This is the official codebase for the paper "WAVELET LATENT DIFFUSION (WALA): BILLION- PARAMETER 3D GENERATIVE MODEL WITH COM-PACT WAVELET ENCODINGS"
- Single-view to 3D inference code
- Sketch to 3D inference code
- Multi-view to 3D inference code
- Multi-view-depth 4 to 3D inference code
- Multi-view-depth 6 to 3D inference code
- 16³ resolution Voxel to 3D inference code
- Point cloud to 3D inference code
- Text to Multi-view infrence code and model weights
- Text to Multi-depthmap infrence code and model weights
- Google Colab demo
- Unconditional 3D generation inference code
- 1.4B models
- Python >= 3.10
- Install CUDA if available
- Install PyTorch according to your platform: https://pytorch.org/get-started/locally/
- Install other dependencies by
pip install -r requirements.txt
For example, on AWS EC2 instances with PyTorch Deep learning AMI, you can setup the environment as follows:
conda create -n wala python==3.10
conda activate wala
pip install -r requirements.txt
For multiple generation use different --seed parameter
Based on our extensive grid search on the validation set across different conditions, the optimal parameters are shown below.
| Model | Scale | Timestep |
|---|---|---|
| Voxel | 1.5 | 5 |
| Pointcloud | 1.3 | 8 |
| Single-View RGB | 1.8 | 5 |
| Sketch | 1.8 | 5 |
| Single-View Depth | 1.8 | 5 |
| Multi-View RGB | 1.3 | 5 |
| Multi-View Depth | 1.3 | 5 |
| 6 Multi-View Depth | 1.5 | 10 |
| Text to 3d | 1.5 | 10 |
| Unconditional | - | 1000 |
Table: Classifier-free scale and timestep used in the paper
The input data for this method is a single-view image of a 3D object.
python run.py --model_name ADSKAILab/WaLa-SV-1B --images examples/single_view/table.png --output_dir examples --output_format obj --scale 1.8 --diffusion_rescale_timestep 5
The model uses sketch input with detailed geometry and complex structures to generate 3D object.
python run.py --model_name ADSKAILab/WaLa-SK-1B --sketch examples/sketch/tree.png --output_dir examples --output_format obj --scale 1.8 --diffusion_rescale_timestep 5
This model uses a voxelized representation of the object with a resolution of 16³. The voxel file is a JSON containing the following keys: resolution, occupancy, and color
python run.py --model_name ADSKAILab/WaLa-VX16-1B --voxel_files examples/voxel/horse_16.json --output_dir examples --output_format obj --scale 1.5 --diffusion_rescale_timestep 5
The input data for this method is a pointcloud of a 3D object.
python run.py --model_name ADSKAILab/WaLa-PC-1B --pointcloud examples/pointcloud/ring.h5df --output_dir examples --output_format obj --scale 1.3 --diffusion_rescale_timestep 8
The model uses single depth-map image to generate 3D object.
python run.py --model_name ADSKAILab/WaLa-DM1-1B --dm1 examples/single_depth_map/49.png --output_dir examples --output_format obj --scale 1.5 --diffusion_rescale_timestep 5
| "An owl" | "A monkey" | "An octopus" |
|---|---|---|
 |
 |
 |
python run.py --model_name ADSKAILab/WaLa-MVDream-DM6 --text_to_dm6 "generate me a cup" --output_dir examples/text/
mv examples/text/depth_maps/image_0.png examples/text/depth_maps/3.png; \
mv examples/text/depth_maps/image_1.png examples/text/depth_maps/6.png; \
mv examples/text/depth_maps/image_2.png examples/text/depth_maps/10.png; \
mv examples/text/depth_maps/image_3.png examples/text/depth_maps/26.png; \
mv examples/text/depth_maps/image_4.png examples/text/depth_maps/49.png; \
mv examples/text/depth_maps/image_5.png examples/text/depth_maps/50.png
python run.py --model_name ADSKAILab/WaLa-DM6-1B --dm6 examples/text/depth_maps/3.png examples/text/depth_maps/6.png examples/text/depth_maps/10.png examples/text/depth_maps/26.png examples/text/depth_maps/49.png examples/text/depth_maps/50.png --output_dir examples/text/ --output_format obj --output_format obj --scale 1.5 --diffusion_rescale_timestep 10
python run.py --model_name ADSKAILab/WaLa-MVDream-DM6 --text_to_dm6 "generate me a cup" --output_dir examples
python run.py --model_name ADSKAILab/WaLa-MVDream-RGB4 --text_to_mv "generate me a cup" --output_dir examples
For multi-view input, the model utilizes multiple images of the same object captured from different camera angles. These images should be named according to the index of the camera view parameters as described in Data Formats
python run.py --model_name ADSKAILab/WaLa-RGB4-1B --multi_view_images examples/multi_view/003.png examples/multi_view/006.png examples/multi_view/010.png examples/multi_view/026.png --output_dir examples --output_format obj --scale 1.3 --diffusion_rescale_timestep 5
For depth-maps input, the model utilizes 4 depth-map images of the same object captured from different camera angles to create 3D object.
python run.py --model_name ADSKAILab/WaLa-DM4-1B --dm4 examples/depth_maps_4/3.png examples/depth_maps_4/6.png examples/depth_maps_4/10.png examples/depth_maps_4/26.png --output_dir examples --output_format obj --scale 1.3 --diffusion_rescale_timestep 5
For depth-maps input, the model utilizes 6 depth-map images of the same object captured from different camera angles to create 3D object.
python run.py --model_name ADSKAILab/WaLa-DM6-1B --dm6 examples/depth_maps_6/3.png examples/depth_maps_6/6.png examples/depth_maps_6/10.png examples/depth_maps_6/26.png examples/depth_maps_6/49.png examples/depth_maps_6/50.png --output_dir examples --output_format obj --scale 1.5 --diffusion_rescale_timestep 10
-
Single-View Input: A single image file (e.g.,
.png,.jpg) depicting the 3D object. -
Multi-View Input: A set of image files taken from different camera angles. The filenames correspond to specific camera parameters. Below is a table that maps the index of each image to its corresponding camera rotation and elevation:
Index Rotation (degrees) Elevation (degrees) 0 57.37 13.48 1 36.86 6.18 2 11.25 21.62 3 57.27 25.34 4 100.07 9.10 5 116.91 21.32 6 140.94 12.92 7 99.88 3.57 8 5.06 11.38 9 217.88 6.72 10 230.76 13.27 11 180.99 23.99 12 100.59 -6.37 13 65.29 -2.70 14 145.70 6.61 15 271.98 0.15 16 284.36 5.84 17 220.28 0.07 18 145.86 -1.18 19 59.08 -13.59 20 7.35 0.51 21 7.06 -7.82 22 146.05 -15.43 23 182.55 -5.17 24 341.95 3.29 25 353.64 9.75 26 319.81 16.44 27 233.76 -8.56 28 334.96 -2.65 29 207.67 -16.79 30 79.72 -21.20 31 169.69 -26.77 32 237.16 -27.06 33 231.72 25.91 34 284.84 23.44 35 311.22 -14.09 36 285.15 -7.42 37 257.11 -14.38 38 319.14 -23.75 39 355.62 -9.06 40 0.00 60.00 41 40.00 60.00 42 80.00 60.00 43 120.00 60.00 44 160.00 60.00 45 200.00 60.00 46 240.00 60.00 47 280.00 60.00 48 320.00 60.00 49 360.00 60.00 50 0.00 -60.00 51 90.00 -60.00 52 180.00 -60.00 53 270.00 -60.00 54 360.00 -60.00 -
Voxel Input: A JSON file containing a voxelized representation of the object. The JSON includes:
- resolution: The grid size of the voxel space (e.g., 16 or 32).
- occupancy: The indices of occupied voxels.
- color: The RGB values for each occupied voxel.
To quickly try out the WaLa models without setting up your local environment, check out the Google Colab Demo.
@misc{sanghi2024waveletlatentdiffusionwala,
title={Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model with Compact Wavelet Encodings},
author={Aditya Sanghi and Aliasghar Khani and Pradyumna Reddy and Arianna Rampini and Derek Cheung and Kamal Rahimi Malekshan and Kanika Madan and Hooman Shayani},
year={2024},
eprint={2411.08017},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2411.08017}
}
