QtMeshEditor-triposg-onnx

fernandotonon/QtMeshEditor-triposg-onnx

QtMeshEditor-triposg-onnx is a image-to-3d model from fernandotonon. Use it for the image-to-3d task on the model card, and read the license before you ship it in a product. It is set up for onnx. The card lists the license as mit.

ONNX re-export of VAST-AI/TripoSG (SIGGRAPH 2025 — MIT code + MIT weights): a 1.5B-parameter rectified-flow diffusion transformer over an SDF VAE, single image → high-fidelity 3D geometry (≈ commercial Tripo 2.0 quali…

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Public

Updated Jul 10, 2026

Repo size

7.8 GB

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Public

Hugging Face

Repo makeup

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.data5.8 GB · 74%

At a glance

Task: Image-to-3D
Library: onnx
License: mit
Access: Public
Created: Jul 10, 2026
Updated: Jul 10, 2026
SHA: 40588776

Model card

From the Hugging Face model README

TripoSG — ONNX export (staged graphs)

ONNX re-export of VAST-AI/TripoSG (SIGGRAPH 2025 — MIT code + MIT weights): a 1.5B-parameter rectified-flow diffusion transformer over an SDF VAE, single image → high-fidelity 3D geometry (≈ commercial Tripo 2.0 quality). All credit for the original weights goes to VAST-AI-Research.

Exported as four staged graphs so the flow loop runs in plain host code — for QtMeshEditor (qtmesh generate3d --backend triposg, GUI Backend dropdown, MCP backend arg), local inference via ONNX Runtime + native marching cubes.

The files QtMeshEditor downloads at runtime live in the shared fernandotonon/QtMeshEditor-models repo under triposg/. This repo is the standalone model card + mirror.

Files

file	role
`triposg_image_encoder.onnx`	DINOv2-224 image encoder (mean/std baked in; CFG unconditional = zeros)
`triposg_dit_step.onnx` + `.data`	one DiT flow step (fp32, ~5.8 GB external weights)
`triposg_vae_latents.onnx`	VAE latent KV-cache graph — run once per generation
`triposg_vae_decoder.onnx`	per-point SDF field decoder

An int8 DiT tier exists in the aggregate repo but is not recommended: even per-channel-quantized, the 1.5B DiT degrades to blobs over the 25-step CFG flow loop, and dynamic-int8 MatMuls are no faster than fp32 on ARM.

Inference contract

Flow loop (host code): σᵢ = 1 − i/N, timestep 1000·σᵢ, update x += (σᵢ − σᵢ₊₁)·v — note the sign is opposite of stock diffusers FlowMatchEulerDiscreteScheduler. CFG as two batch-1 calls, guidance 7.0, 25 steps default.
Background removal should composite over white (TripoSG's reference pipeline), not gray.
The exported field decoder is outside-positive — negate for inside-positive marching cubes at iso 0; query bounds ±1.005.
Query the decoder in chunks of ≤ 8192 points (cross-attention to 2048 kv tokens; huge chunks OOM). Open/release the sessions per stage to keep peak memory ~1 GB instead of the >4 GB sum.
Output is already +Y-up.
Geometry only — TripoSG has no colour decoder. QtMeshEditor bakes colour by projecting the input photo onto the visible front and filling the rest from TripoSR's image-conditioned colour field (see QtMeshEditor-triposr-onnx).

Full measured export contract: docs/TRIPOSG_EXPORT_NOTES.md in the QtMeshEditor repo.

Reproducing

scripts/export-triposg-onnx.py in the QtMeshEditor repo (one-time, offline).

License

MIT (same as the upstream code and weights). Credit: VAST-AI-Research.

Task: Image-to-3D
Library: onnx
License: mit
Created: Jul 10, 2026
Updated: Jul 10, 2026