Open-source alternatives to RePaint

GLIDE is a generative model designed for text-to-image synthesis, image editing, and the contextual filling of masked image regions. It uses a guided diffusion process to transform random noise into high-resolution imagery that aligns with descriptive text prompts. The system provides specialized capabilities for modifying existing visuals, including the ability to alter specific image elements and iteratively refine selected regions through text-driven guidance. It also functions as an inpainting tool, filling missing or masked sections of an image with new content that blends naturally with

GoCV is a computer vision library and Go language binding for OpenCV. It serves as an image processing toolkit and deep learning inference engine, providing programmatic access to a wide range of algorithms for image manipulation, object detection, and video analysis. The project differentiates itself through high-performance native bindings and hardware acceleration. It utilizes a foreign function interface to map Go calls to C++ functions and includes a hardware-agnostic backend dispatch to route neural network tasks to computation engines such as CUDA and OpenVINO. The library covers a br

python - m visdom.server python train.py --noflip --norotation --noaugment --imgfile yourdata --lr 1e-4 python train.py --noflip --norotation --noaugment --imgfile yourdata --lr 1e-5 --continuetrain python test.py --batchSize 1 --masktype 3 --imgfile yourdata --maskfile yourmask

This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior to reconstruct images from noisy or incomplete data. It functions as a neural network image prior, utilizing the inherent biases of the network architecture to restore pixels without the need for a pre-trained dataset or external learning. The system performs zero-shot image restoration by treating the network architecture itself as a regularization term. It uses a randomly initialized encoder-decoder structure and iterative gradient descent to minimize pixel-wise loss, recove

Codes of Learning Prior Feature and Attention Enhanced Image Inpainting (ECCV2022)

\2022.10.03\ Model for FFHQ-512 is available. (Link)

The paper can be found here. If you have any question about the paper/codes, you can contact me through Email(yutao666@mail.ustc.edu.cn).

This repository contains the implmentation of Image Inpainting method proposed in the paper

arXiv|pdf paper|appendix|Project

Official repository for "Deep Fusion Network for Image completion".

StructCompletion

SC-FEGAN : Face Editing Generative Adversarial Network with User's Sketch and Color (ICCV 2019)

This repository is for the CVPR 2023 paper: NÜWA-LIP: Language Guided Image Inpainting with Defect-free VQGAN

Arxiv |ICCV 2019 paper| BibTex

PD-GAN: Probabilistic Diverse GAN for Image Inpainting Hongyu Liu, Ziyu Wan, Wei Huang, Yibing Song, Xintong Han and JIng Liao. In CVPR 2021

Rethinking Image Inpainting via a Mutual Encoder Decoder with Feature Equalizations .

High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis

13/08/2024 The controllable image inpainting is avaliable! In addition, a demo ui is provided to support interactive image editing!

This codes repository provides the pytorch implementation of the KGI virtual try-on method proposed in ICCV23 paper Virtual Try-On with Garment-Pose Keypoints Guided Inpainting.

ArXiv | Project Page | Online Demo | Video(demo)

Codebase to realistically insert objects into images via diffusion inpainting models with applications to anomaly segmentation and dataset extension as presented in Placing Objects in Context via Inpainting for Out-of-distribution Segmentation.

Official implementation of Improving Text-guided Object Inpainting with Semantic Pre-inpainting in ECCV 2024.

ECCV 2024 PowerPaint, a versatile image inpainting model that supports text-guided object inpainting, object removal, image outpainting and shape-guided object inpainting with only a single model. 一个高质量多功能的图像修补模型，可以同时支持插入物体、移除物体、图像扩展、形状可控的物体生成，只需要一个模型

Project Page | Paper | Bibtex

This repository contains the code and model for the curation module as well as test dataset described in our paper: Inpainting at Modern Camera Resolution by Guided PatchMatch with Auto-Curation by Lingzhi Zhang et al.

CVPR 2016 Unsupervised Feature Learning by Image Inpainting using GANs