Papers › Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models
Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models
Xichen Pan, Pengda Qin, Yuhong Li, Hui Xue, Wenhu Chen
Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity. Recently, most works focus on synthesizing independent images; While for real-world applications, it is common and necessary to generate a series of coherent images for story-stelling. In this work, we mainly focus on story visualization and continuation tasks and propose AR-LDM, a latent diffusion model auto-regressively conditioned on history captions and generated images. Moreover, AR-LDM can generalize to new characters through adaptation. To our best knowledge, this is the first work successfully leveraging diffusion models for coherent visual story synthesizing. Quantitative results show that AR-LDM achieves SoTA FID scores on PororoSV, FlintstonesSV, and the newly introduced challenging dataset VIST containing natural images. Large-scale human evaluations show that AR-LDM has superior performance in terms of quality, relevance, and consistency.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Story Continuation | FlintstonesSV | AR-LDM | FID | 19.28 | #2 of 6 | Archive leaderboard | report |
| Story Continuation | PororoSV | AR-LDM | FID | 17.4 | #2 of 6 | Archive leaderboard | report |
| Story Continuation | VIST | AR-LDM (SIS captions) | FID | 16.95 | #1 of 2 | Archive leaderboard | report |
| Story Continuation | VIST | AR-LDM (DII captions) | FID | 17.03 | #2 of 2 | Archive leaderboard | report |
| Story Visualization | Pororo | AR-LDM | FID | 16.59 | #2 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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