TRELLIS vs TRELLIS.2

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.

TRELLIS

Open source · MIT

Generates 3D assets from text or images, exportable as meshes, Gaussians, or radiance fields.

Full review Website

TRELLIS.2

Open source · MIT

Open model that turns a single image into a 3D asset with PBR materials, on NVIDIA GPUs.

Full review Website

The short answer

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

Choose TRELLIS.2 if you need…

Researchers and developers experimenting with image-to-3D generation
Designers who want a quick textured 3D base model from a reference image and have an NVIDIA GPU
People comfortable running Python and CUDA on Linux

Side-by-side

	TRELLIS	TRELLIS.2
Type	AI model / agent	AI model / agent
Price	Free and open source	Free and open source
Licence	MIT	MIT
GitHub stars	14.2k	12.6k
Setup difficulty	Advanced	Advanced
How you get it	Clone and build from source	Clone and build from source
Main language	Python	Python
Last code update	10 days ago	4 months ago

What each one is

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.

TRELLIS.2

TRELLIS.2 is a 4-billion-parameter AI model from Microsoft that generates 3D objects from a single input image. It outputs textured meshes with physically based rendering (PBR) materials such as base color, roughness, metallic, and opacity, and can export them as GLB files. It is released under the MIT license with pretrained weights on Hugging Face.

Best for

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

TRELLIS.2

Researchers and developers experimenting with image-to-3D generation
Designers who want a quick textured 3D base model from a reference image and have an NVIDIA GPU
People comfortable running Python and CUDA on Linux

Strengths

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

TRELLIS.2

Handles open surfaces, non-manifold geometry and internal structures, according to the README
Supports full PBR material outputs including roughness, metallic and opacity
Ships with pretrained weights, an example script, a web demo and an academic paper

Not ideal for

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

TRELLIS.2

Users without an NVIDIA GPU with at least 24GB of memory, since the README says it is necessary
Windows or macOS users, since the README says the code is currently tested only on Linux
Anyone wanting a point-and-click tool, since setup involves conda, CUDA and compiled dependencies

Trade-offs

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

TRELLIS.2

Requires a high-end NVIDIA GPU and a heavy dependency stack with CUDA compilation
Only tested on Linux according to the README; the README also lists no Windows or macOS support
Exported GLB is opaque by default, so transparency requires manual setup in 3D software

Getting started

TRELLIS

Clone the repository with submodules

git clone --recurse-submodules https://github.com/microsoft/TRELLIS.git

Enter the project folder

cd TRELLIS

Step-by-step guide: how to use TRELLIS

TRELLIS.2

Clone the repository with submodules

git clone -b main https://github.com/microsoft/TRELLIS.2.git --recursive

Enter the project directory

cd TRELLIS.2

Step-by-step guide: how to use TRELLIS.2

Frequently asked questions

What is the difference between TRELLIS and TRELLIS.2?

TRELLIS: Generates 3D assets from text or images, exportable as meshes, Gaussians, or radiance fields. TRELLIS.2: Open model that turns a single image into a 3D asset with PBR materials, on NVIDIA GPUs. TRELLIS suits researchers exploring text- or image-to-3d generation, while TRELLIS.2 suits researchers and developers experimenting with image-to-3d generation.

Are TRELLIS and TRELLIS.2 free?

TRELLIS: Free and open source. TRELLIS.2: Free and open source.

Is TRELLIS or TRELLIS.2 more popular?

TRELLIS has more GitHub stars (14.2k) than TRELLIS.2 (12.6k). Popularity isn't the same as fit — check which one matches your use case above.

What are other alternatives to TRELLIS and TRELLIS.2?

Other options people compare them with include Shap-E.

Keep comparing

Shap-E vs TRELLIS

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.