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When using a diffusion model for image editing, there are times when themodified image can differ greatly from the source. To address this, we apply adual-guidance approach to maintain high fidelity to the original in areas thatare not altered. First, we employ text-guided optimization, using textembeddings to direct latent space and classifier-free guidance. Second, we useperceptual similarity guidance, optimizing latent vectors with posteriorsampling via Tweedie formula during the reverse process. This method ensuresthe realistic rendering of both the edited elements and the preservation of theunedited parts of the original image.

Perceptual Similarity guidance and text guidance optimization for Editing Real Images using Guided Diffusion Models