API Reference

microsoft / trellis

Overview

Description:

MSFT TRELLIS 3D is an asset generation model capable of producing detailed meshes directly from text prompts or images. With multiple size variants, TRELLIS offers options for users aiming to maximize quality and/or speed.
This model is ready for non-commercial/commercial use.

Third-Party Community Consideration:

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to:

License/Terms of Use

GOVERNING TERMS: This trial service is governed by the NVIDIA API Trial Terms of Service. Use of this model is governed by the NVIDIA Community Model License. Additional Information: MIT license.

Deployment Geography:

Global

Use Case:

Creators and professionals can use this model to generate high-quality images from text prompts, simplifying visual communication.

Release Date:

References

Model Architecture:

Architecture Type: Transformer
Network Architecture: Sparse Flow Transformer
Number of model parameters: TRELLIS-image-large-1.2B, TRELLIS-text-base-342M, TRELLIS-text-large 1.1B.

Input:

Input Type: Text, Image
Input Parameters: Text: One-Dimensional (1D); Image: Two-Dimensional (2D)
Input Format: Text: String. Image: Red, Green, Blue (RGB)
Other Properties Related to Input: Steps, Classifier-Free Guidance Scale and Seed

Output:

Output Type: 3D Object
Output Parameters: Three-Dimensional (3D)
Output Format: Graphics Library Binary (GLB)

Software Integration:

Runtime Engines:

  • Pytorch

Supported Hardware Platforms:

  • NVIDIA Blackwell
  • NVIDIA Hopper
  • NVIDIA Lovelace

Supported Operating Systems: Linux, Windows Subsystem for Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

  • TRELLIS-image-large
  • TRELLIS-text-large

Training, Testing, and Evaluation Datasets:

Training Dataset:

Link: https://huggingface.co/datasets/JeffreyXiang/TRELLIS-500K

Data Modality: Image, Text, 3D Objects

  • Data Collection Method by dataset: Automated
  • Labeling Method by dataset: Automated

Properties (Quantity, Dataset Descriptions, Sensor(s)): TRELLIS-500K is a dataset of 500K 3D assets curated from Objaverse(XL), ABO, 3D-FUTURE, HSSD, and Toys4k, filtered based on aesthetic scores.

Testing Dataset:

Link: https://huggingface.co/datasets/JeffreyXiang/TRELLIS-500K

Data Modality: Image, Text, 3D Objects

  • Data Collection Method by dataset: Automated
  • Labeling Method by dataset: Automated

Properties (Quantity, Dataset Descriptions, Sensor(s)): TRELLIS-500K is a dataset of 500K 3D assets curated from Objaverse(XL), ABO, 3D-FUTURE, HSSD, and Toys4k, filtered based on aesthetic scores.

Evaluation Dataset:

Link: https://huggingface.co/datasets/JeffreyXiang/TRELLIS-500K

Data Modality: Image, Text, 3D Objects

  • Data Collection Method by dataset: Automated
  • Labeling Method by dataset: Automated

Properties (Quantity, Dataset Descriptions, Sensor(s)): TRELLIS-500K is a dataset of 500K 3D assets curated from Objaverse(XL), ABO, 3D-FUTURE, HSSD, and Toys4k, filtered based on aesthetic scores.

Inference:

Engine: Pytorch
Test Hardware: L40S

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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