TILT: Improving Compositional Generation in Diffusion Models with a Model-Intrinsic Reward

Consider conditional generation p(x mid C=c1, c2, dots ck) where C is a prompt composed of multiple concepts ci. 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(xmid ci) 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/tiltweb/