  Recently, the text-to-3D task has developed rapidly due to the appearance of
the SDS method. However, the SDS method always generates 3D objects with poor
quality due to the over-smooth issue. This issue is attributed to two factors:
1) the DDPM single-step inference produces poor guidance gradients; 2) the
randomness from the input noises and timesteps averages the details of the 3D
contents.In this paper, to address the issue, we propose DreamLCM which
incorporates the Latent Consistency Model (LCM). DreamLCM leverages the
powerful image generation capabilities inherent in LCM, enabling generating
consistent and high-quality guidance, i.e., predicted noises or images. Powered
by the improved guidance, the proposed method can provide accurate and detailed
gradients to optimize the target 3D models.In addition, we propose two
strategies to enhance the generation quality further. Firstly, we propose a
guidance calibration strategy, utilizing Euler Solver to calibrate the guidance
distribution to accelerate 3D models to converge. Secondly, we propose a dual
timestep strategy, increasing the consistency of guidance and optimizing 3D
models from geometry to appearance in DreamLCM. Experiments show that DreamLCM
achieves state-of-the-art results in both generation quality and training
efficiency. The code is available at https://github.com/1YimingZhong/DreamLCM.
