MMGeneration is an open-source toolkit from OpenMMLab with 2,000+ stars on GitHub for training and evaluating generative models — GANs, diffusion models and their relatives — built on PyTorch and the MMCV foundation library.
OpenMMLab’s family of toolboxes standardised how many researchers train computer-vision models, and MMGeneration is the family’s generative-models member. It collects implementations of influential generative approaches — generative adversarial networks in their many variants, and later diffusion models — behind a common training, evaluation and configuration system, so a new method can be compared against established baselines without rewriting the harness each time. Metrics, data pipelines and model zoos are shared, which historically made papers easier to reproduce and benchmarks easier to trust. The codebase was most active through 2023 and has not seen major updates since, so today it serves best as a comprehensive reference and research base — a well-organised snapshot of the GAN-to-diffusion era — rather than as the cutting edge. Its documentation remains available on Read the Docs, and the Apache-2.0 license keeps it freely usable. For students of generative modelling, it also doubles as a well-annotated map of how the field’s standard methods were implemented in practice during those years.
MMGeneration is free and open source under the Apache-2.0 license. Training generative models requires your own GPU compute, and there is no hosted or paid version of the toolkit.
Researchers and engineers working on generative models who want a structured, benchmark-ready codebase covering the classic GAN literature and early diffusion methods, in the familiar OpenMMLab style.