DetailFlow：通过下一细节预测实现1D从粗到精自回归图像生成
DetailFlow: 1D Coarse-to-Fine Autoregressive Image Generation via Next-Detail Prediction
AI总结：
针对图像生成问题，提出DetailFlow方法，通过1D从粗到精自回归模型与下一细节预测策略，用更少标记实现高质量生成，还提出并行推理机制提升速度，在ImageNet基准上优于VAR等方法，兼具质量与效率优势。
摘要：
本文提出DetailFlow，一种从粗到精的1D自回归（AR）图像生成方法，该方法通过新颖的下一细节预测策略对图像进行建模。通过学习由渐进退化图像监督的分辨率感知标记序列，DetailFlow使生成过程能从全局结构开始并逐步细化细节。这种从粗到精的1D标记序列与自回归推理机制高度契合，为AR模型生成复杂视觉内容提供更自然高效的方式。我们紧凑的1D AR模型实现了高质量图像合成，其使用的标记数量显著少于VAR/VQGAN等先前方法。我们进一步提出带自校正的并行推理机制，将生成速度提升约8倍，同时减少教师强制监督中固有的累积采样误差。在ImageNet 256x256基准上，我们的方法使用128个标记达到2.96 gFID，优于使用680个标记的VAR（3.3 FID）和FlexVAR（3.05 FID）。此外，由于标记数大幅减少和并行推理机制，我们的方法推理速度比VAR和FlexVAR快近2倍。大量实验结果表明，DetailFlow在生成质量和效率上优于现有最先进方法。
英文摘要：
This paper presents DetailFlow, a coarse-to-fine 1D autoregressive (AR) image generation method that models images through a novel next-detail prediction strategy. By learning a resolution-aware token sequence supervised with progressively degraded images, DetailFlow enables the generation process to start from the global structure and incrementally refine details. This coarse-to-fine 1D token sequence aligns well with the autoregressive inference mechanism, providing a more natural and efficient way for the AR model to generate complex visual content. Our compact 1D AR model achieves high-quality image synthesis with significantly fewer tokens than previous approaches, i.e. VAR/VQGAN. We further propose a parallel inference mechanism with self-correction that accelerates generation speed by approximately 8x while reducing accumulation sampling error inherent in teacher-forcing supervision. On the ImageNet 256x256 benchmark, our method achieves 2.96 gFID with 128 tokens, outperforming VAR (3.3 FID) and FlexVAR (3.05 FID), which both require 680 tokens in their AR models. Moreover, due to the significantly reduced token count and parallel inference mechanism, our method runs nearly 2x faster inference speed compared to VAR and FlexVAR. Extensive experimental results demonstrate DetailFlow's superior generation quality and efficiency compared to existing state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
