ViSTA: Visual Storytelling using Multi-modal Adapters for Text-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2506.12198v1
• Date: Fri, 13 Jun 2025 19:57:40 GMT
• Title: ViSTA: Visual Storytelling using Multi-modal Adapters for Text-to-Image Diffusion Models
• Authors: Sibo Dong, Ismail Shaheen, Maggie Shen, Rupayan Mallick, Sarah Adel Bargal,
• Abstract summary: We propose a multi-modal history adapter for text-to-image diffusion models, textbfViSTA.<n>It consists of (1) a multi-modal history fusion module to extract relevant history features and (2) a history adapter to condition the generation on the extracted relevant features.<n>Our proposed ViSTA model is not only consistent across different frames, but also well-aligned with the narrative text descriptions.
• Score: 5.753009405589415
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Text-to-image diffusion models have achieved remarkable success, yet generating coherent image sequences for visual storytelling remains challenging. A key challenge is effectively leveraging all previous text-image pairs, referred to as history text-image pairs, which provide contextual information for maintaining consistency across frames. Existing auto-regressive methods condition on all past image-text pairs but require extensive training, while training-free subject-specific approaches ensure consistency but lack adaptability to narrative prompts. To address these limitations, we propose a multi-modal history adapter for text-to-image diffusion models, \textbf{ViSTA}. It consists of (1) a multi-modal history fusion module to extract relevant history features and (2) a history adapter to condition the generation on the extracted relevant features. We also introduce a salient history selection strategy during inference, where the most salient history text-image pair is selected, improving the quality of the conditioning. Furthermore, we propose to employ a Visual Question Answering-based metric TIFA to assess text-image alignment in visual storytelling, providing a more targeted and interpretable assessment of generated images. Evaluated on the StorySalon and FlintStonesSV dataset, our proposed ViSTA model is not only consistent across different frames, but also well-aligned with the narrative text descriptions.

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