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

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 [~optimum.intel.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 [~optimum.intel.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.