> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/diffusers/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/diffusers/optimization-special-hardware/onnx.md).

# ONNX

## How to use ONNX Runtime for inference

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

### Installation

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

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

### Stable Diffusion

#### Inference

To load an ONNX model and run inference with ONNX Runtime, you need to replace [StableDiffusionPipeline](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/stable_diffusion/text2img#diffusers.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`.

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```
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:

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

Then perform inference:

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```
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.

![](https://huggingface.co/datasets/optimum/documentation-images/resolve/main/onnxruntime/stable_diffusion_v1_5_ort_sail_boat.png)

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 :

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

#### Inference

Here is an example of how you can load a SDXL ONNX model from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and run inference with ONNX Runtime :

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```
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]
```

#### 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.