MMagic is an open-source image and video editing/generating toolbox based on PyTorch. It is a part of the OpenMMLab project and provides state-of-the-art generative models.

What are the main features of MMagic?

MMagic provides state-of-the-art generative models, supports popular applications such as image restoration and text-to-image generation, and offers an efficient framework for customized editor frameworks.

What tasks does MMagic support?

MMagic supports multiple image and video generation/editing tasks, including image restoration, text-to-image, 3D-aware generation, inpainting, matting, super-resolution, and more.

What is the underlying framework for MMagic?

MMagic uses MMEngine and MMCV of OpenMMLab 2.0 framework, which decomposes the editing framework into different modules for easy customization.

How do I install MMagic?

First, install PyTorch. Then, install MMCV, MMEngine and MMagic using MIM: `pip3 install openmim; mim install mmcv>=2.0.0; mim install mmengine; mim install mmagic`.

MMagic

MMagic is an open-source PyTorch-based toolbox for multimodal advanced, generative, and intelligent creation (AIGC). Inheriting from MMEditing and MMGeneration, it provides a comprehensive suite of tools and models for image and video editing, generation, and restoration. MMagic leverages OpenMMLab 2.0 framework, using MMEngine and MMCV, offering modular design and customizable workflows. It supports state-of-the-art generative models including Stable Diffusion, Disco Diffusion, and ControlNet. The framework includes features such as refactored DataSample, DataPreprocessor, MultiValLoop and MultiTestLoop. It is designed to provide researchers and AIGC enthusiasts with an agile and flexible experimental environment, facilitating exploration and innovation in generative AI.

MMagic

About MMagic

Core Capabilities

Main Tasks

Edit video content

3D-Aware Generation

What this tool is best suited for

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Key Features

Diffusion Model Support

ControlNet Integration

xFormers Optimization

MultiValLoop and MultiTestLoop

MMSeparateDistributedDataParallel

Use Cases

Generating photorealistic images from text descriptions.

Restoring old or damaged photos.

Creating 3D-aware generative art.

Colorizing black and white images.

Enhancing video resolution.

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Core Tasks

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