DreamCatcher: Efficient Multi-Concept Customization via Representation Finetuning
Abstract
Recent advances in customizing Text-to-Image models allow users to generate personalized images with just a few samples. As demand for multi-concept generation grows, methods using weight fusion and test-time optimization have emerged, integrating multiple concepts within a single image. However, these approaches inject concept knowledge into the parametric space, leading to high overhead in multi-concept generation. We introduce DreamCatcher, an efficient framework based on representation finetuning. Our key innovation embeds conceptual information into the feature space, achieving up to 5x faster multi-concept generation while reducing learnable storage per concept by 88%, all without quality loss. Besides, our method is highly versatile, enabling personalized inpainting without additional training.