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suvadityamuk/TRELLIS-image-large-diffusers-3d

sourceHugging Facemitupdated 5d agoView on Hugging Face

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TRELLIS-image-large for diffusers-3d

microsoft/TRELLIS-image-large converted into a diffusers-3d pipeline: ordinary Diffusers component folders (config.json + safetensors) plus the object3d_model_index.json sidecar that the package's auto-loader validates before downloading anything. Nothing here requires remote code.

Install

bash

pip install git+https://github.com/suvadityamuk/diffusers.git
pip install "git+https://github.com/suvadityamuk/diffusers.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; meshing and PBR export for TRELLIS.2 need the compiled O-Voxel runtime (see the package docs).

Usage

python

import torch
from diffusers_3d import AutoPipelineForImageTo3D, ImageCondition

pipeline = AutoPipelineForImageTo3D.from_pretrained("suvadityamuk/TRELLIS-image-large-diffusers-3d", dtype=torch.bfloat16).to("cuda")
output = pipeline(ImageCondition(image=rgba), formats=("gaussian", "mesh", "radiance_field"))
gaussians, mesh, radiance_field = output.objects

rgba is a (4, H, W) tensor in [0, 1] whose alpha channel masks the object; the pipeline crops and recentres it as the released code does. formats selects any of sparse_structure, slat, gaussian, mesh, and radiance_field.

Components

Folder	Class	Released file
`conditioner`	`TrellisDinov2Conditioner`	`facebook/dinov2-with-registers-large` (DINOv2 ViT-L/14 with registers)
`sparse_structure_flow_model`	`TrellisSparseStructureFlowModel`	`ss_flow_img_dit_L_16l8_fp16`
`sparse_structure_decoder`	`TrellisSparseStructureDecoder`	`ss_dec_conv3d_16l8_fp16`
`slat_flow_model`	`TrellisSLatFlowModel`	`slat_flow_img_dit_L_64l8p2_fp16`
`gaussian_decoder`	`TrellisSLatGaussianDecoder`	`slat_dec_gs_swin8_B_64l8gs32_fp16`
`mesh_decoder`	`TrellisSLatMeshDecoder`	`slat_dec_mesh_swin8_B_64l8m256c_fp16`
`radiance_field_decoder`	`TrellisSLatRadianceFieldDecoder`	`slat_dec_rf_swin8_B_64l8r16_fp16`

Both schedulers carry the released sampler settings (25 steps, guidance 5.0 over the 0.5–1.0 interval, rescale_t=3). Weights are stored as released (float16 for the transformers); load with dtype= to pick the compute precision.

Provenance

Converted with diffusers-3d-convert-trellis from diffusers-3d 0.1.0.dev0 against TRELLIS revision 442aa1e1afb9014e80681d3bf604e8d728a86ee7. The conversion renames parameters into the package layout and reformats configs; it does not change any weight value. Tiny-configuration parity tests against the pinned upstream code are part of the package test suite.

License and attribution

TRELLIS weights and architecture: MIT License, Copyright (c) Microsoft Corporation. The DINOv2 conditioner weights are Apache-2.0, Copyright (c) Meta Platforms, Inc. This repository redistributes both under those terms; it is not affiliated with or endorsed by Microsoft or Meta.