VisualPrompter: Prompt Optimization with Visual Feedback for Text-to-Image Synthesis
Journal:
arXiv
Published Date:
Jun 29, 2025
Abstract
Since there exists a notable gap between user-provided and model-preferred
prompts, generating high-quality and satisfactory images using diffusion models
often requires prompt engineering to optimize user inputs. Current studies on
text-to-image prompt engineering can effectively enhance the style and
aesthetics of generated images. However, they often neglect the semantic
alignment between generated images and user descriptions, resulting in visually
appealing but content-wise unsatisfying outputs. In this work, we propose
VisualPrompter, a novel training-free prompt engineering framework that refines
user inputs to model-preferred sentences. In particular, VisualPrompter
utilizes an automatic self-reflection module to identify the missing concepts
in generated images and a target-specific prompt optimization mechanism to
revise the prompts in a fine-grained manner. Extensive experiments demonstrate
the effectiveness of our VisualPrompter, which achieves new state-of-the-art
performance on multiple benchmarks for text-image alignment evaluation.
Additionally, our framework features a plug-and-play design, making it highly
adaptable to various generative models.
