RAISE: Requirement-Adaptive Evolutionary Refinement for Training-Free Text-to-Image Alignment
Recent text-to-image (T2I) diffusion models achieve remarkable realism, yet faithful prompt-image alignment remains challenging, particularly for complex prompts with multiple objects, relations, and fine-grained attributes. Existing training-free inference-time scaling methods rely on fixed iteration budgets that cannot adapt to prompt difficulty, while reflection-tuned models require carefully curated reflection datasets and extensive joint fine-tuning of diffusion and vision-language models, often overfitting to reflection paths data and lacking transferability across models. We introduce RAISE (Requirement-Adaptive Self-Improving Evolution), a training-free, requirement-driven evoluti...
| Authors | Liyao Jiang, Ruichen Chen, Chao Gao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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