SketchDeco：面向精确草图上色的免训练潜空间组合方法
SketchDeco: Training-Free Latent Composition for Precise Sketch Colourisation
把扩散反演当作精确的局部「上色」工具，再用自定义自注意力把局部编辑与全局底图融合，这套免训练的潜空间组合流程可直接迁移到 inpainting、局部风格编辑等需要区域一致性的可控生成任务。
原文依据 we first leverage diffusion inversion to precisely "paint" user-defined colours into specified regions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 SketchDeco，一种免训练的草图上色方法，弥合了专业设计需求与直观的、基于区域的控制之间的差距。该方法使艺术家能够使用简单的掩码与调色板来精确指定空间与色彩，既避免了手动逐处赋色的繁琐，也避免了文本提示的歧义。我们将该任务重新表述为一个新颖的免训练组合问题。核心技术创新是一个引导式潜空间混合过程：首先利用扩散反演将用户指定的颜色精确「绘制」到指定区域，然后使用定制的自注意力机制将这些局部编辑与全局一致的基础图像和谐地融合。这在不进行任何模型微调的情况下，同时保证了局部色彩保真度与全局协调性。我们的系统在消费级 GPU 上仅需 15–20 步推理即可生成高质量结果，使专业级、可控的上色变得易于获得。
英文原文摘要 1004 字符
We introduce SketchDeco, a training-free approach to sketch colourisation that bridges the gap between professional design needs and intuitive, region-based control. Our method empowers artists to use simple masks and colour palettes for precise spatial and chromatic specification, avoiding both the tediousness of manual assignment and the ambiguity of text-based prompts. We reformulate this task as a novel, training-free composition problem. Our core technical contribution is a guided latent-space blending process: we first leverage diffusion inversion to precisely "paint" user-defined colours into specified regions, and then use a custom self-attention mechanism to harmoniously blend these local edits with a globally consistent base image. This ensures both local colour fidelity and global harmony without requiring any model fine-tuning. Our system produces high-quality results in 15--20 inference steps on consumer GPUs, making professional-quality, controllable colourisation accessible.