For object removal effects, the strongest matches are geekyutao/inpaint-anything (This tool provides a comprehensive suite for AI-powered image), sczhou/propainter (This tool is a specialized deep learning system designed) and gaomingqi/track-anything (This tool provides an AI-driven pipeline for video object). sanster/lama-cleaner and ant-research/magicquill round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
Find the best open-source image inpainting tools for object removal. Compare top-rated repositories by activity and features to pick the right one.
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
This tool provides a comprehensive suite for AI-powered image and video inpainting, specifically featuring object removal, mask-based editing, and deep learning-based temporal consistency for video processing.
This tool is a specialized deep learning system designed for video inpainting and object removal, providing the core capabilities required to mask and reconstruct missing content in video sequences.
Track-Anything is an AI-driven video object segmentation and tracking system. It utilizes the Segment Anything Model to isolate and mask multiple objects across video frames, providing tools for automated mask propagation and background-filling inpainting. The system distinguishes itself through a multi-object segmentation pipeline that can follow several distinct targets simultaneously. It includes a video inpainting utility to remove tracked objects and replace them with synthesized background content, as well as temporal mask refinement to correct tracking drift. The project covers broad
This tool provides an AI-driven pipeline for video object segmentation and inpainting, specifically designed to mask and remove objects from video frames using deep learning models.
Lama Cleaner is an AI-powered image editing application focused on inpainting, object removal, and generative filling. It provides a suite of tools for erasing unwanted elements from photos and filling the resulting gaps using generative artificial intelligence. The project includes specialized capabilities for image outpainting to extend borders, background removal through object segmentation, and face restoration to fix visual defects. It also features an image upscaler to increase resolution and clarity via super-resolution AI, as well as a Stable Diffusion-based editor for replacing speci
Lama Cleaner is an AI-powered image inpainting tool that provides a masking interface and deep learning models for object removal, though it is primarily focused on images rather than video processing.
MagicQuill is a suite of interactive tools for image segmentation, diffusion-based editing, layered composition, and prompt-guided visual synthesis. It functions as a diffusion model image editor and a layered visual composition tool, enabling the addition, removal, and recoloring of image elements through a combination of sketches and text prompts. The system features a prompt-guided image generator that predicts editing instructions by analyzing user drawings to automatically populate text prompts. It allows for visual style control by swapping generative model weights to shift outputs betw
MagicQuill is an AI-powered image editing tool that supports object removal and generative inpainting through a masking and prompt-guided interface, though it lacks explicit video processing capabilities.
A free and open-source inpainting & image-upscaling tool powered by webgpu and wasm on the browser。| 基于 Webgpu 技术和 wasm 技术的免费开源 inpainting & image-upscaling 工具, 纯浏览器端实现。
This is a browser-based image inpainting tool that supports object removal using local WebGPU and WASM, though it lacks the video processing capabilities requested.
Flux is a diffusion model inference engine designed for text-to-image generation and image-to-image manipulation. It provides a system for executing open-weight models to transform natural language descriptions into visual imagery or to modify existing images. The project distinguishes itself through a flow-matching framework for image generation and a structural image controller. This controller allows for guided synthesis by using depth maps and Canny edge detection to constrain the geometry and composition of the output. The toolkit covers a broad range of image editing capabilities, incl
This is a powerful diffusion-based inference engine that natively supports inpainting and regional modifications, making it a highly capable tool for removing objects from images using deep learning models.
IOPaint is an AI image editor and Stable Diffusion inpainting tool providing a web interface for removing objects and replacing image content. It utilizes latent diffusion image processing to synthesize high-resolution replacements for erased sections of an image. The project features a specialized AI background remover for isolating subjects and an AI image upscaler that employs super-resolution models for general photos and anime artwork. The software covers a broad range of capabilities including image segmentation for object isolation, face restoration for improving facial details, and t
IOPaint is a dedicated AI-powered image inpainting tool that provides a web interface for object removal and content replacement using deep learning models, though it lacks native video processing capabilities.
Official repo for “VORNet: Spatio-temporally Consistent Video Inpainting for Object Removal, Chang et al., CVPRW 2019” arxiv
This repository provides a deep learning model specifically designed for spatio-temporally consistent video inpainting and object removal, serving as a core implementation of the requested technology.
OmniGen is a unified image generation model and diffusion framework that processes text, images, and vision tasks through a single system. It functions as a multimodal diffusion framework that treats diverse vision operations as unified image synthesis problems using shared model weights, removing the need for external adapter modules. The system supports subject-driven image generation to preserve the identity of objects from reference photos and allows for multi-reference image synthesis. It also operates as an instruction-based image editor, modifying visual content through natural languag
OmniGen is a unified diffusion-based image editing framework that supports instruction-based object manipulation and removal, though it is primarily designed as a generative synthesis model rather than a dedicated inpainting tool.
InvokeAI is a self-hosted, professional-grade platform designed for managing generative models and performing complex image synthesis. It provides a local application environment that allows users to execute diffusion models directly on their own hardware, ensuring data privacy and complete ownership of all generated assets. The platform distinguishes itself through a node-based workflow system that enables the construction of reproducible and automated image generation pipelines. By chaining modular functional units into directed acyclic graphs, users can automate intricate production tasks
This platform provides a robust canvas-based interface for inpainting and generative image manipulation, though it is primarily focused on image synthesis rather than dedicated video object removal.
Lama is an image restoration framework and deep learning model designed for image inpainting and object removal. It provides the tools necessary to train and evaluate neural networks that fill masked areas and repair corrupted visual data. The system utilizes a Fourier convolution neural network to maintain global image structure and reconstruct periodic patterns. This architecture allows for resolution-independent inference, enabling the processing of high-resolution images without increasing memory or computational requirements. The project includes a synthetic dataset generator that creat
This repository provides a deep learning framework specifically designed for image inpainting and object removal, though it focuses on the model and training pipeline rather than providing a ready-to-use end-user application with a graphical masking interface.
This project is a local AI inpainting tool designed to erase hard-coded subtitles and watermarks from videos and images. It functions as a content-aware media restorer that uses deep learning to reconstruct missing pixels and preserve the original resolution of the source files. The software is distinguished by its local execution model, running inference on host hardware to process media without relying on external cloud APIs. It employs content-aware model selection, allowing the use of different generative algorithms based on media types, such as animation or live action, to optimize visua
This tool is a specialized AI inpainting application that performs object and watermark removal on both images and videos using deep learning, fitting the core requirements for content-aware media restoration.
RapidRAW is a non-destructive RAW photo editor and digital asset manager designed for decoding manufacturer RAW formats and applying tonal and color adjustments. It functions as a professional image processor that ensures original source data remains unmodified by saving all edits, masks, and crops to sidecar files. The software features a specialized color grading suite using 3D LUTs, color wheels, and HSL mixers, alongside AI-powered utilities for subject isolation, automatic masking, and generative inpainting for object removal. It distinguishes itself with AI-assisted photo retouching and
This is a professional RAW photo editor that includes AI-powered generative inpainting and subject isolation tools, making it a capable solution for object removal despite its broader focus on color grading and asset management.
This is a comprehensive computer vision framework that provides deep learning models for image and video inpainting, serving as a powerful tool for object removal tasks.
This project is an integrated creative interface for image manipulation that combines generative canvas expansion, object segmentation, and diffusion-based inpainting. It functions as an extension for Stable Diffusion, providing a workflow to isolate specific elements and perform targeted content modifications through prompt-guided synthesis. The tool distinguishes itself by automating the creation of precise selection masks, allowing users to identify and isolate objects by pointing to them rather than manually drawing selections. By chaining segmentation models with generative diffusion pip
This tool provides an interface for object removal and image inpainting using deep learning models, though it is designed as an extension for Stable Diffusion rather than a standalone video processing application.
Unofficial pytorch implementation of 'Image Inpainting for Irregular Holes Using Partial Convolutions' Liu+, ECCV2018
This repository provides a deep learning implementation for image inpainting using partial convolutions, which directly addresses the core capability of removing unwanted objects from images.
This repository provides a deep learning-based implementation for image inpainting, specifically designed to fill in missing or masked regions of an image, which directly addresses the core requirement for object removal.
Just draw a bounding box and you can remove the object you want to remove.
This tool provides a deep learning-based solution for removing objects from videos using a masking interface, directly addressing the core requirements for AI-powered video inpainting.
This software is a watermark removal system that uses machine learning and image inpainting to delete unwanted text or logos from images. It reconstructs missing pixels to match the original background, ensuring visual consistency through pretrained models. The project includes a masking utility to isolate specific regions for content replacement using binary masks, bounding boxes, or brush strokes. It also features a batch processor that applies these cleaning tasks to large sets of images via a predefined file list. The system handles image preparation by normalizing dimensions and aspect
This tool provides AI-powered image inpainting and object removal using masking and batch processing, though it is specifically optimized for watermark removal rather than general-purpose video object removal.
| المستودع | النجوم | اللغة | الترخيص | آخر تحديث |
|---|---|---|---|---|
| geekyutao/inpaint-anything | 7.6K | Jupyter Notebook | Apache-2.0 | |
| sczhou/propainter | 6.5K | Python | other | |
| gaomingqi/track-anything | 6.9K | Python | mit | |
| sanster/lama-cleaner | 23.2K | Python | Apache-2.0 | |
| ant-research/magicquill | 3.7K | Python | NOASSERTION | |
| lxfater/inpaint-web | 5.8K | TypeScript | GPL-3.0 | |
| black-forest-labs/flux | 25.6K | Python | Apache-2.0 | |
| sanster/iopaint | 23.2K | Python | Apache-2.0 | |
| amjltc295/vornet-spatio-temporally-consistent-video-inpainting-for-object-removal | 0 | — | — | — |
| vectorspacelab/omnigen | 4.3K | Jupyter Notebook | MIT |