Conceptrol: Concept Control of Zero-shot Personalized Image Generation
This addresses a critical design flaw in zero-shot adapters for personalized image generation, offering a solution that enhances performance without computational overhead, which is significant for users needing efficient and accurate text-to-image personalization.
The paper tackles the problem of zero-shot personalized image generation where existing adapters struggle to balance preserving personalized content with text prompt adherence, and proposes Conceptrol, a framework that improves subject-driven generation capabilities, achieving up to 89% improvement on benchmarks over vanilla IP-Adapter and outperforming fine-tuning approaches like Dreambooth LoRA.
Personalized image generation with text-to-image diffusion models generates unseen images based on reference image content. Zero-shot adapter methods such as IP-Adapter and OminiControl are especially interesting because they do not require test-time fine-tuning. However, they struggle to balance preserving personalized content and adherence to the text prompt. We identify a critical design flaw resulting in this performance gap: current adapters inadequately integrate personalization images with the textual descriptions. The generated images, therefore, replicate the personalized content rather than adhere to the text prompt instructions. Yet the base text-to-image has strong conceptual understanding capabilities that can be leveraged. We propose Conceptrol, a simple yet effective framework that enhances zero-shot adapters without adding computational overhead. Conceptrol constrains the attention of visual specification with a textual concept mask that improves subject-driven generation capabilities. It achieves as much as 89% improvement on personalization benchmarks over the vanilla IP-Adapter and can even outperform fine-tuning approaches such as Dreambooth LoRA. The source code is available at https://github.com/QY-H00/Conceptrol.