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model · Documentation reviewed · 5 October 2026

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Shap-E

Studying early text/image-to-3D generation and running local research experiments

Availability
legacy
Where it runs
local
Costs
No fee for the MIT repository code; budget for compute, environment setup and mesh cleanup. If you use third-party hosting, its prices and terms apply. Hosted access and billing are unverified.
Adult content policy
conditional

Shap-E is useful for learning how a text prompt or a single object image becomes a 3D asset. Its 2023 research release generates an implicit representation that can be rendered from different angles or exported as a mesh; the text example writes PLY and OBJ files. The catch is what comes after export: OpenAI describes rough edges, holes and blurry textures, with a tendency toward simple, cartoonish assets. Complex prompts and hidden surfaces are further weak spots. Use it as an educational baseline or to experiment with simple props. A spinning preview does not supply clean topology, UV maps or a rig, and OpenAI's model card advises against commercial use of this release.

Commercial use & license

Repository code is MIT, permitting commercial use and redistribution with copyright and permission notices retained. Confirm the terms for the exact checkpoints; the code license does not clear input or third-party asset rights.

Adult content rules

The cited MIT code license has no adult-content clause, but it does not clear rights to reference images, likenesses or third-party assets. Shap-E's model card advises against commercial use; the CLIP dependency's card lists any deployed use as out of scope. These intended-use cautions sit alongside the permissive code license. Check exact checkpoint terms and any hosting provider's policies.

Read the policy ↗

What you need

Python/PyTorch with the repository's CLIP dependency and released checkpoints/configs; the notebooks select CUDA if available, otherwise CPU. Encoding an existing 3D asset requires Blender 3.3.1+ and BLENDER_PATH. Plan for mesh inspection and cleanup after export.

Ranks are recommendations for the task described, based on reviewed documentation. Model ranks cover non-explicit development work. We have not run practical benchmarks. Check content policies and commercial terms separately. How the ranks work →

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