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Demo for running stable diffusion with OpenVINO Model Server {#ovms_demo_stable_diffusion}

Download the models from HuggingFaces and convert them the ONNX format:

git clone https://github.com/openvinotoolkit/model_server 
cd model_server/demos/stable-diffusion/python
pip install -r requirements.txt
python vae.py
python text_encoder.py 
python unet.py

Start OVMS with the models:

docker run -p 9000:9000 -d -v $(pwd)/:/models openvino/model_server:latest --config_path /models/config.json --port 9000

The demo can be execute in 2 modes:

  • with local execution using OpenVINO Runtime
  • with remote execution via calls to the model server

The executor is abstracted using an adapter which be be selected via --adapter parameter.

Local execution:

python generate.py --adapter openvino --seed 10
Initializing models
Models initialized
Pipeline settings
Input text: sea shore at midnight, epic vista, beach
Negative text: frames, borderline, text, character, duplicate, error, out of frame, watermark
Seed: 10
Number of steps: 20
getting initial random noise (1, 4, 96, 96) {}
100%|████████████████████████████████████████████████| 20/20 [01:15<00:00,  3.75s/it]

Remote execution:

python generate.py --adapter ovms --url localhost:9000 --seed 10
Initializing models
Models initialized
Pipeline settings
Input text: sea shore at midnight, epic vista, beach
Negative text: frames, borderline, text, character, duplicate, error, out of frame, watermark
Seed: 10
Number of steps: 20
getting initial random noise (1, 4, 96, 96) {}
100%|████████████████████████████████████████████████| 20/20 [00:53<00:00,  2.66s/it]

In both cases the result is the same and stored in a file result.png. result

The advantages of using the model server are:

  • the client can delegate majority of the pipeline load to a remote host
  • the loaded models can be shared with multiple clients