Diffusion models are a powerful generative framework, but come with expensive
inference. Existing acceleration methods often compromise image quality or fail
under complex conditioning when operating in an extremely low-step regime. In
this work, we propose a novel distillation framework tailored to enable
high-fidelity, diverse sample generation using just one to three steps. Our
approach comprises three key components: (i) Backward Distillation, which
mitigates training-inference discrepancies by calibrating the student on its
own backward trajectory; (ii) Shifted Reconstruction Loss that dynamically
adapts knowledge transfer based on the current time step; and (iii) Noise
Correction, an inference-time technique that enhances sample quality by
addressing singularities in noise prediction. Through extensive experiments, we
demonstrate that our method outperforms existing competitors in quantitative
metrics and human evaluations. Remarkably, it achieves performance comparable
to the teacher model using only three denoising steps, enabling efficient
high-quality generation.

通过反向蒸馏、动态适应的位移重构损失和噪声修正技术，我们提出了一种新的蒸馏框架，用于实现高保真度、多样化的样本生成，仅使用一到三个步骤即可。通过大量实验证明，我们的方法在定量指标和人工评估中优于现有竞争对手，仅仅使用三个降噪步骤即可达到与教师模型相当的性能，从而实现了高效高质量的生成。

想象 Flash：使用向后蒸馏加速鸵鸟扩散模型

Imagine Flash: Accelerating Emu Diffusion Models with Backward  Distillation

Talking head video generation aims to animate a human face in a still image
with dynamic poses and expressions using motion information derived from a
target-driving video, while maintaining the person's identity in the source
image. However, dramatic and complex motions in the driving video cause
ambiguous generation, because the still source image cannot provide sufficient
appearance information for occluded regions or delicate expression variations,
which produces severe artifacts and significantly degrades the generation
quality. To tackle this problem, we propose to learn a global facial
representation space, and design a novel implicit identity representation
conditioned memory compensation network, coined as MCNet, for high-fidelity
talking head generation.~Specifically, we devise a network module to learn a
unified spatial facial meta-memory bank from all training samples, which can
provide rich facial structure and appearance priors to compensate warped source
facial features for the generation. Furthermore, we propose an effective query
mechanism based on implicit identity representations learned from the discrete
keypoints of the source image. It can greatly facilitate the retrieval of more
correlated information from the memory bank for the compensation. Extensive
experiments demonstrate that MCNet can learn representative and complementary
facial memory, and can clearly outperform previous state-of-the-art talking
head generation methods on VoxCeleb1 and CelebV datasets. Please check our
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通过学习全局面部表达空间并设计了一个新颖的隐式身份表示条件化内存补偿网络（MCNet），我们提出了一种高保真的谈话头生成方法，能够从训练样本中学习统一的空间面部元记忆库，并通过离散关键点的隐式身份表示来有效查询这个记忆库以获得更相关的信息进行补偿，实验证明 MCNet 可以学习到代表性和互补的面部记忆，并在 VoxCeleb1 和 CelebV 数据集上明显优于现有的谈话头生成方法。