With the rapid development of diffusion models in image generation, the demand for more powerful and flexible controllable frameworks is increasing. Although existing methods can guide generation beyond text prompts, the challenge of effectively combining multiple conditional inputs while maintaining consistency with all of them remains unsolved. To address this, we introduce UniCombine, a DiT-based multi-conditional controllable generative framework capable of handling any combination of conditions, including but not limited to text prompts, spatial maps, and subject images. Specifically, we introduce a novel Conditional MMDiT Attention mechanism and incorporate a trainable LoRA module to build both the training-free and training-based versions. Additionally, we propose a new pipeline to construct SubjectSpatial200K, the first dataset designed for multi-conditional generative tasks covering both the subject-driven and spatially-aligned conditions. Extensive experimental results on multi-conditional generation demonstrate the outstanding universality and powerful capability of our approach with state-of-the-art performance.

本文解决了图像生成领域中现有方法无法有效结合多种条件输入的问题，提出了UniCombine框架，能够处理包括文本提示、空间映射和主题图像在内的任意条件组合。研究创新了条件性多模态注意力机制，并开发了可训练的LoRA模块，使得框架在多条件生成任务中表现出色，具有广泛应用潜力。

UniCombine：统一多条件结合的扩散变换器