Shap-E vs TRELLIS
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Research code that generates 3D objects from a text prompt or an image.
Open source · MIT
Generates 3D assets from text or images, exportable as meshes, Gaussians, or radiance fields.
The short answer
Choose Shap-E if you need…
- 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
Choose TRELLIS if you need…
- Researchers exploring text- or image-to-3D generation
- Developers with an NVIDIA GPU who want to prototype 3D asset pipelines in Python
- Creators who need quick 3D starting points from a single image
- People who want to export one generated asset in several formats
Side-by-side
| Shap-E | TRELLIS | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 12.3k | 14.2k |
| Setup difficulty | Intermediate | Advanced |
| How you get it | Clone and build from source | Clone and build from source |
| Main language | Python | Python |
| Last code update | 2 years ago | 10 days ago |
What each one is
Shap-E
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.
TRELLIS
TRELLIS is a large 3D generation model from Microsoft Research. You give it a text prompt or an image, and it produces a 3D object in formats such as meshes, 3D Gaussians, or radiance fields. It is built on a shared 'structured latent' representation that can be decoded into different output formats, and it also supports local editing and generating variants of an object.
Best for
Shap-E
- 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
TRELLIS
- Researchers exploring text- or image-to-3D generation
- Developers with an NVIDIA GPU who want to prototype 3D asset pipelines in Python
- Creators who need quick 3D starting points from a single image
- People who want to export one generated asset in several formats
Strengths
Shap-E
- 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
TRELLIS
- Produces 3D output in several formats from one latent representation, per the README
- Pretrained models are published on Hugging Face and load by repository name
- Released under the MIT license with training code and a dataset toolkit
Not ideal for
Shap-E
- 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
TRELLIS
- Users without an NVIDIA GPU with at least 16GB of memory, since the README says this is necessary
- Windows users, because the code is tested only on Linux and Windows setup is not fully tested
- Anyone wanting a polished no-code tool, since the project is a research codebase run from the command line and Python
- Text-only workflows expecting top quality, since the README recommends generating images first for better results
Trade-offs
Shap-E
- 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
TRELLIS
- Requires a high-end NVIDIA GPU and tested only on Linux, per the README
- Text-conditioned models are described by the authors as less creative and detailed than image-conditioned ones
- Installation involves many dependencies and CUDA version considerations that the README warns may take a while
Getting started
Shap-E
Install the package from the cloned repository
pip install -e .TRELLIS
Clone the repository with submodules
git clone --recurse-submodules https://github.com/microsoft/TRELLIS.gitEnter the project folder
cd TRELLISFrequently asked questions
What is the difference between Shap-E and TRELLIS?
Shap-E: Research code that generates 3D objects from a text prompt or an image. TRELLIS: Generates 3D assets from text or images, exportable as meshes, Gaussians, or radiance fields. Shap-E suits researchers exploring text-conditioned 3d generation, while TRELLIS suits researchers exploring text- or image-to-3d generation.
Are Shap-E and TRELLIS free?
Shap-E: Free and open source. TRELLIS: Free and open source.
Which is easier to set up, Shap-E or TRELLIS?
Shap-E is easier to get started with (rated intermediate); TRELLIS is rated advanced and usually needs more setup.
Is Shap-E or TRELLIS more popular?
TRELLIS has more GitHub stars (14.2k) than Shap-E (12.3k). Popularity isn't the same as fit — check which one matches your use case above.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.