逐层实例绑定用于文本到图像扩散变换器中的区域和遮挡控制
Layer-wise Instance Binding for Regional and Occlusion Control in Text-to-Image Diffusion Transformers
AI总结：
LayerBind通过逐层实例绑定实现文本到图像扩散变换器中的区域和遮挡控制，无需训练即可提升生成质量和遮挡管理能力。
摘要：
在文本到图像生成中，区域指导的布局控制具有高度实用性，但现有方法存在局限性：（i）基于训练的方法继承数据偏见，往往降低图像质量；（ii）当前技术难以处理遮挡顺序，限制了实际应用。为解决这些问题，我们提出了LayerBind。通过将区域生成建模为不同的层次并在生成过程中进行绑定，我们的方法实现了精确的区域和遮挡可控性。我们的动机源于观察到空间布局和遮挡在非常早期的去噪阶段就已建立，这表明重新排列早期的潜在结构足以修改最终输出。基于此，我们将方案分为两个阶段：实例初始化和随后的语义护理。（1）首先，利用多模态联合注意机制中的上下文共享机制，逐层实例初始化创建了针对各自区域的分支，同时锚定到共享的背景。在指定的早期步骤中，这些分支根据层次顺序融合，形成具有预设布局的统一潜在结构。（2）然后，逐层语义护理通过逐层注意增强来加强区域细节并保持遮挡顺序。具体而言，一个顺序层注意路径与标准全局路径并行运行，更新在层透明度调度器下进行复合。LayerBind是无训练的，并且可以即插即用，作为扩散变换器中的区域和遮挡控制器。除了生成之外，它原生支持可编辑的工作流程，允许灵活的修改，如更改实例或重新排列可见顺序。定性和定量结果证明了LayerBind的有效性，突显了其在创意应用中的强大潜力。
英文摘要：
Region-instructed layout control in text-to-image generation is highly practical, yet existing methods suffer from limitations: (i) training-based approaches inherit data bias and often degrade image quality, and (ii) current techniques struggle with occlusion order, limiting real-world usability. To address these issues, we propose LayerBind. By modeling regional generation as distinct layers and binding them during the generation, our method enables precise regional and occlusion controllability. Our motivation stems from the observation that spatial layout and occlusion are established at a very early denoising stage, suggesting that rearranging the early latent structure is sufficient to modify the final output. Building on this, we structure the scheme into two phases: instance initialization and subsequent semantic nursing. (1) First, leveraging the contextual sharing mechanism in multimodal joint attention, Layer-wise Instance Initialization creates per-instance branches that attend to their own regions while anchoring to the shared background. At a designated early step, these branches are fused according to the layer order to form a unified latent with a pre-established layout. (2) Then, Layer-wise Semantic Nursing reinforces regional details and maintains the occlusion order via a layer-wise attention enhancement. Specifically, a sequential layered attention path operates alongside the standard global path, with updates composited under a layer-transparency scheduler. LayerBind is training-free and plug-and-play, serving as a regional and occlusion controller across Diffusion Transformers. Beyond generation, it natively supports editable workflows, allowing for flexible modifications like changing instances or rearranging visible orders. Both qualitative and quantitative results demonstrate LayerBind's effectiveness, highlighting its strong potential for creative applications.
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。
