Conceptrol: Concept Control of Zero-shot Personalized Image Generation

2025-03-09

AI Summary

In zero-shot personalized image generation, existing adapters (e.g., IP-Adapter) struggle to simultaneously ensure text prompt adherence and subject fidelity, often over-replicating reference images while neglecting semantic instructions. To address this, we propose a lightweight, fine-tuning-free framework with zero computational overhead. Our key innovation is the Text Concept Mask—a novel mechanism that explicitly constrains cross-modal attention to enable fine-grained alignment between visual features and textual semantics. Integrated into diffusion-based adapter architectures, our method is fully compatible with mainstream designs such as IP-Adapter. On standard personalized generation benchmarks, it achieves an 89% improvement over IP-Adapter and outperforms fine-tuning approaches including DreamBooth and LoRA. This work overcomes the longstanding limitation of insufficient multimodal fusion in adapter-based personalization, establishing a new state-of-the-art for zero-shot, plug-and-play subject customization.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Balances personalized content and text prompt adherence
• Improves zero-shot adapters without computational overhead
• Enhances subject-driven generation with textual concept mask

Innovation

• Enhances zero-shot adapters without computational overhead
• Uses textual concept mask to constrain visual attention
• Improves personalization benchmarks by 89%