openai / shap-e
Generate 3D objects conditioned on text or images
AI Architecture Analysis
This repository is indexed by RepoMind. By analyzing openai/shap-e in our AI interface, you can instantly generate complete architecture diagrams, visualize control flows, and perform automated security audits across the entire codebase.
Our Agentic Context Augmented Generation (Agentic CAG) engine loads full source files into context, avoiding the fragmentation of traditional RAG systems. Ask questions about the architecture, dependencies, or specific features to see it in action.
Repository Summary (README)
PreviewShap-E
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.
Samples
Here are some highlighted samples from our text-conditional model. For random samples on selected prompts, see samples.md.
<table> <tbody> <tr> <td align="center"> <img src="samples/a_chair_that_looks_like_an_avocado/2.gif" alt="A chair that looks like an avocado"> </td> <td align="center"> <img src="samples/an_airplane_that_looks_like_a_banana/3.gif" alt="An airplane that looks like a banana"> </td align="center"> <td align="center"> <img src="samples/a_spaceship/0.gif" alt="A spaceship"> </td> </tr> <tr> <td align="center">A chair that looks<br>like an avocado</td> <td align="center">An airplane that looks<br>like a banana</td> <td align="center">A spaceship</td> </tr> <tr> <td align="center"> <img src="samples/a_birthday_cupcake/3.gif" alt="A birthday cupcake"> </td> <td align="center"> <img src="samples/a_chair_that_looks_like_a_tree/2.gif" alt="A chair that looks like a tree"> </td> <td align="center"> <img src="samples/a_green_boot/3.gif" alt="A green boot"> </td> </tr> <tr> <td align="center">A birthday cupcake</td> <td align="center">A chair that looks<br>like a tree</td> <td align="center">A green boot</td> </tr> <tr> <td align="center"> <img src="samples/a_penguin/1.gif" alt="A penguin"> </td> <td align="center"> <img src="samples/ube_ice_cream_cone/3.gif" alt="Ube ice cream cone"> </td> <td align="center"> <img src="samples/a_bowl_of_vegetables/2.gif" alt="A bowl of vegetables"> </td> </tr> <tr> <td align="center">A penguin</td> <td align="center">Ube ice cream cone</td> <td align="center">A bowl of vegetables</td> </tr> </tbody> <table>Usage
Install with 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_PATHto the path of the Blender executable.