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[NeurIPS 2023] PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance
The main features of pq-yang/pgdiff are: Face Restoration, Inverse Problems.
Projects with overlapping indexed features include: zsyoaoa/difface — Zongsheng Yue, Chen Change Loy. xpixelgroup/diffbir — DiffBIR is a diffusion-based image restoration framework designed for blind image reconstruction. It utilizes… ballerin/v1diffusion — Implementation of sub-Riemannian diffusion for image inpainting and enhancement. chenxx89/bfrffusion — Official codes of Towards Real-World Blind Face Restoration with Generative Diffusion Prior. crosszamirski/guided-i2i — Combining image-to-image and class guided denoising diffusion probabilistic models. biomedai-ucsc/inversesr — [Early Accepted at MICCAI 2023] Pytorch Code of "InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion…
DiffBIR is a diffusion-based image restoration framework designed for blind image reconstruction. It utilizes generative diffusion priors to recover high-quality images from sources with unknown or complex degradations without requiring explicit degradation models. The system includes specialized models for face restoration, enabling the recovery of facial landmarks, textures, and backgrounds in degraded portraits. To support high-resolution outputs on hardware with limited memory, it employs a tiled image upscaler that divides images into smaller patches during sampling. The framework cover
Implementation of sub-Riemannian diffusion for image inpainting and enhancement
Early Accepted at MICCAI 2023 Pytorch Code of "InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model"
