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

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# How to use OpenVINO for inference

🤗 [Optimum](https://github.com/huggingface/optimum-intel) provides Stable Diffusion pipelines compatible with OpenVINO. You can now easily perform inference with OpenVINO Runtime on a variety of Intel processors ([see](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html) the full list of supported devices).

## Installation

Install 🤗 Optimum Intel with the following command:

```
pip install --upgrade-strategy eager optimum["openvino"]
```

The `--upgrade-strategy eager` option is needed to ensure [`optimum-intel`](https://github.com/huggingface/optimum-intel) is upgraded to its latest version.


## Stable Diffusion

### Inference

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

```python
from optimum.intel import OVStableDiffusionPipeline

model_id = "runwayml/stable-diffusion-v1-5"
pipeline = OVStableDiffusionPipeline.from_pretrained(model_id, export=True)
prompt = "sailing ship in storm by Rembrandt"
image = pipeline(prompt).images[0]

# Don't forget to save the exported model
pipeline.save_pretrained("openvino-sd-v1-5")
```

To further speed up inference, the model can be statically reshaped :

```python
# Define the shapes related to the inputs and desired outputs
batch_size, num_images, height, width = 1, 1, 512, 512

# Statically reshape the model
pipeline.reshape(batch_size, height, width, num_images)
# Compile the model before inference
pipeline.compile()

image = pipeline(
    prompt,
    height=height,
    width=width,
    num_images_per_prompt=num_images,
).images[0]
```

In case you want to change any parameters such as the outputs height or width, you’ll need to statically reshape your model once again.

<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/optimum/documentation-images/resolve/main/intel/openvino/stable_diffusion_v1_5_sail_boat_rembrandt.png">
</div>


### Supported tasks

| Task                                 | Loading Class                        |
|--------------------------------------|--------------------------------------|
| `text-to-image`                      | `OVStableDiffusionPipeline`          |
| `image-to-image`                     | `OVStableDiffusionImg2ImgPipeline`   |
| `inpaint`                            | `OVStableDiffusionInpaintPipeline`   |

You can find more examples in the optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion).


## Stable Diffusion XL

### Inference

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

```python
from optimum.intel import OVStableDiffusionXLPipeline

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

To further speed up inference, the model can be statically reshaped as showed above.
You can find more examples in the optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion-xl).

### Supported tasks

| Task                                 | Loading Class                        |
|--------------------------------------|--------------------------------------|
| `text-to-image`                      | `OVStableDiffusionXLPipeline`        |
| `image-to-image`                     | `OVStableDiffusionXLImg2ImgPipeline` |