Visual AutoRegressive (VAR) modeling redefines image generation with a novel coarse-to-fine approach, producing higher quality images faster than current models. On benchmarks such as ImageNet, VAR greatly surpasses traditional autoregressive models and even diffusion transformers, showing vast improvements in metrics like Frechet inception distance and inception score. Notable points include:

• VAR’s methodology of ‘next-scale prediction’ speeds up the inference process substantially.
• Evidence of power-law scaling laws, which were previously associated with Large Language Models (LLMs).
• VAR models exhibit zero-shot generalization in tasks like in-painting, out-painting, and editing.

This paper is crucial to the progress of AI image generation technologies, indicating a shift toward more efficient and scalable models. The emergence of VAR models could revolutionize the field, making tasks previously thought computationally prohibitive, now more feasible. For an in-depth understanding, explore the full research.