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Hunyuan3D 2.1 is an open-source generative AI model for converting 2D images into high-fidelity 3D assets. It combines two specialized modules—Hunyuan3D-Shape for precise image-to-mesh reconstruction and Hunyuan3D-Paint for physically based rendering (PBR) texture synthesis. Designed to run on MacOS, Windows, and Linux, Hunyuan3D 2.1 supports full fine-tuning and customization. Developers and 3D artists benefit from photorealistic outputs with accurate reflections, subsurface scattering, and metalness effects, outpacing many closed-source and research models.
num_chunks
(1,000–200,000) and max_facenum
(10,000–200,000) for mesh complexity.guidance_scale
(1–20) balances adherence to input vs. creative variation.To maximize output quality and consistency:
seed=42
for reproducible results or random for exploration.steps=30
(range 5–50) for a balance of speed and fidelity.num_chunks
to 8,000+ and max_facenum
to 20,000+ for detailed surfaces.generate_texture=true
and choose octree_resolution=512
for higher texture resolution.remove_background=true
to isolate objects cleanly.guidance_scale=7.5
preserves input likeness; increase to 10 for creative variations.Q: How do I convert a product image to a 3D model?
A: Pass your image URL to the image
parameter and run Hunyuan3D-Shape. Adjust steps
and max_facenum
to refine mesh fidelity.
Q: Can I fine-tune Hunyuan3D 2.1 on my own dataset?
A: Yes. The repository includes training scripts and weight checkpoints. You can fine-tune on domain-specific images.
Q: What file formats are supported for export?
A: Generated meshes export as OBJ or glTF, with PBR textures in PNG or JPEG maps.
Q: Is Hunyuan3D 2.1 suitable for real-time applications?
A: For real-time, reduce steps
and num_chunks
settings, then optimize output in your engine’s LOD pipeline.
Q: Where can I get the code and weights?
A: Visit the official GitHub repository under an open-source license for full access and community contributions.