This is the official code and model release for Shap-E: Generating Conditional 3D Implicit Functions.

• See Usage for guidance on how to use this repository.
• See Samples for examples of what our text-conditional model can generate.

Here are some highlighted samples from our text-conditional model. For random samples on selected prompts, see samples.md.

A chair that looks like an avocado	An airplane that looks like a banana	A spaceship
A birthday cupcake	A chair that looks like a tree	A green boot
A penguin	Ube ice cream cone	A bowl of vegetables

Clone the repository

git clone https://github.com/openai/shap-e.git cd shap-e

Install with pip

pip install -e .

To get started with examples, see the following notebooks:

• sample_text_to_3d.ipynb - sample a 3D model, conditioned on a text prompt.
• sample_image_to_3d.ipynb - sample a 3D model, conditioned on a synthetic view image. To get the best result, you should remove background from the input image.
• encode_model.ipynb - loads a 3D model or a trimesh, creates a batch of multiview renders and a point cloud, encodes them into a latent, and renders it back. For this to work, install Blender version 3.3.1 or higher, and set the environment variable BLENDER_PATH to the path of the Blender executable.