Text-to-3d
Image-guided 3d generation
Image-to-3d reconstruction
conda create -n hifa python=3.9
pip install -r requirements.txt
(Suppose your are using torch2.0 + cu117, install torch-scatter:)
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
make sure torch is with cuda.is_available()
By default, we use load to build the extension at runtime.
We also provide the setup.py to build each extension:
# install all extension modules
bash scripts/install_ext.sh
# if you want to install manually, here is an example:
pip install ./raymarching # install to python path (you still need the raymarching/ folder, since this only installs the built extension.)This is a lot more convenient, since you wont have to rebuild it
CUDA_VISIBLE_DEVICES=0 python main.py --text "a baby bunny sitting on top of a stack of pancakes" --workspace trials_throne_sanity --dir_text --albedo --phi_range 0 120
For both of those, you need to generate some predicted views following instruction in SyncDreamer by first removing the background and then generating 16 views. Copy over the output 0.png to this project's folder and specify the file with image-path. We provided some example images under raw_input and gt_images.
After you get the predicted views from SyncDreamer, for image to 3d generation:
CUDA_VISIBLE_DEVICES=0 python main.py --text "A toy grabber with dinosaur head" --learned_embeds_path "gt_images/dinosaur/learned_embeds.bin" --image_path "gt_images/dinosaur/0.png" --workspace "trials_dinosaur(textprompt)_imgto3d" --dir_text --albedo --gt_image_rate 0.5 --h 256 --w 256
For image-guided 3d generation:
CUDA_VISIBLE_DEVICES=0 python main.py --text "A toy grabber with dinosaur head" --learned_embeds_path "gt_images/dinosaur/learned_embeds.bin" --image_path "gt_images/dinosaur/0.png" --workspace "trials_dinosaur(textprompt)_imgguided" --dir_text --albedo --gt_image_rate 0.5 --h 256 --w 256 --anneal_gt 0.7
To use textual inversion, first compute token:
python textual-inversion/textual_inversion.py --output_dir="gt_images/teapot" --train_data_dir="raw_input/no_bg/teapot" --initializer_token="teapot" --placeholder_token="_teapot_placeholder_" --pretrained_model_name_or_path="SG161222/Realistic_Vision_V5.1_noVAE" --learnable_property="object" --resolution=256 --train_batch_size=1 --gradient_accumulation_steps=4 --max_train_steps=5000 --learning_rate=5.0e-4 --scale_lr --lr_scheduler="constant" --lr_warmup_steps=0 --use_augmentations
python main.py --text "a DSLR photo of <token>" --learned_embeds_path "gt_images/teapot/learned_embeds.bin" --image_path "gt_images/teapot/0.png" --workspace "trials_teapot_gtrate=0.5_v9" --dir_text --albedo --gt_image_rate 0.5 --h 256 --w 256
However, we don't find that textual inversion visibly brings a benefit
Some notable additions compared to the paper:
- More kernel smoothing strategies for coarse-to-fine sampling
- New regularizers akin to z-variance: monotonicity loss and z-entropy loss Intuitively, monotonicity loss ensures that the blending weight first increases then decreases monotonically along a ray, ensuring that there is a consistent surface. z-entropy loss regulates the entropy of the blending weight distribution along the ray, similar to how z-variance loss regulates its variance.
- This is just a clarification, since I noticed this issue in threestudio's re-implementation of z-variance loss: the rendering weights have to be normalized(divided by sum) along each ray before z-variance is computed. This is because if you don't normalize, two things will happen: i) the depth and z-variance will both be zero for background pixels 2) you cannot set lambda_zvar to be big, since then the model will be encouraged to have more background pixels(which have low z-variance), leading to small objects. This issue goes away with normalization.
- This repo is modified from stable-dreamfusion. We also provide an alternative version on threestudio, which makes our contributions fully integrated with ProlificDreamer and Magic123
@misc{zhu2023hifa,
title={HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance},
author={Junzhe Zhu and Peiye Zhuang},
year={2023},
eprint={2305.18766},
archivePrefix={arXiv},
primaryClass={cs.CV}
}