Pixal3D-GGUF

Pixal3D — GGUF build

The Pixal3D image-to-3D pipeline with its three 1.3B DiTs quantized to GGUF — ≈2.7 GB each instead of ≈5.5 GB, roughly halving the download and the resident footprint. ≈85–120 s per asset on an RTX 5090, with PBR material baked into the mesh.

What this repo is: GGUF quantizations of the Pixal3D sparse-structure, shape and texture transformers, plus the fp16 decoders and the background remover / DINOv3 encoder the pipeline needs — weights only, not a retrain. Every sample below was generated with these exact GGUF weights.

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Samples

Single image in, textured mesh out. Source images were rendered with Z-Image Turbo; the meshes are Blender Workbench renders of the raw .glb — no cleanup, no retopology, no separate paint stage.

<table> <tr><td><img src="https://huggingface.co/ChrisColeTech/Pixal3D-GGUF/resolve/main/samples/src-robot.png" width="330" alt="source photograph of a retro robot toy"></td><td><img src="https://huggingface.co/ChrisColeTech/Pixal3D-GGUF/resolve/main/samples/mesh-robot.png" width="330" alt="generated mesh with baked material"></td></tr> <tr><td>_{**source** — `a cute chunky retro robot toy standing upright, rounded metal body, simple friendly face, studio product photograph on a plain white background`}</td><td>_{**mesh** — 17.1 MB `.glb`, generated in 119 s, seed 42}</td></tr> <tr><td><img src="https://huggingface.co/ChrisColeTech/Pixal3D-GGUF/resolve/main/samples/src-teapot.png" width="330" alt="source photograph of a porcelain teapot"></td><td><img src="https://huggingface.co/ChrisColeTech/Pixal3D-GGUF/resolve/main/samples/mesh-teapot.png" width="330" alt="generated mesh with baked material"></td></tr> <tr><td>_{**source** — `an ornate ceramic teapot with a curved spout and handle, glazed blue and white porcelain, studio product photograph`}</td><td>_{**mesh** — 15.5 MB `.glb`, generated in 85 s, seed 42}</td></tr> </table>

Turntable

Four views, 90° apart.

The chest display and dial, the ear knobs and the boot flare all survive quantization, and the surface reads as brushed metal — material is produced with the geometry rather than in a later pass.

Recommended settings

Supported modes: img2mesh, txt2mesh, and the _textured variants

Notes and gotchas

• ⚠ Meshes export rotated 180° relative to some other generators. Pixal3D puts the subject's front along the opposite axis from TripoSG/TRELLIS.2, so a fixed camera that frames those correctly renders Pixal3D output from behind. Orbit the camera 180°, or check one contact sheet per model before trusting a shared preset.
• Material comes for free — no separate paint stage, which is the main reason to pick this over a geometry-only model.
• Loads in a low-VRAM mode by default, reporting ≈0 GiB resident after load and streaming weights in as it runs. Generation still wants the card largely to itself.
• Two shape DiTs ship here — _512_ and _1024_, differing in latent resolution. Both are the same 1.3B architecture; pick one.
• Sparse-conv backend — logs [SPARSE] Conv backend: flex_gemm; Attention backend: flash_attn on load and pulls a valeoai/NAF estimator from torch.hub on first run, so a fresh machine needs network access.

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Files

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Provenance

• Upstream base model: Pixal3D (TencentARC). Full-precision safetensors of the same weights live in ChrisColeTech/Pixal3D.
• This build: GGUF quantizations of the three transformers; decoders and extras are unquantized. Weights are not retrained here.
• License: MIT, per upstream.