diffusers/docs/source/en/optimization/onnx.mdx

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# How to use the ONNX Runtime for inference

🤗 [Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with ONNX Runtime. 

## Installation

Install 🤗 Optimum with the following command for ONNX Runtime support:

```
pip install optimum["onnxruntime"]
```

## Stable Diffusion Inference

To load an ONNX model and run inference with the ONNX Runtime, you need to replace [`StableDiffusionPipeline`] with `ORTStableDiffusionPipeline`. In case you want to load
a PyTorch model and convert it to the ONNX format on-the-fly, you can set `export=True`.

```python
from optimum.onnxruntime import ORTStableDiffusionPipeline

model_id = "runwayml/stable-diffusion-v1-5"
pipe = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)
prompt = "a photo of an astronaut riding a horse on mars"
images = pipe(prompt).images[0]
pipe.save_pretrained("./onnx-stable-diffusion-v1-5")
```

If you want to export the pipeline in the ONNX format offline and later use it for inference,
you can use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command: 

```bash
optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 sd_v15_onnx/
```

Then perform inference:

```python 
from optimum.onnxruntime import ORTStableDiffusionPipeline

model_id = "sd_v15_onnx"
pipe = ORTStableDiffusionPipeline.from_pretrained(model_id)
prompt = "a photo of an astronaut riding a horse on mars"
images = pipe(prompt).images[0]
```

Notice that we didn't have to specify `export=True` above.

You can find more examples in [optimum documentation](https://huggingface.co/docs/optimum/).

## Known Issues

- Generating multiple prompts in a batch seems to take too much memory. While we look into it, you may need to iterate instead of batching.