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.

| Stage | F16 MiB | F32 MiB | Q4_K MiB | Q8_0 MiB | Role |

| --- | ---: | ---: | ---: | ---: | --- |

| MoGe ViT-L | 600.0 | removed | not distributed | not distributed | image/mask point-map conditioning |

| SS generator | 3052.4 | removed | 864.8 | 2191.6 | condition encoder, structure diffusion and pose |

| SS decoder | 140.6 | removed | 39.7 | 74.8 | sparse support |

| SLat generator | 2341.9 | removed | 663.9 | 1247.5 | structured latent diffusion |

| Gaussian decoder | 163.7 | removed | 47.2 | 87.7 | Gaussian attributes |

| Gaussian stride-4 decoder | 162.4 | removed | 45.9 | 86.4 | optional alternative, not the current raw matrix |

| Mesh decoder | 173.5 | removed | 49.1 | 92.4 | FlexiCubes mesh features |

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.