Stable-Dreamfusion

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

The original paper's project page: DreamFusion: Text-to-3D using 2D Diffusion.

Examples generated from text prompt a DSLR photo of a pineapple viewed with the GUI in real time:

pineapple.mp4

Gallery | Update Logs

Important Notice

This project is a work-in-progress, and contains lots of differences from the paper. Also, many features are still not implemented now. The current generation quality cannot match the results from the original paper, and still fail badly for many prompts.

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. Currently, 15000 training steps take about 5 hours to train on a V100.
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 Adam optimizer with a larger initial learning rate.

TODOs

The normal evaluation & shading part.
Better mesh (improve the surface quality).

Install

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

Important: To download the Stable Diffusion model checkpoint, you should create a file called TOKEN under this directory (i.e., stable-dreamfusion/TOKEN) and copy your hugging face access token into it.

Install with pip

pip install -r requirements.txt

# (optional) install the tcnn backbone if using --tcnn
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

# (optional) install CLIP guidance for the dreamfield setting
pip install git+https://github.com/openai/CLIP.git

# (optional) install nvdiffrast for exporting textured mesh
pip install git+https://github.com/NVlabs/nvdiffrast/

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.)

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
# train with text prompt
# `-O` equals `--cuda_ray --fp16 --dir_text`
python main_nerf.py --text "a hamburger" --workspace trial -O

# test (exporting 360 video, and an obj mesh with png texture)
python main_nerf.py --text "a hamburger" --workspace trial -O --test

# test with a GUI (free view control!)
python main_nerf.py --text "a hamburger" --workspace trial -O --test --gui

### dreamfields (CLIP) setting
python main_nerf.py --text "a hamburger" --workspace trial_clip -O --guidance clip
python main_nerf.py --text "a hamburger" --workspace trial_clip -O --test --gui --guidance clip

Code organization

This is a simple description of the most important implementation details. If you are interested in improving this repo, this might be a starting point. Any contribution would be greatly appreciated!

The SDS loss is located at ./nerf/sd.py > StableDiffusion > train_step:

# 1. we need to interpolate the NeRF rendering to 512x512, to feed it to SD's VAE.
pred_rgb_512 = F.interpolate(pred_rgb, (512, 512), mode='bilinear', align_corners=False)
# 2. image (512x512) --- VAE --> latents (64x64), this is SD's difference from Imagen.
latents = self.encode_imgs(pred_rgb_512)
... # timestep sampling, noise adding and UNet noise predicting
# 3. the SDS loss, since UNet part is ignored and cannot simply audodiff, we manually set the grad for latents.
w = (1 - self.scheduler.alphas_cumprod[t]).to(self.device)
grad = w * (noise_pred - noise)
latents.backward(gradient=grad, retain_graph=True)

Other regularizations are in ./nerf/utils.py > Trainer > train_step.
NeRF Rendering core function: ./nerf/renderer.py > NeRFRenderer > run_cuda.

Acknowledgement

The amazing original work: DreamFusion: Text-to-3D using 2D Diffusion.
Huge thanks to the Stable Diffusion and the diffusers library.
The GUI is developed with DearPyGui.

sudheerbabuu / stable-dreamfusion