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- CodeFormer
On the recordRead 11 Oct 202622 facts
CodeFormer
On the record
- Additional tools
- It includes face colorization for black-and-white or faded cropped faces and face inpainting for cropped faces masked with a white brush.github.com · read 4 Oct 2026
- API restriction
- The Replicate model page says its CodeFormer API cannot be used commercially.replicate.com · read 4 Oct 2026
- Colorization and inpainting
- Separate scripts provide colorization and inpainting for cropped and aligned face images.github.com · read 11 Oct 2026
- Image enhancement
- The project supports face restoration and whole-image enhancement, with optional Real-ESRGAN upsampling for faces and backgrounds.github.com · read 4 Oct 2026
- Integrations
- The project lists Stable Diffusion WebUI, ChaiNNer, and ComfyUI among integrations, and identifies Hugging Face, Replicate, and OpenXLab demos as author-maintained deployments.github.com · read 4 Oct 2026
- Intended users
- The project page presents CodeFormer as a face-restoration method and provides code and a demo for researchers and other users working with degraded face images.shangchenzhou.com · read 11 Oct 2026
- Known limitation
- For whole-image processing, the face-background fusion step may damage hair texture at the boundary, according to the repository's comparison note.github.com · read 11 Oct 2026
- License
- The project uses the NTU S-Lab License 1.0, and the repository says redistribution and use should follow that license.github.com · read 11 Oct 2026
- Local requirements
- The installation instructions list PyTorch 1.7.1 or later, CUDA 10.1 or later, and additional packages in requirements.txt.github.com · read 11 Oct 2026
- Official demos
- The authors identify their Hugging Face Space, Replicate, and OpenXLab demos as official deployments they maintain.github.com · read 11 Oct 2026
- Purpose
- CodeFormer is a Transformer-based method for blind face restoration that predicts codes from low-quality face images.shangchenzhou.com · read 11 Oct 2026
- Quality and fidelity control
- A scalar weight lets users trade image quality against fidelity, with smaller values tending toward higher quality and larger values toward higher fidelity.github.com · read 11 Oct 2026
- Quality control
- A controllable weight trades image quality against fidelity, with lower values tending toward higher quality and higher values toward higher fidelity.shangchenzhou.com · read 4 Oct 2026
- Requirements
- The README lists PyTorch 1.7.1 or later, CUDA 10.1 or later, Python 3.8 in its setup example, and other packages in requirements.txt.github.com · read 4 Oct 2026
- Restoration method
- It uses a learned discrete codebook and Transformer-based code prediction to restore faces from degraded inputs.shangchenzhou.com · read 4 Oct 2026
- Security and compliance
- The opened project and demo pages do not state security certifications or compliance standards.github.com · read 4 Oct 2026
- Support
- The repository directs questions to [email protected].github.com · read 11 Oct 2026
- Third-party integrations
- The repository lists integrations including Stable Diffusion WebUI and chaiNNer, and warns that other listed demos and services are third-party.github.com · read 11 Oct 2026
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