LanPaint：基于渐近精确且快速的条件采样的免训练扩散图像修复
LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling
AI总结：
针对扩散模型部分条件采样缺乏高效免训练方法的问题，提出基于朗之万动力学蒙特卡洛采样的免训练、渐近精确的LanPaint方法，实现了无需反向传播的快速采样并在图像修复等任务中表现优异。
摘要：
扩散模型在图像生成的联合像素采样方面表现出色，但缺乏用于部分条件采样（例如对已知像素进行图像修复）的高效免训练方法。先前的工作通常将其表述为难以处理的逆问题，依赖于粗糙的变分近似、需要昂贵反向传播的启发式损失，或缓慢的随机采样。这些局限性阻碍了：（1）图像修复结果中准确的分布匹配，（2）无需梯度的快速推理，（3）对快速基于梯度的采样器的兼容性。为了解决这些局限性，我们提出了LanPaint：一种用于基于梯度的扩散模型的免训练、渐近精确的部分条件采样方法。通过精心设计的朗之万动力学蒙特卡洛采样，LanPaint实现了快速、免反向传播的蒙特卡洛采样。实验表明，我们的方法在精确的部分条件采样方面取得了卓越的性能，并在包括图像修复在内的各种任务中提供了视觉上连贯的结果。
英文摘要：
Diffusion models excel at joint pixel sampling for image generation but lack efficient training-free methods for partial conditional sampling (e.g., inpainting with known pixels). Prior work typically formulates this as an intractable inverse problem, relying on coarse variational approximations, heuristic losses requiring expensive backpropagation, or slow stochastic sampling. These limitations preclude: (1) accurate distributional matching in inpainting results, (2) efficient inference modes without gradient, (3) compatibility with fast ODE-based samplers. To address these limitations, we propose LanPaint: a training-free, asymptotically exact partial conditional sampling methods for ODE-based and rectified flow diffusion models. By leveraging carefully designed Langevin dynamics, LanPaint enables fast, backpropagation-free Monte Carlo sampling. Experiments demonstrate that our approach achieves superior performance with precise partial conditioning and visually coherent inpainting across diverse tasks.
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。
