From Random Streaks to Recognizable Digits: Building an Autoregressive Image Generation Model
This article details building a basic autoregressive image generation model using a Multilayer Perceptron (MLP) to generate images of handwritten digits. The author explains the core concept of predicting the next pixel based on its predecessors. Three models are progressively built: Model V1 uses one-hot encoding and ignores spatial information; Model V2 introduces positional encodings, improving image structure; Model V3 uses learned token embeddings and positional encodings, achieving conditional generation, generating images based on a given digit class. While the generated images fall short of state-of-the-art models, the tutorial clearly demonstrates core autoregressive concepts and the building process, providing valuable insights into generative AI.
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