Audit & Repair: An Agentic Framework for Consistent Story Visualization
  in Text-to-Image Diffusion Models
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.
685ab2ad0e4ad7e21975879b
Kiymet Akdemir
admin_assigned
2025-06-24T17:43:52.586Z
6658e05bb20730965d6d3046
/avatars/ca295711e0795d956025265ba1598acd.svg
Kiymet Akdemir
user
kakdemir
685ab2ad0e4ad7e21975879c
Tahira Kazimi
claimed_verified
2025-06-24T14:43:45.414Z
66fec311343c151be4fb0b73
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/FXeHseCn4DKSVUzk-VKdA.png
Tahira Kazimi
user
tahirakazimi77
685ab2ad0e4ad7e21975879d
Pinar Yanardag
Audit & Repair: An Agentic Framework for Consistent Story Visualization
  in Text-to-Image Diffusion Models
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.
A collaborative multi-agent framework improves consistency in multi-panel story visualizations by refining inconsistencies across panels using diffusion models like Flux and Stable Diffusion.
collaborative multi-agent framework diffusion models rectified flow transformers Flux latent diffusion models Stable Diffusion multi-panel consistency