RAISE: Requirement-Adaptive Evolutionary Refinement for Training-Free Text-to-Image Alignment

• URL: http://arxiv.org/abs/2603.00483v1
• Date: Sat, 28 Feb 2026 05:53:01 GMT
• Title: RAISE: Requirement-Adaptive Evolutionary Refinement for Training-Free Text-to-Image Alignment
• Authors: Liyao Jiang, Ruichen Chen, Chao Gao, Di Niu,
• Abstract summary: We introduce RAISE, a training-free, requirement-driven evolutionary framework for adaptive T2I generation.<n> RAISE formulates image generation as a requirement-driven adaptive scaling process.<n>On GenEval and DrawBench, RAISE attains state-of-the-art alignment.
• Score: 37.59966317174412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 evolutionary framework for adaptive T2I generation. RAISE formulates image generation as a requirement-driven adaptive scaling process, evolving a population of candidates at inference time through a diverse set of refinement actions-including prompt rewriting, noise resampling, and instructional editing. Each generation is verified against a structured checklist of requirements, enabling the system to dynamically identify unsatisfied items and allocate further computation only where needed. This achieves adaptive test-time scaling that aligns computational effort with semantic query complexity. On GenEval and DrawBench, RAISE attains state-of-the-art alignment (0.94 overall GenEval) while incurring fewer generated samples (reduced by 30-40%) and VLM calls (reduced by 80%) than prior scaling and reflection-tuned baselines, demonstrating efficient, generalizable, and model-agnostic multi-round self-improvement. Code is available at https://github.com/LiyaoJiang1998/RAISE.

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