#
OpenVINO

🤗 Optimum provides Stable Diffusion pipelines compatible with OpenVINO to perform inference on a variety of Intel processors (see the full list of supported devices).

You’ll need to install 🤗 Optimum Intel with the `--upgrade-strategy eager` option to ensure `optimum-intel` is using the latest version:

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

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

##
Stable Diffusion

To load and run inference, use the `OVStableDiffusionPipeline`. If you want to load a PyTorch model and convert it to the OpenVINO format on-the-fly, set `export=True`:

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

    # 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]

You can find more examples in the 🤗 Optimum documentation, 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 `OVStableDiffusionXLPipeline`:

    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, statically reshape the model as shown in the Stable Diffusion section.

You can find more examples in the 🤗 Optimum documentation, and running SDXL in OpenVINO is supported for text-to-image and image-to-image.

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