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The Diffusion Model Keeps the Details That Are Still Moving
DaTMe decides which image tokens to merge from denoising dynamics and local structure, not prompt relevance alone.
Summary
DaTMe decides which image tokens to merge from denoising dynamics and local structure, not prompt relevance alone.
Training-free token merging can speed image diffusion by collapsing redundant computation, but preserving only prompt-aligned foreground regions can erase background structure and unmentioned detail. Dynamics-aware Token Merging builds an importance map from the predicted clean image: temporal change marks unresolved regions, while local high-frequency content identifies boundaries and fine structure. Important tokens remain anchors; stable, homogeneous regions become merge candidates. Across PixArt-a and FLUX, the authors report better fidelity across the full image than comparison methods at matched compression settings. The evidence is benchmark- and model-specific, but it replaces a semantic shortcut with signals from the generation process itself.
Why it matters
DaTMe decides which image tokens to merge from denoising dynamics and local structure, not prompt relevance alone.
Limits and context
- Training-free token merging can speed image diffusion by collapsing redundant computation, but preserving only prompt-aligned foreground regions can erase background structure and unmentioned detail.
- Important tokens remain anchors; stable, homogeneous regions become merge candidates.
Key claims
DaTMe decides which image tokens to merge from denoising dynamics and local structure, not prompt relevance alone.
Qualification: Training-free token merging can speed image diffusion by collapsing redundant computation, but preserving only prompt-aligned foreground regions can erase background structure and unmentioned detail.
Evidence: source-2026-10-17-015
Sources
- arXiv preprint 2610.22370, original v1arXiv · primary research
Corrections
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