Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models

Understand

This paper proposes a method for generating images of customized objects specified by users.

• The method is based on a general framework that bypasses the lengthy optimization required by previous approaches, which often employ a per-object optimization paradigm.
• Our framework adopts an encoder to capture high-level identifiable semantics of objects, producing an object-specific embedding with only a single feed-forward pass.
• The acquired object embedding is then passed to a text-to-image synthesis model for subsequent generation.