  Generative 3D Painting is among the top productivity boosters in
high-resolution 3D asset management and recycling. Ever since text-to-image
models became accessible for inference on consumer hardware, the performance of
3D Painting methods has consistently improved and is currently close to
plateauing. At the core of most such models lies denoising diffusion in the
latent space, an inherently time-consuming iterative process. Multiple
techniques have been developed recently to accelerate generation and reduce
sampling iterations by orders of magnitude. Designed for 2D generative imaging,
these techniques do not come with recipes for lifting them into 3D. In this
paper, we address this shortcoming by proposing a Latent Consistency Model
(LCM) adaptation for the task at hand. We analyze the strengths and weaknesses
of the proposed model and evaluate it quantitatively and qualitatively. Based
on the Objaverse dataset samples study, our 3D painting method attains strong
preference in all evaluations. Source code is available at
https://github.com/kongdai123/consistency2.
