#
How to use the ONNX Runtime for inference

🤗 Optimum provides a Stable Diffusion pipeline compatible with ONNX Runtime.

##
Installation

Install 🤗 Optimum with the following command for ONNX Runtime support:

    pip install optimum["onnxruntime"]

##
Stable Diffusion

###
Inference

To load an ONNX model and run inference with the 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`.

    from optimum.onnxruntime import ORTStableDiffusionPipeline

    model_id = "runwayml/stable-diffusion-v1-5"
    pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)
    prompt = "sailing ship in storm by Leonardo da Vinci"
    image = pipeline(prompt).images[0]
    pipeline.save_pretrained("./onnx-stable-diffusion-v1-5")

If you want to export the pipeline in the ONNX format offline and later use it for inference, you can use the `optimum-cli export` command:

    optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 sd_v15_onnx/

Then perform inference:

    from optimum.onnxruntime import ORTStableDiffusionPipeline

    model_id = "sd_v15_onnx"
    pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id)
    prompt = "sailing ship in storm by Leonardo da Vinci"
    image = pipeline(prompt).images[0]

Notice that we didn’t have to specify `export=True` above.

You can find more examples in optimum documentation.

###
Supported tasks

Task | Loading Class
--- | ---
`text-to-image` | `ORTStableDiffusionPipeline`
`image-to-image` | `ORTStableDiffusionImg2ImgPipeline`
`inpaint` | `ORTStableDiffusionInpaintPipeline`

##
Stable Diffusion XL

###
Export

To export your model to ONNX, you can use the Optimum CLI as follows :

    optimum-cli export onnx --model stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl sd_xl_onnx/

###
Inference

To load an ONNX model and run inference with ONNX Runtime, you need to replace `StableDiffusionPipelineXL` with `ORTStableDiffusionPipelineXL` :

    from optimum.onnxruntime import ORTStableDiffusionXLPipeline

    pipeline = ORTStableDiffusionXLPipeline.from_pretrained("sd_xl_onnx")
    prompt = "sailing ship in storm by Leonardo da Vinci"
    image = pipeline(prompt).images[0]

###
Supported tasks

Task | Loading Class
--- | ---
`text-to-image` | `ORTStableDiffusionXLPipeline`
`image-to-image` | `ORTStableDiffusionXLImg2ImgPipeline`

##
Known Issues

- 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.