# OpenVINO 🤗 [Optimum](https://github.com/huggingface/optimum-intel) provides Stable Diffusion pipelines compatible with OpenVINO to perform inference on a variety of Intel processors (see the [full list](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html) of supported devices). You'll need to install 🤗 Optimum Intel with the `--upgrade-strategy eager` option to ensure [`optimum-intel`](https://github.com/huggingface/optimum-intel) is using the latest version: ```bash 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`: ```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, 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. ```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] ```

You can find more examples in the 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion), 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`]: ```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, [statically reshape](#stable-diffusion) the model as shown in the Stable Diffusion section. You can find more examples in the 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion-xl), and running SDXL in OpenVINO is supported for text-to-image and image-to-image.