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

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.