LISA: Likelihood Score Alignment for Visual-condition Controllable Generation
The prevalent dual-branch paradigm, i.e., training a side network to encode visual conditions and fusing its intermediate-layer features to a frozen pretrained main network, has shown remarkable success in visual-condition controllable generation. Despite its widespread adoption, the role of the side branch and its training efficiency remain underexplored. In this paper, we first revisit this mainstream paradigm through the lens of score-based generative modeling: 1) The main network preserves visual perceptual quality by providing a prior unconditional score. 2) The side network steers conditional control by implicitly contributing a likelihood score. Guided by this perspective, we propo...
| Authors | Yanghao Wang, Hongxu Chen, Jiazhen Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 June 2026 |
| Last updated at source | 28 September 2026 |
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