| Anthropomorphic cat dressed as a fire fighter |
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Würstchen is a diffusion model, whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images, is way more expensive than training at 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, authors achieve a 42x spatial compression. This was unseen before because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what is called Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the paper). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, allowing also cheaper and faster inference.
The notebook provides a simple interface that allows communication with a model using text instruction. In this demonstration user can provide input instructions and the model generates an image. An additional part demonstrates how to run quantization with NNCF to speed up pipeline.
This tutorial consists of the following steps:
- Prerequisites
- Load the original model
- Infer the original model
- Convert the model to OpenVINO IR
- Prior pipeline
- Decoder pipeline
- Compiling models
- Building the pipeline
- Inference
- Optimize
Würstchenwith NNCF quantization- Compare results of original and optimized pipelines
- Interactive inference
This is a self-contained example that relies solely on its own code.
We recommend running the notebook in a virtual environment. You only need a Jupyter server to start.
For details, please refer to Installation Guide.