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81 lines
3.5 KiB
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81 lines
3.5 KiB
Markdown
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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-->
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# OpenVINO
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🤗 [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).
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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:
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```bash
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pip install --upgrade-strategy eager optimum["openvino"]
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```
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This guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelines with OpenVINO.
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## Stable Diffusion
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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`:
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```python
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from optimum.intel import OVStableDiffusionPipeline
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model_id = "runwayml/stable-diffusion-v1-5"
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pipeline = OVStableDiffusionPipeline.from_pretrained(model_id, export=True)
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prompt = "sailing ship in storm by Rembrandt"
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image = pipeline(prompt).images[0]
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# Don't forget to save the exported model
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pipeline.save_pretrained("openvino-sd-v1-5")
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```
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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.
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```python
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# Define the shapes related to the inputs and desired outputs
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batch_size, num_images, height, width = 1, 1, 512, 512
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# Statically reshape the model
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pipeline.reshape(batch_size, height, width, num_images)
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# Compile the model before inference
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pipeline.compile()
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image = pipeline(
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prompt,
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height=height,
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width=width,
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num_images_per_prompt=num_images,
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).images[0]
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```
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<div class="flex justify-center">
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<img src="https://huggingface.co/datasets/optimum/documentation-images/resolve/main/intel/openvino/stable_diffusion_v1_5_sail_boat_rembrandt.png">
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</div>
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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.
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## Stable Diffusion XL
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To load and run inference with SDXL, use the [`~optimum.intel.OVStableDiffusionXLPipeline`]:
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```python
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from optimum.intel import OVStableDiffusionXLPipeline
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model_id = "stabilityai/stable-diffusion-xl-base-1.0"
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pipeline = OVStableDiffusionXLPipeline.from_pretrained(model_id)
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prompt = "sailing ship in storm by Rembrandt"
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image = pipeline(prompt).images[0]
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```
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To further speed-up inference, [statically reshape](#stable-diffusion) the model as shown in the Stable Diffusion section.
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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.
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