@@ -114,7 +114,7 @@ Running the pipeline is then identical to the code above as it's the same model
>>> image.save("image_of_squirrel_painting.png")
```
Diffusion systems can be used with multiple different [schedulers]("api/schedulers") each with their
Diffusion systems can be used with multiple different [schedulers](./api/schedulers) each with their
pros and cons. By default, Stable Diffusion runs with [`PNDMScheduler`], but it's very simple to
use a different scheduler. *E.g.* if you would instead like to use the [`LMSDiscreteScheduler`] scheduler,
you could use it as follows:
...
...
@@ -131,15 +131,15 @@ you could use it as follows:
[Stability AI's](https://stability.ai/) Stable Diffusion model is an impressive image generation model
and can do much more than just generating images from text. We have dedicated a whole documentation page,
just for Stable Diffusion [here]("./conceptual/stable_diffusion").
just for Stable Diffusion [here](./conceptual/stable_diffusion).
If you want to know how to optimize Stable Diffusion to run on less memory, higher inference speeds, on specific hardware, such as Mac, or with [ONNX Runtime](https://onnxruntime.ai/), please have a look at our
optimization pages:
- [Optimized PyTorch on GPU]("./optimization/fp16")
- [Mac OS with PyTorch]("./optimization/mps")
- [ONNX]("./optimization/onnx)
- [OpenVINO]("./optimization/open_vino)
- [Optimized PyTorch on GPU](./optimization/fp16)
- [Mac OS with PyTorch](./optimization/mps)
- [ONNX](./optimization/onnx)
- [OpenVINO](./optimization/open_vino)
If you want to fine-tune or train your diffusion model, please have a look at the [**training section**](./training/overview)
