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.

使用扩散模型进行图像编辑时，我们采用了双向引导的方法来保持未改变区域与原图的高度保真度。首先，我们使用文本嵌入来指导潜空间，并使用无分类器的引导进行优化。其次，我们使用感知相似性引导，在反向过程中通过 Tweedie 公式进行后验采样来优化潜空间向量。此方法既可以保证对编辑元素进行逼真渲染，又可以保护原图未编辑部分的完整性。