docs/source/en/optimization/onnx.md

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# How to use 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 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"
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")
```

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

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

<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/).


### Supported tasks

| Task                                 | Loading Class                        |
|--------------------------------------|--------------------------------------|
| `text-to-image`                      | `ORTStableDiffusionPipeline`         |
| `image-to-image`                     | `ORTStableDiffusionImg2ImgPipeline`  |
| `inpaint`                            | `ORTStableDiffusionInpaintPipeline`  |

## Stable Diffusion XL

### Export

To export your model to ONNX, you can use the [Optimum CLI](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) as follows :

```bash
optimum-cli export onnx --model stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl sd_xl_onnx/
```

### Inference

To load an ONNX model and run inference with ONNX Runtime, you need to replace `StableDiffusionPipelineXL` with `ORTStableDiffusionPipelineXL` :

```python
from optimum.onnxruntime import ORTStableDiffusionXLPipeline

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

### Supported tasks

| Task                                 | Loading Class                        |
|--------------------------------------|--------------------------------------|
| `text-to-image`                      | `ORTStableDiffusionXLPipeline`       |
| `image-to-image`                     | `ORTStableDiffusionXLImg2ImgPipeline`|

## 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.