Pick-and-Draw: Training-free Semantic Guidance for Text-to-Image Personalization

Understand

Diffusion-based text-to-image personalization have achieved great success in generating subjects specified by users among various contexts.

• Even though, existing finetuning-based methods still suffer from model overfitting, which greatly harms the generative diversity, especially when given subject images are few.
• To this end, we propose Pick-and-Draw, a training-free semantic guidance approach to boost identity consistency and generative diversity for personalization methods.
• Our approach consists of two components: appearance picking guidance and layout drawing guidance.