TILT：使用模型内在奖励改进扩散模型中的组合生成
TILT: Model-Intrinsic Reward Alignment For Compositional Diffusion
AI总结：
研究如何改进扩散模型的组合生成，提出无训练框架TILT，通过测试时奖励对齐，将组合失败解释为分布重叠模式并定义固有奖励，产生KL约束目标及指导步骤，实验表明该方法能在保图质时提升组合对齐。
AI中文摘要：
强大的文本到图像生成模型的最新进展使得开发在测试时修改采样轨迹以生成更符合复杂组合提示的图像的方法变得越来越重要。我们提出了TILT，这是一个通过测试时奖励对齐进行组合文本到图像生成的无训练框架。我们将组合失败解释为联合和单概念分布之间的重叠模式，并定义了一个奖励，该奖励有利于所有概念共同出现的样本。此奖励是基础模型固有的，不需要任何外部监督或奖励模型。这产生了一个具有闭式倾斜目标分布和扩散采样的原则性指导步骤的KL约束目标。概念分布的相互作用与上述奖励自然地导致了两种不同的指导策略，而平衡它们各自优点的混合方法产生了更强的性能。在T2ICompBench的提示上进行的实验表明，与以前的基线相比，我们的方法在保持图像质量的同时提高了组合对齐。
英文摘要：
Consider conditional generation $p(x \mid C=\{c_1, c_2, \dots c_k\})$ where $C$ is a prompt composed of multiple concepts $c_i$. Diffusion models often struggle with compositional prompts, producing samples in which some concepts dominate while others are missing or weakly represented. Prior work attributes these failures to mode collision, where single-concept modes of $p(x\mid c_i)$ overlap with modes of the joint $p(x \mid C)$. To seek out collision-free modes of $p(x \mid C)$, or "pure modes", corrector-based approaches have attempted to suppress collisions at intermediate diffusion times. However, local corrections are often heuristic and do not necessarily steer the generation to a "pure mode" in the final data space. Derived from a principled formulation, we present TILT (Test-time model-Intrinsic reward aLignment via Tilting), a training-free framework that poses eventual pure mode sampling as a reward for intermediate-time alignment. This reward offers valuable advantages: (1) it is intrinsic to the model, hence external reward models need not be trained by modality-specific datasets, (2) it yields a closed-form target under a variational approximation, which makes it realizable through standard diffusion sampling, and (3) it is interpretable, hence amenable to preference-based modifications. Project page: https://debottam-dutta7.github.io/tilt_web/
