TILT: Model-Intrinsic Reward Alignment For Compositional Diffusion
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 ...
| Authors | Debottam Dutta, Jianchong Chen, Jaehoon Hahm et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 May 2026 |
| Last updated at source | 5 October 2026 |
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