SAM_3D_OBJECTS_GGUF

github: https://github.com/Asher-1/sam-3d-objects-ggml

SAM 3D Objects GGUF Models

All GGUF weights belong in gguf/, never in benchmarks. Native raw reconstruction consumes the MoGe model plus five generative stages and exports a Gaussian PLY, a textured GLB and its base-color PNG. Vulkan inference hands its raw mesh and Gaussian outputs to a separate CUDA PBR process.

Download and conversion

The matching model repository is Asher-1/SAM_3D_OBJECTS_GGUF. Weights inherit the upstream SAM 3D Objects license. Use the helper from the checkout root; it refuses to overwrite an existing local model by default:

bash cpp_ggml/scripts/download_gguf.sh --help
bash cpp_ggml/scripts/download_gguf.sh --dtype q8_0
SAM3D_PYTHON=/path/to/sam3d-objects/bin/python \
  bash cpp_ggml/scripts/prepare_moge_gguf.sh

The published MoGe ViT-L file is the F16 GGUF (moge_vitl-f16.gguf), also in the public model repository and part of the downloader's default f16 set. The helper still reconverts from the official Ruicheng/moge-vitl checkpoint (huggingface_hub) when another dtype or a fresh validation against upstream is needed. It preserves existing GGUF files unless explicitly given --force.

For conversion from local official checkpoints (conversion writes the chosen output filenames; do not overwrite a benchmark baseline unintentionally):

SAM3D_PYTHON=/path/to/sam3d-objects/bin/python
"$SAM3D_PYTHON" cpp_ggml/scripts/convert_sam3d_to_gguf.py \
  --checkpoint-dir checkpoints/hf --model all --dtype q8_0 \
  --keep-f16-regex '^cemb\.' \
  --output cpp_ggml/models/gguf

Repeat with --dtype f16 or --dtype q8_0 for the other full-matrix families. --model all converts the six generative stages, not MoGe; download the published F16 file with the downloader or prepare it with the separate helper above. The checkpoint directory must contain the converter's expected stage files; see the runtime guide. Do not substitute LingBot-Map GGUF weights: that is a different architecture.

Current local inventory

Sizes below are actual local file sizes after the 2026-09-14 asset ruling, in MiB (2^20 bytes). They are disk sizes, not inference VRAM requirements or a promise that a model downloaded under the same filename has identical contents.

The 2026-09-14 ruling deleted every Q4_0/Q4_1 GGUF (12 files, 3.6 GB) and moge_vitl-f32.gguf after measured A/B arbitration (see "Weight-Asset Rulings" in the parity contract): q4_k is the accuracy/speed winner inside the Q4 family and matches the historical q4_best selection; the F16-weight MoGe matches the official F32 checkpoint to MAE 2.6e-4 - better than the F32-weight GGUF - at half the size. ss_generator-q8_0.gguf was re-exported with --keep-f16-regex '^cemb\.' so the DINO/PointPatch/fuser weights stay F16 regardless of the deployment dtype (hence 2191.6 MiB, not 1625.7). convert_sam3d_to_gguf.py keeps q4_0 in its choices only for the QAT retraining pipeline; q4_1 was removed from it.

Precision policy

F16, Q8_0 and Q4_K select the five generative files of that suffix; the raw matrix always uses the same explicitly recorded MoGe F16 file. Norm/bias and other tensors unsupported by a quantization layout retain the converter's floating-point types. Execution uses mixed intermediate dtypes, not uniformly F32 activations. The actual graph is recorded by --dtype-contract-out. The historical sensitive-layer SS Q4_K candidate provenance remains in q4_best. No GGUF is moved, replaced or retrained when publishing benchmarks.

End-to-end verification

Current per-model latency, final GLB renders, standalone textures and explicit gate results are linked from benchmarks. Only these complete raw-input results represent release evidence. A conditioned-stage replay, a load test or an improved Q4 trajectory does not establish complete reconstruction parity.

The root run_ggml.sh launcher uses strict SS attention. Full output matches the official material scope: baked base color with a PBR material, not independently inferred metallic/roughness/normal maps. Native CUDA or Vulkan-to-CUDA reconstruction does not require Torch or cuDNN at runtime. VTK >= 9.3, OpenCV and the explicit native rasterizer/MeshFix license options are required for complete post-processing.

VAE encoders are training components and are not required by generation.