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Shap-E

Research code that generates 3D objects from a text prompt or an image.

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Forks
1.1k
Licence
MIT
Language
Python
Last update
2.4 years ago
Type
AI model / agent

No commits in over a year — check for forks or alternatives before adopting.

What it is

Shap-E is OpenAI's official research release for generating 3D objects from a text description or from an image. It provides model code and examples in Jupyter notebooks, based on the paper linked in the README. People use it to explore text-to-3D generation and to see what such models can produce.

  • Generate a 3D object from a text prompt using the text-to-3D notebook
  • Generate a 3D object from an image using the image-to-3D notebook, after removing the background
  • Encode an existing 3D model or trimesh into a latent and render it back using the encode notebook

Great for

  • Researchers exploring text-conditioned 3D generation
  • Developers who want to try generating 3D models from prompts in Python
  • Students learning how generative 3D models work
  • Designers curious about quick 3D concept sketches

Not ideal for

  • People who need a polished, ready-made 3D tool; the README describes research code and notebooks, not an end-user app
  • Anyone without a Python setup, since installation requires pip and the examples are notebooks
  • Projects needing production-grade, print-ready meshes; the README shows sample outputs but does not claim production quality
Step-by-step guide: how to use Shap-EInstall, first run, examples, tips and FAQs
Shap-E vs TRELLIS

How to get it

  1. 1

    Clone and build from source

    You'll need: Python · pip · Blender 3.3.1 or higher (only for the encode notebook)

    Install the package from the cloned repository

    pip install -e .
  2. 2

    Run it

    • Clone the repository and install it with pip install -e .
    • Open shap_e/examples/sample_text_to_3d.ipynb in Jupyter
    • Run the notebook cells with a text prompt to sample a 3D model
    • Try sample_image_to_3d.ipynb with a background-removed image for image input
  3. 3

    Get the most out of it

    • Use the image notebook with a background-removed input for better results, as the README recommends
    • Check samples.md to see random outputs on selected prompts before writing your own
    • Install Blender 3.3.1 or higher and set BLENDER_PATH to use the encode_model notebook

Downloads

Source code (.zip)main branch · straight from GitHub

Strengths

  • Official code and model release from OpenAI, tied to a published paper
  • Supports both text and image conditioning, per the README
  • Includes runnable example notebooks for each workflow

Trade-offs

  • The README is short and gives little detail on hardware, model sizes, or run time
  • The encode notebook needs Blender 3.3.1 or higher installed separately
  • Image input works best with a background removed, so extra preprocessing may be needed

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.