triposg-onnx-webgpu

TripoSG, compressed ONNX for the browser

TripoSG (SIGGRAPH 2025, VAST-AI-Research) is a 1.5B rectified-flow diffusion transformer over an SDF VAE: one image in, high fidelity geometry out. This is that pipeline compressed so it runs entirely inside a web browser on WebGPU.

7.8 GB down to 3.5 GB, with the graphs checked one at a time against the originals.

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Make a 3D model at freegen.ai

Files

Why the graphs are not all treated the same

Run-once graphs quantize cleanly; iterated ones do not, and this pipeline runs its transformer a hundred times per generation with classifier-free guidance. So the two single-shot graphs are int8 and the DiT is float16. Measured on the same input, against the float32 originals:

The field decoder stays float32: it is small, it is evaluated at every sample point, and its sign is what decides which side of the surface a point is on.

Inference contract

image  -> cut out, composited over WHITE, cropped to the subject with 10%
          padding, shortest edge to 256, centre crop to 224, divided by 255
          (ImageNet mean and std are inside the graph)
       -> image_embeds [1,257,1024]

latents = randn(1, 2048, 64)
for i in 0..49:
    sigma      = 1 - i/50
    sigma_next = 1 - (i+1)/50
    v_uncond = dit(latents, 1000*sigma, zeros[1,257,1024])
    v_cond   = dit(latents, 1000*sigma, image_embeds)
    latents += (sigma - sigma_next) * (v_uncond + 7.0*(v_cond - v_uncond))

kv_cache = vae_latents(latents)                      once
sdf      = vae_decoder(kv_cache, points)             chunks of <= 8192
mesh     = marching_cubes(-sdf, level=0) over (-1.005 .. 1.005)

Four ways to get this wrong

• The subject must be cut out. Handed a photograph with a room behind it this model reconstructs the room: the field comes back half inside and half outside, which is a long way of saying noise. This is the failure that looks most like a broken port and is not one.
• The background is white, not the grey TripoSR wants.
• The update adds (sigma_i - sigma_next) * v. Stock diffusers' FlowMatchEulerDiscreteScheduler uses the opposite sign, because its models predict noise - x0 where this one predicts x0 - noise.
• The unconditional branch is a zero embedding, not an encoded black image. DINOv2 turns a black square into a confident, very non-zero embedding, and guidance of 7.0 applied against it produces noise.

On the field's polarity

The upstream export notes say the decoder ends in * -1 and is inside positive; the standalone model card says it is outside positive and must be negated. Measured on a cut out chair, 95.2% of the box comes back positive, and an object filling 95% of its bounding box is not an object. This export is outside positive: negate it, or take the object to be where the field is negative.

Verification

scripts/triposg-verify.py compares each compressed graph with its original on identical input, and separately runs the whole pipeline to a mesh, because the two fail differently: a compression problem shows up as a cosine, and a contract problem shows up as a field that never crosses zero or crosses it everywhere. On a cut out chair the finished field is negative over 4.8% of its box.

Note on the upstream ONNX export

Derived from the export by fernandotonon for QtMeshEditor, whose docs/TRIPOSG_EXPORT_NOTES.md is the most useful description of this model's inference contract anywhere, and was read from the upstream sources rather than guessed at.

Licence

MIT, from the upstream code and weights. The DINOv2-large encoder is Apache-2.0 (credit Meta AI). Both permit commercial use.

Note for anyone reproducing this: TripoSG's own reference pipeline mattes with BriaRMBG, which is non-commercial. It is not used here and not needed; any permissively licensed matting model works.