  By leveraging the text-to-image diffusion priors, score distillation can
synthesize 3D contents without paired text-3D training data. Instead of
spending hours of online optimization per text prompt, recent studies have been
focused on learning a text-to-3D generative network for amortizing multiple
text-3D relations, which can synthesize 3D contents in seconds. However,
existing score distillation methods are hard to scale up to a large amount of
text prompts due to the difficulties in aligning pretrained diffusion prior
with the distribution of rendered images from various text prompts. Current
state-of-the-arts such as Variational Score Distillation finetune the
pretrained diffusion model to minimize the noise prediction error so as to
align the distributions, which are however unstable to train and will impair
the model's comprehension capability to numerous text prompts. Based on the
observation that the diffusion models tend to have lower noise prediction
errors at earlier timesteps, we propose Asynchronous Score Distillation (ASD),
which minimizes the noise prediction error by shifting the diffusion timestep
to earlier ones. ASD is stable to train and can scale up to 100k prompts. It
reduces the noise prediction error without changing the weights of pre-trained
diffusion model, thus keeping its strong comprehension capability to prompts.
We conduct extensive experiments across different 2D diffusion models,
including Stable Diffusion and MVDream, and text-to-3D generators, including
Hyper-iNGP, 3DConv-Net and Triplane-Transformer. The results demonstrate ASD's
effectiveness in stable 3D generator training, high-quality 3D content
synthesis, and its superior prompt-consistency, especially under large prompt
corpus.
