firtoz / trellis

A powerful 3D asset generation model

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  • 3.5K runs
  • GitHub
  • Paper
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Examples

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Run time and cost

This model costs approximately $0.058 to run on Replicate, or 17 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 42 seconds. The predict time for this model varies significantly based on the inputs.

Readme

TRELLIS

Note: This Replicate deployment is maintained by firtoz, a fan of the TRELLIS project, and is not officially affiliated with Microsoft or the TRELLIS team. All rights, licenses, and intellectual property belong to Microsoft.

TRELLIS is a powerful 3D asset generation model that converts text or image prompts into high-quality 3D assets. This Replicate deployment focuses on the image-to-3D generation capabilities of TRELLIS.

Model Description

TRELLIS uses a unified Structured LATent (SLAT) representation that enables generation of different 3D output formats. The model deployed here is TRELLIS-image-large, which contains 1.2B parameters and is trained on a diverse dataset of 500K 3D objects.

Key features: - Generate high-quality 3D assets from input images - Multiple output formats: 3D Gaussians, Radiance Fields, and textured meshes - Detailed shape and texture generation - Support for various viewpoint renderings

For more examples and to try it directly in your browser, visit the Replicate model page.

Input Format

The model accepts: - An input image (PNG or JPEG format) - Optional parameters for controlling the generation process

Output Format

The model outputs: - A GLB file containing the generated 3D model with textures - Preview renders from multiple angles - Optional: Raw 3D Gaussians or Radiance Field representations

Example Usage

import replicate

output = replicate.run(
    "firtoz/trellis:version",
    input={
        "seed": 0,
        "image": "https://replicate.delivery/pbxt/M6rvlcKpjcTijzvLfJw8SCWQ74M1jrxowbVDT6nNTxREcvxO/ephemeros_cartoonish_character_art_cyberpunk_crocodile_white_ba_486fb649-bc68-46a0-b429-751b43734b89.png",
        "texture_size": 1024,
        "mesh_simplify": 0.95,
        "generate_color": True,
        "generate_model": True,
        "randomize_seed": True,
        "generate_normal": True,
        "ss_sampling_steps": 12,
        "slat_sampling_steps": 12,
        "ss_guidance_strength": 7.5,
        "slat_guidance_strength": 3
    }
)
print(output)

Citations

@article{xiang2024structured,
    title   = {Structured 3D Latents for Scalable and Versatile 3D Generation},
    author  = {Xiang, Jianfeng and Lv, Zelong and Xu, Sicheng and Deng, Yu and Wang, Ruicheng and Zhang, Bowen and Chen, Dong and Tong, Xin and Yang, Jiaolong},
    journal = {arXiv preprint arXiv:2412.01506},
    year    = {2024}
}

License

TRELLIS is released under the MIT License. See LICENSE for details.