A Training-Free Approach for Multi-ID Customization via Attention Adjustment and Spatial Control

TL;DR

This paper introduces MultiID, a training-free method for multi-ID image customization in computer vision, using attention adjustment and spatial control to improve quality and text controllability without additional training.

Contribution

The paper proposes a novel training-free approach that adapts existing single-ID models for multi-ID customization using ID-decoupled attention and spatial control strategies.

Findings

Achieves high-quality multi-ID customization without training.

Outperforms some training-based methods in quality and controllability.

Provides a new benchmark for evaluation called IDBench.

Abstract

Multi-ID customization is an interesting topic in computer vision and attracts considerable attention recently. Given the ID images of multiple individuals, its purpose is to generate a customized image that seamlessly integrates them while preserving their respective identities. Compared to single-ID customization, multi-ID customization is much more difficult and poses two major challenges. First, since the multi-ID customization model is trained to reconstruct an image from the cropped person regions, it often encounters the copy-paste issue during inference, leading to lower quality. Second, the model also suffers from inferior text controllability. The generated result simply combines multiple persons into one image, regardless of whether it is aligned with the input text. In this work, we propose MultiID to tackle this challenging task in a training-free manner. Since the existing single-ID customization models have less copy-paste issue, our key idea is to adapt these models to achieve multi-ID customization. To this end, we present an ID-decoupled cross-attention mechanism, injecting distinct ID embeddings into the corresponding image regions and thus generating multi-ID outputs. To enhance the generation controllability, we introduce three critical strategies, namely the local prompt, depth-guided spatial control, and extended self-attention, making the results more consistent with the text prompts and ID images. We also carefully build a benchmark, called IDBench, for evaluation. The extensive qualitative and quantitative results demonstrate the effectiveness of MultiID in solving the aforementioned two challenges. Its performance is comparable or even better than the training-based multi-ID customization methods.