open-mmlab/mmagic

MMagic is a toolkit for working with AI-generated images and videos. It is part of the OpenMMLab family of open-source research tools and is built on PyTorch, a widely used machine learning framework. The name stands for Multimodal Advanced, Generative, and Intelligent Creation, and the library grew out of two older OpenMMLab projects called MMEditing and MMGeneration, which were merged into this single combined package. The toolkit covers a wide range of tasks. On the generation side, it supports creating images from text descriptions using models like Stable Diffusion, ControlNet, GLIDE, Guided Diffusion, and Disco Diffusion. ControlNet in particular lets you guide the image generation process with additional constraints, such as a rough sketch or pose outline. The toolkit also includes Dreambooth and Dreambooth LoRA, which are fine-tuning approaches that teach a model to generate images of a specific subject or style based on a small set of example photos. Beyond generation, MMagic supports image restoration and enhancement, including tools for removing noise or blur, sharpening low-resolution images, and colorizing black-and-white photos. It also includes a feature called PowerPaint for inpainting, which means filling in or replacing selected regions of an image in a way that looks natural. Video generation through a technique called MultiFrame Render is also supported. For researchers and developers who need to measure quality, the toolkit provides metrics for both generative tasks and reconstruction tasks, and it supports visualizing training results through tools like TensorBoard and Weights and Biases. It is also optimized to take advantage of PyTorch 2.0 speed improvements across more than 33 of its included algorithms. The project is aimed at researchers and practitioners in the field of image and video AI. Getting started requires familiarity with Python environments and installing packages like PyTorch. The repository includes Jupyter notebooks demonstrating various use cases. Documentation, a changelog, and a model zoo listing all supported pretrained models are available on the project's website.