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3.5 KiB
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87 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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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# ONNX Runtime
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🤗 [Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with ONNX Runtime. You'll need to install 🤗 Optimum with the following command for ONNX Runtime support:
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```bash
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pip install -q optimum["onnxruntime"]
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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 ONNX Runtime.
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## Stable Diffusion
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To load and run inference, use the [`~optimum.onnxruntime.ORTStableDiffusionPipeline`]. If you want to load a PyTorch model and convert it to the ONNX format on-the-fly, set `export=True`:
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```python
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from optimum.onnxruntime import ORTStableDiffusionPipeline
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model_id = "runwayml/stable-diffusion-v1-5"
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pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)
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prompt = "sailing ship in storm by Leonardo da Vinci"
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image = pipeline(prompt).images[0]
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pipeline.save_pretrained("./onnx-stable-diffusion-v1-5")
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```
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<Tip warning={true}>
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Generating multiple prompts in a batch seems to take too much memory. While we look into it, you may need to iterate instead of batching.
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</Tip>
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To export the pipeline in the ONNX format offline and use it later for inference,
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use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:
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```bash
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optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 sd_v15_onnx/
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```
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Then to perform inference (you don't have to specify `export=True` again):
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```python
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from optimum.onnxruntime import ORTStableDiffusionPipeline
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model_id = "sd_v15_onnx"
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pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id)
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prompt = "sailing ship in storm by Leonardo da Vinci"
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image = pipeline(prompt).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/onnxruntime/stable_diffusion_v1_5_ort_sail_boat.png">
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</div>
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You can find more examples in 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/), 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.onnxruntime.ORTStableDiffusionXLPipeline`]:
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```python
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from optimum.onnxruntime import ORTStableDiffusionXLPipeline
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model_id = "stabilityai/stable-diffusion-xl-base-1.0"
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pipeline = ORTStableDiffusionXLPipeline.from_pretrained(model_id)
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prompt = "sailing ship in storm by Leonardo da Vinci"
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image = pipeline(prompt).images[0]
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```
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To export the pipeline in the ONNX format and use it later for inference, use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:
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```bash
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optimum-cli export onnx --model stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl sd_xl_onnx/
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```
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SDXL in the ONNX format is supported for text-to-image and image-to-image.
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