CRAFT: Continuous Reasoning and Agentic Feedback Tuning for Multimodal Text-to-Image Generation

TL;DR

CRAFT introduces a training-free, structured inference framework that enhances multimodal text-to-image generation by iteratively verifying and refining images through explicit constraints, improving quality and control without retraining.

Contribution

It presents CRAFT, a novel, model-agnostic method for inference-time refinement using explicit constraints, improving image quality and interpretability in multimodal generation.

Findings

Consistently improves compositional accuracy and text rendering.

Achieves strong gains for lightweight models.

Incur negligible inference overhead.

Abstract

Recent work has shown that inference-time reasoning and reflection can improve text-to-image generation without retraining. However, existing approaches often rely on implicit, holistic critiques or unconstrained prompt rewrites, making their behavior difficult to interpret, control, or stop reliably. In contrast, large language models have benefited from explicit, structured forms of **thinking** based on verification, targeted correction, and early stopping. We introduce CRAFT (Continuous Reasoning and Agentic Feedback Tuning), a training-free and model-agnostic framework for multimodal image generation. CRAFT transforms a user prompt into a set of explicit, dependency-structured visual constraints, verifies generated images using a vision-language model, and performs targeted prompt updates only when specific constraints are violated. This iterative process includes an explicit stopping criterion, resulting in an interpretable and controllable inference-time refinement loop. Across multiple model families and challenging benchmarks, CRAFT consistently improves compositional accuracy, text rendering, and preference-based evaluations, with particularly strong gains for lightweight generators. Importantly, these improvements incur only a negligible inference-time overhead, allowing smaller or cheaper models to approach the quality of substantially more expensive systems. Our results suggest that explicitly structured, constraint-driven inference-time reasoning is a key ingredient for improving the reliability of multimodal generative models.