@@ -23,7 +23,7 @@ More precisely, 🤗 Diffusers offers:
- State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)).
- Various noise schedulers that can be used interchangeably for the prefered speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)).
- Multiple types of diffusion models, such as UNet, that can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)).
- Multiple types of models, such as UNet, that can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)).
- Training examples to show how to train the most popular diffusion models (see [examples](https://github.com/huggingface/diffusers/tree/main/examples)).
