Introduction to the Normal-Depth Diffusion Model
The Normal-Depth Diffusion Model is a cutting-edge technology in the realm of computer vision, designed to aid in the generation of 3D models from textual descriptions. This generalizable model focuses on creating detailed representations by leveraging normal and depth data from images. It offers advancements in how computers interpret and recreate three-dimensional spaces based on inputs from simple text prompts.
Overview
The Normal-Depth Diffusion Model presents a suite of tools and models for generating 3D reconstructions from text inputs. This includes ready-to-use inference and training codes accompanied by pretrained models. The project features multiple models, such as ND (Normal-Depth), ND-MV (MultiView Normal-Depth), and Albedo-MV, each designed to tackle different aspects of visual data processing. Currently, the ND-MV-VAE model is still in development.
Key Features
- Inference and Training: The repository provides both inference code for sampling outputs and training code for users interested in fine-tuning or developing new models.
- Pretrained Models: Users can access multiple pretrained models for various applications. These include the Normal-Depth model trained on a large dataset like Laion-2B, and MultiView adaptations for more complex scenarios.
- Support for 3D Generation: While the primary focus of this repository is on diffusion models and 2D to 3D transformations, additional resources for robust 3D generation are available through the associated RichDreamer project.
Recent Updates
The project is continually being enhanced with new datasets and script optimizations. For instance, a training dataset was recently released on December 25, 2023, alongside tools for efficient data download.
Getting Started
To begin using the Normal-Depth Diffusion Model, users need to prepare their computing environment by installing the necessary dependencies. A provided Dockerfile facilitates setup for environments using Docker, ensuring consistency and ease of deployment. After setting up the environment, users can download various pretrained weights required for the initial model operations.
Performing Inference
For inference, the project includes specialized scripts that allow users to generate images from text prompts via several solvers—DMP, PLMS, and DDIM for Regular and MultiView Outputs. The models can extrapolate text inputs into detailed normal and depth maps, which can then be visualized or further analyzed.
Training New Models
Training involves several steps:
- Setting up datasets by downloading the necessary sources such as the Laion-2B dataset.
- Acquiring weights for Monocular Prior models.
- Utilizing rendered images from datasets like the Objaverse-dataset.
Scripts are available to assist users through these processes, allowing the training of Normal-Depth VAE Models and the refinement of multi-view models both with and without VAE denoising.
Acknowledgments
The Normal-Depth Diffusion Model has been developed with substantial support and code borrowed from established projects such as Stable Diffusion and MVDream, demonstrating collaborative advancements in AI and machine learning fields.
This comprehensive setup makes the Normal-Depth Diffusion Model a robust platform for exploring the convergence of text-driven instructions and 3D model creation, fostering advancements in how machines understand and generate complex visual data.