面向文本到图像扩散 Transformer 中区域与遮挡控制的逐层实例绑定
Layer-wise Instance Binding for Regional and Occlusion Control in Text-to-Image Diffusion Transformers
利用多模态联合注意力中的上下文共享机制，在去噪早期为每个实例建立独立分支并按层序融合成统一潜变量，从而在免训练条件下实现区域布局与遮挡顺序控制；这一「早期潜变量结构重排」思路可直接迁移到 DiT 类生成器的空间可控生成与分层编辑。
原文依据 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
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论文本到图像生成中的区域指令布局控制具有很高的实用价值，但现有方法存在局限：(i) 基于训练的方法会继承数据偏差，且常常导致图像质量下降；(ii) 现有技术难以处理遮挡顺序，限制了其在真实场景中的可用性。为解决这些问题，我们提出 LayerBind。该方法将区域生成建模为相互独立的层，并在生成过程中对这些层进行绑定，从而实现精确的区域与遮挡可控性。我们的动机源于以下观察：空间布局与遮挡在去噪的极早期阶段就已确立，这表明重新排列早期的潜变量结构就足以改变最终输出。基于此，我们将方案组织为两个阶段：实例初始化与随后的语义培育。(1) 首先，利用多模态联合注意力中的上下文共享机制，逐层实例初始化创建出每个实例的分支，这些分支关注各自的区域，同时锚定到共享背景。在指定的早期步骤，这些分支按层顺序融合，形成具有预先确立布局的统一潜变量。(2) 随后，逐层语义培育通过逐层注意力增强来强化区域细节并维持遮挡顺序。具体而言，一条顺序分层注意力路径与标准全局路径并行运行，其更新在层透明度调度器的控制下进行合成。LayerBind 无需训练且即插即用，可作为扩散 Transformer 上的区域与遮挡控制器。它还支持可编辑的工作流，允许进行诸如更换实例或重排可见顺序等灵活修改。实验结果证明了 LayerBind 的有效性，凸显了其在创意应用中的潜力。项目主页：https://littlefatshiba.github.io/layerbind-page
英文原文摘要 1930 字符
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. It also supports editable workflows, allowing for flexible modifications like changing instances or rearranging visible orders. Experimental results demonstrate LayerBind's effectiveness, highlighting its potential for creative applications. Project page: https://littlefatshiba.github.io/layerbind-page