Audit & Repair: An Agentic Framework for Consistent Story Visualization
  in Text-to-Image Diffusion Models
Authors: name: Kiymet Akdemir, name: Tahira Kazimi, name: Pinar Yanardag

Story visualization has become a popular task where visual scenes are
generated to depict a narrative across multiple panels. A central challenge in
this setting is maintaining visual consistency, particularly in how characters
and objects persist and evolve throughout the story. Despite recent advances in
diffusion models, current approaches often fail to preserve key character
attributes, leading to incoherent narratives. In this work, we propose a
collaborative multi-agent framework that autonomously identifies, corrects, and
refines inconsistencies across multi-panel story visualizations. The agents
operate in an iterative loop, enabling fine-grained, panel-level updates
without re-generating entire sequences. Our framework is model-agnostic and
flexibly integrates with a variety of diffusion models, including rectified
flow transformers such as Flux and latent diffusion models such as Stable
Diffusion. Quantitative and qualitative experiments show that our method
outperforms prior approaches in terms of multi-panel consistency.