无需训练即可分层生成图像，保持各层一致性。
TAUE: Training-free Noise Transplant and Cultivation Diffusion Model
- 将去噪过程中的结构信息注入初始噪声，保持空间连贯性。
- 通过跨层注意力共享语义线索，提升多层视觉一致性。
- 支持布局感知编辑、多对象组合等新应用，适合创意设计场景。
尽管文本到图像的扩散模型取得了显著进展，但其仅输出单一扁平化图像的特性，成为需要分层控制的专业应用的关键瓶颈。现有方法或依赖大规模不可获取的数据微调，或虽无需训练但仅能生成孤立前景元素，无法生成完整连贯的场景。为此，我们提出无需训练的噪声移植与培育扩散模型（TAUE），一种无需微调和额外数据的分层图像生成框架。TAUE将中间去噪潜在表示中的全局结构信息嵌入初始噪声，以保持空间连贯性，并通过跨层注意力共享语义线索，确保各层间的上下文与视觉一致性。大量实验表明，TAUE在无需训练的方法中达到领先性能，图像质量接近微调模型，同时显著提升层间一致性。此外，该模型支持布局感知编辑、多对象组合和背景替换等新应用，展现出在真实创作流程中实现交互式分层生成系统的潜力。
原文摘要 · Abstract (English)
Despite the remarkable success of text-to-image diffusion models, their output of a single, flattened image remains a critical bottleneck for professional applications requiring layer-wise control. Existing solutions either rely on fine-tuning with large, inaccessible datasets or are training-free yet limited to generating isolated foreground elements, failing to produce a complete and coherent scene. To address this, we introduce the Training-free Noise Transplantation and Cultivation Diffusion Model (TAUE), a novel framework for layer-wise image generation that requires neither fine-tuning nor additional data. TAUE embeds global structural information from intermediate denoising latents into the initial noise to preserve spatial coherence, and integrates semantic cues through cross-layer attention sharing to maintain contextual and visual consistency across layers. Extensive experiments demonstrate that TAUE achieves state-of-the-art performance among training-free methods, delivering image quality comparable to fine-tuned models while improving inter-layer consistency. Moreover, it enables new applications, such as layout-aware editing, multi-object composition, and background replacement, indicating potential for interactive, layer-separated generation systems in real-world creative workflows.
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