformer import PriorTransformer`, instead."
    deprecate("PriorTransformer", "0.29", deprecation_message)

 import flax.linen as nn
 import jax
 import jax.numpy as jnp


 class FlaxUpsample2D(nn.Module):
    out_channels: int
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.conv = nn.Conv(
            self.out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

    def __call__(self, hidden_states):
        batch, height, width, channels = hidden_states.shape
        hidden_states = jax.image.resize(
            hidden_states,
            shape=(batch, height * 2, width * 2, channels),
            method="nearest",
        )
        hidden_states = self.conv(hidden_states)
        return hidden_states


 class FlaxDownsample2D(nn.Module):
    out_channels: int
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.conv = nn.Conv(
            self.out_channels,
            kernel_size=(3, 3),
            strides=(2, 2),
            padding=((1, 1), (1, 1)),  # padding="VALID",
            dtype=self.dtype,
        )

    def __call__(self, hidden_states):
        # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
        # hidden_states = jnp.pad(hidden_states, pad_width=pad)
        hidden_states = self.conv(hidden_states)
        return hidden_states


 class FlaxResnetBlock2D(nn.Module):
    in_channels: int
    out_channels: int = None
    dropout_prob: float = 0.0
    use_nin_shortcut: bool = None
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        out_channels = self.in_channels if self.out_channels is None else self.out_channels

        self.norm1 = nn.GroupNorm(num_groups=32, epsilon=1e-5)
        self.conv1 = nn.Conv(
            out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        self.time_emb_proj = nn.Dense(out_channels, dtype=self.dtype)

        self.norm2 = nn.GroupNorm(num_groups=32, epsilon=1e-5)
        self.dropout = nn.Dropout(self.dropout_prob)
        self.conv2 = nn.Conv(
            out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        self.conv_shortcut = None
        if use_nin_shortcut:
            self.conv_shortcut = nn.Conv(
                out_channels,
                kernel_size=(1, 1),
                strides=(1, 1),
                padding="VALID",
                dtype=self.dtype,
            )

    def __call__(self, hidden_states, temb, deterministic=True):
        residual = hidden_states
        hidden_states = self.norm1(hidden_states)
        hidden_states = nn.swish(hidden_states)
        hidden_states = self.conv1(hidden_states)

        temb = self.time_emb_proj(nn.swish(temb))
        temb = jnp.expand_dims(jnp.expand_dims(temb, 1), 1)
        hidden_states = hidden_states + temb

        hidden_states = self.norm2(hidden_states)
        hidden_states = nn.swish(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic)
        hidden_states = self.conv2(hidden_states)

        if self.conv_shortcut is not None:
            residual = self.conv_shortcut(residual)

        return hidden_states + residual
 # Copyright 2022 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
 from typing import Callable, List, Optional, Union

 import torch
 import torch.nn as nn

 from ..configuration_utils import ConfigMixin, register_to_config
 from ..utils import logging
 from .modeling_utils import ModelMixin


 logger = logging.get_logger(__name__)


 class MultiAdapter(ModelMixin):
    r"""
    MultiAdapter is a wrapper model that contains multiple adapter models and merges their outputs according to
    user-assigned weighting.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
    implements for all the model (such as downloading or saving, etc.)

    Parameters:
        adapters (`List[T2IAdapter]`, *optional*, defaults to None):
            A list of `T2IAdapter` model instances.
    """

    def __init__(self, adapters: List["T2IAdapter"]):
        super(MultiAdapter, self).__init__()

        self.num_adapter = len(adapters)
        self.adapters = nn.ModuleList(adapters)

        if len(adapters) == 0:
            raise ValueError("Expecting at least one adapter")

        if len(adapters) == 1:
            raise ValueError("For a single adapter, please use the `T2IAdapter` class instead of `MultiAdapter`")

        # The outputs from each adapter are added together with a weight.
        # This means that the change in dimensions from downsampling must
        # be the same for all adapters. Inductively, it also means the
        # downscale_factor and total_downscale_factor must be the same for all
        # adapters.
        first_adapter_total_downscale_factor = adapters[0].total_downscale_factor
        first_adapter_downscale_factor = adapters[0].downscale_factor
        for idx in range(1, len(adapters)):
            if (
                adapters[idx].total_downscale_factor != first_adapter_total_downscale_factor
                or adapters[idx].downscale_factor != first_adapter_downscale_factor
            ):
                raise ValueError(
                    f"Expecting all adapters to have the same downscaling behavior, but got:\n"
                    f"adapters[0].total_downscale_factor={first_adapter_total_downscale_factor}\n"
                    f"adapters[0].downscale_factor={first_adapter_downscale_factor}\n"
                    f"adapter[`{idx}`].total_downscale_factor={adapters[idx].total_downscale_factor}\n"
                    f"adapter[`{idx}`].downscale_factor={adapters[idx].downscale_factor}"
                )

        self.total_downscale_factor = first_adapter_total_downscale_factor
        self.downscale_factor = first_adapter_downscale_factor

    def forward(self, xs: torch.Tensor, adapter_weights: Optional[List[float]] = None) -> List[torch.Tensor]:
        r"""
        Args:
            xs (`torch.Tensor`):
                (batch, channel, height, width) input images for multiple adapter models concated along dimension 1,
                `channel` should equal to `num_adapter` * "number of channel of image".
            adapter_weights (`List[float]`, *optional*, defaults to None):
                List of floats representing the weight which will be multiply to each adapter's output before adding
                them together.
        """
        if adapter_weights is None:
            adapter_weights = torch.tensor([1 / self.num_adapter] * self.num_adapter)
        else:
            adapter_weights = torch.tensor(adapter_weights)

        accume_state = None
        for x, w, adapter in zip(xs, adapter_weights, self.adapters):
            features = adapter(x)
            if accume_state is None:
                accume_state = features
                for i in range(len(accume_state)):
                    accume_state[i] = w * accume_state[i]
            else:
                for i in range(len(features)):
                    accume_state[i] += w * features[i]
        return accume_state

    def save_pretrained(
        self,
        save_directory: Union[str, os.PathLike],
        is_main_process: bool = True,
        save_function: Callable = None,
        safe_serialization: bool = True,
        variant: Optional[str] = None,
    ):
        """
        Save a model and its configuration file to a directory, so that it can be re-loaded using the
        `[`~models.adapter.MultiAdapter.from_pretrained`]` class method.

        Arguments:
            save_directory (`str` or `os.PathLike`):
                Directory to which to save. Will be created if it doesn't exist.
            is_main_process (`bool`, *optional*, defaults to `True`):
                Whether the process calling this is the main process or not. Useful when in distributed training like
                TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
                the main process to avoid race conditions.
            save_function (`Callable`):
                The function to use to save the state dictionary. Useful on distributed training like TPUs when one
                need to replace `torch.save` by another method. Can be configured with the environment variable
                `DIFFUSERS_SAVE_MODE`.
            safe_serialization (`bool`, *optional*, defaults to `True`):
                Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
            variant (`str`, *optional*):
                If specified, weights are saved in the format pytorch_model.<variant>.bin.
        """
        idx = 0
        model_path_to_save = save_directory
        for adapter in self.adapters:
            adapter.save_pretrained(
                model_path_to_save,
                is_main_process=is_main_process,
                save_function=save_function,
                safe_serialization=safe_serialization,
                variant=variant,
            )

            idx += 1
            model_path_to_save = model_path_to_save + f"_{idx}"

    @classmethod
    def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]], **kwargs):
        r"""
        Instantiate a pretrained MultiAdapter model from multiple pre-trained adapter models.

        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
        the model, you should first set it back in training mode with `model.train()`.

        The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
        pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
        task.

        The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
        weights are discarded.

        Parameters:
            pretrained_model_path (`os.PathLike`):
                A path to a *directory* containing model weights saved using
                [`~diffusers.models.adapter.MultiAdapter.save_pretrained`], e.g., `./my_model_directory/adapter`.
            torch_dtype (`str` or `torch.dtype`, *optional*):
                Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
                will be automatically derived from the model's weights.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
                A map that specifies where each submodule should go. It doesn't need to be refined to each
                parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
                same device.

                To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
                more information about each option see [designing a device
                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
            max_memory (`Dict`, *optional*):
                A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
                GPU and the available CPU RAM if unset.
            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
                Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
                also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
                model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
                setting this argument to `True` will raise an error.
            variant (`str`, *optional*):
                If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. `variant` is
                ignored when using `from_flax`.
            use_safetensors (`bool`, *optional*, defaults to `None`):
                If set to `None`, the `safetensors` weights will be downloaded if they're available **and** if the
                `safetensors` library is installed. If set to `True`, the model will be forcibly loaded from
                `safetensors` weights. If set to `False`, loading will *not* use `safetensors`.
        """
        idx = 0
        adapters = []

        # load adapter and append to list until no adapter directory exists anymore
        # first adapter has to be saved under `./mydirectory/adapter` to be compliant with `DiffusionPipeline.from_pretrained`
        # second, third, ... adapters have to be saved under `./mydirectory/adapter_1`, `./mydirectory/adapter_2`, ...
        model_path_to_load = pretrained_model_path
        while os.path.isdir(model_path_to_load):
            adapter = T2IAdapter.from_pretrained(model_path_to_load, **kwargs)
            adapters.append(adapter)

            idx += 1
            model_path_to_load = pretrained_model_path + f"_{idx}"

        logger.info(f"{len(adapters)} adapters loaded from {pretrained_model_path}.")

        if len(adapters) == 0:
            raise ValueError(
                f"No T2IAdapters found under {os.path.dirname(pretrained_model_path)}. Expected at least {pretrained_model_path + '_0'}."
            )

        return cls(adapters)


 class T2IAdapter(ModelMixin, ConfigMixin):
    r"""
    A simple ResNet-like model that accepts images containing control signals such as keyposes and depth. The model
    generates multiple feature maps that are used as additional conditioning in [`UNet2DConditionModel`]. The model's
    architecture follows the original implementation of
    [Adapter](https://github.com/TencentARC/T2I-Adapter/blob/686de4681515662c0ac2ffa07bf5dda83af1038a/ldm/modules/encoders/adapter.py#L97)
     and
     [AdapterLight](https://github.com/TencentARC/T2I-Adapter/blob/686de4681515662c0ac2ffa07bf5dda83af1038a/ldm/modules/encoders/adapter.py#L235).

    This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
    implements for all the model (such as downloading or saving, etc.)

    Parameters:
        in_channels (`int`, *optional*, defaults to 3):
            Number of channels of Aapter's input(*control image*). Set this parameter to 1 if you're using gray scale
            image as *control image*.
        channels (`List[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
            The number of channel of each downsample block's output hidden state. The `len(block_out_channels)` will
            also determine the number of downsample blocks in the Adapter.
        num_res_blocks (`int`, *optional*, defaults to 2):
            Number of ResNet blocks in each downsample block.
        downscale_factor (`int`, *optional*, defaults to 8):
            A factor that determines the total downscale factor of the Adapter.
        adapter_type (`str`, *optional*, defaults to `full_adapter`):
            The type of Adapter to use. Choose either `full_adapter` or `full_adapter_xl` or `light_adapter`.
    """

    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        channels: List[int] = [320, 640, 1280, 1280],
        num_res_blocks: int = 2,
        downscale_factor: int = 8,
        adapter_type: str = "full_adapter",
    ):
        super().__init__()

        if adapter_type == "full_adapter":
            self.adapter = FullAdapter(in_channels, channels, num_res_blocks, downscale_factor)
        elif adapter_type == "full_adapter_xl":
            self.adapter = FullAdapterXL(in_channels, channels, num_res_blocks, downscale_factor)
        elif adapter_type == "light_adapter":
            self.adapter = LightAdapter(in_channels, channels, num_res_blocks, downscale_factor)
        else:
            raise ValueError(
                f"Unsupported adapter_type: '{adapter_type}'. Choose either 'full_adapter' or "
                "'full_adapter_xl' or 'light_adapter'."
            )

    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
        r"""
        This function processes the input tensor `x` through the adapter model and returns a list of feature tensors,
        each representing information extracted at a different scale from the input. The length of the list is
        determined by the number of downsample blocks in the Adapter, as specified by the `channels` and
        `num_res_blocks` parameters during initialization.
        """
        return self.adapter(x)

    @property
    def total_downscale_factor(self):
        return self.adapter.total_downscale_factor

    @property
    def downscale_factor(self):
        """The downscale factor applied in the T2I-Adapter's initial pixel unshuffle operation. If an input image's dimensions are
        not evenly divisible by the downscale_factor then an exception will be raised.
        """
        return self.adapter.unshuffle.downscale_factor


 # full adapter


 class FullAdapter(nn.Module):
    r"""
    See [`T2IAdapter`] for more information.
    """

    def __init__(
        self,
        in_channels: int = 3,
        channels: List[int] = [320, 640, 1280, 1280],
        num_res_blocks: int = 2,
        downscale_factor: int = 8,
    ):
        super().__init__()

        in_channels = in_channels * downscale_factor**2

        self.unshuffle = nn.PixelUnshuffle(downscale_factor)
        self.conv_in = nn.Conv2d(in_channels, channels[0], kernel_size=3, padding=1)

        self.body = nn.ModuleList(
            [
                AdapterBlock(channels[0], channels[0], num_res_blocks),
                *[
                    AdapterBlock(channels[i - 1], channels[i], num_res_blocks, down=True)
                    for i in range(1, len(channels))
                ],
            ]
        )

        self.total_downscale_factor = downscale_factor * 2 ** (len(channels) - 1)

    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
        r"""
        This method processes the input tensor `x` through the FullAdapter model and performs operations including
        pixel unshuffling, convolution, and a stack of AdapterBlocks. It returns a list of feature tensors, each
        capturing information at a different stage of processing within the FullAdapter model. The number of feature
        tensors in the list is determined by the number of downsample blocks specified during initialization.
        """
        x = self.unshuffle(x)
        x = self.conv_in(x)

        features = []

        for block in self.body:
            x = block(x)
            features.append(x)

        return features


 class FullAdapterXL(nn.Module):
    r"""
    See [`T2IAdapter`] for more information.
    """

    def __init__(
        self,
        in_channels: int = 3,
        channels: List[int] = [320, 640, 1280, 1280],
        num_res_blocks: int = 2,
        downscale_factor: int = 16,
    ):
        super().__init__()

        in_channels = in_channels * downscale_factor**2

        self.unshuffle = nn.PixelUnshuffle(downscale_factor)
        self.conv_in = nn.Conv2d(in_channels, channels[0], kernel_size=3, padding=1)

        self.body = []
        # blocks to extract XL features with dimensions of [320, 64, 64], [640, 64, 64], [1280, 32, 32], [1280, 32, 32]
        for i in range(len(channels)):
            if i == 1:
                self.body.append(AdapterBlock(channels[i - 1], channels[i], num_res_blocks))
            elif i == 2:
                self.body.append(AdapterBlock(channels[i - 1], channels[i], num_res_blocks, down=True))
            else:
                self.body.append(AdapterBlock(channels[i], channels[i], num_res_blocks))

        self.body = nn.ModuleList(self.body)
        # XL has only one downsampling AdapterBlock.
        self.total_downscale_factor = downscale_factor * 2

    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
        r"""
        This method takes the tensor x as input and processes it through FullAdapterXL model. It consists of operations
        including unshuffling pixels, applying convolution layer and appending each block into list of feature tensors.
        """
        x = self.unshuffle(x)
        x = self.conv_in(x)

        features = []

        for block in self.body:
            x = block(x)
            features.append(x)

        return features


 class AdapterBlock(nn.Module):
    r"""
    An AdapterBlock is a helper model that contains multiple ResNet-like blocks. It is used in the `FullAdapter` and
    `FullAdapterXL` models.

    Parameters:
        in_channels (`int`):
            Number of channels of AdapterBlock's input.
        out_channels (`int`):
            Number of channels of AdapterBlock's output.
        num_res_blocks (`int`):
            Number of ResNet blocks in the AdapterBlock.
        down (`bool`, *optional*, defaults to `False`):
            Whether to perform downsampling on AdapterBlock's input.
    """

    def __init__(self, in_channels: int, out_channels: int, num_res_blocks: int, down: bool = False):
        super().__init__()

        self.downsample = None
        if down:
            self.downsample = nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True)

        self.in_conv = None
        if in_channels != out_channels:
            self.in_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

        self.resnets = nn.Sequential(
            *[AdapterResnetBlock(out_channels) for _ in range(num_res_blocks)],
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        r"""
        This method takes tensor x as input and performs operations downsampling and convolutional layers if the
        self.downsample and self.in_conv properties of AdapterBlock model are specified. Then it applies a series of
        residual blocks to the input tensor.
        """
        if self.downsample is not None:
            x = self.downsample(x)

        if self.in_conv is not None:
            x = self.in_conv(x)

        x = self.resnets(x)

        return x


 class AdapterResnetBlock(nn.Module):
    r"""
    An `AdapterResnetBlock` is a helper model that implements a ResNet-like block.

    Parameters:
        channels (`int`):
            Number of channels of AdapterResnetBlock's input and output.
    """

    def __init__(self, channels: int):
        super().__init__()
        self.block1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.act = nn.ReLU()
        self.block2 = nn.Conv2d(channels, channels, kernel_size=1)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        r"""
        This method takes input tensor x and applies a convolutional layer, ReLU activation, and another convolutional
        layer on the input tensor. It returns addition with the input tensor.
        """

        h = self.act(self.block1(x))
        h = self.block2(h)

        return h + x


 # light adapter


 class LightAdapter(nn.Module):
    r"""
    See [`T2IAdapter`] for more information.
    """

    def __init__(
        self,
        in_channels: int = 3,
        channels: List[int] = [320, 640, 1280],
        num_res_blocks: int = 4,
        downscale_factor: int = 8,
    ):
        super().__init__()

        in_channels = in_channels * downscale_factor**2

        self.unshuffle = nn.PixelUnshuffle(downscale_factor)

        self.body = nn.ModuleList(
            [
                LightAdapterBlock(in_channels, channels[0], num_res_blocks),
                *[
                    LightAdapterBlock(channels[i], channels[i + 1], num_res_blocks, down=True)
                    for i in range(len(channels) - 1)
                ],
                LightAdapterBlock(channels[-1], channels[-1], num_res_blocks, down=True),
            ]
        )

        self.total_downscale_factor = downscale_factor * (2 ** len(channels))

    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
        r"""
        This method takes the input tensor x and performs downscaling and appends it in list of feature tensors. Each
        feature tensor corresponds to a different level of processing within the LightAdapter.
        """
        x = self.unshuffle(x)

        features = []

        for block in self.body:
            x = block(x)
            features.append(x)

        return features


 class LightAdapterBlock(nn.Module):
    r"""
    A `LightAdapterBlock` is a helper model that contains multiple `LightAdapterResnetBlocks`. It is used in the
    `LightAdapter` model.

    Parameters:
        in_channels (`int`):
            Number of channels of LightAdapterBlock's input.
        out_channels (`int`):
            Number of channels of LightAdapterBlock's output.
        num_res_blocks (`int`):
            Number of LightAdapterResnetBlocks in the LightAdapterBlock.
        down (`bool`, *optional*, defaults to `False`):
            Whether to perform downsampling on LightAdapterBlock's input.
    """

    def __init__(self, in_channels: int, out_channels: int, num_res_blocks: int, down: bool = False):
        super().__init__()
        mid_channels = out_channels // 4

        self.downsample = None
        if down:
            self.downsample = nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True)

        self.in_conv = nn.Conv2d(in_channels, mid_channels, kernel_size=1)
        self.resnets = nn.Sequential(*[LightAdapterResnetBlock(mid_channels) for _ in range(num_res_blocks)])
        self.out_conv = nn.Conv2d(mid_channels, out_channels, kernel_size=1)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        r"""
        This method takes tensor x as input and performs downsampling if required. Then it applies in convolution
        layer, a sequence of residual blocks, and out convolutional layer.
        """
        if self.downsample is not None:
            x = self.downsample(x)

        x = self.in_conv(x)
        x = self.resnets(x)
        x = self.out_conv(x)

        return x


 class LightAdapterResnetBlock(nn.Module):
    """
    A `LightAdapterResnetBlock` is a helper model that implements a ResNet-like block with a slightly different
    architecture than `AdapterResnetBlock`.

    Parameters:
        channels (`int`):
            Number of channels of LightAdapterResnetBlock's input and output.
    """

    def __init__(self, channels: int):
        super().__init__()
        self.block1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.act = nn.ReLU()
        self.block2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        r"""
        This function takes input tensor x and processes it through one convolutional layer, ReLU activation, and
        another convolutional layer and adds it to input tensor.
        """

        h = self.act(self.block1(x))
        h = self.block2(h)

        return h + x

 import inspect
 from importlib import import_module
 from typing import Callable, Optional, Union

 import torch
 import torch.nn.functional as F
 from torch import nn

 from ..image_processor import IPAdapterMaskProcessor
 from ..utils import USE_PEFT_BACKEND, deprecate, logging
 from ..utils.import_utils import is_xformers_available
 from ..utils.torch_utils import maybe_allow_in_graph
 from .lora import LoRACompatibleLinear, LoRALinearLayer


 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


 if is_xformers_available():
    import xformers
    import xformers.ops
 else:
    xformers = None


 @maybe_allow_in_graph
 class Attention(nn.Module):
    r"""
    A cross attention layer.

    Parameters:
        query_dim (`int`):
            The number of channels in the query.
        cross_attention_dim (`int`, *optional*):
            The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
        heads (`int`,  *optional*, defaults to 8):
            The number of heads to use for multi-head attention.
        dim_head (`int`,  *optional*, defaults to 64):
            The number of channels in each head.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability to use.
        bias (`bool`, *optional*, defaults to False):
            Set to `True` for the query, key, and value linear layers to contain a bias parameter.
        upcast_attention (`bool`, *optional*, defaults to False):
            Set to `True` to upcast the attention computation to `float32`.
        upcast_softmax (`bool`, *optional*, defaults to False):
            Set to `True` to upcast the softmax computation to `float32`.
        cross_attention_norm (`str`, *optional*, defaults to `None`):
            The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
        cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
            The number of groups to use for the group norm in the cross attention.
        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
            The number of channels to use for the added key and value projections. If `None`, no projection is used.
        norm_num_groups (`int`, *optional*, defaults to `None`):
            The number of groups to use for the group norm in the attention.
        spatial_norm_dim (`int`, *optional*, defaults to `None`):
            The number of channels to use for the spatial normalization.
        out_bias (`bool`, *optional*, defaults to `True`):
            Set to `True` to use a bias in the output linear layer.
        scale_qk (`bool`, *optional*, defaults to `True`):
            Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
        only_cross_attention (`bool`, *optional*, defaults to `False`):
            Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
            `added_kv_proj_dim` is not `None`.
        eps (`float`, *optional*, defaults to 1e-5):
            An additional value added to the denominator in group normalization that is used for numerical stability.
        rescale_output_factor (`float`, *optional*, defaults to 1.0):
            A factor to rescale the output by dividing it with this value.
        residual_connection (`bool`, *optional*, defaults to `False`):
            Set to `True` to add the residual connection to the output.
        _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
            Set to `True` if the attention block is loaded from a deprecated state dict.
        processor (`AttnProcessor`, *optional*, defaults to `None`):
            The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
            `AttnProcessor` otherwise.
    """

    def __init__(
        self,
        query_dim: int,
        cross_attention_dim: Optional[int] = None,
        heads: int = 8,
        dim_head: int = 64,
        dropout: float = 0.0,
        bias: bool = False,
        upcast_attention: bool = False,
        upcast_softmax: bool = False,
        cross_attention_norm: Optional[str] = None,
        cross_attention_norm_num_groups: int = 32,
        added_kv_proj_dim: Optional[int] = None,
        norm_num_groups: Optional[int] = None,
        spatial_norm_dim: Optional[int] = None,
        out_bias: bool = True,
        scale_qk: bool = True,
        only_cross_attention: bool = False,
        eps: float = 1e-5,
        rescale_output_factor: float = 1.0,
        residual_connection: bool = False,
        _from_deprecated_attn_block: bool = False,
        processor: Optional["AttnProcessor"] = None,
        out_dim: int = None,
    ):
        super().__init__()
        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
        self.query_dim = query_dim
        self.use_bias = bias
        self.is_cross_attention = cross_attention_dim is not None
        self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
        self.upcast_attention = upcast_attention
        self.upcast_softmax = upcast_softmax
        self.rescale_output_factor = rescale_output_factor
        self.residual_connection = residual_connection
        self.dropout = dropout
        self.fused_projections = False
        self.out_dim = out_dim if out_dim is not None else query_dim

        # we make use of this private variable to know whether this class is loaded
        # with an deprecated state dict so that we can convert it on the fly
        self._from_deprecated_attn_block = _from_deprecated_attn_block

        self.scale_qk = scale_qk
        self.scale = dim_head**-0.5 if self.scale_qk else 1.0

        self.heads = out_dim // dim_head if out_dim is not None else heads
        # for slice_size > 0 the attention score computation
        # is split across the batch axis to save memory
        # You can set slice_size with `set_attention_slice`
        self.sliceable_head_dim = heads

        self.added_kv_proj_dim = added_kv_proj_dim
        self.only_cross_attention = only_cross_attention

        if self.added_kv_proj_dim is None and self.only_cross_attention:
            raise ValueError(
                "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
            )

        if norm_num_groups is not None:
            self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True)
        else:
            self.group_norm = None

        if spatial_norm_dim is not None:
            self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim)
        else:
            self.spatial_norm = None

        if cross_attention_norm is None:
            self.norm_cross = None
        elif cross_attention_norm == "layer_norm":
            self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
        elif cross_attention_norm == "group_norm":
            if self.added_kv_proj_dim is not None:
                # The given `encoder_hidden_states` are initially of shape
                # (batch_size, seq_len, added_kv_proj_dim) before being projected
                # to (batch_size, seq_len, cross_attention_dim). The norm is applied
                # before the projection, so we need to use `added_kv_proj_dim` as
                # the number of channels for the group norm.
                norm_cross_num_channels = added_kv_proj_dim
            else:
                norm_cross_num_channels = self.cross_attention_dim

            self.norm_cross = nn.GroupNorm(
                num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True
            )
        else:
            raise ValueError(
                f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
            )

        if USE_PEFT_BACKEND:
            linear_cls = nn.Linear
        else:
            linear_cls = LoRACompatibleLinear

        self.linear_cls = linear_cls
        self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias)

        if not self.only_cross_attention:
            # only relevant for the `AddedKVProcessor` classes
            self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
            self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
        else:
            self.to_k = None
            self.to_v = None

        if self.added_kv_proj_dim is not None:
            self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
            self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim)

        self.to_out = nn.ModuleList([])
        self.to_out.append(linear_cls(self.inner_dim, self.out_dim, bias=out_bias))
        self.to_out.append(nn.Dropout(dropout))

        # set attention processor
        # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
        # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
        # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
        if processor is None:
            processor = (
                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
            )
        self.set_processor(processor)

    def set_use_memory_efficient_attention_xformers(
        self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
    ) -> None:
        r"""
        Set whether to use memory efficient attention from `xformers` or not.

        Args:
            use_memory_efficient_attention_xformers (`bool`):
                Whether to use memory efficient attention from `xformers` or not.
            attention_op (`Callable`, *optional*):
                The attention operation to use. Defaults to `None` which uses the default attention operation from
                `xformers`.
        """
        is_lora = hasattr(self, "processor") and isinstance(
            self.processor,
            LORA_ATTENTION_PROCESSORS,
        )
        is_custom_diffusion = hasattr(self, "processor") and isinstance(
            self.processor,
            (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, CustomDiffusionAttnProcessor2_0),
        )
        is_added_kv_processor = hasattr(self, "processor") and isinstance(
            self.processor,
            (
                AttnAddedKVProcessor,
                AttnAddedKVProcessor2_0,
                SlicedAttnAddedKVProcessor,
                XFormersAttnAddedKVProcessor,
                LoRAAttnAddedKVProcessor,
            ),
        )

        if use_memory_efficient_attention_xformers:
            if is_added_kv_processor and (is_lora or is_custom_diffusion):
                raise NotImplementedError(
                    f"Memory efficient attention is currently not supported for LoRA or custom diffusion for attention processor type {self.processor}"
                )
            if not is_xformers_available():
                raise ModuleNotFoundError(
                    (
                        "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
                        " xformers"
                    ),
                    name="xformers",
                )
            elif not torch.cuda.is_available():
                raise ValueError(
                    "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
                    " only available for GPU "
                )
            else:
                try:
                    # Make sure we can run the memory efficient attention
                    _ = xformers.ops.memory_efficient_attention(
                        torch.randn((1, 2, 40), device="cuda"),
                        torch.randn((1, 2, 40), device="cuda"),
                        torch.randn((1, 2, 40), device="cuda"),
                    )
                except Exception as e:
                    raise e

            if is_lora:
                # TODO (sayakpaul): should we throw a warning if someone wants to use the xformers
                # variant when using PT 2.0 now that we have LoRAAttnProcessor2_0?
                processor = LoRAXFormersAttnProcessor(
                    hidden_size=self.processor.hidden_size,
                    cross_attention_dim=self.processor.cross_attention_dim,
                    rank=self.processor.rank,
                    attention_op=attention_op,
                )
                processor.load_state_dict(self.processor.state_dict())
                processor.to(self.processor.to_q_lora.up.weight.device)
            elif is_custom_diffusion:
                processor = CustomDiffusionXFormersAttnProcessor(
                    train_kv=self.processor.train_kv,
                    train_q_out=self.processor.train_q_out,
                    hidden_size=self.processor.hidden_size,
                    cross_attention_dim=self.processor.cross_attention_dim,
                    attention_op=attention_op,
                )
                processor.load_state_dict(self.processor.state_dict())
                if hasattr(self.processor, "to_k_custom_diffusion"):
                    processor.to(self.processor.to_k_custom_diffusion.weight.device)
            elif is_added_kv_processor:
                # TODO(Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP
                # which uses this type of cross attention ONLY because the attention mask of format
                # [0, ..., -10.000, ..., 0, ...,] is not supported
                # throw warning
                logger.info(
                    "Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation."
                )
                processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)
            else:
                processor = XFormersAttnProcessor(attention_op=attention_op)
        else:
            if is_lora:
                attn_processor_class = (
                    LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
                )
                processor = attn_processor_class(
                    hidden_size=self.processor.hidden_size,
                    cross_attention_dim=self.processor.cross_attention_dim,
                    rank=self.processor.rank,
                )
                processor.load_state_dict(self.processor.state_dict())
                processor.to(self.processor.to_q_lora.up.weight.device)
            elif is_custom_diffusion:
                attn_processor_class = (
                    CustomDiffusionAttnProcessor2_0
                    if hasattr(F, "scaled_dot_product_attention")
                    else CustomDiffusionAttnProcessor
                )
                processor = attn_processor_class(
                    train_kv=self.processor.train_kv,
                    train_q_out=self.processor.train_q_out,
                    hidden_size=self.processor.hidden_size,
                    cross_attention_dim=self.processor.cross_attention_dim,
                )
                processor.load_state_dict(self.processor.state_dict())
                if hasattr(self.processor, "to_k_custom_diffusion"):
                    processor.to(self.processor.to_k_custom_diffusion.weight.device)
            else:
                # set attention processor
                # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
                # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
                # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
                processor = (
                    AttnProcessor2_0()
                    if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
                    else AttnProcessor()
                )

        self.set_processor(processor)

    def set_attention_slice(self, slice_size: int) -> None:
        r"""
        Set the slice size for attention computation.

        Args:
            slice_size (`int`):
                The slice size for attention computation.
        """
        if slice_size is not None and slice_size > self.sliceable_head_dim:
            raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")

        if slice_size is not None and self.added_kv_proj_dim is not None:
            processor = SlicedAttnAddedKVProcessor(slice_size)
        elif slice_size is not None:
            processor = SlicedAttnProcessor(slice_size)
        elif self.added_kv_proj_dim is not None:
            processor = AttnAddedKVProcessor()
        else:
            # set attention processor
            # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
            # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
            # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
            processor = (
                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
            )

        self.set_processor(processor)

    def set_processor(self, processor: "AttnProcessor") -> None:
        r"""
        Set the attention processor to use.

        Args:
            processor (`AttnProcessor`):
                The attention processor to use.
        """
        # if current processor is in `self._modules` and if passed `processor` is not, we need to
        # pop `processor` from `self._modules`
        if (
            hasattr(self, "processor")
            and isinstance(self.processor, torch.nn.Module)
            and not isinstance(processor, torch.nn.Module)
        ):
            logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
            self._modules.pop("processor")

        self.processor = processor

    def get_processor(self, return_deprecated_lora: bool = False) -> "AttentionProcessor":
        r"""
        Get the attention processor in use.

        Args:
            return_deprecated_lora (`bool`, *optional*, defaults to `False`):
                Set to `True` to return the deprecated LoRA attention processor.

        Returns:
            "AttentionProcessor": The attention processor in use.
        """
        if not return_deprecated_lora:
            return self.processor

        # TODO(Sayak, Patrick). The rest of the function is needed to ensure backwards compatible
        # serialization format for LoRA Attention Processors. It should be deleted once the integration
        # with PEFT is completed.
        is_lora_activated = {
            name: module.lora_layer is not None
            for name, module in self.named_modules()
            if hasattr(module, "lora_layer")
        }

        # 1. if no layer has a LoRA activated we can return the processor as usual
        if not any(is_lora_activated.values()):
            return self.processor

        # If doesn't apply LoRA do `add_k_proj` or `add_v_proj`
        is_lora_activated.pop("add_k_proj", None)
        is_lora_activated.pop("add_v_proj", None)
        # 2. else it is not posssible that only some layers have LoRA activated
        if not all(is_lora_activated.values()):
            raise ValueError(
                f"Make sure that either all layers or no layers have LoRA activated, but have {is_lora_activated}"
            )

        # 3. And we need to merge the current LoRA layers into the corresponding LoRA attention processor
        non_lora_processor_cls_name = self.processor.__class__.__name__
        lora_processor_cls = getattr(import_module(__name__), "LoRA" + non_lora_processor_cls_name)

        hidden_size = self.inner_dim

        # now create a LoRA attention processor from the LoRA layers
        if lora_processor_cls in [LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor]:
            kwargs = {
                "cross_attention_dim": self.cross_attention_dim,
                "rank": self.to_q.lora_layer.rank,
                "network_alpha": self.to_q.lora_layer.network_alpha,
                "q_rank": self.to_q.lora_layer.rank,
                "q_hidden_size": self.to_q.lora_layer.out_features,
                "k_rank": self.to_k.lora_layer.rank,
                "k_hidden_size": self.to_k.lora_layer.out_features,
                "v_rank": self.to_v.lora_layer.rank,
                "v_hidden_size": self.to_v.lora_layer.out_features,
                "out_rank": self.to_out[0].lora_layer.rank,
                "out_hidden_size": self.to_out[0].lora_layer.out_features,
            }

            if hasattr(self.processor, "attention_op"):
                kwargs["attention_op"] = self.processor.attention_op

            lora_processor = lora_processor_cls(hidden_size, **kwargs)
            lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict())
            lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict())
            lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict())
            lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict())
        elif lora_processor_cls == LoRAAttnAddedKVProcessor:
            lora_processor = lora_processor_cls(
                hidden_size,
                cross_attention_dim=self.add_k_proj.weight.shape[0],
                rank=self.to_q.lora_layer.rank,
                network_alpha=self.to_q.lora_layer.network_alpha,
            )
            lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict())
            lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict())
            lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict())
            lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict())

            # only save if used
            if self.add_k_proj.lora_layer is not None:
                lora_processor.add_k_proj_lora.load_state_dict(self.add_k_proj.lora_layer.state_dict())
                lora_processor.add_v_proj_lora.load_state_dict(self.add_v_proj.lora_layer.state_dict())
            else:
                lora_processor.add_k_proj_lora = None
                lora_processor.add_v_proj_lora = None
        else:
            raise ValueError(f"{lora_processor_cls} does not exist.")

        return lora_processor

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        **cross_attention_kwargs,
    ) -> torch.Tensor:
        r"""
        The forward method of the `Attention` class.

        Args:
            hidden_states (`torch.Tensor`):
                The hidden states of the query.
            encoder_hidden_states (`torch.Tensor`, *optional*):
                The hidden states of the encoder.
            attention_mask (`torch.Tensor`, *optional*):
                The attention mask to use. If `None`, no mask is applied.
            **cross_attention_kwargs:
                Additional keyword arguments to pass along to the cross attention.

        Returns:
            `torch.Tensor`: The output of the attention layer.
        """
        # The `Attention` class can call different attention processors / attention functions
        # here we simply pass along all tensors to the selected processor class
        # For standard processors that are defined here, `**cross_attention_kwargs` is empty

        attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())
        unused_kwargs = [k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters]
        if len(unused_kwargs) > 0:
            logger.warning(
                f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
            )
        cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters}

        return self.processor(
            self,
            hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            attention_mask=attention_mask,
            **cross_attention_kwargs,
        )

    def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
        r"""
        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
        is the number of heads initialized while constructing the `Attention` class.

        Args:
            tensor (`torch.Tensor`): The tensor to reshape.

        Returns:
            `torch.Tensor`: The reshaped tensor.
        """
        head_size = self.heads
        batch_size, seq_len, dim = tensor.shape
        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
        return tensor

    def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
        r"""
        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
        the number of heads initialized while constructing the `Attention` class.

        Args:
            tensor (`torch.Tensor`): The tensor to reshape.
            out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
                reshaped to `[batch_size * heads, seq_len, dim // heads]`.

        Returns:
            `torch.Tensor`: The reshaped tensor.
        """
        head_size = self.heads
        if tensor.ndim == 3:
            batch_size, seq_len, dim = tensor.shape
            extra_dim = 1
        else:
            batch_size, extra_dim, seq_len, dim = tensor.shape
        tensor = tensor.reshape(batch_size, seq_len * extra_dim, head_size, dim // head_size)
        tensor = tensor.permute(0, 2, 1, 3)

        if out_dim == 3:
            tensor = tensor.reshape(batch_size * head_size, seq_len * extra_dim, dim // head_size)

        return tensor

    def get_attention_scores(
        self, query: torch.Tensor, key: torch.Tensor, attention_mask: torch.Tensor = None
    ) -> torch.Tensor:
        r"""
        Compute the attention scores.

        Args:
            query (`torch.Tensor`): The query tensor.
            key (`torch.Tensor`): The key tensor.
            attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.

        Returns:
            `torch.Tensor`: The attention probabilities/scores.
        """
        dtype = query.dtype
        if self.upcast_attention:
            query = query.float()
            key = key.float()

        if attention_mask is None:
            baddbmm_input = torch.empty(
                query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
            )
            beta = 0
        else:
            baddbmm_input = attention_mask
            beta = 1

        attention_scores = torch.baddbmm(
            baddbmm_input,
            query,
            key.transpose(-1, -2),
            beta=beta,
            alpha=self.scale,
        )
        del baddbmm_input

        if self.upcast_softmax:
            attention_scores = attention_scores.float()

        attention_probs = attention_scores.softmax(dim=-1)
        del attention_scores

        attention_probs = attention_probs.to(dtype)

        return attention_probs

    def prepare_attention_mask(
        self, attention_mask: torch.Tensor, target_length: int, batch_size: int, out_dim: int = 3
    ) -> torch.Tensor:
        r"""
        Prepare the attention mask for the attention computation.

        Args:
            attention_mask (`torch.Tensor`):
                The attention mask to prepare.
            target_length (`int`):
                The target length of the attention mask. This is the length of the attention mask after padding.
            batch_size (`int`):
                The batch size, which is used to repeat the attention mask.
            out_dim (`int`, *optional*, defaults to `3`):
                The output dimension of the attention mask. Can be either `3` or `4`.

        Returns:
            `torch.Tensor`: The prepared attention mask.
        """
        head_size = self.heads
        if attention_mask is None:
            return attention_mask

        current_length: int = attention_mask.shape[-1]
        if current_length != target_length:
            if attention_mask.device.type == "mps":
                # HACK: MPS: Does not support padding by greater than dimension of input tensor.
                # Instead, we can manually construct the padding tensor.
                padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length)
                padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device)
                attention_mask = torch.cat([attention_mask, padding], dim=2)
            else:
                # TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
                #       we want to instead pad by (0, remaining_length), where remaining_length is:
                #       remaining_length: int = target_length - current_length
                # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)

        if out_dim == 3:
            if attention_mask.shape[0] < batch_size * head_size:
                attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
        elif out_dim == 4:
            attention_mask = attention_mask.unsqueeze(1)
            attention_mask = attention_mask.repeat_interleave(head_size, dim=1)

        return attention_mask

    def norm_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
        r"""
        Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
        `Attention` class.

        Args:
            encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.

        Returns:
            `torch.Tensor`: The normalized encoder hidden states.
        """
        assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states"

        if isinstance(self.norm_cross, nn.LayerNorm):
            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
        elif isinstance(self.norm_cross, nn.GroupNorm):
            # Group norm norms along the channels dimension and expects
            # input to be in the shape of (N, C, *). In this case, we want
            # to norm along the hidden dimension, so we need to move
            # (batch_size, sequence_length, hidden_size) ->
            # (batch_size, hidden_size, sequence_length)
            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
        else:
            assert False

        return encoder_hidden_states

    @torch.no_grad()
    def fuse_projections(self, fuse=True):
        device = self.to_q.weight.data.device
        dtype = self.to_q.weight.data.dtype

        if not self.is_cross_attention:
            # fetch weight matrices.
            concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])
            in_features = concatenated_weights.shape[1]
            out_features = concatenated_weights.shape[0]

            # create a new single projection layer and copy over the weights.
            self.to_qkv = self.linear_cls(in_features, out_features, bias=self.use_bias, device=device, dtype=dtype)
            self.to_qkv.weight.copy_(concatenated_weights)
            if self.use_bias:
                concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])
                self.to_qkv.bias.copy_(concatenated_bias)

        else:
            concatenated_weights = torch.cat([self.to_k.weight.data, self.to_v.weight.data])
            in_features = concatenated_weights.shape[1]
            out_features = concatenated_weights.shape[0]

            self.to_kv = self.linear_cls(in_features, out_features, bias=self.use_bias, device=device, dtype=dtype)
            self.to_kv.weight.copy_(concatenated_weights)
            if self.use_bias:
                concatenated_bias = torch.cat([self.to_k.bias.data, self.to_v.bias.data])
                self.to_kv.bias.copy_(concatenated_bias)

        self.fused_projections = fuse


 class AttnProcessor:
    r"""
    Default processor for performing attention-related computations.
    """

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.Tensor:
        residual = hidden_states

        args = () if USE_PEFT_BACKEND else (scale,)

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states, *args)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states, *args)
        value = attn.to_v(encoder_hidden_states, *args)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


 class CustomDiffusionAttnProcessor(nn.Module):
    r"""
    Processor for implementing attention for the Custom Diffusion method.

    Args:
        train_kv (`bool`, defaults to `True`):
            Whether to newly train the key and value matrices corresponding to the text features.
        train_q_out (`bool`, defaults to `True`):
            Whether to newly train query matrices corresponding to the latent image features.
        hidden_size (`int`, *optional*, defaults to `None`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`, *optional*, defaults to `None`):
            The number of channels in the `encoder_hidden_states`.
        out_bias (`bool`, defaults to `True`):
            Whether to include the bias parameter in `train_q_out`.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability to use.
    """

    def __init__(
        self,
        train_kv: bool = True,
        train_q_out: bool = True,
        hidden_size: Optional[int] = None,
        cross_attention_dim: Optional[int] = None,
        out_bias: bool = True,
        dropout: float = 0.0,
    ):
        super().__init__()
        self.train_kv = train_kv
        self.train_q_out = train_q_out

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim

        # `_custom_diffusion` id for easy serialization and loading.
        if self.train_kv:
            self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
            self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        if self.train_q_out:
            self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
            self.to_out_custom_diffusion = nn.ModuleList([])
            self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
            self.to_out_custom_diffusion.append(nn.Dropout(dropout))

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.Tensor:
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
        if self.train_q_out:
            query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype)
        else:
            query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype))

        if encoder_hidden_states is None:
            crossattn = False
            encoder_hidden_states = hidden_states
        else:
            crossattn = True
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        if self.train_kv:
            key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))
            value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))
            key = key.to(attn.to_q.weight.dtype)
            value = value.to(attn.to_q.weight.dtype)
        else:
            key = attn.to_k(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states)

        if crossattn:
            detach = torch.ones_like(key)
            detach[:, :1, :] = detach[:, :1, :] * 0.0
            key = detach * key + (1 - detach) * key.detach()
            value = detach * value + (1 - detach) * value.detach()

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        if self.train_q_out:
            # linear proj
            hidden_states = self.to_out_custom_diffusion[0](hidden_states)
            # dropout
            hidden_states = self.to_out_custom_diffusion[1](hidden_states)
        else:
            # linear proj
            hidden_states = attn.to_out[0](hidden_states)
            # dropout
            hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


 class AttnAddedKVProcessor:
    r"""
    Processor for performing attention-related computations with extra learnable key and value matrices for the text
    encoder.
    """

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.Tensor:
        residual = hidden_states

        args = () if USE_PEFT_BACKEND else (scale,)

        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
        batch_size, sequence_length, _ = hidden_states.shape

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states, *args)
        query = attn.head_to_batch_dim(query)

        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states, *args)
        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states, *args)
        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)

        if not attn.only_cross_attention:
            key = attn.to_k(hidden_states, *args)
            value = attn.to_v(hidden_states, *args)
            key = attn.head_to_batch_dim(key)
            value = attn.head_to_batch_dim(value)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
        else:
            key = encoder_hidden_states_key_proj
            value = encoder_hidden_states_value_proj

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
        hidden_states = hidden_states + residual

        return hidden_states


 class AttnAddedKVProcessor2_0:
    r"""
    Processor for performing scaled dot-product attention (enabled by default if you're using PyTorch 2.0), with extra
    learnable key and value matrices for the text encoder.
    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "AttnAddedKVProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
            )

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.Tensor:
        residual = hidden_states

        args = () if USE_PEFT_BACKEND else (scale,)

        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
        batch_size, sequence_length, _ = hidden_states.shape

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, out_dim=4)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states, *args)
        query = attn.head_to_batch_dim(query, out_dim=4)

        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj, out_dim=4)
        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4)

        if not attn.only_cross_attention:
            key = attn.to_k(hidden_states, *args)
            value = attn.to_v(hidden_states, *args)
            key = attn.head_to_batch_dim(key, out_dim=4)
            value = attn.head_to_batch_dim(value, out_dim=4)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
        else:
            key = encoder_hidden_states_key_proj
            value = encoder_hidden_states_value_proj

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, residual.shape[1])

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
        hidden_states = hidden_states + residual

        return hidden_states


 class XFormersAttnAddedKVProcessor:
    r"""
    Processor for implementing memory efficient attention using xFormers.

    Args:
        attention_op (`Callable`, *optional*, defaults to `None`):
            The base
            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
            operator.
    """

    def __init__(self, attention_op: Optional[Callable] = None):
        self.attention_op = attention_op

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
        batch_size, sequence_length, _ = hidden_states.shape

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)
        query = attn.head_to_batch_dim(query)

        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)

        if not attn.only_cross_attention:
            key = attn.to_k(hidden_states)
            value = attn.to_v(hidden_states)
            key = attn.head_to_batch_dim(key)
            value = attn.head_to_batch_dim(value)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
        else:
            key = encoder_hidden_states_key_proj
            value = encoder_hidden_states_value_proj

        hidden_states = xformers.ops.memory_efficient_attention(
            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
        )
        hidden_states = hidden_states.to(query.dtype)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
        hidden_states = hidden_states + residual

        return hidden_states


 class XFormersAttnProcessor:
    r"""
    Processor for implementing memory efficient attention using xFormers.

    Args:
        attention_op (`Callable`, *optional*, defaults to `None`):
            The base
            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
            operator.
    """

    def __init__(self, attention_op: Optional[Callable] = None):
        self.attention_op = attention_op

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.FloatTensor:
        residual = hidden_states

        args = () if USE_PEFT_BACKEND else (scale,)

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, key_tokens, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)
        if attention_mask is not None:
            # expand our mask's singleton query_tokens dimension:
            #   [batch*heads,            1, key_tokens] ->
            #   [batch*heads, query_tokens, key_tokens]
            # so that it can be added as a bias onto the attention scores that xformers computes:
            #   [batch*heads, query_tokens, key_tokens]
            # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
            _, query_tokens, _ = hidden_states.shape
            attention_mask = attention_mask.expand(-1, query_tokens, -1)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states, *args)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states, *args)
        value = attn.to_v(encoder_hidden_states, *args)

        query = attn.head_to_batch_dim(query).contiguous()
        key = attn.head_to_batch_dim(key).contiguous()
        value = attn.head_to_batch_dim(value).contiguous()

        hidden_states = xformers.ops.memory_efficient_attention(
            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
        )
        hidden_states = hidden_states.to(query.dtype)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


 class AttnProcessor2_0:
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.FloatTensor:
        residual = hidden_states
        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        args = () if USE_PEFT_BACKEND else (scale,)
        query = attn.to_q(hidden_states, *args)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states, *args)
        value = attn.to_v(encoder_hidden_states, *args)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


 class FusedAttnProcessor2_0:
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
    It uses fused projection layers. For self-attention modules, all projection matrices (i.e., query,
    key, value) are fused. For cross-attention modules, key and value projection matrices are fused.

    <Tip warning={true}>

    This API is currently 🧪 experimental in nature and can change in future.

    </Tip>
    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "FusedAttnProcessor2_0 requires at least PyTorch 2.0, to use it. Please upgrade PyTorch to > 2.0."
            )

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.FloatTensor:
        residual = hidden_states
        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        args = () if USE_PEFT_BACKEND else (scale,)
        if encoder_hidden_states is None:
            qkv = attn.to_qkv(hidden_states, *args)
            split_size = qkv.shape[-1] // 3
            query, key, value = torch.split(qkv, split_size, dim=-1)
        else:
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
            query = attn.to_q(hidden_states, *args)

            kv = attn.to_kv(encoder_hidden_states, *args)
            split_size = kv.shape[-1] // 2
            key, value = torch.split(kv, split_size, dim=-1)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


 class CustomDiffusionXFormersAttnProcessor(nn.Module):
    r"""
    Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method.

    Args:
    train_kv (`bool`, defaults to `True`):
        Whether to newly train the key and value matrices corresponding to the text features.
    train_q_out (`bool`, defaults to `True`):
        Whether to newly train query matrices corresponding to the latent image features.
    hidden_size (`int`, *optional*, defaults to `None`):
        The hidden size of the attention layer.
    cross_attention_dim (`int`, *optional*, defaults to `None`):
        The number of channels in the `encoder_hidden_states`.
    out_bias (`bool`, defaults to `True`):
        Whether to include the bias parameter in `train_q_out`.
    dropout (`float`, *optional*, defaults to 0.0):
        The dropout probability to use.
    attention_op (`Callable`, *optional*, defaults to `None`):
        The base
        [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to use
        as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best operator.
    """

    def __init__(
        self,
        train_kv: bool = True,
        train_q_out: bool = False,
        hidden_size: Optional[int] = None,
        cross_attention_dim: Optional[int] = None,
        out_bias: bool = True,
        dropout: float = 0.0,
        attention_op: Optional[Callable] = None,
    ):
        super().__init__()
        self.train_kv = train_kv
        self.train_q_out = train_q_out

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.attention_op = attention_op

        # `_custom_diffusion` id for easy serialization and loading.
        if self.train_kv:
            self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
            self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        if self.train_q_out:
            self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
            self.to_out_custom_diffusion = nn.ModuleList([])
            self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
            self.to_out_custom_diffusion.append(nn.Dropout(dropout))

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if self.train_q_out:
            query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype)
        else:
            query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype))

        if encoder_hidden_states is None:
            crossattn = False
            encoder_hidden_states = hidden_states
        else:
            crossattn = True
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        if self.train_kv:
            key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))
            value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))
            key = key.to(attn.to_q.weight.dtype)
            value = value.to(attn.to_q.weight.dtype)
        else:
            key = attn.to_k(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states)

        if crossattn:
            detach = torch.ones_like(key)
            detach[:, :1, :] = detach[:, :1, :] * 0.0
            key = detach * key + (1 - detach) * key.detach()
            value = detach * value + (1 - detach) * value.detach()

        query = attn.head_to_batch_dim(query).contiguous()
        key = attn.head_to_batch_dim(key).contiguous()
        value = attn.head_to_batch_dim(value).contiguous()

        hidden_states = xformers.ops.memory_efficient_attention(
            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
        )
        hidden_states = hidden_states.to(query.dtype)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        if self.train_q_out:
            # linear proj
            hidden_states = self.to_out_custom_diffusion[0](hidden_states)
            # dropout
            hidden_states = self.to_out_custom_diffusion[1](hidden_states)
        else:
            # linear proj
            hidden_states = attn.to_out[0](hidden_states)
            # dropout
            hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


 class CustomDiffusionAttnProcessor2_0(nn.Module):
    r"""
    Processor for implementing attention for the Custom Diffusion method using PyTorch 2.0’s memory-efficient scaled
    dot-product attention.

    Args:
        train_kv (`bool`, defaults to `True`):
            Whether to newly train the key and value matrices corresponding to the text features.
        train_q_out (`bool`, defaults to `True`):
            Whether to newly train query matrices corresponding to the latent image features.
        hidden_size (`int`, *optional*, defaults to `None`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`, *optional*, defaults to `None`):
            The number of channels in the `encoder_hidden_states`.
        out_bias (`bool`, defaults to `True`):
            Whether to include the bias parameter in `train_q_out`.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability to use.
    """

    def __init__(
        self,
        train_kv: bool = True,
        train_q_out: bool = True,
        hidden_size: Optional[int] = None,
        cross_attention_dim: Optional[int] = None,
        out_bias: bool = True,
        dropout: float = 0.0,
    ):
        super().__init__()
        self.train_kv = train_kv
        self.train_q_out = train_q_out

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim

        # `_custom_diffusion` id for easy serialization and loading.
        if self.train_kv:
            self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
            self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        if self.train_q_out:
            self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
            self.to_out_custom_diffusion = nn.ModuleList([])
            self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
            self.to_out_custom_diffusion.append(nn.Dropout(dropout))

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
        if self.train_q_out:
            query = self.to_q_custom_diffusion(hidden_states)
        else:
            query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            crossattn = False
            encoder_hidden_states = hidden_states
        else:
            crossattn = True
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        if self.train_kv:
            key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))
            value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))
            key = key.to(attn.to_q.weight.dtype)
            value = value.to(attn.to_q.weight.dtype)

        else:
            key = attn.to_k(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states)

        if crossattn:
            detach = torch.ones_like(key)
            detach[:, :1, :] = detach[:, :1, :] * 0.0
            key = detach * key + (1 - detach) * key.detach()
            value = detach * value + (1 - detach) * value.detach()

        inner_dim = hidden_states.shape[-1]

        head_dim = inner_dim // attn.heads
        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        if self.train_q_out:
            # linear proj
            hidden_states = self.to_out_custom_diffusion[0](hidden_states)
            # dropout
            hidden_states = self.to_out_custom_diffusion[1](hidden_states)
        else:
            # linear proj
            hidden_states = attn.to_out[0](hidden_states)
            # dropout
            hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


 class SlicedAttnProcessor:
    r"""
    Processor for implementing sliced attention.

    Args:
        slice_size (`int`, *optional*):
            The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
            `attention_head_dim` must be a multiple of the `slice_size`.
    """

    def __init__(self, slice_size: int):
        self.slice_size = slice_size

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        residual = hidden_states

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)
        dim = query.shape[-1]
        query = attn.head_to_batch_dim(query)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        batch_size_attention, query_tokens, _ = query.shape
        hidden_states = torch.zeros(
            (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
        )

        for i in range(batch_size_attention // self.slice_size):
            start_idx = i * self.slice_size
            end_idx = (i + 1) * self.slice_size

            query_slice = query[start_idx:end_idx]
            key_slice = key[start_idx:end_idx]
            attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None

            attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)

            attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])

            hidden_states[start_idx:end_idx] = attn_slice

        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


 class SlicedAttnAddedKVProcessor:
    r"""
    Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.

    Args:
        slice_size (`int`, *optional*):
            The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
            `attention_head_dim` must be a multiple of the `slice_size`.
    """

    def __init__(self, slice_size):
        self.slice_size = slice_size

    def __call__(
        self,
        attn: "Attention",
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)

        batch_size, sequence_length, _ = hidden_states.shape

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)
        dim = query.shape[-1]
        query = attn.head_to_batch_dim(query)

        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)

        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)

        if not attn.only_cross_attention:
            key = attn.to_k(hidden_states)
            value = attn.to_v(hidden_states)
            key = attn.head_to_batch_dim(key)
            value = attn.head_to_batch_dim(value)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
        else:
            key = encoder_hidden_states_key_proj
            value = encoder_hidden_states_value_proj

        batch_size_attention, query_tokens, _ = query.shape
        hidden_states = torch.zeros(
            (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
        )

        for i in range(batch_size_attention // self.slice_size):
            start_idx = i * self.slice_size
            end_idx = (i + 1) * self.slice_size

            query_slice = query[start_idx:end_idx]
            key_slice = key[start_idx:end_idx]
            attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None

            attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)

            attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])

            hidden_states[start_idx:end_idx] = attn_slice

        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
        hidden_states = hidden_states + residual

        return hidden_states


 class SpatialNorm(nn.Module):
    """
    Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002.

    Args:
        f_channels (`int`):
            The number of channels for input to group normalization layer, and output of the spatial norm layer.
        zq_channels (`int`):
            The number of channels for the quantized vector as described in the paper.
    """

    def __init__(
        self,
        f_channels: int,
        zq_channels: int,
    ):
        super().__init__()
        self.norm_layer = nn.GroupNorm(num_channels=f_channels, num_groups=32, eps=1e-6, affine=True)
        self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
        self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)

    def forward(self, f: torch.FloatTensor, zq: torch.FloatTensor) -> torch.FloatTensor:
        f_size = f.shape[-2:]
        zq = F.interpolate(zq, size=f_size, mode="nearest")
        norm_f = self.norm_layer(f)
        new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
        return new_f


 class LoRAAttnProcessor(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        cross_attention_dim: Optional[int] = None,
        rank: int = 4,
        network_alpha: Optional[int] = None,
        **kwargs,
    ):
        deprecation_message = "Using LoRAAttnProcessor is deprecated. Please use the PEFT backend for all things LoRA. You can install PEFT by running `pip install peft`."
        deprecate("LoRAAttnProcessor", "0.30.0", deprecation_message, standard_warn=False)

        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.rank = rank

        q_rank = kwargs.pop("q_rank", None)
        q_hidden_size = kwargs.pop("q_hidden_size", None)
        q_rank = q_rank if q_rank is not None else rank
        q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size

        v_rank = kwargs.pop("v_rank", None)
        v_hidden_size = kwargs.pop("v_hidden_size", None)
        v_rank = v_rank if v_rank is not None else rank
        v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size

        out_rank = kwargs.pop("out_rank", None)
        out_hidden_size = kwargs.pop("out_hidden_size", None)
        out_rank = out_rank if out_rank is not None else rank
        out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size

        self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)
        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
        self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)
        self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)

    def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
        self_cls_name = self.__class__.__name__
        deprecate(
            self_cls_name,
            "0.26.0",
            (
                f"Make sure use {self_cls_name[4:]} instead by setting"
                "LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
                " `LoraLoaderMixin.load_lora_weights`"
            ),
        )
        attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
        attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
        attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
        attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)

        attn._modules.pop("processor")
        attn.processor = AttnProcessor()
        return attn.processor(attn, hidden_states, *args, **kwargs)


 class LoRAAttnProcessor2_0(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        cross_attention_dim: Optional[int] = None,
        rank: int = 4,
        network_alpha: Optional[int] = None,
        **kwargs,
    ):
        deprecation_message = "Using LoRAAttnProcessor is deprecated. Please use the PEFT backend for all things LoRA. You can install PEFT by running `pip install peft`."
        deprecate("LoRAAttnProcessor2_0", "0.30.0", deprecation_message, standard_warn=False)

        super().__init__()
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.rank = rank

        q_rank = kwargs.pop("q_rank", None)
        q_hidden_size = kwargs.pop("q_hidden_size", None)
        q_rank = q_rank if q_rank is not None else rank
        q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size

        v_rank = kwargs.pop("v_rank", None)
        v_hidden_size = kwargs.pop("v_hidden_size", None)
        v_rank = v_rank if v_rank is not None else rank
        v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size

        out_rank = kwargs.pop("out_rank", None)
        out_hidden_size = kwargs.pop("out_hidden_size", None)
        out_rank = out_rank if out_rank is not None else rank
        out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size

        self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)
        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
        self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)
        self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)

    def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
        self_cls_name = self.__class__.__name__
        deprecate(
            self_cls_name,
            "0.26.0",
            (
                f"Make sure use {self_cls_name[4:]} instead by setting"
                "LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
                " `LoraLoaderMixin.load_lora_weights`"
            ),
        )
        attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
        attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
        attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
        attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)

        attn._modules.pop("processor")
        attn.processor = AttnProcessor2_0()
        return attn.processor(attn, hidden_states, *args, **kwargs)


 class LoRAXFormersAttnProcessor(nn.Module):
    r"""
    Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers.

    Args:
        hidden_size (`int`, *optional*):
            The hidden size of the attention layer.
        cross_attention_dim (`int`, *optional*):
            The number of channels in the `encoder_hidden_states`.
        rank (`int`, defaults to 4):
            The dimension of the LoRA update matrices.
        attention_op (`Callable`, *optional*, defaults to `None`):
            The base
            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
            operator.
        network_alpha (`int`, *optional*):
            Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
        kwargs (`dict`):
            Additional keyword arguments to pass to the `LoRALinearLayer` layers.
    """

    def __init__(
        self,
        hidden_size: int,
        cross_attention_dim: int,
        rank: int = 4,
        attention_op: Optional[Callable] = None,
        network_alpha: Optional[int] = None,
        **kwargs,
    ):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.rank = rank
        self.attention_op = attention_op

        q_rank = kwargs.pop("q_rank", None)
        q_hidden_size = kwargs.pop("q_hidden_size", None)
        q_rank = q_rank if q_rank is not None else rank
        q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size

        v_rank = kwargs.pop("v_rank", None)
        v_hidden_size = kwargs.pop("v_hidden_size", None)
        v_rank = v_rank if v_rank is not None else rank
        v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size

        out_rank = kwargs.pop("out_rank", None)
        out_hidden_size = kwargs.pop("out_hidden_size", None)
        out_rank = out_rank if out_rank is not None else rank
        out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size

        self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)
        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
        self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)
        self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)

    def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
        self_cls_name = self.__class__.__name__
        deprecate(
            self_cls_name,
            "0.26.0",
            (
                f"Make sure use {self_cls_name[4:]} instead by setting"
                "LoRA layers to `self.{to_q,to_k,to_v,add_k_proj,add_v_proj,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
                " `LoraLoaderMixin.load_lora_weights`"
            ),
        )
        attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
        attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
        attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
        attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)

        attn._modules.pop("processor")
        attn.processor = XFormersAttnProcessor()
        return attn.processor(attn, hidden_states, *args, **kwargs)


 class LoRAAttnAddedKVProcessor(nn.Module):
    r"""
    Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text
    encoder.

    Args:
        hidden_size (`int`, *optional*):
            The hidden size of the attention layer.
        cross_attention_dim (`int`, *optional*, defaults to `None`):
            The number of channels in the `encoder_hidden_states`.
        rank (`int`, defaults to 4):
            The dimension of the LoRA update matrices.
        network_alpha (`int`, *optional*):
            Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
        kwargs (`dict`):
            Additional keyword arguments to pass to the `LoRALinearLayer` layers.
    """

    def __init__(
        self,
        hidden_size: int,
        cross_attention_dim: Optional[int] = None,
        rank: int = 4,
        network_alpha: Optional[int] = None,
    ):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.rank = rank

        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
        self.add_k_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
        self.add_v_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
        self.to_k_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
        self.to_v_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)

    def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
        self_cls_name = self.__class__.__name__
        deprecate(
            self_cls_name,
            "0.26.0",
            (
                f"Make sure use {self_cls_name[4:]} instead by setting"
                "LoRA layers to `self.{to_q,to_k,to_v,add_k_proj,add_v_proj,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
                " `LoraLoaderMixin.load_lora_weights`"
            ),
        )
        attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
        attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
        attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
        attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)

        attn._modules.pop("processor")
        attn.processor = AttnAddedKVProcessor()
        return attn.processor(attn, hidden_states, *args, **kwargs)


 class IPAdapterAttnProcessor(nn.Module):
    r"""
    Attention processor for Multiple IP-Adapater.

    Args:
        hidden_size (`int`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`):
            The number of channels in the `encoder_hidden_states`.
        num_tokens (`int`, `Tuple[int]` or `List[int]`, defaults to `(4,)`):
            The context length of the image features.
        scale (`float` or List[`float`], defaults to 1.0):
            the weight scale of image prompt.
    """

    def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=(4,), scale=1.0):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim

        if not isinstance(num_tokens, (tuple, list)):
            num_tokens = [num_tokens]
        self.num_tokens = num_tokens

        if not isinstance(scale, list):
            scale = [scale] * len(num_tokens)
        if len(scale) != len(num_tokens):
            raise ValueError("`scale` should be a list of integers with the same length as `num_tokens`.")
        self.scale = scale

        self.to_k_ip = nn.ModuleList(
            [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))]
        )
        self.to_v_ip = nn.ModuleList(
            [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))]
        )

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
        ip_adapter_masks: Optional[torch.FloatTensor] = None,
    ):
        residual = hidden_states

        # separate ip_hidden_states from encoder_hidden_states
        if encoder_hidden_states is not None:
            if isinstance(encoder_hidden_states, tuple):
                encoder_hidden_states, ip_hidden_states = encoder_hidden_states
            else:
                deprecation_message = (
                    "You have passed a tensor as `encoder_hidden_states`.This is deprecated and will be removed in a future release."
                    " Please make sure to update your script to pass `encoder_hidden_states` as a tuple to supress this warning."
                )
                deprecate("encoder_hidden_states not a tuple", "1.0.0", deprecation_message, standard_warn=False)
                end_pos = encoder_hidden_states.shape[1] - self.num_tokens[0]
                encoder_hidden_states, ip_hidden_states = (
                    encoder_hidden_states[:, :end_pos, :],
                    [encoder_hidden_states[:, end_pos:, :]],
                )

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        if ip_adapter_masks is not None:
            if not isinstance(ip_adapter_masks, torch.Tensor) or ip_adapter_masks.ndim != 4:
                raise ValueError(
                    " ip_adapter_mask should be a tensor with shape [num_ip_adapter, 1, height, width]."
                    " Please use `IPAdapterMaskProcessor` to preprocess your mask"
                )
            if len(ip_adapter_masks) != len(self.scale):
                raise ValueError(
                    f"Number of ip_adapter_masks ({len(ip_adapter_masks)}) must match number of IP-Adapters ({len(self.scale)})"
                )
        else:
            ip_adapter_masks = [None] * len(self.scale)

        # for ip-adapter
        for current_ip_hidden_states, scale, to_k_ip, to_v_ip, mask in zip(
            ip_hidden_states, self.scale, self.to_k_ip, self.to_v_ip, ip_adapter_masks
        ):
            ip_key = to_k_ip(current_ip_hidden_states)
            ip_value = to_v_ip(current_ip_hidden_states)

            ip_key = attn.head_to_batch_dim(ip_key)
            ip_value = attn.head_to_batch_dim(ip_value)

            ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
            current_ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
            current_ip_hidden_states = attn.batch_to_head_dim(current_ip_hidden_states)

            if mask is not None:
                mask_downsample = IPAdapterMaskProcessor.downsample(
                    mask, batch_size, current_ip_hidden_states.shape[1], current_ip_hidden_states.shape[2]
                )

                mask_downsample = mask_downsample.to(dtype=query.dtype, device=query.device)

                current_ip_hidden_states = current_ip_hidden_states * mask_downsample

            hidden_states = hidden_states + scale * current_ip_hidden_states

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


 class IPAdapterAttnProcessor2_0(torch.nn.Module):
    r"""
    Attention processor for IP-Adapater for PyTorch 2.0.

    Args:
        hidden_size (`int`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`):
            The number of channels in the `encoder_hidden_states`.
        num_tokens (`int`, `Tuple[int]` or `List[int]`, defaults to `(4,)`):
            The context length of the image features.
        scale (`float` or `List[float]`, defaults to 1.0):
            the weight scale of image prompt.
    """

    def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=(4,), scale=1.0):
        super().__init__()

        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                f"{self.__class__.__name__} requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
            )

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim

        if not isinstance(num_tokens, (tuple, list)):
            num_tokens = [num_tokens]
        self.num_tokens = num_tokens

        if not isinstance(scale, list):
            scale = [scale] * len(num_tokens)
        if len(scale) != len(num_tokens):
            raise ValueError("`scale` should be a list of integers with the same length as `num_tokens`.")
        self.scale = scale

        self.to_k_ip = nn.ModuleList(
            [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))]
        )
        self.to_v_ip = nn.ModuleList(
            [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))]
        )

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
        ip_adapter_masks: Optional[torch.FloatTensor] = None,
    ):
        residual = hidden_states

        # separate ip_hidden_states from encoder_hidden_states
        if encoder_hidden_states is not None:
            if isinstance(encoder_hidden_states, tuple):
                encoder_hidden_states, ip_hidden_states = encoder_hidden_states
            else:
                deprecation_message = (
                    "You have passed a tensor as `encoder_hidden_states`.This is deprecated and will be removed in a future release."
                    " Please make sure to update your script to pass `encoder_hidden_states` as a tuple to supress this warning."
                )
                deprecate("encoder_hidden_states not a tuple", "1.0.0", deprecation_message, standard_warn=False)
                end_pos = encoder_hidden_states.shape[1] - self.num_tokens[0]
                encoder_hidden_states, ip_hidden_states = (
                    encoder_hidden_states[:, :end_pos, :],
                    [encoder_hidden_states[:, end_pos:, :]],
                )

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        if ip_adapter_masks is not None:
            if not isinstance(ip_adapter_masks, torch.Tensor) or ip_adapter_masks.ndim != 4:
                raise ValueError(
                    " ip_adapter_mask should be a tensor with shape [num_ip_adapter, 1, height, width]."
                    " Please use `IPAdapterMaskProcessor` to preprocess your mask"
                )
            if len(ip_adapter_masks) != len(self.scale):
                raise ValueError(
                    f"Number of ip_adapter_masks ({len(ip_adapter_masks)}) must match number of IP-Adapters ({len(self.scale)})"
                )
        else:
            ip_adapter_masks = [None] * len(self.scale)

        # for ip-adapter
        for current_ip_hidden_states, scale, to_k_ip, to_v_ip, mask in zip(
            ip_hidden_states, self.scale, self.to_k_ip, self.to_v_ip, ip_adapter_masks
        ):
            ip_key = to_k_ip(current_ip_hidden_states)
            ip_value = to_v_ip(current_ip_hidden_states)

            ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
            ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

            # the output of sdp = (batch, num_heads, seq_len, head_dim)
            # TODO: add support for attn.scale when we move to Torch 2.1
            current_ip_hidden_states = F.scaled_dot_product_attention(
                query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
            )

            current_ip_hidden_states = current_ip_hidden_states.transpose(1, 2).reshape(
                batch_size, -1, attn.heads * head_dim
            )
            current_ip_hidden_states = current_ip_hidden_states.to(query.dtype)

            if mask is not None:
                mask_downsample = IPAdapterMaskProcessor.downsample(
                    mask, batch_size, current_ip_hidden_states.shape[1], current_ip_hidden_states.shape[2]
                )

                mask_downsample = mask_downsample.to(dtype=query.dtype, device=query.device)

                current_ip_hidden_states = current_ip_hidden_states * mask_downsample

            hidden_states = hidden_states + scale * current_ip_hidden_states

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


 LORA_ATTENTION_PROCESSORS = (
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    LoRAAttnAddedKVProcessor,
 )

 ADDED_KV_ATTENTION_PROCESSORS = (
    AttnAddedKVProcessor,
    SlicedAttnAddedKVProcessor,
    AttnAddedKVProcessor2_0,
    XFormersAttnAddedKVProcessor,
    LoRAAttnAddedKVProcessor,
 )

 CROSS_ATTENTION_PROCESSORS = (
    AttnProcessor,
    AttnProcessor2_0,
    XFormersAttnProcessor,
    SlicedAttnProcessor,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    IPAdapterAttnProcessor,
    IPAdapterAttnProcessor2_0,
 )

 AttentionProcessor = Union[
    AttnProcessor,
    AttnProcessor2_0,
    FusedAttnProcessor2_0,
    XFormersAttnProcessor,
    SlicedAttnProcessor,
    AttnAddedKVProcessor,
    SlicedAttnAddedKVProcessor,
    AttnAddedKVProcessor2_0,
    XFormersAttnAddedKVProcessor,
    CustomDiffusionAttnProcessor,
    CustomDiffusionXFormersAttnProcessor,
    CustomDiffusionAttnProcessor2_0,
    # deprecated
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    LoRAAttnAddedKVProcessor,
 ]
 # coding=utf-8
 # Copyright 2024 HuggingFace Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.

 import numbers
 from typing import Dict, Optional, Tuple

 import torch
 import torch.nn as nn
 import torch.nn.functional as F

 from ..utils import is_torch_version
 from .activations import get_activation
 from .embeddings import CombinedTimestepLabelEmbeddings, PixArtAlphaCombinedTimestepSizeEmbeddings


 class AdaLayerNorm(nn.Module):
    r"""
    Norm layer modified to incorporate timestep embeddings.

    Parameters:
        embedding_dim (`int`): The size of each embedding vector.
        num_embeddings (`int`): The size of the embeddings dictionary.
    """

    def __init__(self, embedding_dim: int, num_embeddings: int):
        super().__init__()
        self.emb = nn.Embedding(num_embeddings, embedding_dim)
        self.silu = nn.SiLU()
        self.linear = nn.Linear(embedding_dim, embedding_dim * 2)
        self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False)

    def forward(self, x: torch.Tensor, timestep: torch.Tensor) -> torch.Tensor:
        emb = self.linear(self.silu(self.emb(timestep)))
        scale, shift = torch.chunk(emb, 2)
        x = self.norm(x) * (1 + scale) + shift
        return x


 class AdaLayerNormZero(nn.Module):
    r"""
    Norm layer adaptive layer norm zero (adaLN-Zero).

    Parameters:
        embedding_dim (`int`): The size of each embedding vector.
        num_embeddings (`int`): The size of the embeddings dictionary.
    """

    def __init__(self, embedding_dim: int, num_embeddings: int):
        super().__init__()

        self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim)

        self.silu = nn.SiLU()
        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
        self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)

    def forward(
        self,
        x: torch.Tensor,
        timestep: torch.Tensor,
        class_labels: torch.LongTensor,
        hidden_dtype: Optional[torch.dtype] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        emb = self.linear(self.silu(self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)))
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=1)
        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp


 class AdaLayerNormSingle(nn.Module):
    r"""
    Norm layer adaptive layer norm single (adaLN-single).

    As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).

    Parameters:
        embedding_dim (`int`): The size of each embedding vector.
        use_additional_conditions (`bool`): To use additional conditions for normalization or not.
    """

    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False):
        super().__init__()

        self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
            embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions
        )

        self.silu = nn.SiLU()
        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)

    def forward(
        self,
        timestep: torch.Tensor,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        batch_size: Optional[int] = None,
        hidden_dtype: Optional[torch.dtype] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        # No modulation happening here.
        embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)
        return self.linear(self.silu(embedded_timestep)), embedded_timestep


 class AdaGroupNorm(nn.Module):
    r"""
    GroupNorm layer modified to incorporate timestep embeddings.

    Parameters:
        embedding_dim (`int`): The size of each embedding vector.
        num_embeddings (`int`): The size of the embeddings dictionary.
        num_groups (`int`): The number of groups to separate the channels into.
        act_fn (`str`, *optional*, defaults to `None`): The activation function to use.
        eps (`float`, *optional*, defaults to `1e-5`): The epsilon value to use for numerical stability.
    """

    def __init__(
        self, embedding_dim: int, out_dim: int, num_groups: int, act_fn: Optional[str] = None, eps: float = 1e-5
    ):
        super().__init__()
        self.num_groups = num_groups
        self.eps = eps

        if act_fn is None:
            self.act = None
        else:
            self.act = get_activation(act_fn)

        self.linear = nn.Linear(embedding_dim, out_dim * 2)

    def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:
        if self.act:
            emb = self.act(emb)
        emb = self.linear(emb)
        emb = emb[:, :, None, None]
        scale, shift = emb.chunk(2, dim=1)

        x = F.group_norm(x, self.num_groups, eps=self.eps)
        x = x * (1 + scale) + shift
        return x


 class AdaLayerNormContinuous(nn.Module):
    def __init__(
        self,
        embedding_dim: int,
        conditioning_embedding_dim: int,
        # NOTE: It is a bit weird that the norm layer can be configured to have scale and shift parameters
        # because the output is immediately scaled and shifted by the projected conditioning embeddings.
        # Note that AdaLayerNorm does not let the norm layer have scale and shift parameters.
        # However, this is how it was implemented in the original code, and it's rather likely you should
        # set `elementwise_affine` to False.
        elementwise_affine=True,
        eps=1e-5,
        bias=True,
        norm_type="layer_norm",
    ):
        super().__init__()
        self.silu = nn.SiLU()
        self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
        if norm_type == "layer_norm":
            self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)
        elif norm_type == "rms_norm":
            self.norm = RMSNorm(embedding_dim, eps, elementwise_affine)
        else:
            raise ValueError(f"unknown norm_type {norm_type}")

    def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
        emb = self.linear(self.silu(conditioning_embedding))
        scale, shift = torch.chunk(emb, 2, dim=1)
        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
        return x


 if is_torch_version(">=", "2.1.0"):
    LayerNorm = nn.LayerNorm
 else:
    # Has optional bias parameter compared to torch layer norm
    # TODO: replace with torch layernorm once min required torch version >= 2.1
    class LayerNorm(nn.Module):
        def __init__(self, dim, eps: float = 1e-5, elementwise_affine: bool = True, bias: bool = True):
            super().__init__()

            self.eps = eps

            if isinstance(dim, numbers.Integral):
                dim = (dim,)

            self.dim = torch.Size(dim)

            if elementwise_affine:
                self.weight = nn.Parameter(torch.ones(dim))
                self.bias = nn.Parameter(torch.zeros(dim)) if bias else None
            else:
                self.weight = None
                self.bias = None

        def forward(self, input):
            return F.layer_norm(input, self.dim, self.weight, self.bias, self.eps)


 class RMSNorm(nn.Module):
    def __init__(self, dim, eps: float, elementwise_affine: bool = True):
        super().__init__()

        self.eps = eps

        if isinstance(dim, numbers.Integral):
            dim = (dim,)

        self.dim = torch.Size(dim)

        if elementwise_affine:
            self.weight = nn.Parameter(torch.ones(dim))
        else:
            self.weight = None

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)

        if self.weight is not None:
            # convert into half-precision if necessary
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                hidden_states = hidden_states.to(self.weight.dtype)
            hidden_states = hidden_states * self.weight
        else:
            hidden_states = hidden_states.to(input_dtype)

        return hidden_states


 class GlobalResponseNorm(nn.Module):
    # Taken from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105
    def __init__(self, dim):
        super().__init__()
        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
        self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))

    def forward(self, x):
        gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
        nx = gx / (gx.mean(dim=-1, keepdim=True) + 1e-6)
        return self.gamma * (x * nx) + self.beta + x


 from typing import Optional, Tuple

 import torch
 import torch.nn as nn
 import torch.nn.functional as F

 from ..utils import USE_PEFT_BACKEND
 from .lora import LoRACompatibleConv
 from .normalization import RMSNorm
 from .upsampling import upfirdn2d_native


 class Downsample1D(nn.Module):
    """A 1D downsampling layer with an optional convolution.

    Parameters:
        channels (`int`):
            number of channels in the inputs and outputs.
        use_conv (`bool`, default `False`):
            option to use a convolution.
        out_channels (`int`, optional):
            number of output channels. Defaults to `channels`.
        padding (`int`, default `1`):
            padding for the convolution.
        name (`str`, default `conv`):
            name of the downsampling 1D layer.
    """

    def __init__(
        self,
        channels: int,
        use_conv: bool = False,
        out_channels: Optional[int] = None,
        padding: int = 1,
        name: str = "conv",
    ):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.padding = padding
        stride = 2
        self.name = name

        if use_conv:
            self.conv = nn.Conv1d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
        else:
            assert self.channels == self.out_channels
            self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride)

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        assert inputs.shape[1] == self.channels
        return self.conv(inputs)


 class Downsample2D(nn.Module):
    """A 2D downsampling layer with an optional convolution.

    Parameters:
        channels (`int`):
            number of channels in the inputs and outputs.
        use_conv (`bool`, default `False`):
            option to use a convolution.
        out_channels (`int`, optional):
            number of output channels. Defaults to `channels`.
        padding (`int`, default `1`):
            padding for the convolution.
        name (`str`, default `conv`):
            name of the downsampling 2D layer.
    """

    def __init__(
        self,
        channels: int,
        use_conv: bool = False,
        out_channels: Optional[int] = None,
        padding: int = 1,
        name: str = "conv",
        kernel_size=3,
        norm_type=None,
        eps=None,
        elementwise_affine=None,
        bias=True,
    ):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.padding = padding
        stride = 2
        self.name = name
        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv

        if norm_type == "ln_norm":
            self.norm = nn.LayerNorm(channels, eps, elementwise_affine)
        elif norm_type == "rms_norm":
            self.norm = RMSNorm(channels, eps, elementwise_affine)
        elif norm_type is None:
            self.norm = None
        else:
            raise ValueError(f"unknown norm_type: {norm_type}")

        if use_conv:
            conv = conv_cls(
                self.channels, self.out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias
            )
        else:
            assert self.channels == self.out_channels
            conv = nn.AvgPool2d(kernel_size=stride, stride=stride)

        # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
        if name == "conv":
            self.Conv2d_0 = conv
            self.conv = conv
        elif name == "Conv2d_0":
            self.conv = conv
        else:
            self.conv = conv

    def forward(self, hidden_states: torch.FloatTensor, scale: float = 1.0) -> torch.FloatTensor:
        assert hidden_states.shape[1] == self.channels

        if self.norm is not None:
            hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)

        if self.use_conv and self.padding == 0:
            pad = (0, 1, 0, 1)
            hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)

        assert hidden_states.shape[1] == self.channels

        if not USE_PEFT_BACKEND:
            if isinstance(self.conv, LoRACompatibleConv):
                hidden_states = self.conv(hidden_states, scale)
            else:
                hidden_states = self.conv(hidden_states)
        else:
            hidden_states = self.conv(hidden_states)

        return hidden_states


 class FirDownsample2D(nn.Module):
    """A 2D FIR downsampling layer with an optional convolution.

    Parameters:
        channels (`int`):
            number of channels in the inputs and outputs.
        use_conv (`bool`, default `False`):
            option to use a convolution.
        out_channels (`int`, optional):
            number of output channels. Defaults to `channels`.
        fir_kernel (`tuple`, default `(1, 3, 3, 1)`):
            kernel for the FIR filter.
    """

    def __init__(
        self,
        channels: Optional[int] = None,
        out_channels: Optional[int] = None,
        use_conv: bool = False,
        fir_kernel: Tuple[int, int, int, int] = (1, 3, 3, 1),
    ):
        super().__init__()
        out_channels = out_channels if out_channels else channels
        if use_conv:
            self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.fir_kernel = fir_kernel
        self.use_conv = use_conv
        self.out_channels = out_channels

    def _downsample_2d(
        self,
        hidden_states: torch.FloatTensor,
        weight: Optional[torch.FloatTensor] = None,
        kernel: Optional[torch.FloatTensor] = None,
        factor: int = 2,
        gain: float = 1,
    ) -> torch.FloatTensor:
        """Fused `Conv2d()` followed by `downsample_2d()`.
        Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
        arbitrary order.

        Args:
            hidden_states (`torch.FloatTensor`):
                Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
            weight (`torch.FloatTensor`, *optional*):
                Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be
                performed by `inChannels = x.shape[0] // numGroups`.
            kernel (`torch.FloatTensor`, *optional*):
                FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which
                corresponds to average pooling.
            factor (`int`, *optional*, default to `2`):
                Integer downsampling factor.
            gain (`float`, *optional*, default to `1.0`):
                Scaling factor for signal magnitude.

        Returns:
            output (`torch.FloatTensor`):
                Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same
                datatype as `x`.
        """

        assert isinstance(factor, int) and factor >= 1
        if kernel is None:
            kernel = [1] * factor

        # setup kernel
        kernel = torch.tensor(kernel, dtype=torch.float32)
        if kernel.ndim == 1:
            kernel = torch.outer(kernel, kernel)
        kernel /= torch.sum(kernel)

        kernel = kernel * gain

        if self.use_conv:
            _, _, convH, convW = weight.shape
            pad_value = (kernel.shape[0] - factor) + (convW - 1)
            stride_value = [factor, factor]
            upfirdn_input = upfirdn2d_native(
                hidden_states,
                torch.tensor(kernel, device=hidden_states.device),
                pad=((pad_value + 1) // 2, pad_value // 2),
            )
            output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)
        else:
            pad_value = kernel.shape[0] - factor
            output = upfirdn2d_native(
                hidden_states,
                torch.tensor(kernel, device=hidden_states.device),
                down=factor,
                pad=((pad_value + 1) // 2, pad_value // 2),
            )

        return output

    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        if self.use_conv:
            downsample_input = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
            hidden_states = downsample_input + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
        else:
            hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)

        return hidden_states


 # downsample/upsample layer used in k-upscaler, might be able to use FirDownsample2D/DirUpsample2D instead
 class KDownsample2D(nn.Module):
    r"""A 2D K-downsampling layer.

    Parameters:
        pad_mode (`str`, *optional*, default to `"reflect"`): the padding mode to use.
    """

    def __init__(self, pad_mode: str = "reflect"):
        super().__init__()
        self.pad_mode = pad_mode
        kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]])
        self.pad = kernel_1d.shape[1] // 2 - 1
        self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        inputs = F.pad(inputs, (self.pad,) * 4, self.pad_mode)
        weight = inputs.new_zeros(
            [
                inputs.shape[1],
                inputs.shape[1],
                self.kernel.shape[0],
                self.kernel.shape[1],
            ]
        )
        indices = torch.arange(inputs.shape[1], device=inputs.device)
        kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
        weight[indices, indices] = kernel
        return F.conv2d(inputs, weight, stride=2)


 def downsample_2d(
    hidden_states: torch.FloatTensor,
    kernel: Optional[torch.FloatTensor] = None,
    factor: int = 2,
    gain: float = 1,
 ) -> torch.FloatTensor:
    r"""Downsample2D a batch of 2D images with the given filter.
    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
    given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the
    specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its
    shape is a multiple of the downsampling factor.

    Args:
        hidden_states (`torch.FloatTensor`)
            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        kernel (`torch.FloatTensor`, *optional*):
            FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which
            corresponds to average pooling.
        factor (`int`, *optional*, default to `2`):
            Integer downsampling factor.
        gain (`float`, *optional*, default to `1.0`):
            Scaling factor for signal magnitude.

    Returns:
        output (`torch.FloatTensor`):
            Tensor of the shape `[N, C, H // factor, W // factor]`
    """

    assert isinstance(factor, int) and factor >= 1
    if kernel is None:
        kernel = [1] * factor

    kernel = torch.tensor(kernel, dtype=torch.float32)
    if kernel.ndim == 1:
        kernel = torch.outer(kernel, kernel)
    kernel /= torch.sum(kernel)

    kernel = kernel * gain
    pad_value = kernel.shape[0] - factor
    output = upfirdn2d_native(
        hidden_states,
        kernel.to(device=hidden_states.device),
        down=factor,
        pad=((pad_value + 1) // 2, pad_value // 2),
    )
    return output
 # coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team.
 # Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.

 import inspect
 import itertools
 import os
 import re
 from collections import OrderedDict
 from functools import partial
 from typing import Any, Callable, List, Optional, Tuple, Union

 import safetensors
 import torch
 from huggingface_hub import create_repo
 from huggingface_hub.utils import validate_hf_hub_args
 from torch import Tensor, nn

 from .. import __version__
 from ..utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    SAFETENSORS_FILE_EXTENSION,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
    _add_variant,
    _get_model_file,
    deprecate,
    is_accelerate_available,
    is_torch_version,
    logging,
 )
 from ..utils.hub_utils import PushToHubMixin, load_or_create_model_card, populate_model_card


 logger = logging.get_logger(__name__)


 if is_torch_version(">=", "1.9.0"):
    _LOW_CPU_MEM_USAGE_DEFAULT = True
 else:
    _LOW_CPU_MEM_USAGE_DEFAULT = False


 if is_accelerate_available():
    import accelerate
    from accelerate.utils import set_module_tensor_to_device
    from accelerate.utils.versions import is_torch_version


 def get_parameter_device(parameter: torch.nn.Module) -> torch.device:
    try:
        parameters_and_buffers = itertools.chain(parameter.parameters(), parameter.buffers())
        return next(parameters_and_buffers).device
    except StopIteration:
        # For torch.nn.DataParallel compatibility in PyTorch 1.5

        def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:
            tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
            return tuples

        gen = parameter._named_members(get_members_fn=find_tensor_attributes)
        first_tuple = next(gen)
        return first_tuple[1].device


 def get_parameter_dtype(parameter: torch.nn.Module) -> torch.dtype:
    try:
        params = tuple(parameter.parameters())
        if len(params) > 0:
            return params[0].dtype

        buffers = tuple(parameter.buffers())
        if len(buffers) > 0:
            return buffers[0].dtype

    except StopIteration:
        # For torch.nn.DataParallel compatibility in PyTorch 1.5

        def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:
            tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
            return tuples

        gen = parameter._named_members(get_members_fn=find_tensor_attributes)
        first_tuple = next(gen)
        return first_tuple[1].dtype


 def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None):
    """
    Reads a checkpoint file, returning properly formatted errors if they arise.
    """
    try:
        file_extension = os.path.basename(checkpoint_file).split(".")[-1]
        if file_extension == SAFETENSORS_FILE_EXTENSION:
            return safetensors.torch.load_file(checkpoint_file, device="cpu")
        else:
            return torch.load(checkpoint_file, map_location="cpu")
    except Exception as e:
        try:
            with open(checkpoint_file) as f:
                if f.read().startswith("version"):
                    raise OSError(
                        "You seem to have cloned a repository without having git-lfs installed. Please install "
                        "git-lfs and run `git lfs install` followed by `git lfs pull` in the folder "
                        "you cloned."
                    )
                else:
                    raise ValueError(
                        f"Unable to locate the file {checkpoint_file} which is necessary to load this pretrained "
                        "model. Make sure you have saved the model properly."
                    ) from e
        except (UnicodeDecodeError, ValueError):
            raise OSError(
                f"Unable to load weights from checkpoint file for '{checkpoint_file}' " f"at '{checkpoint_file}'. "
            )


 def load_model_dict_into_meta(
    model,
    state_dict: OrderedDict,
    device: Optional[Union[str, torch.device]] = None,
    dtype: Optional[Union[str, torch.dtype]] = None,
    model_name_or_path: Optional[str] = None,
 ) -> List[str]:
    device = device or torch.device("cpu")
    dtype = dtype or torch.float32

    accepts_dtype = "dtype" in set(inspect.signature(set_module_tensor_to_device).parameters.keys())

    unexpected_keys = []
    empty_state_dict = model.state_dict()
    for param_name, param in state_dict.items():
        if param_name not in empty_state_dict:
            unexpected_keys.append(param_name)
            continue

        if empty_state_dict[param_name].shape != param.shape:
            model_name_or_path_str = f"{model_name_or_path} " if model_name_or_path is not None else ""
            raise ValueError(
                f"Cannot load {model_name_or_path_str}because {param_name} expected shape {empty_state_dict[param_name]}, but got {param.shape}. If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example."
            )

        if accepts_dtype:
            set_module_tensor_to_device(model, param_name, device, value=param, dtype=dtype)
        else:
            set_module_tensor_to_device(model, param_name, device, value=param)
    return unexpected_keys


 def _load_state_dict_into_model(model_to_load, state_dict: OrderedDict) -> List[str]:
    # Convert old format to new format if needed from a PyTorch state_dict
    # copy state_dict so _load_from_state_dict can modify it
    state_dict = state_dict.copy()
    error_msgs = []

    # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
    # so we need to apply the function recursively.
    def load(module: torch.nn.Module, prefix: str = ""):
        args = (state_dict, prefix, {}, True, [], [], error_msgs)
        module._load_from_state_dict(*args)

        for name, child in module._modules.items():
            if child is not None:
                load(child, prefix + name + ".")

    load(model_to_load)

    return error_msgs


 class ModelMixin(torch.nn.Module, PushToHubMixin):
    r"""
    Base class for all models.

    [`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and
    saving models.

        - **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`].
    """

    config_name = CONFIG_NAME
    _automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
    _supports_gradient_checkpointing = False
    _keys_to_ignore_on_load_unexpected = None

    def __init__(self):
        super().__init__()

    def __getattr__(self, name: str) -> Any:
        """The only reason we overwrite `getattr` here is to gracefully deprecate accessing
        config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 We need to overwrite
        __getattr__ here in addition so that we don't trigger `torch.nn.Module`'s __getattr__':
        https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module
        """

        is_in_config = "_internal_dict" in self.__dict__ and hasattr(self.__dict__["_internal_dict"], name)
        is_attribute = name in self.__dict__

        if is_in_config and not is_attribute:
            deprecation_message = f"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'unet.config.{name}'."
            deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False, stacklevel=3)
            return self._internal_dict[name]

        # call PyTorch's https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module
        return super().__getattr__(name)

    @property
    def is_gradient_checkpointing(self) -> bool:
        """
        Whether gradient checkpointing is activated for this model or not.
        """
        return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules())

    def enable_gradient_checkpointing(self) -> None:
        """
        Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or
        *checkpoint activations* in other frameworks).
        """
        if not self._supports_gradient_checkpointing:
            raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
        self.apply(partial(self._set_gradient_checkpointing, value=True))

    def disable_gradient_checkpointing(self) -> None:
        """
        Deactivates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or
        *checkpoint activations* in other frameworks).
        """
        if self._supports_gradient_checkpointing:
            self.apply(partial(self._set_gradient_checkpointing, value=False))

    def set_use_memory_efficient_attention_xformers(
        self, valid: bool, attention_op: Optional[Callable] = None
    ) -> None:
        # Recursively walk through all the children.
        # Any children which exposes the set_use_memory_efficient_attention_xformers method
        # gets the message
        def fn_recursive_set_mem_eff(module: torch.nn.Module):
            if hasattr(module, "set_use_memory_efficient_attention_xformers"):
                module.set_use_memory_efficient_attention_xformers(valid, attention_op)

            for child in module.children():
                fn_recursive_set_mem_eff(child)

        for module in self.children():
            if isinstance(module, torch.nn.Module):
                fn_recursive_set_mem_eff(module)

    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None) -> None:
        r"""
        Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).

        When this option is enabled, you should observe lower GPU memory usage and a potential speed up during
        inference. Speed up during training is not guaranteed.

        <Tip warning={true}>

        ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
        precedent.

        </Tip>

        Parameters:
            attention_op (`Callable`, *optional*):
                Override the default `None` operator for use as `op` argument to the
                [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
                function of xFormers.

        Examples:

        ```py
        >>> import torch
        >>> from diffusers import UNet2DConditionModel
        >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

        >>> model = UNet2DConditionModel.from_pretrained(
        ...     "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16
        ... )
        >>> model = model.to("cuda")
        >>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
        ```
        """
        self.set_use_memory_efficient_attention_xformers(True, attention_op)

    def disable_xformers_memory_efficient_attention(self) -> None:
        r"""
        Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).
        """
        self.set_use_memory_efficient_attention_xformers(False)

    def save_pretrained(
        self,
        save_directory: Union[str, os.PathLike],
        is_main_process: bool = True,
        save_function: Optional[Callable] = None,
        safe_serialization: bool = True,
        variant: Optional[str] = None,
        push_to_hub: bool = False,
        **kwargs,
    ):
        """
        Save a model and its configuration file to a directory so that it can be reloaded using the
        [`~models.ModelMixin.from_pretrained`] class method.

        Arguments:
            save_directory (`str` or `os.PathLike`):
                Directory to save a model and its configuration file to. Will be created if it doesn't exist.
            is_main_process (`bool`, *optional*, defaults to `True`):
                Whether the process calling this is the main process or not. Useful during distributed training and you
                need to call this function on all processes. In this case, set `is_main_process=True` only on the main
                process to avoid race conditions.
            save_function (`Callable`):
                The function to use to save the state dictionary. Useful during distributed training when you need to
                replace `torch.save` with another method. Can be configured with the environment variable
                `DIFFUSERS_SAVE_MODE`.
            safe_serialization (`bool`, *optional*, defaults to `True`):
                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
            variant (`str`, *optional*):
                If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
            push_to_hub (`bool`, *optional*, defaults to `False`):
                Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                namespace).
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
        """
        if os.path.isfile(save_directory):
            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
            return

        os.makedirs(save_directory, exist_ok=True)

        if push_to_hub:
            commit_message = kwargs.pop("commit_message", None)
            private = kwargs.pop("private", False)
            create_pr = kwargs.pop("create_pr", False)
            token = kwargs.pop("token", None)
            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
            repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id

        # Only save the model itself if we are using distributed training
        model_to_save = self

        # Attach architecture to the config
        # Save the config
        if is_main_process:
            model_to_save.save_config(save_directory)

        # Save the model
        state_dict = model_to_save.state_dict()

        weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
        weights_name = _add_variant(weights_name, variant)

        # Save the model
        if safe_serialization:
            safetensors.torch.save_file(
                state_dict, os.path.join(save_directory, weights_name), metadata={"format": "pt"}
            )
        else:
            torch.save(state_dict, os.path.join(save_directory, weights_name))

        logger.info(f"Model weights saved in {os.path.join(save_directory, weights_name)}")

        if push_to_hub:
            # Create a new empty model card and eventually tag it
            model_card = load_or_create_model_card(repo_id, token=token)
            model_card = populate_model_card(model_card)
            model_card.save(os.path.join(save_directory, "README.md"))

            self._upload_folder(
                save_directory,
                repo_id,
                token=token,
                commit_message=commit_message,
                create_pr=create_pr,
            )

    @classmethod
    @validate_hf_hub_args
    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
        r"""
        Instantiate a pretrained PyTorch model from a pretrained model configuration.

        The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To
        train the model, set it back in training mode with `model.train()`.

        Parameters:
            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
                Can be either:

                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
                      the Hub.
                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
                      with [`~ModelMixin.save_pretrained`].

            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                is not used.
            torch_dtype (`str` or `torch.dtype`, *optional*):
                Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the
                dtype is automatically derived from the model's weights.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
                incompletely downloaded files are deleted.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            output_loading_info (`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
            local_files_only(`bool`, *optional*, defaults to `False`):
                Whether to only load local model weights and configuration files or not. If set to `True`, the model
                won't be downloaded from the Hub.
            token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                `diffusers-cli login` (stored in `~/.huggingface`) is used.
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                allowed by Git.
            from_flax (`bool`, *optional*, defaults to `False`):
                Load the model weights from a Flax checkpoint save file.
            subfolder (`str`, *optional*, defaults to `""`):
                The subfolder location of a model file within a larger model repository on the Hub or locally.
            mirror (`str`, *optional*):
                Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
                guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
                information.
            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
                A map that specifies where each submodule should go. It doesn't need to be defined for each
                parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
                same device.

                Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
                more information about each option see [designing a device
                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
            max_memory (`Dict`, *optional*):
                A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
                each GPU and the available CPU RAM if unset.
            offload_folder (`str` or `os.PathLike`, *optional*):
                The path to offload weights if `device_map` contains the value `"disk"`.
            offload_state_dict (`bool`, *optional*):
                If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
                the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
                when there is some disk offload.
            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
                argument to `True` will raise an error.
            variant (`str`, *optional*):
                Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when
                loading `from_flax`.
            use_safetensors (`bool`, *optional*, defaults to `None`):
                If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the
                `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`
                weights. If set to `False`, `safetensors` weights are not loaded.

        <Tip>

        To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with
        `huggingface-cli login`. You can also activate the special
        ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a
        firewalled environment.

        </Tip>

        Example:

        ```py
        from diffusers import UNet2DConditionModel

        unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
        ```

        If you get the error message below, you need to finetune the weights for your downstream task:

        ```bash
        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
        - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
        ```
        """
        cache_dir = kwargs.pop("cache_dir", None)
        ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
        force_download = kwargs.pop("force_download", False)
        from_flax = kwargs.pop("from_flax", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        output_loading_info = kwargs.pop("output_loading_info", False)
        local_files_only = kwargs.pop("local_files_only", None)
        token = kwargs.pop("token", None)
        revision = kwargs.pop("revision", None)
        torch_dtype = kwargs.pop("torch_dtype", None)
        subfolder = kwargs.pop("subfolder", None)
        device_map = kwargs.pop("device_map", None)
        max_memory = kwargs.pop("max_memory", None)
        offload_folder = kwargs.pop("offload_folder", None)
        offload_state_dict = kwargs.pop("offload_state_dict", False)
        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
        variant = kwargs.pop("variant", None)
        use_safetensors = kwargs.pop("use_safetensors", None)

        allow_pickle = False
        if use_safetensors is None:
            use_safetensors = True
            allow_pickle = True

        if low_cpu_mem_usage and not is_accelerate_available():
            low_cpu_mem_usage = False
            logger.warning(
                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
                " install accelerate\n```\n."
            )

        if device_map is not None and not is_accelerate_available():
            raise NotImplementedError(
                "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"
                " `device_map=None`. You can install accelerate with `pip install accelerate`."
            )

        # Check if we can handle device_map and dispatching the weights
        if device_map is not None and not is_torch_version(">=", "1.9.0"):
            raise NotImplementedError(
                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
                " `device_map=None`."
            )

        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
            raise NotImplementedError(
                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
                " `low_cpu_mem_usage=False`."
            )

        if low_cpu_mem_usage is False and device_map is not None:
            raise ValueError(
                f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and"
                " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
            )

        # Load config if we don't provide a configuration
        config_path = pretrained_model_name_or_path

        user_agent = {
            "diffusers": __version__,
            "file_type": "model",
            "framework": "pytorch",
        }

        # load config
        config, unused_kwargs, commit_hash = cls.load_config(
            config_path,
            cache_dir=cache_dir,
            return_unused_kwargs=True,
            return_commit_hash=True,
            force_download=force_download,
            resume_download=resume_download,
            proxies=proxies,
            local_files_only=local_files_only,
            token=token,
            revision=revision,
            subfolder=subfolder,
            device_map=device_map,
            max_memory=max_memory,
            offload_folder=offload_folder,
            offload_state_dict=offload_state_dict,
            user_agent=user_agent,
            **kwargs,
        )

        # load model
        model_file = None
        if from_flax:
            model_file = _get_model_file(
                pretrained_model_name_or_path,
                weights_name=FLAX_WEIGHTS_NAME,
                cache_dir=cache_dir,
                force_download=force_download,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                token=token,
                revision=revision,
                subfolder=subfolder,
                user_agent=user_agent,
                commit_hash=commit_hash,
            )
            model = cls.from_config(config, **unused_kwargs)

            # Convert the weights
            from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model

            model = load_flax_checkpoint_in_pytorch_model(model, model_file)
        else:
            if use_safetensors:
                try:
                    model_file = _get_model_file(
                        pretrained_model_name_or_path,
                        weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),
                        cache_dir=cache_dir,
                        force_download=force_download,
                        resume_download=resume_download,
                        proxies=proxies,
                        local_files_only=local_files_only,
                        token=token,
                        revision=revision,
                        subfolder=subfolder,
                        user_agent=user_agent,
                        commit_hash=commit_hash,
                    )
                except IOError as e:
                    if not allow_pickle:
                        raise e
                    pass
            if model_file is None:
                model_file = _get_model_file(
                    pretrained_model_name_or_path,
                    weights_name=_add_variant(WEIGHTS_NAME, variant),
                    cache_dir=cache_dir,
                    force_download=force_download,
                    resume_download=resume_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    token=token,
                    revision=revision,
                    subfolder=subfolder,
                    user_agent=user_agent,
                    commit_hash=commit_hash,
                )

            if low_cpu_mem_usage:
                # Instantiate model with empty weights
                with accelerate.init_empty_weights():
                    model = cls.from_config(config, **unused_kwargs)

                # if device_map is None, load the state dict and move the params from meta device to the cpu
                if device_map is None:
                    param_device = "cpu"
                    state_dict = load_state_dict(model_file, variant=variant)
                    model._convert_deprecated_attention_blocks(state_dict)
                    # move the params from meta device to cpu
                    missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
                    if len(missing_keys) > 0:
                        raise ValueError(
                            f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are"
                            f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
                            " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"
                            " those weights or else make sure your checkpoint file is correct."
                        )

                    unexpected_keys = load_model_dict_into_meta(
                        model,
                        state_dict,
                        device=param_device,
                        dtype=torch_dtype,
                        model_name_or_path=pretrained_model_name_or_path,
                    )

                    if cls._keys_to_ignore_on_load_unexpected is not None:
                        for pat in cls._keys_to_ignore_on_load_unexpected:
                            unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]

                    if len(unexpected_keys) > 0:
                        logger.warn(
                            f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
                        )

                else:  # else let accelerate handle loading and dispatching.
                    # Load weights and dispatch according to the device_map
                    # by default the device_map is None and the weights are loaded on the CPU
                    try:
                        accelerate.load_checkpoint_and_dispatch(
                            model,
                            model_file,
                            device_map,
                            max_memory=max_memory,
                            offload_folder=offload_folder,
                            offload_state_dict=offload_state_dict,
                            dtype=torch_dtype,
                        )
                    except AttributeError as e:
                        # When using accelerate loading, we do not have the ability to load the state
                        # dict and rename the weight names manually. Additionally, accelerate skips
                        # torch loading conventions and directly writes into `module.{_buffers, _parameters}`
                        # (which look like they should be private variables?), so we can't use the standard hooks
                        # to rename parameters on load. We need to mimic the original weight names so the correct
                        # attributes are available. After we have loaded the weights, we convert the deprecated
                        # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert
                        # the weights so we don't have to do this again.

                        if "'Attention' object has no attribute" in str(e):
                            logger.warn(
                                f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}"
                                " was saved with deprecated attention block weight names. We will load it with the deprecated attention block"
                                " names and convert them on the fly to the new attention block format. Please re-save the model after this conversion,"
                                " so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint,"
                                " please also re-upload it or open a PR on the original repository."
                            )
                            model._temp_convert_self_to_deprecated_attention_blocks()
                            accelerate.load_checkpoint_and_dispatch(
                                model,
                                model_file,
                                device_map,
                                max_memory=max_memory,
                                offload_folder=offload_folder,
                                offload_state_dict=offload_state_dict,
                                dtype=torch_dtype,
                            )
                            model._undo_temp_convert_self_to_deprecated_attention_blocks()
                        else:
                            raise e

                loading_info = {
                    "missing_keys": [],
                    "unexpected_keys": [],
                    "mismatched_keys": [],
                    "error_msgs": [],
                }
            else:
                model = cls.from_config(config, **unused_kwargs)

                state_dict = load_state_dict(model_file, variant=variant)
                model._convert_deprecated_attention_blocks(state_dict)

                model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
                    model,
                    state_dict,
                    model_file,
                    pretrained_model_name_or_path,
                    ignore_mismatched_sizes=ignore_mismatched_sizes,
                )

                loading_info = {
                    "missing_keys": missing_keys,
                    "unexpected_keys": unexpected_keys,
                    "mismatched_keys": mismatched_keys,
                    "error_msgs": error_msgs,
                }

        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
            raise ValueError(
                f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
            )
        elif torch_dtype is not None:
            model = model.to(torch_dtype)

        model.register_to_config(_name_or_path=pretrained_model_name_or_path)

        # Set model in evaluation mode to deactivate DropOut modules by default
        model.eval()
        if output_loading_info:
            return model, loading_info

        return model

    @classmethod
    def _load_pretrained_model(
        cls,
        model,
        state_dict: OrderedDict,
        resolved_archive_file,
        pretrained_model_name_or_path: Union[str, os.PathLike],
        ignore_mismatched_sizes: bool = False,
    ):
        # Retrieve missing & unexpected_keys
        model_state_dict = model.state_dict()
        loaded_keys = list(state_dict.keys())

        expected_keys = list(model_state_dict.keys())

        original_loaded_keys = loaded_keys

        missing_keys = list(set(expected_keys) - set(loaded_keys))
        unexpected_keys = list(set(loaded_keys) - set(expected_keys))

        # Make sure we are able to load base models as well as derived models (with heads)
        model_to_load = model

        def _find_mismatched_keys(
            state_dict,
            model_state_dict,
            loaded_keys,
            ignore_mismatched_sizes,
        ):
            mismatched_keys = []
            if ignore_mismatched_sizes:
                for checkpoint_key in loaded_keys:
                    model_key = checkpoint_key

                    if (
                        model_key in model_state_dict
                        and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape
                    ):
                        mismatched_keys.append(
                            (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
                        )
                        del state_dict[checkpoint_key]
            return mismatched_keys

        if state_dict is not None:
            # Whole checkpoint
            mismatched_keys = _find_mismatched_keys(
                state_dict,
                model_state_dict,
                original_loaded_keys,
                ignore_mismatched_sizes,
            )
            error_msgs = _load_state_dict_into_model(model_to_load, state_dict)

        if len(error_msgs) > 0:
            error_msg = "\n\t".join(error_msgs)
            if "size mismatch" in error_msg:
                error_msg += (
                    "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
                )
            raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")

        if len(unexpected_keys) > 0:
            logger.warning(
                f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
                f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
                f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task"
                " or with another architecture (e.g. initializing a BertForSequenceClassification model from a"
                " BertForPreTraining model).\n- This IS NOT expected if you are initializing"
                f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly"
                " identical (initializing a BertForSequenceClassification model from a"
                " BertForSequenceClassification model)."
            )
        else:
            logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
        if len(missing_keys) > 0:
            logger.warning(
                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
                " TRAIN this model on a down-stream task to be able to use it for predictions and inference."
            )
        elif len(mismatched_keys) == 0:
            logger.info(
                f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the"
                f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions"
                " without further training."
            )
        if len(mismatched_keys) > 0:
            mismatched_warning = "\n".join(
                [
                    f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
                    for key, shape1, shape2 in mismatched_keys
                ]
            )
            logger.warning(
                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not"
                f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be"
                " able to use it for predictions and inference."
            )

        return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs

    @property
    def device(self) -> torch.device:
        """
        `torch.device`: The device on which the module is (assuming that all the module parameters are on the same
        device).
        """
        return get_parameter_device(self)

    @property
    def dtype(self) -> torch.dtype:
        """
        `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
        """
        return get_parameter_dtype(self)

    def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:
        """
        Get number of (trainable or non-embedding) parameters in the module.

        Args:
            only_trainable (`bool`, *optional*, defaults to `False`):
                Whether or not to return only the number of trainable parameters.
            exclude_embeddings (`bool`, *optional*, defaults to `False`):
                Whether or not to return only the number of non-embedding parameters.

        Returns:
            `int`: The number of parameters.

        Example:

        ```py
        from diffusers import UNet2DConditionModel

        model_id = "runwayml/stable-diffusion-v1-5"
        unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet")
        unet.num_parameters(only_trainable=True)
        859520964
        ```
        """

        if exclude_embeddings:
            embedding_param_names = [
                f"{name}.weight"
                for name, module_type in self.named_modules()
                if isinstance(module_type, torch.nn.Embedding)
            ]
            non_embedding_parameters = [
                parameter for name, parameter in self.named_parameters() if name not in embedding_param_names
            ]
            return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable)
        else:
            return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable)

    def _convert_deprecated_attention_blocks(self, state_dict: OrderedDict) -> None:
        deprecated_attention_block_paths = []

        def recursive_find_attn_block(name, module):
            if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block:
                deprecated_attention_block_paths.append(name)

            for sub_name, sub_module in module.named_children():
                sub_name = sub_name if name == "" else f"{name}.{sub_name}"
                recursive_find_attn_block(sub_name, sub_module)

        recursive_find_attn_block("", self)

        # NOTE: we have to check if the deprecated parameters are in the state dict
        # because it is possible we are loading from a state dict that was already
        # converted

        for path in deprecated_attention_block_paths:
            # group_norm path stays the same

            # query -> to_q
            if f"{path}.query.weight" in state_dict:
                state_dict[f"{path}.to_q.weight"] = state_dict.pop(f"{path}.query.weight")
            if f"{path}.query.bias" in state_dict:
                state_dict[f"{path}.to_q.bias"] = state_dict.pop(f"{path}.query.bias")

            # key -> to_k
            if f"{path}.key.weight" in state_dict:
                state_dict[f"{path}.to_k.weight"] = state_dict.pop(f"{path}.key.weight")
            if f"{path}.key.bias" in state_dict:
                state_dict[f"{path}.to_k.bias"] = state_dict.pop(f"{path}.key.bias")

            # value -> to_v
            if f"{path}.value.weight" in state_dict:
                state_dict[f"{path}.to_v.weight"] = state_dict.pop(f"{path}.value.weight")
            if f"{path}.value.bias" in state_dict:
                state_dict[f"{path}.to_v.bias"] = state_dict.pop(f"{path}.value.bias")

            # proj_attn -> to_out.0
            if f"{path}.proj_attn.weight" in state_dict:
                state_dict[f"{path}.to_out.0.weight"] = state_dict.pop(f"{path}.proj_attn.weight")
            if f"{path}.proj_attn.bias" in state_dict:
                state_dict[f"{path}.to_out.0.bias"] = state_dict.pop(f"{path}.proj_attn.bias")

    def _temp_convert_self_to_deprecated_attention_blocks(self) -> None:
        deprecated_attention_block_modules = []

        def recursive_find_attn_block(module):
            if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block:
                deprecated_attention_block_modules.append(module)

            for sub_module in module.children():
                recursive_find_attn_block(sub_module)

        recursive_find_attn_block(self)

        for module in deprecated_attention_block_modules:
            module.query = module.to_q
            module.key = module.to_k
            module.value = module.to_v
            module.proj_attn = module.to_out[0]

            # We don't _have_ to delete the old attributes, but it's helpful to ensure
            # that _all_ the weights are loaded into the new attributes and we're not
            # making an incorrect assumption that this model should be converted when
            # it really shouldn't be.
            del module.to_q
            del module.to_k
            del module.to_v
            del module.to_out

    def _undo_temp_convert_self_to_deprecated_attention_blocks(self) -> None:
        deprecated_attention_block_modules = []

        def recursive_find_attn_block(module) -> None:
            if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block:
                deprecated_attention_block_modules.append(module)

            for sub_module in module.children():
                recursive_find_attn_block(sub_module)

        recursive_find_attn_block(self)

        for module in deprecated_attention_block_modules:
            module.to_q = module.query
            module.to_k = module.key
            module.to_v = module.value
            module.to_out = nn.ModuleList([module.proj_attn, nn.Dropout(module.dropout)])

            del module.query
            del module.key
            del module.value
            del module.proj_attn

 from typing import Any, Dict, Optional

 import torch
 import torch.nn.functional as F
 from torch import nn

 from ..utils import USE_PEFT_BACKEND
 from ..utils.torch_utils import maybe_allow_in_graph
 from .activations import GEGLU, GELU, ApproximateGELU
 from .attention_processor import Attention
 from .embeddings import SinusoidalPositionalEmbedding
 from .lora import LoRACompatibleLinear
 from .normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm


 def _chunked_feed_forward(
    ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int, lora_scale: Optional[float] = None
 ):
    # "feed_forward_chunk_size" can be used to save memory
    if hidden_states.shape[chunk_dim] % chunk_size != 0:
        raise ValueError(
            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
        )

    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
    if lora_scale is None:
        ff_output = torch.cat(
            [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
            dim=chunk_dim,
        )
    else:
        # TOOD(Patrick): LoRA scale can be removed once PEFT refactor is complete
        ff_output = torch.cat(
            [ff(hid_slice, scale=lora_scale) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
            dim=chunk_dim,
        )

    return ff_output


 @maybe_allow_in_graph
 class GatedSelfAttentionDense(nn.Module):
    r"""
    A gated self-attention dense layer that combines visual features and object features.

    Parameters:
        query_dim (`int`): The number of channels in the query.
        context_dim (`int`): The number of channels in the context.
        n_heads (`int`): The number of heads to use for attention.
        d_head (`int`): The number of channels in each head.
    """

    def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int):
        super().__init__()

        # we need a linear projection since we need cat visual feature and obj feature
        self.linear = nn.Linear(context_dim, query_dim)

        self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
        self.ff = FeedForward(query_dim, activation_fn="geglu")

        self.norm1 = nn.LayerNorm(query_dim)
        self.norm2 = nn.LayerNorm(query_dim)

        self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0)))
        self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0)))

        self.enabled = True

    def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor:
        if not self.enabled:
            return x

        n_visual = x.shape[1]
        objs = self.linear(objs)

        x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :]
        x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x))

        return x


 @maybe_allow_in_graph
 class BasicTransformerBlock(nn.Module):
    r"""
    A basic Transformer block.

    Parameters:
        dim (`int`): The number of channels in the input and output.
        num_attention_heads (`int`): The number of heads to use for multi-head attention.
        attention_head_dim (`int`): The number of channels in each head.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
        num_embeds_ada_norm (:
            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
        attention_bias (:
            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
        only_cross_attention (`bool`, *optional*):
            Whether to use only cross-attention layers. In this case two cross attention layers are used.
        double_self_attention (`bool`, *optional*):
            Whether to use two self-attention layers. In this case no cross attention layers are used.
        upcast_attention (`bool`, *optional*):
            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
            Whether to use learnable elementwise affine parameters for normalization.
        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
        final_dropout (`bool` *optional*, defaults to False):
            Whether to apply a final dropout after the last feed-forward layer.
        attention_type (`str`, *optional*, defaults to `"default"`):
            The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
        positional_embeddings (`str`, *optional*, defaults to `None`):
            The type of positional embeddings to apply to.
        num_positional_embeddings (`int`, *optional*, defaults to `None`):
            The maximum number of positional embeddings to apply.
    """

    def __init__(
        self,
        dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
        dropout=0.0,
        cross_attention_dim: Optional[int] = None,
        activation_fn: str = "geglu",
        num_embeds_ada_norm: Optional[int] = None,
        attention_bias: bool = False,
        only_cross_attention: bool = False,
        double_self_attention: bool = False,
        upcast_attention: bool = False,
        norm_elementwise_affine: bool = True,
        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'
        norm_eps: float = 1e-5,
        final_dropout: bool = False,
        attention_type: str = "default",
        positional_embeddings: Optional[str] = None,
        num_positional_embeddings: Optional[int] = None,
        ada_norm_continous_conditioning_embedding_dim: Optional[int] = None,
        ada_norm_bias: Optional[int] = None,
        ff_inner_dim: Optional[int] = None,
        ff_bias: bool = True,
        attention_out_bias: bool = True,
    ):
        super().__init__()
        self.only_cross_attention = only_cross_attention

        # We keep these boolean flags for backward-compatibility.
        self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
        self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
        self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
        self.use_layer_norm = norm_type == "layer_norm"
        self.use_ada_layer_norm_continuous = norm_type == "ada_norm_continuous"

        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
            raise ValueError(
                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
            )

        self.norm_type = norm_type
        self.num_embeds_ada_norm = num_embeds_ada_norm

        if positional_embeddings and (num_positional_embeddings is None):
            raise ValueError(
                "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
            )

        if positional_embeddings == "sinusoidal":
            self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
        else:
            self.pos_embed = None

        # Define 3 blocks. Each block has its own normalization layer.
        # 1. Self-Attn
        if norm_type == "ada_norm":
            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
        elif norm_type == "ada_norm_zero":
            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
        elif norm_type == "ada_norm_continuous":
            self.norm1 = AdaLayerNormContinuous(
                dim,
                ada_norm_continous_conditioning_embedding_dim,
                norm_elementwise_affine,
                norm_eps,
                ada_norm_bias,
                "rms_norm",
            )
        else:
            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)

        self.attn1 = Attention(
            query_dim=dim,
            heads=num_attention_heads,
            dim_head=attention_head_dim,
            dropout=dropout,
            bias=attention_bias,
            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
            upcast_attention=upcast_attention,
            out_bias=attention_out_bias,
        )

        # 2. Cross-Attn
        if cross_attention_dim is not None or double_self_attention:
            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
            # the second cross attention block.
            if norm_type == "ada_norm":
                self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm)
            elif norm_type == "ada_norm_continuous":
                self.norm2 = AdaLayerNormContinuous(
                    dim,
                    ada_norm_continous_conditioning_embedding_dim,
                    norm_elementwise_affine,
                    norm_eps,
                    ada_norm_bias,
                    "rms_norm",
                )
            else:
                self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)

            self.attn2 = Attention(
                query_dim=dim,
                cross_attention_dim=cross_attention_dim if not double_self_attention else None,
                heads=num_attention_heads,
                dim_head=attention_head_dim,
                dropout=dropout,
                bias=attention_bias,
                upcast_attention=upcast_attention,
                out_bias=attention_out_bias,
            )  # is self-attn if encoder_hidden_states is none
        else:
            self.norm2 = None
            self.attn2 = None

        # 3. Feed-forward
        if norm_type == "ada_norm_continuous":
            self.norm3 = AdaLayerNormContinuous(
                dim,
                ada_norm_continous_conditioning_embedding_dim,
                norm_elementwise_affine,
                norm_eps,
                ada_norm_bias,
                "layer_norm",
            )

        elif norm_type in ["ada_norm_zero", "ada_norm", "layer_norm", "ada_norm_continuous"]:
            self.norm3 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
        elif norm_type == "layer_norm_i2vgen":
            self.norm3 = None

        self.ff = FeedForward(
            dim,
            dropout=dropout,
            activation_fn=activation_fn,
            final_dropout=final_dropout,
            inner_dim=ff_inner_dim,
            bias=ff_bias,
        )

        # 4. Fuser
        if attention_type == "gated" or attention_type == "gated-text-image":
            self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)

        # 5. Scale-shift for PixArt-Alpha.
        if norm_type == "ada_norm_single":
            self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)

        # let chunk size default to None
        self._chunk_size = None
        self._chunk_dim = 0

    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
        # Sets chunk feed-forward
        self._chunk_size = chunk_size
        self._chunk_dim = dim

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        timestep: Optional[torch.LongTensor] = None,
        cross_attention_kwargs: Dict[str, Any] = None,
        class_labels: Optional[torch.LongTensor] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
    ) -> torch.FloatTensor:
        # Notice that normalization is always applied before the real computation in the following blocks.
        # 0. Self-Attention
        batch_size = hidden_states.shape[0]

        if self.norm_type == "ada_norm":
            norm_hidden_states = self.norm1(hidden_states, timestep)
        elif self.norm_type == "ada_norm_zero":
            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
            )
        elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
            norm_hidden_states = self.norm1(hidden_states)
        elif self.norm_type == "ada_norm_continuous":
            norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
        elif self.norm_type == "ada_norm_single":
            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
            ).chunk(6, dim=1)
            norm_hidden_states = self.norm1(hidden_states)
            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
            norm_hidden_states = norm_hidden_states.squeeze(1)
        else:
            raise ValueError("Incorrect norm used")

        if self.pos_embed is not None:
            norm_hidden_states = self.pos_embed(norm_hidden_states)

        # 1. Retrieve lora scale.
        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0

        # 2. Prepare GLIGEN inputs
        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)

        attn_output = self.attn1(
            norm_hidden_states,
            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
            attention_mask=attention_mask,
            **cross_attention_kwargs,
        )
        if self.norm_type == "ada_norm_zero":
            attn_output = gate_msa.unsqueeze(1) * attn_output
        elif self.norm_type == "ada_norm_single":
            attn_output = gate_msa * attn_output

        hidden_states = attn_output + hidden_states
        if hidden_states.ndim == 4:
            hidden_states = hidden_states.squeeze(1)

        # 2.5 GLIGEN Control
        if gligen_kwargs is not None:
            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])

        # 3. Cross-Attention
        if self.attn2 is not None:
            if self.norm_type == "ada_norm":
                norm_hidden_states = self.norm2(hidden_states, timestep)
            elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
                norm_hidden_states = self.norm2(hidden_states)
            elif self.norm_type == "ada_norm_single":
                # For PixArt norm2 isn't applied here:
                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
                norm_hidden_states = hidden_states
            elif self.norm_type == "ada_norm_continuous":
                norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
            else:
                raise ValueError("Incorrect norm")

            if self.pos_embed is not None and self.norm_type != "ada_norm_single":
                norm_hidden_states = self.pos_embed(norm_hidden_states)

            attn_output = self.attn2(
                norm_hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                **cross_attention_kwargs,
            )
            hidden_states = attn_output + hidden_states

        # 4. Feed-forward
        # i2vgen doesn't have this norm 🤷‍♂️
        if self.norm_type == "ada_norm_continuous":
            norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
        elif not self.norm_type == "ada_norm_single":
            norm_hidden_states = self.norm3(hidden_states)

        if self.norm_type == "ada_norm_zero":
            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

        if self.norm_type == "ada_norm_single":
            norm_hidden_states = self.norm2(hidden_states)
            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp

        if self._chunk_size is not None:
            # "feed_forward_chunk_size" can be used to save memory
            ff_output = _chunked_feed_forward(
                self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size, lora_scale=lora_scale
            )
        else:
            ff_output = self.ff(norm_hidden_states, scale=lora_scale)

        if self.norm_type == "ada_norm_zero":
            ff_output = gate_mlp.unsqueeze(1) * ff_output
        elif self.norm_type == "ada_norm_single":
            ff_output = gate_mlp * ff_output

        hidden_states = ff_output + hidden_states
        if hidden_states.ndim == 4:
            hidden_states = hidden_states.squeeze(1)

        return hidden_states


 @maybe_allow_in_graph
 class TemporalBasicTransformerBlock(nn.Module):
    r"""
    A basic Transformer block for video like data.

    Parameters:
        dim (`int`): The number of channels in the input and output.
        time_mix_inner_dim (`int`): The number of channels for temporal attention.
        num_attention_heads (`int`): The number of heads to use for multi-head attention.
        attention_head_dim (`int`): The number of channels in each head.
        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
    """

    def __init__(
        self,
        dim: int,
        time_mix_inner_dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
        cross_attention_dim: Optional[int] = None,
    ):
        super().__init__()
        self.is_res = dim == time_mix_inner_dim

        self.norm_in = nn.LayerNorm(dim)

        # Define 3 blocks. Each block has its own normalization layer.
        # 1. Self-Attn
        self.ff_in = FeedForward(
            dim,
            dim_out=time_mix_inner_dim,
            activation_fn="geglu",
        )

        self.norm1 = nn.LayerNorm(time_mix_inner_dim)
        self.attn1 = Attention(
            query_dim=time_mix_inner_dim,
            heads=num_attention_heads,
            dim_head=attention_head_dim,
            cross_attention_dim=None,
        )

        # 2. Cross-Attn
        if cross_attention_dim is not None:
            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
            # the second cross attention block.
            self.norm2 = nn.LayerNorm(time_mix_inner_dim)
            self.attn2 = Attention(
                query_dim=time_mix_inner_dim,
                cross_attention_dim=cross_attention_dim,
                heads=num_attention_heads,
                dim_head=attention_head_dim,
            )  # is self-attn if encoder_hidden_states is none
        else:
            self.norm2 = None
            self.attn2 = None

        # 3. Feed-forward
        self.norm3 = nn.LayerNorm(time_mix_inner_dim)
        self.ff = FeedForward(time_mix_inner_dim, activation_fn="geglu")

        # let chunk size default to None
        self._chunk_size = None
        self._chunk_dim = None

    def set_chunk_feed_forward(self, chunk_size: Optional[int], **kwargs):
        # Sets chunk feed-forward
        self._chunk_size = chunk_size
        # chunk dim should be hardcoded to 1 to have better speed vs. memory trade-off
        self._chunk_dim = 1

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        num_frames: int,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        # Notice that normalization is always applied before the real computation in the following blocks.
        # 0. Self-Attention
        batch_size = hidden_states.shape[0]

        batch_frames, seq_length, channels = hidden_states.shape
        batch_size = batch_frames // num_frames

        hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, seq_length, channels)
        hidden_states = hidden_states.permute(0, 2, 1, 3)
        hidden_states = hidden_states.reshape(batch_size * seq_length, num_frames, channels)

        residual = hidden_states
        hidden_states = self.norm_in(hidden_states)

        if self._chunk_size is not None:
            hidden_states = _chunked_feed_forward(self.ff_in, hidden_states, self._chunk_dim, self._chunk_size)
        else:
            hidden_states = self.ff_in(hidden_states)

        if self.is_res:
            hidden_states = hidden_states + residual

        norm_hidden_states = self.norm1(hidden_states)
        attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
        hidden_states = attn_output + hidden_states

        # 3. Cross-Attention
        if self.attn2 is not None:
            norm_hidden_states = self.norm2(hidden_states)
            attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
            hidden_states = attn_output + hidden_states

        # 4. Feed-forward
        norm_hidden_states = self.norm3(hidden_states)

        if self._chunk_size is not None:
            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
        else:
            ff_output = self.ff(norm_hidden_states)

        if self.is_res:
            hidden_states = ff_output + hidden_states
        else:
            hidden_states = ff_output

        hidden_states = hidden_states[None, :].reshape(batch_size, seq_length, num_frames, channels)
        hidden_states = hidden_states.permute(0, 2, 1, 3)
        hidden_states = hidden_states.reshape(batch_size * num_frames, seq_length, channels)

        return hidden_states


 class SkipFFTransformerBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
        kv_input_dim: int,
        kv_input_dim_proj_use_bias: bool,
        dropout=0.0,
        cross_attention_dim: Optional[int] = None,
        attention_bias: bool = False,
        attention_out_bias: bool = True,
    ):
        super().__init__()
        if kv_input_dim != dim:
            self.kv_mapper = nn.Linear(kv_input_dim, dim, kv_input_dim_proj_use_bias)
        else:
            self.kv_mapper = None

        self.norm1 = RMSNorm(dim, 1e-06)

        self.attn1 = Attention(
            query_dim=dim,
            heads=num_attention_heads,
            dim_head=attention_head_dim,
            dropout=dropout,
            bias=attention_bias,
            cross_attention_dim=cross_attention_dim,
            out_bias=attention_out_bias,
        )

        self.norm2 = RMSNorm(dim, 1e-06)

        self.attn2 = Attention(
            query_dim=dim,
            cross_attention_dim=cross_attention_dim,
            heads=num_attention_heads,
            dim_head=attention_head_dim,
            dropout=dropout,
            bias=attention_bias,
            out_bias=attention_out_bias,
        )

    def forward(self, hidden_states, encoder_hidden_states, cross_attention_kwargs):
        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}

        if self.kv_mapper is not None:
            encoder_hidden_states = self.kv_mapper(F.silu(encoder_hidden_states))

        norm_hidden_states = self.norm1(hidden_states)

        attn_output = self.attn1(
            norm_hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            **cross_attention_kwargs,
        )

        hidden_states = attn_output + hidden_states

        norm_hidden_states = self.norm2(hidden_states)

        attn_output = self.attn2(
            norm_hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            **cross_attention_kwargs,
        )

        hidden_states = attn_output + hidden_states

        return hidden_states


 class FeedForward(nn.Module):
    r"""
    A feed-forward layer.

    Parameters:
        dim (`int`): The number of channels in the input.
        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
    """

    def __init__(
        self,
        dim: int,
        dim_out: Optional[int] = None,
        mult: int = 4,
        dropout: float = 0.0,
        activation_fn: str = "geglu",
        final_dropout: bool = False,
        inner_dim=None,
        bias: bool = True,
    ):
        super().__init__()
        if inner_dim is None:
            inner_dim = int(dim * mult)
        dim_out = dim_out if dim_out is not None else dim
        linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear

        if activation_fn == "gelu":
            act_fn = GELU(dim, inner_dim, bias=bias)
        if activation_fn == "gelu-approximate":
            act_fn = GELU(dim, inner_dim, approximate="tanh", bias=bias)
        elif activation_fn == "geglu":
            act_fn = GEGLU(dim, inner_dim, bias=bias)
        elif activation_fn == "geglu-approximate":
            act_fn = ApproximateGELU(dim, inner_dim, bias=bias)

        self.net = nn.ModuleList([])
        # project in
        self.net.append(act_fn)
        # project dropout
        self.net.append(nn.Dropout(dropout))
        # project out
        self.net.append(linear_cls(inner_dim, dim_out, bias=bias))
        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
        if final_dropout:
            self.net.append(nn.Dropout(dropout))

    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
        compatible_cls = (GEGLU,) if USE_PEFT_BACKEND else (GEGLU, LoRACompatibleLinear)
        for module in self.net:
            if isinstance(module, compatible_cls):
                hidden_states = module(hidden_states, scale)
            else:
                hidden_states = module(hidden_states)
        return hidden_states

 from dataclasses import dataclass
 from typing import Optional, Tuple, Union

 import torch
 import torch.nn as nn

 from ..configuration_utils import ConfigMixin, register_to_config
 from ..utils import BaseOutput
 from ..utils.accelerate_utils import apply_forward_hook
 from .autoencoders.vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
 from .modeling_utils import ModelMixin


 @dataclass
 class VQEncoderOutput(BaseOutput):
    """
    Output of VQModel encoding method.

    Args:
        latents (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            The encoded output sample from the last layer of the model.
    """

    latents: torch.FloatTensor


 class VQModel(ModelMixin, ConfigMixin):
    r"""
    A VQ-VAE model for decoding latent representations.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).

    Parameters:
        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
            Tuple of downsample block types.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
            Tuple of upsample block types.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
            Tuple of block output channels.
        layers_per_block (`int`, *optional*, defaults to `1`): Number of layers per block.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space.
        sample_size (`int`, *optional*, defaults to `32`): Sample input size.
        num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE.
        norm_num_groups (`int`, *optional*, defaults to `32`): Number of groups for normalization layers.
        vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE.
        scaling_factor (`float`, *optional*, defaults to `0.18215`):
            The component-wise standard deviation of the trained latent space computed using the first batch of the
            training set. This is used to scale the latent space to have unit variance when training the diffusion
            model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
            diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
            / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
        norm_type (`str`, *optional*, defaults to `"group"`):
            Type of normalization layer to use. Can be one of `"group"` or `"spatial"`.
    """

    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",),
        up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",),
        block_out_channels: Tuple[int, ...] = (64,),
        layers_per_block: int = 1,
        act_fn: str = "silu",
        latent_channels: int = 3,
        sample_size: int = 32,
        num_vq_embeddings: int = 256,
        norm_num_groups: int = 32,
        vq_embed_dim: Optional[int] = None,
        scaling_factor: float = 0.18215,
        norm_type: str = "group",  # group, spatial
        mid_block_add_attention=True,
        lookup_from_codebook=False,
        force_upcast=False,
    ):
        super().__init__()

        # pass init params to Encoder
        self.encoder = Encoder(
            in_channels=in_channels,
            out_channels=latent_channels,
            down_block_types=down_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            act_fn=act_fn,
            norm_num_groups=norm_num_groups,
            double_z=False,
            mid_block_add_attention=mid_block_add_attention,
        )

        vq_embed_dim = vq_embed_dim if vq_embed_dim is not None else latent_channels

        self.quant_conv = nn.Conv2d(latent_channels, vq_embed_dim, 1)
        self.quantize = VectorQuantizer(num_vq_embeddings, vq_embed_dim, beta=0.25, remap=None, sane_index_shape=False)
        self.post_quant_conv = nn.Conv2d(vq_embed_dim, latent_channels, 1)

        # pass init params to Decoder
        self.decoder = Decoder(
            in_channels=latent_channels,
            out_channels=out_channels,
            up_block_types=up_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            act_fn=act_fn,
            norm_num_groups=norm_num_groups,
            norm_type=norm_type,
            mid_block_add_attention=mid_block_add_attention,
        )

    @apply_forward_hook
    def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> VQEncoderOutput:
        h = self.encoder(x)
        h = self.quant_conv(h)

        if not return_dict:
            return (h,)

        return VQEncoderOutput(latents=h)

    @apply_forward_hook
    def decode(
        self, h: torch.FloatTensor, force_not_quantize: bool = False, return_dict: bool = True, shape=None
    ) -> Union[DecoderOutput, torch.FloatTensor]:
        # also go through quantization layer
        if not force_not_quantize:
            quant, _, _ = self.quantize(h)
        elif self.config.lookup_from_codebook:
            quant = self.quantize.get_codebook_entry(h, shape)
        else:
            quant = h
        quant2 = self.post_quant_conv(quant)
        dec = self.decoder(quant2, quant if self.config.norm_type == "spatial" else None)

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    def forward(
        self, sample: torch.FloatTensor, return_dict: bool = True
    ) -> Union[DecoderOutput, Tuple[torch.FloatTensor, ...]]:
        r"""
        The [`VQModel`] forward method.

        Args:
            sample (`torch.FloatTensor`): Input sample.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`models.vq_model.VQEncoderOutput`] instead of a plain tuple.

        Returns:
            [`~models.vq_model.VQEncoderOutput`] or `tuple`:
                If return_dict is True, a [`~models.vq_model.VQEncoderOutput`] is returned, otherwise a plain `tuple`
                is returned.
        """

        h = self.encode(sample).latents
        dec = self.decode(h).sample

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)
 # coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ PyTorch - Flax general utilities."""

 from pickle import UnpicklingError

 import jax
 import jax.numpy as jnp
 import numpy as np
 from flax.serialization import from_bytes
 from flax.traverse_util import flatten_dict

 from ..utils import logging


 logger = logging.get_logger(__name__)


 #####################
 # Flax => PyTorch #
 #####################


 # from https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_flax_pytorch_utils.py#L224-L352
 def load_flax_checkpoint_in_pytorch_model(pt_model, model_file):
    try:
        with open(model_file, "rb") as flax_state_f:
            flax_state = from_bytes(None, flax_state_f.read())
    except UnpicklingError as e:
        try:
            with open(model_file) as f:
                if f.read().startswith("version"):
                    raise OSError(
                        "You seem to have cloned a repository without having git-lfs installed. Please"
                        " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
                        " folder you cloned."
                    )
                else:
                    raise ValueError from e
        except (UnicodeDecodeError, ValueError):
            raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ")

    return load_flax_weights_in_pytorch_model(pt_model, flax_state)


 def load_flax_weights_in_pytorch_model(pt_model, flax_state):
    """Load flax checkpoints in a PyTorch model"""

    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
            " instructions."
        )
        raise

    # check if we have bf16 weights
    is_type_bf16 = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype == jnp.bfloat16, flax_state)).values()
    if any(is_type_bf16):
        # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

        # and bf16 is not fully supported in PT yet.
        logger.warning(
            "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
            "before loading those in PyTorch model."
        )
        flax_state = jax.tree_util.tree_map(
            lambda params: params.astype(np.float32) if params.dtype == jnp.bfloat16 else params, flax_state
        )

    pt_model.base_model_prefix = ""

    flax_state_dict = flatten_dict(flax_state, sep=".")
    pt_model_dict = pt_model.state_dict()

    # keep track of unexpected & missing keys
    unexpected_keys = []
    missing_keys = set(pt_model_dict.keys())

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        flax_key_tuple_array = flax_key_tuple.split(".")

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"]
            flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1))
        elif flax_key_tuple_array[-1] == "kernel":
            flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"]
            flax_tensor = flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"]

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(flax_key_tuple_array):
                flax_key_tuple_array[i] = (
                    flax_key_tuple_string.replace("_0", ".0")
                    .replace("_1", ".1")
                    .replace("_2", ".2")
                    .replace("_3", ".3")
                    .replace("_4", ".4")
                    .replace("_5", ".5")
                    .replace("_6", ".6")
                    .replace("_7", ".7")
                    .replace("_8", ".8")
                    .replace("_9", ".9")
                )

        flax_key = ".".join(flax_key_tuple_array)

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
                    f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."
                )
            else:
                # add weight to pytorch dict
                flax_tensor = np.asarray(flax_tensor) if not isinstance(flax_tensor, np.ndarray) else flax_tensor
                pt_model_dict[flax_key] = torch.from_numpy(flax_tensor)
                # remove from missing keys
                missing_keys.remove(flax_key)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(flax_key)

    pt_model.load_state_dict(pt_model_dict)

    # re-transform missing_keys to list
    missing_keys = list(missing_keys)

    if len(unexpected_keys) > 0:
        logger.warning(
            "Some weights of the Flax model were not used when initializing the PyTorch model"
            f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
            f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
            " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
            f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
            " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
            " FlaxBertForSequenceClassification model)."
        )
    if len(missing_keys) > 0:
        logger.warning(
            f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
            f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
            " use it for predictions and inference."
        )

    return pt_model


 import functools
 import math

 import flax.linen as nn
 import jax
 import jax.numpy as jnp


 def _query_chunk_attention(query, key, value, precision, key_chunk_size: int = 4096):
    """Multi-head dot product attention with a limited number of queries."""
    num_kv, num_heads, k_features = key.shape[-3:]
    v_features = value.shape[-1]
    key_chunk_size = min(key_chunk_size, num_kv)
    query = query / jnp.sqrt(k_features)

    @functools.partial(jax.checkpoint, prevent_cse=False)
    def summarize_chunk(query, key, value):
        attn_weights = jnp.einsum("...qhd,...khd->...qhk", query, key, precision=precision)

        max_score = jnp.max(attn_weights, axis=-1, keepdims=True)
        max_score = jax.lax.stop_gradient(max_score)
        exp_weights = jnp.exp(attn_weights - max_score)

        exp_values = jnp.einsum("...vhf,...qhv->...qhf", value, exp_weights, precision=precision)
        max_score = jnp.einsum("...qhk->...qh", max_score)

        return (exp_values, exp_weights.sum(axis=-1), max_score)

    def chunk_scanner(chunk_idx):
        # julienne key array
        key_chunk = jax.lax.dynamic_slice(
            operand=key,
            start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0],  # [...,k,h,d]
            slice_sizes=list(key.shape[:-3]) + [key_chunk_size, num_heads, k_features],  # [...,k,h,d]
        )

        # julienne value array
        value_chunk = jax.lax.dynamic_slice(
            operand=value,
            start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0],  # [...,v,h,d]
            slice_sizes=list(value.shape[:-3]) + [key_chunk_size, num_heads, v_features],  # [...,v,h,d]
        )

        return summarize_chunk(query, key_chunk, value_chunk)

    chunk_values, chunk_weights, chunk_max = jax.lax.map(f=chunk_scanner, xs=jnp.arange(0, num_kv, key_chunk_size))

    global_max = jnp.max(chunk_max, axis=0, keepdims=True)
    max_diffs = jnp.exp(chunk_max - global_max)

    chunk_values *= jnp.expand_dims(max_diffs, axis=-1)
    chunk_weights *= max_diffs

    all_values = chunk_values.sum(axis=0)
    all_weights = jnp.expand_dims(chunk_weights, -1).sum(axis=0)

    return all_values / all_weights


 def jax_memory_efficient_attention(
    query, key, value, precision=jax.lax.Precision.HIGHEST, query_chunk_size: int = 1024, key_chunk_size: int = 4096
 ):
    r"""
    Flax Memory-efficient multi-head dot product attention. https://arxiv.org/abs/2112.05682v2
    https://github.com/AminRezaei0x443/memory-efficient-attention

    Args:
        query (`jnp.ndarray`): (batch..., query_length, head, query_key_depth_per_head)
        key (`jnp.ndarray`): (batch..., key_value_length, head, query_key_depth_per_head)
        value (`jnp.ndarray`): (batch..., key_value_length, head, value_depth_per_head)
        precision (`jax.lax.Precision`, *optional*, defaults to `jax.lax.Precision.HIGHEST`):
            numerical precision for computation
        query_chunk_size (`int`, *optional*, defaults to 1024):
            chunk size to divide query array value must divide query_length equally without remainder
        key_chunk_size (`int`, *optional*, defaults to 4096):
            chunk size to divide key and value array value must divide key_value_length equally without remainder

    Returns:
        (`jnp.ndarray`) with shape of (batch..., query_length, head, value_depth_per_head)
    """
    num_q, num_heads, q_features = query.shape[-3:]

    def chunk_scanner(chunk_idx, _):
        # julienne query array
        query_chunk = jax.lax.dynamic_slice(
            operand=query,
            start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0],  # [...,q,h,d]
            slice_sizes=list(query.shape[:-3]) + [min(query_chunk_size, num_q), num_heads, q_features],  # [...,q,h,d]
        )

        return (
            chunk_idx + query_chunk_size,  # unused ignore it
            _query_chunk_attention(
                query=query_chunk, key=key, value=value, precision=precision, key_chunk_size=key_chunk_size
            ),
        )

    _, res = jax.lax.scan(
        f=chunk_scanner,
        init=0,
        xs=None,
        length=math.ceil(num_q / query_chunk_size),  # start counter  # stop counter
    )

    return jnp.concatenate(res, axis=-3)  # fuse the chunked result back


 class FlaxAttention(nn.Module):
    r"""
    A Flax multi-head attention module as described in: https://arxiv.org/abs/1706.03762

    Parameters:
        query_dim (:obj:`int`):
            Input hidden states dimension
        heads (:obj:`int`, *optional*, defaults to 8):
            Number of heads
        dim_head (:obj:`int`, *optional*, defaults to 64):
            Hidden states dimension inside each head
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
            enable memory efficient attention https://arxiv.org/abs/2112.05682
        split_head_dim (`bool`, *optional*, defaults to `False`):
            Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
            enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`

    """

    query_dim: int
    heads: int = 8
    dim_head: int = 64
    dropout: float = 0.0
    use_memory_efficient_attention: bool = False
    split_head_dim: bool = False
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        inner_dim = self.dim_head * self.heads
        self.scale = self.dim_head**-0.5

        # Weights were exported with old names {to_q, to_k, to_v, to_out}
        self.query = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_q")
        self.key = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_k")
        self.value = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name="to_v")

        self.proj_attn = nn.Dense(self.query_dim, dtype=self.dtype, name="to_out_0")
        self.dropout_layer = nn.Dropout(rate=self.dropout)

    def reshape_heads_to_batch_dim(self, tensor):
        batch_size, seq_len, dim = tensor.shape
        head_size = self.heads
        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
        tensor = jnp.transpose(tensor, (0, 2, 1, 3))
        tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
        return tensor

    def reshape_batch_dim_to_heads(self, tensor):
        batch_size, seq_len, dim = tensor.shape
        head_size = self.heads
        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
        tensor = jnp.transpose(tensor, (0, 2, 1, 3))
        tensor = tensor.reshape(batch_size // head_size, seq_len, dim * head_size)
        return tensor

    def __call__(self, hidden_states, context=None, deterministic=True):
        context = hidden_states if context is None else context

        query_proj = self.query(hidden_states)
        key_proj = self.key(context)
        value_proj = self.value(context)

        if self.split_head_dim:
            b = hidden_states.shape[0]
            query_states = jnp.reshape(query_proj, (b, -1, self.heads, self.dim_head))
            key_states = jnp.reshape(key_proj, (b, -1, self.heads, self.dim_head))
            value_states = jnp.reshape(value_proj, (b, -1, self.heads, self.dim_head))
        else:
            query_states = self.reshape_heads_to_batch_dim(query_proj)
            key_states = self.reshape_heads_to_batch_dim(key_proj)
            value_states = self.reshape_heads_to_batch_dim(value_proj)

        if self.use_memory_efficient_attention:
            query_states = query_states.transpose(1, 0, 2)
            key_states = key_states.transpose(1, 0, 2)
            value_states = value_states.transpose(1, 0, 2)

            # this if statement create a chunk size for each layer of the unet
            # the chunk size is equal to the query_length dimension of the deepest layer of the unet

            flatten_latent_dim = query_states.shape[-3]
            if flatten_latent_dim % 64 == 0:
                query_chunk_size = int(flatten_latent_dim / 64)
            elif flatten_latent_dim % 16 == 0:
                query_chunk_size = int(flatten_latent_dim / 16)
            elif flatten_latent_dim % 4 == 0:
                query_chunk_size = int(flatten_latent_dim / 4)
            else:
                query_chunk_size = int(flatten_latent_dim)

            hidden_states = jax_memory_efficient_attention(
                query_states, key_states, value_states, query_chunk_size=query_chunk_size, key_chunk_size=4096 * 4
            )

            hidden_states = hidden_states.transpose(1, 0, 2)
        else:
            # compute attentions
            if self.split_head_dim:
                attention_scores = jnp.einsum("b t n h, b f n h -> b n f t", key_states, query_states)
            else:
                attention_scores = jnp.einsum("b i d, b j d->b i j", query_states, key_states)

            attention_scores = attention_scores * self.scale
            attention_probs = nn.softmax(attention_scores, axis=-1 if self.split_head_dim else 2)

            # attend to values
            if self.split_head_dim:
                hidden_states = jnp.einsum("b n f t, b t n h -> b f n h", attention_probs, value_states)
                b = hidden_states.shape[0]
                hidden_states = jnp.reshape(hidden_states, (b, -1, self.heads * self.dim_head))
            else:
                hidden_states = jnp.einsum("b i j, b j d -> b i d", attention_probs, value_states)
                hidden_states = self.reshape_batch_dim_to_heads(hidden_states)

        hidden_states = self.proj_attn(hidden_states)
        return self.dropout_layer(hidden_states, deterministic=deterministic)


 class FlaxBasicTransformerBlock(nn.Module):
    r"""
    A Flax transformer block layer with `GLU` (Gated Linear Unit) activation function as described in:
    https://arxiv.org/abs/1706.03762


    Parameters:
        dim (:obj:`int`):
            Inner hidden states dimension
        n_heads (:obj:`int`):
            Number of heads
        d_head (:obj:`int`):
            Hidden states dimension inside each head
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        only_cross_attention (`bool`, defaults to `False`):
            Whether to only apply cross attention.
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
            enable memory efficient attention https://arxiv.org/abs/2112.05682
        split_head_dim (`bool`, *optional*, defaults to `False`):
            Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
            enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
    """

    dim: int
    n_heads: int
    d_head: int
    dropout: float = 0.0
    only_cross_attention: bool = False
    dtype: jnp.dtype = jnp.float32
    use_memory_efficient_attention: bool = False
    split_head_dim: bool = False

    def setup(self):
        # self attention (or cross_attention if only_cross_attention is True)
        self.attn1 = FlaxAttention(
            self.dim,
            self.n_heads,
            self.d_head,
            self.dropout,
            self.use_memory_efficient_attention,
            self.split_head_dim,
            dtype=self.dtype,
        )
        # cross attention
        self.attn2 = FlaxAttention(
            self.dim,
            self.n_heads,
            self.d_head,
            self.dropout,
            self.use_memory_efficient_attention,
            self.split_head_dim,
            dtype=self.dtype,
        )
        self.ff = FlaxFeedForward(dim=self.dim, dropout=self.dropout, dtype=self.dtype)
        self.norm1 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
        self.norm2 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
        self.norm3 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)
        self.dropout_layer = nn.Dropout(rate=self.dropout)

    def __call__(self, hidden_states, context, deterministic=True):
        # self attention
        residual = hidden_states
        if self.only_cross_attention:
            hidden_states = self.attn1(self.norm1(hidden_states), context, deterministic=deterministic)
        else:
            hidden_states = self.attn1(self.norm1(hidden_states), deterministic=deterministic)
        hidden_states = hidden_states + residual

        # cross attention
        residual = hidden_states
        hidden_states = self.attn2(self.norm2(hidden_states), context, deterministic=deterministic)
        hidden_states = hidden_states + residual

        # feed forward
        residual = hidden_states
        hidden_states = self.ff(self.norm3(hidden_states), deterministic=deterministic)
        hidden_states = hidden_states + residual

        return self.dropout_layer(hidden_states, deterministic=deterministic)


 class FlaxTransformer2DModel(nn.Module):
    r"""
    A Spatial Transformer layer with Gated Linear Unit (GLU) activation function as described in:
    https://arxiv.org/pdf/1506.02025.pdf


    Parameters:
        in_channels (:obj:`int`):
            Input number of channels
        n_heads (:obj:`int`):
            Number of heads
        d_head (:obj:`int`):
            Hidden states dimension inside each head
        depth (:obj:`int`, *optional*, defaults to 1):
            Number of transformers block
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        use_linear_projection (`bool`, defaults to `False`): tbd
        only_cross_attention (`bool`, defaults to `False`): tbd
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
            enable memory efficient attention https://arxiv.org/abs/2112.05682
        split_head_dim (`bool`, *optional*, defaults to `False`):
            Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
            enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
    """

    in_channels: int
    n_heads: int
    d_head: int
    depth: int = 1
    dropout: float = 0.0
    use_linear_projection: bool = False
    only_cross_attention: bool = False
    dtype: jnp.dtype = jnp.float32
    use_memory_efficient_attention: bool = False
    split_head_dim: bool = False

    def setup(self):
        self.norm = nn.GroupNorm(num_groups=32, epsilon=1e-5)

        inner_dim = self.n_heads * self.d_head
        if self.use_linear_projection:
            self.proj_in = nn.Dense(inner_dim, dtype=self.dtype)
        else:
            self.proj_in = nn.Conv(
                inner_dim,
                kernel_size=(1, 1),
                strides=(1, 1),
                padding="VALID",
                dtype=self.dtype,
            )

        self.transformer_blocks = [
            FlaxBasicTransformerBlock(
                inner_dim,
                self.n_heads,
                self.d_head,
                dropout=self.dropout,
                only_cross_attention=self.only_cross_attention,
                dtype=self.dtype,
                use_memory_efficient_attention=self.use_memory_efficient_attention,
                split_head_dim=self.split_head_dim,
            )
            for _ in range(self.depth)
        ]

        if self.use_linear_projection:
            self.proj_out = nn.Dense(inner_dim, dtype=self.dtype)
        else:
            self.proj_out = nn.Conv(
                inner_dim,
                kernel_size=(1, 1),
                strides=(1, 1),
                padding="VALID",
                dtype=self.dtype,
            )

        self.dropout_layer = nn.Dropout(rate=self.dropout)

    def __call__(self, hidden_states, context, deterministic=True):
        batch, height, width, channels = hidden_states.shape
        residual = hidden_states
        hidden_states = self.norm(hidden_states)
        if self.use_linear_projection:
            hidden_states = hidden_states.reshape(batch, height * width, channels)
            hidden_states = self.proj_in(hidden_states)
        else:
            hidden_states = self.proj_in(hidden_states)
            hidden_states = hidden_states.reshape(batch, height * width, channels)

        for transformer_block in self.transformer_blocks:
            hidden_states = transformer_block(hidden_states, context, deterministic=deterministic)

        if self.use_linear_projection:
            hidden_states = self.proj_out(hidden_states)
            hidden_states = hidden_states.reshape(batch, height, width, channels)
        else:
            hidden_states = hidden_states.reshape(batch, height, width, channels)
            hidden_states = self.proj_out(hidden_states)

        hidden_states = hidden_states + residual
        return self.dropout_layer(hidden_states, deterministic=deterministic)


 class FlaxFeedForward(nn.Module):
    r"""
    Flax module that encapsulates two Linear layers separated by a non-linearity. It is the counterpart of PyTorch's
    [`FeedForward`] class, with the following simplifications:
    - The activation function is currently hardcoded to a gated linear unit from:
    https://arxiv.org/abs/2002.05202
    - `dim_out` is equal to `dim`.
    - The number of hidden dimensions is hardcoded to `dim * 4` in [`FlaxGELU`].

    Parameters:
        dim (:obj:`int`):
            Inner hidden states dimension
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    dim: int
    dropout: float = 0.0
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        # The second linear layer needs to be called
        # net_2 for now to match the index of the Sequential layer
        self.net_0 = FlaxGEGLU(self.dim, self.dropout, self.dtype)
        self.net_2 = nn.Dense(self.dim, dtype=self.dtype)

    def __call__(self, hidden_states, deterministic=True):
        hidden_states = self.net_0(hidden_states, deterministic=deterministic)
        hidden_states = self.net_2(hidden_states)
        return hidden_states


 class FlaxGEGLU(nn.Module):
    r"""
    Flax implementation of a Linear layer followed by the variant of the gated linear unit activation function from
    https://arxiv.org/abs/2002.05202.

    Parameters:
        dim (:obj:`int`):
            Input hidden states dimension
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    dim: int
    dropout: float = 0.0
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        inner_dim = self.dim * 4
        self.proj = nn.Dense(inner_dim * 2, dtype=self.dtype)
        self.dropout_layer = nn.Dropout(rate=self.dropout)

    def __call__(self, hidden_states, deterministic=True):
        hidden_states = self.proj(hidden_states)
        hidden_linear, hidden_gelu = jnp.split(hidden_states, 2, axis=2)
        return self.dropout_layer(hidden_linear * nn.gelu(hidden_gelu), deterministic=deterministic)



 from ..utils import deprecate
 from .unets.unet_2d import UNet2DModel, UNet2DOutput


 class UNet2DOutput(UNet2DOutput):
    deprecation_message = "Importing `UNet2DOutput` from `diffusers.models.unet_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d import UNet2DOutput`, instead."
    deprecate("UNet2DOutput", "0.29", deprecation_message)


 class UNet2DModel(UNet2DModel):
    deprecation_message = "Importing `UNet2DModel` from `diffusers.models.unet_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d import UNet2DModel`, instead."
    deprecate("UNet2DModel", "0.29", deprecation_message)
 # Copyright 2024 The HuggingFace Team. All rights reserved.
 # `TemporalConvLayer` Copyright 2024 Alibaba DAMO-VILAB, The ModelScope Team and The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.

 from functools import partial
 from typing import Optional, Tuple, Union

 import torch
 import torch.nn as nn
 import torch.nn.functional as F

 from ..utils import USE_PEFT_BACKEND
 from .activations import get_activation
 from .attention_processor import SpatialNorm
 from .downsampling import (  # noqa
    Downsample1D,
    Downsample2D,
    FirDownsample2D,
    KDownsample2D,
    downsample_2d,
 )
 from .lora import LoRACompatibleConv, LoRACompatibleLinear
 from .normalization import AdaGroupNorm
 from .upsampling import (  # noqa
    FirUpsample2D,
    KUpsample2D,
    Upsample1D,
    Upsample2D,
    upfirdn2d_native,
    upsample_2d,
 )


 class ResnetBlockCondNorm2D(nn.Module):
    r"""
    A Resnet block that use normalization layer that incorporate conditioning information.

    Parameters:
        in_channels (`int`): The number of channels in the input.
        out_channels (`int`, *optional*, default to be `None`):
            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
        dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
        groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
        groups_out (`int`, *optional*, default to None):
            The number of groups to use for the second normalization layer. if set to None, same as `groups`.
        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
        non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
        time_embedding_norm (`str`, *optional*, default to `"ada_group"` ):
            The normalization layer for time embedding `temb`. Currently only support "ada_group" or "spatial".
        kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
            [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
        output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
        use_in_shortcut (`bool`, *optional*, default to `True`):
            If `True`, add a 1x1 nn.conv2d layer for skip-connection.
        up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
        down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
        conv_shortcut_bias (`bool`, *optional*, default to `True`):  If `True`, adds a learnable bias to the
            `conv_shortcut` output.
        conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
            If None, same as `out_channels`.
    """

    def __init__(
        self,
        *,
        in_channels: int,
        out_channels: Optional[int] = None,
        conv_shortcut: bool = False,
        dropout: float = 0.0,
        temb_channels: int = 512,
        groups: int = 32,
        groups_out: Optional[int] = None,
        eps: float = 1e-6,
        non_linearity: str = "swish",
        time_embedding_norm: str = "ada_group",  # ada_group, spatial
        output_scale_factor: float = 1.0,
        use_in_shortcut: Optional[bool] = None,
        up: bool = False,
        down: bool = False,
        conv_shortcut_bias: bool = True,
        conv_2d_out_channels: Optional[int] = None,
    ):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.use_conv_shortcut = conv_shortcut
        self.up = up
        self.down = down
        self.output_scale_factor = output_scale_factor
        self.time_embedding_norm = time_embedding_norm

        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv

        if groups_out is None:
            groups_out = groups

        if self.time_embedding_norm == "ada_group":  # ada_group
            self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
        elif self.time_embedding_norm == "spatial":
            self.norm1 = SpatialNorm(in_channels, temb_channels)
        else:
            raise ValueError(f" unsupported time_embedding_norm: {self.time_embedding_norm}")

        self.conv1 = conv_cls(in_channels, out_channels, kernel_size=3, stride=1, padding=1)

        if self.time_embedding_norm == "ada_group":  # ada_group
            self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
        elif self.time_embedding_norm == "spatial":  # spatial
            self.norm2 = SpatialNorm(out_channels, temb_channels)
        else:
            raise ValueError(f" unsupported time_embedding_norm: {self.time_embedding_norm}")

        self.dropout = torch.nn.Dropout(dropout)

        conv_2d_out_channels = conv_2d_out_channels or out_channels
        self.conv2 = conv_cls(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)

        self.nonlinearity = get_activation(non_linearity)

        self.upsample = self.downsample = None
        if self.up:
            self.upsample = Upsample2D(in_channels, use_conv=False)
        elif self.down:
            self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")

        self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut

        self.conv_shortcut = None
        if self.use_in_shortcut:
            self.conv_shortcut = conv_cls(
                in_channels,
                conv_2d_out_channels,
                kernel_size=1,
                stride=1,
                padding=0,
                bias=conv_shortcut_bias,
            )

    def forward(
        self,
        input_tensor: torch.FloatTensor,
        temb: torch.FloatTensor,
        scale: float = 1.0,
    ) -> torch.FloatTensor:
        hidden_states = input_tensor

        hidden_states = self.norm1(hidden_states, temb)

        hidden_states = self.nonlinearity(hidden_states)

        if self.upsample is not None:
            # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
            if hidden_states.shape[0] >= 64:
                input_tensor = input_tensor.contiguous()
                hidden_states = hidden_states.contiguous()
            input_tensor = self.upsample(input_tensor, scale=scale)
            hidden_states = self.upsample(hidden_states, scale=scale)

        elif self.downsample is not None:
            input_tensor = self.downsample(input_tensor, scale=scale)
            hidden_states = self.downsample(hidden_states, scale=scale)

        hidden_states = self.conv1(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv1(hidden_states)

        hidden_states = self.norm2(hidden_states, temb)

        hidden_states = self.nonlinearity(hidden_states)

        hidden_states = self.dropout(hidden_states)
        hidden_states = self.conv2(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv2(hidden_states)

        if self.conv_shortcut is not None:
            input_tensor = (
                self.conv_shortcut(input_tensor, scale) if not USE_PEFT_BACKEND else self.conv_shortcut(input_tensor)
            )

        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor

        return output_tensor


 class ResnetBlock2D(nn.Module):
    r"""
    A Resnet block.

    Parameters:
        in_channels (`int`): The number of channels in the input.
        out_channels (`int`, *optional*, default to be `None`):
            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
        dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
        groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
        groups_out (`int`, *optional*, default to None):
            The number of groups to use for the second normalization layer. if set to None, same as `groups`.
        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
        non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
        time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config.
            By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift"
            for a stronger conditioning with scale and shift.
        kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
            [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
        output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
        use_in_shortcut (`bool`, *optional*, default to `True`):
            If `True`, add a 1x1 nn.conv2d layer for skip-connection.
        up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
        down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
        conv_shortcut_bias (`bool`, *optional*, default to `True`):  If `True`, adds a learnable bias to the
            `conv_shortcut` output.
        conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
            If None, same as `out_channels`.
    """

    def __init__(
        self,
        *,
        in_channels: int,
        out_channels: Optional[int] = None,
        conv_shortcut: bool = False,
        dropout: float = 0.0,
        temb_channels: int = 512,
        groups: int = 32,
        groups_out: Optional[int] = None,
        pre_norm: bool = True,
        eps: float = 1e-6,
        non_linearity: str = "swish",
        skip_time_act: bool = False,
        time_embedding_norm: str = "default",  # default, scale_shift,
        kernel: Optional[torch.FloatTensor] = None,
        output_scale_factor: float = 1.0,
        use_in_shortcut: Optional[bool] = None,
        up: bool = False,
        down: bool = False,
        conv_shortcut_bias: bool = True,
        conv_2d_out_channels: Optional[int] = None,
    ):
        super().__init__()
        if time_embedding_norm == "ada_group":
            raise ValueError(
                "This class cannot be used with `time_embedding_norm==ada_group`, please use `ResnetBlockCondNorm2D` instead",
            )
        if time_embedding_norm == "spatial":
            raise ValueError(
                "This class cannot be used with `time_embedding_norm==spatial`, please use `ResnetBlockCondNorm2D` instead",
            )

        self.pre_norm = True
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.use_conv_shortcut = conv_shortcut
        self.up = up
        self.down = down
        self.output_scale_factor = output_scale_factor
        self.time_embedding_norm = time_embedding_norm
        self.skip_time_act = skip_time_act

        linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv

        if groups_out is None:
            groups_out = groups

        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)

        self.conv1 = conv_cls(in_channels, out_channels, kernel_size=3, stride=1, padding=1)

        if temb_channels is not None:
            if self.time_embedding_norm == "default":
                self.time_emb_proj = linear_cls(temb_channels, out_channels)
            elif self.time_embedding_norm == "scale_shift":
                self.time_emb_proj = linear_cls(temb_channels, 2 * out_channels)
            else:
                raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
        else:
            self.time_emb_proj = None

        self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)

        self.dropout = torch.nn.Dropout(dropout)
        conv_2d_out_channels = conv_2d_out_channels or out_channels
        self.conv2 = conv_cls(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)

        self.nonlinearity = get_activation(non_linearity)

        self.upsample = self.downsample = None
        if self.up:
            if kernel == "fir":
                fir_kernel = (1, 3, 3, 1)
                self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel)
            elif kernel == "sde_vp":
                self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
            else:
                self.upsample = Upsample2D(in_channels, use_conv=False)
        elif self.down:
            if kernel == "fir":
                fir_kernel = (1, 3, 3, 1)
                self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel)
            elif kernel == "sde_vp":
                self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
            else:
                self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")

        self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut

        self.conv_shortcut = None
        if self.use_in_shortcut:
            self.conv_shortcut = conv_cls(
                in_channels,
                conv_2d_out_channels,
                kernel_size=1,
                stride=1,
                padding=0,
                bias=conv_shortcut_bias,
            )

    def forward(
        self,
        input_tensor: torch.FloatTensor,
        temb: torch.FloatTensor,
        scale: float = 1.0,
    ) -> torch.FloatTensor:
        hidden_states = input_tensor

        hidden_states = self.norm1(hidden_states)
        hidden_states = self.nonlinearity(hidden_states)

        if self.upsample is not None:
            # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
            if hidden_states.shape[0] >= 64:
                input_tensor = input_tensor.contiguous()
                hidden_states = hidden_states.contiguous()
            input_tensor = (
                self.upsample(input_tensor, scale=scale)
                if isinstance(self.upsample, Upsample2D)
                else self.upsample(input_tensor)
            )
            hidden_states = (
                self.upsample(hidden_states, scale=scale)
                if isinstance(self.upsample, Upsample2D)
                else self.upsample(hidden_states)
            )
        elif self.downsample is not None:
            input_tensor = (
                self.downsample(input_tensor, scale=scale)
                if isinstance(self.downsample, Downsample2D)
                else self.downsample(input_tensor)
            )
            hidden_states = (
                self.downsample(hidden_states, scale=scale)
                if isinstance(self.downsample, Downsample2D)
                else self.downsample(hidden_states)
            )

        hidden_states = self.conv1(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv1(hidden_states)

        if self.time_emb_proj is not None:
            if not self.skip_time_act:
                temb = self.nonlinearity(temb)
            temb = (
                self.time_emb_proj(temb, scale)[:, :, None, None]
                if not USE_PEFT_BACKEND
                else self.time_emb_proj(temb)[:, :, None, None]
            )

        if self.time_embedding_norm == "default":
            if temb is not None:
                hidden_states = hidden_states + temb
            hidden_states = self.norm2(hidden_states)
        elif self.time_embedding_norm == "scale_shift":
            if temb is None:
                raise ValueError(
                    f" `temb` should not be None when `time_embedding_norm` is {self.time_embedding_norm}"
                )
            time_scale, time_shift = torch.chunk(temb, 2, dim=1)
            hidden_states = self.norm2(hidden_states)
            hidden_states = hidden_states * (1 + time_scale) + time_shift
        else:
            hidden_states = self.norm2(hidden_states)

        hidden_states = self.nonlinearity(hidden_states)

        hidden_states = self.dropout(hidden_states)
        hidden_states = self.conv2(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv2(hidden_states)

        if self.conv_shortcut is not None:
            input_tensor = (
                self.conv_shortcut(input_tensor, scale) if not USE_PEFT_BACKEND else self.conv_shortcut(input_tensor)
            )

        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor

        return output_tensor


 # unet_rl.py
 def rearrange_dims(tensor: torch.Tensor) -> torch.Tensor:
    if len(tensor.shape) == 2:
        return tensor[:, :, None]
    if len(tensor.shape) == 3:
        return tensor[:, :, None, :]
    elif len(tensor.shape) == 4:
        return tensor[:, :, 0, :]
    else:
        raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.")


 class Conv1dBlock(nn.Module):
    """
    Conv1d --> GroupNorm --> Mish

    Parameters:
        inp_channels (`int`): Number of input channels.
        out_channels (`int`): Number of output channels.
        kernel_size (`int` or `tuple`): Size of the convolving kernel.
        n_groups (`int`, default `8`): Number of groups to separate the channels into.
        activation (`str`, defaults to `mish`): Name of the activation function.
    """

    def __init__(
        self,
        inp_channels: int,
        out_channels: int,
        kernel_size: Union[int, Tuple[int, int]],
        n_groups: int = 8,
        activation: str = "mish",
    ):
        super().__init__()

        self.conv1d = nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2)
        self.group_norm = nn.GroupNorm(n_groups, out_channels)
        self.mish = get_activation(activation)

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        intermediate_repr = self.conv1d(inputs)
        intermediate_repr = rearrange_dims(intermediate_repr)
        intermediate_repr = self.group_norm(intermediate_repr)
        intermediate_repr = rearrange_dims(intermediate_repr)
        output = self.mish(intermediate_repr)
        return output


 # unet_rl.py
 class ResidualTemporalBlock1D(nn.Module):
    """
    Residual 1D block with temporal convolutions.

    Parameters:
        inp_channels (`int`): Number of input channels.
        out_channels (`int`): Number of output channels.
        embed_dim (`int`): Embedding dimension.
        kernel_size (`int` or `tuple`): Size of the convolving kernel.
        activation (`str`, defaults `mish`): It is possible to choose the right activation function.
    """

    def __init__(
        self,
        inp_channels: int,
        out_channels: int,
        embed_dim: int,
        kernel_size: Union[int, Tuple[int, int]] = 5,
        activation: str = "mish",
    ):
        super().__init__()
        self.conv_in = Conv1dBlock(inp_channels, out_channels, kernel_size)
        self.conv_out = Conv1dBlock(out_channels, out_channels, kernel_size)

        self.time_emb_act = get_activation(activation)
        self.time_emb = nn.Linear(embed_dim, out_channels)

        self.residual_conv = (
            nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
        )

    def forward(self, inputs: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
        """
        Args:
            inputs : [ batch_size x inp_channels x horizon ]
            t : [ batch_size x embed_dim ]

        returns:
            out : [ batch_size x out_channels x horizon ]
        """
        t = self.time_emb_act(t)
        t = self.time_emb(t)
        out = self.conv_in(inputs) + rearrange_dims(t)
        out = self.conv_out(out)
        return out + self.residual_conv(inputs)


 class TemporalConvLayer(nn.Module):
    """
    Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from:
    https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016

    Parameters:
        in_dim (`int`): Number of input channels.
        out_dim (`int`): Number of output channels.
        dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
    """

    def __init__(
        self,
        in_dim: int,
        out_dim: Optional[int] = None,
        dropout: float = 0.0,
        norm_num_groups: int = 32,
    ):
        super().__init__()
        out_dim = out_dim or in_dim
        self.in_dim = in_dim
        self.out_dim = out_dim

        # conv layers
        self.conv1 = nn.Sequential(
            nn.GroupNorm(norm_num_groups, in_dim),
            nn.SiLU(),
            nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0)),
        )
        self.conv2 = nn.Sequential(
            nn.GroupNorm(norm_num_groups, out_dim),
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
        )
        self.conv3 = nn.Sequential(
            nn.GroupNorm(norm_num_groups, out_dim),
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
        )
        self.conv4 = nn.Sequential(
            nn.GroupNorm(norm_num_groups, out_dim),
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
        )

        # zero out the last layer params,so the conv block is identity
        nn.init.zeros_(self.conv4[-1].weight)
        nn.init.zeros_(self.conv4[-1].bias)

    def forward(self, hidden_states: torch.Tensor, num_frames: int = 1) -> torch.Tensor:
        hidden_states = (
            hidden_states[None, :].reshape((-1, num_frames) + hidden_states.shape[1:]).permute(0, 2, 1, 3, 4)
        )

        identity = hidden_states
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.conv2(hidden_states)
        hidden_states = self.conv3(hidden_states)
        hidden_states = self.conv4(hidden_states)

        hidden_states = identity + hidden_states

        hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(
            (hidden_states.shape[0] * hidden_states.shape[2], -1) + hidden_states.shape[3:]
        )
        return hidden_states


 class TemporalResnetBlock(nn.Module):
    r"""
    A Resnet block.

    Parameters:
        in_channels (`int`): The number of channels in the input.
        out_channels (`int`, *optional*, default to be `None`):
            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: Optional[int] = None,
        temb_channels: int = 512,
        eps: float = 1e-6,
    ):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels

        kernel_size = (3, 1, 1)
        padding = [k // 2 for k in kernel_size]

        self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=eps, affine=True)
        self.conv1 = nn.Conv3d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=1,
            padding=padding,
        )

        if temb_channels is not None:
            self.time_emb_proj = nn.Linear(temb_channels, out_channels)
        else:
            self.time_emb_proj = None

        self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=eps, affine=True)

        self.dropout = torch.nn.Dropout(0.0)
        self.conv2 = nn.Conv3d(
            out_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=1,
            padding=padding,
        )

        self.nonlinearity = get_activation("silu")

        self.use_in_shortcut = self.in_channels != out_channels

        self.conv_shortcut = None
        if self.use_in_shortcut:
            self.conv_shortcut = nn.Conv3d(
                in_channels,
                out_channels,
                kernel_size=1,
                stride=1,
                padding=0,
            )

    def forward(self, input_tensor: torch.FloatTensor, temb: torch.FloatTensor) -> torch.FloatTensor:
        hidden_states = input_tensor

        hidden_states = self.norm1(hidden_states)
        hidden_states = self.nonlinearity(hidden_states)
        hidden_states = self.conv1(hidden_states)

        if self.time_emb_proj is not None:
            temb = self.nonlinearity(temb)
            temb = self.time_emb_proj(temb)[:, :, :, None, None]
            temb = temb.permute(0, 2, 1, 3, 4)
            hidden_states = hidden_states + temb

        hidden_states = self.norm2(hidden_states)
        hidden_states = self.nonlinearity(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.conv2(hidden_states)

        if self.conv_shortcut is not None:
            input_tensor = self.conv_shortcut(input_tensor)

        output_tensor = input_tensor + hidden_states

        return output_tensor


 # VideoResBlock
 class SpatioTemporalResBlock(nn.Module):
    r"""
    A SpatioTemporal Resnet block.

    Parameters:
        in_channels (`int`): The number of channels in the input.
        out_channels (`int`, *optional*, default to be `None`):
            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the spatial resenet.
        temporal_eps (`float`, *optional*, defaults to `eps`): The epsilon to use for the temporal resnet.
        merge_factor (`float`, *optional*, defaults to `0.5`): The merge factor to use for the temporal mixing.
        merge_strategy (`str`, *optional*, defaults to `learned_with_images`):
            The merge strategy to use for the temporal mixing.
        switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):
            If `True`, switch the spatial and temporal mixing.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: Optional[int] = None,
        temb_channels: int = 512,
        eps: float = 1e-6,
        temporal_eps: Optional[float] = None,
        merge_factor: float = 0.5,
        merge_strategy="learned_with_images",
        switch_spatial_to_temporal_mix: bool = False,
    ):
        super().__init__()

        self.spatial_res_block = ResnetBlock2D(
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            eps=eps,
        )

        self.temporal_res_block = TemporalResnetBlock(
            in_channels=out_channels if out_channels is not None else in_channels,
            out_channels=out_channels if out_channels is not None else in_channels,
            temb_channels=temb_channels,
            eps=temporal_eps if temporal_eps is not None else eps,
        )

        self.time_mixer = AlphaBlender(
            alpha=merge_factor,
            merge_strategy=merge_strategy,
            switch_spatial_to_temporal_mix=switch_spatial_to_temporal_mix,
        )

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        image_only_indicator: Optional[torch.Tensor] = None,
    ):
        num_frames = image_only_indicator.shape[-1]
        hidden_states = self.spatial_res_block(hidden_states, temb)

        batch_frames, channels, height, width = hidden_states.shape
        batch_size = batch_frames // num_frames

        hidden_states_mix = (
            hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
        )
        hidden_states = (
            hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
        )

        if temb is not None:
            temb = temb.reshape(batch_size, num_frames, -1)

        hidden_states = self.temporal_res_block(hidden_states, temb)
        hidden_states = self.time_mixer(
            x_spatial=hidden_states_mix,
            x_temporal=hidden_states,
            image_only_indicator=image_only_indicator,
        )

        hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)
        return hidden_states


 class AlphaBlender(nn.Module):
    r"""
    A module to blend spatial and temporal features.

    Parameters:
        alpha (`float`): The initial value of the blending factor.
        merge_strategy (`str`, *optional*, defaults to `learned_with_images`):
            The merge strategy to use for the temporal mixing.
        switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):
            If `True`, switch the spatial and temporal mixing.
    """

    strategies = ["learned", "fixed", "learned_with_images"]

    def __init__(
        self,
        alpha: float,
        merge_strategy: str = "learned_with_images",
        switch_spatial_to_temporal_mix: bool = False,
    ):
        super().__init__()
        self.merge_strategy = merge_strategy
        self.switch_spatial_to_temporal_mix = switch_spatial_to_temporal_mix  # For TemporalVAE

        if merge_strategy not in self.strategies:
            raise ValueError(f"merge_strategy needs to be in {self.strategies}")

        if self.merge_strategy == "fixed":
            self.register_buffer("mix_factor", torch.Tensor([alpha]))
        elif self.merge_strategy == "learned" or self.merge_strategy == "learned_with_images":
            self.register_parameter("mix_factor", torch.nn.Parameter(torch.Tensor([alpha])))
        else:
            raise ValueError(f"Unknown merge strategy {self.merge_strategy}")

    def get_alpha(self, image_only_indicator: torch.Tensor, ndims: int) -> torch.Tensor:
        if self.merge_strategy == "fixed":
            alpha = self.mix_factor

        elif self.merge_strategy == "learned":
            alpha = torch.sigmoid(self.mix_factor)

        elif self.merge_strategy == "learned_with_images":
            if image_only_indicator is None:
                raise ValueError("Please provide image_only_indicator to use learned_with_images merge strategy")

            alpha = torch.where(
                image_only_indicator.bool(),
                torch.ones(1, 1, device=image_only_indicator.device),
                torch.sigmoid(self.mix_factor)[..., None],
            )

            # (batch, channel, frames, height, width)
            if ndims == 5:
                alpha = alpha[:, None, :, None, None]
            # (batch*frames, height*width, channels)
            elif ndims == 3:
                alpha = alpha.reshape(-1)[:, None, None]
            else:
                raise ValueError(f"Unexpected ndims {ndims}. Dimensions should be 3 or 5")

        else:
            raise NotImplementedError

        return alpha

    def forward(
        self,
        x_spatial: torch.Tensor,
        x_temporal: torch.Tensor,
        image_only_indicator: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        alpha = self.get_alpha(image_only_indicator, x_spatial.ndim)
        alpha = alpha.to(x_spatial.dtype)

        if self.switch_spatial_to_temporal_mix:
            alpha = 1.0 - alpha

        x = alpha * x_spatial + (1.0 - alpha) * x_temporal
        return x
 # coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.

 import os
 from pickle import UnpicklingError
 from typing import Any, Dict, Union

 import jax
 import jax.numpy as jnp
 import msgpack.exceptions
 from flax.core.frozen_dict import FrozenDict, unfreeze
 from flax.serialization import from_bytes, to_bytes
 from flax.traverse_util import flatten_dict, unflatten_dict
 from huggingface_hub import create_repo, hf_hub_download
 from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    validate_hf_hub_args,
 )
 from requests import HTTPError

 from .. import __version__, is_torch_available
 from ..utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    WEIGHTS_NAME,
    PushToHubMixin,
    logging,
 )
 from .modeling_flax_pytorch_utils import convert_pytorch_state_dict_to_flax


 logger = logging.get_logger(__name__)


 class FlaxModelMixin(PushToHubMixin):
    r"""
    Base class for all Flax models.

    [`FlaxModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and
    saving models.

        - **config_name** ([`str`]) -- Filename to save a model to when calling [`~FlaxModelMixin.save_pretrained`].
    """

    config_name = CONFIG_NAME
    _automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
    _flax_internal_args = ["name", "parent", "dtype"]

    @classmethod
    def _from_config(cls, config, **kwargs):
        """
        All context managers that the model should be initialized under go here.
        """
        return cls(config, **kwargs)

    def _cast_floating_to(self, params: Union[Dict, FrozenDict], dtype: jnp.dtype, mask: Any = None) -> Any:
        """
        Helper method to cast floating-point values of given parameter `PyTree` to given `dtype`.
        """

        # taken from https://github.com/deepmind/jmp/blob/3a8318abc3292be38582794dbf7b094e6583b192/jmp/_src/policy.py#L27
        def conditional_cast(param):
            if isinstance(param, jnp.ndarray) and jnp.issubdtype(param.dtype, jnp.floating):
                param = param.astype(dtype)
            return param

        if mask is None:
            return jax.tree_map(conditional_cast, params)

        flat_params = flatten_dict(params)
        flat_mask, _ = jax.tree_flatten(mask)

        for masked, key in zip(flat_mask, flat_params.keys()):
            if masked:
                param = flat_params[key]
                flat_params[key] = conditional_cast(param)

        return unflatten_dict(flat_params)

    def to_bf16(self, params: Union[Dict, FrozenDict], mask: Any = None):
        r"""
        Cast the floating-point `params` to `jax.numpy.bfloat16`. This returns a new `params` tree and does not cast
        the `params` in place.

        This method can be used on a TPU to explicitly convert the model parameters to bfloat16 precision to do full
        half-precision training or to save weights in bfloat16 for inference in order to save memory and improve speed.

        Arguments:
            params (`Union[Dict, FrozenDict]`):
                A `PyTree` of model parameters.
            mask (`Union[Dict, FrozenDict]`):
                A `PyTree` with same structure as the `params` tree. The leaves should be booleans. It should be `True`
                for params you want to cast, and `False` for those you want to skip.

        Examples:

        ```python
        >>> from diffusers import FlaxUNet2DConditionModel

        >>> # load model
        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> # By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision
        >>> params = model.to_bf16(params)
        >>> # If you don't want to cast certain parameters (for example layer norm bias and scale)
        >>> # then pass the mask as follows
        >>> from flax import traverse_util

        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> flat_params = traverse_util.flatten_dict(params)
        >>> mask = {
        ...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
        ...     for path in flat_params
        ... }
        >>> mask = traverse_util.unflatten_dict(mask)
        >>> params = model.to_bf16(params, mask)
        ```"""
        return self._cast_floating_to(params, jnp.bfloat16, mask)

    def to_fp32(self, params: Union[Dict, FrozenDict], mask: Any = None):
        r"""
        Cast the floating-point `params` to `jax.numpy.float32`. This method can be used to explicitly convert the
        model parameters to fp32 precision. This returns a new `params` tree and does not cast the `params` in place.

        Arguments:
            params (`Union[Dict, FrozenDict]`):
                A `PyTree` of model parameters.
            mask (`Union[Dict, FrozenDict]`):
                A `PyTree` with same structure as the `params` tree. The leaves should be booleans. It should be `True`
                for params you want to cast, and `False` for those you want to skip.

        Examples:

        ```python
        >>> from diffusers import FlaxUNet2DConditionModel

        >>> # Download model and configuration from huggingface.co
        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> # By default, the model params will be in fp32, to illustrate the use of this method,
        >>> # we'll first cast to fp16 and back to fp32
        >>> params = model.to_f16(params)
        >>> # now cast back to fp32
        >>> params = model.to_fp32(params)
        ```"""
        return self._cast_floating_to(params, jnp.float32, mask)

    def to_fp16(self, params: Union[Dict, FrozenDict], mask: Any = None):
        r"""
        Cast the floating-point `params` to `jax.numpy.float16`. This returns a new `params` tree and does not cast the
        `params` in place.

        This method can be used on a GPU to explicitly convert the model parameters to float16 precision to do full
        half-precision training or to save weights in float16 for inference in order to save memory and improve speed.

        Arguments:
            params (`Union[Dict, FrozenDict]`):
                A `PyTree` of model parameters.
            mask (`Union[Dict, FrozenDict]`):
                A `PyTree` with same structure as the `params` tree. The leaves should be booleans. It should be `True`
                for params you want to cast, and `False` for those you want to skip.

        Examples:

        ```python
        >>> from diffusers import FlaxUNet2DConditionModel

        >>> # load model
        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> # By default, the model params will be in fp32, to cast these to float16
        >>> params = model.to_fp16(params)
        >>> # If you want don't want to cast certain parameters (for example layer norm bias and scale)
        >>> # then pass the mask as follows
        >>> from flax import traverse_util

        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> flat_params = traverse_util.flatten_dict(params)
        >>> mask = {
        ...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
        ...     for path in flat_params
        ... }
        >>> mask = traverse_util.unflatten_dict(mask)
        >>> params = model.to_fp16(params, mask)
        ```"""
        return self._cast_floating_to(params, jnp.float16, mask)

    def init_weights(self, rng: jax.Array) -> Dict:
        raise NotImplementedError(f"init_weights method has to be implemented for {self}")

    @classmethod
    @validate_hf_hub_args
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Union[str, os.PathLike],
        dtype: jnp.dtype = jnp.float32,
        *model_args,
        **kwargs,
    ):
        r"""
        Instantiate a pretrained Flax model from a pretrained model configuration.

        Parameters:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                Can be either:

                    - A string, the *model id* (for example `runwayml/stable-diffusion-v1-5`) of a pretrained model
                      hosted on the Hub.
                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
                      using [`~FlaxModelMixin.save_pretrained`].
            dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
                The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
                `jax.numpy.bfloat16` (on TPUs).

                This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
                specified, all the computation will be performed with the given `dtype`.

                <Tip>

                This only specifies the dtype of the *computation* and does not influence the dtype of model
                parameters.

                If you wish to change the dtype of the model parameters, see [`~FlaxModelMixin.to_fp16`] and
                [`~FlaxModelMixin.to_bf16`].

                </Tip>

            model_args (sequence of positional arguments, *optional*):
                All remaining positional arguments are passed to the underlying model's `__init__` method.
            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                is not used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
                incompletely downloaded files are deleted.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            local_files_only(`bool`, *optional*, defaults to `False`):
                Whether to only load local model weights and configuration files or not. If set to `True`, the model
                won't be downloaded from the Hub.
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                allowed by Git.
            from_pt (`bool`, *optional*, defaults to `False`):
                Load the model weights from a PyTorch checkpoint save file.
            kwargs (remaining dictionary of keyword arguments, *optional*):
                Can be used to update the configuration object (after it is loaded) and initiate the model (for
                example, `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
                automatically loaded:

                    - If a configuration is provided with `config`, `kwargs` are directly passed to the underlying
                      model's `__init__` method (we assume all relevant updates to the configuration have already been
                      done).
                    - If a configuration is not provided, `kwargs` are first passed to the configuration class
                      initialization function [`~ConfigMixin.from_config`]. Each key of the `kwargs` that corresponds
                      to a configuration attribute is used to override said attribute with the supplied `kwargs` value.
                      Remaining keys that do not correspond to any configuration attribute are passed to the underlying
                      model's `__init__` function.

        Examples:

        ```python
        >>> from diffusers import FlaxUNet2DConditionModel

        >>> # Download model and configuration from huggingface.co and cache.
        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("./test/saved_model/")
        ```

        If you get the error message below, you need to finetune the weights for your downstream task:

        ```bash
        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
        - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
        ```
        """
        config = kwargs.pop("config", None)
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        from_pt = kwargs.pop("from_pt", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", False)
        token = kwargs.pop("token", None)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", None)

        user_agent = {
            "diffusers": __version__,
            "file_type": "model",
            "framework": "flax",
        }

        # Load config if we don't provide one
        if config is None:
            config, unused_kwargs = cls.load_config(
                pretrained_model_name_or_path,
                cache_dir=cache_dir,
                return_unused_kwargs=True,
                force_download=force_download,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                token=token,
                revision=revision,
                subfolder=subfolder,
                **kwargs,
            )

        model, model_kwargs = cls.from_config(config, dtype=dtype, return_unused_kwargs=True, **unused_kwargs)

        # Load model
        pretrained_path_with_subfolder = (
            pretrained_model_name_or_path
            if subfolder is None
            else os.path.join(pretrained_model_name_or_path, subfolder)
        )
        if os.path.isdir(pretrained_path_with_subfolder):
            if from_pt:
                if not os.path.isfile(os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)):
                    raise EnvironmentError(
                        f"Error no file named {WEIGHTS_NAME} found in directory {pretrained_path_with_subfolder} "
                    )
                model_file = os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)
            elif os.path.isfile(os.path.join(pretrained_path_with_subfolder, FLAX_WEIGHTS_NAME)):
                # Load from a Flax checkpoint
                model_file = os.path.join(pretrained_path_with_subfolder, FLAX_WEIGHTS_NAME)
            # Check if pytorch weights exist instead
            elif os.path.isfile(os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)):
                raise EnvironmentError(
                    f"{WEIGHTS_NAME} file found in directory {pretrained_path_with_subfolder}. Please load the model"
                    " using `from_pt=True`."
                )
            else:
                raise EnvironmentError(
                    f"Error no file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME} found in directory "
                    f"{pretrained_path_with_subfolder}."
                )
        else:
            try:
                model_file = hf_hub_download(
                    pretrained_model_name_or_path,
                    filename=FLAX_WEIGHTS_NAME if not from_pt else WEIGHTS_NAME,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    proxies=proxies,
                    resume_download=resume_download,
                    local_files_only=local_files_only,
                    token=token,
                    user_agent=user_agent,
                    subfolder=subfolder,
                    revision=revision,
                )

            except RepositoryNotFoundError:
                raise EnvironmentError(
                    f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
                    "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
                    "token having permission to this repo with `token` or log in with `huggingface-cli "
                    "login`."
                )
            except RevisionNotFoundError:
                raise EnvironmentError(
                    f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
                    "this model name. Check the model page at "
                    f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
                )
            except EntryNotFoundError:
                raise EnvironmentError(
                    f"{pretrained_model_name_or_path} does not appear to have a file named {FLAX_WEIGHTS_NAME}."
                )
            except HTTPError as err:
                raise EnvironmentError(
                    f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n"
                    f"{err}"
                )
            except ValueError:
                raise EnvironmentError(
                    f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
                    f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                    f" directory containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}.\nCheckout your"
                    " internet connection or see how to run the library in offline mode at"
                    " 'https://huggingface.co/docs/transformers/installation#offline-mode'."
                )
            except EnvironmentError:
                raise EnvironmentError(
                    f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
                    f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                    f"containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}."
                )

        if from_pt:
            if is_torch_available():
                from .modeling_utils import load_state_dict
            else:
                raise EnvironmentError(
                    "Can't load the model in PyTorch format because PyTorch is not installed. "
                    "Please, install PyTorch or use native Flax weights."
                )

            # Step 1: Get the pytorch file
            pytorch_model_file = load_state_dict(model_file)

            # Step 2: Convert the weights
            state = convert_pytorch_state_dict_to_flax(pytorch_model_file, model)
        else:
            try:
                with open(model_file, "rb") as state_f:
                    state = from_bytes(cls, state_f.read())
            except (UnpicklingError, msgpack.exceptions.ExtraData) as e:
                try:
                    with open(model_file) as f:
                        if f.read().startswith("version"):
                            raise OSError(
                                "You seem to have cloned a repository without having git-lfs installed. Please"
                                " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
                                " folder you cloned."
                            )
                        else:
                            raise ValueError from e
                except (UnicodeDecodeError, ValueError):
                    raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ")
            # make sure all arrays are stored as jnp.ndarray
            # NOTE: This is to prevent a bug this will be fixed in Flax >= v0.3.4:
            # https://github.com/google/flax/issues/1261
        state = jax.tree_util.tree_map(lambda x: jax.device_put(x, jax.local_devices(backend="cpu")[0]), state)

        # flatten dicts
        state = flatten_dict(state)

        params_shape_tree = jax.eval_shape(model.init_weights, rng=jax.random.PRNGKey(0))
        required_params = set(flatten_dict(unfreeze(params_shape_tree)).keys())

        shape_state = flatten_dict(unfreeze(params_shape_tree))

        missing_keys = required_params - set(state.keys())
        unexpected_keys = set(state.keys()) - required_params

        if missing_keys:
            logger.warning(
                f"The checkpoint {pretrained_model_name_or_path} is missing required keys: {missing_keys}. "
                "Make sure to call model.init_weights to initialize the missing weights."
            )
            cls._missing_keys = missing_keys

        for key in state.keys():
            if key in shape_state and state[key].shape != shape_state[key].shape:
                raise ValueError(
                    f"Trying to load the pretrained weight for {key} failed: checkpoint has shape "
                    f"{state[key].shape} which is incompatible with the model shape {shape_state[key].shape}. "
                )

        # remove unexpected keys to not be saved again
        for unexpected_key in unexpected_keys:
            del state[unexpected_key]

        if len(unexpected_keys) > 0:
            logger.warning(
                f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
                f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
                f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task or"
                " with another architecture."
            )
        else:
            logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")

        if len(missing_keys) > 0:
            logger.warning(
                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
                " TRAIN this model on a down-stream task to be able to use it for predictions and inference."
            )
        else:
            logger.info(
                f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the checkpoint"
                f" was trained on, you can already use {model.__class__.__name__} for predictions without further"
                " training."
            )

        return model, unflatten_dict(state)

    def save_pretrained(
        self,
        save_directory: Union[str, os.PathLike],
        params: Union[Dict, FrozenDict],
        is_main_process: bool = True,
        push_to_hub: bool = False,
        **kwargs,
    ):
        """
        Save a model and its configuration file to a directory so that it can be reloaded using the
        [`~FlaxModelMixin.from_pretrained`] class method.

        Arguments:
            save_directory (`str` or `os.PathLike`):
                Directory to save a model and its configuration file to. Will be created if it doesn't exist.
            params (`Union[Dict, FrozenDict]`):
                A `PyTree` of model parameters.
            is_main_process (`bool`, *optional*, defaults to `True`):
                Whether the process calling this is the main process or not. Useful during distributed training and you
                need to call this function on all processes. In this case, set `is_main_process=True` only on the main
                process to avoid race conditions.
            push_to_hub (`bool`, *optional*, defaults to `False`):
                Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                namespace).
            kwargs (`Dict[str, Any]`, *optional*):
                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
        """
        if os.path.isfile(save_directory):
            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
            return

        os.makedirs(save_directory, exist_ok=True)

        if push_to_hub:
            commit_message = kwargs.pop("commit_message", None)
            private = kwargs.pop("private", False)
            create_pr = kwargs.pop("create_pr", False)
            token = kwargs.pop("token", None)
            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
            repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id

        model_to_save = self

        # Attach architecture to the config
        # Save the config
        if is_main_process:
            model_to_save.save_config(save_directory)

        # save model
        output_model_file = os.path.join(save_directory, FLAX_WEIGHTS_NAME)
        with open(output_model_file, "wb") as f:
            model_bytes = to_bytes(params)
            f.write(model_bytes)

        logger.info(f"Model weights saved in {output_model_file}")

        if push_to_hub:
            self._upload_folder(
                save_directory,
                repo_id,
                token=token,
                commit_message=commit_message,
                create_pr=create_pr,
            )


 from ..utils import deprecate
 from .unets.unet_1d_blocks import (
    AttnDownBlock1D,
    AttnUpBlock1D,
    DownBlock1D,
    DownBlock1DNoSkip,
    DownResnetBlock1D,
    Downsample1d,
    MidResTemporalBlock1D,
    OutConv1DBlock,
    OutValueFunctionBlock,
    ResConvBlock,
    SelfAttention1d,
    UNetMidBlock1D,
    UpBlock1D,
    UpBlock1DNoSkip,
    UpResnetBlock1D,
    Upsample1d,
    ValueFunctionMidBlock1D,
 )


 class DownResnetBlock1D(DownResnetBlock1D):
    deprecation_message = "Importing `DownResnetBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import DownResnetBlock1D`, instead."
    deprecate("DownResnetBlock1D", "0.29", deprecation_message)


 class UpResnetBlock1D(UpResnetBlock1D):
    deprecation_message = "Importing `UpResnetBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UpResnetBlock1D`, instead."
    deprecate("UpResnetBlock1D", "0.29", deprecation_message)


 class ValueFunctionMidBlock1D(ValueFunctionMidBlock1D):
    deprecation_message = "Importing `ValueFunctionMidBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import ValueFunctionMidBlock1D`, instead."
    deprecate("ValueFunctionMidBlock1D", "0.29", deprecation_message)


 class OutConv1DBlock(OutConv1DBlock):
    deprecation_message = "Importing `OutConv1DBlock` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import OutConv1DBlock`, instead."
    deprecate("OutConv1DBlock", "0.29", deprecation_message)


 class OutValueFunctionBlock(OutValueFunctionBlock):
    deprecation_message = "Importing `OutValueFunctionBlock` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import OutValueFunctionBlock`, instead."
    deprecate("OutValueFunctionBlock", "0.29", deprecation_message)


 class Downsample1d(Downsample1d):
    deprecation_message = "Importing `Downsample1d` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import Downsample1d`, instead."
    deprecate("Downsample1d", "0.29", deprecation_message)


 class Upsample1d(Upsample1d):
    deprecation_message = "Importing `Upsample1d` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import Upsample1d`, instead."
    deprecate("Upsample1d", "0.29", deprecation_message)


 class SelfAttention1d(SelfAttention1d):
    deprecation_message = "Importing `SelfAttention1d` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import SelfAttention1d`, instead."
    deprecate("SelfAttention1d", "0.29", deprecation_message)


 class ResConvBlock(ResConvBlock):
    deprecation_message = "Importing `ResConvBlock` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import ResConvBlock`, instead."
    deprecate("ResConvBlock", "0.29", deprecation_message)


 class UNetMidBlock1D(UNetMidBlock1D):
    deprecation_message = "Importing `UNetMidBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UNetMidBlock1D`, instead."
    deprecate("UNetMidBlock1D", "0.29", deprecation_message)


 class AttnDownBlock1D(AttnDownBlock1D):
    deprecation_message = "Importing `AttnDownBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import AttnDownBlock1D`, instead."
    deprecate("AttnDownBlock1D", "0.29", deprecation_message)


 class DownBlock1D(DownBlock1D):
    deprecation_message = "Importing `DownBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import DownBlock1D`, instead."
    deprecate("DownBlock1D", "0.29", deprecation_message)


 class DownBlock1DNoSkip(DownBlock1DNoSkip):
    deprecation_message = "Importing `DownBlock1DNoSkip` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import DownBlock1DNoSkip`, instead."
    deprecate("DownBlock1DNoSkip", "0.29", deprecation_message)


 class AttnUpBlock1D(AttnUpBlock1D):
    deprecation_message = "Importing `AttnUpBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import AttnUpBlock1D`, instead."
    deprecate("AttnUpBlock1D", "0.29", deprecation_message)


 class UpBlock1D(UpBlock1D):
    deprecation_message = "Importing `UpBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UpBlock1D`, instead."
    deprecate("UpBlock1D", "0.29", deprecation_message)


 class UpBlock1DNoSkip(UpBlock1DNoSkip):
    deprecation_message = "Importing `UpBlock1DNoSkip` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UpBlock1DNoSkip`, instead."
    deprecate("UpBlock1DNoSkip", "0.29", deprecation_message)


 class MidResTemporalBlock1D(MidResTemporalBlock1D):
    deprecation_message = "Importing `MidResTemporalBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import MidResTemporalBlock1D`, instead."
    deprecate("MidResTemporalBlock1D", "0.29", deprecation_message)


 def get_down_block(
    down_block_type: str,
    num_layers: int,
    in_channels: int,
    out_channels: int,
    temb_channels: int,
    add_downsample: bool,
 ):
    deprecation_message = "Importing `get_down_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_down_block`, instead."
    deprecate("get_down_block", "0.29", deprecation_message)

    from .unets.unet_1d_blocks import get_down_block

    return get_down_block(
        down_block_type=down_block_type,
        num_layers=num_layers,
        in_channels=in_channels,
        out_channels=out_channels,
        temb_channels=temb_channels,
        add_downsample=add_downsample,
    )


 def get_up_block(
    up_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channels: int, add_upsample: bool
 ):
    deprecation_message = "Importing `get_up_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_up_block`, instead."
    deprecate("get_up_block", "0.29", deprecation_message)

    from .unets.unet_1d_blocks import get_up_block

    return get_up_block(
        up_block_type=up_block_type,
        num_layers=num_layers,
        in_channels=in_channels,
        out_channels=out_channels,
        temb_channels=temb_channels,
        add_upsample=add_upsample,
    )


 def get_mid_block(
    mid_block_type: str,
    num_layers: int,
    in_channels: int,
    mid_channels: int,
    out_channels: int,
    embed_dim: int,
    add_downsample: bool,
 ):
    deprecation_message = "Importing `get_mid_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_mid_block`, instead."
    deprecate("get_mid_block", "0.29", deprecation_message)

    from .unets.unet_1d_blocks import get_mid_block

    return get_mid_block(
        mid_block_type=mid_block_type,
        num_layers=num_layers,
        in_channels=in_channels,
        mid_channels=mid_channels,
        out_channels=out_channels,
        embed_dim=embed_dim,
        add_downsample=add_downsample,
    )


 def get_out_block(
    *, out_block_type: str, num_groups_out: int, embed_dim: int, out_channels: int, act_fn: str, fc_dim: int
 ):
    deprecation_message = "Importing `get_out_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_out_block`, instead."
    deprecate("get_out_block", "0.29", deprecation_message)

    from .unets.unet_1d_blocks import get_out_block

    return get_out_block(
        out_block_type=out_block_type,
        num_groups_out=num_groups_out,
        embed_dim=embed_dim,
        out_channels=out_channels,
        act_fn=act_fn,
        fc_dim=fc_dim,
    )

 import math
 from typing import List, Optional, Tuple, Union

 import numpy as np
 import torch
 from torch import nn

 from ..utils import USE_PEFT_BACKEND, deprecate
 from .activations import get_activation
 from .attention_processor import Attention
 from .lora import LoRACompatibleLinear


 def get_timestep_embedding(
    timesteps: torch.Tensor,
    embedding_dim: int,
    flip_sin_to_cos: bool = False,
    downscale_freq_shift: float = 1,
    scale: float = 1,
    max_period: int = 10000,
 ):
    """
    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.

    :param timesteps: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
    embeddings. :return: an [N x dim] Tensor of positional embeddings.
    """
    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"

    half_dim = embedding_dim // 2
    exponent = -math.log(max_period) * torch.arange(
        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
    )
    exponent = exponent / (half_dim - downscale_freq_shift)

    emb = torch.exp(exponent)
    emb = timesteps[:, None].float() * emb[None, :]

    # scale embeddings
    emb = scale * emb

    # concat sine and cosine embeddings
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)

    # flip sine and cosine embeddings
    if flip_sin_to_cos:
        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)

    # zero pad
    if embedding_dim % 2 == 1:
        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
    return emb


 def get_2d_sincos_pos_embed(
    embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16
 ):
    """
    grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
    [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    if isinstance(grid_size, int):
        grid_size = (grid_size, grid_size)

    grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale
    grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token and extra_tokens > 0:
        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
    return pos_embed


 def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    if embed_dim % 2 != 0:
        raise ValueError("embed_dim must be divisible by 2")

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
    return emb


 def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)
    """
    if embed_dim % 2 != 0:
        raise ValueError("embed_dim must be divisible by 2")

    omega = np.arange(embed_dim // 2, dtype=np.float64)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out)  # (M, D/2)
    emb_cos = np.cos(out)  # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb


 class PatchEmbed(nn.Module):
    """2D Image to Patch Embedding"""

    def __init__(
        self,
        height=224,
        width=224,
        patch_size=16,
        in_channels=3,
        embed_dim=768,
        layer_norm=False,
        flatten=True,
        bias=True,
        interpolation_scale=1,
    ):
        super().__init__()

        num_patches = (height // patch_size) * (width // patch_size)
        self.flatten = flatten
        self.layer_norm = layer_norm

        self.proj = nn.Conv2d(
            in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
        )
        if layer_norm:
            self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)
        else:
            self.norm = None

        self.patch_size = patch_size
        # See:
        # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L161
        self.height, self.width = height // patch_size, width // patch_size
        self.base_size = height // patch_size
        self.interpolation_scale = interpolation_scale
        pos_embed = get_2d_sincos_pos_embed(
            embed_dim, int(num_patches**0.5), base_size=self.base_size, interpolation_scale=self.interpolation_scale
        )
        self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False)

    def forward(self, latent):
        height, width = latent.shape[-2] // self.patch_size, latent.shape[-1] // self.patch_size

        latent = self.proj(latent)
        if self.flatten:
            latent = latent.flatten(2).transpose(1, 2)  # BCHW -> BNC
        if self.layer_norm:
            latent = self.norm(latent)

        # Interpolate positional embeddings if needed.
        # (For PixArt-Alpha: https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L162C151-L162C160)
        if self.height != height or self.width != width:
            pos_embed = get_2d_sincos_pos_embed(
                embed_dim=self.pos_embed.shape[-1],
                grid_size=(height, width),
                base_size=self.base_size,
                interpolation_scale=self.interpolation_scale,
            )
            pos_embed = torch.from_numpy(pos_embed)
            pos_embed = pos_embed.float().unsqueeze(0).to(latent.device)
        else:
            pos_embed = self.pos_embed

        return (latent + pos_embed).to(latent.dtype)


 class TimestepEmbedding(nn.Module):
    def __init__(
        self,
        in_channels: int,
        time_embed_dim: int,
        act_fn: str = "silu",
        out_dim: int = None,
        post_act_fn: Optional[str] = None,
        cond_proj_dim=None,
        sample_proj_bias=True,
    ):
        super().__init__()
        linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear

        self.linear_1 = linear_cls(in_channels, time_embed_dim, sample_proj_bias)

        if cond_proj_dim is not None:
            self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
        else:
            self.cond_proj = None

        self.act = get_activation(act_fn)

        if out_dim is not None:
            time_embed_dim_out = out_dim
        else:
            time_embed_dim_out = time_embed_dim
        self.linear_2 = linear_cls(time_embed_dim, time_embed_dim_out, sample_proj_bias)

        if post_act_fn is None:
            self.post_act = None
        else:
            self.post_act = get_activation(post_act_fn)

    def forward(self, sample, condition=None):
        if condition is not None:
            sample = sample + self.cond_proj(condition)
        sample = self.linear_1(sample)

        if self.act is not None:
            sample = self.act(sample)

        sample = self.linear_2(sample)

        if self.post_act is not None:
            sample = self.post_act(sample)
        return sample


 class Timesteps(nn.Module):
    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
        super().__init__()
        self.num_channels = num_channels
        self.flip_sin_to_cos = flip_sin_to_cos
        self.downscale_freq_shift = downscale_freq_shift

    def forward(self, timesteps):
        t_emb = get_timestep_embedding(
            timesteps,
            self.num_channels,
            flip_sin_to_cos=self.flip_sin_to_cos,
            downscale_freq_shift=self.downscale_freq_shift,
        )
        return t_emb


 class GaussianFourierProjection(nn.Module):
    """Gaussian Fourier embeddings for noise levels."""

    def __init__(
        self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False
    ):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
        self.log = log
        self.flip_sin_to_cos = flip_sin_to_cos

        if set_W_to_weight:
            # to delete later
            self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)

            self.weight = self.W

    def forward(self, x):
        if self.log:
            x = torch.log(x)

        x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi

        if self.flip_sin_to_cos:
            out = torch.cat([torch.cos(x_proj), torch.sin(x_proj)], dim=-1)
        else:
            out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
        return out


 class SinusoidalPositionalEmbedding(nn.Module):
    """Apply positional information to a sequence of embeddings.

    Takes in a sequence of embeddings with shape (batch_size, seq_length, embed_dim) and adds positional embeddings to
    them

    Args:
        embed_dim: (int): Dimension of the positional embedding.
        max_seq_length: Maximum sequence length to apply positional embeddings

    """

    def __init__(self, embed_dim: int, max_seq_length: int = 32):
        super().__init__()
        position = torch.arange(max_seq_length).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim))
        pe = torch.zeros(1, max_seq_length, embed_dim)
        pe[0, :, 0::2] = torch.sin(position * div_term)
        pe[0, :, 1::2] = torch.cos(position * div_term)
        self.register_buffer("pe", pe)

    def forward(self, x):
        _, seq_length, _ = x.shape
        x = x + self.pe[:, :seq_length]
        return x


 class ImagePositionalEmbeddings(nn.Module):
    """
    Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the
    height and width of the latent space.

    For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092

    For VQ-diffusion:

    Output vector embeddings are used as input for the transformer.

    Note that the vector embeddings for the transformer are different than the vector embeddings from the VQVAE.

    Args:
        num_embed (`int`):
            Number of embeddings for the latent pixels embeddings.
        height (`int`):
            Height of the latent image i.e. the number of height embeddings.
        width (`int`):
            Width of the latent image i.e. the number of width embeddings.
        embed_dim (`int`):
            Dimension of the produced vector embeddings. Used for the latent pixel, height, and width embeddings.
    """

    def __init__(
        self,
        num_embed: int,
        height: int,
        width: int,
        embed_dim: int,
    ):
        super().__init__()

        self.height = height
        self.width = width
        self.num_embed = num_embed
        self.embed_dim = embed_dim

        self.emb = nn.Embedding(self.num_embed, embed_dim)
        self.height_emb = nn.Embedding(self.height, embed_dim)
        self.width_emb = nn.Embedding(self.width, embed_dim)

    def forward(self, index):
        emb = self.emb(index)

        height_emb = self.height_emb(torch.arange(self.height, device=index.device).view(1, self.height))

        # 1 x H x D -> 1 x H x 1 x D
        height_emb = height_emb.unsqueeze(2)

        width_emb = self.width_emb(torch.arange(self.width, device=index.device).view(1, self.width))

        # 1 x W x D -> 1 x 1 x W x D
        width_emb = width_emb.unsqueeze(1)

        pos_emb = height_emb + width_emb

        # 1 x H x W x D -> 1 x L xD
        pos_emb = pos_emb.view(1, self.height * self.width, -1)

        emb = emb + pos_emb[:, : emb.shape[1], :]

        return emb


 class LabelEmbedding(nn.Module):
    """
    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.

    Args:
        num_classes (`int`): The number of classes.
        hidden_size (`int`): The size of the vector embeddings.
        dropout_prob (`float`): The probability of dropping a label.
    """

    def __init__(self, num_classes, hidden_size, dropout_prob):
        super().__init__()
        use_cfg_embedding = dropout_prob > 0
        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
        self.num_classes = num_classes
        self.dropout_prob = dropout_prob

    def token_drop(self, labels, force_drop_ids=None):
        """
        Drops labels to enable classifier-free guidance.
        """
        if force_drop_ids is None:
            drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
        else:
            drop_ids = torch.tensor(force_drop_ids == 1)
        labels = torch.where(drop_ids, self.num_classes, labels)
        return labels

    def forward(self, labels: torch.LongTensor, force_drop_ids=None):
        use_dropout = self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
            labels = self.token_drop(labels, force_drop_ids)
        embeddings = self.embedding_table(labels)
        return embeddings


 class TextImageProjection(nn.Module):
    def __init__(
        self,
        text_embed_dim: int = 1024,
        image_embed_dim: int = 768,
        cross_attention_dim: int = 768,
        num_image_text_embeds: int = 10,
    ):
        super().__init__()

        self.num_image_text_embeds = num_image_text_embeds
        self.image_embeds = nn.Linear(image_embed_dim, self.num_image_text_embeds * cross_attention_dim)
        self.text_proj = nn.Linear(text_embed_dim, cross_attention_dim)

    def forward(self, text_embeds: torch.FloatTensor, image_embeds: torch.FloatTensor):
        batch_size = text_embeds.shape[0]

        # image
        image_text_embeds = self.image_embeds(image_embeds)
        image_text_embeds = image_text_embeds.reshape(batch_size, self.num_image_text_embeds, -1)

        # text
        text_embeds = self.text_proj(text_embeds)

        return torch.cat([image_text_embeds, text_embeds], dim=1)


 class ImageProjection(nn.Module):
    def __init__(
        self,
        image_embed_dim: int = 768,
        cross_attention_dim: int = 768,
        num_image_text_embeds: int = 32,
    ):
        super().__init__()

        self.num_image_text_embeds = num_image_text_embeds
        self.image_embeds = nn.Linear(image_embed_dim, self.num_image_text_embeds * cross_attention_dim)
        self.norm = nn.LayerNorm(cross_attention_dim)

    def forward(self, image_embeds: torch.FloatTensor):
        batch_size = image_embeds.shape[0]

        # image
        image_embeds = self.image_embeds(image_embeds)
        image_embeds = image_embeds.reshape(batch_size, self.num_image_text_embeds, -1)
        image_embeds = self.norm(image_embeds)
        return image_embeds


 class IPAdapterFullImageProjection(nn.Module):
    def __init__(self, image_embed_dim=1024, cross_attention_dim=1024):
        super().__init__()
        from .attention import FeedForward

        self.ff = FeedForward(image_embed_dim, cross_attention_dim, mult=1, activation_fn="gelu")
        self.norm = nn.LayerNorm(cross_attention_dim)

    def forward(self, image_embeds: torch.FloatTensor):
        return self.norm(self.ff(image_embeds))


 class CombinedTimestepLabelEmbeddings(nn.Module):
    def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1):
        super().__init__()

        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)
        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
        self.class_embedder = LabelEmbedding(num_classes, embedding_dim, class_dropout_prob)

    def forward(self, timestep, class_labels, hidden_dtype=None):
        timesteps_proj = self.time_proj(timestep)
        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)

        class_labels = self.class_embedder(class_labels)  # (N, D)

        conditioning = timesteps_emb + class_labels  # (N, D)

        return conditioning


 class TextTimeEmbedding(nn.Module):
    def __init__(self, encoder_dim: int, time_embed_dim: int, num_heads: int = 64):
        super().__init__()
        self.norm1 = nn.LayerNorm(encoder_dim)
        self.pool = AttentionPooling(num_heads, encoder_dim)
        self.proj = nn.Linear(encoder_dim, time_embed_dim)
        self.norm2 = nn.LayerNorm(time_embed_dim)

    def forward(self, hidden_states):
        hidden_states = self.norm1(hidden_states)
        hidden_states = self.pool(hidden_states)
        hidden_states = self.proj(hidden_states)
        hidden_states = self.norm2(hidden_states)
        return hidden_states


 class TextImageTimeEmbedding(nn.Module):
    def __init__(self, text_embed_dim: int = 768, image_embed_dim: int = 768, time_embed_dim: int = 1536):
        super().__init__()
        self.text_proj = nn.Linear(text_embed_dim, time_embed_dim)
        self.text_norm = nn.LayerNorm(time_embed_dim)
        self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)

    def forward(self, text_embeds: torch.FloatTensor, image_embeds: torch.FloatTensor):
        # text
        time_text_embeds = self.text_proj(text_embeds)
        time_text_embeds = self.text_norm(time_text_embeds)

        # image
        time_image_embeds = self.image_proj(image_embeds)

        return time_image_embeds + time_text_embeds


 class ImageTimeEmbedding(nn.Module):
    def __init__(self, image_embed_dim: int = 768, time_embed_dim: int = 1536):
        super().__init__()
        self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)
        self.image_norm = nn.LayerNorm(time_embed_dim)

    def forward(self, image_embeds: torch.FloatTensor):
        # image
        time_image_embeds = self.image_proj(image_embeds)
        time_image_embeds = self.image_norm(time_image_embeds)
        return time_image_embeds


 class ImageHintTimeEmbedding(nn.Module):
    def __init__(self, image_embed_dim: int = 768, time_embed_dim: int = 1536):
        super().__init__()
        self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)
        self.image_norm = nn.LayerNorm(time_embed_dim)
        self.input_hint_block = nn.Sequential(
            nn.Conv2d(3, 16, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(16, 16, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(16, 32, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(32, 32, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(32, 96, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(96, 96, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(96, 256, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(256, 4, 3, padding=1),
        )

    def forward(self, image_embeds: torch.FloatTensor, hint: torch.FloatTensor):
        # image
        time_image_embeds = self.image_proj(image_embeds)
        time_image_embeds = self.image_norm(time_image_embeds)
        hint = self.input_hint_block(hint)
        return time_image_embeds, hint


 class AttentionPooling(nn.Module):
    # Copied from https://github.com/deep-floyd/IF/blob/2f91391f27dd3c468bf174be5805b4cc92980c0b/deepfloyd_if/model/nn.py#L54

    def __init__(self, num_heads, embed_dim, dtype=None):
        super().__init__()
        self.dtype = dtype
        self.positional_embedding = nn.Parameter(torch.randn(1, embed_dim) / embed_dim**0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype)
        self.q_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype)
        self.v_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype)
        self.num_heads = num_heads
        self.dim_per_head = embed_dim // self.num_heads

    def forward(self, x):
        bs, length, width = x.size()

        def shape(x):
            # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
            x = x.view(bs, -1, self.num_heads, self.dim_per_head)
            # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
            x = x.transpose(1, 2)
            # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
            x = x.reshape(bs * self.num_heads, -1, self.dim_per_head)
            # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
            x = x.transpose(1, 2)
            return x

        class_token = x.mean(dim=1, keepdim=True) + self.positional_embedding.to(x.dtype)
        x = torch.cat([class_token, x], dim=1)  # (bs, length+1, width)

        # (bs*n_heads, class_token_length, dim_per_head)
        q = shape(self.q_proj(class_token))
        # (bs*n_heads, length+class_token_length, dim_per_head)
        k = shape(self.k_proj(x))
        v = shape(self.v_proj(x))

        # (bs*n_heads, class_token_length, length+class_token_length):
        scale = 1 / math.sqrt(math.sqrt(self.dim_per_head))
        weight = torch.einsum("bct,bcs->bts", q * scale, k * scale)  # More stable with f16 than dividing afterwards
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)

        # (bs*n_heads, dim_per_head, class_token_length)
        a = torch.einsum("bts,bcs->bct", weight, v)

        # (bs, length+1, width)
        a = a.reshape(bs, -1, 1).transpose(1, 2)

        return a[:, 0, :]  # cls_token


 def get_fourier_embeds_from_boundingbox(embed_dim, box):
    """
    Args:
        embed_dim: int
        box: a 3-D tensor [B x N x 4] representing the bounding boxes for GLIGEN pipeline
    Returns:
        [B x N x embed_dim] tensor of positional embeddings
    """

    batch_size, num_boxes = box.shape[:2]

    emb = 100 ** (torch.arange(embed_dim) / embed_dim)
    emb = emb[None, None, None].to(device=box.device, dtype=box.dtype)
    emb = emb * box.unsqueeze(-1)

    emb = torch.stack((emb.sin(), emb.cos()), dim=-1)
    emb = emb.permute(0, 1, 3, 4, 2).reshape(batch_size, num_boxes, embed_dim * 2 * 4)

    return emb


 class GLIGENTextBoundingboxProjection(nn.Module):
    def __init__(self, positive_len, out_dim, feature_type="text-only", fourier_freqs=8):
        super().__init__()
        self.positive_len = positive_len
        self.out_dim = out_dim

        self.fourier_embedder_dim = fourier_freqs
        self.position_dim = fourier_freqs * 2 * 4  # 2: sin/cos, 4: xyxy

        if isinstance(out_dim, tuple):
            out_dim = out_dim[0]

        if feature_type == "text-only":
            self.linears = nn.Sequential(
                nn.Linear(self.positive_len + self.position_dim, 512),
                nn.SiLU(),
                nn.Linear(512, 512),
                nn.SiLU(),
                nn.Linear(512, out_dim),
            )
            self.null_positive_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))

        elif feature_type == "text-image":
            self.linears_text = nn.Sequential(
                nn.Linear(self.positive_len + self.position_dim, 512),
                nn.SiLU(),
                nn.Linear(512, 512),
                nn.SiLU(),
                nn.Linear(512, out_dim),
            )
            self.linears_image = nn.Sequential(
                nn.Linear(self.positive_len + self.position_dim, 512),
                nn.SiLU(),
                nn.Linear(512, 512),
                nn.SiLU(),
                nn.Linear(512, out_dim),
            )
            self.null_text_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))
            self.null_image_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))

        self.null_position_feature = torch.nn.Parameter(torch.zeros([self.position_dim]))

    def forward(
        self,
        boxes,
        masks,
        positive_embeddings=None,
        phrases_masks=None,
        image_masks=None,
        phrases_embeddings=None,
        image_embeddings=None,
    ):
        masks = masks.unsqueeze(-1)

        # embedding position (it may includes padding as placeholder)
        xyxy_embedding = get_fourier_embeds_from_boundingbox(self.fourier_embedder_dim, boxes)  # B*N*4 -> B*N*C

        # learnable null embedding
        xyxy_null = self.null_position_feature.view(1, 1, -1)

        # replace padding with learnable null embedding
        xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null

        # positionet with text only information
        if positive_embeddings is not None:
            # learnable null embedding
            positive_null = self.null_positive_feature.view(1, 1, -1)

            # replace padding with learnable null embedding
            positive_embeddings = positive_embeddings * masks + (1 - masks) * positive_null

            objs = self.linears(torch.cat([positive_embeddings, xyxy_embedding], dim=-1))

        # positionet with text and image infomation
        else:
            phrases_masks = phrases_masks.unsqueeze(-1)
            image_masks = image_masks.unsqueeze(-1)

            # learnable null embedding
            text_null = self.null_text_feature.view(1, 1, -1)
            image_null = self.null_image_feature.view(1, 1, -1)

            # replace padding with learnable null embedding
            phrases_embeddings = phrases_embeddings * phrases_masks + (1 - phrases_masks) * text_null
            image_embeddings = image_embeddings * image_masks + (1 - image_masks) * image_null

            objs_text = self.linears_text(torch.cat([phrases_embeddings, xyxy_embedding], dim=-1))
            objs_image = self.linears_image(torch.cat([image_embeddings, xyxy_embedding], dim=-1))
            objs = torch.cat([objs_text, objs_image], dim=1)

        return objs


 class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
    """
    For PixArt-Alpha.

    Reference:
    https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
    """

    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False):
        super().__init__()

        self.outdim = size_emb_dim
        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)

        self.use_additional_conditions = use_additional_conditions
        if use_additional_conditions:
            self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
            self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
            self.aspect_ratio_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)

    def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
        timesteps_proj = self.time_proj(timestep)
        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)

        if self.use_additional_conditions:
            resolution_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype)
            resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1)
            aspect_ratio_emb = self.additional_condition_proj(aspect_ratio.flatten()).to(hidden_dtype)
            aspect_ratio_emb = self.aspect_ratio_embedder(aspect_ratio_emb).reshape(batch_size, -1)
            conditioning = timesteps_emb + torch.cat([resolution_emb, aspect_ratio_emb], dim=1)
        else:
            conditioning = timesteps_emb

        return conditioning


 class PixArtAlphaTextProjection(nn.Module):
    """
    Projects caption embeddings. Also handles dropout for classifier-free guidance.

    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
    """

    def __init__(self, in_features, hidden_size, num_tokens=120):
        super().__init__()
        self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True)
        self.act_1 = nn.GELU(approximate="tanh")
        self.linear_2 = nn.Linear(in_features=hidden_size, out_features=hidden_size, bias=True)

    def forward(self, caption):
        hidden_states = self.linear_1(caption)
        hidden_states = self.act_1(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states


 class IPAdapterPlusImageProjection(nn.Module):
    """Resampler of IP-Adapter Plus.

    Args:
    ----
        embed_dims (int): The feature dimension. Defaults to 768.
        output_dims (int): The number of output channels, that is the same
            number of the channels in the
            `unet.config.cross_attention_dim`. Defaults to 1024.
        hidden_dims (int): The number of hidden channels. Defaults to 1280.
        depth (int): The number of blocks. Defaults to 8.
        dim_head (int): The number of head channels. Defaults to 64.
        heads (int): Parallel attention heads. Defaults to 16.
        num_queries (int): The number of queries. Defaults to 8.
        ffn_ratio (float): The expansion ratio of feedforward network hidden
            layer channels. Defaults to 4.
    """

    def __init__(
        self,
        embed_dims: int = 768,
        output_dims: int = 1024,
        hidden_dims: int = 1280,
        depth: int = 4,
        dim_head: int = 64,
        heads: int = 16,
        num_queries: int = 8,
        ffn_ratio: float = 4,
    ) -> None:
        super().__init__()
        from .attention import FeedForward  # Lazy import to avoid circular import

        self.latents = nn.Parameter(torch.randn(1, num_queries, hidden_dims) / hidden_dims**0.5)

        self.proj_in = nn.Linear(embed_dims, hidden_dims)

        self.proj_out = nn.Linear(hidden_dims, output_dims)
        self.norm_out = nn.LayerNorm(output_dims)

        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                nn.ModuleList(
                    [
                        nn.LayerNorm(hidden_dims),
                        nn.LayerNorm(hidden_dims),
                        Attention(
                            query_dim=hidden_dims,
                            dim_head=dim_head,
                            heads=heads,
                            out_bias=False,
                        ),
                        nn.Sequential(
                            nn.LayerNorm(hidden_dims),
                            FeedForward(hidden_dims, hidden_dims, activation_fn="gelu", mult=ffn_ratio, bias=False),
                        ),
                    ]
                )
            )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass.

        Args:
        ----
            x (torch.Tensor): Input Tensor.

        Returns:
        -------
            torch.Tensor: Output Tensor.
        """
        latents = self.latents.repeat(x.size(0), 1, 1)

        x = self.proj_in(x)

        for ln0, ln1, attn, ff in self.layers:
            residual = latents

            encoder_hidden_states = ln0(x)
            latents = ln1(latents)
            encoder_hidden_states = torch.cat([encoder_hidden_states, latents], dim=-2)
            latents = attn(latents, encoder_hidden_states) + residual
            latents = ff(latents) + latents

        latents = self.proj_out(latents)
        return self.norm_out(latents)


 class MultiIPAdapterImageProjection(nn.Module):
    def __init__(self, IPAdapterImageProjectionLayers: Union[List[nn.Module], Tuple[nn.Module]]):
        super().__init__()
        self.image_projection_layers = nn.ModuleList(IPAdapterImageProjectionLayers)

    def forward(self, image_embeds: List[torch.FloatTensor]):
        projected_image_embeds = []

        # currently, we accept `image_embeds` as
        #  1. a tensor (deprecated) with shape [batch_size, embed_dim] or [batch_size, sequence_length, embed_dim]
        #  2. list of `n` tensors where `n` is number of ip-adapters, each tensor can hae shape [batch_size, num_images, embed_dim] or [batch_size, num_images, sequence_length, embed_dim]
        if not isinstance(image_embeds, list):
            deprecation_message = (
                "You have passed a tensor as `image_embeds`.This is deprecated and will be removed in a future release."
                " Please make sure to update your script to pass `image_embeds` as a list of tensors to supress this warning."
            )
            deprecate("image_embeds not a list", "1.0.0", deprecation_message, standard_warn=False)
            image_embeds = [image_embeds.unsqueeze(1)]

        if len(image_embeds) != len(self.image_projection_layers):
            raise ValueError(
                f"image_embeds must have the same length as image_projection_layers, got {len(image_embeds)} and {len(self.image_projection_layers)}"
            )

        for image_embed, image_projection_layer in zip(image_embeds, self.image_projection_layers):
            batch_size, num_images = image_embed.shape[0], image_embed.shape[1]
            image_embed = image_embed.reshape((batch_size * num_images,) + image_embed.shape[2:])
            image_embed = image_projection_layer(image_embed)
            image_embed = image_embed.reshape((batch_size, num_images) + image_embed.shape[1:])

            projected_image_embeds.append(image_embed)

        return projected_image_embeds

 from ..utils import deprecate
 from .transformers.t5_film_transformer import (
    DecoderLayer,
    NewGELUActivation,
    T5DenseGatedActDense,
    T5FilmDecoder,
    T5FiLMLayer,
    T5LayerCrossAttention,
    T5LayerFFCond,
    T5LayerNorm,
    T5LayerSelfAttentionCond,
 )


 class T5FilmDecoder(T5FilmDecoder):
    deprecation_message = "Importing `T5FilmDecoder` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5FilmDecoder`, instead."
    deprecate("T5FilmDecoder", "0.29", deprecation_message)


 class DecoderLayer(DecoderLayer):
    deprecation_message = "Importing `DecoderLayer` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import DecoderLayer`, instead."
    deprecate("DecoderLayer", "0.29", deprecation_message)


 class T5LayerSelfAttentionCond(T5LayerSelfAttentionCond):
    deprecation_message = "Importing `T5LayerSelfAttentionCond` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5LayerSelfAttentionCond`, instead."
    deprecate("T5LayerSelfAttentionCond", "0.29", deprecation_message)


 class T5LayerCrossAttention(T5LayerCrossAttention):
    deprecation_message = "Importing `T5LayerCrossAttention` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5LayerCrossAttention`, instead."
    deprecate("T5LayerCrossAttention", "0.29", deprecation_message)


 class T5LayerFFCond(T5LayerFFCond):
    deprecation_message = "Importing `T5LayerFFCond` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5LayerFFCond`, instead."
    deprecate("T5LayerFFCond", "0.29", deprecation_message)


 class T5DenseGatedActDense(T5DenseGatedActDense):
    deprecation_message = "Importing `T5DenseGatedActDense` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5DenseGatedActDense`, instead."
    deprecate("T5DenseGatedActDense", "0.29", deprecation_message)


 class T5LayerNorm(T5LayerNorm):
    deprecation_message = "Importing `T5LayerNorm` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5LayerNorm`, instead."
    deprecate("T5LayerNorm", "0.29", deprecation_message)


 class NewGELUActivation(NewGELUActivation):
    deprecation_message = "Importing `T5LayerNorm` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import NewGELUActivation`, instead."
    deprecate("NewGELUActivation", "0.29", deprecation_message)


 class T5FiLMLayer(T5FiLMLayer):
    deprecation_message = "Importing `T5FiLMLayer` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5FiLMLayer`, instead."
    deprecate("T5FiLMLayer", "0.29", deprecation_message)
 from ...utils import is_flax_available, is_torch_available


 if is_torch_available():
    from .unet_1d import UNet1DModel
    from .unet_2d import UNet2DModel
    from .unet_2d_condition import UNet2DConditionModel
    from .unet_3d_condition import UNet3DConditionModel
    from .unet_i2vgen_xl import I2VGenXLUNet
    from .unet_kandinsky3 import Kandinsky3UNet
    from .unet_motion_model import MotionAdapter, UNetMotionModel
    from .unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel
    from .unet_stable_cascade import StableCascadeUNet
    from .uvit_2d import UVit2DModel


 if is_flax_available():
    from .unet_2d_condition_flax import FlaxUNet2DConditionModel


 from typing import Any, Dict, Optional, Tuple, Union

 import torch
 from torch import nn

 from ...utils import is_torch_version
 from ...utils.torch_utils import apply_freeu
 from ..attention import Attention
 from ..resnet import (
    Downsample2D,
    ResnetBlock2D,
    SpatioTemporalResBlock,
    TemporalConvLayer,
    Upsample2D,
 )
 from ..transformers.dual_transformer_2d import DualTransformer2DModel
 from ..transformers.transformer_2d import Transformer2DModel
 from ..transformers.transformer_temporal import (
    TransformerSpatioTemporalModel,
    TransformerTemporalModel,
 )


 def get_down_block(
    down_block_type: str,
    num_layers: int,
    in_channels: int,
    out_channels: int,
    temb_channels: int,
    add_downsample: bool,
    resnet_eps: float,
    resnet_act_fn: str,
    num_attention_heads: int,
    resnet_groups: Optional[int] = None,
    cross_attention_dim: Optional[int] = None,
    downsample_padding: Optional[int] = None,
    dual_cross_attention: bool = False,
    use_linear_projection: bool = True,
    only_cross_attention: bool = False,
    upcast_attention: bool = False,
    resnet_time_scale_shift: str = "default",
    temporal_num_attention_heads: int = 8,
    temporal_max_seq_length: int = 32,
    transformer_layers_per_block: int = 1,
 ) -> Union[
    "DownBlock3D",
    "CrossAttnDownBlock3D",
    "DownBlockMotion",
    "CrossAttnDownBlockMotion",
    "DownBlockSpatioTemporal",
    "CrossAttnDownBlockSpatioTemporal",
 ]:
    if down_block_type == "DownBlock3D":
        return DownBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            downsample_padding=downsample_padding,
            resnet_time_scale_shift=resnet_time_scale_shift,
        )
    elif down_block_type == "CrossAttnDownBlock3D":
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
        return CrossAttnDownBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            downsample_padding=downsample_padding,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            only_cross_attention=only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
        )
    if down_block_type == "DownBlockMotion":
        return DownBlockMotion(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            downsample_padding=downsample_padding,
            resnet_time_scale_shift=resnet_time_scale_shift,
            temporal_num_attention_heads=temporal_num_attention_heads,
            temporal_max_seq_length=temporal_max_seq_length,
        )
    elif down_block_type == "CrossAttnDownBlockMotion":
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockMotion")
        return CrossAttnDownBlockMotion(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            downsample_padding=downsample_padding,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            only_cross_attention=only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            temporal_num_attention_heads=temporal_num_attention_heads,
            temporal_max_seq_length=temporal_max_seq_length,
        )
    elif down_block_type == "DownBlockSpatioTemporal":
        # added for SDV
        return DownBlockSpatioTemporal(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
        )
    elif down_block_type == "CrossAttnDownBlockSpatioTemporal":
        # added for SDV
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockSpatioTemporal")
        return CrossAttnDownBlockSpatioTemporal(
            in_channels=in_channels,
            out_channels=out_channels,
            temb_channels=temb_channels,
            num_layers=num_layers,
            transformer_layers_per_block=transformer_layers_per_block,
            add_downsample=add_downsample,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
        )

    raise ValueError(f"{down_block_type} does not exist.")


 def get_up_block(
    up_block_type: str,
    num_layers: int,
    in_channels: int,
    out_channels: int,
    prev_output_channel: int,
    temb_channels: int,
    add_upsample: bool,
    resnet_eps: float,
    resnet_act_fn: str,
    num_attention_heads: int,
    resolution_idx: Optional[int] = None,
    resnet_groups: Optional[int] = None,
    cross_attention_dim: Optional[int] = None,
    dual_cross_attention: bool = False,
    use_linear_projection: bool = True,
    only_cross_attention: bool = False,
    upcast_attention: bool = False,
    resnet_time_scale_shift: str = "default",
    temporal_num_attention_heads: int = 8,
    temporal_cross_attention_dim: Optional[int] = None,
    temporal_max_seq_length: int = 32,
    transformer_layers_per_block: int = 1,
    dropout: float = 0.0,
 ) -> Union[
    "UpBlock3D",
    "CrossAttnUpBlock3D",
    "UpBlockMotion",
    "CrossAttnUpBlockMotion",
    "UpBlockSpatioTemporal",
    "CrossAttnUpBlockSpatioTemporal",
 ]:
    if up_block_type == "UpBlock3D":
        return UpBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            resnet_time_scale_shift=resnet_time_scale_shift,
            resolution_idx=resolution_idx,
        )
    elif up_block_type == "CrossAttnUpBlock3D":
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
        return CrossAttnUpBlock3D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            only_cross_attention=only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            resolution_idx=resolution_idx,
        )
    if up_block_type == "UpBlockMotion":
        return UpBlockMotion(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            resnet_time_scale_shift=resnet_time_scale_shift,
            resolution_idx=resolution_idx,
            temporal_num_attention_heads=temporal_num_attention_heads,
            temporal_max_seq_length=temporal_max_seq_length,
        )
    elif up_block_type == "CrossAttnUpBlockMotion":
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockMotion")
        return CrossAttnUpBlockMotion(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            add_upsample=add_upsample,
            resnet_eps=resnet_eps,
            resnet_act_fn=resnet_act_fn,
            resnet_groups=resnet_groups,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            only_cross_attention=only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            resolution_idx=resolution_idx,
            temporal_num_attention_heads=temporal_num_attention_heads,
            temporal_max_seq_length=temporal_max_seq_length,
        )
    elif up_block_type == "UpBlockSpatioTemporal":
        # added for SDV
        return UpBlockSpatioTemporal(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            resolution_idx=resolution_idx,
            add_upsample=add_upsample,
        )
    elif up_block_type == "CrossAttnUpBlockSpatioTemporal":
        # added for SDV
        if cross_attention_dim is None:
            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockSpatioTemporal")
        return CrossAttnUpBlockSpatioTemporal(
            in_channels=in_channels,
            out_channels=out_channels,
            prev_output_channel=prev_output_channel,
            temb_channels=temb_channels,
            num_layers=num_layers,
            transformer_layers_per_block=transformer_layers_per_block,
            add_upsample=add_upsample,
            cross_attention_dim=cross_attention_dim,
            num_attention_heads=num_attention_heads,
            resolution_idx=resolution_idx,
        )

    raise ValueError(f"{up_block_type} does not exist.")


 class UNetMidBlock3DCrossAttn(nn.Module):
    def __init__(
        self,
        in_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        num_attention_heads: int = 1,
        output_scale_factor: float = 1.0,
        cross_attention_dim: int = 1280,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = True,
        upcast_attention: bool = False,
    ):
        super().__init__()

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads
        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)

        # there is always at least one resnet
        resnets = [
            ResnetBlock2D(
                in_channels=in_channels,
                out_channels=in_channels,
                temb_channels=temb_channels,
                eps=resnet_eps,
                groups=resnet_groups,
                dropout=dropout,
                time_embedding_norm=resnet_time_scale_shift,
                non_linearity=resnet_act_fn,
                output_scale_factor=output_scale_factor,
                pre_norm=resnet_pre_norm,
            )
        ]
        temp_convs = [
            TemporalConvLayer(
                in_channels,
                in_channels,
                dropout=0.1,
                norm_num_groups=resnet_groups,
            )
        ]
        attentions = []
        temp_attentions = []

        for _ in range(num_layers):
            attentions.append(
                Transformer2DModel(
                    in_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=in_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                    use_linear_projection=use_linear_projection,
                    upcast_attention=upcast_attention,
                )
            )
            temp_attentions.append(
                TransformerTemporalModel(
                    in_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=in_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                )
            )
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=in_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )
            temp_convs.append(
                TemporalConvLayer(
                    in_channels,
                    in_channels,
                    dropout=0.1,
                    norm_num_groups=resnet_groups,
                )
            )

        self.resnets = nn.ModuleList(resnets)
        self.temp_convs = nn.ModuleList(temp_convs)
        self.attentions = nn.ModuleList(attentions)
        self.temp_attentions = nn.ModuleList(temp_attentions)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        num_frames: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
    ) -> torch.FloatTensor:
        hidden_states = self.resnets[0](hidden_states, temb)
        hidden_states = self.temp_convs[0](hidden_states, num_frames=num_frames)
        for attn, temp_attn, resnet, temp_conv in zip(
            self.attentions, self.temp_attentions, self.resnets[1:], self.temp_convs[1:]
        ):
            hidden_states = attn(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]
            hidden_states = temp_attn(
                hidden_states,
                num_frames=num_frames,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]
            hidden_states = resnet(hidden_states, temb)
            hidden_states = temp_conv(hidden_states, num_frames=num_frames)

        return hidden_states


 class CrossAttnDownBlock3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        output_scale_factor: float = 1.0,
        downsample_padding: int = 1,
        add_downsample: bool = True,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        upcast_attention: bool = False,
    ):
        super().__init__()
        resnets = []
        attentions = []
        temp_attentions = []
        temp_convs = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )
            temp_convs.append(
                TemporalConvLayer(
                    out_channels,
                    out_channels,
                    dropout=0.1,
                    norm_num_groups=resnet_groups,
                )
            )
            attentions.append(
                Transformer2DModel(
                    out_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=out_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                    use_linear_projection=use_linear_projection,
                    only_cross_attention=only_cross_attention,
                    upcast_attention=upcast_attention,
                )
            )
            temp_attentions.append(
                TransformerTemporalModel(
                    out_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=out_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                )
            )
        self.resnets = nn.ModuleList(resnets)
        self.temp_convs = nn.ModuleList(temp_convs)
        self.attentions = nn.ModuleList(attentions)
        self.temp_attentions = nn.ModuleList(temp_attentions)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        padding=downsample_padding,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        num_frames: int = 1,
        cross_attention_kwargs: Dict[str, Any] = None,
    ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
        # TODO(Patrick, William) - attention mask is not used
        output_states = ()

        for resnet, temp_conv, attn, temp_attn in zip(
            self.resnets, self.temp_convs, self.attentions, self.temp_attentions
        ):
            hidden_states = resnet(hidden_states, temb)
            hidden_states = temp_conv(hidden_states, num_frames=num_frames)
            hidden_states = attn(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]
            hidden_states = temp_attn(
                hidden_states,
                num_frames=num_frames,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]

            output_states += (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states)

            output_states += (hidden_states,)

        return hidden_states, output_states


 class DownBlock3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        output_scale_factor: float = 1.0,
        add_downsample: bool = True,
        downsample_padding: int = 1,
    ):
        super().__init__()
        resnets = []
        temp_convs = []

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )
            temp_convs.append(
                TemporalConvLayer(
                    out_channels,
                    out_channels,
                    dropout=0.1,
                    norm_num_groups=resnet_groups,
                )
            )

        self.resnets = nn.ModuleList(resnets)
        self.temp_convs = nn.ModuleList(temp_convs)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        padding=downsample_padding,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        num_frames: int = 1,
    ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
        output_states = ()

        for resnet, temp_conv in zip(self.resnets, self.temp_convs):
            hidden_states = resnet(hidden_states, temb)
            hidden_states = temp_conv(hidden_states, num_frames=num_frames)

            output_states += (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states)

            output_states += (hidden_states,)

        return hidden_states, output_states


 class CrossAttnUpBlock3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        prev_output_channel: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        output_scale_factor: float = 1.0,
        add_upsample: bool = True,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        upcast_attention: bool = False,
        resolution_idx: Optional[int] = None,
    ):
        super().__init__()
        resnets = []
        temp_convs = []
        attentions = []
        temp_attentions = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                ResnetBlock2D(
                    in_channels=resnet_in_channels + res_skip_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )
            temp_convs.append(
                TemporalConvLayer(
                    out_channels,
                    out_channels,
                    dropout=0.1,
                    norm_num_groups=resnet_groups,
                )
            )
            attentions.append(
                Transformer2DModel(
                    out_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=out_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                    use_linear_projection=use_linear_projection,
                    only_cross_attention=only_cross_attention,
                    upcast_attention=upcast_attention,
                )
            )
            temp_attentions.append(
                TransformerTemporalModel(
                    out_channels // num_attention_heads,
                    num_attention_heads,
                    in_channels=out_channels,
                    num_layers=1,
                    cross_attention_dim=cross_attention_dim,
                    norm_num_groups=resnet_groups,
                )
            )
        self.resnets = nn.ModuleList(resnets)
        self.temp_convs = nn.ModuleList(temp_convs)
        self.attentions = nn.ModuleList(attentions)
        self.temp_attentions = nn.ModuleList(temp_attentions)

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        upsample_size: Optional[int] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        num_frames: int = 1,
        cross_attention_kwargs: Dict[str, Any] = None,
    ) -> torch.FloatTensor:
        is_freeu_enabled = (
            getattr(self, "s1", None)
            and getattr(self, "s2", None)
            and getattr(self, "b1", None)
            and getattr(self, "b2", None)
        )

        # TODO(Patrick, William) - attention mask is not used
        for resnet, temp_conv, attn, temp_attn in zip(
            self.resnets, self.temp_convs, self.attentions, self.temp_attentions
        ):
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            # FreeU: Only operate on the first two stages
            if is_freeu_enabled:
                hidden_states, res_hidden_states = apply_freeu(
                    self.resolution_idx,
                    hidden_states,
                    res_hidden_states,
                    s1=self.s1,
                    s2=self.s2,
                    b1=self.b1,
                    b2=self.b2,
                )

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            hidden_states = resnet(hidden_states, temb)
            hidden_states = temp_conv(hidden_states, num_frames=num_frames)
            hidden_states = attn(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]
            hidden_states = temp_attn(
                hidden_states,
                num_frames=num_frames,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size)

        return hidden_states


 class UpBlock3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        prev_output_channel: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        output_scale_factor: float = 1.0,
        add_upsample: bool = True,
        resolution_idx: Optional[int] = None,
    ):
        super().__init__()
        resnets = []
        temp_convs = []

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                ResnetBlock2D(
                    in_channels=resnet_in_channels + res_skip_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )
            temp_convs.append(
                TemporalConvLayer(
                    out_channels,
                    out_channels,
                    dropout=0.1,
                    norm_num_groups=resnet_groups,
                )
            )

        self.resnets = nn.ModuleList(resnets)
        self.temp_convs = nn.ModuleList(temp_convs)

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
        upsample_size: Optional[int] = None,
        num_frames: int = 1,
    ) -> torch.FloatTensor:
        is_freeu_enabled = (
            getattr(self, "s1", None)
            and getattr(self, "s2", None)
            and getattr(self, "b1", None)
            and getattr(self, "b2", None)
        )
        for resnet, temp_conv in zip(self.resnets, self.temp_convs):
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            # FreeU: Only operate on the first two stages
            if is_freeu_enabled:
                hidden_states, res_hidden_states = apply_freeu(
                    self.resolution_idx,
                    hidden_states,
                    res_hidden_states,
                    s1=self.s1,
                    s2=self.s2,
                    b1=self.b1,
                    b2=self.b2,
                )

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            hidden_states = resnet(hidden_states, temb)
            hidden_states = temp_conv(hidden_states, num_frames=num_frames)

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size)

        return hidden_states


 class DownBlockMotion(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        output_scale_factor: float = 1.0,
        add_downsample: bool = True,
        downsample_padding: int = 1,
        temporal_num_attention_heads: int = 1,
        temporal_cross_attention_dim: Optional[int] = None,
        temporal_max_seq_length: int = 32,
    ):
        super().__init__()
        resnets = []
        motion_modules = []

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )
            motion_modules.append(
                TransformerTemporalModel(
                    num_attention_heads=temporal_num_attention_heads,
                    in_channels=out_channels,
                    norm_num_groups=resnet_groups,
                    cross_attention_dim=temporal_cross_attention_dim,
                    attention_bias=False,
                    activation_fn="geglu",
                    positional_embeddings="sinusoidal",
                    num_positional_embeddings=temporal_max_seq_length,
                    attention_head_dim=out_channels // temporal_num_attention_heads,
                )
            )

        self.resnets = nn.ModuleList(resnets)
        self.motion_modules = nn.ModuleList(motion_modules)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        padding=downsample_padding,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
        num_frames: int = 1,
    ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
        output_states = ()

        blocks = zip(self.resnets, self.motion_modules)
        for resnet, motion_module in blocks:
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                if is_torch_version(">=", "1.11.0"):
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet),
                        hidden_states,
                        temb,
                        use_reentrant=False,
                    )
                else:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet), hidden_states, temb, scale
                    )

            else:
                hidden_states = resnet(hidden_states, temb, scale=scale)
            hidden_states = motion_module(hidden_states, num_frames=num_frames)[0]

            output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states, scale=scale)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states


 class CrossAttnDownBlockMotion(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        transformer_layers_per_block: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        output_scale_factor: float = 1.0,
        downsample_padding: int = 1,
        add_downsample: bool = True,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        upcast_attention: bool = False,
        attention_type: str = "default",
        temporal_cross_attention_dim: Optional[int] = None,
        temporal_num_attention_heads: int = 8,
        temporal_max_seq_length: int = 32,
    ):
        super().__init__()
        resnets = []
        attentions = []
        motion_modules = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )

            if not dual_cross_attention:
                attentions.append(
                    Transformer2DModel(
                        num_attention_heads,
                        out_channels // num_attention_heads,
                        in_channels=out_channels,
                        num_layers=transformer_layers_per_block,
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                        use_linear_projection=use_linear_projection,
                        only_cross_attention=only_cross_attention,
                        upcast_attention=upcast_attention,
                        attention_type=attention_type,
                    )
                )
            else:
                attentions.append(
                    DualTransformer2DModel(
                        num_attention_heads,
                        out_channels // num_attention_heads,
                        in_channels=out_channels,
                        num_layers=1,
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                    )
                )

            motion_modules.append(
                TransformerTemporalModel(
                    num_attention_heads=temporal_num_attention_heads,
                    in_channels=out_channels,
                    norm_num_groups=resnet_groups,
                    cross_attention_dim=temporal_cross_attention_dim,
                    attention_bias=False,
                    activation_fn="geglu",
                    positional_embeddings="sinusoidal",
                    num_positional_embeddings=temporal_max_seq_length,
                    attention_head_dim=out_channels // temporal_num_attention_heads,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)
        self.motion_modules = nn.ModuleList(motion_modules)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        padding=downsample_padding,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        num_frames: int = 1,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        additional_residuals: Optional[torch.FloatTensor] = None,
    ):
        output_states = ()

        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0

        blocks = list(zip(self.resnets, self.attentions, self.motion_modules))
        for i, (resnet, attn, motion_module) in enumerate(blocks):
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet),
                    hidden_states,
                    temb,
                    **ckpt_kwargs,
                )
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )[0]
            else:
                hidden_states = resnet(hidden_states, temb, scale=lora_scale)
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )[0]
            hidden_states = motion_module(
                hidden_states,
                num_frames=num_frames,
            )[0]

            # apply additional residuals to the output of the last pair of resnet and attention blocks
            if i == len(blocks) - 1 and additional_residuals is not None:
                hidden_states = hidden_states + additional_residuals

            output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states, scale=lora_scale)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states


 class CrossAttnUpBlockMotion(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        prev_output_channel: int,
        temb_channels: int,
        resolution_idx: Optional[int] = None,
        dropout: float = 0.0,
        num_layers: int = 1,
        transformer_layers_per_block: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        output_scale_factor: float = 1.0,
        add_upsample: bool = True,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        upcast_attention: bool = False,
        attention_type: str = "default",
        temporal_cross_attention_dim: Optional[int] = None,
        temporal_num_attention_heads: int = 8,
        temporal_max_seq_length: int = 32,
    ):
        super().__init__()
        resnets = []
        attentions = []
        motion_modules = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                ResnetBlock2D(
                    in_channels=resnet_in_channels + res_skip_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )

            if not dual_cross_attention:
                attentions.append(
                    Transformer2DModel(
                        num_attention_heads,
                        out_channels // num_attention_heads,
                        in_channels=out_channels,
                        num_layers=transformer_layers_per_block,
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                        use_linear_projection=use_linear_projection,
                        only_cross_attention=only_cross_attention,
                        upcast_attention=upcast_attention,
                        attention_type=attention_type,
                    )
                )
            else:
                attentions.append(
                    DualTransformer2DModel(
                        num_attention_heads,
                        out_channels // num_attention_heads,
                        in_channels=out_channels,
                        num_layers=1,
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                    )
                )
            motion_modules.append(
                TransformerTemporalModel(
                    num_attention_heads=temporal_num_attention_heads,
                    in_channels=out_channels,
                    norm_num_groups=resnet_groups,
                    cross_attention_dim=temporal_cross_attention_dim,
                    attention_bias=False,
                    activation_fn="geglu",
                    positional_embeddings="sinusoidal",
                    num_positional_embeddings=temporal_max_seq_length,
                    attention_head_dim=out_channels // temporal_num_attention_heads,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)
        self.motion_modules = nn.ModuleList(motion_modules)

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        upsample_size: Optional[int] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        num_frames: int = 1,
    ) -> torch.FloatTensor:
        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
        is_freeu_enabled = (
            getattr(self, "s1", None)
            and getattr(self, "s2", None)
            and getattr(self, "b1", None)
            and getattr(self, "b2", None)
        )

        blocks = zip(self.resnets, self.attentions, self.motion_modules)
        for resnet, attn, motion_module in blocks:
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            # FreeU: Only operate on the first two stages
            if is_freeu_enabled:
                hidden_states, res_hidden_states = apply_freeu(
                    self.resolution_idx,
                    hidden_states,
                    res_hidden_states,
                    s1=self.s1,
                    s2=self.s2,
                    b1=self.b1,
                    b2=self.b2,
                )

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet),
                    hidden_states,
                    temb,
                    **ckpt_kwargs,
                )
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )[0]
            else:
                hidden_states = resnet(hidden_states, temb, scale=lora_scale)
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )[0]
            hidden_states = motion_module(
                hidden_states,
                num_frames=num_frames,
            )[0]

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size, scale=lora_scale)

        return hidden_states


 class UpBlockMotion(nn.Module):
    def __init__(
        self,
        in_channels: int,
        prev_output_channel: int,
        out_channels: int,
        temb_channels: int,
        resolution_idx: Optional[int] = None,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        output_scale_factor: float = 1.0,
        add_upsample: bool = True,
        temporal_norm_num_groups: int = 32,
        temporal_cross_attention_dim: Optional[int] = None,
        temporal_num_attention_heads: int = 8,
        temporal_max_seq_length: int = 32,
    ):
        super().__init__()
        resnets = []
        motion_modules = []

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                ResnetBlock2D(
                    in_channels=resnet_in_channels + res_skip_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )

            motion_modules.append(
                TransformerTemporalModel(
                    num_attention_heads=temporal_num_attention_heads,
                    in_channels=out_channels,
                    norm_num_groups=temporal_norm_num_groups,
                    cross_attention_dim=temporal_cross_attention_dim,
                    attention_bias=False,
                    activation_fn="geglu",
                    positional_embeddings="sinusoidal",
                    num_positional_embeddings=temporal_max_seq_length,
                    attention_head_dim=out_channels // temporal_num_attention_heads,
                )
            )

        self.resnets = nn.ModuleList(resnets)
        self.motion_modules = nn.ModuleList(motion_modules)

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
        upsample_size=None,
        scale: float = 1.0,
        num_frames: int = 1,
    ) -> torch.FloatTensor:
        is_freeu_enabled = (
            getattr(self, "s1", None)
            and getattr(self, "s2", None)
            and getattr(self, "b1", None)
            and getattr(self, "b2", None)
        )

        blocks = zip(self.resnets, self.motion_modules)

        for resnet, motion_module in blocks:
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            # FreeU: Only operate on the first two stages
            if is_freeu_enabled:
                hidden_states, res_hidden_states = apply_freeu(
                    self.resolution_idx,
                    hidden_states,
                    res_hidden_states,
                    s1=self.s1,
                    s2=self.s2,
                    b1=self.b1,
                    b2=self.b2,
                )

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                if is_torch_version(">=", "1.11.0"):
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet),
                        hidden_states,
                        temb,
                        use_reentrant=False,
                    )
                else:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet), hidden_states, temb
                    )

            else:
                hidden_states = resnet(hidden_states, temb, scale=scale)
            hidden_states = motion_module(hidden_states, num_frames=num_frames)[0]

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size, scale=scale)

        return hidden_states


 class UNetMidBlockCrossAttnMotion(nn.Module):
    def __init__(
        self,
        in_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        transformer_layers_per_block: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        num_attention_heads: int = 1,
        output_scale_factor: float = 1.0,
        cross_attention_dim: int = 1280,
        dual_cross_attention: float = False,
        use_linear_projection: float = False,
        upcast_attention: float = False,
        attention_type: str = "default",
        temporal_num_attention_heads: int = 1,
        temporal_cross_attention_dim: Optional[int] = None,
        temporal_max_seq_length: int = 32,
    ):
        super().__init__()

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads
        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)

        # there is always at least one resnet
        resnets = [
            ResnetBlock2D(
                in_channels=in_channels,
                out_channels=in_channels,
                temb_channels=temb_channels,
                eps=resnet_eps,
                groups=resnet_groups,
                dropout=dropout,
                time_embedding_norm=resnet_time_scale_shift,
                non_linearity=resnet_act_fn,
                output_scale_factor=output_scale_factor,
                pre_norm=resnet_pre_norm,
            )
        ]
        attentions = []
        motion_modules = []

        for _ in range(num_layers):
            if not dual_cross_attention:
                attentions.append(
                    Transformer2DModel(
                        num_attention_heads,
                        in_channels // num_attention_heads,
                        in_channels=in_channels,
                        num_layers=transformer_layers_per_block,
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                        use_linear_projection=use_linear_projection,
                        upcast_attention=upcast_attention,
                        attention_type=attention_type,
                    )
                )
            else:
                attentions.append(
                    DualTransformer2DModel(
                        num_attention_heads,
                        in_channels // num_attention_heads,
                        in_channels=in_channels,
                        num_layers=1,
                        cross_attention_dim=cross_attention_dim,
                        norm_num_groups=resnet_groups,
                    )
                )
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=in_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )
            motion_modules.append(
                TransformerTemporalModel(
                    num_attention_heads=temporal_num_attention_heads,
                    attention_head_dim=in_channels // temporal_num_attention_heads,
                    in_channels=in_channels,
                    norm_num_groups=resnet_groups,
                    cross_attention_dim=temporal_cross_attention_dim,
                    attention_bias=False,
                    positional_embeddings="sinusoidal",
                    num_positional_embeddings=temporal_max_seq_length,
                    activation_fn="geglu",
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)
        self.motion_modules = nn.ModuleList(motion_modules)

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        num_frames: int = 1,
    ) -> torch.FloatTensor:
        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
        hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale)

        blocks = zip(self.attentions, self.resnets[1:], self.motion_modules)
        for attn, resnet, motion_module in blocks:
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )[0]
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(motion_module),
                    hidden_states,
                    temb,
                    **ckpt_kwargs,
                )
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet),
                    hidden_states,
                    temb,
                    **ckpt_kwargs,
                )
            else:
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                    return_dict=False,
                )[0]
                hidden_states = motion_module(
                    hidden_states,
                    num_frames=num_frames,
                )[0]
                hidden_states = resnet(hidden_states, temb, scale=lora_scale)

        return hidden_states


 class MidBlockTemporalDecoder(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        attention_head_dim: int = 512,
        num_layers: int = 1,
        upcast_attention: bool = False,
    ):
        super().__init__()

        resnets = []
        attentions = []
        for i in range(num_layers):
            input_channels = in_channels if i == 0 else out_channels
            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=input_channels,
                    out_channels=out_channels,
                    temb_channels=None,
                    eps=1e-6,
                    temporal_eps=1e-5,
                    merge_factor=0.0,
                    merge_strategy="learned",
                    switch_spatial_to_temporal_mix=True,
                )
            )

        attentions.append(
            Attention(
                query_dim=in_channels,
                heads=in_channels // attention_head_dim,
                dim_head=attention_head_dim,
                eps=1e-6,
                upcast_attention=upcast_attention,
                norm_num_groups=32,
                bias=True,
                residual_connection=True,
            )
        )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        image_only_indicator: torch.FloatTensor,
    ):
        hidden_states = self.resnets[0](
            hidden_states,
            image_only_indicator=image_only_indicator,
        )
        for resnet, attn in zip(self.resnets[1:], self.attentions):
            hidden_states = attn(hidden_states)
            hidden_states = resnet(
                hidden_states,
                image_only_indicator=image_only_indicator,
            )

        return hidden_states


 class UpBlockTemporalDecoder(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        num_layers: int = 1,
        add_upsample: bool = True,
    ):
        super().__init__()
        resnets = []
        for i in range(num_layers):
            input_channels = in_channels if i == 0 else out_channels

            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=input_channels,
                    out_channels=out_channels,
                    temb_channels=None,
                    eps=1e-6,
                    temporal_eps=1e-5,
                    merge_factor=0.0,
                    merge_strategy="learned",
                    switch_spatial_to_temporal_mix=True,
                )
            )
        self.resnets = nn.ModuleList(resnets)

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        image_only_indicator: torch.FloatTensor,
    ) -> torch.FloatTensor:
        for resnet in self.resnets:
            hidden_states = resnet(
                hidden_states,
                image_only_indicator=image_only_indicator,
            )

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states)

        return hidden_states


 class UNetMidBlockSpatioTemporal(nn.Module):
    def __init__(
        self,
        in_channels: int,
        temb_channels: int,
        num_layers: int = 1,
        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
    ):
        super().__init__()

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        # support for variable transformer layers per block
        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * num_layers

        # there is always at least one resnet
        resnets = [
            SpatioTemporalResBlock(
                in_channels=in_channels,
                out_channels=in_channels,
                temb_channels=temb_channels,
                eps=1e-5,
            )
        ]
        attentions = []

        for i in range(num_layers):
            attentions.append(
                TransformerSpatioTemporalModel(
                    num_attention_heads,
                    in_channels // num_attention_heads,
                    in_channels=in_channels,
                    num_layers=transformer_layers_per_block[i],
                    cross_attention_dim=cross_attention_dim,
                )
            )

            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=in_channels,
                    out_channels=in_channels,
                    temb_channels=temb_channels,
                    eps=1e-5,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        image_only_indicator: Optional[torch.Tensor] = None,
    ) -> torch.FloatTensor:
        hidden_states = self.resnets[0](
            hidden_states,
            temb,
            image_only_indicator=image_only_indicator,
        )

        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            if self.training and self.gradient_checkpointing:  # TODO

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet),
                    hidden_states,
                    temb,
                    image_only_indicator,
                    **ckpt_kwargs,
                )
            else:
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]
                hidden_states = resnet(
                    hidden_states,
                    temb,
                    image_only_indicator=image_only_indicator,
                )

        return hidden_states


 class DownBlockSpatioTemporal(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        num_layers: int = 1,
        add_downsample: bool = True,
    ):
        super().__init__()
        resnets = []

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=1e-5,
                )
            )

        self.resnets = nn.ModuleList(resnets)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        image_only_indicator: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
        output_states = ()
        for resnet in self.resnets:
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                if is_torch_version(">=", "1.11.0"):
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet),
                        hidden_states,
                        temb,
                        image_only_indicator,
                        use_reentrant=False,
                    )
                else:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet),
                        hidden_states,
                        temb,
                        image_only_indicator,
                    )
            else:
                hidden_states = resnet(
                    hidden_states,
                    temb,
                    image_only_indicator=image_only_indicator,
                )

            output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states


 class CrossAttnDownBlockSpatioTemporal(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        num_layers: int = 1,
        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        add_downsample: bool = True,
    ):
        super().__init__()
        resnets = []
        attentions = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads
        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * num_layers

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=1e-6,
                )
            )
            attentions.append(
                TransformerSpatioTemporalModel(
                    num_attention_heads,
                    out_channels // num_attention_heads,
                    in_channels=out_channels,
                    num_layers=transformer_layers_per_block[i],
                    cross_attention_dim=cross_attention_dim,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels,
                        use_conv=True,
                        out_channels=out_channels,
                        padding=1,
                        name="op",
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        image_only_indicator: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
        output_states = ()

        blocks = list(zip(self.resnets, self.attentions))
        for resnet, attn in blocks:
            if self.training and self.gradient_checkpointing:  # TODO

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet),
                    hidden_states,
                    temb,
                    image_only_indicator,
                    **ckpt_kwargs,
                )

                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]
            else:
                hidden_states = resnet(
                    hidden_states,
                    temb,
                    image_only_indicator=image_only_indicator,
                )
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]

            output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states


 class UpBlockSpatioTemporal(nn.Module):
    def __init__(
        self,
        in_channels: int,
        prev_output_channel: int,
        out_channels: int,
        temb_channels: int,
        resolution_idx: Optional[int] = None,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        add_upsample: bool = True,
    ):
        super().__init__()
        resnets = []

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=resnet_in_channels + res_skip_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                )
            )

        self.resnets = nn.ModuleList(resnets)

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
        image_only_indicator: Optional[torch.Tensor] = None,
    ) -> torch.FloatTensor:
        for resnet in self.resnets:
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                if is_torch_version(">=", "1.11.0"):
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet),
                        hidden_states,
                        temb,
                        image_only_indicator,
                        use_reentrant=False,
                    )
                else:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet),
                        hidden_states,
                        temb,
                        image_only_indicator,
                    )
            else:
                hidden_states = resnet(
                    hidden_states,
                    temb,
                    image_only_indicator=image_only_indicator,
                )

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states)

        return hidden_states


 class CrossAttnUpBlockSpatioTemporal(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        prev_output_channel: int,
        temb_channels: int,
        resolution_idx: Optional[int] = None,
        num_layers: int = 1,
        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
        resnet_eps: float = 1e-6,
        num_attention_heads: int = 1,
        cross_attention_dim: int = 1280,
        add_upsample: bool = True,
    ):
        super().__init__()
        resnets = []
        attentions = []

        self.has_cross_attention = True
        self.num_attention_heads = num_attention_heads

        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * num_layers

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                SpatioTemporalResBlock(
                    in_channels=resnet_in_channels + res_skip_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                )
            )
            attentions.append(
                TransformerSpatioTemporalModel(
                    num_attention_heads,
                    out_channels // num_attention_heads,
                    in_channels=out_channels,
                    num_layers=transformer_layers_per_block[i],
                    cross_attention_dim=cross_attention_dim,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        if add_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False
        self.resolution_idx = resolution_idx

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        image_only_indicator: Optional[torch.Tensor] = None,
    ) -> torch.FloatTensor:
        for resnet, attn in zip(self.resnets, self.attentions):
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]

            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

            if self.training and self.gradient_checkpointing:  # TODO

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(resnet),
                    hidden_states,
                    temb,
                    image_only_indicator,
                    **ckpt_kwargs,
                )
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]
            else:
                hidden_states = resnet(
                    hidden_states,
                    temb,
                    image_only_indicator=image_only_indicator,
                )
                hidden_states = attn(
                    hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                    return_dict=False,
                )[0]

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states)

        return hidden_states

 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Tuple, Union

 import torch
 import torch.nn as nn
 import torch.utils.checkpoint

 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin
 from ...utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
 from ..activations import get_activation
 from ..attention_processor import (
    ADDED_KV_ATTENTION_PROCESSORS,
    CROSS_ATTENTION_PROCESSORS,
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor,
 )
 from ..embeddings import (
    GaussianFourierProjection,
    GLIGENTextBoundingboxProjection,
    ImageHintTimeEmbedding,
    ImageProjection,
    ImageTimeEmbedding,
    TextImageProjection,
    TextImageTimeEmbedding,
    TextTimeEmbedding,
    TimestepEmbedding,
    Timesteps,
 )
 from ..modeling_utils import ModelMixin
 from .unet_2d_blocks import (
    get_down_block,
    get_mid_block,
    get_up_block,
 )


 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


 @dataclass
 class UNet2DConditionOutput(BaseOutput):
    """
    The output of [`UNet2DConditionModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
    """

    sample: torch.FloatTensor = None


 class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
    r"""
    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
    shaped output.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).

    Parameters:
        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
            Height and width of input/output sample.
        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
            Whether to flip the sin to cos in the time embedding.
        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
            The tuple of downsample blocks to use.
        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
            Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
            The tuple of upsample blocks to use.
        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
            Whether to include self-attention in the basic transformer blocks, see
            [`~models.attention.BasicTransformerBlock`].
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each block.
        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
            If `None`, normalization and activation layers is skipped in post-processing.
        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
            The dimension of the cross attention features.
        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
       reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
            blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
        encoder_hid_dim (`int`, *optional*, defaults to None):
            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
            dimension to `cross_attention_dim`.
        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
        num_attention_heads (`int`, *optional*):
            The number of attention heads. If not defined, defaults to `attention_head_dim`
        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
        class_embed_type (`str`, *optional*, defaults to `None`):
            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
        addition_embed_type (`str`, *optional*, defaults to `None`):
            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
            "text". "text" will use the `TextTimeEmbedding` layer.
        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
            Dimension for the timestep embeddings.
        num_class_embeds (`int`, *optional*, defaults to `None`):
            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
            class conditioning with `class_embed_type` equal to `None`.
        time_embedding_type (`str`, *optional*, defaults to `positional`):
            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
        time_embedding_dim (`int`, *optional*, defaults to `None`):
            An optional override for the dimension of the projected time embedding.
        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
            Optional activation function to use only once on the time embeddings before they are passed to the rest of
            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
        timestep_post_act (`str`, *optional*, defaults to `None`):
            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
            The dimension of `cond_proj` layer in the timestep embedding.
        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,
        *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,
        *optional*): The dimension of the `class_labels` input when
            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
            embeddings with the class embeddings.
        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
            otherwise.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        sample_size: Optional[int] = None,
        in_channels: int = 4,
        out_channels: int = 4,
        center_input_sample: bool = False,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        down_block_types: Tuple[str] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
        only_cross_attention: Union[bool, Tuple[bool]] = False,
        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
        layers_per_block: Union[int, Tuple[int]] = 2,
        downsample_padding: int = 1,
        mid_block_scale_factor: float = 1,
        dropout: float = 0.0,
        act_fn: str = "silu",
        norm_num_groups: Optional[int] = 32,
        norm_eps: float = 1e-5,
        cross_attention_dim: Union[int, Tuple[int]] = 1280,
        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
        encoder_hid_dim: Optional[int] = None,
        encoder_hid_dim_type: Optional[str] = None,
        attention_head_dim: Union[int, Tuple[int]] = 8,
        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        class_embed_type: Optional[str] = None,
        addition_embed_type: Optional[str] = None,
        addition_time_embed_dim: Optional[int] = None,
        num_class_embeds: Optional[int] = None,
        upcast_attention: bool = False,
        resnet_time_scale_shift: str = "default",
        resnet_skip_time_act: bool = False,
        resnet_out_scale_factor: float = 1.0,
        time_embedding_type: str = "positional",
        time_embedding_dim: Optional[int] = None,
        time_embedding_act_fn: Optional[str] = None,
        timestep_post_act: Optional[str] = None,
        time_cond_proj_dim: Optional[int] = None,
        conv_in_kernel: int = 3,
        conv_out_kernel: int = 3,
        projection_class_embeddings_input_dim: Optional[int] = None,
        attention_type: str = "default",
        class_embeddings_concat: bool = False,
        mid_block_only_cross_attention: Optional[bool] = None,
        cross_attention_norm: Optional[str] = None,
        addition_embed_type_num_heads: int = 64,
    ):
        super().__init__()

        self.sample_size = sample_size

        if num_attention_heads is not None:
            raise ValueError(
                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
            )

        # If `num_attention_heads` is not defined (which is the case for most models)
        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
        # The reason for this behavior is to correct for incorrectly named variables that were introduced
        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
        # which is why we correct for the naming here.
        num_attention_heads = num_attention_heads or attention_head_dim

        # Check inputs
        self._check_config(
            down_block_types=down_block_types,
            up_block_types=up_block_types,
            only_cross_attention=only_cross_attention,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            cross_attention_dim=cross_attention_dim,
            transformer_layers_per_block=transformer_layers_per_block,
            reverse_transformer_layers_per_block=reverse_transformer_layers_per_block,
            attention_head_dim=attention_head_dim,
            num_attention_heads=num_attention_heads,
        )

        # input
        conv_in_padding = (conv_in_kernel - 1) // 2
        self.conv_in = nn.Conv2d(
            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
        )

        # time
        time_embed_dim, timestep_input_dim = self._set_time_proj(
            time_embedding_type,
            block_out_channels=block_out_channels,
            flip_sin_to_cos=flip_sin_to_cos,
            freq_shift=freq_shift,
            time_embedding_dim=time_embedding_dim,
        )

        self.time_embedding = TimestepEmbedding(
            timestep_input_dim,
            time_embed_dim,
            act_fn=act_fn,
            post_act_fn=timestep_post_act,
            cond_proj_dim=time_cond_proj_dim,
        )

        self._set_encoder_hid_proj(
            encoder_hid_dim_type,
            cross_attention_dim=cross_attention_dim,
            encoder_hid_dim=encoder_hid_dim,
        )

        # class embedding
        self._set_class_embedding(
            class_embed_type,
            act_fn=act_fn,
            num_class_embeds=num_class_embeds,
            projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
            time_embed_dim=time_embed_dim,
            timestep_input_dim=timestep_input_dim,
        )

        self._set_add_embedding(
            addition_embed_type,
            addition_embed_type_num_heads=addition_embed_type_num_heads,
            addition_time_embed_dim=addition_time_embed_dim,
            cross_attention_dim=cross_attention_dim,
            encoder_hid_dim=encoder_hid_dim,
            flip_sin_to_cos=flip_sin_to_cos,
            freq_shift=freq_shift,
            projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
            time_embed_dim=time_embed_dim,
        )

        if time_embedding_act_fn is None:
            self.time_embed_act = None
        else:
            self.time_embed_act = get_activation(time_embedding_act_fn)

        self.down_blocks = nn.ModuleList([])
        self.up_blocks = nn.ModuleList([])

        if isinstance(only_cross_attention, bool):
            if mid_block_only_cross_attention is None:
                mid_block_only_cross_attention = only_cross_attention

            only_cross_attention = [only_cross_attention] * len(down_block_types)

        if mid_block_only_cross_attention is None:
            mid_block_only_cross_attention = False

        if isinstance(num_attention_heads, int):
            num_attention_heads = (num_attention_heads,) * len(down_block_types)

        if isinstance(attention_head_dim, int):
            attention_head_dim = (attention_head_dim,) * len(down_block_types)

        if isinstance(cross_attention_dim, int):
            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)

        if isinstance(layers_per_block, int):
            layers_per_block = [layers_per_block] * len(down_block_types)

        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)

        if class_embeddings_concat:
            # The time embeddings are concatenated with the class embeddings. The dimension of the
            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
            # regular time embeddings
            blocks_time_embed_dim = time_embed_dim * 2
        else:
            blocks_time_embed_dim = time_embed_dim

        # down
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block[i],
                transformer_layers_per_block=transformer_layers_per_block[i],
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=blocks_time_embed_dim,
                add_downsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=cross_attention_dim[i],
                num_attention_heads=num_attention_heads[i],
                downsample_padding=downsample_padding,
                dual_cross_attention=dual_cross_attention,
                use_linear_projection=use_linear_projection,
                only_cross_attention=only_cross_attention[i],
                upcast_attention=upcast_attention,
                resnet_time_scale_shift=resnet_time_scale_shift,
                attention_type=attention_type,
                resnet_skip_time_act=resnet_skip_time_act,
                resnet_out_scale_factor=resnet_out_scale_factor,
                cross_attention_norm=cross_attention_norm,
                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
                dropout=dropout,
            )
            self.down_blocks.append(down_block)

        # mid
        self.mid_block = get_mid_block(
            mid_block_type,
            temb_channels=blocks_time_embed_dim,
            in_channels=block_out_channels[-1],
            resnet_eps=norm_eps,
            resnet_act_fn=act_fn,
            resnet_groups=norm_num_groups,
            output_scale_factor=mid_block_scale_factor,
            transformer_layers_per_block=transformer_layers_per_block[-1],
            num_attention_heads=num_attention_heads[-1],
            cross_attention_dim=cross_attention_dim[-1],
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            mid_block_only_cross_attention=mid_block_only_cross_attention,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            attention_type=attention_type,
            resnet_skip_time_act=resnet_skip_time_act,
            cross_attention_norm=cross_attention_norm,
            attention_head_dim=attention_head_dim[-1],
            dropout=dropout,
        )

        # count how many layers upsample the images
        self.num_upsamplers = 0

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        reversed_num_attention_heads = list(reversed(num_attention_heads))
        reversed_layers_per_block = list(reversed(layers_per_block))
        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
        reversed_transformer_layers_per_block = (
            list(reversed(transformer_layers_per_block))
            if reverse_transformer_layers_per_block is None
            else reverse_transformer_layers_per_block
        )
        only_cross_attention = list(reversed(only_cross_attention))

        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            is_final_block = i == len(block_out_channels) - 1

            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            # add upsample block for all BUT final layer
            if not is_final_block:
                add_upsample = True
                self.num_upsamplers += 1
            else:
                add_upsample = False

            up_block = get_up_block(
                up_block_type,
                num_layers=reversed_layers_per_block[i] + 1,
                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
                temb_channels=blocks_time_embed_dim,
                add_upsample=add_upsample,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resolution_idx=i,
                resnet_groups=norm_num_groups,
                cross_attention_dim=reversed_cross_attention_dim[i],
                num_attention_heads=reversed_num_attention_heads[i],
                dual_cross_attention=dual_cross_attention,
                use_linear_projection=use_linear_projection,
                only_cross_attention=only_cross_attention[i],
                upcast_attention=upcast_attention,
                resnet_time_scale_shift=resnet_time_scale_shift,
                attention_type=attention_type,
                resnet_skip_time_act=resnet_skip_time_act,
                resnet_out_scale_factor=resnet_out_scale_factor,
                cross_attention_norm=cross_attention_norm,
                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
                dropout=dropout,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        if norm_num_groups is not None:
            self.conv_norm_out = nn.GroupNorm(
                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
            )

            self.conv_act = get_activation(act_fn)

        else:
            self.conv_norm_out = None
            self.conv_act = None

        conv_out_padding = (conv_out_kernel - 1) // 2
        self.conv_out = nn.Conv2d(
            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
        )

        self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim)

    def _check_config(
        self,
        down_block_types: Tuple[str],
        up_block_types: Tuple[str],
        only_cross_attention: Union[bool, Tuple[bool]],
        block_out_channels: Tuple[int],
        layers_per_block: Union[int, Tuple[int]],
        cross_attention_dim: Union[int, Tuple[int]],
        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]],
        reverse_transformer_layers_per_block: bool,
        attention_head_dim: int,
        num_attention_heads: Optional[Union[int, Tuple[int]]],
    ):
        if len(down_block_types) != len(up_block_types):
            raise ValueError(
                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
            )

        if len(block_out_channels) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
            )

        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
            )
        if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
            for layer_number_per_block in transformer_layers_per_block:
                if isinstance(layer_number_per_block, list):
                    raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")

    def _set_time_proj(
        self,
        time_embedding_type: str,
        block_out_channels: int,
        flip_sin_to_cos: bool,
        freq_shift: float,
        time_embedding_dim: int,
    ) -> Tuple[int, int]:
        if time_embedding_type == "fourier":
            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
            if time_embed_dim % 2 != 0:
                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
            self.time_proj = GaussianFourierProjection(
                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
            )
            timestep_input_dim = time_embed_dim
        elif time_embedding_type == "positional":
            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4

            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
            timestep_input_dim = block_out_channels[0]
        else:
            raise ValueError(
                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
            )

        return time_embed_dim, timestep_input_dim

    def _set_encoder_hid_proj(
        self,
        encoder_hid_dim_type: Optional[str],
        cross_attention_dim: Union[int, Tuple[int]],
        encoder_hid_dim: Optional[int],
    ):
        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
            encoder_hid_dim_type = "text_proj"
            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")

        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
            raise ValueError(
                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
            )

        if encoder_hid_dim_type == "text_proj":
            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
        elif encoder_hid_dim_type == "text_image_proj":
            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
            self.encoder_hid_proj = TextImageProjection(
                text_embed_dim=encoder_hid_dim,
                image_embed_dim=cross_attention_dim,
                cross_attention_dim=cross_attention_dim,
            )
        elif encoder_hid_dim_type == "image_proj":
            # Kandinsky 2.2
            self.encoder_hid_proj = ImageProjection(
                image_embed_dim=encoder_hid_dim,
                cross_attention_dim=cross_attention_dim,
            )
        elif encoder_hid_dim_type is not None:
            raise ValueError(
                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
            )
        else:
            self.encoder_hid_proj = None

    def _set_class_embedding(
        self,
        class_embed_type: Optional[str],
        act_fn: str,
        num_class_embeds: Optional[int],
        projection_class_embeddings_input_dim: Optional[int],
        time_embed_dim: int,
        timestep_input_dim: int,
    ):
        if class_embed_type is None and num_class_embeds is not None:
            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
        elif class_embed_type == "timestep":
            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
        elif class_embed_type == "identity":
            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
        elif class_embed_type == "projection":
            if projection_class_embeddings_input_dim is None:
                raise ValueError(
                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
                )
            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
            # 2. it projects from an arbitrary input dimension.
            #
            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
        elif class_embed_type == "simple_projection":
            if projection_class_embeddings_input_dim is None:
                raise ValueError(
                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
                )
            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
        else:
            self.class_embedding = None

    def _set_add_embedding(
        self,
        addition_embed_type: str,
        addition_embed_type_num_heads: int,
        addition_time_embed_dim: Optional[int],
        flip_sin_to_cos: bool,
        freq_shift: float,
        cross_attention_dim: Optional[int],
        encoder_hid_dim: Optional[int],
        projection_class_embeddings_input_dim: Optional[int],
        time_embed_dim: int,
    ):
        if addition_embed_type == "text":
            if encoder_hid_dim is not None:
                text_time_embedding_from_dim = encoder_hid_dim
            else:
                text_time_embedding_from_dim = cross_attention_dim

            self.add_embedding = TextTimeEmbedding(
                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
            )
        elif addition_embed_type == "text_image":
            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
            self.add_embedding = TextImageTimeEmbedding(
                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
            )
        elif addition_embed_type == "text_time":
            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
        elif addition_embed_type == "image":
            # Kandinsky 2.2
            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
        elif addition_embed_type == "image_hint":
            # Kandinsky 2.2 ControlNet
            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
        elif addition_embed_type is not None:
            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")

    def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int):
        if attention_type in ["gated", "gated-text-image"]:
            positive_len = 768
            if isinstance(cross_attention_dim, int):
                positive_len = cross_attention_dim
            elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):
                positive_len = cross_attention_dim[0]

            feature_type = "text-only" if attention_type == "gated" else "text-image"
            self.position_net = GLIGENTextBoundingboxProjection(
                positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
            )

    @property
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnAddedKVProcessor()
        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnProcessor()
        else:
            raise ValueError(
                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
            )

        self.set_attn_processor(processor)

    def set_attention_slice(self, slice_size: Union[str, int, List[int]] = "auto"):
        r"""
        Enable sliced attention computation.

        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
        several steps. This is useful for saving some memory in exchange for a small decrease in speed.

        Args:
            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                must be a multiple of `slice_size`.
        """
        sliceable_head_dims = []

        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
            if hasattr(module, "set_attention_slice"):
                sliceable_head_dims.append(module.sliceable_head_dim)

            for child in module.children():
                fn_recursive_retrieve_sliceable_dims(child)

        # retrieve number of attention layers
        for module in self.children():
            fn_recursive_retrieve_sliceable_dims(module)

        num_sliceable_layers = len(sliceable_head_dims)

        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = [dim // 2 for dim in sliceable_head_dims]
        elif slice_size == "max":
            # make smallest slice possible
            slice_size = num_sliceable_layers * [1]

        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size

        if len(slice_size) != len(sliceable_head_dims):
            raise ValueError(
                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
            )

        for i in range(len(slice_size)):
            size = slice_size[i]
            dim = sliceable_head_dims[i]
            if size is not None and size > dim:
                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")

        # Recursively walk through all the children.
        # Any children which exposes the set_attention_slice method
        # gets the message
        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
            if hasattr(module, "set_attention_slice"):
                module.set_attention_slice(slice_size.pop())

            for child in module.children():
                fn_recursive_set_attention_slice(child, slice_size)

        reversed_slice_size = list(reversed(slice_size))
        for module in self.children():
            fn_recursive_set_attention_slice(module, reversed_slice_size)

    def _set_gradient_checkpointing(self, module, value=False):
        if hasattr(module, "gradient_checkpointing"):
            module.gradient_checkpointing = value

    def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
        r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.

        The suffixes after the scaling factors represent the stage blocks where they are being applied.

        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
        are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.

        Args:
            s1 (`float`):
                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
                mitigate the "oversmoothing effect" in the enhanced denoising process.
            s2 (`float`):
                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
                mitigate the "oversmoothing effect" in the enhanced denoising process.
            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
        """
        for i, upsample_block in enumerate(self.up_blocks):
            setattr(upsample_block, "s1", s1)
            setattr(upsample_block, "s2", s2)
            setattr(upsample_block, "b1", b1)
            setattr(upsample_block, "b2", b2)

    def disable_freeu(self):
        """Disables the FreeU mechanism."""
        freeu_keys = {"s1", "s2", "b1", "b2"}
        for i, upsample_block in enumerate(self.up_blocks):
            for k in freeu_keys:
                if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
                    setattr(upsample_block, k, None)

    def fuse_qkv_projections(self):
        """
        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,
        key, value) are fused. For cross-attention modules, key and value projection matrices are fused.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>
        """
        self.original_attn_processors = None

        for _, attn_processor in self.attn_processors.items():
            if "Added" in str(attn_processor.__class__.__name__):
                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")

        self.original_attn_processors = self.attn_processors

        for module in self.modules():
            if isinstance(module, Attention):
                module.fuse_projections(fuse=True)

    def unfuse_qkv_projections(self):
        """Disables the fused QKV projection if enabled.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>

        """
        if self.original_attn_processors is not None:
            self.set_attn_processor(self.original_attn_processors)

    def unload_lora(self):
        """Unloads LoRA weights."""
        deprecate(
            "unload_lora",
            "0.28.0",
            "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().",
        )
        for module in self.modules():
            if hasattr(module, "set_lora_layer"):
                module.set_lora_layer(None)

    def get_time_embed(
        self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int]
    ) -> Optional[torch.Tensor]:
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            is_mps = sample.device.type == "mps"
            if isinstance(timestep, float):
                dtype = torch.float32 if is_mps else torch.float64
            else:
                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps.expand(sample.shape[0])

        t_emb = self.time_proj(timesteps)
        # `Timesteps` does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=sample.dtype)
        return t_emb

    def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
        class_emb = None
        if self.class_embedding is not None:
            if class_labels is None:
                raise ValueError("class_labels should be provided when num_class_embeds > 0")

            if self.config.class_embed_type == "timestep":
                class_labels = self.time_proj(class_labels)

                # `Timesteps` does not contain any weights and will always return f32 tensors
                # there might be better ways to encapsulate this.
                class_labels = class_labels.to(dtype=sample.dtype)

            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
        return class_emb

    def get_aug_embed(
        self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]
    ) -> Optional[torch.Tensor]:
        aug_emb = None
        if self.config.addition_embed_type == "text":
            aug_emb = self.add_embedding(encoder_hidden_states)
        elif self.config.addition_embed_type == "text_image":
            # Kandinsky 2.1 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
                )

            image_embs = added_cond_kwargs.get("image_embeds")
            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
            aug_emb = self.add_embedding(text_embs, image_embs)
        elif self.config.addition_embed_type == "text_time":
            # SDXL - style
            if "text_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
                )
            text_embeds = added_cond_kwargs.get("text_embeds")
            if "time_ids" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
                )
            time_ids = added_cond_kwargs.get("time_ids")
            time_embeds = self.add_time_proj(time_ids.flatten())
            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
            add_embeds = add_embeds.to(emb.dtype)
            aug_emb = self.add_embedding(add_embeds)
        elif self.config.addition_embed_type == "image":
            # Kandinsky 2.2 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
                )
            image_embs = added_cond_kwargs.get("image_embeds")
            aug_emb = self.add_embedding(image_embs)
        elif self.config.addition_embed_type == "image_hint":
            # Kandinsky 2.2 - style
            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
                )
            image_embs = added_cond_kwargs.get("image_embeds")
            hint = added_cond_kwargs.get("hint")
            aug_emb = self.add_embedding(image_embs, hint)
        return aug_emb

    def process_encoder_hidden_states(
        self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]
    ) -> torch.Tensor:
        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
            # Kadinsky 2.1 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
                )

            image_embeds = added_cond_kwargs.get("image_embeds")
            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
            # Kandinsky 2.2 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
                )
            image_embeds = added_cond_kwargs.get("image_embeds")
            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
                )
            image_embeds = added_cond_kwargs.get("image_embeds")
            image_embeds = self.encoder_hid_proj(image_embeds)
            encoder_hidden_states = (encoder_hidden_states, image_embeds)
        return encoder_hidden_states

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        class_labels: Optional[torch.Tensor] = None,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        mid_block_additional_residual: Optional[torch.Tensor] = None,
        down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ) -> Union[UNet2DConditionOutput, Tuple]:
        r"""
        The [`UNet2DConditionModel`] forward method.

        Args:
            sample (`torch.FloatTensor`):
                The noisy input tensor with the following shape `(batch, channel, height, width)`.
            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.FloatTensor`):
                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
            timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
                Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
                through the `self.time_embedding` layer to obtain the timestep embeddings.
            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            added_cond_kwargs: (`dict`, *optional*):
                A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
                are passed along to the UNet blocks.
            down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
                A tuple of tensors that if specified are added to the residuals of down unet blocks.
            mid_block_additional_residual: (`torch.Tensor`, *optional*):
                A tensor that if specified is added to the residual of the middle unet block.
            encoder_attention_mask (`torch.Tensor`):
                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
                which adds large negative values to the attention scores corresponding to "discard" tokens.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
            added_cond_kwargs: (`dict`, *optional*):
                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
                are passed along to the UNet blocks.
            down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
                additional residuals to be added to UNet long skip connections from down blocks to up blocks for
                example from ControlNet side model(s)
            mid_block_additional_residual (`torch.Tensor`, *optional*):
                additional residual to be added to UNet mid block output, for example from ControlNet side model
            down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
                additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)

        Returns:
            [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
                a `tuple` is returned where the first element is the sample tensor.
        """
        # By default samples have to be AT least a multiple of the overall upsampling factor.
        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
        # However, the upsampling interpolation output size can be forced to fit any upsampling size
        # on the fly if necessary.
        default_overall_up_factor = 2**self.num_upsamplers

        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
        forward_upsample_size = False
        upsample_size = None

        for dim in sample.shape[-2:]:
            if dim % default_overall_up_factor != 0:
                # Forward upsample size to force interpolation output size.
                forward_upsample_size = True
                break

        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
        # expects mask of shape:
        #   [batch, key_tokens]
        # adds singleton query_tokens dimension:
        #   [batch,                    1, key_tokens]
        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
        if attention_mask is not None:
            # assume that mask is expressed as:
            #   (1 = keep,      0 = discard)
            # convert mask into a bias that can be added to attention scores:
            #       (keep = +0,     discard = -10000.0)
            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        # convert encoder_attention_mask to a bias the same way we do for attention_mask
        if encoder_attention_mask is not None:
            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

        # 0. center input if necessary
        if self.config.center_input_sample:
            sample = 2 * sample - 1.0

        # 1. time
        t_emb = self.get_time_embed(sample=sample, timestep=timestep)
        emb = self.time_embedding(t_emb, timestep_cond)
        aug_emb = None

        class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
        if class_emb is not None:
            if self.config.class_embeddings_concat:
                emb = torch.cat([emb, class_emb], dim=-1)
            else:
                emb = emb + class_emb

        aug_emb = self.get_aug_embed(
            emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
        )
        if self.config.addition_embed_type == "image_hint":
            aug_emb, hint = aug_emb
            sample = torch.cat([sample, hint], dim=1)

        emb = emb + aug_emb if aug_emb is not None else emb

        if self.time_embed_act is not None:
            emb = self.time_embed_act(emb)

        encoder_hidden_states = self.process_encoder_hidden_states(
            encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
        )

        # 2. pre-process
        sample = self.conv_in(sample)

        # 2.5 GLIGEN position net
        if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
            cross_attention_kwargs = cross_attention_kwargs.copy()
            gligen_args = cross_attention_kwargs.pop("gligen")
            cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}

        # 3. down
        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
        if USE_PEFT_BACKEND:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self, lora_scale)

        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
        # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
        is_adapter = down_intrablock_additional_residuals is not None
        # maintain backward compatibility for legacy usage, where
        #       T2I-Adapter and ControlNet both use down_block_additional_residuals arg
        #       but can only use one or the other
        if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
            deprecate(
                "T2I should not use down_block_additional_residuals",
                "1.3.0",
                "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
                       and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
                       for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
                standard_warn=False,
            )
            down_intrablock_additional_residuals = down_block_additional_residuals
            is_adapter = True

        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                # For t2i-adapter CrossAttnDownBlock2D
                additional_residuals = {}
                if is_adapter and len(down_intrablock_additional_residuals) > 0:
                    additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)

                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                    encoder_attention_mask=encoder_attention_mask,
                    **additional_residuals,
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)
                if is_adapter and len(down_intrablock_additional_residuals) > 0:
                    sample += down_intrablock_additional_residuals.pop(0)

            down_block_res_samples += res_samples

        if is_controlnet:
            new_down_block_res_samples = ()

            for down_block_res_sample, down_block_additional_residual in zip(
                down_block_res_samples, down_block_additional_residuals
            ):
                down_block_res_sample = down_block_res_sample + down_block_additional_residual
                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)

            down_block_res_samples = new_down_block_res_samples

        # 4. mid
        if self.mid_block is not None:
            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
                sample = self.mid_block(
                    sample,
                    emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                    encoder_attention_mask=encoder_attention_mask,
                )
            else:
                sample = self.mid_block(sample, emb)

            # To support T2I-Adapter-XL
            if (
                is_adapter
                and len(down_intrablock_additional_residuals) > 0
                and sample.shape == down_intrablock_additional_residuals[0].shape
            ):
                sample += down_intrablock_additional_residuals.pop(0)

        if is_controlnet:
            sample = sample + mid_block_additional_residual

        # 5. up
        for i, upsample_block in enumerate(self.up_blocks):
            is_final_block = i == len(self.up_blocks) - 1

            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

            # if we have not reached the final block and need to forward the
            # upsample size, we do it here
            if not is_final_block and forward_upsample_size:
                upsample_size = down_block_res_samples[-1].shape[2:]

            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    upsample_size=upsample_size,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                )
            else:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    upsample_size=upsample_size,
                    scale=lora_scale,
                )

        # 6. post-process
        if self.conv_norm_out:
            sample = self.conv_norm_out(sample)
            sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        if USE_PEFT_BACKEND:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self, lora_scale)

        if not return_dict:
            return (sample,)

        return UNet2DConditionOutput(sample=sample)


 from dataclasses import dataclass
 from typing import Dict, Tuple, Union

 import torch
 import torch.utils.checkpoint
 from torch import nn

 from ...configuration_utils import ConfigMixin, register_to_config
 from ...utils import BaseOutput, logging
 from ..attention_processor import Attention, AttentionProcessor, AttnProcessor
 from ..embeddings import TimestepEmbedding, Timesteps
 from ..modeling_utils import ModelMixin


 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


 @dataclass
 class Kandinsky3UNetOutput(BaseOutput):
    sample: torch.FloatTensor = None


 class Kandinsky3EncoderProj(nn.Module):
    def __init__(self, encoder_hid_dim, cross_attention_dim):
        super().__init__()
        self.projection_linear = nn.Linear(encoder_hid_dim, cross_attention_dim, bias=False)
        self.projection_norm = nn.LayerNorm(cross_attention_dim)

    def forward(self, x):
        x = self.projection_linear(x)
        x = self.projection_norm(x)
        return x


 class Kandinsky3UNet(ModelMixin, ConfigMixin):
    @register_to_config
    def __init__(
        self,
        in_channels: int = 4,
        time_embedding_dim: int = 1536,
        groups: int = 32,
        attention_head_dim: int = 64,
        layers_per_block: Union[int, Tuple[int]] = 3,
        block_out_channels: Tuple[int] = (384, 768, 1536, 3072),
        cross_attention_dim: Union[int, Tuple[int]] = 4096,
        encoder_hid_dim: int = 4096,
    ):
        super().__init__()

        # TOOD(Yiyi): Give better name and put into config for the following 4 parameters
        expansion_ratio = 4
        compression_ratio = 2
        add_cross_attention = (False, True, True, True)
        add_self_attention = (False, True, True, True)

        out_channels = in_channels
        init_channels = block_out_channels[0] // 2
        self.time_proj = Timesteps(init_channels, flip_sin_to_cos=False, downscale_freq_shift=1)

        self.time_embedding = TimestepEmbedding(
            init_channels,
            time_embedding_dim,
        )

        self.add_time_condition = Kandinsky3AttentionPooling(
            time_embedding_dim, cross_attention_dim, attention_head_dim
        )

        self.conv_in = nn.Conv2d(in_channels, init_channels, kernel_size=3, padding=1)

        self.encoder_hid_proj = Kandinsky3EncoderProj(encoder_hid_dim, cross_attention_dim)

        hidden_dims = [init_channels] + list(block_out_channels)
        in_out_dims = list(zip(hidden_dims[:-1], hidden_dims[1:]))
        text_dims = [cross_attention_dim if is_exist else None for is_exist in add_cross_attention]
        num_blocks = len(block_out_channels) * [layers_per_block]
        layer_params = [num_blocks, text_dims, add_self_attention]
        rev_layer_params = map(reversed, layer_params)

        cat_dims = []
        self.num_levels = len(in_out_dims)
        self.down_blocks = nn.ModuleList([])
        for level, ((in_dim, out_dim), res_block_num, text_dim, self_attention) in enumerate(
            zip(in_out_dims, *layer_params)
        ):
            down_sample = level != (self.num_levels - 1)
            cat_dims.append(out_dim if level != (self.num_levels - 1) else 0)
            self.down_blocks.append(
                Kandinsky3DownSampleBlock(
                    in_dim,
                    out_dim,
                    time_embedding_dim,
                    text_dim,
                    res_block_num,
                    groups,
                    attention_head_dim,
                    expansion_ratio,
                    compression_ratio,
                    down_sample,
                    self_attention,
                )
            )

        self.up_blocks = nn.ModuleList([])
        for level, ((out_dim, in_dim), res_block_num, text_dim, self_attention) in enumerate(
            zip(reversed(in_out_dims), *rev_layer_params)
        ):
            up_sample = level != 0
            self.up_blocks.append(
                Kandinsky3UpSampleBlock(
                    in_dim,
                    cat_dims.pop(),
                    out_dim,
                    time_embedding_dim,
                    text_dim,
                    res_block_num,
                    groups,
                    attention_head_dim,
                    expansion_ratio,
                    compression_ratio,
                    up_sample,
                    self_attention,
                )
            )

        self.conv_norm_out = nn.GroupNorm(groups, init_channels)
        self.conv_act_out = nn.SiLU()
        self.conv_out = nn.Conv2d(init_channels, out_channels, kernel_size=3, padding=1)

    @property
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "set_processor"):
                processors[f"{name}.processor"] = module.processor

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        self.set_attn_processor(AttnProcessor())

    def _set_gradient_checkpointing(self, module, value=False):
        if hasattr(module, "gradient_checkpointing"):
            module.gradient_checkpointing = value

    def forward(self, sample, timestep, encoder_hidden_states=None, encoder_attention_mask=None, return_dict=True):
        if encoder_attention_mask is not None:
            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

        if not torch.is_tensor(timestep):
            dtype = torch.float32 if isinstance(timestep, float) else torch.int32
            timestep = torch.tensor([timestep], dtype=dtype, device=sample.device)
        elif len(timestep.shape) == 0:
            timestep = timestep[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timestep = timestep.expand(sample.shape[0])
        time_embed_input = self.time_proj(timestep).to(sample.dtype)
        time_embed = self.time_embedding(time_embed_input)

        encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)

        if encoder_hidden_states is not None:
            time_embed = self.add_time_condition(time_embed, encoder_hidden_states, encoder_attention_mask)

        hidden_states = []
        sample = self.conv_in(sample)
        for level, down_sample in enumerate(self.down_blocks):
            sample = down_sample(sample, time_embed, encoder_hidden_states, encoder_attention_mask)
            if level != self.num_levels - 1:
                hidden_states.append(sample)

        for level, up_sample in enumerate(self.up_blocks):
            if level != 0:
                sample = torch.cat([sample, hidden_states.pop()], dim=1)
            sample = up_sample(sample, time_embed, encoder_hidden_states, encoder_attention_mask)

        sample = self.conv_norm_out(sample)
        sample = self.conv_act_out(sample)
        sample = self.conv_out(sample)

        if not return_dict:
            return (sample,)
        return Kandinsky3UNetOutput(sample=sample)


 class Kandinsky3UpSampleBlock(nn.Module):
    def __init__(
        self,
        in_channels,
        cat_dim,
        out_channels,
        time_embed_dim,
        context_dim=None,
        num_blocks=3,
        groups=32,
        head_dim=64,
        expansion_ratio=4,
        compression_ratio=2,
        up_sample=True,
        self_attention=True,
    ):
        super().__init__()
        up_resolutions = [[None, True if up_sample else None, None, None]] + [[None] * 4] * (num_blocks - 1)
        hidden_channels = (
            [(in_channels + cat_dim, in_channels)]
            + [(in_channels, in_channels)] * (num_blocks - 2)
            + [(in_channels, out_channels)]
        )
        attentions = []
        resnets_in = []
        resnets_out = []

        self.self_attention = self_attention
        self.context_dim = context_dim

        if self_attention:
            attentions.append(
                Kandinsky3AttentionBlock(out_channels, time_embed_dim, None, groups, head_dim, expansion_ratio)
            )
        else:
            attentions.append(nn.Identity())

        for (in_channel, out_channel), up_resolution in zip(hidden_channels, up_resolutions):
            resnets_in.append(
                Kandinsky3ResNetBlock(in_channel, in_channel, time_embed_dim, groups, compression_ratio, up_resolution)
            )

            if context_dim is not None:
                attentions.append(
                    Kandinsky3AttentionBlock(
                        in_channel, time_embed_dim, context_dim, groups, head_dim, expansion_ratio
                    )
                )
            else:
                attentions.append(nn.Identity())

            resnets_out.append(
                Kandinsky3ResNetBlock(in_channel, out_channel, time_embed_dim, groups, compression_ratio)
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets_in = nn.ModuleList(resnets_in)
        self.resnets_out = nn.ModuleList(resnets_out)

    def forward(self, x, time_embed, context=None, context_mask=None, image_mask=None):
        for attention, resnet_in, resnet_out in zip(self.attentions[1:], self.resnets_in, self.resnets_out):
            x = resnet_in(x, time_embed)
            if self.context_dim is not None:
                x = attention(x, time_embed, context, context_mask, image_mask)
            x = resnet_out(x, time_embed)

        if self.self_attention:
            x = self.attentions[0](x, time_embed, image_mask=image_mask)
        return x


 class Kandinsky3DownSampleBlock(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        time_embed_dim,
        context_dim=None,
        num_blocks=3,
        groups=32,
        head_dim=64,
        expansion_ratio=4,
        compression_ratio=2,
        down_sample=True,
        self_attention=True,
    ):
        super().__init__()
        attentions = []
        resnets_in = []
        resnets_out = []

        self.self_attention = self_attention
        self.context_dim = context_dim

        if self_attention:
            attentions.append(
                Kandinsky3AttentionBlock(in_channels, time_embed_dim, None, groups, head_dim, expansion_ratio)
            )
        else:
            attentions.append(nn.Identity())

        up_resolutions = [[None] * 4] * (num_blocks - 1) + [[None, None, False if down_sample else None, None]]
        hidden_channels = [(in_channels, out_channels)] + [(out_channels, out_channels)] * (num_blocks - 1)
        for (in_channel, out_channel), up_resolution in zip(hidden_channels, up_resolutions):
            resnets_in.append(
                Kandinsky3ResNetBlock(in_channel, out_channel, time_embed_dim, groups, compression_ratio)
            )

            if context_dim is not None:
                attentions.append(
                    Kandinsky3AttentionBlock(
                        out_channel, time_embed_dim, context_dim, groups, head_dim, expansion_ratio
                    )
                )
            else:
                attentions.append(nn.Identity())

            resnets_out.append(
                Kandinsky3ResNetBlock(
                    out_channel, out_channel, time_embed_dim, groups, compression_ratio, up_resolution
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets_in = nn.ModuleList(resnets_in)
        self.resnets_out = nn.ModuleList(resnets_out)

    def forward(self, x, time_embed, context=None, context_mask=None, image_mask=None):
        if self.self_attention:
            x = self.attentions[0](x, time_embed, image_mask=image_mask)

        for attention, resnet_in, resnet_out in zip(self.attentions[1:], self.resnets_in, self.resnets_out):
            x = resnet_in(x, time_embed)
            if self.context_dim is not None:
                x = attention(x, time_embed, context, context_mask, image_mask)
            x = resnet_out(x, time_embed)
        return x


 class Kandinsky3ConditionalGroupNorm(nn.Module):
    def __init__(self, groups, normalized_shape, context_dim):
        super().__init__()
        self.norm = nn.GroupNorm(groups, normalized_shape, affine=False)
        self.context_mlp = nn.Sequential(nn.SiLU(), nn.Linear(context_dim, 2 * normalized_shape))
        self.context_mlp[1].weight.data.zero_()
        self.context_mlp[1].bias.data.zero_()

    def forward(self, x, context):
        context = self.context_mlp(context)

        for _ in range(len(x.shape[2:])):
            context = context.unsqueeze(-1)

        scale, shift = context.chunk(2, dim=1)
        x = self.norm(x) * (scale + 1.0) + shift
        return x


 class Kandinsky3Block(nn.Module):
    def __init__(self, in_channels, out_channels, time_embed_dim, kernel_size=3, norm_groups=32, up_resolution=None):
        super().__init__()
        self.group_norm = Kandinsky3ConditionalGroupNorm(norm_groups, in_channels, time_embed_dim)
        self.activation = nn.SiLU()
        if up_resolution is not None and up_resolution:
            self.up_sample = nn.ConvTranspose2d(in_channels, in_channels, kernel_size=2, stride=2)
        else:
            self.up_sample = nn.Identity()

        padding = int(kernel_size > 1)
        self.projection = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=padding)

        if up_resolution is not None and not up_resolution:
            self.down_sample = nn.Conv2d(out_channels, out_channels, kernel_size=2, stride=2)
        else:
            self.down_sample = nn.Identity()

    def forward(self, x, time_embed):
        x = self.group_norm(x, time_embed)
        x = self.activation(x)
        x = self.up_sample(x)
        x = self.projection(x)
        x = self.down_sample(x)
        return x


 class Kandinsky3ResNetBlock(nn.Module):
    def __init__(
        self, in_channels, out_channels, time_embed_dim, norm_groups=32, compression_ratio=2, up_resolutions=4 * [None]
    ):
        super().__init__()
        kernel_sizes = [1, 3, 3, 1]
        hidden_channel = max(in_channels, out_channels) // compression_ratio
        hidden_channels = (
            [(in_channels, hidden_channel)] + [(hidden_channel, hidden_channel)] * 2 + [(hidden_channel, out_channels)]
        )
        self.resnet_blocks = nn.ModuleList(
            [
                Kandinsky3Block(in_channel, out_channel, time_embed_dim, kernel_size, norm_groups, up_resolution)
                for (in_channel, out_channel), kernel_size, up_resolution in zip(
                    hidden_channels, kernel_sizes, up_resolutions
                )
            ]
        )
        self.shortcut_up_sample = (
            nn.ConvTranspose2d(in_channels, in_channels, kernel_size=2, stride=2)
            if True in up_resolutions
            else nn.Identity()
        )
        self.shortcut_projection = (
            nn.Conv2d(in_channels, out_channels, kernel_size=1) if in_channels != out_channels else nn.Identity()
        )
        self.shortcut_down_sample = (
            nn.Conv2d(out_channels, out_channels, kernel_size=2, stride=2)
            if False in up_resolutions
            else nn.Identity()
        )

    def forward(self, x, time_embed):
        out = x
        for resnet_block in self.resnet_blocks:
            out = resnet_block(out, time_embed)

        x = self.shortcut_up_sample(x)
        x = self.shortcut_projection(x)
        x = self.shortcut_down_sample(x)
        x = x + out
        return x


 class Kandinsky3AttentionPooling(nn.Module):
    def __init__(self, num_channels, context_dim, head_dim=64):
        super().__init__()
        self.attention = Attention(
            context_dim,
            context_dim,
            dim_head=head_dim,
            out_dim=num_channels,
            out_bias=False,
        )

    def forward(self, x, context, context_mask=None):
        context_mask = context_mask.to(dtype=context.dtype)
        context = self.attention(context.mean(dim=1, keepdim=True), context, context_mask)
        return x + context.squeeze(1)


 class Kandinsky3AttentionBlock(nn.Module):
    def __init__(self, num_channels, time_embed_dim, context_dim=None, norm_groups=32, head_dim=64, expansion_ratio=4):
        super().__init__()
        self.in_norm = Kandinsky3ConditionalGroupNorm(norm_groups, num_channels, time_embed_dim)
        self.attention = Attention(
            num_channels,
            context_dim or num_channels,
            dim_head=head_dim,
            out_dim=num_channels,
            out_bias=False,
        )

        hidden_channels = expansion_ratio * num_channels
        self.out_norm = Kandinsky3ConditionalGroupNorm(norm_groups, num_channels, time_embed_dim)
        self.feed_forward = nn.Sequential(
            nn.Conv2d(num_channels, hidden_channels, kernel_size=1, bias=False),
            nn.SiLU(),
            nn.Conv2d(hidden_channels, num_channels, kernel_size=1, bias=False),
        )

    def forward(self, x, time_embed, context=None, context_mask=None, image_mask=None):
        height, width = x.shape[-2:]
        out = self.in_norm(x, time_embed)
        out = out.reshape(x.shape[0], -1, height * width).permute(0, 2, 1)
        context = context if context is not None else out
        if context_mask is not None:
            context_mask = context_mask.to(dtype=context.dtype)

        out = self.attention(out, context, context_mask)
        out = out.permute(0, 2, 1).unsqueeze(-1).reshape(out.shape[0], -1, height, width)
        x = x + out

        out = self.out_norm(x, time_embed)
        out = self.feed_forward(out)
        x = x + out
        return x
 from dataclasses import dataclass
 from typing import Dict, Optional, Tuple, Union

 import torch
 import torch.nn as nn

 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import UNet2DConditionLoadersMixin
 from ...utils import BaseOutput, logging
 from ..attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor
 from ..embeddings import TimestepEmbedding, Timesteps
 from ..modeling_utils import ModelMixin
 from .unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block


 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


 @dataclass
 class UNetSpatioTemporalConditionOutput(BaseOutput):
    """
    The output of [`UNetSpatioTemporalConditionModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
    """

    sample: torch.FloatTensor = None


 class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
    r"""
    A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample
    shaped output.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).

    Parameters:
        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
            Height and width of input/output sample.
        in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`):
            The tuple of downsample blocks to use.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`):
            The tuple of upsample blocks to use.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each block.
        addition_time_embed_dim: (`int`, defaults to 256):
            Dimension to to encode the additional time ids.
        projection_class_embeddings_input_dim (`int`, defaults to 768):
            The dimension of the projection of encoded `added_time_ids`.
        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
            The dimension of the cross attention features.
        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
            [`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`], [`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`],
            [`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`].
        num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`):
            The number of attention heads.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        sample_size: Optional[int] = None,
        in_channels: int = 8,
        out_channels: int = 4,
        down_block_types: Tuple[str] = (
            "CrossAttnDownBlockSpatioTemporal",
            "CrossAttnDownBlockSpatioTemporal",
            "CrossAttnDownBlockSpatioTemporal",
            "DownBlockSpatioTemporal",
        ),
        up_block_types: Tuple[str] = (
            "UpBlockSpatioTemporal",
            "CrossAttnUpBlockSpatioTemporal",
            "CrossAttnUpBlockSpatioTemporal",
            "CrossAttnUpBlockSpatioTemporal",
        ),
        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
        addition_time_embed_dim: int = 256,
        projection_class_embeddings_input_dim: int = 768,
        layers_per_block: Union[int, Tuple[int]] = 2,
        cross_attention_dim: Union[int, Tuple[int]] = 1024,
        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
        num_attention_heads: Union[int, Tuple[int]] = (5, 10, 20, 20),
        num_frames: int = 25,
    ):
        super().__init__()

        self.sample_size = sample_size

        # Check inputs
        if len(down_block_types) != len(up_block_types):
            raise ValueError(
                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
            )

        if len(block_out_channels) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
            )

        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
            )

        # input
        self.conv_in = nn.Conv2d(
            in_channels,
            block_out_channels[0],
            kernel_size=3,
            padding=1,
        )

        # time
        time_embed_dim = block_out_channels[0] * 4

        self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0)
        timestep_input_dim = block_out_channels[0]

        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)

        self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0)
        self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)

        self.down_blocks = nn.ModuleList([])
        self.up_blocks = nn.ModuleList([])

        if isinstance(num_attention_heads, int):
            num_attention_heads = (num_attention_heads,) * len(down_block_types)

        if isinstance(cross_attention_dim, int):
            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)

        if isinstance(layers_per_block, int):
            layers_per_block = [layers_per_block] * len(down_block_types)

        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)

        blocks_time_embed_dim = time_embed_dim

        # down
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block[i],
                transformer_layers_per_block=transformer_layers_per_block[i],
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=blocks_time_embed_dim,
                add_downsample=not is_final_block,
                resnet_eps=1e-5,
                cross_attention_dim=cross_attention_dim[i],
                num_attention_heads=num_attention_heads[i],
                resnet_act_fn="silu",
            )
            self.down_blocks.append(down_block)

        # mid
        self.mid_block = UNetMidBlockSpatioTemporal(
            block_out_channels[-1],
            temb_channels=blocks_time_embed_dim,
            transformer_layers_per_block=transformer_layers_per_block[-1],
            cross_attention_dim=cross_attention_dim[-1],
            num_attention_heads=num_attention_heads[-1],
        )

        # count how many layers upsample the images
        self.num_upsamplers = 0

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        reversed_num_attention_heads = list(reversed(num_attention_heads))
        reversed_layers_per_block = list(reversed(layers_per_block))
        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))

        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            is_final_block = i == len(block_out_channels) - 1

            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            # add upsample block for all BUT final layer
            if not is_final_block:
                add_upsample = True
                self.num_upsamplers += 1
            else:
                add_upsample = False

            up_block = get_up_block(
                up_block_type,
                num_layers=reversed_layers_per_block[i] + 1,
                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
                temb_channels=blocks_time_embed_dim,
                add_upsample=add_upsample,
                resnet_eps=1e-5,
                resolution_idx=i,
                cross_attention_dim=reversed_cross_attention_dim[i],
                num_attention_heads=reversed_num_attention_heads[i],
                resnet_act_fn="silu",
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5)
        self.conv_act = nn.SiLU()

        self.conv_out = nn.Conv2d(
            block_out_channels[0],
            out_channels,
            kernel_size=3,
            padding=1,
        )

    @property
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(
            name: str,
            module: torch.nn.Module,
            processors: Dict[str, AttentionProcessor],
        ):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnProcessor()
        else:
            raise ValueError(
                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
            )

        self.set_attn_processor(processor)

    def _set_gradient_checkpointing(self, module, value=False):
        if hasattr(module, "gradient_checkpointing"):
            module.gradient_checkpointing = value

    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
    def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
        """
        Sets the attention processor to use [feed forward
        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).

        Parameters:
            chunk_size (`int`, *optional*):
                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
                over each tensor of dim=`dim`.
            dim (`int`, *optional*, defaults to `0`):
                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
                or dim=1 (sequence length).
        """
        if dim not in [0, 1]:
            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")

        # By default chunk size is 1
        chunk_size = chunk_size or 1

        def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
            if hasattr(module, "set_chunk_feed_forward"):
                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

            for child in module.children():
                fn_recursive_feed_forward(child, chunk_size, dim)

        for module in self.children():
            fn_recursive_feed_forward(module, chunk_size, dim)

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        added_time_ids: torch.Tensor,
        return_dict: bool = True,
    ) -> Union[UNetSpatioTemporalConditionOutput, Tuple]:
        r"""
        The [`UNetSpatioTemporalConditionModel`] forward method.

        Args:
            sample (`torch.FloatTensor`):
                The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`.
            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.FloatTensor`):
                The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`.
            added_time_ids: (`torch.FloatTensor`):
                The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal
                embeddings and added to the time embeddings.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead of a plain
                tuple.
        Returns:
            [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is returned, otherwise
                a `tuple` is returned where the first element is the sample tensor.
        """
        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            is_mps = sample.device.type == "mps"
            if isinstance(timestep, float):
                dtype = torch.float32 if is_mps else torch.float64
            else:
                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        batch_size, num_frames = sample.shape[:2]
        timesteps = timesteps.expand(batch_size)

        t_emb = self.time_proj(timesteps)

        # `Timesteps` does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=sample.dtype)

        emb = self.time_embedding(t_emb)

        time_embeds = self.add_time_proj(added_time_ids.flatten())
        time_embeds = time_embeds.reshape((batch_size, -1))
        time_embeds = time_embeds.to(emb.dtype)
        aug_emb = self.add_embedding(time_embeds)
        emb = emb + aug_emb

        # Flatten the batch and frames dimensions
        # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width]
        sample = sample.flatten(0, 1)
        # Repeat the embeddings num_video_frames times
        # emb: [batch, channels] -> [batch * frames, channels]
        emb = emb.repeat_interleave(num_frames, dim=0)
        # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels]
        encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0)

        # 2. pre-process
        sample = self.conv_in(sample)

        image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device)

        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                )
            else:
                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    image_only_indicator=image_only_indicator,
                )

            down_block_res_samples += res_samples

        # 4. mid
        sample = self.mid_block(
            hidden_states=sample,
            temb=emb,
            encoder_hidden_states=encoder_hidden_states,
            image_only_indicator=image_only_indicator,
        )

        # 5. up
        for i, upsample_block in enumerate(self.up_blocks):
            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    encoder_hidden_states=encoder_hidden_states,
                    image_only_indicator=image_only_indicator,
                )
            else:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    image_only_indicator=image_only_indicator,
                )

        # 6. post-process
        sample = self.conv_norm_out(sample)
        sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        # 7. Reshape back to original shape
        sample = sample.reshape(batch_size, num_frames, *sample.shape[1:])

        if not return_dict:
            return (sample,)

        return UNetSpatioTemporalConditionOutput(sample=sample)


 import flax.linen as nn
 import jax.numpy as jnp

 from ..attention_flax import FlaxTransformer2DModel
 from ..resnet_flax import FlaxDownsample2D, FlaxResnetBlock2D, FlaxUpsample2D


 class FlaxCrossAttnDownBlock2D(nn.Module):
    r"""
    Cross Attention 2D Downsizing block - original architecture from Unet transformers:
    https://arxiv.org/abs/2103.06104

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        out_channels (:obj:`int`):
            Output channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of attention blocks layers
        num_attention_heads (:obj:`int`, *optional*, defaults to 1):
            Number of attention heads of each spatial transformer block
        add_downsample (:obj:`bool`, *optional*, defaults to `True`):
            Whether to add downsampling layer before each final output
        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
            enable memory efficient attention https://arxiv.org/abs/2112.05682
        split_head_dim (`bool`, *optional*, defaults to `False`):
            Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
            enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    out_channels: int
    dropout: float = 0.0
    num_layers: int = 1
    num_attention_heads: int = 1
    add_downsample: bool = True
    use_linear_projection: bool = False
    only_cross_attention: bool = False
    use_memory_efficient_attention: bool = False
    split_head_dim: bool = False
    dtype: jnp.dtype = jnp.float32
    transformer_layers_per_block: int = 1

    def setup(self):
        resnets = []
        attentions = []

        for i in range(self.num_layers):
            in_channels = self.in_channels if i == 0 else self.out_channels

            res_block = FlaxResnetBlock2D(
                in_channels=in_channels,
                out_channels=self.out_channels,
                dropout_prob=self.dropout,
                dtype=self.dtype,
            )
            resnets.append(res_block)

            attn_block = FlaxTransformer2DModel(
                in_channels=self.out_channels,
                n_heads=self.num_attention_heads,
                d_head=self.out_channels // self.num_attention_heads,
                depth=self.transformer_layers_per_block,
                use_linear_projection=self.use_linear_projection,
                only_cross_attention=self.only_cross_attention,
                use_memory_efficient_attention=self.use_memory_efficient_attention,
                split_head_dim=self.split_head_dim,
                dtype=self.dtype,
            )
            attentions.append(attn_block)

        self.resnets = resnets
        self.attentions = attentions

        if self.add_downsample:
            self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)

    def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
        output_states = ()

        for resnet, attn in zip(self.resnets, self.attentions):
            hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
            hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
            output_states += (hidden_states,)

        if self.add_downsample:
            hidden_states = self.downsamplers_0(hidden_states)
            output_states += (hidden_states,)

        return hidden_states, output_states


 class FlaxDownBlock2D(nn.Module):
    r"""
    Flax 2D downsizing block

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        out_channels (:obj:`int`):
            Output channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of attention blocks layers
        add_downsample (:obj:`bool`, *optional*, defaults to `True`):
            Whether to add downsampling layer before each final output
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    out_channels: int
    dropout: float = 0.0
    num_layers: int = 1
    add_downsample: bool = True
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        resnets = []

        for i in range(self.num_layers):
            in_channels = self.in_channels if i == 0 else self.out_channels

            res_block = FlaxResnetBlock2D(
                in_channels=in_channels,
                out_channels=self.out_channels,
                dropout_prob=self.dropout,
                dtype=self.dtype,
            )
            resnets.append(res_block)
        self.resnets = resnets

        if self.add_downsample:
            self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)

    def __call__(self, hidden_states, temb, deterministic=True):
        output_states = ()

        for resnet in self.resnets:
            hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
            output_states += (hidden_states,)

        if self.add_downsample:
            hidden_states = self.downsamplers_0(hidden_states)
            output_states += (hidden_states,)

        return hidden_states, output_states


 class FlaxCrossAttnUpBlock2D(nn.Module):
    r"""
    Cross Attention 2D Upsampling block - original architecture from Unet transformers:
    https://arxiv.org/abs/2103.06104

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        out_channels (:obj:`int`):
            Output channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of attention blocks layers
        num_attention_heads (:obj:`int`, *optional*, defaults to 1):
            Number of attention heads of each spatial transformer block
        add_upsample (:obj:`bool`, *optional*, defaults to `True`):
            Whether to add upsampling layer before each final output
        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
            enable memory efficient attention https://arxiv.org/abs/2112.05682
        split_head_dim (`bool`, *optional*, defaults to `False`):
            Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
            enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    out_channels: int
    prev_output_channel: int
    dropout: float = 0.0
    num_layers: int = 1
    num_attention_heads: int = 1
    add_upsample: bool = True
    use_linear_projection: bool = False
    only_cross_attention: bool = False
    use_memory_efficient_attention: bool = False
    split_head_dim: bool = False
    dtype: jnp.dtype = jnp.float32
    transformer_layers_per_block: int = 1

    def setup(self):
        resnets = []
        attentions = []

        for i in range(self.num_layers):
            res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
            resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels

            res_block = FlaxResnetBlock2D(
                in_channels=resnet_in_channels + res_skip_channels,
                out_channels=self.out_channels,
                dropout_prob=self.dropout,
                dtype=self.dtype,
            )
            resnets.append(res_block)

            attn_block = FlaxTransformer2DModel(
                in_channels=self.out_channels,
                n_heads=self.num_attention_heads,
                d_head=self.out_channels // self.num_attention_heads,
                depth=self.transformer_layers_per_block,
                use_linear_projection=self.use_linear_projection,
                only_cross_attention=self.only_cross_attention,
                use_memory_efficient_attention=self.use_memory_efficient_attention,
                split_head_dim=self.split_head_dim,
                dtype=self.dtype,
            )
            attentions.append(attn_block)

        self.resnets = resnets
        self.attentions = attentions

        if self.add_upsample:
            self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)

    def __call__(self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states, deterministic=True):
        for resnet, attn in zip(self.resnets, self.attentions):
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
            hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)

            hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
            hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)

        if self.add_upsample:
            hidden_states = self.upsamplers_0(hidden_states)

        return hidden_states


 class FlaxUpBlock2D(nn.Module):
    r"""
    Flax 2D upsampling block

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        out_channels (:obj:`int`):
            Output channels
        prev_output_channel (:obj:`int`):
            Output channels from the previous block
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of attention blocks layers
        add_downsample (:obj:`bool`, *optional*, defaults to `True`):
            Whether to add downsampling layer before each final output
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    out_channels: int
    prev_output_channel: int
    dropout: float = 0.0
    num_layers: int = 1
    add_upsample: bool = True
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        resnets = []

        for i in range(self.num_layers):
            res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
            resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels

            res_block = FlaxResnetBlock2D(
                in_channels=resnet_in_channels + res_skip_channels,
                out_channels=self.out_channels,
                dropout_prob=self.dropout,
                dtype=self.dtype,
            )
            resnets.append(res_block)

        self.resnets = resnets

        if self.add_upsample:
            self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)

    def __call__(self, hidden_states, res_hidden_states_tuple, temb, deterministic=True):
        for resnet in self.resnets:
            # pop res hidden states
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
            hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)

            hidden_states = resnet(hidden_states, temb, deterministic=deterministic)

        if self.add_upsample:
            hidden_states = self.upsamplers_0(hidden_states)

        return hidden_states


 class FlaxUNetMidBlock2DCrossAttn(nn.Module):
    r"""
    Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of attention blocks layers
        num_attention_heads (:obj:`int`, *optional*, defaults to 1):
            Number of attention heads of each spatial transformer block
        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
            enable memory efficient attention https://arxiv.org/abs/2112.05682
        split_head_dim (`bool`, *optional*, defaults to `False`):
            Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
            enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    dropout: float = 0.0
    num_layers: int = 1
    num_attention_heads: int = 1
    use_linear_projection: bool = False
    use_memory_efficient_attention: bool = False
    split_head_dim: bool = False
    dtype: jnp.dtype = jnp.float32
    transformer_layers_per_block: int = 1

    def setup(self):
        # there is always at least one resnet
        resnets = [
            FlaxResnetBlock2D(
                in_channels=self.in_channels,
                out_channels=self.in_channels,
                dropout_prob=self.dropout,
                dtype=self.dtype,
            )
        ]

        attentions = []

        for _ in range(self.num_layers):
            attn_block = FlaxTransformer2DModel(
                in_channels=self.in_channels,
                n_heads=self.num_attention_heads,
                d_head=self.in_channels // self.num_attention_heads,
                depth=self.transformer_layers_per_block,
                use_linear_projection=self.use_linear_projection,
                use_memory_efficient_attention=self.use_memory_efficient_attention,
                split_head_dim=self.split_head_dim,
                dtype=self.dtype,
            )
            attentions.append(attn_block)

            res_block = FlaxResnetBlock2D(
                in_channels=self.in_channels,
                out_channels=self.in_channels,
                dropout_prob=self.dropout,
                dtype=self.dtype,
            )
            resnets.append(res_block)

        self.resnets = resnets
        self.attentions = attentions

    def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
        hidden_states = self.resnets[0](hidden_states, temb)
        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
            hidden_states = resnet(hidden_states, temb, deterministic=deterministic)

        return hidden_states

 from dataclasses import dataclass
 from typing import Optional, Tuple, Union

 import torch
 import torch.nn as nn

 from ...configuration_utils import ConfigMixin, register_to_config
 from ...utils import BaseOutput
 from ..embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
 from ..modeling_utils import ModelMixin
 from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block


 @dataclass
 class UNet2DOutput(BaseOutput):
    """
    The output of [`UNet2DModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            The hidden states output from the last layer of the model.
    """

    sample: torch.FloatTensor


 class UNet2DModel(ModelMixin, ConfigMixin):
    r"""
    A 2D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).

    Parameters:
        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
            Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) -
            1)`.
        in_channels (`int`, *optional*, defaults to 3): Number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
        time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use.
        freq_shift (`int`, *optional*, defaults to 0): Frequency shift for Fourier time embedding.
        flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
            Whether to flip sin to cos for Fourier time embedding.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`):
            Tuple of downsample block types.
        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`):
            Block type for middle of UNet, it can be either `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`):
            Tuple of upsample block types.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`):
            Tuple of block output channels.
        layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.
        mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.
        downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution.
        downsample_type (`str`, *optional*, defaults to `conv`):
            The downsample type for downsampling layers. Choose between "conv" and "resnet"
        upsample_type (`str`, *optional*, defaults to `conv`):
            The upsample type for upsampling layers. Choose between "conv" and "resnet"
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
        norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization.
        attn_norm_num_groups (`int`, *optional*, defaults to `None`):
            If set to an integer, a group norm layer will be created in the mid block's [`Attention`] layer with the
            given number of groups. If left as `None`, the group norm layer will only be created if
            `resnet_time_scale_shift` is set to `default`, and if created will have `norm_num_groups` groups.
        norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for normalization.
        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
        class_embed_type (`str`, *optional*, defaults to `None`):
            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
            `"timestep"`, or `"identity"`.
        num_class_embeds (`int`, *optional*, defaults to `None`):
            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim` when performing class
            conditioning with `class_embed_type` equal to `None`.
    """

    @register_to_config
    def __init__(
        self,
        sample_size: Optional[Union[int, Tuple[int, int]]] = None,
        in_channels: int = 3,
        out_channels: int = 3,
        center_input_sample: bool = False,
        time_embedding_type: str = "positional",
        freq_shift: int = 0,
        flip_sin_to_cos: bool = True,
        down_block_types: Tuple[str, ...] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
        up_block_types: Tuple[str, ...] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
        block_out_channels: Tuple[int, ...] = (224, 448, 672, 896),
        layers_per_block: int = 2,
        mid_block_scale_factor: float = 1,
        downsample_padding: int = 1,
        downsample_type: str = "conv",
        upsample_type: str = "conv",
        dropout: float = 0.0,
        act_fn: str = "silu",
        attention_head_dim: Optional[int] = 8,
        norm_num_groups: int = 32,
        attn_norm_num_groups: Optional[int] = None,
        norm_eps: float = 1e-5,
        resnet_time_scale_shift: str = "default",
        add_attention: bool = True,
        class_embed_type: Optional[str] = None,
        num_class_embeds: Optional[int] = None,
        num_train_timesteps: Optional[int] = None,
    ):
        super().__init__()

        self.sample_size = sample_size
        time_embed_dim = block_out_channels[0] * 4

        # Check inputs
        if len(down_block_types) != len(up_block_types):
            raise ValueError(
                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
            )

        if len(block_out_channels) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
            )

        # input
        self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))

        # time
        if time_embedding_type == "fourier":
            self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16)
            timestep_input_dim = 2 * block_out_channels[0]
        elif time_embedding_type == "positional":
            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
            timestep_input_dim = block_out_channels[0]
        elif time_embedding_type == "learned":
            self.time_proj = nn.Embedding(num_train_timesteps, block_out_channels[0])
            timestep_input_dim = block_out_channels[0]

        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
        elif class_embed_type == "timestep":
            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
        elif class_embed_type == "identity":
            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
        else:
            self.class_embedding = None

        self.down_blocks = nn.ModuleList([])
        self.mid_block = None
        self.up_blocks = nn.ModuleList([])

        # down
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block,
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=time_embed_dim,
                add_downsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
                downsample_padding=downsample_padding,
                resnet_time_scale_shift=resnet_time_scale_shift,
                downsample_type=downsample_type,
                dropout=dropout,
            )
            self.down_blocks.append(down_block)

        # mid
        self.mid_block = UNetMidBlock2D(
            in_channels=block_out_channels[-1],
            temb_channels=time_embed_dim,
            dropout=dropout,
            resnet_eps=norm_eps,
            resnet_act_fn=act_fn,
            output_scale_factor=mid_block_scale_factor,
            resnet_time_scale_shift=resnet_time_scale_shift,
            attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],
            resnet_groups=norm_num_groups,
            attn_groups=attn_norm_num_groups,
            add_attention=add_attention,
        )

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            is_final_block = i == len(block_out_channels) - 1

            up_block = get_up_block(
                up_block_type,
                num_layers=layers_per_block + 1,
                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
                temb_channels=time_embed_dim,
                add_upsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
                resnet_time_scale_shift=resnet_time_scale_shift,
                upsample_type=upsample_type,
                dropout=dropout,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps)
        self.conv_act = nn.SiLU()
        self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        class_labels: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ) -> Union[UNet2DOutput, Tuple]:
        r"""
        The [`UNet2DModel`] forward method.

        Args:
            sample (`torch.FloatTensor`):
                The noisy input tensor with the following shape `(batch, channel, height, width)`.
            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
            class_labels (`torch.FloatTensor`, *optional*, defaults to `None`):
                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple.

        Returns:
            [`~models.unet_2d.UNet2DOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is
                returned where the first element is the sample tensor.
        """
        # 0. center input if necessary
        if self.config.center_input_sample:
            sample = 2 * sample - 1.0

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device)

        t_emb = self.time_proj(timesteps)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=self.dtype)
        emb = self.time_embedding(t_emb)

        if self.class_embedding is not None:
            if class_labels is None:
                raise ValueError("class_labels should be provided when doing class conditioning")

            if self.config.class_embed_type == "timestep":
                class_labels = self.time_proj(class_labels)

            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
            emb = emb + class_emb
        elif self.class_embedding is None and class_labels is not None:
            raise ValueError("class_embedding needs to be initialized in order to use class conditioning")

        # 2. pre-process
        skip_sample = sample
        sample = self.conv_in(sample)

        # 3. down
        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "skip_conv"):
                sample, res_samples, skip_sample = downsample_block(
                    hidden_states=sample, temb=emb, skip_sample=skip_sample
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)

            down_block_res_samples += res_samples

        # 4. mid
        sample = self.mid_block(sample, emb)

        # 5. up
        skip_sample = None
        for upsample_block in self.up_blocks:
            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

            if hasattr(upsample_block, "skip_conv"):
                sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample)
            else:
                sample = upsample_block(sample, res_samples, emb)

        # 6. post-process
        sample = self.conv_norm_out(sample)
        sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        if skip_sample is not None:
            sample += skip_sample

        if self.config.time_embedding_type == "fourier":
            timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
            sample = sample / timesteps

        if not return_dict:
            return (sample,)

        return UNet2DOutput(sample=sample)

 from typing import Any, Dict, Optional, Tuple, Union

 import torch
 import torch.nn as nn
 import torch.utils.checkpoint

 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import UNet2DConditionLoadersMixin
 from ...utils import logging
 from ..attention_processor import (
    ADDED_KV_ATTENTION_PROCESSORS,
    CROSS_ATTENTION_PROCESSORS,
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor,
 )
 from ..embeddings import TimestepEmbedding, Timesteps
 from ..modeling_utils import ModelMixin
 from ..transformers.transformer_temporal import TransformerTemporalModel
 from .unet_2d_blocks import UNetMidBlock2DCrossAttn
 from .unet_2d_condition import UNet2DConditionModel
 from .unet_3d_blocks import (
    CrossAttnDownBlockMotion,
    CrossAttnUpBlockMotion,
    DownBlockMotion,
    UNetMidBlockCrossAttnMotion,
    UpBlockMotion,
    get_down_block,
    get_up_block,
 )
 from .unet_3d_condition import UNet3DConditionOutput


 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


 class MotionModules(nn.Module):
    def __init__(
        self,
        in_channels: int,
        layers_per_block: int = 2,
        num_attention_heads: int = 8,
        attention_bias: bool = False,
        cross_attention_dim: Optional[int] = None,
        activation_fn: str = "geglu",
        norm_num_groups: int = 32,
        max_seq_length: int = 32,
    ):
        super().__init__()
        self.motion_modules = nn.ModuleList([])

        for i in range(layers_per_block):
            self.motion_modules.append(
                TransformerTemporalModel(
                    in_channels=in_channels,
                    norm_num_groups=norm_num_groups,
                    cross_attention_dim=cross_attention_dim,
                    activation_fn=activation_fn,
                    attention_bias=attention_bias,
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=in_channels // num_attention_heads,
                    positional_embeddings="sinusoidal",
                    num_positional_embeddings=max_seq_length,
                )
            )


 class MotionAdapter(ModelMixin, ConfigMixin):
    @register_to_config
    def __init__(
        self,
        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
        motion_layers_per_block: int = 2,
        motion_mid_block_layers_per_block: int = 1,
        motion_num_attention_heads: int = 8,
        motion_norm_num_groups: int = 32,
        motion_max_seq_length: int = 32,
        use_motion_mid_block: bool = True,
        conv_in_channels: Optional[int] = None,
    ):
        """Container to store AnimateDiff Motion Modules

        Args:
            block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each UNet block.
            motion_layers_per_block (`int`, *optional*, defaults to 2):
                The number of motion layers per UNet block.
            motion_mid_block_layers_per_block (`int`, *optional*, defaults to 1):
                The number of motion layers in the middle UNet block.
            motion_num_attention_heads (`int`, *optional*, defaults to 8):
                The number of heads to use in each attention layer of the motion module.
            motion_norm_num_groups (`int`, *optional*, defaults to 32):
                The number of groups to use in each group normalization layer of the motion module.
            motion_max_seq_length (`int`, *optional*, defaults to 32):
                The maximum sequence length to use in the motion module.
            use_motion_mid_block (`bool`, *optional*, defaults to True):
                Whether to use a motion module in the middle of the UNet.
        """

        super().__init__()
        down_blocks = []
        up_blocks = []

        if conv_in_channels:
            # input
            self.conv_in = nn.Conv2d(conv_in_channels, block_out_channels[0], kernel_size=3, padding=1)
        else:
            self.conv_in = None

        for i, channel in enumerate(block_out_channels):
            output_channel = block_out_channels[i]
            down_blocks.append(
                MotionModules(
                    in_channels=output_channel,
                    norm_num_groups=motion_norm_num_groups,
                    cross_attention_dim=None,
                    activation_fn="geglu",
                    attention_bias=False,
                    num_attention_heads=motion_num_attention_heads,
                    max_seq_length=motion_max_seq_length,
                    layers_per_block=motion_layers_per_block,
                )
            )

        if use_motion_mid_block:
            self.mid_block = MotionModules(
                in_channels=block_out_channels[-1],
                norm_num_groups=motion_norm_num_groups,
                cross_attention_dim=None,
                activation_fn="geglu",
                attention_bias=False,
                num_attention_heads=motion_num_attention_heads,
                layers_per_block=motion_mid_block_layers_per_block,
                max_seq_length=motion_max_seq_length,
            )
        else:
            self.mid_block = None

        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        for i, channel in enumerate(reversed_block_out_channels):
            output_channel = reversed_block_out_channels[i]
            up_blocks.append(
                MotionModules(
                    in_channels=output_channel,
                    norm_num_groups=motion_norm_num_groups,
                    cross_attention_dim=None,
                    activation_fn="geglu",
                    attention_bias=False,
                    num_attention_heads=motion_num_attention_heads,
                    max_seq_length=motion_max_seq_length,
                    layers_per_block=motion_layers_per_block + 1,
                )
            )

        self.down_blocks = nn.ModuleList(down_blocks)
        self.up_blocks = nn.ModuleList(up_blocks)

    def forward(self, sample):
        pass


 class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
    r"""
    A modified conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a
    sample shaped output.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        sample_size: Optional[int] = None,
        in_channels: int = 4,
        out_channels: int = 4,
        down_block_types: Tuple[str, ...] = (
            "CrossAttnDownBlockMotion",
            "CrossAttnDownBlockMotion",
            "CrossAttnDownBlockMotion",
            "DownBlockMotion",
        ),
        up_block_types: Tuple[str, ...] = (
            "UpBlockMotion",
            "CrossAttnUpBlockMotion",
            "CrossAttnUpBlockMotion",
            "CrossAttnUpBlockMotion",
        ),
        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
        layers_per_block: int = 2,
        downsample_padding: int = 1,
        mid_block_scale_factor: float = 1,
        act_fn: str = "silu",
        norm_num_groups: int = 32,
        norm_eps: float = 1e-5,
        cross_attention_dim: int = 1280,
        use_linear_projection: bool = False,
        num_attention_heads: Union[int, Tuple[int, ...]] = 8,
        motion_max_seq_length: int = 32,
        motion_num_attention_heads: int = 8,
        use_motion_mid_block: int = True,
        encoder_hid_dim: Optional[int] = None,
        encoder_hid_dim_type: Optional[str] = None,
        time_cond_proj_dim: Optional[int] = None,
    ):
        super().__init__()

        self.sample_size = sample_size

        # Check inputs
        if len(down_block_types) != len(up_block_types):
            raise ValueError(
                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
            )

        if len(block_out_channels) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
            )

        # input
        conv_in_kernel = 3
        conv_out_kernel = 3
        conv_in_padding = (conv_in_kernel - 1) // 2
        self.conv_in = nn.Conv2d(
            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
        )

        # time
        time_embed_dim = block_out_channels[0] * 4
        self.time_proj = Timesteps(block_out_channels[0], True, 0)
        timestep_input_dim = block_out_channels[0]

        self.time_embedding = TimestepEmbedding(
            timestep_input_dim, time_embed_dim, act_fn=act_fn, cond_proj_dim=time_cond_proj_dim
        )

        if encoder_hid_dim_type is None:
            self.encoder_hid_proj = None

        # class embedding
        self.down_blocks = nn.ModuleList([])
        self.up_blocks = nn.ModuleList([])

        if isinstance(num_attention_heads, int):
            num_attention_heads = (num_attention_heads,) * len(down_block_types)

        # down
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block,
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=time_embed_dim,
                add_downsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=cross_attention_dim,
                num_attention_heads=num_attention_heads[i],
                downsample_padding=downsample_padding,
                use_linear_projection=use_linear_projection,
                dual_cross_attention=False,
                temporal_num_attention_heads=motion_num_attention_heads,
                temporal_max_seq_length=motion_max_seq_length,
            )
            self.down_blocks.append(down_block)

        # mid
        if use_motion_mid_block:
            self.mid_block = UNetMidBlockCrossAttnMotion(
                in_channels=block_out_channels[-1],
                temb_channels=time_embed_dim,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                output_scale_factor=mid_block_scale_factor,
                cross_attention_dim=cross_attention_dim,
                num_attention_heads=num_attention_heads[-1],
                resnet_groups=norm_num_groups,
                dual_cross_attention=False,
                use_linear_projection=use_linear_projection,
                temporal_num_attention_heads=motion_num_attention_heads,
                temporal_max_seq_length=motion_max_seq_length,
            )

        else:
            self.mid_block = UNetMidBlock2DCrossAttn(
                in_channels=block_out_channels[-1],
                temb_channels=time_embed_dim,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                output_scale_factor=mid_block_scale_factor,
                cross_attention_dim=cross_attention_dim,
                num_attention_heads=num_attention_heads[-1],
                resnet_groups=norm_num_groups,
                dual_cross_attention=False,
                use_linear_projection=use_linear_projection,
            )

        # count how many layers upsample the images
        self.num_upsamplers = 0

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        reversed_num_attention_heads = list(reversed(num_attention_heads))

        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            is_final_block = i == len(block_out_channels) - 1

            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            # add upsample block for all BUT final layer
            if not is_final_block:
                add_upsample = True
                self.num_upsamplers += 1
            else:
                add_upsample = False

            up_block = get_up_block(
                up_block_type,
                num_layers=layers_per_block + 1,
                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
                temb_channels=time_embed_dim,
                add_upsample=add_upsample,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                cross_attention_dim=cross_attention_dim,
                num_attention_heads=reversed_num_attention_heads[i],
                dual_cross_attention=False,
                resolution_idx=i,
                use_linear_projection=use_linear_projection,
                temporal_num_attention_heads=motion_num_attention_heads,
                temporal_max_seq_length=motion_max_seq_length,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        if norm_num_groups is not None:
            self.conv_norm_out = nn.GroupNorm(
                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
            )
            self.conv_act = nn.SiLU()
        else:
            self.conv_norm_out = None
            self.conv_act = None

        conv_out_padding = (conv_out_kernel - 1) // 2
        self.conv_out = nn.Conv2d(
            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
        )

    @classmethod
    def from_unet2d(
        cls,
        unet: UNet2DConditionModel,
        motion_adapter: Optional[MotionAdapter] = None,
        load_weights: bool = True,
    ):
        has_motion_adapter = motion_adapter is not None

        # based on https://github.com/guoyww/AnimateDiff/blob/895f3220c06318ea0760131ec70408b466c49333/animatediff/models/unet.py#L459
        config = unet.config
        config["_class_name"] = cls.__name__

        down_blocks = []
        for down_blocks_type in config["down_block_types"]:
            if "CrossAttn" in down_blocks_type:
                down_blocks.append("CrossAttnDownBlockMotion")
            else:
                down_blocks.append("DownBlockMotion")
        config["down_block_types"] = down_blocks

        up_blocks = []
        for down_blocks_type in config["up_block_types"]:
            if "CrossAttn" in down_blocks_type:
                up_blocks.append("CrossAttnUpBlockMotion")
            else:
                up_blocks.append("UpBlockMotion")

        config["up_block_types"] = up_blocks

        if has_motion_adapter:
            config["motion_num_attention_heads"] = motion_adapter.config["motion_num_attention_heads"]
            config["motion_max_seq_length"] = motion_adapter.config["motion_max_seq_length"]
            config["use_motion_mid_block"] = motion_adapter.config["use_motion_mid_block"]

            # For PIA UNets we need to set the number input channels to 9
            if motion_adapter.config["conv_in_channels"]:
                config["in_channels"] = motion_adapter.config["conv_in_channels"]

        # Need this for backwards compatibility with UNet2DConditionModel checkpoints
        if not config.get("num_attention_heads"):
            config["num_attention_heads"] = config["attention_head_dim"]

        model = cls.from_config(config)

        if not load_weights:
            return model

        # Logic for loading PIA UNets which allow the first 4 channels to be any UNet2DConditionModel conv_in weight
        # while the last 5 channels must be PIA conv_in weights.
        if has_motion_adapter and motion_adapter.config["conv_in_channels"]:
            model.conv_in = motion_adapter.conv_in
            updated_conv_in_weight = torch.cat(
                [unet.conv_in.weight, motion_adapter.conv_in.weight[:, 4:, :, :]], dim=1
            )
            model.conv_in.load_state_dict({"weight": updated_conv_in_weight, "bias": unet.conv_in.bias})
        else:
            model.conv_in.load_state_dict(unet.conv_in.state_dict())

        model.time_proj.load_state_dict(unet.time_proj.state_dict())
        model.time_embedding.load_state_dict(unet.time_embedding.state_dict())

        for i, down_block in enumerate(unet.down_blocks):
            model.down_blocks[i].resnets.load_state_dict(down_block.resnets.state_dict())
            if hasattr(model.down_blocks[i], "attentions"):
                model.down_blocks[i].attentions.load_state_dict(down_block.attentions.state_dict())
            if model.down_blocks[i].downsamplers:
                model.down_blocks[i].downsamplers.load_state_dict(down_block.downsamplers.state_dict())

        for i, up_block in enumerate(unet.up_blocks):
            model.up_blocks[i].resnets.load_state_dict(up_block.resnets.state_dict())
            if hasattr(model.up_blocks[i], "attentions"):
                model.up_blocks[i].attentions.load_state_dict(up_block.attentions.state_dict())
            if model.up_blocks[i].upsamplers:
                model.up_blocks[i].upsamplers.load_state_dict(up_block.upsamplers.state_dict())

        model.mid_block.resnets.load_state_dict(unet.mid_block.resnets.state_dict())
        model.mid_block.attentions.load_state_dict(unet.mid_block.attentions.state_dict())

        if unet.conv_norm_out is not None:
            model.conv_norm_out.load_state_dict(unet.conv_norm_out.state_dict())
        if unet.conv_act is not None:
            model.conv_act.load_state_dict(unet.conv_act.state_dict())
        model.conv_out.load_state_dict(unet.conv_out.state_dict())

        if has_motion_adapter:
            model.load_motion_modules(motion_adapter)

        # ensure that the Motion UNet is the same dtype as the UNet2DConditionModel
        model.to(unet.dtype)

        return model

    def freeze_unet2d_params(self) -> None:
        """Freeze the weights of just the UNet2DConditionModel, and leave the motion modules
        unfrozen for fine tuning.
        """
        # Freeze everything
        for param in self.parameters():
            param.requires_grad = False

        # Unfreeze Motion Modules
        for down_block in self.down_blocks:
            motion_modules = down_block.motion_modules
            for param in motion_modules.parameters():
                param.requires_grad = True

        for up_block in self.up_blocks:
            motion_modules = up_block.motion_modules
            for param in motion_modules.parameters():
                param.requires_grad = True

        if hasattr(self.mid_block, "motion_modules"):
            motion_modules = self.mid_block.motion_modules
            for param in motion_modules.parameters():
                param.requires_grad = True

    def load_motion_modules(self, motion_adapter: Optional[MotionAdapter]) -> None:
        for i, down_block in enumerate(motion_adapter.down_blocks):
            self.down_blocks[i].motion_modules.load_state_dict(down_block.motion_modules.state_dict())
        for i, up_block in enumerate(motion_adapter.up_blocks):
            self.up_blocks[i].motion_modules.load_state_dict(up_block.motion_modules.state_dict())

        # to support older motion modules that don't have a mid_block
        if hasattr(self.mid_block, "motion_modules"):
            self.mid_block.motion_modules.load_state_dict(motion_adapter.mid_block.motion_modules.state_dict())

    def save_motion_modules(
        self,
        save_directory: str,
        is_main_process: bool = True,
        safe_serialization: bool = True,
        variant: Optional[str] = None,
        push_to_hub: bool = False,
        **kwargs,
    ) -> None:
        state_dict = self.state_dict()

        # Extract all motion modules
        motion_state_dict = {}
        for k, v in state_dict.items():
            if "motion_modules" in k:
                motion_state_dict[k] = v

        adapter = MotionAdapter(
            block_out_channels=self.config["block_out_channels"],
            motion_layers_per_block=self.config["layers_per_block"],
            motion_norm_num_groups=self.config["norm_num_groups"],
            motion_num_attention_heads=self.config["motion_num_attention_heads"],
            motion_max_seq_length=self.config["motion_max_seq_length"],
            use_motion_mid_block=self.config["use_motion_mid_block"],
        )
        adapter.load_state_dict(motion_state_dict)
        adapter.save_pretrained(
            save_directory=save_directory,
            is_main_process=is_main_process,
            safe_serialization=safe_serialization,
            variant=variant,
            push_to_hub=push_to_hub,
            **kwargs,
        )

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
    def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
        """
        Sets the attention processor to use [feed forward
        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).

        Parameters:
            chunk_size (`int`, *optional*):
                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
                over each tensor of dim=`dim`.
            dim (`int`, *optional*, defaults to `0`):
                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
                or dim=1 (sequence length).
        """
        if dim not in [0, 1]:
            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")

        # By default chunk size is 1
        chunk_size = chunk_size or 1

        def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
            if hasattr(module, "set_chunk_feed_forward"):
                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

            for child in module.children():
                fn_recursive_feed_forward(child, chunk_size, dim)

        for module in self.children():
            fn_recursive_feed_forward(module, chunk_size, dim)

    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
    def disable_forward_chunking(self) -> None:
        def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
            if hasattr(module, "set_chunk_feed_forward"):
                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

            for child in module.children():
                fn_recursive_feed_forward(child, chunk_size, dim)

        for module in self.children():
            fn_recursive_feed_forward(module, None, 0)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
    def set_default_attn_processor(self) -> None:
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnAddedKVProcessor()
        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnProcessor()
        else:
            raise ValueError(
                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
            )

        self.set_attn_processor(processor)

    def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
        if isinstance(module, (CrossAttnDownBlockMotion, DownBlockMotion, CrossAttnUpBlockMotion, UpBlockMotion)):
            module.gradient_checkpointing = value

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu
    def enable_freeu(self, s1: float, s2: float, b1: float, b2: float) -> None:
        r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.

        The suffixes after the scaling factors represent the stage blocks where they are being applied.

        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
        are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.

        Args:
            s1 (`float`):
                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
                mitigate the "oversmoothing effect" in the enhanced denoising process.
            s2 (`float`):
                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
                mitigate the "oversmoothing effect" in the enhanced denoising process.
            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
        """
        for i, upsample_block in enumerate(self.up_blocks):
            setattr(upsample_block, "s1", s1)
            setattr(upsample_block, "s2", s2)
            setattr(upsample_block, "b1", b1)
            setattr(upsample_block, "b2", b2)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu
    def disable_freeu(self) -> None:
        """Disables the FreeU mechanism."""
        freeu_keys = {"s1", "s2", "b1", "b2"}
        for i, upsample_block in enumerate(self.up_blocks):
            for k in freeu_keys:
                if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
                    setattr(upsample_block, k, None)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
    def fuse_qkv_projections(self):
        """
        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,
        key, value) are fused. For cross-attention modules, key and value projection matrices are fused.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>
        """
        self.original_attn_processors = None

        for _, attn_processor in self.attn_processors.items():
            if "Added" in str(attn_processor.__class__.__name__):
                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")

        self.original_attn_processors = self.attn_processors

        for module in self.modules():
            if isinstance(module, Attention):
                module.fuse_projections(fuse=True)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
    def unfuse_qkv_projections(self):
        """Disables the fused QKV projection if enabled.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>

        """
        if self.original_attn_processors is not None:
            self.set_attn_processor(self.original_attn_processors)

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        mid_block_additional_residual: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ) -> Union[UNet3DConditionOutput, Tuple[torch.Tensor]]:
        r"""
        The [`UNetMotionModel`] forward method.

        Args:
            sample (`torch.FloatTensor`):
                The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.
            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.FloatTensor`):
                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
            timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
                Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
                through the `self.time_embedding` layer to obtain the timestep embeddings.
            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
                A tuple of tensors that if specified are added to the residuals of down unet blocks.
            mid_block_additional_residual: (`torch.Tensor`, *optional*):
                A tensor that if specified is added to the residual of the middle unet block.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_3d_condition.UNet3DConditionOutput`] instead of a plain
                tuple.

        Returns:
            [`~models.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.unet_3d_condition.UNet3DConditionOutput`] is returned, otherwise
                a `tuple` is returned where the first element is the sample tensor.
        """
        # By default samples have to be AT least a multiple of the overall upsampling factor.
        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
        # However, the upsampling interpolation output size can be forced to fit any upsampling size
        # on the fly if necessary.
        default_overall_up_factor = 2**self.num_upsamplers

        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
        forward_upsample_size = False
        upsample_size = None

        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
            logger.info("Forward upsample size to force interpolation output size.")
            forward_upsample_size = True

        # prepare attention_mask
        if attention_mask is not None:
            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            is_mps = sample.device.type == "mps"
            if isinstance(timestep, float):
                dtype = torch.float32 if is_mps else torch.float64
            else:
                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        num_frames = sample.shape[2]
        timesteps = timesteps.expand(sample.shape[0])

        t_emb = self.time_proj(timesteps)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=self.dtype)

        emb = self.time_embedding(t_emb, timestep_cond)
        emb = emb.repeat_interleave(repeats=num_frames, dim=0)
        encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)

        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
                )
            image_embeds = added_cond_kwargs.get("image_embeds")
            image_embeds = self.encoder_hid_proj(image_embeds)
            image_embeds = [image_embed.repeat_interleave(repeats=num_frames, dim=0) for image_embed in image_embeds]
            encoder_hidden_states = (encoder_hidden_states, image_embeds)

        # 2. pre-process
        sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])
        sample = self.conv_in(sample)

        # 3. down
        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    num_frames=num_frames,
                    cross_attention_kwargs=cross_attention_kwargs,
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)

            down_block_res_samples += res_samples

        if down_block_additional_residuals is not None:
            new_down_block_res_samples = ()

            for down_block_res_sample, down_block_additional_residual in zip(
                down_block_res_samples, down_block_additional_residuals
            ):
                down_block_res_sample = down_block_res_sample + down_block_additional_residual
                new_down_block_res_samples += (down_block_res_sample,)

            down_block_res_samples = new_down_block_res_samples

        # 4. mid
        if self.mid_block is not None:
            # To support older versions of motion modules that don't have a mid_block
            if hasattr(self.mid_block, "motion_modules"):
                sample = self.mid_block(
                    sample,
                    emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    num_frames=num_frames,
                    cross_attention_kwargs=cross_attention_kwargs,
                )
            else:
                sample = self.mid_block(
                    sample,
                    emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                )

        if mid_block_additional_residual is not None:
            sample = sample + mid_block_additional_residual

        # 5. up
        for i, upsample_block in enumerate(self.up_blocks):
            is_final_block = i == len(self.up_blocks) - 1

            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

            # if we have not reached the final block and need to forward the
            # upsample size, we do it here
            if not is_final_block and forward_upsample_size:
                upsample_size = down_block_res_samples[-1].shape[2:]

            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    encoder_hidden_states=encoder_hidden_states,
                    upsample_size=upsample_size,
                    attention_mask=attention_mask,
                    num_frames=num_frames,
                    cross_attention_kwargs=cross_attention_kwargs,
                )
            else:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    upsample_size=upsample_size,
                    num_frames=num_frames,
                )

        # 6. post-process
        if self.conv_norm_out:
            sample = self.conv_norm_out(sample)
            sample = self.conv_act(sample)

        sample = self.conv_out(sample)

        # reshape to (batch, channel, framerate, width, height)
        sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)

        if not return_dict:
            return (sample,)

        return UNet3DConditionOutput(sample=sample)

 import math
 from typing import Optional, Tuple, Union

 import torch
 import torch.nn.functional as F
 from torch import nn

 from ..activations import get_activation
 from ..resnet import Downsample1D, ResidualTemporalBlock1D, Upsample1D, rearrange_dims


 class DownResnetBlock1D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: Optional[int] = None,
        num_layers: int = 1,
        conv_shortcut: bool = False,
        temb_channels: int = 32,
        groups: int = 32,
        groups_out: Optional[int] = None,
        non_linearity: Optional[str] = None,
        time_embedding_norm: str = "default",
        output_scale_factor: float = 1.0,
        add_downsample: bool = True,
    ):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.use_conv_shortcut = conv_shortcut
        self.time_embedding_norm = time_embedding_norm
        self.add_downsample = add_downsample
        self.output_scale_factor = output_scale_factor

        if groups_out is None:
            groups_out = groups

        # there will always be at least one resnet
        resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=temb_channels)]

        for _ in range(num_layers):
            resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))

        self.resnets = nn.ModuleList(resnets)

        if non_linearity is None:
            self.nonlinearity = None
        else:
            self.nonlinearity = get_activation(non_linearity)

        self.downsample = None
        if add_downsample:
            self.downsample = Downsample1D(out_channels, use_conv=True, padding=1)

    def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
        output_states = ()

        hidden_states = self.resnets[0](hidden_states, temb)
        for resnet in self.resnets[1:]:
            hidden_states = resnet(hidden_states, temb)

        output_states += (hidden_states,)

        if self.nonlinearity is not None:
            hidden_states = self.nonlinearity(hidden_states)

        if self.downsample is not None:
            hidden_states = self.downsample(hidden_states)

        return hidden_states, output_states


 class UpResnetBlock1D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: Optional[int] = None,
        num_layers: int = 1,
        temb_channels: int = 32,
        groups: int = 32,
        groups_out: Optional[int] = None,
        non_linearity: Optional[str] = None,
        time_embedding_norm: str = "default",
        output_scale_factor: float = 1.0,
        add_upsample: bool = True,
    ):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.time_embedding_norm = time_embedding_norm
        self.add_upsample = add_upsample
        self.output_scale_factor = output_scale_factor

        if groups_out is None:
            groups_out = groups

        # there will always be at least one resnet
        resnets = [ResidualTemporalBlock1D(2 * in_channels, out_channels, embed_dim=temb_channels)]

        for _ in range(num_layers):
            resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))

        self.resnets = nn.ModuleList(resnets)

        if non_linearity is None:
            self.nonlinearity = None
        else:
            self.nonlinearity = get_activation(non_linearity)

        self.upsample = None
        if add_upsample:
            self.upsample = Upsample1D(out_channels, use_conv_transpose=True)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Optional[Tuple[torch.FloatTensor, ...]] = None,
        temb: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        if res_hidden_states_tuple is not None:
            res_hidden_states = res_hidden_states_tuple[-1]
            hidden_states = torch.cat((hidden_states, res_hidden_states), dim=1)

        hidden_states = self.resnets[0](hidden_states, temb)
        for resnet in self.resnets[1:]:
            hidden_states = resnet(hidden_states, temb)

        if self.nonlinearity is not None:
            hidden_states = self.nonlinearity(hidden_states)

        if self.upsample is not None:
            hidden_states = self.upsample(hidden_states)

        return hidden_states


 class ValueFunctionMidBlock1D(nn.Module):
    def __init__(self, in_channels: int, out_channels: int, embed_dim: int):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.embed_dim = embed_dim

        self.res1 = ResidualTemporalBlock1D(in_channels, in_channels // 2, embed_dim=embed_dim)
        self.down1 = Downsample1D(out_channels // 2, use_conv=True)
        self.res2 = ResidualTemporalBlock1D(in_channels // 2, in_channels // 4, embed_dim=embed_dim)
        self.down2 = Downsample1D(out_channels // 4, use_conv=True)

    def forward(self, x: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
        x = self.res1(x, temb)
        x = self.down1(x)
        x = self.res2(x, temb)
        x = self.down2(x)
        return x


 class MidResTemporalBlock1D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        embed_dim: int,
        num_layers: int = 1,
        add_downsample: bool = False,
        add_upsample: bool = False,
        non_linearity: Optional[str] = None,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.add_downsample = add_downsample

        # there will always be at least one resnet
        resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=embed_dim)]

        for _ in range(num_layers):
            resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=embed_dim))

        self.resnets = nn.ModuleList(resnets)

        if non_linearity is None:
            self.nonlinearity = None
        else:
            self.nonlinearity = get_activation(non_linearity)

        self.upsample = None
        if add_upsample:
            self.upsample = Downsample1D(out_channels, use_conv=True)

        self.downsample = None
        if add_downsample:
            self.downsample = Downsample1D(out_channels, use_conv=True)

        if self.upsample and self.downsample:
            raise ValueError("Block cannot downsample and upsample")

    def forward(self, hidden_states: torch.FloatTensor, temb: torch.FloatTensor) -> torch.FloatTensor:
        hidden_states = self.resnets[0](hidden_states, temb)
        for resnet in self.resnets[1:]:
            hidden_states = resnet(hidden_states, temb)

        if self.upsample:
            hidden_states = self.upsample(hidden_states)
        if self.downsample:
            self.downsample = self.downsample(hidden_states)

        return hidden_states


 class OutConv1DBlock(nn.Module):
    def __init__(self, num_groups_out: int, out_channels: int, embed_dim: int, act_fn: str):
        super().__init__()
        self.final_conv1d_1 = nn.Conv1d(embed_dim, embed_dim, 5, padding=2)
        self.final_conv1d_gn = nn.GroupNorm(num_groups_out, embed_dim)
        self.final_conv1d_act = get_activation(act_fn)
        self.final_conv1d_2 = nn.Conv1d(embed_dim, out_channels, 1)

    def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
        hidden_states = self.final_conv1d_1(hidden_states)
        hidden_states = rearrange_dims(hidden_states)
        hidden_states = self.final_conv1d_gn(hidden_states)
        hidden_states = rearrange_dims(hidden_states)
        hidden_states = self.final_conv1d_act(hidden_states)
        hidden_states = self.final_conv1d_2(hidden_states)
        return hidden_states


 class OutValueFunctionBlock(nn.Module):
    def __init__(self, fc_dim: int, embed_dim: int, act_fn: str = "mish"):
        super().__init__()
        self.final_block = nn.ModuleList(
            [
                nn.Linear(fc_dim + embed_dim, fc_dim // 2),
                get_activation(act_fn),
                nn.Linear(fc_dim // 2, 1),
            ]
        )

    def forward(self, hidden_states: torch.FloatTensor, temb: torch.FloatTensor) -> torch.FloatTensor:
        hidden_states = hidden_states.view(hidden_states.shape[0], -1)
        hidden_states = torch.cat((hidden_states, temb), dim=-1)
        for layer in self.final_block:
            hidden_states = layer(hidden_states)

        return hidden_states


 _kernels = {
    "linear": [1 / 8, 3 / 8, 3 / 8, 1 / 8],
    "cubic": [-0.01171875, -0.03515625, 0.11328125, 0.43359375, 0.43359375, 0.11328125, -0.03515625, -0.01171875],
    "lanczos3": [
        0.003689131001010537,
        0.015056144446134567,
        -0.03399861603975296,
        -0.066637322306633,
        0.13550527393817902,
        0.44638532400131226,
        0.44638532400131226,
        0.13550527393817902,
        -0.066637322306633,
        -0.03399861603975296,
        0.015056144446134567,
        0.003689131001010537,
    ],
 }


 class Downsample1d(nn.Module):
    def __init__(self, kernel: str = "linear", pad_mode: str = "reflect"):
        super().__init__()
        self.pad_mode = pad_mode
        kernel_1d = torch.tensor(_kernels[kernel])
        self.pad = kernel_1d.shape[0] // 2 - 1
        self.register_buffer("kernel", kernel_1d)

    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        hidden_states = F.pad(hidden_states, (self.pad,) * 2, self.pad_mode)
        weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])
        indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)
        kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)
        weight[indices, indices] = kernel
        return F.conv1d(hidden_states, weight, stride=2)


 class Upsample1d(nn.Module):
    def __init__(self, kernel: str = "linear", pad_mode: str = "reflect"):
        super().__init__()
        self.pad_mode = pad_mode
        kernel_1d = torch.tensor(_kernels[kernel]) * 2
        self.pad = kernel_1d.shape[0] // 2 - 1
        self.register_buffer("kernel", kernel_1d)

    def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
        hidden_states = F.pad(hidden_states, ((self.pad + 1) // 2,) * 2, self.pad_mode)
        weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])
        indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)
        kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)
        weight[indices, indices] = kernel
        return F.conv_transpose1d(hidden_states, weight, stride=2, padding=self.pad * 2 + 1)


 class SelfAttention1d(nn.Module):
    def __init__(self, in_channels: int, n_head: int = 1, dropout_rate: float = 0.0):
        super().__init__()
        self.channels = in_channels
        self.group_norm = nn.GroupNorm(1, num_channels=in_channels)
        self.num_heads = n_head

        self.query = nn.Linear(self.channels, self.channels)
        self.key = nn.Linear(self.channels, self.channels)
        self.value = nn.Linear(self.channels, self.channels)

        self.proj_attn = nn.Linear(self.channels, self.channels, bias=True)

        self.dropout = nn.Dropout(dropout_rate, inplace=True)

    def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor:
        new_projection_shape = projection.size()[:-1] + (self.num_heads, -1)
        # move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
        new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)
        return new_projection

    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        residual = hidden_states
        batch, channel_dim, seq = hidden_states.shape

        hidden_states = self.group_norm(hidden_states)
        hidden_states = hidden_states.transpose(1, 2)

        query_proj = self.query(hidden_states)
        key_proj = self.key(hidden_states)
        value_proj = self.value(hidden_states)

        query_states = self.transpose_for_scores(query_proj)
        key_states = self.transpose_for_scores(key_proj)
        value_states = self.transpose_for_scores(value_proj)

        scale = 1 / math.sqrt(math.sqrt(key_states.shape[-1]))

        attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)
        attention_probs = torch.softmax(attention_scores, dim=-1)

        # compute attention output
        hidden_states = torch.matmul(attention_probs, value_states)

        hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()
        new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,)
        hidden_states = hidden_states.view(new_hidden_states_shape)

        # compute next hidden_states
        hidden_states = self.proj_attn(hidden_states)
        hidden_states = hidden_states.transpose(1, 2)
        hidden_states = self.dropout(hidden_states)

        output = hidden_states + residual

        return output


 class ResConvBlock(nn.Module):
    def __init__(self, in_channels: int, mid_channels: int, out_channels: int, is_last: bool = False):
        super().__init__()
        self.is_last = is_last
        self.has_conv_skip = in_channels != out_channels

        if self.has_conv_skip:
            self.conv_skip = nn.Conv1d(in_channels, out_channels, 1, bias=False)

        self.conv_1 = nn.Conv1d(in_channels, mid_channels, 5, padding=2)
        self.group_norm_1 = nn.GroupNorm(1, mid_channels)
        self.gelu_1 = nn.GELU()
        self.conv_2 = nn.Conv1d(mid_channels, out_channels, 5, padding=2)

        if not self.is_last:
            self.group_norm_2 = nn.GroupNorm(1, out_channels)
            self.gelu_2 = nn.GELU()

    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        residual = self.conv_skip(hidden_states) if self.has_conv_skip else hidden_states

        hidden_states = self.conv_1(hidden_states)
        hidden_states = self.group_norm_1(hidden_states)
        hidden_states = self.gelu_1(hidden_states)
        hidden_states = self.conv_2(hidden_states)

        if not self.is_last:
            hidden_states = self.group_norm_2(hidden_states)
            hidden_states = self.gelu_2(hidden_states)

        output = hidden_states + residual
        return output


 class UNetMidBlock1D(nn.Module):
    def __init__(self, mid_channels: int, in_channels: int, out_channels: Optional[int] = None):
        super().__init__()

        out_channels = in_channels if out_channels is None else out_channels

        # there is always at least one resnet
        self.down = Downsample1d("cubic")
        resnets = [
            ResConvBlock(in_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, out_channels),
        ]
        attentions = [
            SelfAttention1d(mid_channels, mid_channels // 32),
            SelfAttention1d(mid_channels, mid_channels // 32),
            SelfAttention1d(mid_channels, mid_channels // 32),
            SelfAttention1d(mid_channels, mid_channels // 32),
            SelfAttention1d(mid_channels, mid_channels // 32),
            SelfAttention1d(out_channels, out_channels // 32),
        ]
        self.up = Upsample1d(kernel="cubic")

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

    def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
        hidden_states = self.down(hidden_states)
        for attn, resnet in zip(self.attentions, self.resnets):
            hidden_states = resnet(hidden_states)
            hidden_states = attn(hidden_states)

        hidden_states = self.up(hidden_states)

        return hidden_states


 class AttnDownBlock1D(nn.Module):
    def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):
        super().__init__()
        mid_channels = out_channels if mid_channels is None else mid_channels

        self.down = Downsample1d("cubic")
        resnets = [
            ResConvBlock(in_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, out_channels),
        ]
        attentions = [
            SelfAttention1d(mid_channels, mid_channels // 32),
            SelfAttention1d(mid_channels, mid_channels // 32),
            SelfAttention1d(out_channels, out_channels // 32),
        ]

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

    def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
        hidden_states = self.down(hidden_states)

        for resnet, attn in zip(self.resnets, self.attentions):
            hidden_states = resnet(hidden_states)
            hidden_states = attn(hidden_states)

        return hidden_states, (hidden_states,)


 class DownBlock1D(nn.Module):
    def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):
        super().__init__()
        mid_channels = out_channels if mid_channels is None else mid_channels

        self.down = Downsample1d("cubic")
        resnets = [
            ResConvBlock(in_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, out_channels),
        ]

        self.resnets = nn.ModuleList(resnets)

    def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
        hidden_states = self.down(hidden_states)

        for resnet in self.resnets:
            hidden_states = resnet(hidden_states)

        return hidden_states, (hidden_states,)


 class DownBlock1DNoSkip(nn.Module):
    def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):
        super().__init__()
        mid_channels = out_channels if mid_channels is None else mid_channels

        resnets = [
            ResConvBlock(in_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, out_channels),
        ]

        self.resnets = nn.ModuleList(resnets)

    def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
        hidden_states = torch.cat([hidden_states, temb], dim=1)
        for resnet in self.resnets:
            hidden_states = resnet(hidden_states)

        return hidden_states, (hidden_states,)


 class AttnUpBlock1D(nn.Module):
    def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):
        super().__init__()
        mid_channels = out_channels if mid_channels is None else mid_channels

        resnets = [
            ResConvBlock(2 * in_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, out_channels),
        ]
        attentions = [
            SelfAttention1d(mid_channels, mid_channels // 32),
            SelfAttention1d(mid_channels, mid_channels // 32),
            SelfAttention1d(out_channels, out_channels // 32),
        ]

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)
        self.up = Upsample1d(kernel="cubic")

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        res_hidden_states = res_hidden_states_tuple[-1]
        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

        for resnet, attn in zip(self.resnets, self.attentions):
            hidden_states = resnet(hidden_states)
            hidden_states = attn(hidden_states)

        hidden_states = self.up(hidden_states)

        return hidden_states


 class UpBlock1D(nn.Module):
    def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):
        super().__init__()
        mid_channels = in_channels if mid_channels is None else mid_channels

        resnets = [
            ResConvBlock(2 * in_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, out_channels),
        ]

        self.resnets = nn.ModuleList(resnets)
        self.up = Upsample1d(kernel="cubic")

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        res_hidden_states = res_hidden_states_tuple[-1]
        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

        for resnet in self.resnets:
            hidden_states = resnet(hidden_states)

        hidden_states = self.up(hidden_states)

        return hidden_states


 class UpBlock1DNoSkip(nn.Module):
    def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):
        super().__init__()
        mid_channels = in_channels if mid_channels is None else mid_channels

        resnets = [
            ResConvBlock(2 * in_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, out_channels, is_last=True),
        ]

        self.resnets = nn.ModuleList(resnets)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
        temb: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        res_hidden_states = res_hidden_states_tuple[-1]
        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

        for resnet in self.resnets:
            hidden_states = resnet(hidden_states)

        return hidden_states


 DownBlockType = Union[DownResnetBlock1D, DownBlock1D, AttnDownBlock1D, DownBlock1DNoSkip]
 MidBlockType = Union[MidResTemporalBlock1D, ValueFunctionMidBlock1D, UNetMidBlock1D]
 OutBlockType = Union[OutConv1DBlock, OutValueFunctionBlock]
 UpBlockType = Union[UpResnetBlock1D, UpBlock1D, AttnUpBlock1D, UpBlock1DNoSkip]


 def get_down_block(
    down_block_type: str,
    num_layers: int,
    in_channels: int,
    out_channels: int,
    temb_channels: int,
    add_downsample: bool,
 ) -> DownBlockType:
    if down_block_type == "DownResnetBlock1D":
        return DownResnetBlock1D(
            in_channels=in_channels,
            num_layers=num_layers,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_downsample=add_downsample,
        )
    elif down_block_type == "DownBlock1D":
        return DownBlock1D(out_channels=out_channels, in_channels=in_channels)
    elif down_block_type == "AttnDownBlock1D":
        return AttnDownBlock1D(out_channels=out_channels, in_channels=in_channels)
    elif down_block_type == "DownBlock1DNoSkip":
        return DownBlock1DNoSkip(out_channels=out_channels, in_channels=in_channels)
    raise ValueError(f"{down_block_type} does not exist.")


 def get_up_block(
    up_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channels: int, add_upsample: bool
 ) -> UpBlockType:
    if up_block_type == "UpResnetBlock1D":
        return UpResnetBlock1D(
            in_channels=in_channels,
            num_layers=num_layers,
            out_channels=out_channels,
            temb_channels=temb_channels,
            add_upsample=add_upsample,
        )
    elif up_block_type == "UpBlock1D":
        return UpBlock1D(in_channels=in_channels, out_channels=out_channels)
    elif up_block_type == "AttnUpBlock1D":
        return AttnUpBlock1D(in_channels=in_channels, out_channels=out_channels)
    elif up_block_type == "UpBlock1DNoSkip":
        return UpBlock1DNoSkip(in_channels=in_channels, out_channels=out_channels)
    raise ValueError(f"{up_block_type} does not exist.")


 def get_mid_block(
    mid_block_type: str,
    num_layers: int,
    in_channels: int,
    mid_channels: int,
    out_channels: int,
    embed_dim: int,
    add_downsample: bool,
 ) -> MidBlockType:
    if mid_block_type == "MidResTemporalBlock1D":
        return MidResTemporalBlock1D(
            num_layers=num_layers,
            in_channels=in_channels,
            out_channels=out_channels,
            embed_dim=embed_dim,
            add_downsample=add_downsample,
        )
    elif mid_block_type == "ValueFunctionMidBlock1D":
        return ValueFunctionMidBlock1D(in_channels=in_channels, out_channels=out_channels, embed_dim=embed_dim)
    elif mid_block_type == "UNetMidBlock1D":
        return UNetMidBlock1D(in_channels=in_channels, mid_channels=mid_channels, out_channels=out_channels)
    raise ValueError(f"{mid_block_type} does not exist.")


 def get_out_block(
    *, out_block_type: str, num_groups_out: int, embed_dim: int, out_channels: int, act_fn: str, fc_dim: int
 ) -> Optional[OutBlockType]:
    if out_block_type == "OutConv1DBlock":
        return OutConv1DBlock(num_groups_out, out_channels, embed_dim, act_fn)
    elif out_block_type == "ValueFunction":
        return OutValueFunctionBlock(fc_dim, embed_dim, act_fn)
    return None
 # coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.

 from typing import Dict, Union

 import torch
 import torch.nn.functional as F
 from torch import nn
 from torch.utils.checkpoint import checkpoint

 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
 from ..attention import BasicTransformerBlock, SkipFFTransformerBlock
 from ..attention_processor import (
    ADDED_KV_ATTENTION_PROCESSORS,
    CROSS_ATTENTION_PROCESSORS,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor,
 )
 from ..embeddings import TimestepEmbedding, get_timestep_embedding
 from ..modeling_utils import ModelMixin
 from ..normalization import GlobalResponseNorm, RMSNorm
 from ..resnet import Downsample2D, Upsample2D


 class UVit2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        # global config
        hidden_size: int = 1024,
        use_bias: bool = False,
        hidden_dropout: float = 0.0,
        # conditioning dimensions
        cond_embed_dim: int = 768,
        micro_cond_encode_dim: int = 256,
        micro_cond_embed_dim: int = 1280,
        encoder_hidden_size: int = 768,
        # num tokens
        vocab_size: int = 8256,  # codebook_size + 1 (for the mask token) rounded
        codebook_size: int = 8192,
        # `UVit2DConvEmbed`
        in_channels: int = 768,
        block_out_channels: int = 768,
        num_res_blocks: int = 3,
        downsample: bool = False,
        upsample: bool = False,
        block_num_heads: int = 12,
        # `TransformerLayer`
        num_hidden_layers: int = 22,
        num_attention_heads: int = 16,
        # `Attention`
        attention_dropout: float = 0.0,
        # `FeedForward`
        intermediate_size: int = 2816,
        # `Norm`
        layer_norm_eps: float = 1e-6,
        ln_elementwise_affine: bool = True,
        sample_size: int = 64,
    ):
        super().__init__()

        self.encoder_proj = nn.Linear(encoder_hidden_size, hidden_size, bias=use_bias)
        self.encoder_proj_layer_norm = RMSNorm(hidden_size, layer_norm_eps, ln_elementwise_affine)

        self.embed = UVit2DConvEmbed(
            in_channels, block_out_channels, vocab_size, ln_elementwise_affine, layer_norm_eps, use_bias
        )

        self.cond_embed = TimestepEmbedding(
            micro_cond_embed_dim + cond_embed_dim, hidden_size, sample_proj_bias=use_bias
        )

        self.down_block = UVitBlock(
            block_out_channels,
            num_res_blocks,
            hidden_size,
            hidden_dropout,
            ln_elementwise_affine,
            layer_norm_eps,
            use_bias,
            block_num_heads,
            attention_dropout,
            downsample,
            False,
        )

        self.project_to_hidden_norm = RMSNorm(block_out_channels, layer_norm_eps, ln_elementwise_affine)
        self.project_to_hidden = nn.Linear(block_out_channels, hidden_size, bias=use_bias)

        self.transformer_layers = nn.ModuleList(
            [
                BasicTransformerBlock(
                    dim=hidden_size,
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=hidden_size // num_attention_heads,
                    dropout=hidden_dropout,
                    cross_attention_dim=hidden_size,
                    attention_bias=use_bias,
                    norm_type="ada_norm_continuous",
                    ada_norm_continous_conditioning_embedding_dim=hidden_size,
                    norm_elementwise_affine=ln_elementwise_affine,
                    norm_eps=layer_norm_eps,
                    ada_norm_bias=use_bias,
                    ff_inner_dim=intermediate_size,
                    ff_bias=use_bias,
                    attention_out_bias=use_bias,
                )
                for _ in range(num_hidden_layers)
            ]
        )

        self.project_from_hidden_norm = RMSNorm(hidden_size, layer_norm_eps, ln_elementwise_affine)
        self.project_from_hidden = nn.Linear(hidden_size, block_out_channels, bias=use_bias)

        self.up_block = UVitBlock(
            block_out_channels,
            num_res_blocks,
            hidden_size,
            hidden_dropout,
            ln_elementwise_affine,
            layer_norm_eps,
            use_bias,
            block_num_heads,
            attention_dropout,
            downsample=False,
            upsample=upsample,
        )

        self.mlm_layer = ConvMlmLayer(
            block_out_channels, in_channels, use_bias, ln_elementwise_affine, layer_norm_eps, codebook_size
        )

        self.gradient_checkpointing = False

    def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
        pass

    def forward(self, input_ids, encoder_hidden_states, pooled_text_emb, micro_conds, cross_attention_kwargs=None):
        encoder_hidden_states = self.encoder_proj(encoder_hidden_states)
        encoder_hidden_states = self.encoder_proj_layer_norm(encoder_hidden_states)

        micro_cond_embeds = get_timestep_embedding(
            micro_conds.flatten(), self.config.micro_cond_encode_dim, flip_sin_to_cos=True, downscale_freq_shift=0
        )

        micro_cond_embeds = micro_cond_embeds.reshape((input_ids.shape[0], -1))

        pooled_text_emb = torch.cat([pooled_text_emb, micro_cond_embeds], dim=1)
        pooled_text_emb = pooled_text_emb.to(dtype=self.dtype)
        pooled_text_emb = self.cond_embed(pooled_text_emb).to(encoder_hidden_states.dtype)

        hidden_states = self.embed(input_ids)

        hidden_states = self.down_block(
            hidden_states,
            pooled_text_emb=pooled_text_emb,
            encoder_hidden_states=encoder_hidden_states,
            cross_attention_kwargs=cross_attention_kwargs,
        )

        batch_size, channels, height, width = hidden_states.shape
        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels)

        hidden_states = self.project_to_hidden_norm(hidden_states)
        hidden_states = self.project_to_hidden(hidden_states)

        for layer in self.transformer_layers:
            if self.training and self.gradient_checkpointing:

                def layer_(*args):
                    return checkpoint(layer, *args)

            else:
                layer_ = layer

            hidden_states = layer_(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                cross_attention_kwargs=cross_attention_kwargs,
                added_cond_kwargs={"pooled_text_emb": pooled_text_emb},
            )

        hidden_states = self.project_from_hidden_norm(hidden_states)
        hidden_states = self.project_from_hidden(hidden_states)

        hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)

        hidden_states = self.up_block(
            hidden_states,
            pooled_text_emb=pooled_text_emb,
            encoder_hidden_states=encoder_hidden_states,
            cross_attention_kwargs=cross_attention_kwargs,
        )

        logits = self.mlm_layer(hidden_states)

        return logits

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnAddedKVProcessor()
        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
            processor = AttnProcessor()
        else:
            raise ValueError(
                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
            )

        self.set_attn_processor(processor)


 class UVit2DConvEmbed(nn.Module):
    def __init__(self, in_channels, block_out_channels, vocab_size, elementwise_affine, eps, bias):
        super().__init__()
        self.embeddings = nn.Embedding(vocab_size, in_channels)
        self.layer_norm = RMSNorm(in_channels, eps, elementwise_affine)
        self.conv = nn.Conv2d(in_channels, block_out_channels, kernel_size=1, bias=bias)

    def forward(self, input_ids):
        embeddings = self.embeddings(input_ids)
        embeddings = self.layer_norm(embeddings)
        embeddings = embeddings.permute(0, 3, 1, 2)
        embeddings = self.conv(embeddings)
        return embeddings


 class UVitBlock(nn.Module):
    def __init__(
        self,
        channels,
        num_res_blocks: int,
        hidden_size,
        hidden_dropout,
        ln_elementwise_affine,
        layer_norm_eps,
        use_bias,
        block_num_heads,
        attention_dropout,
        downsample: bool,
        upsample: bool,
    ):
        super().__init__()

        if downsample:
            self.downsample = Downsample2D(
                channels,
                use_conv=True,
                padding=0,
                name="Conv2d_0",
                kernel_size=2,
                norm_type="rms_norm",
                eps=layer_norm_eps,
                elementwise_affine=ln_elementwise_affine,
                bias=use_bias,
            )
        else:
            self.downsample = None

        self.res_blocks = nn.ModuleList(
            [
                ConvNextBlock(
                    channels,
                    layer_norm_eps,
                    ln_elementwise_affine,
                    use_bias,
                    hidden_dropout,
                    hidden_size,
                )
                for i in range(num_res_blocks)
            ]
        )

        self.attention_blocks = nn.ModuleList(
            [
                SkipFFTransformerBlock(
                    channels,
                    block_num_heads,
                    channels // block_num_heads,
                    hidden_size,
                    use_bias,
                    attention_dropout,
                    channels,
                    attention_bias=use_bias,
                    attention_out_bias=use_bias,
                )
                for _ in range(num_res_blocks)
            ]
        )

        if upsample:
            self.upsample = Upsample2D(
                channels,
                use_conv_transpose=True,
                kernel_size=2,
                padding=0,
                name="conv",
                norm_type="rms_norm",
                eps=layer_norm_eps,
                elementwise_affine=ln_elementwise_affine,
                bias=use_bias,
                interpolate=False,
            )
        else:
            self.upsample = None

    def forward(self, x, pooled_text_emb, encoder_hidden_states, cross_attention_kwargs):
        if self.downsample is not None:
            x = self.downsample(x)

        for res_block, attention_block in zip(self.res_blocks, self.attention_blocks):
            x = res_block(x, pooled_text_emb)

            batch_size, channels, height, width = x.shape
            x = x.view(batch_size, channels, height * width).permute(0, 2, 1)
            x = attention_block(
                x, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs
            )
            x = x.permute(0, 2, 1).view(batch_size, channels, height, width)

        if self.upsample is not None:
            x = self.upsample(x)

        return x


 class ConvNextBlock(nn.Module):
    def __init__(
        self, channels, layer_norm_eps, ln_elementwise_affine, use_bias, hidden_dropout, hidden_size, res_ffn_factor=4
    ):
        super().__init__()
        self.depthwise = nn.Conv2d(
            channels,
            channels,
            kernel_size=3,
            padding=1,
            groups=channels,
            bias=use_bias,
        )
        self.norm = RMSNorm(channels, layer_norm_eps, ln_elementwise_affine)
        self.channelwise_linear_1 = nn.Linear(channels, int(channels * res_ffn_factor), bias=use_bias)
        self.channelwise_act = nn.GELU()
        self.channelwise_norm = GlobalResponseNorm(int(channels * res_ffn_factor))
        self.channelwise_linear_2 = nn.Linear(int(channels * res_ffn_factor), channels, bias=use_bias)
        self.channelwise_dropout = nn.Dropout(hidden_dropout)
        self.cond_embeds_mapper = nn.Linear(hidden_size, channels * 2, use_bias)

    def forward(self, x, cond_embeds):
        x_res = x

        x = self.depthwise(x)

        x = x.permute(0, 2, 3, 1)
        x = self.norm(x)

        x = self.channelwise_linear_1(x)
        x = self.channelwise_act(x)
        x = self.channelwise_norm(x)
        x = self.channelwise_linear_2(x)
        x = self.channelwise_dropout(x)

        x = x.permute(0, 3, 1, 2)

        x = x + x_res

        scale, shift = self.cond_embeds_mapper(F.silu(cond_embeds)).chunk(2, dim=1)
        x = x * (1 + scale[:, :, None, None]) + shift[:, :, None, None]

        return x


 class ConvMlmLayer(nn.Module):
    def __init__(
        self,
        block_out_channels: int,
        in_channels: int,
        use_bias: bool,
        ln_elementwise_affine: bool,
        layer_norm_eps: float,
        codebook_size: int,
    ):
        super().__init__()
        self.conv1 = nn.Conv2d(block_out_channels, in_channels, kernel_size=1, bias=use_bias)
        self.layer_norm = RMSNorm(in_channels, layer_norm_eps, ln_elementwise_affine)
        self.conv2 = nn.Conv2d(in_channels, codebook_size, kernel_size=1, bias=use_bias)

    def forward(self, hidden_states):
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.layer_norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
        logits = self.conv2(hidden_states)
        return logits


 from dataclasses import dataclass
 from typing import Optional, Tuple, Union

 import torch
 import torch.nn as nn

 from ...configuration_utils import ConfigMixin, register_to_config
 from ...utils import BaseOutput
 from ..embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
 from ..modeling_utils import ModelMixin
 from .unet_1d_blocks import get_down_block, get_mid_block, get_out_block, get_up_block


 @dataclass
 class UNet1DOutput(BaseOutput):
    """
    The output of [`UNet1DModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, sample_size)`):
            The hidden states output from the last layer of the model.
    """

    sample: torch.FloatTensor


 class UNet1DModel(ModelMixin, ConfigMixin):
    r"""
    A 1D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).

    Parameters:
        sample_size (`int`, *optional*): Default length of sample. Should be adaptable at runtime.
        in_channels (`int`, *optional*, defaults to 2): Number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 2): Number of channels in the output.
        extra_in_channels (`int`, *optional*, defaults to 0):
            Number of additional channels to be added to the input of the first down block. Useful for cases where the
            input data has more channels than what the model was initially designed for.
        time_embedding_type (`str`, *optional*, defaults to `"fourier"`): Type of time embedding to use.
        freq_shift (`float`, *optional*, defaults to 0.0): Frequency shift for Fourier time embedding.
        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
            Whether to flip sin to cos for Fourier time embedding.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")`):
            Tuple of downsample block types.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")`):
            Tuple of upsample block types.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(32, 32, 64)`):
            Tuple of block output channels.
        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock1D"`): Block type for middle of UNet.
        out_block_type (`str`, *optional*, defaults to `None`): Optional output processing block of UNet.
        act_fn (`str`, *optional*, defaults to `None`): Optional activation function in UNet blocks.
        norm_num_groups (`int`, *optional*, defaults to 8): The number of groups for normalization.
        layers_per_block (`int`, *optional*, defaults to 1): The number of layers per block.
        downsample_each_block (`int`, *optional*, defaults to `False`):
            Experimental feature for using a UNet without upsampling.
    """

    @register_to_config
    def __init__(
        self,
        sample_size: int = 65536,
        sample_rate: Optional[int] = None,
        in_channels: int = 2,
        out_channels: int = 2,
        extra_in_channels: int = 0,
        time_embedding_type: str = "fourier",
        flip_sin_to_cos: bool = True,
        use_timestep_embedding: bool = False,
        freq_shift: float = 0.0,
        down_block_types: Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"),
        up_block_types: Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"),
        mid_block_type: Tuple[str] = "UNetMidBlock1D",
        out_block_type: str = None,
        block_out_channels: Tuple[int] = (32, 32, 64),
        act_fn: str = None,
        norm_num_groups: int = 8,
        layers_per_block: int = 1,
        downsample_each_block: bool = False,
    ):
        super().__init__()
        self.sample_size = sample_size

        # time
        if time_embedding_type == "fourier":
            self.time_proj = GaussianFourierProjection(
                embedding_size=8, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
            )
            timestep_input_dim = 2 * block_out_channels[0]
        elif time_embedding_type == "positional":
            self.time_proj = Timesteps(
                block_out_channels[0], flip_sin_to_cos=flip_sin_to_cos, downscale_freq_shift=freq_shift
            )
            timestep_input_dim = block_out_channels[0]

        if use_timestep_embedding:
            time_embed_dim = block_out_channels[0] * 4
            self.time_mlp = TimestepEmbedding(
                in_channels=timestep_input_dim,
                time_embed_dim=time_embed_dim,
                act_fn=act_fn,
                out_dim=block_out_channels[0],
            )

        self.down_blocks = nn.ModuleList([])
        self.mid_block = None
        self.up_blocks = nn.ModuleList([])
        self.out_block = None

        # down
        output_channel = in_channels
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]

            if i == 0:
                input_channel += extra_in_channels

            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block,
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=block_out_channels[0],
                add_downsample=not is_final_block or downsample_each_block,
            )
            self.down_blocks.append(down_block)

        # mid
        self.mid_block = get_mid_block(
            mid_block_type,
            in_channels=block_out_channels[-1],
            mid_channels=block_out_channels[-1],
            out_channels=block_out_channels[-1],
            embed_dim=block_out_channels[0],
            num_layers=layers_per_block,
            add_downsample=downsample_each_block,
        )

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        if out_block_type is None:
            final_upsample_channels = out_channels
        else:
            final_upsample_channels = block_out_channels[0]

        for i, up_block_type in enumerate(up_block_types):
            prev_output_channel = output_channel
            output_channel = (
                reversed_block_out_channels[i + 1] if i < len(up_block_types) - 1 else final_upsample_channels
            )

            is_final_block = i == len(block_out_channels) - 1

            up_block = get_up_block(
                up_block_type,
                num_layers=layers_per_block,
                in_channels=prev_output_channel,
                out_channels=output_channel,
                temb_channels=block_out_channels[0],
                add_upsample=not is_final_block,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
        self.out_block = get_out_block(
            out_block_type=out_block_type,
            num_groups_out=num_groups_out,
            embed_dim=block_out_channels[0],
            out_channels=out_channels,
            act_fn=act_fn,
            fc_dim=block_out_channels[-1] // 4,
        )

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        return_dict: bool = True,
    ) -> Union[UNet1DOutput, Tuple]:
        r"""
        The [`UNet1DModel`] forward method.

        Args:
            sample (`torch.FloatTensor`):
                The noisy input tensor with the following shape `(batch_size, num_channels, sample_size)`.
            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_1d.UNet1DOutput`] instead of a plain tuple.

        Returns:
            [`~models.unet_1d.UNet1DOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.unet_1d.UNet1DOutput`] is returned, otherwise a `tuple` is
                returned where the first element is the sample tensor.
        """

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        timestep_embed = self.time_proj(timesteps)
        if self.config.use_timestep_embedding:
            timestep_embed = self.time_mlp(timestep_embed)
        else:
            timestep_embed = timestep_embed[..., None]
            timestep_embed = timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
            timestep_embed = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

        # 2. down
        down_block_res_samples = ()
        for downsample_block in self.down_blocks:
            sample, res_samples = downsample_block(hidden_states=sample, temb=timestep_embed)
            down_block_res_samples += res_samples

        # 3. mid
        if self.mid_block:
            sample = self.mid_block(sample, timestep_embed)

        # 4. up
        for i, upsample_block in enumerate(self.up_blocks):
            res_samples = down_block_res_samples[-1:]
            down_block_res_samples = down_block_res_samples[:-1]
            sample = upsample_block(sample, res_hidden_states_tuple=res_samples, temb=timestep_embed)

        # 5. post-process
        if self.out_block:
            sample = self.out_block(sample, timestep_embed)

        if not return_dict:
            return (sample,)

        return UNet1DOutput(sample=sample)

 from typing import Dict, Optional, Tuple, Union

 import flax
 import flax.linen as nn
 import jax
 import jax.numpy as jnp
 from flax.core.frozen_dict import FrozenDict

 from ...configuration_utils import ConfigMixin, flax_register_to_config
 from ...utils import BaseOutput
 from ..embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
 from ..modeling_flax_utils import FlaxModelMixin
 from .unet_2d_blocks_flax import (
    FlaxCrossAttnDownBlock2D,
    FlaxCrossAttnUpBlock2D,
    FlaxDownBlock2D,
    FlaxUNetMidBlock2DCrossAttn,
    FlaxUpBlock2D,
 )


 @flax.struct.dataclass
 class FlaxUNet2DConditionOutput(BaseOutput):
    """
    The output of [`FlaxUNet2DConditionModel`].

    Args:
        sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
    """

    sample: jnp.ndarray


 @flax_register_to_config
 class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
    r"""
    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
    shaped output.

    This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it's generic methods
    implemented for all models (such as downloading or saving).

    This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matters related to its
    general usage and behavior.

    Inherent JAX features such as the following are supported:
    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        sample_size (`int`, *optional*):
            The size of the input sample.
        in_channels (`int`, *optional*, defaults to 4):
            The number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 4):
            The number of channels in the output.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D")`):
            The tuple of downsample blocks to use.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("FlaxUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D")`):
            The tuple of upsample blocks to use.
        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
            Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`. If `None`, the mid block layer is skipped.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each block.
        layers_per_block (`int`, *optional*, defaults to 2):
            The number of layers per block.
        attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8):
            The dimension of the attention heads.
        num_attention_heads (`int` or `Tuple[int]`, *optional*):
            The number of attention heads.
        cross_attention_dim (`int`, *optional*, defaults to 768):
            The dimension of the cross attention features.
        dropout (`float`, *optional*, defaults to 0):
            Dropout probability for down, up and bottleneck blocks.
        flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
            Whether to flip the sin to cos in the time embedding.
        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
            Enable memory efficient attention as described [here](https://arxiv.org/abs/2112.05682).
        split_head_dim (`bool`, *optional*, defaults to `False`):
            Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
            enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
    """

    sample_size: int = 32
    in_channels: int = 4
    out_channels: int = 4
    down_block_types: Tuple[str, ...] = (
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    up_block_types: Tuple[str, ...] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
    mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn"
    only_cross_attention: Union[bool, Tuple[bool]] = False
    block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280)
    layers_per_block: int = 2
    attention_head_dim: Union[int, Tuple[int, ...]] = 8
    num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None
    cross_attention_dim: int = 1280
    dropout: float = 0.0
    use_linear_projection: bool = False
    dtype: jnp.dtype = jnp.float32
    flip_sin_to_cos: bool = True
    freq_shift: int = 0
    use_memory_efficient_attention: bool = False
    split_head_dim: bool = False
    transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1
    addition_embed_type: Optional[str] = None
    addition_time_embed_dim: Optional[int] = None
    addition_embed_type_num_heads: int = 64
    projection_class_embeddings_input_dim: Optional[int] = None

    def init_weights(self, rng: jax.Array) -> FrozenDict:
        # init input tensors
        sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
        sample = jnp.zeros(sample_shape, dtype=jnp.float32)
        timesteps = jnp.ones((1,), dtype=jnp.int32)
        encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32)

        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}

        added_cond_kwargs = None
        if self.addition_embed_type == "text_time":
            # we retrieve the expected `text_embeds_dim` by first checking if the architecture is a refiner
            # or non-refiner architecture and then by "reverse-computing" from `projection_class_embeddings_input_dim`
            is_refiner = (
                5 * self.config.addition_time_embed_dim + self.config.cross_attention_dim
                == self.config.projection_class_embeddings_input_dim
            )
            num_micro_conditions = 5 if is_refiner else 6

            text_embeds_dim = self.config.projection_class_embeddings_input_dim - (
                num_micro_conditions * self.config.addition_time_embed_dim
            )

            time_ids_channels = self.projection_class_embeddings_input_dim - text_embeds_dim
            time_ids_dims = time_ids_channels // self.addition_time_embed_dim
            added_cond_kwargs = {
                "text_embeds": jnp.zeros((1, text_embeds_dim), dtype=jnp.float32),
                "time_ids": jnp.zeros((1, time_ids_dims), dtype=jnp.float32),
            }
        return self.init(rngs, sample, timesteps, encoder_hidden_states, added_cond_kwargs)["params"]

    def setup(self) -> None:
        block_out_channels = self.block_out_channels
        time_embed_dim = block_out_channels[0] * 4

        if self.num_attention_heads is not None:
            raise ValueError(
                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
            )

        # If `num_attention_heads` is not defined (which is the case for most models)
        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
        # The reason for this behavior is to correct for incorrectly named variables that were introduced
        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
        # which is why we correct for the naming here.
        num_attention_heads = self.num_attention_heads or self.attention_head_dim

        # input
        self.conv_in = nn.Conv(
            block_out_channels[0],
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        # time
        self.time_proj = FlaxTimesteps(
            block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift
        )
        self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)

        only_cross_attention = self.only_cross_attention
        if isinstance(only_cross_attention, bool):
            only_cross_attention = (only_cross_attention,) * len(self.down_block_types)

        if isinstance(num_attention_heads, int):
            num_attention_heads = (num_attention_heads,) * len(self.down_block_types)

        # transformer layers per block
        transformer_layers_per_block = self.transformer_layers_per_block
        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * len(self.down_block_types)

        # addition embed types
        if self.addition_embed_type is None:
            self.add_embedding = None
        elif self.addition_embed_type == "text_time":
            if self.addition_time_embed_dim is None:
                raise ValueError(
                    f"addition_embed_type {self.addition_embed_type} requires `addition_time_embed_dim` to not be None"
                )
            self.add_time_proj = FlaxTimesteps(self.addition_time_embed_dim, self.flip_sin_to_cos, self.freq_shift)
            self.add_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)
        else:
            raise ValueError(f"addition_embed_type: {self.addition_embed_type} must be None or `text_time`.")

        # down
        down_blocks = []
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(self.down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            if down_block_type == "CrossAttnDownBlock2D":
                down_block = FlaxCrossAttnDownBlock2D(
                    in_channels=input_channel,
                    out_channels=output_channel,
                    dropout=self.dropout,
                    num_layers=self.layers_per_block,
                    transformer_layers_per_block=transformer_layers_per_block[i],
                    num_attention_heads=num_attention_heads[i],
                    add_downsample=not is_final_block,
                    use_linear_projection=self.use_linear_projection,
                    only_cross_attention=only_cross_attention[i],
                    use_memory_efficient_attention=self.use_memory_efficient_attention,
                    split_head_dim=self.split_head_dim,
                    dtype=self.dtype,
                )
            else:
                down_block = FlaxDownBlock2D(
                    in_channels=input_channel,
                    out_channels=output_channel,
                    dropout=self.dropout,
                    num_layers=self.layers_per_block,
                    add_downsample=not is_final_block,
                    dtype=self.dtype,
                )

            down_blocks.append(down_block)
        self.down_blocks = down_blocks

        # mid
        if self.config.mid_block_type == "UNetMidBlock2DCrossAttn":
            self.mid_block = FlaxUNetMidBlock2DCrossAttn(
                in_channels=block_out_channels[-1],
                dropout=self.dropout,
                num_attention_heads=num_attention_heads[-1],
                transformer_layers_per_block=transformer_layers_per_block[-1],
                use_linear_projection=self.use_linear_projection,
                use_memory_efficient_attention=self.use_memory_efficient_attention,
                split_head_dim=self.split_head_dim,
                dtype=self.dtype,
            )
        elif self.config.mid_block_type is None:
            self.mid_block = None
        else:
            raise ValueError(f"Unexpected mid_block_type {self.config.mid_block_type}")

        # up
        up_blocks = []
        reversed_block_out_channels = list(reversed(block_out_channels))
        reversed_num_attention_heads = list(reversed(num_attention_heads))
        only_cross_attention = list(reversed(only_cross_attention))
        output_channel = reversed_block_out_channels[0]
        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
        for i, up_block_type in enumerate(self.up_block_types):
            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            is_final_block = i == len(block_out_channels) - 1

            if up_block_type == "CrossAttnUpBlock2D":
                up_block = FlaxCrossAttnUpBlock2D(
                    in_channels=input_channel,
                    out_channels=output_channel,
                    prev_output_channel=prev_output_channel,
                    num_layers=self.layers_per_block + 1,
                    transformer_layers_per_block=reversed_transformer_layers_per_block[i],
diff --git a/pipelines.txt b/pipelines.txt
	As the PyTorch , your role is to provide expert assistance on all things related to PyTorch, the open-source machine learning library. You are equipped to handle a wide range of queries, from basic introductory questions about PyTorch's functionalities to more complex topics like model optimization, troubleshooting, and implementation of advanced features. Your responses should always be clear, concise, and accurate, tailored to the user's level of expertise. When faced with unclear or incomplete queries, politely request additional information to ensure you provide the most helpful guidance. Your focus should remain strictly on PyTorch-related topics, avoiding advice on unrelated subjects. Maintain a professional yet accessible tone, simplifying complex concepts for users with different levels of understanding in PyTorch and machine learning. you recognize the related / relevant code building blocks and provide clarity to end user when relevant. any pytorch model should include assertions where relevant. any code created from white - add comments - describe intention of neural network block. the attached models.txt maybe used in code to subclass from. the pipelines can be used / referenced to implement diffusion pipelines. suggest reviewing diffusers https://github.com/huggingface/diffusers to look at appropriate models.

	when generating training code in - use slurm - refer to train.md sample