HiCoGen：基于强化学习的扩散模型分层组合式文本到图像生成
HiCoGen: Hierarchical Compositional Text-to-Image Generation in Diffusion Models via Reinforcement Learning
把复杂提示拆成最小语义单元、逐步合成并让每步图像作为下一步视觉上下文，再用分层奖励的强化学习优化采样；其中「将随机性集中在前段以最大化样本多样性」的衰减随机性调度可直接用于任何扩散采样的探索增强。
原文依据 sample diversity is maximized by concentrating stochasticity in the early generation stages
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期扩散模型的进展在简单提示下生成高质量图像方面展现出令人印象深刻的能力。然而，面对涉及多物体与分层结构的复杂提示时，现有模型难以准确遵循指令，导致概念遗漏、混淆以及组合性差等问题。为解决这些局限，我们提出分层组合式生成框架（HiCoGen），它建立在一种新的合成链（Chain of Synthesis, CoS）范式之上。HiCoGen 不采用整体式生成，而是首先利用大语言模型（LLM）将复杂提示分解为最小语义单元，随后迭代地合成这些单元，其中每一步生成的图像都为下一步提供关键的视觉上下文，从而确保所有文本概念都被忠实地构建进最终场景。为进一步优化该过程，我们引入强化学习（RL）框架。关键在于，我们发现标准扩散采样器的有限探索会阻碍有效的强化学习。我们从理论上证明，将随机性集中于生成早期阶段可使样本多样性最大化，并基于这一洞见提出一种新的衰减随机性调度（Decaying Stochasticity Schedule）以增强探索。随后，我们的强化学习算法由分层奖励机制引导，该机制在全局、主体与关系三个层面联合评估图像。我们还构建了 HiCoPrompt，一个带有分层提示的新文本到图像基准，用于严格评测。实验表明，我们的方法在概念覆盖度与组合准确性上均显著优于现有方法。
英文原文摘要 1681 字符
Recent advances in diffusion models have demonstrated impressive capability in generating high-quality images for simple prompts. However, when confronted with complex prompts involving multiple objects and hierarchical structures, existing models struggle to accurately follow instructions, leading to issues such as concept omission, confusion, and poor compositionality. To address these limitations, we propose a Hierarchical Compositional Generative framework (HiCoGen) built upon a novel Chain of Synthesis (CoS) paradigm. Instead of monolithic generation, HiCoGen first leverages a Large Language Model (LLM) to decompose complex prompts into minimal semantic units. It then synthesizes these units iteratively, where the image generated in each step provides crucial visual context for the next, ensuring all textual concepts are faithfully constructed into the final scene. To further optimize this process, we introduce a reinforcement learning (RL) framework. Crucially, we identify that the limited exploration of standard diffusion samplers hinders effective RL. We theoretically prove that sample diversity is maximized by concentrating stochasticity in the early generation stages and, based on this insight, propose a novel Decaying Stochasticity Schedule to enhance exploration. Our RL algorithm is then guided by a hierarchical reward mechanism that jointly evaluates the image at the global, subject, and relationship levels. We also construct HiCoPrompt, a new text-to-image benchmark with hierarchical prompts for rigorous evaluation. Experiments show our approach significantly outperforms existing methods in both concept coverage and compositional accuracy.