TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models

By Tripo

TripoSG is an advanced high-fidelity, high-quality and high-generalizability image-to-3D generation foundation model. It leverages large-scale rectified flow transformers, hybrid supervised training, and a high-quality dataset to achieve state-of-the-art performance in 3D shape generation.

✨ Key Features

• High-Fidelity Generation: Produces meshes with sharp geometric features, fine surface details, and complex structures
• Semantic Consistency: Generated shapes accurately reflect input image semantics and appearance
• Strong Generalization: Handles diverse input styles including photorealistic images, cartoons, and sketches
• Robust Performance: Creates coherent shapes even for challenging inputs with complex topology

🔬 Technical Highlights

• Large-Scale Rectified Flow Transformer: Combines RF's linear trajectory modeling with transformer architecture for stable, efficient training
• Advanced VAE Architecture: Uses Signed Distance Functions (SDFs) with hybrid supervision combining SDF loss, surface normal guidance, and eikonal loss
• High-Quality Dataset: Trained on 2 million meticulously curated Image-SDF pairs, ensuring superior output quality
• Efficient Scaling: Implements architecture optimizations for high performance even at smaller model scales

🔥 Updates

• [2025-04] Release TripoSG-scribble, a CFG-distilled, 512 token model for fast shape prototyping from scribble+prompt! Try the online demo here.
• [2025-03] Release of TripoSG 1.5B parameter rectified flow model and VAE trained on 2048 latent tokens, along with inference code and interactive demo

🧩 ComfyUI Support

This is a wrapper implementation of TripoSG in ComfyUI.

🔨 Installation

To use these nodes, simply clone the repo to your ComfyUI custom nodes directory and restart ComfyUI. Then from the repo dir run:

pip install -r requirements.txt

You can then load the provided workflows and generate high-fidelity 3D meshes directly from images or scribbles.

Update

2025 Jul 23:

• Add support for PartCrafter - a finetune of TripoSG.
• ⚠️ Breaking Change: TripoSGInference now return TRIMESH type. Use SaveTrimesh to export to 3D model. Or use TrimeshToMesh to convert back to ComfyUI native MESH format.

Supported Models

The ComfyUI wrapper supports three different models:

• TripoSG: Standard high-fidelity image-to-3D model for detailed mesh generation
• TripoSG-scribble: CFG-distilled model for fast prototyping from scribbles + prompts
• PartCrafter: Multi-part generation model for complex object composition

Available Nodes

Core Nodes:

• TripoSG Model Loader: Loads TripoSG, TripoSG-scribble, or PartCrafter models for inference.
• TripoSG Prepare Image: Preprocesses and crops input images for optimal 3D generation (used for standard TripoSG model).
• TripoSG Inference: Runs 3D mesh generation from an input image with optional conditioning.

Conditioning Nodes:

• TripoSG Scribble Conditioning: Prepares prompt and scribble confidence conditioning for the TripoSG-scribble model.
• PartCrafter Conditioning: Prepares settings for PartCrafter multi-part generation.

Mesh Processing Nodes:

• Save Trimesh: Exports TRIMESH objects to various 3D model formats (GLB, OBJ, PLY, STL, 3MF, DAE).
• Mesh to Trimesh: Converts ComfyUI MESH objects to TRIMESH objects.
• Trimesh to Mesh: Converts TRIMESH objects to ComfyUI MESH objects.
• Simplify Mesh: Reduces mesh complexity by decreasing face count (requires pymeshlab).

Example Workflows

Example ComfyUI workflows are provided in example_workflows.

• Image to 3D: Standard image-to-3D workflow

• Scribble to 3D: Scribble+prompt to 3D workflow.

• PartCrafter: Generate 3D in multile parts.

💻 System Requirements

• CUDA-enabled GPU with at least 8GB VRAM

🤝 Community & Support

• Issues & Discussions: Use GitHub Issues for bug reports and feature requests.
• Contributing: We welcome contributions!

📚 Citation

@article{li2025triposg,
  title={TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models},
  author={Li, Yangguang and Zou, Zi-Xin and Liu, Zexiang and Wang, Dehu and Liang, Yuan and Yu, Zhipeng and Liu, Xingchao and Guo, Yuan-Chen and Liang, Ding and Ouyang, Wanli and others},
  journal={arXiv preprint arXiv:2502.06608},
  year={2025}
}

⭐ Acknowledgements

We would like to thank the following open-source projects and research works that made TripoSG possible:

• DINOv2 for their powerful visual features
• RMBG-1.4 for background removal
• 🤗 Diffusers for their excellent diffusion model framework
• HunyuanDiT for DiT
• FlashVDM for their lightning vecset decoder
• 3DShape2VecSet for 3D shape representation

We are grateful to the broader research community for their open exploration and contributions to the field of 3D generation.