Rectify Then Diffuse: Disentangling Concepts Before Denoising Trajectory Unfolds
Text-to-image diffusion models can generate individual concepts well, but they often omit or merge concepts incorrectly with multiple concepts. We trace these failures to an early coordination bottleneck: before denoising begins, prompt-conditioned attention may allocate different concepts to strongly overlapping spatial support, which can keep their attention coupled as denoising proceeds. This observation motivates treating compositional generation as a boundary-condition problem rather than repeatedly controlling the evolving trajectory. To this end, we propose Rectify-then-Diffuse (RTD), a training-free framework that rectifies the initial allocation once before standard denoising. Fi...
| Authors | Ning Zhu, An Chen, Mengfei Zhao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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