DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

Paper | Project Page

Xinqi Lin^1,*, Jingwen He^2,3,*, Ziyan Chen¹, Zhaoyang Lyu², Bo Dai², Fanghua Yu¹, Wanli Ouyang², Yu Qiao², Chao Dong^1,2

¹Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
²Shanghai AI Laboratory
³The Chinese University of Hong Kong

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⭐If DiffBIR is helpful for you, please help star this repo. Thanks!🤗

📖Table Of Contents

• Update
• Visual Results On Real-world Images
• TODO
• Installation
• Pretrained Models
• Inference
• Train

🆕Update

• 2024.04.08: ✅ Release everything about our updated manuscript, including (1) a new model trained on subset of laion2b-en and (2) a more readable code base, etc. DiffBIR is now a general restoration pipeline that could handle different blind image restoration tasks with a unified generation module.
• 2023.09.19: ✅ Add support for Apple Silicon! Check installation_xOS.md to work with CPU/CUDA/MPS device!
• 2023.09.14: ✅ Integrate a patch-based sampling strategy (mixture-of-diffusers). Try it! Here is an example with a resolution of 2396 x 1596. GPU memory usage will continue to be optimized in the future and we are looking forward to your pull requests!
• 2023.09.14: ✅ Add support for background upsampler (DiffBIR/RealESRGAN) in face enhancement! 🚀 Try it!
• 2023.09.13: 🚀 Provide online demo (DiffBIR-official) in OpenXLab, which integrates both general model and face model. Please have a try! camenduru also implements an online demo, thanks for his work.:hugs:
• 2023.09.12: ✅ Upload inference code of latent image guidance and release real47 testset.
• 2023.09.08: ✅ Add support for restoring unaligned faces.
• 2023.09.06: 🚀 Update colab demo. Thanks to camenduru!:hugs:
• 2023.08.30: This repo is released.

👀Visual Results On Real-world Images

Blind Image Super-Resolution

Blind Face Restoration

⭐ Face and the background enhanced by DiffBIR.

Blind Image Denoising

8x Blind Super-Resolution With Patch-based Sampling

I often think of Bag End. I miss my books and my arm chair, and my garden. See, that's where I belong. That's home. --- Bilbo Baggins

🧗TODO

• Release code and pretrained models 💻.
• Update links to paper and project page 🔗.
• Release real47 testset 💽.
• Provide webui.
• Reduce the vram usage of DiffBIR 🔥🔥🔥.
• Provide HuggingFace demo 📓.
• Add a patch-based sampling schedule 🔍.
• Upload inference code of latent image guidance 📄.
• Improve the performance 🦸.
• Support MPS acceleration for MacOS users.
• DiffBIR-turbo 🔥🔥🔥.
• Speed up inference, such as using fp16/bf16, torch.compile 🔥🔥🔥.

⚙️Installation

# clone this repo
git clone https://github.com/XPixelGroup/DiffBIR.git
cd DiffBIR

# create environment
conda create -n diffbir python=3.10
conda activate diffbir
pip install -r requirements.txt

Our new code is based on pytorch 2.2.2 for the built-in support of memory-efficient attention. If you are working on a GPU that is not compatible with the latest pytorch, just downgrade pytorch to 1.13.1+cu116 and install xformers 0.0.16 as an alternative.

🧬Pretrained Models

Here we list pretrained weight of stage 2 model (IRControlNet) and our trained SwinIR, which was used for degradation removal during the training of stage 2 model.

Model Name	Description	HuggingFace	BaiduNetdisk	OpenXLab
v2.pth	IRControlNet trained on filtered laion2b-en	download	download (pwd: xiu3)	download
v1_general.pth	IRControlNet trained on ImageNet-1k	download	download (pwd: 79n9)	download
v1_face.pth	IRControlNet trained on FFHQ	download	download (pwd: n7dx)	download
codeformer_swinir.ckpt	SwinIR trained on ImageNet-1k	download	download (pwd: vfif)	download

During inference, we use off-the-shelf models from other papers as the stage 1 model: BSRNet for BSR, SwinIR-Face used in DifFace for BFR, and SCUNet-PSNR for BID, while the trained IRControlNet remains unchanged for all tasks. Please check code for more details. Thanks for their work!

⚔️Inference

We provide some examples for inference, check inference.py for more arguments. Pretrained weights will be automatically downloaded.

Blind Image Super-Resolution

python -u inference.py \
--version v2 \
--task sr \
--upscale 4 \
--cfg_scale 4.0 \
--input inputs/demo/bsr \
--output results/demo_bsr \
--device cuda

Blind Face Restoration

# for aligned face inputs
python -u inference.py \
--version v2 \
--task fr \
--upscale 1 \
--cfg_scale 4.0 \
--input inputs/demo/bfr/aligned \
--output results/demo_bfr_aligned \
--device cuda

# for unaligned face inputs
python -u inference.py \
--version v2 \
--task fr_bg \
--upscale 2 \
--cfg_scale 4.0 \
--input inputs/demo/bfr/whole_img \
--output results/demo_bfr_unaligned \
--device cuda

Blind Image Denoising

python -u inference.py \
--version v2
--task dn \
--upscale 1 \
--cfg_scale 4.0 \
--input inputs/demo/bid \
--output results/demo_bid \
--device cuda

Other options

Patch-based sampling

Add the following arguments to enable patch-based sampling:

[command...] --tiled --tile_size 512 --tile_stride 256

Patch-based sampling supports super-resolution with a large scale factor. Our patch-based sampling is built upon mixture-of-diffusers. Thanks for their work!

Restoration Guidance

Restoration guidance is used to achieve a trade-off bwtween quality and fidelity. We default to closing it since we prefer quality rather than fidelity. Here is an example:

python -u inference.py \
--version v2 \
--task sr \
--upscale 4 \
--cfg_scale 4.0 \
--input inputs/demo/bsr \
--guidance --g_loss w_mse --g_scale 0.5 --g_space rgb \
--output results/demo_bsr_wg \
--device cuda

You will see that the results become more smooth.

Better Start Point For Sampling

Add the following argument to offer better start point for reverse sampling:

[command...] --better_start

This option prevents our model from generating noise in image background.

🌠Train

Stage 1

First, we train a SwinIR, which will be used for degradation removal during the training of stage 2.

1. Generate file list of training set and validation set, a file list looks like:

   /path/to/image_1
   /path/to/image_2
   /path/to/image_3
   ...

   You can write a simple python script or directly use shell command to produce file lists. Here is an example:

   # collect all iamge files in img_dir
   find [img_dir] -type f > files.list
   # shuffle collected files
   shuf files.list > files_shuf.list
   # pick train_size files in the front as training set
   head -n [train_size] files_shuf.list > files_shuf_train.list
   # pick remaining files as validation set
   tail -n +[train_size + 1] files_shuf.list > files_shuf_val.list

2. Fill in the training configuration file with appropriate values.

3. Start training!

   accelerate launch train_stage1.py --config configs/train/train_stage1.yaml

Stage 2

1. Download pretrained Stable Diffusion v2.1 to provide generative capabilities. 💡: If you have ran the inference script, the SD v2.1 checkpoint can be found in weights.

   wget https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.ckpt --no-check-certificate

2. Generate file list as mentioned above. Currently, the training script of stage 2 doesn't support validation set, so you only need to create training file list.

3. Fill in the training configuration file with appropriate values.

4. Start training!

   accelerate launch train_stage2.py --config configs/train/train_stage2.yaml

Citation

Please cite us if our work is useful for your research.

@misc{lin2024diffbir,
      title={DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior}, 
      author={Xinqi Lin and Jingwen He and Ziyan Chen and Zhaoyang Lyu and Bo Dai and Fanghua Yu and Wanli Ouyang and Yu Qiao and Chao Dong},
      year={2024},
      eprint={2308.15070},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

License

This project is released under the Apache 2.0 license.

Acknowledgement

This project is based on ControlNet and BasicSR. Thanks for their awesome work.

Contact

If you have any questions, please feel free to contact with me at linxinqi23@mails.ucas.ac.cn.