Art restoration in camera capute images of Art.

Image restoration and super resolution using pretrained and finetuned Diffusion Model.

Update

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 a 8x example with a result beyond 4K (5120x3680). The example image is taken from here.

• We further directly test StableSR on AIGC and compared with several diffusion-based upscalers following the suggestions. A 4K demo is here, which is a 4x SR on the image from here. More comparisons can be found 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. 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 | Google Drive | OneDrive | 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

• 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 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:

@inproceedings{wang2023exploiting,
    author = {Wang, Jianyi and Yue, Zongsheng and Zhou, Shangchen and Chan, Kelvin CK and Loy, Chen Change},
    title = {Exploiting Diffusion Prior for Real-World Image Super-Resolution},
    booktitle = {arXiv preprint arXiv:2305.07015},
    year = {2023}
}

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 StableSR, 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].