Important

You are viewing the NeMo 2.0 documentation. This release introduces significant changes to the API and a new library, NeMo Run. We are currently porting all features from NeMo 1.0 to 2.0. For documentation on previous versions or features not yet available in 2.0, please refer to the NeMo 24.07 documentation.

Training with Predefined Configurations

DreamFusion trains a Neural Radiance Field (NeRF) using a 2D diffusion model as a prior. The recommended configuration can be found in the conf/training/nerf/dreamfusion directory. You can access and modify the parameters to customize the hyperparameters according to your specific training requirements.

DreamFusion-DMTet

DreamFusion-DMTet fine tunes a DreamFusion checkpoint using a DMTet model. The recommended configuration can be found in the conf/training/nerf/dreamfusion-dmtet directory. You can access and modify the parameters to customize the hyperparameters according to your specific training requirements. You will need to provide a pretrained model using DreamFusion in the model.resume_from_checkpoint parameter, and the initial tetrahedral grid using the model.renderer.quartet_fileresume_from_checkpoint parameter.