Text Template Tokens Are Implicit Semantic Registers in Diffusion Transformers
Text-to-image diffusion transformers (DiTs) jointly process text and image tokens, yet their internal computation during denoising remains poorly understood. We introduce a causal interpretability framework for modern large-scale DiTs that combines attention decomposition with targeted interventions across token spans, heads, and layers. Using it to separate prompt-content tokens from structural template tokens, we find that the structural tokens carry little prompt-specific information at the encoder output. Yet surprisingly, they emerge as dominant image-to-text attention sinks and causally
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “Text Template Tokens Are Implicit Semantic Registers in Diff” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
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- PossiblePossibly related (embedding) · 54%What if context compression is a diffusion noise function? Proposal + honest results from untrained-model experiments [R] →
- FuzzySimilar title/name (fuzzy) · 87%lucidrains/x-transformers →
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