TIDE让扩散Transformer任意分辨率生成更清晰，不增加计算量。
TIDE: Text-Informed Dynamic Extrapolation with Step-Aware Temperature Control for Diffusion Transformers
- 用文本锚定机制平衡图文令牌信息，防止提示词丢失
- 动态温度控制抑制生成伪影，提升细节保真度
- 无需训练即可适配任意分辨率，兼容主流生成模型
扩散Transformer（DiT）在生成高于训练分辨率的图像时，因注意力稀释导致结构退化。现有方法通过锐化注意力分布缓解问题，但无法保留细粒度语义信息并引入明显伪影。本文分析DiT特性，提出TIDE——一种无需训练的文本到图像扩展方法，可在任意分辨率和长宽比下生成图像且无额外采样开销。我们识别出提示信息丢失的核心原因，引入文本锚定机制以纠正图文令牌间的不平衡；为消除伪影，设计基于扩散过程频谱演进模式的动态温度控制机制。大量实验表明，TIDE具备高质量分辨率外推能力，可无缝集成至现有最先进方法中。
原文摘要 · Abstract (English)
Diffusion Transformer (DiT) faces challenges when generating images with higher resolution compared at training resolution, causing especially structural degradation due to attention dilution. Previous approaches attempt to mitigate this by sharpening attention distributions, but fail to preserve fine-grained semantic details and introduce obvious artifacts. In this work, we analyze the characteristics of DiTs and propose TIDE, a training-free text-to-image (T2I) extrapolation method that enables generation with arbitrary resolution and aspect ratio without additional sampling overhead. We identify the core factor for prompt information loss, and introduce a text anchoring mechanism to correct the imbalance between text and image tokens. To further eliminate artifacts, we design a dynamic temperature control mechanism that leverages the pattern of spectral progression in the diffusion process. Extensive evaluations demonstrate that TIDE delivers high-quality resolution extrapolation capability and integrates seamlessly with existing state-of-the-art methods.
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