Machine Learning Advanced Pro

Autoregressive Image Generation

Design an unconditional high-resolution image generator by treating pixels as a token sequence — VQ-VAE tokenization and decoder-only Transformer generation

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Design a high-resolution image synthesis system the way ByteByteGo-style interviews frame it: why VAEs and GANs stall past a few hundred pixels, why autoregressive beats diffusion on raw sampling speed, how a VQ-VAE/VQGAN tokenizer turns an image into a sequence of discrete tokens, how a decoder-only Transformer generates that sequence, the two-stage training pipeline and its four tokenizer losses, top-p sampling with a worked 1024x1024 example, and the evaluation and service-separation decisions interviewers probe.

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Practice questions (6)

Why Quantize? Posterior Collapse and the VQ-VAE Codebook

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Why a Decoder-Only Transformer for the Image Generator

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Diagnosing a Blurry VQGAN Reconstruction

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Worked Example: Sampling a 2048x2048 Image

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FID vs. Inception Score: What Would Move Each

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Quizzes (1)

Autoregressive Image Generation Quiz

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