DeepFloyd IF is an advanced open-source text-to-image model that delivers remarkable photorealism and language comprehension. DeepFloyd IF consists of a frozen text encoder and three cascaded pixel diffusion modules: a base model that creates 64x64 px images based on text prompts and two super-resolution models, each designed to generate images with increasing resolution: 256x256 px and 1024x1024 px. All stages of the model employ a frozen text encoder, built on the T5 transformer, to derive text embeddings, which are then passed to a UNet architecture enhanced with cross-attention and attention pooling.
This notebook demonstrates how to convert and run DeepFloyd IF models using OpenVINO.
The notebook contains the following steps:
- Convert PyTorch models to OpenVINO IR format, using Model Optimizer tool.
- Run DeepFloyd IF pipeline with OpenVINO.
Please be aware that a machine with at least 32GB of RAM is necessary to run this example.
The Jupyter notebook contains its own set of requirements installed directly within the notebook, allowing it to run independently as a standalone example.