Constructing fast samplers for unconditional diffusion and flow-matching
models has received much attention recently; however, existing methods for
solving inverse problems, such as super-resolution, inpainting, or deblurring,
still require hundreds to thousands of iterative steps to obtain high-quality
results. We propose a plug-and-play framework for constructing efficient
samplers for inverse problems, requiring only pre-trained diffusion or
flow-matching models. We present Conditional Conjugate Integrators, which
leverage the specific form of the inverse problem to project the respective
conditional diffusion/flow dynamics into a more amenable space for sampling.
Our method complements popular posterior approximation methods for solving
inverse problems using diffusion/flow models. We evaluate the proposed method's
performance on various linear image restoration tasks across multiple datasets,
employing diffusion and flow-matching models. Notably, on challenging inverse
problems like 4$\times$ super-resolution on the ImageNet dataset, our method
can generate high-quality samples in as few as 5 conditional sampling steps and
outperforms competing baselines requiring 20-1000 steps. Our code and models
will be publicly available at this https URL

构建快速采样器以进行无条件扩散和流匹配模型近期备受关注；然而，现有方法在解决超分辨率、修复或去模糊等逆问题时仍需要数百到数千次迭代步骤以获得高质量结果。我们提出了一种插拔式框架用于构建逆问题的高效采样器，只需要预先训练的扩散模型或流匹配模型。我们提出了条件共轭积分器，利用逆问题的具体形式将各自的条件扩散 / 流动动力学投影到更易处理的采样空间中。我们评估了所提方法在多个数据集上的各种线性图像修复任务的性能，采用了扩散和流匹配模型。尤其是在 ImageNet 数据集上的 4 倍超分辨率等具有挑战性的逆问题中，我们的方法能够在仅 5 个条件采样步骤中生成高质量样本，并优于需要 20-1000 步的竞争基准。我们的代码和模型将在此 https URL 公开。

迭代精化模型中的逆问题快速抽样器

Fast Samplers for Inverse Problems in Iterative Refinement Models

Taming the generation outcome of state of the art Diffusion and Flow-Matching
(FM) models without having to re-train a task-specific model unlocks a powerful
tool for solving inverse problems, conditional generation, and controlled
generation in general. In this work we introduce D-Flow, a simple framework for
controlling the generation process by differentiating through the flow,
optimizing for the source (noise) point. We motivate this framework by our key
observation stating that for Diffusion/FM models trained with Gaussian
probability paths, differentiating through the generation process projects
gradient on the data manifold, implicitly injecting the prior into the
optimization process. We validate our framework on linear and non-linear
controlled generation problems including: image and audio inverse problems and
conditional molecule generation reaching state of the art performance across
all.

通过与流动匹配模型（Flow-Matching models）不再训练特定任务模型，我们引入 D-Flow，这是一个简单的框架，通过在流动过程中进行微分来控制生成过程，并优化源（噪声）点。我们验证了该框架在线性和非线性控制生成问题上的有效性，包括图像和音频逆问题以及有条件分子生成，并在所有问题上达到了最先进的性能。