How to use 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 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

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

Here is an example of how you can load a SDXL ONNX model from stabilityai/stable-diffusion-xl-base-1.0 and run inference with ONNX Runtime :

from optimum.onnxruntime import ORTStableDiffusionXLPipeline

model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipeline = ORTStableDiffusionXLPipeline.from_pretrained(model_id)
prompt = "sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt).images[0]

Supported tasks

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.