GitHub - finninmunich/StableSR: [IJCV2024] Exploiting Diffusion Prior for Real-World Image Super-Resolution

Exploiting Diffusion Prior for Real-World Image Super-Resolution

Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin C.K. Chan, Chen Change Loy

S-Lab, Nanyang Technological University

⭐ If StableSR is helpful to your images or projects, please help star this repo. Thanks! 🤗

Update

2024.06.28: Accepted by IJCV. See the latest Full paper.
2024.02.29: Support StableSR with SD-Turbo. Thank Andray for the finding!

Now the ComfyUI of StableSR is also available. Thank gameltb and WSJUSA for the implementation!
2023.11.30: Code Update.
- Support DDIM and negative prompts
- Add CFW training scripts
- Add FaceSR training and test scripts
2023.10.08: Our test sets associated with the results in our paper are now available at [HuggingFace] and [OpenXLab]. You may have an easy comparison with StableSR now.
2023.08.19: Integrated to 🤗 Hugging Face. Try out online demo! .
2023.08.19: Integrated to 🐼 OpenXLab. Try out online demo! .
2023.07.31: Integrated to 🚀 Replicate. Try out online demo! Thank Chenxi for the implementation!
2023.07.16: You may reproduce the LDM baseline used in our paper using LDM-SRtuning .
2023.07.14: 🐳 ModelScope for StableSR is released!
2023.06.30: 🐳 New model trained on SD-2.1-768v is released! Better performance with fewer artifacts!
2023.06.28: Support training on SD-2.1-768v.
2023.05.22: 🐳 Improve the code to save more GPU memory, now 128 --> 512 needs 8.9G. Enable start from intermediate steps.
2023.05.20: 🐳 The WebUI of StableSR is available. Thank Li Yi for the implementation!
2023.05.13: Add Colab demo of StableSR.
2023.05.11: Repo is released.

TODO

Demo on real-world SR

For more evaluation, please refer to our paper for details.

Demo on 4K Results

StableSR is capable of achieving arbitrary upscaling in theory, below is an 4x example with a result beyond 4K (4096x6144).

# DDIM w/ negative prompts
python scripts/sr_val_ddim_text_T_negativeprompt_canvas_tile.py --config configs/stableSRNew/v2-finetune_text_T_768v.yaml --ckpt stablesr_768v_000139.ckpt --vqgan_ckpt vqgan_finetune_00011.ckpt --init-img ./inputs/test_example/ --outdir ../output/ --ddim_steps 20 --dec_w 0.0 --colorfix_type wavelet --scale 7.0 --use_negative_prompt --upscale 4 --seed 42 --n_samples 1 --input_size 768 --tile_overlap 48 --ddim_eta 1.0

More examples.
- 4K Demo1, which is a 4x SR on the image from here.
- 4K Demo2, which is a 8x SR on the image from here.
- More comparisons can be found here and here.

Dependencies and Installation

Pytorch == 1.12.1
CUDA == 11.7
pytorch-lightning==1.4.2
xformers == 0.0.16 (Optional)
Other required packages in environment.yaml

# git clone this repository
git clone https://github.com/IceClear/StableSR.git
cd StableSR

# Create a conda environment and activate it
conda env create --file environment.yaml
conda activate stablesr

# Install xformers
conda install xformers -c xformers/label/dev

# Install taming & clip
pip install -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
pip install -e git+https://github.com/openai/CLIP.git@main#egg=clip
pip install -e .

Running Examples

Train

Download the pretrained Stable Diffusion models from [HuggingFace]

Train Time-aware encoder with SFT: set the ckpt_path in config files (Line 22 and Line 55)

python main.py --train --base configs/stableSRNew/v2-finetune_text_T_512.yaml --gpus GPU_ID, --name NAME --scale_lr False

Train CFW: set the ckpt_path in config files (Line 6).

You need to first generate training data using the finetuned diffusion model in the first stage.

# General SR
python scripts/generate_vqgan_data.py --config configs/stableSRdata/test_data.yaml --ckpt CKPT_PATH --outdir OUTDIR --skip_grid --ddpm_steps 200 --base_i 0 --seed 10000

# For face data
python scripts/generate_vqgan_data_face.py --config configs/stableSRdata/test_data_face.yaml --ckpt CKPT_PATH --outdir OUTDIR --skip_grid --ddpm_steps 200 --base_i 0 --seed 10000

The data folder should be like this:

CFW_trainingdata/
    └── inputs
          └── 00000001.png # LQ images, (512, 512, 3) (resize to 512x512)
          └── ...
    └── gts
          └── 00000001.png # GT images, (512, 512, 3) (512x512)
          └── ...
    └── latents
          └── 00000001.npy # Latent codes (N, 4, 64, 64) of HR images generated by the diffusion U-net, saved in .npy format.
          └── ...
    └── samples
          └── 00000001.png # The HR images generated from latent codes, just to make sure the generated latents are correct.
          └── ...

Then you can train CFW:

python main.py --train --base configs/autoencoder/autoencoder_kl_64x64x4_resi.yaml --gpus GPU_ID, --name NAME --scale_lr False

Resume

python main.py --train --base configs/stableSRNew/v2-finetune_text_T_512.yaml --gpus GPU_ID, --resume RESUME_PATH --scale_lr False

Test directly

Download the Diffusion and autoencoder pretrained models from [HuggingFace | OpenXLab]. We use the same color correction scheme introduced in paper by default. You may change --colorfix_type wavelet for better color correction. You may also disable color correction by --colorfix_type nofix

StableSR-Turbo: Get the ckpt first from [HuggingFace or OpenXLab]. Then you just need to modify --ckpt_path and set --ddpm_steps to 4. See examples below:

python scripts/sr_val_ddpm_text_T_vqganfin_old.py --config configs/stableSRNew/v2-finetune_text_T_512.yaml --ckpt ./stablesr_turbo.ckpt --init-img LQ_PATH --outdir OUT_PATH --ddpm_steps 4 --dec_w 0.5 --seed 42 --n_samples 1 --vqgan_ckpt ./vqgan_cfw_00011.ckpt --colorfix_type wavelet

python scripts/sr_val_ddpm_text_T_vqganfin_oldcanvas_tile.py --config configs/stableSRNew/v2-finetune_text_T_512.yaml --ckpt ./stablesr_turbo.ckpt --init-img LQ_PATH --outdir OUT_PATH --ddpm_steps 4 --dec_w 0.5 --seed 42 --n_samples 1 --vqgan_ckpt ./vqgan_cfw_00011.ckpt --colorfix_type wavelet --upscale 4

DDIM is supported now. See here
Test on 128 --> 512: You need at least 10G GPU memory to run this script (batchsize 2 by default)

python scripts/sr_val_ddpm_text_T_vqganfin_old.py --config configs/stableSRNew/v2-finetune_text_T_512.yaml --ckpt CKPT_PATH --vqgan_ckpt VQGANCKPT_PATH --init-img INPUT_PATH --outdir OUT_DIR --ddpm_steps 200 --dec_w 0.5 --colorfix_type adain

Test on arbitrary size w/o chop for autoencoder (for results beyond 512): The memory cost depends on your image size, but is usually above 10G.

python scripts/sr_val_ddpm_text_T_vqganfin_oldcanvas.py --config configs/stableSRNew/v2-finetune_text_T_512.yaml --ckpt CKPT_PATH --vqgan_ckpt VQGANCKPT_PATH --init-img INPUT_PATH --outdir OUT_DIR --ddpm_steps 200 --dec_w 0.5 --colorfix_type adain

Test on arbitrary size w/ chop for autoencoder: Current default setting needs at least 18G to run, you may reduce the autoencoder tile size by setting --vqgantile_size and --vqgantile_stride. Note the min tile size is 512 and the stride should be smaller than the tile size. A smaller size may introduce more border artifacts.

python scripts/sr_val_ddpm_text_T_vqganfin_oldcanvas_tile.py --config configs/stableSRNew/v2-finetune_text_T_512.yaml --ckpt CKPT_PATH --vqgan_ckpt VQGANCKPT_PATH --init-img INPUT_PATH --outdir OUT_DIR --ddpm_steps 200 --dec_w 0.5 --colorfix_type adain

For test on 768 model, you need to set --config configs/stableSRNew/v2-finetune_text_T_768v.yaml, --input_size 768 and --ckpt. You can also adjust --tile_overlap, --vqgantile_size and --vqgantile_stride accordingly. We did not finetune CFW.

Test FaceSR

You need to first generate reference images using [CodeFormer] or other blind face models.
Pretrained Models: [HuggingFace | OpenXLab].

python scripts/sr_val_ddpm_text_T_vqganfin_facerefersampling.py --init-img LR_PATH --ref-img REF_PATH --outdir OUTDIR --config ./configs/stableSRNew/v2-finetune_face_T_512.yaml --ckpt face_stablesr_000050.ckpt
 --vqgan_ckpt face_vqgan_cfw_00011.ckpt --ddpm_steps 200 --dec_w 0.0 --facesr

Test using Replicate API

import replicate
model = replicate.models.get(<model_name>)
model.predict(input_image=...)

You may see here for more information.

Citation

If our work is useful for your research, please consider citing:

@article{wang2024exploiting,
  author = {Wang, Jianyi and Yue, Zongsheng and Zhou, Shangchen and Chan, Kelvin C.K. and Loy, Chen Change},
  title = {Exploiting Diffusion Prior for Real-World Image Super-Resolution},
  article = {International Journal of Computer Vision},
  year = {2024}
}

License

This project is licensed under NTU S-Lab License 1.0. Redistribution and use should follow this license.

Acknowledgement

This project is based on stablediffusion, latent-diffusion, SPADE, mixture-of-diffusers and BasicSR. Thanks for their awesome work.

Contact

If you have any questions, please feel free to reach me out at [email protected].

Name		Name	Last commit message	Last commit date
Latest commit History 63 Commits
assets		assets
basicsr		basicsr
configs		configs
inputs/test_example		inputs/test_example
ldm		ldm
scripts		scripts
sensetime_scripts		sensetime_scripts
.gitignore		.gitignore
LICENSE.txt		LICENSE.txt
README.md		README.md
app.py		app.py
cog.yaml		cog.yaml
environment.yaml		environment.yaml
main.py		main.py
predict.py		predict.py
requirements.txt		requirements.txt
setup.py		setup.py

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Exploiting Diffusion Prior for Real-World Image Super-Resolution

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TODO

Demo on real-world SR

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Test using Replicate API

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Exploiting Diffusion Prior for Real-World Image Super-Resolution

Update

TODO

Demo on real-world SR

Demo on 4K Results

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Test directly

Test FaceSR

Test using Replicate API

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Acknowledgement

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