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Microsoft TRELLIS.2 turns a single image into a 3D asset that can be exported as a GLB mesh with PBR materials. Microsoft advertises a browser experience that requires no setup; local inference is a developer workflow requiring Linux and a high-memory NVIDIA GPU. Check the live browser experience for current availability and terms, which the reviewed sources do not establish.
TRELLIS.2 is distinct from Microsoft’s earlier TRELLIS project. For the newer model, see the TRELLIS.2 project page and its model card.
Two ways to try TRELLIS.2
| Route | What to expect | Considerations |
|---|---|---|
| Browser experience | Microsoft advertises a no-setup way to try TRELLIS.2. | Current availability, account requirements, queues, limits, privacy terms, and service pricing are not established by the reviewed sources. Check the live experience. |
| Local inference | Run the model pipeline on your own machine and export a mesh as GLB. | The official model card says the setup is tested on Linux and requires an NVIDIA GPU with at least 24 GB of memory. Installation and hardware are significant considerations. |
Using the browser experience
Microsoft presents its browser experience as a way to try TRELLIS.2 without installing the model. Follow the current browser link from Microsoft’s TRELLIS.2 Workbench page, then check the live interface for its current input and export steps. The reviewed official materials do not verify current controls, usage limits, or continuous availability, so do not rely on a particular button sequence or assume the service is unlimited.
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Use an image you have the right to submit. The reviewed sources do not establish the browser service’s data-handling or privacy terms; avoid uploading sensitive or proprietary images unless you have checked the live service terms.
Running TRELLIS.2 locally
Local inference is intended for technical users comfortable installing and running a Python model stack. The official TRELLIS.2 model card says the implementation is currently tested on Linux, requires an NVIDIA GPU with at least 24 GB of memory, recommends CUDA Toolkit 12.4, and requires Python 3.8 or higher. Check the current model card and repository instructions before installing because setup details can change.
The documented workflow is to load the TRELLIS.2 pipeline, open an image, run inference, and export the generated mesh as a GLB. Consult the official model card for its current code example and setup guidance.
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What the model produces
TRELLIS.2 is a 4-billion-parameter image-to-3D model. Microsoft says it can generate at resolutions up to 1536³ and describes its output as a mesh with PBR materials. PBR materials are a supported output property, not a guarantee that every generated asset is production-ready.
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Inspect and clean up the mesh
A generated mesh is not necessarily a finished printable object. The model card warns that small holes and minor topological discontinuities can occur, and provides hole-filling post-processing for applications that need watertight geometry.
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- Export the result as a GLB using the current workflow documented by Microsoft.
- Open it in a compatible 3D application and inspect the shape, materials, and mesh from multiple angles.
- If you need a watertight mesh, check for holes and discontinuities and apply suitable cleanup or the provided hole-filling post-processing.
- For 3D printing or fabrication, verify dimensions and repair the mesh for the requirements of your intended process before using it.
A single image does not specify every hidden surface or exact dimension. Treat the result as a generated interpretation, not a measurement-accurate scan.
TRELLIS.2 versus the original TRELLIS
Microsoft’s earlier TRELLIS project is a separate model family. Its requirements and workflows should not be substituted for TRELLIS.2’s. The original repository is useful for identifying that distinction, but this guide’s local requirements refer to TRELLIS.2.
Is TRELLIS.2 free for commercial use?
The TRELLIS.2 model card labels the model MIT, while Microsoft’s project page states: “The materials made available on this page are provided solely for academic and research purposes in connection with the exploration of 3D generation technologies, as described in our tech report. These materials are not intended for commercial exploitation or use.” These statements require careful review; do not treat the model card label alone as resolving commercial-use rights. Check the current model card, project-page terms, and any applicable service terms before commercial use.
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You are also responsible for having the rights needed to use an input image, including any copyright, trademark, or other restrictions that apply to it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Frequently Asked Questions
Can I use TRELLIS.2 without installing it?
Microsoft advertises a browser experience requiring no setup. Its current availability, account requirements, usage limits, and terms are not established by the reviewed sources, so check the live experience.
What hardware do I need to run TRELLIS.2 locally?
The official model card says it is currently tested on Linux and specifies an NVIDIA GPU with at least 24 GB of memory. It recommends CUDA Toolkit 12.4 and Python 3.8 or higher.
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What file does the local workflow export?
The model card documents exporting the generated mesh as a GLB file.
Is the generated mesh ready for 3D printing?
Not necessarily. The model card warns that small holes and minor topological discontinuities can occur. Inspect and repair the mesh, and verify dimensions and suitability for your intended application.
Can I use TRELLIS.2 commercially?
The model card labels the model MIT, but Microsoft’s project page says its materials are for academic and research purposes and are not intended for commercial exploitation or use. Review the current terms rather than assuming commercial rights are settled.
The Bottom Line
Start with Microsoft’s browser experience if you want to try TRELLIS.2 without setup, and check its live terms and availability. For local inference, plan on Linux, an NVIDIA GPU with at least 24 GB of memory, and a developer setup. Treat the GLB as a starting asset: inspect and clean it up before relying on it for printing or production.
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