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Make 🤗 D🧨ffusers Run on MindSpore

State-of-the-art diffusion models for image and audio generation in MindSpore. We've tried to provide a completely consistent interface and usage with the huggingface/diffusers. Only necessary changes are made to the huggingface/diffusers to make it seamless for users from torch.

Info

Due to differences in framework, some APIs will not be identical to huggingface/diffusers in the foreseeable future, see Limitations for details.

🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or want to train your own diffusion model, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions.

The library has three main components:

  • State-of-the-art diffusion pipelines for inference with just a few lines of code. There are many pipelines in 🤗 Diffusers, check out the table in the pipeline overview for a complete list of available pipelines and the task they solve.
  • Interchangeable noise schedulers for balancing trade-offs between generation speed and quality.
  • Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.

  • Tutorials

    Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. We recommend starting here if you're using 🤗 Diffusers for the first time!

  • How-to guides

    Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques.

  • Conceptual guides

    Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.

  • Reference

    Technical descriptions of how 🤗 Diffusers classes and methods work.