threestudio - 将2D文本到图像生成模型提升为3D内容创作的统一框架

threestudio

threestudio is a unified framework for 3D content creation from text prompts, single images, and few-shot images, by lifting 2D text-to-image generation models.

threestudio

Did not find what you want? Checkout threestudio-extension or submit a feature request here!

threestudio

News

12/03/2024: Thank Matthew Kwak and Inès Hyeonsu Kim for implementation of 3DFuse! Follow the instructions on its website to give it a try.
08/03/2024: Thank Xinhua Cheng for implementation of GaussianDreamer! Follow the instructions on its website to give it a try.
01/03/2024: Thank Xinhua Cheng for implementation of Progressive3D! Follow the instructions on its website to give it a try.
09/01/2024: Thank Zehuan Huang for implementation of 3D human avatar generation Dreamwaltz! Follow the instructions on its website to give it a try.
06/01/2024: Thank Baorui Ma for implementation of GeoGream extensions! Follow the instructions on its website to give it a try.
05/01/2024: Implemented HiFA. Follow the instructions here to try all three variants.
23/12/2023: Thank Yuyang Zhao for implementation of image-to-4D generation extensions Animate-124! Follow the instructions on the extensions website to give it a try.
18/12/2023: Implementation of 4D-fy for 4D generation and DreamCraft3D for high-quality image-to-3D generation as the custom extensions! Follow the instructions on the extensions website to give it a try.
13/12/2023: Implementation supporting Stable Zero123 for 3D generation from a single image! Follow the instructions here to give it a try.
30/11/2023: Implementation of MVDream, Gaussian Splatting as the custom extensions. You can also use neural representation to fit a mesh by Mesh-Fitting.
30/11/2023: Implementation of custom extension system and you can add your extensions in this project.
25/06/2023: Implementation of Magic123! Follow the instructions here to give it a try.
06/07/2023: Join our Discord server for lively discussions!
03/07/2023: Try text-to-3D online in HuggingFace Spaces or using our self-hosted service (GPU support from Tencent). To host the web interface locally, see here.
20/06/2023: Implementations of Instruct-NeRF2NeRF and Control4D for high-fidelity 3D editing! Follow the instructions for Control4D and Instruct-NeRF2NeRF to give it a try.
14/06/2023: Implementation of TextMesh! Follow the instructions here to give it a try.
14/06/2023: Implementation of prompt debiasing and Perp-Neg! Follow the instructions here to give it a try.
29/05/2023: An experimental implementation of using Zero-1-to-3 for 3D generation from a single image! Follow the instructions here to give it a try.
26/05/2023: Implementation of ProlificDreamer! Follow the instructions here to give it a try.
14/05/2023: You can experiment with the SDS loss on 2D images using our 2dplayground.
13/05/2023: You can now try threestudio on Google Colab!
11/05/2023: We now support exporting textured meshes! See here for instructions.

export-blender

Installation

See installation.md for additional information, including installation via Docker.

The following steps have been tested on Ubuntu20.04.

You must have an NVIDIA graphics card with at least 6GB VRAM and have CUDA installed.
Install Python >= 3.8.
(Optional, Recommended) Create a virtual environment:

python3 -m virtualenv venv
. venv/bin/activate

# Newer pip versions, e.g. pip-23.x, can be much faster than old versions, e.g. pip-20.x.
# For instance, it caches the wheels of git packages to avoid unnecessarily rebuilding them later.
python3 -m pip install --upgrade pip

Install PyTorch >= 1.12. We have tested on torch1.12.1+cu113 and torch2.0.0+cu118, but other versions should also work fine.

# torch1.12.1+cu113
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
# or torch2.0.0+cu118
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

(Optional, Recommended) Install ninja to speed up the compilation of CUDA extensions:

pip install ninja

Install dependencies:

pip install -r requirements.txt

(Optional) tiny-cuda-nn installation might require downgrading pip to 23.0.1
(Optional, Recommended) The best-performing models in