Diffusion distillation methods aim to compress the diffusion models into
efficient one-step generators while trying to preserve quality. Among them,
Distribution Matching Distillation (DMD) offers a suitable framework for
training general-form one-step generators, applicable beyond unconditional
generation. In this work, we introduce its modification, called Regularized
Distribution Matching Distillation, applicable to unpaired image-to-image (I2I)
problems. We demonstrate its empirical performance in application to several
translation tasks, including 2D examples and I2I between different image
datasets, where it performs on par or better than multi-step diffusion
baselines.

通过压缩扩散模型为高效的单步生成器，保持质量作为目标的扩散蒸馏方法是一种训练通用形式单步生成器的合适框架，适用于除无条件生成之外的应用。本研究中，我们引入其修改版称为规则化分布匹配蒸馏，适用于无配对图像到图像问题。我们展示了其在几个翻译任务中的实证表现，包括 2D 示例和不同图像数据集之间的图像转换任务，其中其性能与多步扩散基准相当或更好。

正则化分布匹配蒸馏用于一步非配对图像到图像转换

Regularized Distribution Matching Distillation for One-step Unpaired  Image-to-Image Translation

Recent approaches have shown promises distilling diffusion models into
efficient one-step generators. Among them, Distribution Matching Distillation
(DMD) produces one-step generators that match their teacher in distribution,
without enforcing a one-to-one correspondence with the sampling trajectories of
their teachers. However, to ensure stable training, DMD requires an additional
regression loss computed using a large set of noise-image pairs generated by
the teacher with many steps of a deterministic sampler. This is costly for
large-scale text-to-image synthesis and limits the student's quality, tying it
too closely to the teacher's original sampling paths. We introduce DMD2, a set
of techniques that lift this limitation and improve DMD training. First, we
eliminate the regression loss and the need for expensive dataset construction.
We show that the resulting instability is due to the fake critic not estimating
the distribution of generated samples accurately and propose a two time-scale
update rule as a remedy. Second, we integrate a GAN loss into the distillation
procedure, discriminating between generated samples and real images. This lets
us train the student model on real data, mitigating the imperfect real score
estimation from the teacher model, and enhancing quality. Lastly, we modify the
training procedure to enable multi-step sampling. We identify and address the
training-inference input mismatch problem in this setting, by simulating
inference-time generator samples during training time. Taken together, our
improvements set new benchmarks in one-step image generation, with FID scores
of 1.28 on ImageNet-64x64 and 8.35 on zero-shot COCO 2014, surpassing the
original teacher despite a 500X reduction in inference cost. Further, we show
our approach can generate megapixel images by distilling SDXL, demonstrating
exceptional visual quality among few-step methods.

通过引入 DMD2 技术，将 Distribution Matching Distillation 应用于一步图像生成，通过 GAN loss 以及多步采样等技巧的改进，在降低推理成本的情况下，取得了在图像生成任务中新的最优结果。

快速图像合成的改进分布匹配蒸馏

Improved Distribution Matching Distillation for Fast Image Synthesis

Diffusion models generate high-quality images but require dozens of forward
passes. We introduce Distribution Matching Distillation (DMD), a procedure to
transform a diffusion model into a one-step image generator with minimal impact
on image quality. We enforce the one-step image generator match the diffusion
model at distribution level, by minimizing an approximate KL divergence whose
gradient can be expressed as the difference between 2 score functions, one of
the target distribution and the other of the synthetic distribution being
produced by our one-step generator. The score functions are parameterized as
two diffusion models trained separately on each distribution. Combined with a
simple regression loss matching the large-scale structure of the multi-step
diffusion outputs, our method outperforms all published few-step diffusion
approaches, reaching 2.62 FID on ImageNet 64x64 and 11.49 FID on zero-shot
COCO-30k, comparable to Stable Diffusion but orders of magnitude faster.
Utilizing FP16 inference, our model can generate images at 20 FPS on modern
hardware.

通过实施分布匹配蒸馏 (Distribution Matching Distillation) 以及多步扩散输出的大规模结构简单回归损失相匹配的方法，我们将扩散模型转化为一步图像生成器，以显著降低对图像质量的影响，使其在 ImageNet 64x64 上达到 2.62 FID，在无监督 COCO-30k 上达到 11.49 FID，并可通过 FP16 推理在现代硬件上以 20 FPS 生成图像。