Perceptual Similarity guidance and text guidance optimization for
Editing Real Images using Guided Diffusion Models
- URL: http://arxiv.org/abs/2312.06680v1
- Date: Sat, 9 Dec 2023 02:55:35 GMT
- Title: Perceptual Similarity guidance and text guidance optimization for
Editing Real Images using Guided Diffusion Models
- Authors: Ruichen Zhang
- Abstract summary: We apply a dual-guidance approach to maintain high fidelity to the original in areas that are not altered.
This method ensures the realistic rendering of both the edited elements and the preservation of the unedited parts of the original image.
- Score: 0.6345523830122168
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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