Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion.
| Date | Stars |
|---|---|
| 2026-07-24 | 8851 |
| 2026-07-25 | 8850 |
| 2026-07-28 | 8850 |
| 2026-07-30 | 8850 |
| 2026-08-06 | 8850 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Stable-Dreamfusion
A pytorch implementation of the text-to-3D model **Dreamfusion**, powered by the [Stable Diffusion](https://github.com/CompVis/stable-diffusion) text-to-2D model.
**ADVERTISEMENT: Please check out [threestudio](https://github.com/threestudio-project/threestudio) for recent improvements and better implementation in 3D content generation!**
**NEWS (2023.6.12)**:
* Support of [Perp-Neg](https://perp-neg.github.io/) to alleviate multi-head problem in Text-to-3D.
* Support of Perp-Neg for both [Stable Diffusion](https://github.com/CompVis/stable-diffusion) and [DeepFloyd-IF](https://github.com/deep-floyd/IF).
https://user-images.githubusercontent.com/25863658/236712982-9f93bd32-83bf-423a-bb7c-f73df7ece2e3.mp4
https://user-images.githubusercontent.com/25863658/232403162-51b69000-a242-4b8c-9cd9-4242b09863fa.mp4
### [Update Logs](assets/update_logs.md)
### Colab notebooks:
* Instant-NGP backbone (`-O`): [](https://colab.research.google.com/drive/1MXT3yfOFvO0ooKEfiUUvTKwUkrrlCHpF?usp=sharing)
* Vanilla NeRF backbone (`-O2`): [](https://colab.research.google.com/drive/1mvfxG-S_n_gZafWoattku7rLJ2kPoImL?usp=sharing)
# Important Notice
This project is a **work-in-progress**, and contains lots of differences from the paper. **The current generation quality cannot match the results from the original paper, and many prompts still fail badly!**
## Notable differences from the paper
* Since the Imagen model is not publicly available, we use [Stable Diffusion](https://github.com/CompVis/stable-diffusion) to replace it (implementation from [diffusers](https://github.com/huggingface/diffusers)). Different from Imagen, Stable-Diffusion is a latent diffusion model, which diffuses in a latent space instead of the original image space. Therefore, we need the loss to propagate back from the VAE's encoder part too, which introduces extra time cost in training.
* We use the [multi-resolution grid encoder](https://github.com/NVlabs/instant-ngp/) to implement the NeRF backbone (implementation from [torch-ngp](https://github.com/ashawkey/torch-ngp)), which enables much faster rendering (~10FPS at 800x800).
* We use the [Adan](https://github.com/sail-sg/Adan) optimizer as default.
# Install
```bash
git clone https://github.com/ashawkey/stable-dreamfusion.git
cd stable-dreamfusion
```
### Optional: create a python virtual environment
To avoid python package conflicts, we recommend using a virtual environment, e.g.: using conda or venv:
```bash
python -m venv venv_stable-dreamfusion
source venv_stable-dreamfusion/bin/activate # you need to repeat this step for every new terminal
```
### Install with pip
```bash
pip install -r requirements.txt
```
### Download pre-trained models
To use image-conditioned 3D generation, you need to download some pretrained checkpoints manually:
* [Zero-1-to-3](https://github.com/cvlab-columbia/zero123) for diffusion backend.
We use `zero123-xl.ckpt` by default, and it is hard-coded in `guidance/zero123_utils.py`.
```bash
cd pretrained/zero123
wget https://zero123.cs.columbia.edu/assets/zero123-xl.ckpt
```
* [Omnidata](https://github.com/EPFL-VILAB/omnidata/tree/main/omnidata_tools/torch) for depth and normal prediction.
These ckpts are hardcoded in `preprocess_image.py`.
```bash
mkdir pretrained/omnidata
cd pretrained/omnidata
# assume gdown is installed
gdown '1Jrh-bRnJEjyMCS7f-WsaFlccfPjJPPHI&confirm=t' # omnidata_dpt_depth_v2.ckpt
gdown '1wNxVO4vVbDEMEpnAi_jwQObf2MFodcBR&confirm=t' # omnidata_dpt_normal_v2.ckpt
```
To use [DeepFloyd-IF](https://github.com/deep-floyd/IF), you need to accept the usage conditions from [hugging face](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0), and login with `huggingface-cli login` in command line.
For DMTet, we port the pre-generated `32/64/128Excerpt of 17,611 characters
Read on GitHub
kiui · NVIDIA · China
112
Christian Laforte · NVIDIA · Canada
15
9
Mingrui Zhang · Imperial College London · United Kingdom
9
8
Vikram Voleti · @mila-udem · Canada
7
3
Reza Armandpour · Apple · United States
2
Ikko Eltociear Ashimine · Japan
1
1
1
1
Mathieu Tuli · Some Company
1
1
1
Elliott Zheng
1
Denis · United States
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6c0aca099be6ae93, topic:text-to-3d, desc:text-to-3d, readme:text-to-3d
matched fp:6c0aca099be6ae93, topic:nerf
matched fp:6c0aca099be6ae93, topic:stable-diffusion, readme:diffusion model, readme:stable diffusion