Ella Charlaix
110 lines
4.2 KiB
Markdown
110 lines
4.2 KiB
Markdown
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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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# How to use ONNX Runtime for inference
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🤗 [Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with ONNX Runtime.
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## Installation
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Install 🤗 Optimum with the following command for ONNX Runtime support:
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```
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pip install optimum["onnxruntime"]
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```
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## Stable Diffusion
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### Inference
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To load an ONNX model and run inference with ONNX Runtime, you need to replace [`StableDiffusionPipeline`] with `ORTStableDiffusionPipeline`. In case you want to load a PyTorch model and convert it to the ONNX format on-the-fly, you can 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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If you want to export the pipeline in the ONNX format offline and later use it for inference,
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you can 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 perform inference:
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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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Notice that we didn't have to specify `export=True` above.
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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/).
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### Supported tasks
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| Task | Loading Class |
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|--------------------------------------|--------------------------------------|
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| `text-to-image` | `ORTStableDiffusionPipeline` |
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| `image-to-image` | `ORTStableDiffusionImg2ImgPipeline` |
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| `inpaint` | `ORTStableDiffusionInpaintPipeline` |
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## Stable Diffusion XL
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### Export
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To export your model to ONNX, you can use the [Optimum CLI](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) as follows :
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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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### Inference
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Here is an example of how you can load a SDXL ONNX model from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and run inference with ONNX Runtime :
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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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### Supported tasks
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| Task | Loading Class |
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|--------------------------------------|--------------------------------------|
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| `text-to-image` | `ORTStableDiffusionXLPipeline` |
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| `image-to-image` | `ORTStableDiffusionXLImg2ImgPipeline`|
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## Known Issues
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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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