Perceptual Similarity guidance and text guidance optimization for
  Editing Real Images using Guided Diffusion Models
Ruichen Zhang
When using a diffusion model for image editing, there are times when the
modified image can differ greatly from the source. To address this, we apply a
dual-guidance approach to maintain high fidelity to the original in areas that
are not altered. First, we employ text-guided optimization, using text
embeddings to direct latent space and classifier-free guidance. Second, we use
perceptual similarity guidance, optimizing latent vectors with posterior
sampling via Tweedie formula during the reverse process. This method ensures
the realistic rendering of both the edited elements and the preservation of the
unedited parts of the original image.