Image-to-3D
Diffusers
Safetensors
TripoSplatImageTo3DPipeline
diffusers-3d
triposplat
3d
gaussian-splatting
Instructions to use suvadityamuk/TripoSplat-diffusers-3d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use suvadityamuk/TripoSplat-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/TripoSplat-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
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Download README.md from suvadityamuk/TripoSplat-diffusers-3d: direct link, hf CLI and curl.
- Browser
- Download file 3.55 kB
-
https://huggingface.co/suvadityamuk/TripoSplat-diffusers-3d/resolve/main/README.md
- Command line
-
hf download hf://suvadityamuk/TripoSplat-diffusers-3d/README.md
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curl -L -o README.md https://huggingface.co/suvadityamuk/TripoSplat-diffusers-3d/resolve/main/README.md
3.55 kB
| license: mit | |
| base_model: VAST-AI/TripoSplat | |
| pipeline_tag: image-to-3d | |
| tags: | |
| - diffusers-3d | |
| - triposplat | |
| - 3d | |
| - image-to-3d | |
| - gaussian-splatting | |
| # TripoSplat for diffusers-3d | |
| [VAST-AI/TripoSplat](https://huggingface.co/VAST-AI/TripoSplat) converted into a [diffusers-3d](https://github.com/suvadityamuk/diffusers-3d) | |
| pipeline: ordinary Diffusers and Transformers component folders that the installed package loads. Nothing here | |
| requires remote code. | |
| ## Install | |
| ```bash | |
| 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 | |
| ```python | |
| import torch | |
| from diffusers_3d import AutoPipelineForImageTo3D, ImageCondition | |
| pipeline = AutoPipelineForImageTo3D.from_pretrained("suvadityamuk/TripoSplat-diffusers-3d").to("cuda") | |
| splat = pipeline(ImageCondition(image=rgba), generator=torch.Generator("cuda").manual_seed(42)).objects[0] | |
| ``` | |
| **DINOv3 and the FLUX.2 VAE are not included.** Both are gated third-party weights, so | |
| `from_pretrained` loads `image_encoder` from | |
| [facebook/dinov3-vith16plus-pretrain-lvd1689m](https://huggingface.co/facebook/dinov3-vith16plus-pretrain-lvd1689m) | |
| (DINOv3 License) and `vae` from the `vae/` folder of | |
| [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev) (FLUX non-commercial license), | |
| each at a pinned revision. Accept both licenses and log in (`hf auth login`) first; without access, loading raises an | |
| error that says so. Inputs without usable alpha are cut out with BiRefNet using | |
| [ZhengPeng7/BiRefNet](https://huggingface.co/ZhengPeng7/BiRefNet) weights (MIT), as upstream does | |
| (`pipeline.disable_background_removal()` skips it for inputs that already carry alpha). | |
| The result is a `GaussianSplatAsset` (SH degree 0, inside `[-0.5, 0.5]^3`, Z-up). Defaults match upstream: 20 steps, guidance 3.0, 262144 Gaussians. Without `dtype`, each component loads in upstream's precision (DINOv3 and the VAE in bfloat16, the transformer and decoder in their released float16); pass `dtype=torch.float32` on CPU. | |
| ## Components | |
| | Folder | Class | | |
| |---|---| | |
| | `decoder` | `diffusers_3d.families.triposplat.decoder.TripoSplatGaussianDecoder` | | |
| | `image_encoder` | `transformers.DINOv3ViTModel` (not included; loads from `facebook/dinov3-vith16plus-pretrain-lvd1689m`) | | |
| | `scheduler` | `diffusers.FlowMatchEulerDiscreteScheduler` | | |
| | `transformer` | `diffusers_3d.families.triposplat.models.TripoSplatTransformerModel` | | |
| | `vae` | `diffusers_3d.families.triposplat.models.TripoSplatImageVAE` (not included; loads from `black-forest-labs/FLUX.2-dev`) | | |
| ## Provenance | |
| Converted with `diffusers-3d-convert-triposplat` 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 | |
| TripoSplat weights and code: MIT License, Copyright (c) 2026 VAST. Only TripoSplat's own flow | |
| transformer and Gaussian decoder are included; the DINOv3 and FLUX.2 VAE copies bundled in the release are not | |
| redistributed and load from their original repositories under their own licenses. See `LICENSE-MIT` and `NOTICE`. | |
| Not affiliated with or endorsed by VAST, Meta, or Black Forest Labs. | |