Recent advancements in Text-to-3D generation have yielded remarkable
progress, particularly through methods that rely on Score Distillation Sampling
(SDS). While SDS exhibits the capability to create impressive 3D assets, it is
hindered by its inherent maximum-likelihood-seeking essence, resulting in
limited diversity in generation outcomes. In this paper, we discover that the
Denoise Diffusion Implicit Models (DDIM) generation process (\ie PF-ODE) can be
succinctly expressed using an analogue of SDS loss. One step further, one can
see SDS as a generalized DDIM generation process. Following this insight, we
show that the noise sampling strategy in the noise addition stage significantly
restricts the diversity of generation results. To address this limitation, we
present an innovative noise sampling approach and introduce a novel text-to-3D
method called Flow Score Distillation (FSD). Our validation experiments across
various text-to-image Diffusion Models demonstrate that FSD substantially
enhances generation diversity without compromising quality.

通过 DDIM 生成过程的类似 SDS 损失的表达方式，本论文发现 SDS 可以看作是一种广义的 DDIM 生成过程，结合创新的噪声抽样方法和流分数蒸馏（FSD）方法，实验证明 FSD 方法显著增强了生成的多样性而不损害质量。

多样化文本到 3D 生成的流动得分蒸馏

Flow Score Distillation for Diverse Text-to-3D Generation

We propose SE-Bridge, a novel method for speech enhancement (SE). After
recently applying the diffusion models to speech enhancement, we can achieve
speech enhancement by solving a stochastic differential equation (SDE). Each
SDE corresponds to a probabilistic flow ordinary differential equation
(PF-ODE), and the trajectory of the PF-ODE solution consists of the speech
states at different moments. Our approach is based on consistency model that
ensure any speech states on the same PF-ODE trajectory, correspond to the same
initial state. By integrating the Brownian Bridge process, the model is able to
generate high-intelligibility speech samples without adversarial training. This
is the first attempt that applies the consistency models to SE task, achieving
state-of-the-art results in several metrics while saving 15 x the time required
for sampling compared to the diffusion-based baseline. Our experiments on
multiple datasets demonstrate the effectiveness of SE-Bridge in SE.
Furthermore, we show through extensive experiments on downstream tasks,
including Automatic Speech Recognition (ASR) and Speaker Verification (SV),
that SE-Bridge can effectively support multiple downstream tasks.

SE-Bridge 是一种基于稳定性模型的语音增强方法，通过解决一个随机微分方程并结合布朗桥过程，能够在多种指标上达到最先进的效果，并提高 15 倍的采样效率，同时在 ASR 和 SV 等下游任务中也表现出了良好的性能。