What does "Autoregressive Image Generation" mean?
Table of Contents
Autoregressive image generation is a method used to create images one part at a time. It works by looking at what has already been generated to predict the next part of the image. This technique allows for high-quality images because it considers the relationships between different parts as it builds the final picture.
How It Works
In this approach, images are broken down into smaller pieces or tokens. The model starts with a rough version of the image and gradually adds in more details. By focusing on one piece at a time, the model can produce images that look realistic and detailed.
Benefits
This method often produces better results than other types of image generation. The images created using autoregressive techniques usually have fewer errors and look more like real photos. However, it can be slow because the model has to work on one piece after another.
Recent Improvements
Recent advancements have aimed to speed up autoregressive image generation. Some new techniques help the model work faster without losing quality. This means it can generate images more quickly while still looking good.
Tools Used
Some approaches use special techniques for organizing image information. For example, wavelet coding helps the model understand both big and small details in images more effectively. This makes it easier for the model to learn and create high-quality images.