视觉生成的另一条路：Infinity 自回归架构的原理与实践

Core Insights - The article discusses the significant advancements in visual autoregressive models, particularly highlighting the potential of these models in the context of AI-generated content (AIGC) and their competitive edge against diffusion models [2][4][11]. Group 1: Visual Autoregressive Models - Visual autoregressive models (VAR) utilize a "coarse-to-fine" approach, starting with low-resolution images and progressively refining them to high-resolution outputs, which aligns more closely with human visual perception [12][18]. - The VAR model architecture includes an improved VQ-VAE that employs a hierarchical structure, allowing for efficient encoding and reconstruction of images while minimizing token usage [15][30]. - VAR has demonstrated superior image generation quality compared to existing models like DiT, showcasing a robust scaling curve that indicates performance improvements with increased model size and computational resources [18][49]. Group 2: Comparison with Diffusion Models - Diffusion models operate by adding Gaussian noise to images and then training a network to reverse this process, maintaining the original resolution throughout [21][25]. - The key advantages of VAR over diffusion models include higher training parallelism and a more intuitive process that mimics human visual cognition, although diffusion models can correct errors through iterative refinement [27][29]. - VAR's approach allows for faster inference times, with the Infinity model achieving significant speed improvements over comparable diffusion models [46][49]. Group 3: Innovations in Tokenization and Error Correction - The Infinity framework introduces a novel "bitwise tokenizer" that enhances reconstruction quality while allowing for a larger vocabulary size, thus improving detail and instruction adherence in generated images [31][41]. - A self-correction mechanism is integrated into the training process, enabling the model to learn from previous errors and significantly reducing cumulative error during inference [35][40]. - The findings indicate that larger models benefit from larger vocabularies, reinforcing the reliability of scaling laws in model performance [41][49].