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MagicTailor: Component-Controllable Personalization in Text-to-Image Diffusion Models

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arxiv 2410.13370 v3 pith:CEKRQDCB submitted 2024-10-17 cs.CV cs.AI

Donghao Zhou , Jiancheng Huang , Jinbin Bai , Jiaze Wang , Hao Chen , Guangyong Chen , Xiaowei Hu , Pheng-Ann Heng This is my paper
classification cs.CVcs.AI
keywords magictailortaskvisualcomponent-controllableconceptconceptsdiffusionenables
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Text-to-image diffusion models can generate high-quality images but lack fine-grained control of visual concepts, limiting their creativity. Thus, we introduce component-controllable personalization, a new task that enables users to customize and reconfigure individual components within concepts. This task faces two challenges: semantic pollution, where undesired elements disrupt the target concept, and semantic imbalance, which causes disproportionate learning of the target concept and component. To address these, we design MagicTailor, a framework that uses Dynamic Masked Degradation to adaptively perturb unwanted visual semantics and Dual-Stream Balancing for more balanced learning of desired visual semantics. The experimental results show that MagicTailor achieves superior performance in this task and enables more personalized and creative image generation.

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Cited by 6 Pith papers

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