Story-Iter: A Training-free Iterative Paradigm for Long Story Visualization

ICLR 2026 Conference Submission13235 Authors

Keywords: Subject-Consistent Image Generation, Diffusion Model, Story Visualization

Abstract: This paper introduces **Story-Iter**, a new training-free iterative paradigm to enhance long-story generation. Unlike existing methods that rely on fixed reference images to construct a complete story, our approach features a novel external **iterative paradigm**, extending beyond the internal iterative denoising steps of diffusion models, to continuously refine each generated image by incorporating all reference images from the previous round. To achieve this, we propose a plug-and-play, training-free **g**lobal **r**eference **c**ross-**a**ttention (**GRCA**) module, modeling all reference frames with global embeddings, ensuring semantic consistency in long sequences. By progressively incorporating holistic visual context and text constraints, our iterative paradigm enables precise generation with fine-grained interactions, optimizing the story visualization step-by-step. Extensive experiments in the official story visualization dataset and our long story benchmark demonstrate that Story-Iter's state-of-the-art performance in long-story visualization (up to 100 frames) excels in both semantic consistency and fine-grained interactions.

Primary Area: generative models

Submission Number: 13235