Stable-Dreamfusion

A pytorch implementation of the text-to-3D model Dreamfusion, powered by the Stable Diffusion text-to-2D model.

NEWS (2023.4.17): Experimental Image-to-3D generation support!

image-to-3d.mp4

text-to-3d.mp4

Update Logs

Colab notebooks:

• Instant-NGP backbone (-O):

• Vanilla NeRF backbone (-O2):

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 to replace it (implementation from 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 to implement the NeRF backbone (implementation from torch-ngp), which enables much faster rendering (~10FPS at 800x800).
• We use the Adan optimizer as default.

Install

git clone https://github.com/ashawkey/stable-dreamfusion.git
cd stable-dreamfusion

To use image-conditioned 3D generation, you need to download some pretrained checkpoints manually:

• Zero-1-to-3 for diffusion backend. We use 105000.ckpt by default, and it is hard-coded in guidance/zero123_utils.py.

  cd pretrained/zero123
  wget https://huggingface.co/cvlab/zero123-weights/resolve/main/105000.ckpt

• Omnidata for depth and normal prediction. These ckpts are hardcoded in preprocess_image.py.

  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

Install with pip

pip install -r requirements.txt

Build extension (optional)

By default, we use load to build the extension at runtime. We also provide the setup.py to build each extension:

# install all extension modules
bash scripts/install_ext.sh

# if you want to install manually, here is an example:
pip install ./raymarching # install to python path (you still need the raymarching/ folder, since this only installs the built extension.)

Taichi backend (optional)

Use Taichi backend for Instant-NGP. It achieves comparable performance to CUDA implementation while No CUDA build is required. Install Taichi with pip:

pip install -i https://pypi.taichi.graphics/simple/ taichi-nightly

Trouble Shooting:

• we assume working with the latest version of all dependencies, if you meet any problems from a specific dependency, please try to upgrade it first (e.g., pip install -U diffusers). If the problem still holds, reporting a bug issue will be appreciated!
• [F glutil.cpp:338] eglInitialize() failed Aborted (core dumped): this usually indicates problems in OpenGL installation. Try to re-install Nvidia driver, or use nvidia-docker as suggested in ashawkey#131 if you are using a headless server.
• TypeError: xxx_forward(): incompatible function arguments： this happens when we update the CUDA source and you used setup.py to install the extensions earlier. Try to re-install the corresponding extension (e.g., pip install ./gridencoder).

Tested environments

• Ubuntu 22 with torch 1.12 & CUDA 11.6 on a V100.

Usage

First time running will take some time to compile the CUDA extensions.

#### stable-dreamfusion setting

### Instant-NGP NeRF Backbone
# + faster rendering speed
# + less GPU memory (~16G)
# - need to build CUDA extensions (a CUDA-free Taichi backend is available)

## train with text prompt (with the default settings)
# `-O` equals `--cuda_ray --fp16`
# `--cuda_ray` enables instant-ngp-like occupancy grid based acceleration.
python main.py --text "a hamburger" --workspace trial -O

# reduce stable-diffusion memory usage with `--vram_O` 
# enable various vram savings (https://huggingface.co/docs/diffusers/optimization/fp16).
python main.py --text "a hamburger" --workspace trial -O --vram_O
# this makes it possible to train with larger rendering resolution, which leads to better quality (see https://github.com/ashawkey/stable-dreamfusion/pull/174)
python main.py --text "a hamburger" --workspace trial -O --vram_O --w 300 --h 300 # Tested to run fine on 8GB VRAM (Nvidia 3070 Ti).

# use CUDA-free Taichi backend with `--backbone grid_taichi`
python3 main.py --text "a hamburger" --workspace trial -O --backbone grid_taichi

# choose stable-diffusion version (support 1.5, 2.0 and 2.1, default is 2.1 now)
python main.py --text "a hamburger" --workspace trial -O --sd_version 1.5

# we also support negative text prompt now:
python main.py --text "a rose" --negative "red" --workspace trial -O

# A Gradio GUI is also possible (with less options):
python gradio_app.py # open in web browser

## after the training is finished:
# test (exporting 360 degree video)
python main.py --workspace trial -O --test
# also save a mesh (with obj, mtl, and png texture)
python main.py --workspace trial -O --test --save_mesh
# test with a GUI (free view control!)
python main.py --workspace trial -O --test --gui

### Vanilla NeRF backbone
# + pure pytorch, no need to build extensions!
# - slow rendering speed
# - more GPU memory

## train
# `-O2` equals `--backbone vanilla`
python main.py --text "a hotdog" --workspace trial2 -O2

# if CUDA OOM, try to reduce NeRF sampling steps (--num_steps and --upsample_steps)
python main.py --text "a hotdog" --workspace trial2 -O2 --num_steps 64 --upsample_steps 0

## test
python main.py --workspace trial2 -O2 --test
python main.py --workspace trial2 -O2 --test --save_mesh
python main.py --workspace trial2 -O2 --test --gui # not recommended, FPS will be low.

### DMTet finetuning

## use --dmtet and --init_ckpt <nerf checkpoint> to finetune the mesh at higher reslution
python main.py -O --text "a hamburger" --workspace trial_dmtet --dmtet --iters 5000 --init_ckpt trial/checkpoints/df.pth

## test & export the mesh
python main.py -O --text "a hamburger" --workspace trial_dmtet --dmtet --iters 5000 --test --save_mesh

## gui to visualize dmtet
python main.py -O --text "a hamburger" --workspace trial_dmtet --dmtet --iters 5000 --test --gui

### Image-conditioned 3D Generation

## preprocess input image
# note: the results of image-to-3D is dependent on zero-1-to-3's capability. For best performance, the input image should contain a single front-facing object. Check the examples under ./data.
# this will exports `<image>_rgba.png`, `<image>_depth.png`, and `<image>_normal.png` to the directory containing the input image.
python preprocess_image.py <image>.png 

## train
# pass in the processed <image>_rgba.png by --image and do NOT pass in --text to enable zero-1-to-3 backend.
python main.py -O --image <image>_rgba.png --workspace trial_image --iters 5000
# dmtet finetuning (highly recommended)
python main.py -O --image <image>_rgba.png --workspace trial_image_dmtet --dmtet --init_ckpt trial_image/checkpoints/df.pth

# experimental: providing both --text and --image enables stable-diffusion backend, but the result may look very different from the provided image. This is still an option if image-only mode cannot produce a satisfactory result.
python main.py -O --image hamburger_rgba.png --text "a DSLR photo of a delicious hamburger" --workspace trial_image_text

## test / visualize
python main.py -O --image <image>_rgba.png --workspace trial_image_dmtet --dmtet --test --save_mesh
python main.py -O --image <image>_rgba.png --workspace trial_image_dmtet --dmtet --test --gui

For advanced tips and other developing stuff, check Advanced Tips.

Acknowledgement

This work is based on an increasing list of amazing research works and open-source projects, thanks a lot to all the authors for sharing!

• DreamFusion: Text-to-3D using 2D Diffusion

  @article{poole2022dreamfusion,
      author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
      title = {DreamFusion: Text-to-3D using 2D Diffusion},
      journal = {arXiv},
      year = {2022},
  }
  

• Magic3D: High-Resolution Text-to-3D Content Creation

  @inproceedings{lin2023magic3d,
     title={Magic3D: High-Resolution Text-to-3D Content Creation},
     author={Lin, Chen-Hsuan and Gao, Jun and Tang, Luming and Takikawa, Towaki and Zeng, Xiaohui and Huang, Xun and Kreis, Karsten and Fidler, Sanja and Liu, Ming-Yu and Lin, Tsung-Yi},
     booktitle={IEEE Conference on Computer Vision and Pattern Recognition ({CVPR})},
     year={2023}
   }
  

• Zero-1-to-3: Zero-shot One Image to 3D Object

  @misc{liu2023zero1to3,
      title={Zero-1-to-3: Zero-shot One Image to 3D Object}, 
      author={Ruoshi Liu and Rundi Wu and Basile Van Hoorick and Pavel Tokmakov and Sergey Zakharov and Carl Vondrick},
      year={2023},
      eprint={2303.11328},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
  }
  

• RealFusion: 360° Reconstruction of Any Object from a Single Image

  @inproceedings{melaskyriazi2023realfusion,
      author = {Melas-Kyriazi, Luke and Rupprecht, Christian and Laina, Iro and Vedaldi, Andrea},
      title = {RealFusion: 360 Reconstruction of Any Object from a Single Image},
      booktitle={CVPR}
      year = {2023},
      url = {https://arxiv.org/abs/2302.10663},
  }
  

• Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation

  @article{chen2023fantasia3d,
      title={Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation},
      author={Rui Chen and Yongwei Chen and Ningxin Jiao and Kui Jia},
      journal={arXiv preprint arXiv:2303.13873},
      year={2023}
  }
  

• Stable Diffusion and the diffusers library.

  @misc{rombach2021highresolution,
      title={High-Resolution Image Synthesis with Latent Diffusion Models},
      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
      year={2021},
      eprint={2112.10752},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
  }
  
  @misc{von-platen-etal-2022-diffusers,
      author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
      title = {Diffusers: State-of-the-art diffusion models},
      year = {2022},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/huggingface/diffusers}}
  }
  

• The GUI is developed with DearPyGui.

Citation

If you find this work useful, a citation will be appreciated via:

@misc{stable-dreamfusion,
    Author = {Jiaxiang Tang},
    Year = {2022},
    Note = {https://github.com/ashawkey/stable-dreamfusion},
    Title = {Stable-dreamfusion: Text-to-3D with Stable-diffusion}
}