Instructions to use suvadityamuk/TripoSG-diffusers-3d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use suvadityamuk/TripoSG-diffusers-3d with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("suvadityamuk/TripoSG-diffusers-3d", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
TripoSG for diffusers-3d
VAST-AI/TripoSG converted into a diffusers-3d
pipeline: ordinary Diffusers and Transformers component folders plus the object3d_model_index.json sidecar that
the package's auto-loader validates before downloading anything. Nothing here requires remote code.
Install
pip install git+https://github.com/suvadityamuk/diffusers-3d.git
pip install "git+https://github.com/suvadityamuk/diffusers-3d.git#subdirectory=packages/diffusers-3d"
diffusers-3d runs every network in plain PyTorch on CPU or GPU. Rendering Gaussian splats needs the optional
gsplat backend; the PBR-textured mesh for TRELLIS.2 needs CuMesh (see the package docs).
Usage
import torch
from diffusers_3d import AutoPipelineForImageTo3D, ImageCondition
pipeline = AutoPipelineForImageTo3D.from_pretrained("suvadityamuk/TripoSG-diffusers-3d").to("cuda")
mesh = pipeline(ImageCondition(image=rgba), generator=torch.Generator("cuda").manual_seed(0)).objects[0]
rgba is a (4, H, W) tensor in [0, 1] whose alpha channel masks the object; the pipeline crops and pads it as the released code does. The result is an untextured MeshAsset extracted with marching cubes.
Components
| Folder | Class |
|---|---|
feature_extractor |
transformers.BitImageProcessorPil |
image_encoder |
transformers.Dinov2Model |
scheduler |
diffusers.FlowMatchEulerDiscreteScheduler |
transformer |
diffusers_3d.families.triposg.models.TripoSGTransformerModel |
vae |
diffusers_3d.families.triposg.vae.TripoSGVAEDecoder |
Provenance
Converted with diffusers-3d-convert-triposg from diffusers-3d 0.1.0.dev0; the package's family README lists the pinned upstream
revisions and how the conversion maps each tensor. Parity tests against the pinned upstream code are part of the
package test suite.
License and attribution
TripoSG weights and code: MIT License, Copyright (c) VAST-AI-Research. The DINOv2 image encoder
weights are Apache-2.0, Copyright (c) Meta Platforms, Inc. TripoSG's transformer is derived from Tencent HunyuanDiT,
whose Tencent Hunyuan Community License does not apply in the European Union, the United Kingdom, or South Korea;
you are responsible for making sure your use complies with it. See LICENSE-MIT and NOTICE. Not affiliated with
or endorsed by VAST, Tencent, or Meta.
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Model tree for suvadityamuk/TripoSG-diffusers-3d
Base model
VAST-AI/TripoSG