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The use of denoising diffusion models is becoming increasingly popular in thefield of image editing. However, current approaches often rely on eitherimage-guided methods, which provide a visual reference but lack control oversemantic consistency, or text-guided methods, which ensure alignment with thetext guidance but compromise visual quality. To resolve this issue, we proposea framework that integrates a fusion of generated visual references and textguidance into the semantic latent space of a \textit{frozen} pre-traineddiffusion model. Using only a tiny neural network, our framework providescontrol over diverse content and attributes, driven intuitively by the textprompt. Compared to state-of-the-art methods, the framework generates images ofhigher quality while providing realistic editing effects across variousbenchmark datasets.

Textual and Visual Prompt Fusion for Image Editing via Step-Wise Alignment