MRT：用于大规模分层图像生成与编辑的掩码区域变换器
MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale
AI总结：
提出MRT，一个200亿参数的掩码区域扩散模型，通过统一文本到层、图像到层和层到层任务，并引入溢出感知画布层，实现高效的多层透明图像生成与编辑。
摘要：
分层图像生成与编辑是一项基础能力，能够实现生成视觉内容的逐层重用、编辑和组合，类似于自然语言中的词级编辑。尽管其重要性，但在大规模场景下仍是一个未充分探索的领域。为解决这一问题，我们提出了MRT，一个200亿参数的掩码区域扩散模型，专为多层透明图像生成与编辑设计，并在超过1000万个涵盖多种宽高比和文本提示的多语言设计样本上训练。为充分利用这一规模，我们做出了两项关键技术贡献。首先，我们在共享的掩码区域扩散框架内统一了三个互补任务，包括文本到层、图像到层和层到层，其中选择性标记掩码实现了灵活的逐层生成与编辑。其次，为实现溢出层生成，我们引入了一个溢出感知画布层，用于处理边界不一致性并支持半透明背景合成，从而生成超出可见画布边界的完整可编辑层。此外，我们应用扩散蒸馏实现了8步实时多层生成，且质量下降极小。大量实验表明，我们的框架在所有三个任务上显著优于先前的最先进方法（包括各种商业系统），为多层透明图像生成建立了新基准。值得注意的是，根据用户研究结果，我们的模型在图像到层质量上显著优于同期Qwen-Image-Layered模型，同时在图像到层推理中实现了10-100倍的推理速度提升，并将激活GPU内存消耗降低50-90%。
英文摘要：
Layered image generation and editing is a fundamental capability that enables layer-wise reuse, editing, and composition of generated visual content, analogous to word-level editing in natural language. Despite its importance, this remains an underexplored area at scale. To address this gap, we present MRT, a 20B-parameter masked region diffusion model tailored for multi-layer transparent image generation and editing, trained on over 10M multilingual design samples spanning diverse aspect ratios and textual prompts. To fully leverage this scale, we make two key technical contributions. First, we unify three complementary tasks including text-to-layers, image-to-layers, and layers-to-layers within a shared masked region diffusion framework, where selective token masking enables flexible layer-wise generation and editing. Second, to enable overflow layer generation, we introduce an overflow-aware canvas layer that handles boundary inconsistencies and supports semi-transparent background synthesis, enabling complete editable layers extending beyond visible canvas boundaries. Additionally, we apply diffusion distillation to achieve 8-step, real-time multi-layer generation with minimal quality degradation. Extensive experiments demonstrate that our framework substantially outperforms prior state-of-the-art approaches, including various commercial systems, across all three tasks, establishing a new benchmark for multi-layer transparent image generation. Notably, our model significantly outperforms the concurrent Qwen-Image-Layered model in image-to-layers quality according to user-study results, while achieving 10-100\times faster inference and reducing activation GPU memory consumption by 50-90\% during image-to-layer inference.
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