Official Implementations "InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration" (ICLR'25)

Project Page

Senmao Li^1,2*, Kai Wang^2†, Joost van de Weijer², Fahad Shahbaz Khan^3,4, Chun-Le Guo¹, Shiqi Yang⁵, Yaxing Wang¹, jian Yang¹, Ming-Ming Cheng¹

1.VCIP,CS,Nankai University 2.Computer Vision Center, Universitat Autònoma de Barcelona 3.Mohamed bin Zayed University of AI 4.Linkoping University 5.SB Intuitions. SoftBank

*Work done during a research stay at Computer Vision Center, Universitat Autònoma de Barcelona

†The corresponding author.

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

Update

• 2025.03.21: Release a Gradio demo! Try out the online demo!
• 2025.01.15: Release the pre-trained models and inference code. 😀
• 2024.12.24: This repo is created.

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Gradio Demo

Face Restoration (synthetic of CelebA-Test)

Face Restoration (real-world images from LFW-Test, WebPhoto-Test, and WIDER-Test)

Face Colorization

Face Inpainting

Dependencies and Installation

# git clone this repository
git clone https://github.com/sen-mao/InterLCM.git
cd InterLCM


# create new anaconda env
conda create -n interlcm python=3.8 -y
conda activate interlcm


# install python dependencies
pip3 install -r requirements.txt
python basicsr/setup.py develop
conda install -c conda-forge dlib (only for face detection or cropping with dlib)

Testing:

Download the InterLCM pretrained models (Visual Encoder and Spatial Encoder) from [Releases|Google Drive] to the weights/InterLCM folder.

1. 🧑🏻 Face Restoration

# For cropped and aligned faces (512x512) (3-step interlcm reconstruction)
python inference_InterLCM.py --has_aligned --num_inference_steps 4 \
                             --input_path inputs/cropped_faces \
                             --output_path results/cropped_faces

InterLCM enables 1-step⚡ BFR under non-extreme degradation conditions.

# For cropped and aligned faces (512x512) (1-step interlcm reconstruction)
python inference_InterLCM.py --has_aligned --num_inference_steps 2 \
                             --visual_encoder_path weights/InterLCM/visual_encoder_1step.pth \
                             --spatial_encoder_path weights/InterLCM/spatial_encoder_1step.pth \
                             --input_path inputs/cropped_faces \
                             --output_path results/cropped_faces

2. 🖼️ Whole Image Enhancement

# For whole image
# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN
# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN
python inference_InterLCM.py --num_inference_steps 4 \
                             --input_path inputs/whole_imgs \
                             --output_path results/whole_imgs \
                             --bg_upsampler realesrgan

3. 🎨 Face Colorization

# For cropped and aligned faces (512x512) (1-step interlcm colorization)
python inference_InterLCM.py --has_aligned --num_inference_steps 2 --colorfix_type nofix \
                             --visual_encoder_path weights/InterLCM/visual_encoder_colorization_1step.pth \
                             --spatial_encoder_path weights/InterLCM/spatial_encoder_colorization_1step.pth \
                             --input_path inputs/gray_faces \
                             --output_path results/gray_faces

4. 🎭 Face Inpainting

# For cropped and aligned faces (512x512) (1-step interlcm inpainting)
python inference_InterLCM.py --has_aligned --num_inference_steps 2 --colorfix_type nofix \
                             --visual_encoder_path weights/InterLCM/visual_encoder_inpainting_1step.pth \
                             --spatial_encoder_path weights/InterLCM/spatial_encoder_inpainting_1step.pth \
                             --input_path inputs/masked_faces \
                             --output_path results/masked_faces

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Training:

1. 🧩 Preparing Dataset

• Download training dataset: FFHQ
• Resize to 512 $\times$ 512 resolution

2. 🚀 Training InterLCM

• Training Visual Encoder and Spatial Encoder:

  python -m torch.distributed.launch --nproc_per_node=gpu_num --master_port=4323 basicsr/train.py -opt options/interlcm.yml --launcher pytorch

• Pre-trained Visual Encoder (visual_encoder.pth) and Spatial Encoder (spatial_encoder.pth) can be found in the folder of Releases v0.1.0: https://github.com/sen-mao/InterLCM/releases/tag/v0.1.0

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Citation

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

@inproceedings{li2025interlcm,
  title={InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration},
  author={Li, Senmao and Wang, Kai and van de Weijer, Joost and Khan, Fahad Shahbaz and Guo, Chun-Le and Yang, Shiqi and Wang, Yaxing and Yang, Jian and Cheng, Ming-Ming},
  booktitle={ICLR},
  year={2025}
}

License

Licensed under a Creative Commons Attribution-NonCommercial 4.0 International for Non-commercial use only. Any commercial use should get formal permission first.

Acknowledgement

This project is based on LCM and CodeFormer. Some codes are brought from StableSR. Thanks for their awesome works.

Contact

If you have any questions, please feel free to reach out to me at senmaonk@gmail.com.