[[open-in-colab]]  
Shap-E is a conditional model for generating 3D assets which could be used for video game development, interior design, and architecture. It is trained on a large dataset of 3D assets, and post-processed to render more views of each object and produce 16K instead of 4K point clouds. The Shap-E model is trained in two steps:  
1. an encoder accepts the point clouds and rendered views of a 3D asset and outputs the parameters of implicit functions that represent the asset
2. a diffusion model is trained on the latents produced by the encoder to generate either neural radiance fields (NeRFs) or a textured 3D mesh, making it easier to render and use the 3D asset in downstream applications  
This guide will show you how to use Shap-E to start generating your own 3D assets!  
Before you begin, make sure you have the following libraries installed:  
```py
# uncomment to install the necessary libraries in Colab
#!pip install -q diffusers transformers accelerate trimesh
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/shap-e.md