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