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sczhou/CodeFormer
Python tool for blind face restoration, enhancing blurry, low-quality or damaged faces in photos and videos using a codebook lookup transformer.
- Estrellas
- 18,185
- Forks
- 3,723
- Issues abiertos
- 283
- Último commit
- hace 11 meses
¿Qué es?
CodeFormer restores faces in images and videos when the degradation type is unknown, a setting called blind face restoration. It is the official implementation of a NeurIPS 2022 paper that uses a learned codebook of facial priors and a transformer to predict clean facial details from degraded input.
The tool exposes a fidelity weight that trades off output quality against similarity to the original face, so users can favor cleaner results or closer identity. Beyond restoration, it includes scripts for colorizing black-and-white or faded face photos and for inpainting masked regions of aligned faces.
For whole images and video, it can enhance background regions with Real-ESRGAN and upsample the restored face. Pretrained models are downloaded via a provided script, and the project includes training code and configuration files.
Funciones clave
- 01Blind face restoration for images and cropped, aligned 512x512 faces
- 02Adjustable fidelity weight to balance quality and similarity to the input
- 03Video enhancement from .mp4, .mov and .avi inputs
- 04Face colorization for black-and-white or faded photos
- 05Face inpainting for masked regions
- 06Optional Real-ESRGAN background enhancement and face upsampling
Casos de uso
- Restoring old or low-resolution family photos
- Enhancing blurry frames in older video clips
- Fixing distorted faces in AI-generated art
- Researchers comparing blind face restoration methods on aligned face datasets
Temas
Preguntas frecuentes
- ¿Qué es CodeFormer?
- Python tool for blind face restoration, enhancing blurry, low-quality or damaged faces in photos and videos using a codebook lookup transformer. CodeFormer restores faces in images and videos when the degradation type is unknown, a setting called blind face restoration. It is the official implementation of a NeurIPS 2022 paper that uses a learned codebook of facial priors and a transformer to predict clean facial details from degraded input.
- ¿CodeFormer es gratuito? ¿Qué licencia tiene?
- CodeFormer es open source bajo la licencia Other. Revisa los términos de la licencia antes de usarlo en productos comerciales.
- ¿CodeFormer sigue mantenido?
- Sin commits en más de 6 meses. El último commit de CodeFormer fue el 18 nov 2025.
- ¿Para qué puedo usar CodeFormer?
- Restoring old or low-resolution family photos Enhancing blurry frames in older video clips Fixing distorted faces in AI-generated art Researchers comparing blind face restoration methods on aligned face datasets
- ¿Dónde consigo CodeFormer?
- El código fuente y las instrucciones de instalación están en GitHub: https://github.com/sczhou/CodeFormer
Resumen generado por IA a partir del README del proyecto. Las estadísticas de GitHub se actualizan a diario.
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