通过分层粒度序列生成图像，实现从整体到细节的可控生成。
Next Visual Granularity Generation
- 将图像分解为多粒度序列，逐级细化视觉细节。
- 在ImageNet上训练模型，FID分数最高提升0.26点。
- 适合需要精细控制生成过程的研究者使用。
我们提出一种新图像生成方法，将图像分解为结构化序列，序列中每个元素具有相同空间分辨率但使用的唯一标记数量不同，从而捕捉不同层次的视觉粒度。图像生成通过新提出的下一视觉粒度（NVG）框架完成，该框架从空图像开始，按结构化方式逐步生成从全局布局到细粒度细节的视觉粒度序列。此迭代过程编码了分层、分层表示，可在多个粒度层级上实现精细控制。我们在ImageNet数据集上训练了一系列类条件生成的NVG模型，并观察到明显的缩放行为。与VAR系列相比，NVG在FID得分上始终表现更优（3.30 → 3.03，2.57 → 2.44，2.09 → 2.06）。我们还进行了广泛分析，展示NVG框架的能力与潜力。代码与模型已发布于https://yikai-wang.github.io/nvg。
原文摘要 · Abstract (English)
We propose a novel approach to image generation by decomposing an image into a structured sequence, where each element in the sequence shares the same spatial resolution but differs in the number of unique tokens used, capturing different level of visual granularity. Image generation is carried out through our newly introduced Next Visual Granularity (NVG) generation framework, which generates a visual granularity sequence beginning from an empty image and progressively refines it, from global layout to fine details, in a structured manner. This iterative process encodes a hierarchical, layered representation that offers fine-grained control over the generation process across multiple granularity levels. We train a series of NVG models for class-conditional image generation on the ImageNet dataset and observe clear scaling behavior. Compared to the VAR series, NVG consistently outperforms it in terms of FID scores (3.30 $\rightarrow$ 3.03, 2.57 $\rightarrow$ 2.44, 2.09 $\rightarrow$ 2.06). We also conduct extensive analysis to showcase the capability and potential of the NVG framework. Our code and models are released at https://yikai-wang.github.io/nvg.
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