General Introduction
TRELLIS is a large-scale 3D asset generation model developed by Microsoft. It is capable of receiving text or image cues and generating high-quality 3D assets in a variety of formats, such as radial fields, 3D Gaussians, and meshes.At the heart of TRELLIS is a unified Structured Latent Variable (SLAT) representation, which allows it to be decoded into different output formats, backed by a powerful rectifier-flow transformer designed specifically for SLAT. Pre-trained on a large 3D asset dataset containing 500,000 diverse objects, the model significantly outperforms existing methods, demonstrating flexible output format selection and native 3D editing capabilities.
Function List
- High-quality generation: Generate diverse 3D assets with complex shape and texture details.
- Versatility: Receive text or image prompts to generate a variety of 3D representations including radial fields, 3D Gaussians and meshes.
- Flexible Editing: Allows easy editing of generated 3D assets, such as generating variants of the same object or local editing of 3D assets.
Using Help
Installation process
- precondition::
- Recommended to run code on Linux, not tested on other platforms.
- Conda is recommended for managing dependencies.
- Requires Python 3.8 or higher.
- Requires an NVIDIA GPU with 16GB or more of RAM, code has been tested on NVIDIA A100 and A6000 GPUs.
- Requires CUDA toolkit to compile certain submodules, code has been tested on CUDA 11.8 and 12.2.
- Installation steps::
- Cloning repositories:
git clone --recurse-submodules https://github.com/microsoft/TRELLIS.git cd TRELLIS - Install dependencies:
. ./setup.sh --new-env --basic --xformers --flash-attn --diffoctreerast --spconv --mipgaussian --kaolin --nvdiffrast
- Cloning repositories:
Usage Process
- Loading pre-trained models::
from trellis.pipelines import TrellisImageTo3DPipeline pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large") pipeline.cuda() - Load the image and run the pipeline::
from PIL import Image image = Image.open("assets/example_image/T.png") outputs = pipeline.run(image, seed=1) - Render output::
from trellis.utils import render_utils video = render_utils.render_video(outputs['gaussian'][0])['color'] - Save results::
import imageio imageio.mimsave("sample_gs.mp4", video, fps=30)
May not be reproduced without permission:Chief AI Sharing Circle " TRELLIS: Microsoft-developed 3D asset generation model with multiple format support and flexible editing
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