← 研踪 PaperTrailCVF · CVPR 2026:cvf:2b6e375a6cbe2222398cce997739e93a · 记录 #1
Interpretable Prompts made Edit-Friendly: Token-to-Token Similarity Reduction in dLLMs for Edit-Friendly Hard Prompt Inversion
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 01:53
摘要
Crafting prompts via Prompt Engineering that steer a model's internal representations toward specific and pre-defined outcomes can be time-consuming, often requiring multiple iterations. Hard Prompt Inversion offers a complementary workflow: start from a reference image and generate a prompt that conditions a text-to-image (T2I) model to reconstruct the reference image. Existing inversion methods either yield incoherent text, or produce prompts that are overly sensitive to downstream token edits. We propose a dLLM-based prompt inversion framework that yield prompts that are (i) more interpretable to humans, (ii) better aligned with the reference image, and (iii) designed for downstream token swap and token append operations (aka edit-friendly prompts). The method is plug-and-play, requiring no finetuning of either the T2I model or the dLLM. Experiments across three datasets show a ~10xreduction in inversion time relative to existing prompt-inversion baselines, higher interpretability scores, and significantly higher prompt editability, as measured by TIFA, GPT-V preference scoring, and controlled user studies, all while preserving high-fidelity image generation. By coupling diffusion-time sampling with token-similarity control inside a dLLM decoder, our approach extends prompt inversion beyond reconstruction to downstream token-editing tasks, enabling faster, more transferable prompts that generalize across multiple T2I models.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
Interpretable Prompts made Edit-Friendly: Token-to-Token Similarity Reduction in dLLMs for Edit-Friendly Hard Prompt Inversion
Crafting prompts via Prompt Engineering that steer a model's internal representations toward specific and pre-defined outcomes can be time-consuming, often requiring multiple iterations. Hard Prompt Inversion offers a complementary workflow: start from a reference image and generate a prompt that conditions a text-to-image (T2I) model to reconstruct the reference image. Existing inversion methods either yield incoherent text, or produce prompts that are overly sensitive to downstream token edits. We propose a dLLM-based prompt inversion framework that yield prompts that are (i) more interpretable to humans, (ii) better aligned with the reference image, and (iii) designed for downstream token swap and token append operations (aka edit-friendly prompts). The method is plug-and-play, requiring no finetuning of either the T2I model or the dLLM. Experiments across three datasets show a ~10xreduction in inversion time relative to existing prompt-inversion baselines, higher interpretability scores, and significantly higher prompt editability, as measured by TIFA, GPT-V preference scoring, and controlled user studies, all while preserving high-fidelity image generation. By coupling diffusion-time sampling with token-similarity control inside a dLLM decoder, our approach extends prompt inversion beyond reconstruction to downstream token-editing tasks, enabling faster, more transferable prompts that generalize across multiple T2I models.
查看此版本解读 →查看此版本原文 ↗