Score Distillation Sampling with Learned Manifold Corrective

Understand

Score Distillation Sampling (SDS) is a recent but already widely popular method that relies on an image diffusion model to control optimization problems using text prompts.

• In this paper, we conduct an in-depth analysis of the SDS loss function, identify an inherent problem with its formulation, and propose a surprisingly easy but effective fix.
• Specifically, we decompose the loss into different factors and isolate the component responsible for noisy gradients.
• In the original formulation, high text guidance is used to account for the noise, leading to unwanted side effects such as oversaturation or repeated detail.