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Stable Diffusion

Introduction to Stable Diffusion

Stable Diffusion is a recent diffusion generative model, similar to DALL·E 2, capable of converting text prompts into images and performing other tasks. These techniques can generate various data types, including neural data.

Key Components for Generative Models

Creating a good generative model involves integrating several key components:

  1. Method of learning to generate new stuff: Given many examples of pictures of people, training a model to generate pictures with a similar distribution.
  2. Way to link text and images: A method to relate textual descriptions to corresponding images, enabling text-guided image generation, e.g., embedding vectors.
  3. Way to compress images: Compressing images for faster training and generation, reducing computational demands.
  4. Incorporate Inductive Biases: Incorporate image-related inductive biases to ensure safe extrapolation and generate novel images beyond the training dataset.

These components include forward/reverse diffusion, text-image representation models, autoencoders, and U-Net architectures with attention mechanisms.

Stable Diffusion Process

The stable diffusion process can be visualized in a diagram. In short it includes images being converted to latent representations via an encoder, denoted as zz. Gaussian noise is iteratively added to the latent representation. A text prompt is converted via CLIP into a text embedding, and cross-attention is used to condition the denoising U-Net. The U-Net then predicts the noise, which is used to refine the latent representation. This denoising process is iteratively performed until the latent representation is decoded back into an image.

Resources

There are several resources available for further exploration of diffusion models, Stable Diffusion, attention mechanisms, and transformers.

Outline of the Presentation

The presentation covers the following topics:

  • Introduction to Stable Diffusion.
  • Building Stable Diffusion