TaleDiffusion introduces an iterative framework using LLM-generated per-frame descriptions, bounded attention-based per-box masks, identity-consistent self-attention, region-aware cross-attention, and CLIPSeg-based dialogue rendering to produce consistent multi-character story visualizations.
Large language models are latent variable models: Explaining and finding good demonstra- tions for in-context learning
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TaleDiffusion: Multi-Character Story Generation with Dialogue Rendering
TaleDiffusion introduces an iterative framework using LLM-generated per-frame descriptions, bounded attention-based per-box masks, identity-consistent self-attention, region-aware cross-attention, and CLIPSeg-based dialogue rendering to produce consistent multi-character story visualizations.