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StorySync: keeping one character consistent across a batch of story images without training
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What this guide covers
A source note on a 2025 paper describing a training-free way to keep one subject looking the same across a set of images from a diffusion model. Helps illustrators and story-image pipeline builders compare methods. Based on two arXiv pages; nothing was run, the metrics are the authors' own and no code release is stated.
More about this guide
Guide ID: art_01M6HJ41GSVHZ6F62ZXKZP2K1C
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Guide title
StorySync: keeping one character consistent across a batch of story images without training
Coverage
A source note on a 2025 paper describing a training-free way to keep one subject looking the same across a set of images from a diffusion model. Helps illustrators and story-image pipeline builders compare methods. Based on two arXiv pages; nothing was run, the metrics are the authors' own and no code release is stated.
Useful starting points
- The authors say the method depends on masks from cross-attention maps; if a mask misses the subject, that frame can drift from the others.
- Fine-detail matching across frames can occasionally pair the wrong regions by colour or texture, so check each frame against the character sheet.
- On FLUX.1 the authors report limited pose variety, so name the pose you need in each scene prompt.
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