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Flux

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Flux

Flux is a series of text-to-image generation models based on diffusion transformers. To know more about Flux, check out the original blog post by the creators of Flux, Black Forest Labs.

Original model checkpoints for Flux can be found here. Original inference code can be found here.

Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out this section for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to this blog post to learn more. For an exhaustive list of resources, check out this gist.

Flux comes in the following variants:

model type	model id
Timestep-distilled	`black-forest-labs/FLUX.1-schnell`
Guidance-distilled	`black-forest-labs/FLUX.1-dev`
Fill Inpainting/Outpainting (Guidance-distilled)	`black-forest-labs/FLUX.1-Fill-dev`
Canny Control (Guidance-distilled)	`black-forest-labs/FLUX.1-Canny-dev`
Depth Control (Guidance-distilled)	`black-forest-labs/FLUX.1-Depth-dev`
Canny Control (LoRA)	`black-forest-labs/FLUX.1-Canny-dev-lora`
Depth Control (LoRA)	`black-forest-labs/FLUX.1-Depth-dev-lora`
Redux (Adapter)	`black-forest-labs/FLUX.1-Redux-dev`

All checkpoints have different usage which we detail below.

Timestep-distilled

max_sequence_length cannot be more than 256.
guidance_scale needs to be 0.
As this is a timestep-distilled model, it benefits from fewer sampling steps.

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()

prompt = "A cat holding a sign that says hello world"
out = pipe(
    prompt=prompt,
    guidance_scale=0.,
    height=768,
    width=1360,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
out.save("image.png")

Guidance-distilled

The guidance-distilled variant takes about 50 sampling steps for good-quality generation.
It doesn’t have any limitations around the max_sequence_length.

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()

prompt = "a tiny astronaut hatching from an egg on the moon"
out = pipe(
    prompt=prompt,
    guidance_scale=3.5,
    height=768,
    width=1360,
    num_inference_steps=50,
).images[0]
out.save("image.png")

Fill Inpainting/Outpainting

Flux Fill pipeline does not require strength as an input like regular inpainting pipelines.
It supports both inpainting and outpainting.

import torch
from diffusers import FluxFillPipeline
from diffusers.utils import load_image

image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup.png")
mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup_mask.png")

repo_id = "black-forest-labs/FLUX.1-Fill-dev"
pipe = FluxFillPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16).to("cuda")

image = pipe(
    prompt="a white paper cup",
    image=image,
    mask_image=mask,
    height=1632,
    width=1232,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save(f"output.png")

Canny Control

Note: black-forest-labs/Flux.1-Canny-dev is not a ControlNetModel model. ControlNet models are a separate component from the UNet/Transformer whose residuals are added to the actual underlying model. Canny Control is an alternate architecture that achieves effectively the same results as a ControlNet model would, by using channel-wise concatenation with input control condition and ensuring the transformer learns structure control by following the condition as closely as possible.

# !pip install -U controlnet-aux
import torch
from controlnet_aux import CannyDetector
from diffusers import FluxControlPipeline
from diffusers.utils import load_image

pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-Canny-dev", torch_dtype=torch.bfloat16).to("cuda")

prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")

processor = CannyDetector()
control_image = processor(control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024)

image = pipe(
    prompt=prompt,
    control_image=control_image,
    height=1024,
    width=1024,
    num_inference_steps=50,
    guidance_scale=30.0,
).images[0]
image.save("output.png")

Depth Control

Note: black-forest-labs/Flux.1-Depth-dev is not a ControlNet model. ControlNetModel models are a separate component from the UNet/Transformer whose residuals are added to the actual underlying model. Depth Control is an alternate architecture that achieves effectively the same results as a ControlNet model would, by using channel-wise concatenation with input control condition and ensuring the transformer learns structure control by following the condition as closely as possible.

# !pip install git+https://github.com/huggingface/image_gen_aux
import torch
from diffusers import FluxControlPipeline, FluxTransformer2DModel
from diffusers.utils import load_image
from image_gen_aux import DepthPreprocessor

pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-Depth-dev", torch_dtype=torch.bfloat16).to("cuda")

prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")

processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
control_image = processor(control_image)[0].convert("RGB")

image = pipe(
    prompt=prompt,
    control_image=control_image,
    height=1024,
    width=1024,
    num_inference_steps=30,
    guidance_scale=10.0,
    generator=torch.Generator().manual_seed(42),
).images[0]
image.save("output.png")

Redux

Flux Redux pipeline is an adapter for FLUX.1 base models. It can be used with both flux-dev and flux-schnell, for image-to-image generation.
You can first use the FluxPriorReduxPipeline to get the prompt_embeds and pooled_prompt_embeds, and then feed them into the FluxPipeline for image-to-image generation.
When use FluxPriorReduxPipeline with a base pipeline, you can set text_encoder=None and text_encoder_2=None in the base pipeline, in order to save VRAM.

import torch
from diffusers import FluxPriorReduxPipeline, FluxPipeline
from diffusers.utils import load_image
device = "cuda"
dtype = torch.bfloat16


repo_redux = "black-forest-labs/FLUX.1-Redux-dev"
repo_base = "black-forest-labs/FLUX.1-dev" 
pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained(repo_redux, torch_dtype=dtype).to(device)
pipe = FluxPipeline.from_pretrained(
    repo_base, 
    text_encoder=None,
    text_encoder_2=None,
    torch_dtype=torch.bfloat16
).to(device)

image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img5.png")
pipe_prior_output = pipe_prior_redux(image)
images = pipe(
    guidance_scale=2.5,
    num_inference_steps=50,
    generator=torch.Generator("cpu").manual_seed(0),
    **pipe_prior_output,
).images
images[0].save("flux-redux.png")

Running FP16 inference

Flux can generate high-quality images with FP16 (i.e. to accelerate inference on Turing/Volta GPUs) but produces different outputs compared to FP32/BF16. The issue is that some activations in the text encoders have to be clipped when running in FP16, which affects the overall image. Forcing text encoders to run with FP32 inference thus removes this output difference. See here for details.

FP16 inference code:

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) # can replace schnell with dev
# to run on low vram GPUs (i.e. between 4 and 32 GB VRAM)
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()

pipe.to(torch.float16) # casting here instead of in the pipeline constructor because doing so in the constructor loads all models into CPU memory at once

prompt = "A cat holding a sign that says hello world"
out = pipe(
    prompt=prompt,
    guidance_scale=0.,
    height=768,
    width=1360,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
out.save("image.png")

Single File Loading for the FluxTransformer2DModel

The FluxTransformer2DModel supports loading checkpoints in the original format shipped by Black Forest Labs. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.

`FP8` inference can be brittle depending on the GPU type, CUDA version, and `torch` version that you are using. It is recommended that you use the `optimum-quanto` library in order to run FP8 inference on your machine.

The following example demonstrates how to run Flux with less than 16GB of VRAM.

First install optimum-quanto

pip install optimum-quanto

Then run the following example

import torch
from diffusers import FluxTransformer2DModel, FluxPipeline
from transformers import T5EncoderModel, CLIPTextModel
from optimum.quanto import freeze, qfloat8, quantize

bfl_repo = "black-forest-labs/FLUX.1-dev"
dtype = torch.bfloat16

transformer = FluxTransformer2DModel.from_single_file("https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8.safetensors", torch_dtype=dtype)
quantize(transformer, weights=qfloat8)
freeze(transformer)

text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
quantize(text_encoder_2, weights=qfloat8)
freeze(text_encoder_2)

pipe = FluxPipeline.from_pretrained(bfl_repo, transformer=None, text_encoder_2=None, torch_dtype=dtype)
pipe.transformer = transformer
pipe.text_encoder_2 = text_encoder_2

pipe.enable_model_cpu_offload()

prompt = "A cat holding a sign that says hello world"
image = pipe(
    prompt,
    guidance_scale=3.5,
    output_type="pil",
    num_inference_steps=20,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]

image.save("flux-fp8-dev.png")

Diffusers

Flux

Timestep-distilled

Guidance-distilled

Fill Inpainting/Outpainting

Canny Control

Depth Control

Redux

Running FP16 inference

Single File Loading for the FluxTransformer2DModel

FluxPipeline

class diffusers.FluxPipeline

__call__

disable_vae_slicing

disable_vae_tiling

enable_vae_slicing

enable_vae_tiling

encode_prompt

FluxImg2ImgPipeline

class diffusers.FluxImg2ImgPipeline

__call__

encode_prompt

FluxInpaintPipeline

class diffusers.FluxInpaintPipeline

__call__

encode_prompt

FluxControlNetInpaintPipeline

class diffusers.FluxControlNetInpaintPipeline

__call__

encode_prompt

FluxControlNetImg2ImgPipeline

class diffusers.FluxControlNetImg2ImgPipeline

__call__

encode_prompt

FluxControlPipeline

class diffusers.FluxControlPipeline

__call__

disable_vae_slicing

disable_vae_tiling

enable_vae_slicing

enable_vae_tiling

encode_prompt

FluxControlImg2ImgPipeline

class diffusers.FluxControlImg2ImgPipeline

__call__

encode_prompt

FluxPriorReduxPipeline

class diffusers.FluxPriorReduxPipeline

__call__

encode_prompt

FluxFillPipeline

class diffusers.FluxFillPipeline

__call__

disable_vae_slicing

disable_vae_tiling

enable_vae_slicing

enable_vae_tiling

encode_prompt

call

call

call

call

call

call

call

call

call