Our method involves a sophisticated process of exemplar-based image editing using diffusion models. The approach begins with extracting image embeddings from both the original image \(x\) and the edited exemplar \(x_{\text{edit}}\) using the encoder \(\mathcal{E}_{\text{img}}\). The difference between these embeddings is pooled to form \(\Delta_{\text{img}}\), capturing the nuanced details of the edit. Simultaneously, text guidance is incorporated through \(\mathcal{E}_{\text{text}}(g_{\text{caption}})\), providing additional context for the edit. These components are combined into a guidance vector \(g\), which is used to condition the diffusion model \(M\).

The process involves DDIM inversion to map the target image \(y\) into a latent space, followed by denoising to maintain the structural integrity of the original image. The model employs cross attention, self-attention injection, and feature injection to ensure precise and contextually relevant edits. This method allows for high-quality image transformations that respect the original content while applying the desired changes, as demonstrated in the final output \(\hat{y}_{\text{edit}}\).