ModuleTeam: Open-Set Multi-Conditional Image Generation with Training-Free Latent Mixture of Any Control Module

摘要

Multi-conditional image generation aims to create customized images that align with multiple specified conditions. Existing methods, whether through end-to-end training or by fine-tuning adapters to integrate pre-trained control modules of the same category (e.g., LoRA, IP-Adapter, ControlNet, T2I-Adapter), are restricted to a closed set of predefined input conditions. To overcome this limitation, we propose ModuleTeam, a training-free method for latent mixture of arbitrary control modules, capable of handling open-set conditions by incorporating the corresponding modules. The design of ModuleTeam is rooted in two key findings: (i) modules interfere with each other at the level of model parameters, and (ii) module weights contribute to the generated images by affecting the noise predictions within the diffusion process in an approximately linear manner. The first finding motivates our latent mixture approach, which mixes the control modules by aggregating their latent variables between diffusion model blocks. The second finding enables a multi-inference module reweighting strategy that balances module contributions to generation, requiring no additional training or fine-tuning overhead. Extensive results demonstrate that ModuleTeam not only outperforms existing methods but also provides flexibility in the types of conditions and scalability in their number.