276-stable-diffusion-torchdynamo-backend

Image Generation with Stable Diffusion using OpenVINO TorchDynamo backend

This notebook demonstrates how to use a Stable Diffusion model for image generation with OpenVINO TorchDynamo backend. The torch.compile feature enables you to use OpenVINO for PyTorch-native applications. It speeds up PyTorch code by JIT-compiling it into optimized kernels.

By default, Torch code runs in eager-mode, but with the use of torch.compile it goes through the following steps:

1. Graph acquisition - the model is rewritten as blocks of subgraphs that are either:
   • compiled by TorchDynamo and “flattened”,
   • falling back to the eager-mode, due to unsupported Python constructs (like control-flow code).
2. Graph lowering - all PyTorch operations are decomposed into their constituent kernels specific to the chosen backend.
3. Graph compilation - the kernels call their corresponding low-level device-specific operations.

Notebook Contents

This notebook demonstrates how to run stable diffusion using OpenVINO TorchDynamo backend.

Notebook contains the following steps:

1. Create PyTorch models pipeline using Diffusers library.
2. Import OpenVINO backend using torch.compile.
3. Run Stable Diffusion pipeline with OpenVINO TorchDynamo backend.

Installation Instructions

If you have not installed all required dependencies, follow the Installation Guide.