门控条件注入无需多模态注意：迈向可控的线性注意变换器
Gated Condition Injection without Multimodal Attention: Towards Controllable Linear-Attention Transformers
AI总结：
本文提出基于线性注意架构的可控扩散模型，解决现有方法在多类型条件输入灵活性和收敛速度上的不足，通过统一门控条件模块实现高效可控生成。
摘要：
最近基于扩散的可控视觉生成在图像质量上取得了显著进步。然而，这些强大模型通常部署在云服务器上，由于计算需求大，引发了用户数据隐私的严重担忧。为了实现安全高效的设备端生成，本文探索了基于线性注意架构的可控扩散模型，这些模型在边缘设备上也具有优越的可扩展性和效率。然而，我们的实验发现，现有可控生成框架，如ControlNet和OminiControl，要么缺乏支持多种异构条件类型的灵活性，要么在这样的线性注意模型上收敛缓慢。为了解决这些限制，我们提出了一种新的可控扩散框架，专为线性注意骨干如SANA设计。我们的方法核心是一个统一的门控条件模块，通过双路径管道工作，有效整合多种类型的条件输入，如空间对齐和非对齐提示。在多个任务和基准上的广泛实验表明，我们的方法在基于线性注意模型的可控生成中实现了最先进的性能，优于现有方法在保真度和可控性方面。
英文摘要：
Recent advances in diffusion-based controllable visual generation have led to remarkable improvements in image quality. However, these powerful models are typically deployed on cloud servers due to their large computational demands, raising serious concerns about user data privacy. To enable secure and efficient on-device generation, we explore in this paper controllable diffusion models built upon linear attention architectures, which offer superior scalability and efficiency, even on edge devices. Yet, our experiments reveal that existing controllable generation frameworks, such as ControlNet and OminiControl, either lack the flexibility to support multiple heterogeneous condition types or suffer from slow convergence on such linear-attention models. To address these limitations, we propose a novel controllable diffusion framework tailored for linear attention backbones like SANA. The core of our method lies in a unified gated conditioning module working in a dual-path pipeline, which effectively integrates multi-type conditional inputs, such as spatially aligned and non-aligned cues. Extensive experiments on multiple tasks and benchmarks demonstrate that our approach achieves state-of-the-art controllable generation performance based on linear-attention models, surpassing existing methods in terms of fidelity and controllability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
