Token-to-Token Alignment of Text Embeddings for Semantic Blending

Token-to-Token Alignment of Text Embeddings for Semantic Blending proposes a two‑stage framework that first rephrases text prompts into a shared structured form and then aligns token embeddings across prompts based on semantic similarity. By establishing explicit semantic correspondence between tokens, the method enables smooth, coherent transitions through simple linear interpolation, facilitating image blending and continuous editing. The authors demonstrate that properly aligned text embeddings reveal an implicit continuous semantic structure in text‑to‑image models, allowing semantic control without altering the generative model.

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