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

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# ONNX Runtime

🤗 [Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with ONNX Runtime. You'll need to install 🤗 Optimum with the following command for ONNX Runtime support:

```bash
pip install -q optimum["onnxruntime"]
```

This guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelines with ONNX Runtime.

## Stable Diffusion

To load and run inference, use the [`~optimum.onnxruntime.ORTStableDiffusionPipeline`]. If you want to load a PyTorch model and convert it to the ONNX format on-the-fly, set `export=True`:

```python
from optimum.onnxruntime import ORTStableDiffusionPipeline

model_id = "runwayml/stable-diffusion-v1-5"
pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)
prompt = "sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt).images[0]
pipeline.save_pretrained("./onnx-stable-diffusion-v1-5")
```

<Tip warning={true}>

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.

</Tip>

To export the pipeline in the ONNX format offline and use it later for inference,
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 to perform inference (you don't have to specify `export=True` again):

```python
from optimum.onnxruntime import ORTStableDiffusionPipeline

model_id = "sd_v15_onnx"
pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id)
prompt = "sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt).images[0]
```

<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/optimum/documentation-images/resolve/main/onnxruntime/stable_diffusion_v1_5_ort_sail_boat.png">
</div>

You can find more examples in 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/), and Stable Diffusion is supported for text-to-image, image-to-image, and inpainting.

## Stable Diffusion XL

To load and run inference with SDXL, use the [`~optimum.onnxruntime.ORTStableDiffusionXLPipeline`]:

```python
from optimum.onnxruntime import ORTStableDiffusionXLPipeline

model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipeline = ORTStableDiffusionXLPipeline.from_pretrained(model_id)
prompt = "sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt).images[0]
```

To export the pipeline in the ONNX format and use it later for inference, 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 stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl sd_xl_onnx/
```

SDXL in the ONNX format is supported for text-to-image and image-to-image.