Learn Once, Edit Anywhere: Visual Direction Transfer for Diffusion Models

Understand

The rapid advancement of diffusion models has enabled the generation of high-fidelity images from textual prompts, yet achieving precise, disentangled control over specific attributes remains a significant challenge.

• A fundamental limitation arises because visual differences between images are often far more descriptive and nuanced than what can be captured through human-crafted text descriptions, which frequently fail to convey fine-grained semantic details.
• To address this, we introduce ViDiT (Visual Direction Transfer for Diffusion), a framework that expands the editing vocabulary by capturing latent semantics directly from image-edit pairs.
• ViDiT learns the underlying transformation by optimizing a single, global, and continuous editing direction from a small set of ``before-and-after'' examples.