---
language:
- en
license: other
license_name: autodesk-non-commercial-3d-generative-v1.0
tags:
- wala
- sketch-to-3d
---

# Model Card for WaLa-SK-1B

This model is part of the Wavelet Latent Diffusion (WaLa) paper, capable of generating high-quality 3D shapes from sketch inputs with detailed geometry and complex structures.

## Model Details

### Model Description

WaLa-SK-1B is a large-scale 3D generative model trained on a massive dataset of over 10 million publicly-available 3D shapes. It can efficiently generate a wide range of high-quality 3D shapes from sketch inputs in just 2-4 seconds. The model uses a wavelet-based compact latent encoding and a billion-parameter architecture to achieve superior performance in terms of geometric detail and structural plausibility.

- **Developed by:** Aditya Sanghi, Aliasghar Khani, Chinthala Pradyumna Reddy, Arianna Rampini, Derek Cheung, Kamal Rahimi Malekshan, Kanika Madan, Hooman Shayani
- **Model type:** 3D Generative Model
- **License:** Autodesk Non-Commercial (3D Generative) v1.0

For more information please look at the [Project Page](https://autodeskailab.github.io/WaLaProject) and [the paper](TBD).

### Model Sources

- **Project Page:** [WaLa](https://autodeskailab.github.io/WaLaProject)
- **Repository:** [Github](https://github.com/AutodeskAILab/WaLa)
- **Paper:** [ArXiv](https://arxiv.org/abs/2411.08017)
- **Demo:** [Colab](https://colab.research.google.com/drive/1W5zPXw9xWNpLTlU5rnq7g3jtIA2BX6aC?usp=sharing)

## Uses

### Direct Use

This model is released by Autodesk and intended for academic and research purposes only for the theoretical exploration and demonstration of the WaLa 3D generative framework. Please see [here](https://github.com/AutodeskAILab/WaLa?tab=readme-ov-file#getting-started) for inferencing instructions. 

### Out-of-Scope Use

The model should not be used for:

- Commercial purposes 

- Creation of load-bearing physical objects the failure of which could cause property damage or personal injury 

- Any usage not in compliance with the [license](https://huggingface.co/ADSKAILab/WaLa-sketch-1B/blob/main/LICENSE.md), in particular, the "Acceptable Use" section. 

## Bias, Risks, and Limitations

### Bias

- The model may inherit biases present in the publicly-available training datasets, which could lead to uneven representation of certain object types or styles. 

- The model's performance may degrade for object categories or styles that are underrepresented in the training data. 

### Risks and Limitations

- The quality of the generated 3D output may be impacted by the quality and clarity of the input.
- The model may occasionally generate implausible shapes, especially when the input is ambiguous or of low quality. Even theoretically plausible shapes should not be relied upon for real-world structural soundness.

## How to Get Started with the Model

Please refer to the instructions [here](https://github.com/AutodeskAILab/WaLa?tab=readme-ov-file#getting-started)

## Training Details

### Training Data

The model was initially trained on the same dataset as the single-view model, consisting of over 10 million 3D shapes from 19 different publicly-available sub-datasets. It was then fine-tuned using synthetic sketch data generated using 6 different techniques.

### Training Procedure

#### Preprocessing

Each 3D shape in the dataset was converted into a truncated signed distance function (TSDF) with a resolution of 256³. The TSDF was then decomposed using a discrete wavelet transform to create the wavelet-tree representation used by the model. Sketches were generated using various techniques including Grease Pencil, Canny edge detection, HED, and CLIPasso.

#### Training Hyperparameters

- **Training regime:** Please refer to the paper.

#### Speeds, Sizes, Times

- The model contains approximately 956 million parameters.
- The model can generate shapes within 2-4 seconds.

## Technical Specifications

### Model Architecture and Objective

The model uses a U-ViT architecture with modifications. It employs a wavelet-based compact latent encoding to effectively capture both coarse and fine details of 3D shapes from sketch inputs.

### Compute Infrastructure

#### Hardware

The model was trained on NVIDIA H100 GPUs.

## Citation

[Citation information to be added after paper publication]