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Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives

Research Multimodal & Generative

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TL;DR - Dualin jointly inverts a target image’s semantic prompt and latent noise for text-to-image diffusion models. This improves fidelity and interpretability while supporting precise image editing without repeated optimization.

  • Uses vision-language models, CLIP, and an LLM to recover a human-readable prompt.
  • Applies unconditional DDIM inversion to reconstruct latent noise encoding structural details.
  • Reported experiments show state-of-the-art image fidelity alongside high-quality inverted prompts.
  • The recovered noise theoretically enables flexible editing without re-optimization.

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Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives

arXiv cs.CV Xiaolong Liu, Junjian Li, Yuan Xiao, Jiaqi Deng, Dayong Ye, Tianqing Zhu, Huan Huo 2026-07-29 arXiv:2607.26735
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-21 14:32:31.094118 UTC

TL;DR - Dualin jointly inverts a target image’s semantic prompt and latent noise for text-to-image diffusion models. This improves fidelity and interpretability while supporting precise image editing without repeated optimization.

  • Uses vision-language models, CLIP, and an LLM to recover a human-readable prompt.
  • Applies unconditional DDIM inversion to reconstruct latent noise encoding structural details.
  • Reported experiments show state-of-the-art image fidelity alongside high-quality inverted prompts.
  • The recovered noise theoretically enables flexible editing without re-optimization.
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