Salvation Lies Within: Eliciting Inherent Style Transfer in Step-Distilled Diffusion Models
Adapting step-distilled text-to-image (T2I) models through post-training incurs additional computational costs and affects native few-step generation behavior. This motivates a complementary route beyond style-specific adaptation: drawing on the visual knowledge already encoded in step-distilled T2I models to elicit stylistic capabilities through language. Pursuing this direction requires textual guidance that captures how visual attributes jointly define a style and remain applicable as the depicted content changes. To explore this approach, we introduce StyleForge, a fully automatic, training-free framework that expresses reference styles as reusable rendering instructions. By integrati...
| Authors | Shengyin Sun, Yiming Li, Yingzhao Lian et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 October 2026 |
| Last updated at source | 13 October 2026 |
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