CodeFormer is a deep learning framework designed for the restoration and enhancement of facial images and video sequences. It functions as a comprehensive processing engine capable of reconstructing high-quality facial features from degraded, blurry, or damaged inputs, while also providing tools for image upscaling and generative inpainting to fill missing or corrupted regions.
The system distinguishes itself by utilizing a codebook-based quantization approach that maps input patches to high-quality facial representations, supported by transformer-based global modeling to ensure structural consistency. It incorporates latent space manifold projection and multi-scale feature fusion to filter noise and preserve fine-grained textures, while an adversarial training objective enforces realistic output generation. For video applications, the framework employs temporal consistency regularization to maintain stability across sequential frames.
Beyond core restoration, the project includes capabilities for colorizing monochrome or faded portraits by applying natural skin tones. The software is distributed as a Python-based repository, providing the necessary models and utilities to perform these enhancement tasks on both static images and video files.
