Conceptrol: Concept Control of Zero-shot Personalized Image Generation

• URL: http://arxiv.org/abs/2503.06568v1
• Date: Sun, 09 Mar 2025 11:54:08 GMT
• Title: Conceptrol: Concept Control of Zero-shot Personalized Image Generation
• Authors: Qiyuan He, Angela Yao,
• Abstract summary: Conceptrol is a framework that enhances zero-shot adapters without adding computational overhead.<n>It achieves as much as 89% improvement on personalization benchmarks over the vanilla IP-Adapter.
• Score: 36.39574513193442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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