Open-source alternatives to DocDiff

PyTorch Implementation of introducing diffusion approach to 3D depth perception ECCV 2024

Inpaint-Anything is a diffusion-based image editor and inpainting tool designed to remove or replace objects in images, videos, and 3D scenes. It functions as a text-guided manipulator that uses natural language descriptions and mask-based filling to modify visual content. The system provides specialized capabilities for multi-view 3D scene editing and video object removal. It tracks selected objects across multiple frames or perspectives to synthesize consistent backgrounds and maintain spatial coherence after an element is removed. The tool covers a range of image manipulation tasks, inclu

ACM Multimedia 2022 Delving Globally into Texture and Structure for Image Inpainting

Paper | Project Page | Video | WebUI | ModelScope | ComfyUI

This is the code repository of the following paper to train and perform inference with patch-based diffusion models for image restoration under adverse weather conditions.

PyTorch code for paper "Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs"

The repository contains reproducible PyTorch source code of our paper Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models. We present the first framework to solve general inverse problems leveraging pre-trained latent diffusion models. Previously proposed…

This project was built using a fork of Score-SDE

Multitask Brain Tumor Inpainting with Diffusion Models

dmps code

CVPR 2024: Residual Denoising Diffusion Models

SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models

This is the code of the paper "Underwater Image Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip Strategy"

NeurIPS 2023 PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance

Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement

Paper 'Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms' in ACM Multimedia '22.

Official PyTorch implementation of "Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction" (MICCAI 2023)

ICLR2023 Official repository of DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models

This is the official impelmenation of the paper Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models.

Yinhuai Wang, Jiwen Yu, Jian Zhang Peking University and PCL \*denotes equal contribution

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

Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction

Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bihan Wen, Radu Timofte, Luc Van Gool.