  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.
