Publish evaluated Forma3D-Tiny v0.2

ATTRIBUTIONS.json

@@ -0,0 +1,1314 @@
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+    "source_url": "https://huggingface.co/datasets/scenario-labs/3d-collection",
+    "object_id": "scenario-5002697c4e9509fe4da7",
+    "source_object_id": "scenario-5002697c4e9509fe4da7"
+  },
+  {
+    "repo": "scenario-labs/3d-collection",
+    "revision": "55b4c002b4c1529fc48da3dff98516c3069bfa3f",
+    "path": "assets/d3873915ce9e9250/12097a943346f264.glb",
+    "sha256": "39021e31a04464c89faee8d13a9fe996a793f5d57ae177da6c948e040fc55070",
+    "asset_id": "d3873915ce9e9250",
+    "license": "cc-by-4.0",
+    "author": "Scenario",
+    "title": "d3873915ce9e9250",
+    "source_model": "claude-opus-5-5",
+    "source_url": "https://huggingface.co/datasets/scenario-labs/3d-collection",
+    "object_id": "scenario-a76f41b9cbac02843bdd",
+    "source_object_id": "scenario-a76f41b9cbac02843bdd"
+  },
+  {
+    "repo": "scenario-labs/3d-collection",
+    "revision": "55b4c002b4c1529fc48da3dff98516c3069bfa3f",
+    "path": "assets/scenario_asset_Yn4bLidfau88DtfBs3aQZ8GA/output.glb",
+    "sha256": "63fce3dfe0d50d8b9fd807119907d420286e58c56e331ef8989e24498e396982",
+    "asset_id": "scenario:asset_Yn4bLidfau88DtfBs3aQZ8GA",
+    "license": "cc-by-4.0",
+    "author": "Scenario",
+    "title": "scenario:asset_Yn4bLidfau88DtfBs3aQZ8GA",
+    "source_model": "model_tripo-v2-5-multiview-to-3d",
+    "source_url": "https://huggingface.co/datasets/scenario-labs/3d-collection",
+    "object_id": "scenario-b450f77df85a147a528c",
+    "source_object_id": "scenario-b450f77df85a147a528c"
+  }
+]

COMMANDS.md

@@ -0,0 +1,110 @@
+# Mac commands
+
+Run these in macOS Terminal, from the extracted `forma3d` folder. Requires Apple
+Silicon and Python 3.11 (the setup script lets uv manage Python). If `uv` is not
+installed, run `brew install uv` first. Do not paste Hugging Face tokens into chat.
+
+```bash
+bash setup-mac.sh
+source "$HOME/Library/Application Support/Forma3D/venv/bin/activate"
+hf auth login
+hf auth whoami
+
+# Pin the original checkpoint so the run is reproducible.
+hf download Lucasgitguy-co/image3d-student \
+  examples/synthetic-smoke/synthetic-only.pt \
+  --revision 1738cdfaf1bb940bf576a96d11b742ce014b9139 \
+  --local-dir base
+
+# 96 mesh assets, four canonical rendered views per object.
+# Dataset files and original meshes stay on your machine.
+python scripts/prepare_hf.py \
+  --out data/forma3d-v1 --cache data/downloads \
+  --scenario 64 --objaverse 32 --objaverse-shards 8 --resume \
+  --grid 16 --views 4 --max-file-mb 16 --max-download-mb 2048
+
+# The base checkpoint width is 64. All learned weights are loaded.
+# Use a NEW --out directory for every run.
+caffeinate -i image3d train \
+  --manifest data/forma3d-v1/manifest.jsonl \
+  --init-from base/examples/synthetic-smoke/synthetic-only.pt \
+  --out runs/forma3d-v1 --device mps \
+  --width 64 --steps 5000 --batch-size 2 --lr 0.0001 \
+  --eval-every 100 --validation-fraction 0.2 --seed 42
+
+# Independent held-out evaluation, release packaging, then publication.
+python scripts/release.py \
+  --run runs/forma3d-v1 --data data/forma3d-v1 \
+  --base base/examples/synthetic-smoke/synthetic-only.pt \
+  --release release/forma3d-v1 --device mps \
+  --repo Lucasgitguy-co/forma3d-tiny --publish
+```
+
+The last command requires a Hugging Face token with permission to create and write
+model repositories. It creates a private repository, uploads the complete evaluated
+release, and makes it public only after a successful upload. It refuses to overwrite
+an existing repository. If an upload fails, a private partial repository can remain;
+inspect it before retrying. No raw source datasets or authentication files are uploaded.
+
+For a fresh run, the same sequence is available as one command:
+
+```bash
+caffeinate -i bash train-mac.sh --publish
+```
+
+This reuses the runtime in `~/Library/Application Support/Forma3D/venv`, creating it
+only if missing. It uses that interpreter explicitly and verifies/reuses the local
+base checkpoint. With the dataset already prepared, training needs no Hugging Face
+connection. Publication still requires service availability and your saved HF login.
+Use `bash setup-mac.sh` explicitly when installing updated package source.
+Dataset preparation now verifies and resumes completed objects, including the old
+64-Scenario/28-Objaverse shortage. It saves progress after each object and searches
+eight Objaverse metadata shards. Completely black rendered objects are replaced;
+exact image/target duplicates remain in the same object group. Excluded existing
+files are preserved but omitted from the manifests. If preparation stops, rerun the same command.
+Training and release directories still must be fresh; after training has started,
+follow the individual commands from the failed stage. The wrapper does not resume
+optimizer state. Downloaded meshes remain cached in `data/downloads`.
+
+Publication requires completed optimizer steps, lower validation and independent
+test loss than the starting checkpoint, higher test occupancy IoU, test IoU at least
+0.05, and nonempty qualitative predictions. These are baseline research checks,
+not proof of production quality. A failed gate writes `runs/forma3d-v1/evaluation.json`
+and does not publish. Inspect the generated PNG comparisons in `runs/forma3d-v1/inspection`.
+
+To inspect first without publishing, omit `--publish`. When subsequently publishing,
+use a fresh `--release` directory. The evaluation is repeated against the actual weights.
+
+To test inference after training:
+
+```bash
+image3d generate --checkpoint runs/forma3d-v1/best.pt \
+  --image your-centered-cutout.png --out results/object.ply --device mps
+image3d preview --ply results/object.ply --out results/preview.png
+```
+
+Replace `your-centered-cutout.png` with a real file. Output is Gaussian PLY, not GLB
+or a triangle mesh. Real photos are outside the tested proxy-render distribution.
+
+For a larger experiment, increase object counts, shard count and download budget
+in a fresh dataset directory after checking free disk space. Source metadata is
+pinned, selection is deterministic, and all views/known shared inputs stay grouped.
+The starter run is not a claim that 96 objects or 5,000 steps are enough for strong
+generalization. Training time depends on the Mac and is not yet benchmarked for
+the full recipe. `Ctrl-C` saves `last.pt`; `--init-from runs/forma3d-v1/last.pt`
+starts a fresh optimizer in a new output directory and is not exact resume.
+
+If Terminal cannot use Metal, `--device mps` fails explicitly. `--device cpu` is
+available for small tests and Intel Macs but is much slower. Keep the selected
+device consistent in training and evaluation commands.
+
+## Local runtime repair — 2026-11-03
+
+The previous `.venv` under Documents contained offloaded (`dataless`) files, causing
+Python read timeouts. The runtime now lives under Library/Application Support and
+uses a regular package installation rather than an editable `.pth` link. The old
+`.venv`, source data, and checkpoints are preserved.
+
+A Hugging Face 503 can prevent browsing or publishing without affecting local
+training. Wait and retry the evaluation/publish step when the service is available;
+do not rerun completed training. Use a new `--release` directory when restaging.

DATASETS.md

@@ -0,0 +1,32 @@
+# Data selection and attribution
+
+Reviewed 2026-11-02. User requested commercial-use-compatible data and exclusion
+of noncommercial datasets. This recipe uses only the sources explicitly marked
+included below. Repository-level labels do not replace individual asset terms.
+
+| Source | Status | Reason |
+|---|---|---|
+| [scenario-labs/3d-collection](https://huggingface.co/datasets/scenario-labs/3d-collection) | Included, bounded GLB subset | Explicit CC-BY-4.0 row filter; Hunyuan-labeled models excluded; checksums verified against metadata. Attribution: Scenario; modifications: normalization, surface sampling, Gaussian proxy rendering. |
+| [scenario-labs/3d-collection-hunyuan](https://huggingface.co/datasets/scenario-labs/3d-collection-hunyuan) | Excluded | Dataset expressly prohibits using assets to train or improve AI models. |
+| [3dlg-hcvc/MultiScan](https://huggingface.co/datasets/3dlg-hcvc/MultiScan) | Excluded | CC-BY-NC-4.0, gated scene data. |
+| [huanngzh/3D-Front](https://huggingface.co/datasets/huanngzh/3D-Front) | Excluded | CC-BY-NC-4.0, indoor scenes rather than directly aligned object targets. |
+| [facebook/show3d-dataset](https://huggingface.co/datasets/facebook/show3d-dataset) | Excluded | CC-BY-NC-4.0; interaction/video data needs a separate task-specific pipeline. |
+| [Voxel51/gaussian_splatting](https://huggingface.co/datasets/Voxel51/gaussian_splatting) | Deferred; not used | Card labels Apache-2.0 and describes a few scene reconstructions. This object-centered recipe does not establish camera alignment, object crops, or source-scene rights from the card alone. No importer or download is provided. |
+| [allenai/objaverse](https://huggingface.co/datasets/allenai/objaverse) | Added: CC0 assets only | Dataset compilation is ODC-By; assets have separate licenses. Script accepts only individual `cc0` metadata entries, retains author/title/source links, and excludes animated assets. |
+| [allenai/objaverse-xl](https://huggingface.co/datasets/allenai/objaverse-xl) | Future candidate; not used | Heterogeneous per-file licensing; no blanket commercial-use assumption or importer in this kit. |
+
+Pinned revisions:
+
+- Scenario: `55b4c002b4c1529fc48da3dff98516c3069bfa3f`
+- Objaverse: `556637099bf4fa79ea7b239d0d9c328b8a2e9ac8`
+- Original model: `1738cdfaf1bb940bf576a96d11b742ce014b9139`
+
+The actual asset inventory is generated as `sources.json`, with asset IDs, source
+URLs, authors, licenses, revision IDs and SHA-256 hashes. Published releases copy
+this file to `ATTRIBUTIONS.json`. Keep it with the released model. Rejected assets
+are recorded in `skipped.json`. The model card must not list excluded or merely
+considered datasets as training sources.
+
+License references: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/),
+[CC0](https://creativecommons.org/publicdomain/zero/1.0/),
+[ODC-By](https://opendatacommons.org/licenses/by/1-0/).

LICENSE

@@ -0,0 +1,202 @@
+
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NOTICE

@@ -0,0 +1,11 @@
+Forma3D-Tiny v0.2.0, derived from Image3D Student v0.1.0
+Copyright 2026 Lucasgitguy-co.
+
+Original project source, documentation and procedural demonstration artifacts are released under Apache License 2.0.
+
+The optional Idol integration and Gaussian attribute conventions were developed with reference to
+speridlabs/idol-sdk, revision 4702a7f00850e34b6e83a2170cc123c3e9570744 (Apache-2.0).
+Its architecture is imported as an optional dependency; pretrained teacher weights are not redistributed.
+Adaptations in this project: staged loading, query-chunk attention, explicit real-valued RoPE selection,
+independent student training, coordinate-preserving Gaussian IO, and diagnostic rendering.
+Upstream attribution is retained in licenses/Idol-APACHE-2.0.txt and THIRD_PARTY.md.

README.md

@@ -0,0 +1,68 @@
+---
+license: apache-2.0
+pipeline_tag: image-to-3d
+base_model: Lucasgitguy-co/image3d-student
+model_name: Forma3D-Tiny v0.2
+datasets:
+- allenai/objaverse
+- scenario-labs/3d-collection
+tags:
+- pytorch
+- gaussian-splatting
+- research-prototype
+- mesh-proxy-supervision
+---
+# Forma3D-Tiny v0.2
+
+An experimental compact image-to-Gaussian model post-trained from Image3D Student's
+synthetic demonstration checkpoint. This is a coarse research baseline, not a
+general-purpose photo-to-3D model. Its grid is 16 cubed and
+it has 306,321 parameters. Selected checkpoint: step 4800;
+completed run: 5000 steps.
+
+## Actual training data and preprocessing
+
+Only the datasets listed above were used, with per-asset provenance in ATTRIBUTIONS.json.
+Scenario assets are CC-BY-4.0; the Objaverse subset is CC0. Original objects were centered
+and uniformly scaled into a 0.9-wide cube, surface sampled, and converted to isotropic
+Gaussian proxy targets. Orthographic images were rendered from these same proxy clouds.
+This is not teacher distillation from Idol, Hunyuan, InstantMesh, or Hi3D, and it did not
+train on natural photographs, MultiScan, 3D-Front, Show3D, or Voxel51 scene splats.
+
+Related source inputs and all views stay in the same object group. A separate 15% test
+partition is excluded from training and validation-based checkpoint selection.
+
+## Measured results
+
+Independent test objects: 14; test views: 60.
+Base test loss: 0.842823; post-trained test loss:
+0.584055. Base occupancy IoU:
+0.272567; post-trained occupancy IoU:
+0.307005.
+See evaluation.json for the full results and checks. These scores measure coarse proxy
+reconstruction on the rendered data distribution; they do not prove photo realism,
+watertight meshes, production quality, or texture fidelity. Near duplicates can remain
+despite source grouping and exact-byte deduplication.
+
+## Run on a Mac
+
+Download this repository, then run `bash setup-mac.sh` and
+`source "$HOME/Library/Application Support/Forma3D/venv/bin/activate"`.
+
+```sh
+image3d generate --checkpoint model.pt --image your-centered-cutout.png \
+  --out results/object.ply --device mps
+image3d preview --ply results/object.ply --out results/preview.png
+```
+
+Inputs are centered objects on black, matching the training framing. Natural photos
+are outside the verified distribution. Output is Gaussian PLY, not a triangle mesh.
+CPU works with `--device cpu`; CUDA support is implemented but was not tested on the
+Mac used to prepare these scripts.
+
+## License and attribution
+
+Source code and these weights are offered under Apache-2.0. Dataset and individual
+asset licenses remain separate; retain ATTRIBUTIONS.json and consult DATASETS.md.
+This license statement does not grant rights to third-party trademarks or likenesses.
+See COMMANDS.md for reproduction commands.

SHA256SUMS

@@ -0,0 +1,26 @@
+a9ba265d76a139fa5e7d95a3176e6d6053e811cc722afe11ab5ae5dac61cfa14  ATTRIBUTIONS.json
+9988ea29538b73886bfc1fd404edcf5bb64b7acfc7586fa6b7430ce13d02cb4b  COMMANDS.md
+4c09bfeb821d41b9da58c9a20c28c4000f40956f4359a04274556436a4ed009f  DATASETS.md
+cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30  LICENSE
+05f38086626c05b43280173a453858ff2eb4273d63b554c94492c64f09a20ab3  NOTICE
+b4fbe86b0118756c0f22228868d3815c731dbd02866d0a5a8bbf04e66e832c47  README.md
+ef7c689f184a7921b02887eeb8d859e8cca77da6d2082999bb19bf29beda61f1  THIRD_PARTY.md
+25d53c6500d9e45aca8a2ce1fe54030e05a0822c554ab24cfcbfeaa631ee9c91  config.json
+b838b35d053ec45f9de150414f98a6b329e4f0be52d9a1c1095320050d8b22bb  evaluation.json
+cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30  licenses/Idol-APACHE-2.0.txt
+83c065d2a50cf086928542cf8d40dc4416b379e38b9aa61d842dc9106d4b3306  model.pt
+f2beee0eea3e9e76d921fc2b2587df3e55115b7a0e7f331d684b8a32256ebc7d  pyproject.toml
+53dc8f90461d899ed8c15574c3e96f9bf07da4d3ee783a47ec3a3b077b6ee9ca  requirements-tested-macos.txt
+03325cb525f9e0562865f8df123678a147db38680b024f0845482dc2d5bc1651  scripts/prepare_hf.py
+96f7df49a214e05b86c2d86023d6500986a4d17b2dfe8b23ab7d213d75375fad  scripts/release.py
+9eaa2118115870e618fe71e130e3671c419a81ac484c80f30ca89b9e2eb677a5  setup-mac.sh
+c09ce8cb78cd68ae32b618ba49f97508b6ce14bc89e88c5b80cc3e2487bd919c  src/image3d_student/__init__.py
+b4be53a864458a84009e91277ff64767dffc6139ca3afcc50526747743722c02  src/image3d_student/cli.py
+d34f9fa50d574764e5e2c1c31c1ca477f2bc4a1887e103c5c47b754f1f1d8c72  src/image3d_student/data.py
+e01d1b42929c993d92cad1bde8944b14c6a8e325fc279d8fd3e9c7287523ad69  src/image3d_student/demo.py
+7335dcc99fcd9cea5c217841376f0ca8c0ca179809b4df34e344bf929d11da51  src/image3d_student/engine.py
+a18005f75a66846446b2574b4f1b085ebde5321e835c4e5b4507e15e812ee5ef  src/image3d_student/model.py
+41961fae62d2819c1e3f455e74539a66b8c4f57bedd9fb9379497b3a5764c692  src/image3d_student/splats.py
+2e2439bc0a48c52e32a8e273f879ed7328fdb82632a996ed3e239e0391ca3a59  src/image3d_student/teacher_idol.py
+4487becd424209645b3cc463c3f17a3b3b32abb5fc9c3fd50190cf1122757298  train-mac.sh
+c3478f8a0f1708ff19dccba080e8bb089dcde43a4907dde16d25edff430fe559  training.json

THIRD_PARTY.md

@@ -0,0 +1,13 @@
+# Third-party scope
+
+This training kit extends Lucasgitguy-co/image3d-student, Apache-2.0. The original
+checkpoint is downloaded separately. No new trained weights are bundled in the kit.
+
+Optional Idol SDK integration is inherited from the original project, pinned to
+`4702a7f00850e34b6e83a2170cc123c3e9570744`; its Apache notice is retained in
+`licenses/Idol-APACHE-2.0.txt`. The Mac recipe does not install or run Idol, Hi3D,
+InstantMesh, Hunyuan, or gated encoders. Their weights are not redistributed.
+
+Dependencies retain their respective licenses. Third-party dataset terms are
+documented in DATASETS.md; per-asset attribution is generated during preparation
+and copied into any published release. Code licensing does not relicense datasets.

config.json

@@ -0,0 +1,4 @@
+{
+  "grid": 16,
+  "width": 64
+}

evaluation.json

@@ -0,0 +1,38 @@
+{
+  "checkpoint_sha256": "449e14a96d3507f7ad8e8c270d1fd632a09e0753254d0bfc93b659832d8b8ca8",
+  "base_sha256": "dd1a1719541c5603125e7f9379b0ad8ede20b7e9f2816a04e9120244cdbf528e",
+  "test_manifest_sha256": "d29a8be0d5e773874a21e59496b934f3647954213c43f972cf68abbb80dc40df",
+  "test_objects": 14,
+  "test_samples": 60,
+  "baseline_test": {
+    "occupancy": 0.5195401266217232,
+    "position": 0.11201683692634105,
+    "colour": 0.06971513153985143,
+    "scale": 0.6968644132216771,
+    "rotation": 0.006667846724546204,
+    "opacity": 0.0014822189676730582,
+    "geometry": 0.0,
+    "loss": 0.8428226768970489,
+    "occupancy_iou": 0.27256669824322066
+  },
+  "posttrained_test": {
+    "occupancy": 0.4215678572654724,
+    "position": 0.09369222347935041,
+    "colour": 0.03420027456401537,
+    "scale": 0.0023372950294287876,
+    "rotation": 8.206806187445181e-05,
+    "opacity": 0.00015286443649529247,
+    "geometry": 0.0,
+    "loss": 0.5840554296970367,
+    "occupancy_iou": 0.30700542802611985
+  },
+  "checks": {
+    "validation_loss_improved": true,
+    "test_loss_improved": true,
+    "test_occupancy_iou_improved": true,
+    "test_occupancy_iou_at_least_005": true
+  },
+  "passed": true,
+  "scope": "Rendered mesh-proxy evaluation only; does not establish real-photo or mesh reconstruction quality",
+  "published_model_sha256": "83c065d2a50cf086928542cf8d40dc4416b379e38b9aa61d842dc9106d4b3306"
+}

licenses/Idol-APACHE-2.0.txt

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
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+
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model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:83c065d2a50cf086928542cf8d40dc4416b379e38b9aa61d842dc9106d4b3306
+size 1735755

pyproject.toml

@@ -0,0 +1,28 @@
+[build-system]
+requires = ["setuptools>=77"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "forma3d-tiny"
+version = "0.2.0"
+license = "Apache-2.0"
+license-files = ["LICENSE", "NOTICE", "THIRD_PARTY.md", "licenses/*.txt"]
+readme = "README.md"
+authors = [{name = "Lucasgitguy-co"}]
+description = "Experimental local image-to-Gaussian student with cached teacher supervision"
+requires-python = ">=3.11,<3.14"
+dependencies = ["torch>=2.5", "numpy>=1.26", "pillow>=10", "plyfile>=1", "trimesh>=4", "scipy>=1.11"]
+
+[project.optional-dependencies]
+test = ["pytest>=8"]
+data = ["huggingface_hub>=0.34", "pyarrow>=17"]
+teacher = ["idol @ git+https://github.com/speridlabs/idol-sdk.git@4702a7f00850e34b6e83a2170cc123c3e9570744"]
+
+[project.scripts]
+image3d = "image3d_student.cli:main"
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]

requirements-tested-macos.txt

@@ -0,0 +1,32 @@
+anyio==4.16.0
+click==8.5.0
+filelock==4.2.7
+fsspec==2026.9.0
+h11==0.16.0
+hf-xet==1.8.0
+httpcore2==2.16.0
+httpx2==2.16.0
+huggingface-hub==2.4.0
+idna==3.20
+iniconfig==2.3.1
+jinja2==3.1.6
+markupsafe==3.0.4
+mpmath==1.3.0
+networkx==3.6.1
+numpy==2.4.6
+packaging==26.3
+pillow==13.0.0
+pluggy==1.7.0
+plyfile==1.1.5
+pyarrow==26.0.0
+pygments==2.21.0
+pytest==9.2.0
+pyyaml==6.0.3
+scipy==1.17.1
+setuptools==84.2.1
+sympy==1.14.0
+torch==2.15.0
+tqdm==4.70.1
+trimesh==5.2.0
+truststore==0.10.5
+typing-extensions==4.16.0

scripts/prepare_hf.py

@@ -0,0 +1,463 @@
+"""Prepare paired, canonical rendered images and coarse Gaussian targets.
+
+These are mesh-derived proxy targets, NOT pretrained-teacher outputs or photos.
+Only public, explicitly allowed assets are downloaded. Source meshes stay local.
+"""
+import argparse
+import gzip
+import hashlib
+import json
+import random
+import shutil
+import time
+import urllib.error
+import urllib.request
+from pathlib import Path, PurePosixPath
+
+import numpy as np
+import pyarrow.parquet as pq
+import trimesh
+from huggingface_hub import hf_hub_download
+
+from image3d_student.data import cache_sample, splat_targets
+from image3d_student.splats import Splats, preview
+from image3d_student.engine import split_rows
+
+SCENARIO = ("scenario-labs/3d-collection", "55b4c002b4c1529fc48da3dff98516c3069bfa3f")
+OBJAVERSE = ("allenai/objaverse", "556637099bf4fa79ea7b239d0d9c328b8a2e9ac8")
+
+
+def digest(path):
+    h = hashlib.sha256()
+    with Path(path).open("rb") as f:
+        for block in iter(lambda: f.read(1024 * 1024), b""):
+            h.update(block)
+    return h.hexdigest()
+
+
+class Downloader:
+    def __init__(self, root, max_file_mb, max_total_mb, reserve_gb):
+        self.root = Path(root)
+        self.root.mkdir(parents=True, exist_ok=True)
+        self.max_file = int(max_file_mb * 1024**2)
+        self.max_total = int(max_total_mb * 1024**2)
+        self.reserve = int(reserve_gb * 1024**3)
+        self.total = 0
+
+    def get(self, repo, revision, filename, expected=None):
+        parts = PurePosixPath(filename)
+        if parts.is_absolute() or ".." in parts.parts or not filename.endswith(".glb"):
+            raise ValueError("Only relative GLB paths are allowed")
+        out = self.root / repo.replace("/", "--") / revision / filename
+        if out.exists():
+            if expected and digest(out) != expected:
+                raise ValueError("Cached source checksum mismatch")
+            if out.stat().st_size > self.max_file:
+                raise ValueError("Cached asset exceeds per-file limit")
+            return out
+        if self.total >= self.max_total:
+            raise RuntimeError("Download budget exhausted")
+        out.parent.mkdir(parents=True, exist_ok=True)
+        url = f"https://huggingface.co/datasets/{repo}/resolve/{revision}/{filename}"
+        for attempt in range(4):
+            tmp = out.with_suffix(".part")
+            try:
+                # Public URL only: credentials are never forwarded to asset hosts.
+                with urllib.request.urlopen(url, timeout=60) as response, tmp.open("wb") as f:
+                    length = int(response.headers.get("Content-Length", 0))
+                    if length > self.max_file:
+                        raise ValueError("Asset exceeds per-file limit")
+                    count = 0
+                    while block := response.read(1024 * 1024):
+                        count += len(block)
+                        self.total += len(block)
+                        if count > self.max_file:
+                            raise ValueError("Asset exceeds per-file limit")
+                        if self.total > self.max_total:
+                            raise RuntimeError("Download budget exhausted")
+                        if shutil.disk_usage(self.root).free < self.reserve + len(block):
+                            raise RuntimeError("Free disk space below reserve")
+                        f.write(block)
+                if expected and digest(tmp) != expected:
+                    raise ValueError("Source checksum mismatch")
+                tmp.replace(out)
+                return out
+            except urllib.error.HTTPError as exc:
+                if exc.code not in (429, 500, 502, 503, 504) or attempt == 3:
+                    raise
+                time.sleep(min(2**attempt, 8))
+            finally:
+                tmp.unlink(missing_ok=True)
+        raise RuntimeError("Download retries exhausted")
+
+
+def scenario_candidates(cache, seed):
+    repo, revision = SCENARIO
+    path = hf_hub_download(repo, "data/train-00000-of-00001.parquet", repo_type="dataset",
+                           revision=revision, local_dir=Path(cache) / "metadata/scenario")
+    rows = pq.read_table(path, columns=["id", "license", "model", "inputs", "outputs"]).to_pylist()
+    # Connected components keep alternate meshes generated from shared inputs together.
+    parent = {}
+
+    def find(x):
+        parent.setdefault(x, x)
+        if parent[x] != x:
+            parent[x] = find(parent[x])
+        return parent[x]
+
+    candidates = []
+    for row in rows:
+        if row["license"] != "cc-by-4.0" or "hunyuan" in (row["model"] or "").lower():
+            continue
+        assets = (row.get("inputs") or {}).get("assets") or []
+        keys = ["input:" + (a.get("sha256") or a["path"]) for a in assets if a.get("sha256") or a.get("path")]
+        keys += ["output:" + o["sha256"] for o in row["outputs"] if o.get("sha256")]
+        keys += [row["id"]]
+        for key in keys[1:]:
+            parent[find(key)] = find(keys[0])
+        for output in row["outputs"]:
+            if output.get("format") == "glb":
+                candidates.append({"repo": repo, "revision": revision, "path": output["path"],
+                    "sha256": output.get("sha256"), "asset_id": row["id"], "license": "cc-by-4.0",
+                    "author": "Scenario", "title": row["id"], "source_model": row["model"],
+                    "source_url": f"https://huggingface.co/datasets/{repo}", "group_key": keys[0]})
+                break
+    for row in candidates:
+        row["object_id"] = "scenario-" + hashlib.sha256(find(row.pop("group_key")).encode()).hexdigest()[:20]
+    candidates.sort(key=lambda r: r["asset_id"])
+    random.Random(seed).shuffle(candidates)
+    return candidates
+
+
+def objaverse_candidates(cache, seed, shards):
+    repo, revision = OBJAVERSE
+    candidates = []
+    for shard in range(shards):
+        part = f"000-{shard:03d}"
+        path = hf_hub_download(repo, f"metadata/{part}.json.gz", repo_type="dataset", revision=revision,
+                               local_dir=Path(cache) / "metadata/objaverse")
+        with gzip.open(path) as f:
+            records = json.load(f)
+        for uid, row in records.items():
+            # Start with the more conservative CC0 subset; no NC/SA/unknown assets.
+            if row.get("license") != "cc0" or row.get("isAgeRestricted") or row.get("animationCount", 0):
+                continue
+            candidates.append({"repo": repo, "revision": revision, "path": f"glbs/{part}/{uid}.glb",
+                "asset_id": uid, "object_id": "objaverse-" + uid, "license": "cc0-1.0",
+                "author": row.get("user", {}).get("displayName", "Unknown"), "title": row.get("name", uid),
+                "source_url": row.get("viewerUrl", f"https://sketchfab.com/3d-models/{uid}")})
+    candidates.sort(key=lambda r: r["asset_id"])
+    random.Random(seed).shuffle(candidates)
+    return candidates
+
+
+def mesh_splats(path, count, grid, seed):
+    scene = trimesh.load_scene(path, process=False)
+    meshes = []
+    for node in scene.graph.nodes_geometry:
+        matrix, name = scene.graph[node]
+        mesh = scene.geometry[name].copy()
+        if not isinstance(mesh, trimesh.Trimesh) or not len(mesh.faces):
+            continue
+        mesh.apply_transform(matrix)
+        meshes.append(mesh)
+    if not meshes or sum(len(m.faces) for m in meshes) > 500_000:
+        raise ValueError("Missing mesh or more than 500,000 triangles")
+    bounds = np.array([m.bounds for m in meshes])
+    low, high = bounds[:, 0].min(axis=0), bounds[:, 1].max(axis=0)
+    extent = float((high - low).max())
+    if not np.isfinite(bounds).all() or extent < 1e-8:
+        raise ValueError("Degenerate mesh")
+    center = (low + high) / 2
+    factor = .9 / extent
+    areas = np.array([m.area for m in meshes])
+    if not np.isfinite(areas).all() or areas.sum() <= 0:
+        raise ValueError("Invalid surface area")
+    counts = np.random.default_rng(seed).multinomial(count, areas / areas.sum())
+    points, colours = [], []
+    for mesh, n in zip(meshes, counts):
+        if not n:
+            continue
+        xyz, faces = trimesh.sample.sample_surface(mesh, int(n), seed=seed)
+        bary = trimesh.triangles.points_to_barycentric(mesh.triangles[faces], xyz)
+        vis = mesh.visual
+        if vis.kind == "texture" and vis.uv is not None:
+            uv = (vis.uv[mesh.faces[faces]] * bary[:, :, None]).sum(axis=1)
+            sampled = np.asarray(vis.material.to_color(uv))
+            if sampled.ndim == 0:
+                raise ValueError("Material has no usable texture or base colour")
+            # Untextured PBR materials can return one constant RGBA value.
+            if sampled.ndim == 1:
+                sampled = np.broadcast_to(sampled, (n, len(sampled)))
+            colour = sampled[:, :3] / 255.
+        elif vis.kind == "vertex":
+            colour = (vis.vertex_colors[mesh.faces[faces], :3] * bary[:, :, None]).sum(axis=1) / 255.
+        elif vis.kind == "face":
+            colour = vis.face_colors[faces, :3] / 255.
+        else:
+            colour = np.full((n, 3), .65)
+        points.append((xyz - center) * factor)
+        colours.append(colour)
+    xyz = np.concatenate(points).astype(np.float32)
+    rgb = np.concatenate(colours).astype(np.float32)
+    n = len(xyz)
+    splats = Splats(xyz, rgb, np.full((n, 3), .6 / grid),
+                    np.tile([1, 0, 0, 0], (n, 1)), np.full((n, 1), .9))
+    transform = np.eye(4)
+    transform[:3, :3] *= factor
+    transform[:3, 3] = -center * factor
+    return splats, transform.tolist()
+
+
+def save_progress(out, args, rows, provenance, failures):
+    state = {"version": 1, "settings": vars(args), "rows": rows,
+             "sources": provenance, "skipped": failures}
+    temporary = out / "progress.json.tmp"
+    temporary.write_text(json.dumps(state))
+    temporary.replace(out / "progress.json")
+
+
+def check_resume_settings(previous, args):
+    for key in ("grid", "views", "surface_points", "seed", "scenario", "objaverse"):
+        if previous[key] != getattr(args, key):
+            raise ValueError(f"Cannot change {key} while resuming; use a new dataset directory")
+
+
+def recover_legacy(out, args):
+    """Recover the old writer's completed files without rendering or downloading.
+
+    Old runs saved sources and arrays, but lost manifest rows on insufficient
+    candidates. Recreate those rows and verify targets against cached GLBs.
+    """
+    previous = json.loads((out / "preparation.json").read_text())["settings"]
+    check_resume_settings(previous, args)
+    provenance = json.loads((out / "sources.json").read_text())
+    failures = json.loads((out / "skipped.json").read_text())
+    rows = []
+    for index, item in enumerate(provenance):
+        path = Path(args.cache) / item["repo"].replace("/", "--") / item["revision"] / item["path"]
+        if not path.is_file() or digest(path) != item["sha256"]:
+            raise ValueError(f"Missing or corrupt cached source for {item['asset_id']}")
+        splats, transform = mesh_splats(path, args.surface_points, args.grid,
+                                        int(item["sha256"][:8], 16) ^ args.seed)
+        stem = hashlib.sha256((item["repo"] + item["asset_id"]).encode()).hexdigest()[:20]
+        for view in range(args.views):
+            sample_id = f"{stem}-v{view}"
+            image, target = f"images/{sample_id}.png", f"targets/{sample_id}.npz"
+            yaw = 2 * np.pi * view / args.views
+            c, s = np.cos(yaw), np.sin(yaw)
+            rotation = np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]], np.float32)
+            rotated = Splats(splats.xyz @ rotation.T, splats.rgb, splats.scale, splats.quat, splats.opacity)
+            expected = splat_targets(rotated, args.grid)
+            with np.load(out / target, allow_pickle=False) as cached:
+                if set(cached.files) != set(expected):
+                    raise ValueError(f"Target fields differ for {sample_id}")
+                for key, value in expected.items():
+                    if not np.array_equal(cached[key], value):
+                        raise ValueError(f"Recovered target differs for {sample_id}: {key}")
+            from PIL import Image
+            with Image.open(out / image) as check:
+                check.verify()
+            rows.append({"id": sample_id, "image": image, "target": target,
+                "source": "mesh-proxy:" + item["repo"], "grid": args.grid,
+                "teachers": {"appearance": "mesh-proxy:" + item["repo"], "coarse": None, "fine": None},
+                "teacher_gaussians": len(splats.xyz), "retained_gaussians": int(expected["occ"].sum()),
+                "geometry_transforms": {"coarse": None, "fine": None},
+                "image_sha256": digest(out / image), "target_sha256": digest(out / target),
+                "metadata": {**item, "normalization_matrix": transform, "yaw_radians": float(yaw),
+                    "render": "orthographic Gaussian proxy, camera +z",
+                    "target": "isotropic surface-sampled mesh proxy; not a pretrained teacher"}})
+        if (index + 1) % 10 == 0 or index + 1 == len(provenance):
+            print(json.dumps({"recovered_objects": index + 1, "existing_objects": len(provenance)}), flush=True)
+    save_progress(out, args, rows, provenance, failures)
+    return rows, provenance, failures
+
+
+def load_progress(out, args):
+    if not (out / "progress.json").exists():
+        return recover_legacy(out, args)
+    state = json.loads((out / "progress.json").read_text())
+    if state.get("version") != 1:
+        raise ValueError("Unsupported preparation progress version")
+    check_resume_settings(state["settings"], args)
+    for row in state["rows"]:
+        for key in ("image", "target"):
+            path = (out / row[key]).resolve()
+            if not path.is_relative_to(out.resolve()) or digest(path) != row[key + "_sha256"]:
+                raise ValueError(f"Prepared file checksum mismatch: {row['id']} {key}")
+    return state["rows"], state["sources"], state["skipped"]
+
+
+def prune_invisible(out, rows, provenance, failures):
+    from PIL import Image
+    bad = set()
+    for row in rows:
+        with Image.open(out / row["image"]) as image:
+            if image.convert("RGB").getbbox() is None:
+                bad.add((row["metadata"]["repo"], row["metadata"]["asset_id"]))
+    for repo, asset in sorted(bad):
+        failures.append({"repo": repo, "asset": asset,
+                         "error": "Rendered view has no visible pixels", "terminal": True})
+    if bad:
+        print(json.dumps({"excluded_invisible_objects": len(bad), "existing_files_preserved": True}), flush=True)
+    return ([r for r in rows if (r["metadata"]["repo"], r["metadata"]["asset_id"]) not in bad],
+            [p for p in provenance if (p["repo"], p["asset_id"]) not in bad])
+
+
+def group_duplicates(rows, provenance):
+    """Link entire objects when any views or target files are byte-identical."""
+    parent = {}
+    def find(x):
+        parent.setdefault(x, x)
+        if parent[x] != x:
+            parent[x] = find(parent[x])
+        return parent[x]
+    hashes = {}
+    for row in rows:
+        meta = row["metadata"]
+        original = meta.setdefault("source_object_id", meta["object_id"])
+        find(original)
+        for field in ("image_sha256", "target_sha256"):
+            key = (field, row[field])
+            if key in hashes:
+                a, b = find(original), find(hashes[key])
+                parent[max(a, b)] = min(a, b)
+            else:
+                hashes[key] = original
+    for row in rows:
+        row["metadata"]["object_id"] = find(row["metadata"]["source_object_id"])
+    for item in provenance:
+        original = item.setdefault("source_object_id", item["object_id"])
+        item["object_id"] = find(original)
+
+
+def prepare(args):
+    out = Path(args.out)
+    occupied = out.exists() and any(out.iterdir())
+    if occupied and not getattr(args, "resume", False):
+        raise ValueError("Output is not empty. Use --resume to retain completed objects.")
+    if args.scenario + args.objaverse < 4:
+        raise ValueError("Request at least four objects")
+    out.mkdir(parents=True, exist_ok=True)
+    if occupied:
+        all_rows, provenance, failures = load_progress(out, args)
+    else:
+        all_rows, provenance, failures = [], [], []
+        save_progress(out, args, all_rows, provenance, failures)
+    all_rows, provenance = prune_invisible(out, all_rows, provenance, failures)
+    save_progress(out, args, all_rows, provenance, failures)
+    seen = {item["sha256"] for item in provenance}
+    completed = {(item["repo"], item["asset_id"]) for item in provenance}
+    rejected = {(f["repo"], f["asset"]) for f in failures if f.get("terminal")}
+    counts = {}
+    fetch = Downloader(args.cache, args.max_file_mb, args.max_download_mb, args.reserve_gb)
+    sources = []
+    if args.scenario:
+        ready = sum(item["repo"] == SCENARIO[0] for item in provenance)
+        candidates = scenario_candidates(args.cache, args.seed) if ready < args.scenario else []
+        sources.append((SCENARIO[0], candidates, args.scenario, ready))
+    if args.objaverse:
+        ready = sum(item["repo"] == OBJAVERSE[0] for item in provenance)
+        candidates = objaverse_candidates(args.cache, args.seed, args.objaverse_shards) if ready < args.objaverse else []
+        sources.append((OBJAVERSE[0], candidates, args.objaverse, ready))
+    for repo, candidates, wanted, accepted in sources:
+        for item in candidates:
+            if accepted >= wanted:
+                break
+            if (item["repo"], item["asset_id"]) in completed | rejected:
+                continue
+            staging = out / "_staging"
+            try:
+                path = fetch.get(item["repo"], item["revision"], item["path"], item.get("sha256"))
+                checksum = digest(path)
+                if checksum in seen:
+                    continue
+                item["sha256"] = checksum
+                item_seed = int(checksum[:8], 16) ^ args.seed
+                splats, transform = mesh_splats(path, args.surface_points, args.grid, item_seed)
+                stem = hashlib.sha256((item["repo"] + item["asset_id"]).encode()).hexdigest()[:20]
+                staging.mkdir(parents=True, exist_ok=True)
+                rows = []
+                for view in range(args.views):
+                    yaw = 2 * np.pi * view / args.views
+                    c, s = np.cos(yaw), np.sin(yaw)
+                    rotation = np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]], np.float32)
+                    rotated = Splats(splats.xyz @ rotation.T, splats.rgb, splats.scale, splats.quat, splats.opacity)
+                    image = staging / "input.png"
+                    rendered = preview(rotated, size=128)
+                    if rendered.getbbox() is None:
+                        raise ValueError("Rendered view has no visible pixels")
+                    rendered.save(image)
+                    metadata = {**item, "object_id": item["object_id"], "normalization_matrix": transform,
+                                "yaw_radians": float(yaw), "render": "orthographic Gaussian proxy, camera +z",
+                                "target": "isotropic surface-sampled mesh proxy; not a pretrained teacher"}
+                    rows.append(cache_sample(staging, f"{stem}-v{view}", image, rotated, args.grid,
+                                             source="mesh-proxy:" + item["repo"], extra=metadata))
+                for folder in ("images", "targets"):
+                    (out / folder).mkdir(exist_ok=True)
+                    for file in (staging / folder).iterdir():
+                        file.replace(out / folder / file.name)
+                all_rows.extend(rows)
+                seen.add(checksum)
+                provenance.append(item)
+                completed.add((item["repo"], item["asset_id"]))
+                accepted += 1
+                save_progress(out, args, all_rows, provenance, failures)
+                print(json.dumps({"repo": item["repo"], "accepted": accepted, "requested": wanted,
+                                  "asset": item["asset_id"]}), flush=True)
+            except RuntimeError:
+                raise  # resource limits are a hard stop
+            except Exception as exc:
+                failures.append({"repo": item["repo"], "asset": item["asset_id"],
+                                 "error": type(exc).__name__ + ": " + str(exc)[:200],
+                                 "terminal": "no visible pixels" in str(exc)})
+                print(json.dumps({"skipped": item["asset_id"], "reason": failures[-1]["error"]}), flush=True)
+            finally:
+                shutil.rmtree(staging, ignore_errors=True)
+        counts[repo] = {"requested": wanted, "prepared": accepted}
+    group_duplicates(all_rows, provenance)
+    save_progress(out, args, all_rows, provenance, failures)
+    (out / "sources.json").write_text(json.dumps(provenance, indent=2))
+    (out / "skipped.json").write_text(json.dumps(failures, indent=2))
+    report = {"counts": counts, "samples": len(all_rows), "objects": len(provenance),
+              "distinct_groups": len({r["metadata"]["object_id"] for r in all_rows}),
+              "settings": vars(args), "rendered_proxy_data": True,
+              "excluded": ["scenario-labs/3d-collection-hunyuan", "3dlg-hcvc/MultiScan",
+                           "huanngzh/3D-Front", "facebook/show3d-dataset", "Voxel51/gaussian_splatting"]}
+    (out / "preparation.json").write_text(json.dumps(report, indent=2))
+    if any(v["prepared"] < v["requested"] for v in counts.values()):
+        raise ValueError("Not enough eligible assets. Completed objects are saved. Rerun with --resume and a larger --objaverse-shards value.")
+    if report["distinct_groups"] < 4:
+        raise ValueError("Not enough independent object groups")
+    # The test partition never enters training or checkpoint selection.
+    development, test = split_rows(all_rows, .15, args.seed + 1)
+    for name, rows in [("manifest.jsonl", development), ("test-manifest.jsonl", test)]:
+        (out / name).write_text("".join(json.dumps(r) + "\n" for r in rows))
+    print(json.dumps(report, indent=2))
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--out", required=True)
+    p.add_argument("--cache", default="data/downloads")
+    p.add_argument("--scenario", type=int, default=64)
+    p.add_argument("--objaverse", type=int, default=32)
+    p.add_argument("--objaverse-shards", type=int, default=8)
+    p.add_argument("--resume", action="store_true", help="Reuse and verify completed objects, including older failed runs")
+    p.add_argument("--grid", type=int, choices=[8, 16, 32], default=16)
+    p.add_argument("--views", type=int, default=4)
+    p.add_argument("--surface-points", type=int, default=4096)
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument("--max-file-mb", type=int, default=16)
+    p.add_argument("--max-download-mb", type=int, default=2048)
+    p.add_argument("--reserve-gb", type=float, default=2)
+    args = p.parse_args()
+    if min(args.scenario, args.objaverse) < 0 or not 1 <= args.objaverse_shards <= 160:
+        p.error("Invalid object counts or shard count")
+    if min(args.views, args.surface_points, args.max_file_mb, args.max_download_mb, args.reserve_gb) <= 0:
+        p.error("Counts and limits must be positive")
+    prepare(args)
+
+
+if __name__ == "__main__":
+    main()

scripts/release.py

@@ -0,0 +1,228 @@
+"""Evaluate a completed run, stage a self-contained release, optionally publish it."""
+import argparse
+import hashlib
+import json
+import shutil
+from pathlib import Path
+
+import torch
+from torch.utils.data import DataLoader
+
+from image3d_student.data import CachedDataset, load_manifest
+from image3d_student.engine import device_for, evaluate, generate
+from image3d_student.model import Student
+
+
+def sha(path):
+    return hashlib.sha256(Path(path).read_bytes()).hexdigest()
+
+
+def evaluate_release(args):
+    run, dataset = Path(args.run), Path(args.data)
+    meta = json.loads((run / "run.json").read_text())
+    checkpoint = torch.load(run / "best.pt", map_location="cpu", weights_only=True)
+    last = torch.load(run / "last.pt", map_location="cpu", weights_only=True)
+    if last["step"] != meta["steps_requested"]:
+        raise ValueError("Training did not complete the requested steps")
+    if checkpoint.get("step", 0) < 1 or not meta.get("initialization"):
+        raise ValueError("Expected completed post-training from a base checkpoint")
+    if sha(args.base) != meta["initialization"]["sha256"]:
+        raise ValueError("Base checkpoint differs from the training initialization")
+    if sha(dataset / "manifest.jsonl") != meta["manifest_sha256"]:
+        raise ValueError("Training manifest changed")
+    development = load_manifest(dataset / "manifest.jsonl")
+    test = load_manifest(dataset / "test-manifest.jsonl")
+    selected_ids = set(meta["train_ids"]) | set(meta["val_ids"])
+    if selected_ids != {r["id"] for r in development}:
+        raise ValueError("Run split does not match development manifest")
+    if {r["metadata"]["object_id"] for r in development} & {r["metadata"]["object_id"] for r in test}:
+        raise ValueError("Test object leakage")
+    for key in ["image_sha256", "target_sha256"]:
+        if {r[key] for r in development} & {r[key] for r in test}:
+            raise ValueError("Duplicate data across development and test")
+    device = device_for(args.device)
+    model = Student(**checkpoint["config"]).to(device)
+    model.load_state_dict(checkpoint["model"])
+    loader = DataLoader(CachedDataset(dataset / "test-manifest.jsonl"), batch_size=1)
+    final = evaluate(model, loader, device)
+    base = torch.load(args.base, map_location="cpu", weights_only=True)
+    initial = Student(**checkpoint["config"]).to(device)
+    result = initial.load_state_dict({k: v for k, v in base["model"].items() if k not in ("coords", "centers")}, strict=False)
+    if set(result.missing_keys) != {"coords", "centers"} or result.unexpected_keys:
+        raise ValueError("Base architecture mismatch")
+    baseline = evaluate(initial, loader, device)
+    best_validation = min(json.loads(line)["validation"]["loss"] for line in
+                          (run / "metrics.jsonl").read_text().splitlines())
+    checks = {"validation_loss_improved": best_validation < meta["baseline_validation"]["loss"],
+              "test_loss_improved": final["loss"] < baseline["loss"],
+              "test_occupancy_iou_improved": final["occupancy_iou"] > baseline["occupancy_iou"],
+              "test_occupancy_iou_at_least_005": final["occupancy_iou"] >= .05}
+    report = {"checkpoint_sha256": sha(run / "best.pt"), "base_sha256": sha(args.base),
+              "test_manifest_sha256": sha(dataset / "test-manifest.jsonl"),
+              "test_objects": len({r["metadata"]["object_id"] for r in test}),
+              "test_samples": len(test), "baseline_test": baseline, "posttrained_test": final,
+              "checks": checks, "passed": all(checks.values()),
+              "scope": "Rendered mesh-proxy evaluation only; does not establish real-photo or mesh reconstruction quality"}
+    (run / "evaluation.json").write_text(json.dumps(report, indent=2))
+    print(json.dumps(report, indent=2), flush=True)
+    # Keep qualitative inspection files local; all held-out objects, first view.
+    example_dir = run / "inspection"
+    example_dir.mkdir(exist_ok=True)
+    seen = set()
+    from image3d_student.splats import preview, read_ply
+    from PIL import Image, ImageDraw
+    for row in test:
+        group = row["metadata"]["object_id"]
+        if group in seen:
+            continue
+        seen.add(group)
+        out = example_dir / (row["id"] + ".ply")
+        if not out.exists():
+            try:
+                generate(run / "best.pt", dataset / row["image"], out, args.device)
+            except ValueError as exc:
+                if "No occupied cells" not in str(exc):
+                    raise
+                report["checks"]["nonempty_examples"] = False
+                report["passed"] = False
+                continue
+        sheet = Image.new("RGB", (512, 152), "black")
+        sheet.paste(Image.open(dataset / row["image"]).convert("RGB").resize((128, 128)), (0, 24))
+        splats = read_ply(out)
+        for i, angle in enumerate([0, 1.5707963, 3.1415927]):
+            sheet.paste(preview(splats, size=128, yaw=angle), (128 * (i + 1), 24))
+        ImageDraw.Draw(sheet).text((5, 5), "Input proxy | prediction front | side | back", fill="white")
+        sheet.save(out.with_suffix(".png"))
+        if len(seen) >= 8:
+            break
+    (run / "evaluation.json").write_text(json.dumps(report, indent=2))
+    if not report["passed"]:
+        raise ValueError("Evaluation gate failed. Nothing was published. Inspect evaluation.json and improve data/training.")
+    return checkpoint, meta, report
+
+
+def stage(args, checkpoint, meta, report):
+    source = Path(__file__).resolve().parents[1]
+    release = Path(args.release)
+    if release.exists():
+        raise ValueError("Release directory already exists; choose a new --release path")
+    release.mkdir(parents=True)
+    shutil.copytree(source / "src", release / "src", ignore=shutil.ignore_patterns("__pycache__", "*.egg-info"))
+    shutil.copytree(source / "scripts", release / "scripts", ignore=shutil.ignore_patterns("__pycache__"))
+    shutil.copytree(source / "licenses", release / "licenses")
+    for filename in ["pyproject.toml", "LICENSE", "NOTICE", "THIRD_PARTY.md", "DATASETS.md", "COMMANDS.md", "setup-mac.sh", "train-mac.sh", "requirements-tested-macos.txt"]:
+        shutil.copyfile(source / filename, release / filename)
+    # Publish inference weights only; optimizer and local absolute paths stay local.
+    public_meta = {k: v for k, v in meta.items() if k != "dataset"}
+    torch.save({"format": 1, "config": checkpoint["config"], "model": checkpoint["model"],
+                "step": checkpoint["step"], "metadata": public_meta}, release / "model.pt")
+    (release / "config.json").write_text(json.dumps(checkpoint["config"], indent=2))
+    (release / "training.json").write_text(json.dumps(public_meta, indent=2))
+    (release / "evaluation.json").write_text(json.dumps({**report,
+        "published_model_sha256": sha(release / "model.pt")}, indent=2))
+    shutil.copyfile(Path(args.data) / "sources.json", release / "ATTRIBUTIONS.json")
+    used = sorted({r["repo"] for r in json.loads((release / "ATTRIBUTIONS.json").read_text())})
+    datasets_yaml = "\n".join("- " + repo for repo in used)
+    text = f'''---
+license: apache-2.0
+pipeline_tag: image-to-3d
+base_model: Lucasgitguy-co/image3d-student
+model_name: Forma3D-Tiny v0.2
+datasets:
+{datasets_yaml}
+tags:
+- pytorch
+- gaussian-splatting
+- research-prototype
+- mesh-proxy-supervision
+---
+# Forma3D-Tiny v0.2
+
+An experimental compact image-to-Gaussian model post-trained from Image3D Student's
+synthetic demonstration checkpoint. This is a coarse research baseline, not a
+general-purpose photo-to-3D model. Its grid is {checkpoint["config"]["grid"]} cubed and
+it has {meta["parameters"]:,} parameters. Selected checkpoint: step {checkpoint["step"]};
+completed run: {meta["steps_requested"]} steps.
+
+## Actual training data and preprocessing
+
+Only the datasets listed above were used, with per-asset provenance in ATTRIBUTIONS.json.
+Scenario assets are CC-BY-4.0; the Objaverse subset is CC0. Original objects were centered
+and uniformly scaled into a 0.9-wide cube, surface sampled, and converted to isotropic
+Gaussian proxy targets. Orthographic images were rendered from these same proxy clouds.
+This is not teacher distillation from Idol, Hunyuan, InstantMesh, or Hi3D, and it did not
+train on natural photographs, MultiScan, 3D-Front, Show3D, or Voxel51 scene splats.
+
+Related source inputs and all views stay in the same object group. A separate 15% test
+partition is excluded from training and validation-based checkpoint selection.
+
+## Measured results
+
+Independent test objects: {report["test_objects"]}; test views: {report["test_samples"]}.
+Base test loss: {report["baseline_test"]["loss"]:.6f}; post-trained test loss:
+{report["posttrained_test"]["loss"]:.6f}. Base occupancy IoU:
+{report["baseline_test"]["occupancy_iou"]:.6f}; post-trained occupancy IoU:
+{report["posttrained_test"]["occupancy_iou"]:.6f}.
+See evaluation.json for the full results and checks. These scores measure coarse proxy
+reconstruction on the rendered data distribution; they do not prove photo realism,
+watertight meshes, production quality, or texture fidelity. Near duplicates can remain
+despite source grouping and exact-byte deduplication.
+
+## Run on a Mac
+
+Download this repository, then run `bash setup-mac.sh` and
+`source "$HOME/Library/Application Support/Forma3D/venv/bin/activate"`.
+
+```sh
+image3d generate --checkpoint model.pt --image your-centered-cutout.png \\
+  --out results/object.ply --device mps
+image3d preview --ply results/object.ply --out results/preview.png
+```
+
+Inputs are centered objects on black, matching the training framing. Natural photos
+are outside the verified distribution. Output is Gaussian PLY, not a triangle mesh.
+CPU works with `--device cpu`; CUDA support is implemented but was not tested on the
+Mac used to prepare these scripts.
+
+## License and attribution
+
+Source code and these weights are offered under Apache-2.0. Dataset and individual
+asset licenses remain separate; retain ATTRIBUTIONS.json and consult DATASETS.md.
+This license statement does not grant rights to third-party trademarks or likenesses.
+See COMMANDS.md for reproduction commands.
+'''
+    (release / "README.md").write_text(text)
+    sums = [sha(p) + "  " + str(p.relative_to(release)) for p in sorted(release.rglob("*")) if p.is_file()]
+    (release / "SHA256SUMS").write_text("\n".join(sums) + "\n")
+    return release
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--run", required=True)
+    p.add_argument("--data", required=True)
+    p.add_argument("--base", required=True)
+    p.add_argument("--release", default="release/forma3d-tiny")
+    p.add_argument("--repo", default="Lucasgitguy-co/forma3d-tiny")
+    p.add_argument("--device", choices=["mps", "cpu", "cuda", "auto"], default="mps")
+    p.add_argument("--publish", action="store_true")
+    args = p.parse_args()
+    checkpoint, meta, report = evaluate_release(args)
+    release = stage(args, checkpoint, meta, report)
+    if args.publish:
+        from huggingface_hub import HfApi
+        api = HfApi()
+        # Refuse accidental replacement of a previously published model.
+        if api.repo_exists(args.repo, repo_type="model"):
+            raise ValueError("Repository already exists. Choose a new --repo; no overwrite was performed.")
+        api.create_repo(args.repo, repo_type="model", private=True)
+        commit = api.upload_folder(repo_id=args.repo, folder_path=release,
+                                   commit_message="Publish evaluated Forma3D-Tiny v0.2")
+        api.update_repo_settings(args.repo, private=False)
+        print(f"Published https://huggingface.co/{args.repo} at {commit.oid}")
+    else:
+        print(f"Evaluated release staged at {release}; nothing published.")
+
+
+if __name__ == "__main__":
+    main()

setup-mac.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+set -euo pipefail
+cd "$(dirname "$0")"
+if ! command -v uv >/dev/null 2>&1; then
+  echo 'Install uv first: brew install uv' >&2
+  exit 1
+fi
+# Keep native libraries out of Documents, where macOS can offload their contents.
+FORMA3D_RUNTIME_DIR="${FORMA3D_RUNTIME_DIR:-$HOME/Library/Application Support/Forma3D}"
+export UV_CACHE_DIR="$FORMA3D_RUNTIME_DIR/uv-cache"
+mkdir -p "$FORMA3D_RUNTIME_DIR"
+uv venv --python 3.11 --allow-existing "$FORMA3D_RUNTIME_DIR/venv"
+uv pip install --python "$FORMA3D_RUNTIME_DIR/venv/bin/python" \
+  -r requirements-tested-macos.txt "$PWD[data,test]"
+"$FORMA3D_RUNTIME_DIR/venv/bin/python" -m image3d_student.cli doctor

src/image3d_student/__init__.py

@@ -0,0 +1,5 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2026 Lucasgitguy-co
+"""Research prototype; no pretrained general-purpose student weights are bundled."""
+
+__version__ = "0.2.0"

src/image3d_student/cli.py

@@ -0,0 +1,126 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2026 Lucasgitguy-co
+import argparse
+import importlib.util
+import json
+import platform
+import shutil
+import sys
+from pathlib import Path
+
+import torch
+
+
+def parser():
+    p = argparse.ArgumentParser(description="Local image-to-Gaussian student research prototype")
+    sub = p.add_subparsers(dest="command", required=True)
+    sub.add_parser("doctor", help="Check dependencies, Metal availability and disk space; no downloads")
+    demo = sub.add_parser("demo-data", help="Create synthetic code-test fixtures, NOT teacher data")
+    demo.add_argument("--out", required=True)
+    demo.add_argument("--count", type=int, default=12)
+    demo.add_argument("--grid", type=int, choices=[8,16,32], default=8)
+    cache = sub.add_parser("cache", help="Cache Idol PLY with optional aligned geometry teacher meshes")
+    cache.add_argument("--image", required=True, help="Exact prepared/framed input used by teacher")
+    cache.add_argument("--idol-ply", required=True)
+    cache.add_argument("--out", required=True)
+    cache.add_argument("--id", required=True)
+    cache.add_argument("--object-id", help="Shared ID for all images of the same object, to prevent validation leakage")
+    cache.add_argument("--grid", type=int, choices=[8,16,32], default=16)
+    cache.add_argument("--coarse-mesh", help="InstantMesh output; this command imports, does not run InstantMesh")
+    cache.add_argument("--coarse-transform", help="JSON 4x4 mesh-to-Idol-PLY matrix")
+    cache.add_argument("--fine-mesh", help="Hi3D output; this command imports, does not run Hi3D")
+    cache.add_argument("--fine-transform", help="JSON 4x4 mesh-to-Idol-PLY matrix")
+    train = sub.add_parser("train", help="Train the student on cached targets")
+    train.add_argument("--manifest", required=True)
+    train.add_argument("--out", required=True)
+    train.add_argument("--device", choices=["auto","cuda","mps","cpu"], default="auto")
+    train.add_argument("--init-from", help="Post-train checkpoint weights; optimizer starts fresh")
+    train.add_argument("--steps", type=int, default=1000)
+    train.add_argument("--batch-size", type=int, default=1)
+    train.add_argument("--width", type=int, default=128)
+    train.add_argument("--lr", type=float, default=3e-4)
+    train.add_argument("--seed", type=int, default=42)
+    train.add_argument("--eval-every", type=int, default=50)
+    train.add_argument("--validation-fraction", type=float, default=.2)
+    train.add_argument("--allow-synthetic", action="store_true")
+    generate = sub.add_parser("generate", help="Generate standard 3DGS PLY with a trained student")
+    generate.add_argument("--checkpoint", required=True)
+    generate.add_argument("--image", required=True, help="Centered cutout or teacher-prepared image, black background")
+    generate.add_argument("--out", required=True)
+    generate.add_argument("--device", choices=["auto","cuda","mps","cpu"], default="auto")
+    generate.add_argument("--threshold", type=float, default=.5)
+    generate.add_argument("--max-splats", type=int, default=32768)
+    render = sub.add_parser("preview", help="CPU diagnostic turntable contact sheet")
+    render.add_argument("--ply", required=True)
+    render.add_argument("--out", required=True)
+    teacher = sub.add_parser("teacher-idol", help="EXPERIMENTAL staged Mac runner; weights not bundled")
+    teacher.add_argument("--image", required=True)
+    teacher.add_argument("--out", required=True)
+    teacher.add_argument("--cache", default=".cache/huggingface")
+    teacher.add_argument("--weights", default="speridlabs/idol")
+    teacher.add_argument("--device", choices=["mps","cpu"], default="mps")
+    teacher.add_argument("--mode", choices=["real","synthetic"], default="synthetic")
+    teacher.add_argument("--steps", type=int, default=20)
+    teacher.add_argument("--gaussians", type=int, default=32768)
+    teacher.add_argument("--query-chunk", type=int, default=128)
+    teacher.add_argument("--seed", type=int, default=42)
+    teacher.add_argument("--allow-download", action="store_true", help="Explicitly allow downloading model weights and gated encoders")
+    return p
+
+
+def main():
+    p = parser()
+    args = p.parse_args()
+    try:
+        if args.command == "doctor":
+            report = {"python": platform.python_version(), "platform": platform.platform(), "torch": str(torch.__version__),
+                      "mps_available": torch.backends.mps.is_available(), "mps_built": torch.backends.mps.is_built(),
+                      "free_disk_gib": round(shutil.disk_usage(Path.cwd()).free / 1024**3, 2),
+                      "idol_installed": importlib.util.find_spec("idol") is not None,
+                      "teacher_status": {"Idol": "experimental adapter, real weights unverified",
+                                         "InstantMesh": "cached mesh import only; no Mac execution adapter",
+                                         "Hi3D": "cached mesh import only; no Mac execution adapter"},
+                      "weights_bundled": False, "quantization": "FP32 student; no binary kernels or 1-bit claims"}
+            print(json.dumps(report, indent=2))
+        elif args.command == "demo-data":
+            from .demo import create_demo
+            print(create_demo(args.out, args.count, args.grid))
+        elif args.command == "cache":
+            from .data import cache_sample
+            from .splats import read_ply
+            print(json.dumps(cache_sample(args.out, args.id, args.image, read_ply(args.idol_ply), args.grid,
+                coarse=args.coarse_mesh, coarse_transform=args.coarse_transform,
+                fine=args.fine_mesh, fine_transform=args.fine_transform,
+                extra={"object_id": args.object_id or args.id, "idol_ply": str(Path(args.idol_ply).resolve())}), indent=2))
+        elif args.command == "train":
+            from .engine import train
+            train(args)
+        elif args.command == "generate":
+            from .engine import generate
+            print(json.dumps(generate(args.checkpoint, args.image, args.out, args.device, args.threshold, args.max_splats), indent=2))
+        elif args.command == "preview":
+            from PIL import Image, ImageDraw
+            import numpy as np
+            from .splats import preview, read_ply
+            splats = read_ply(args.ply)
+            sheet = Image.new("RGB", (1024, 280), "#111111")
+            draw = ImageDraw.Draw(sheet)
+            for i in range(4):
+                sheet.paste(preview(splats, yaw=i*np.pi/2), (i*256, 24))
+                draw.text((i*256+8, 6), f"{i*90} degrees - diagnostic view", fill="white")
+            out = Path(args.out)
+            if out.exists():
+                raise ValueError("Preview already exists")
+            out.parent.mkdir(parents=True, exist_ok=True)
+            sheet.save(out)
+        elif args.command == "teacher-idol":
+            from .teacher_idol import run_idol
+            run_idol(args)
+    except (ValueError, RuntimeError, FileNotFoundError, ImportError) as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 2
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

src/image3d_student/data.py

@@ -0,0 +1,149 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2026 Lucasgitguy-co
+import hashlib
+import json
+from pathlib import Path
+
+import numpy as np
+import torch
+from PIL import Image, ImageOps
+from torch.utils.data import Dataset
+
+from .splats import Splats, read_ply
+
+
+def grid_centers(grid):
+    axis = (np.arange(grid, dtype=np.float32) + .5) / grid - .5
+    return np.stack(np.meshgrid(axis, axis, axis, indexing="ij"), axis=-1).reshape(-1, 3)
+
+
+def cell_indices(xyz, grid):
+    if not np.isfinite(xyz).all() or (np.abs(xyz) > .50001).any():
+        raise ValueError("Targets must be aligned inside [-0.5,0.5]^3. No silent normalization is performed.")
+    ijk = np.floor((xyz + .5) * grid).astype(np.int64).clip(0, grid-1)
+    return (ijk[:, 0] * grid + ijk[:, 1]) * grid + ijk[:, 2]
+
+
+def splat_targets(splats, grid):
+    """One highest-opacity teacher Gaussian per occupied cell; explicitly lossy."""
+    cells = cell_indices(splats.xyz, grid)
+    order = np.argsort(-splats.opacity[:, 0], kind="stable")
+    _, first = np.unique(cells[order], return_index=True)
+    keep = order[first]
+    slots = cells[keep]
+    result = {"occ": np.zeros((grid**3, 1), np.float32), "xyz": grid_centers(grid),
+              "rgb": np.zeros((grid**3, 3), np.float32),
+              "scale": np.full((grid**3, 3), 1 / grid / 3, np.float32),
+              "quat": np.tile(np.array([1, 0, 0, 0], np.float32), (grid**3, 1)),
+              "opacity": np.zeros((grid**3, 1), np.float32),
+              "geometry": np.zeros((grid**3, 2), np.float32),
+              "geometry_valid": np.zeros(2, np.float32), "grid": np.array(grid, np.int32)}
+    result["occ"][slots] = 1
+    for key in ("xyz", "rgb", "scale", "quat", "opacity"):
+        result[key][slots] = getattr(splats, key)[keep]
+    return result
+
+
+def mesh_occupancy(path, transform_path, grid):
+    import trimesh
+    if not transform_path:
+        raise ValueError("Every geometry teacher needs an explicit --*-transform JSON matrix, even for identity.")
+    transform = np.asarray(json.loads(Path(transform_path).read_text()), dtype=float)
+    if transform.shape != (4, 4) or not np.isfinite(transform).all() or not np.allclose(transform[3], [0,0,0,1]):
+        raise ValueError("Transform must be a finite affine 4x4 matrix")
+    mesh = trimesh.load(str(path), force="mesh", process=False)
+    if not isinstance(mesh, trimesh.Trimesh) or len(mesh.faces) == 0:
+        raise ValueError("Geometry teacher must contain a triangle mesh")
+    mesh.apply_transform(transform)
+    cell_indices(np.asarray(mesh.vertices), grid)  # validates the whole object
+    points, _ = trimesh.sample.sample_surface(mesh, min(200_000, grid**3 * 16), seed=42)
+    mask = np.zeros(grid**3, np.float32)
+    mask[np.unique(cell_indices(points, grid))] = 1
+    return mask
+
+
+def cache_sample(root, sample_id, image_path, splats, grid=16, source="idol",
+                 coarse=None, coarse_transform=None, fine=None, fine_transform=None, extra=None):
+    root = Path(root)
+    if not sample_id or any(c not in "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-_" for c in sample_id):
+        raise ValueError("Sample ID may contain only letters, digits, - and _")
+    if grid not in (8, 16, 32):
+        raise ValueError("Grid must be 8, 16 or 32")
+    target = splat_targets(splats, grid)
+    for channel, mesh, transform in [(0, coarse, coarse_transform), (1, fine, fine_transform)]:
+        if mesh:
+            target["geometry"][:, channel] = mesh_occupancy(mesh, transform, grid)
+            target["geometry_valid"][channel] = 1
+    row = {"id": sample_id, "image": f"images/{sample_id}.png", "target": f"targets/{sample_id}.npz",
+           "source": source, "grid": grid, "teachers": {"appearance": source,
+           "coarse": "InstantMesh" if coarse else None, "fine": "Hi3D" if fine else None},
+           "teacher_gaussians": len(splats.xyz), "retained_gaussians": int(target["occ"].sum()),
+           "geometry_transforms": {"coarse": json.loads(Path(coarse_transform).read_text()) if coarse else None,
+                                   "fine": json.loads(Path(fine_transform).read_text()) if fine else None},
+           "metadata": extra or {}}
+    manifest = root / "manifest.jsonl"
+    rows = load_manifest(manifest) if manifest.exists() else []
+    if any(r["id"] == sample_id for r in rows):
+        raise ValueError(f"Sample {sample_id} already exists; choose a new ID")
+    if rows and any(r["grid"] != grid for r in rows):
+        raise ValueError("Do not mix grid resolutions in one dataset")
+    image = Image.open(image_path)
+    image.load()
+    if root.joinpath(row["image"]).exists() or root.joinpath(row["target"]).exists():
+        raise ValueError("Sample files already exist; refusing to overwrite")
+    root.joinpath("images").mkdir(parents=True, exist_ok=True)
+    root.joinpath("targets").mkdir(parents=True, exist_ok=True)
+    image.save(root / row["image"])
+    row["image_sha256"] = hashlib.sha256((root / row["image"]).read_bytes()).hexdigest()
+    np.savez_compressed(root / row["target"], **target)
+    row["target_sha256"] = hashlib.sha256((root / row["target"]).read_bytes()).hexdigest()
+    # Single-writer dataset. Atomic manifest replacement avoids partial JSON lines.
+    tmp = manifest.with_suffix(".tmp")
+    tmp.write_text("".join(json.dumps(r) + "\n" for r in [*rows, row]))
+    tmp.replace(manifest)
+    return row
+
+
+def load_manifest(path):
+    rows = [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()]
+    if not rows or len({r["id"] for r in rows}) != len(rows):
+        raise ValueError("Manifest must contain nonempty, unique sample IDs")
+    return rows
+
+
+def image_tensor(path, size=128):
+    """Already-framed RGB or RGBA cutout; black background, square letterbox."""
+    with Image.open(path) as original:
+        rgba = ImageOps.exif_transpose(original).convert("RGBA")
+        bg = Image.new("RGBA", rgba.size, (0, 0, 0, 255))
+        bg.alpha_composite(rgba)
+        image = ImageOps.pad(bg.convert("RGB"), (size, size), method=Image.Resampling.LANCZOS, color="black")
+    return torch.from_numpy(np.array(image, dtype=np.float32).transpose(2, 0, 1) / 255)
+
+
+class CachedDataset(Dataset):
+    def __init__(self, manifest, rows=None):
+        self.root = Path(manifest).resolve().parent
+        self.rows = load_manifest(manifest) if rows is None else rows
+        if not self.rows:
+            raise ValueError("Empty dataset")
+        self.grid = self.rows[0]["grid"]
+        for row in self.rows:
+            if row["grid"] != self.grid:
+                raise ValueError("Mixed grids")
+            for key in ("image", "target"):
+                p = (self.root / row[key]).resolve()
+                if not p.is_relative_to(self.root) or not p.is_file():
+                    raise ValueError(f"Invalid dataset {key} path")
+                digest = hashlib.sha256(p.read_bytes()).hexdigest()
+                if digest != row[f"{key}_sha256"]:
+                    raise ValueError(f"Dataset {key} hash mismatch for {row['id']}")
+
+    def __len__(self):
+        return len(self.rows)
+
+    def __getitem__(self, i):
+        row = self.rows[i]
+        with np.load(self.root / row["target"], allow_pickle=False) as cached:
+            target = {k: torch.from_numpy(cached[k].copy()).float() for k in cached.files if k != "grid"}
+        return image_tensor(self.root / row["image"]), target

src/image3d_student/demo.py

@@ -0,0 +1,31 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2026 Lucasgitguy-co
+from pathlib import Path
+
+import numpy as np
+
+from .data import cache_sample
+from .splats import Splats, preview
+
+
+def create_demo(root, count=12, grid=8):
+    """Procedural ellipsoids only. Never presented as outputs of any real teacher."""
+    if count < 4:
+        raise ValueError("Use at least four fixtures")
+    root = Path(root)
+    root.mkdir(parents=True, exist_ok=True)
+    rng = np.random.default_rng(17)
+    phi, theta = np.meshgrid(np.linspace(.06, np.pi-.06, 14), np.linspace(0, 2*np.pi, 28, endpoint=False))
+    unit = np.stack([np.sin(phi)*np.cos(theta), np.cos(phi), np.sin(phi)*np.sin(theta)], axis=-1).reshape(-1, 3)
+    for i in range(count):
+        radii = rng.uniform(.17, .36, 3)
+        xyz = (unit * radii).astype(np.float32)
+        colour = rng.uniform(.2, .95, 3)
+        rgb = np.clip(colour[None] * (.8 + .2 * unit[:, 1:2]), 0, 1)
+        splats = Splats(xyz, rgb, np.full_like(xyz, .035), np.tile([1,0,0,0], (len(xyz),1)), np.full((len(xyz),1), .9))
+        image_path = root / f"fixture-{i:03}.png"
+        preview(splats, size=128).save(image_path)
+        cache_sample(root, f"fixture-{i:03}", image_path, splats, grid, source="synthetic",
+                     extra={"purpose": "code verification only", "object_id": f"fixture-{i:03}"})
+        image_path.unlink()
+    return root / "manifest.jsonl"

src/image3d_student/engine.py

@@ -0,0 +1,204 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2026 Lucasgitguy-co
+import json
+import hashlib
+import random
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+
+from .data import CachedDataset, image_tensor, load_manifest
+from .model import Student, distillation_loss
+from .splats import Splats, write_ply
+
+
+def device_for(name="auto"):
+    if name == "auto":
+        name = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
+    if name == "mps" and not torch.backends.mps.is_available():
+        raise ValueError("Metal GPU unavailable in this process. Run in a normal macOS Terminal, or use --device cpu.")
+    if name == "cuda" and not torch.cuda.is_available():
+        raise ValueError("CUDA requested but unavailable")
+    if name not in ("mps", "cpu", "cuda"):
+        raise ValueError("Supported devices: cpu, mps, cuda")
+    torch.set_num_threads(min(4, torch.get_num_threads()))
+    return torch.device(name)
+
+
+def split_rows(rows, fraction, seed):
+    if not 0 <= fraction < 1:
+        raise ValueError("Validation fraction must lie in [0,1)")
+    # Keep repeated views/crops of the same object together. Supply object_id in cache metadata.
+    groups = {}
+    for row in rows:
+        group = row.get("metadata", {}).get("object_id", row["id"])
+        groups.setdefault(group, []).append(row)
+    keys = sorted(groups)
+    random.Random(seed).shuffle(keys)
+    n = max(1, round(len(keys) * fraction)) if fraction else 0
+    if n >= len(keys):
+        raise ValueError("Need at least two different objects for validation, or explicitly use --validation-fraction 0 for an overfit test")
+    val_keys = set(keys[:n])
+    train = [r for k in keys if k not in val_keys for r in groups[k]]
+    val = [r for k in keys if k in val_keys for r in groups[k]]
+    if {r["image_sha256"] for r in train} & {r["image_sha256"] for r in val}:
+        raise ValueError("Identical images occur across training/validation. Assign the same object-id to related samples.")
+    return train, val
+
+
+def on_device(target, device):
+    return {k: v.to(device) for k, v in target.items()}
+
+
+@torch.no_grad()
+def evaluate(model, loader, device):
+    model.eval()
+    sums = {}
+    for image, target in loader:
+        target = on_device(target, device)
+        pred = model(image.to(device))
+        loss, terms = distillation_loss(pred, target, model.grid)
+        occupied = pred["occ"].sigmoid() > .5
+        truth = target["occ"] > .5
+        terms = {**terms, "loss": loss, "occupancy_iou": (occupied & truth).sum() / (occupied | truth).sum().clamp_min(1)}
+        for key, value in terms.items():
+            sums[key] = sums.get(key, 0.) + float(value)
+    return {k: v / len(loader) for k, v in sums.items()}
+
+
+def save_checkpoint(path, model, optimizer, step, metadata):
+    payload = {"format": 1, "config": model.config, "model": {k: v.detach().cpu() for k, v in model.state_dict().items()},
+               "optimizer": optimizer.state_dict(), "step": step, "metadata": metadata}
+    tmp = Path(path).with_suffix(".tmp")
+    torch.save(payload, tmp)
+    tmp.replace(path)
+
+
+def train(args):
+    if args.steps < 1 or args.batch_size < 1 or args.lr <= 0 or args.eval_every < 1:
+        raise ValueError("Steps, batch size, learning rate, and evaluation interval must be positive")
+    device = device_for(args.device)
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    rows = load_manifest(args.manifest)
+    synthetic = any(r["source"] == "synthetic" for r in rows)
+    if synthetic and not args.allow_synthetic:
+        raise ValueError("Synthetic fixtures are code tests, not teacher data. Add --allow-synthetic for an explicit smoke test.")
+    train_rows, val_rows = split_rows(rows, args.validation_fraction, args.seed)
+    train_data = CachedDataset(args.manifest, train_rows)
+    generator = torch.Generator().manual_seed(args.seed)
+    loader = DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=0, generator=generator)
+    val_loader = DataLoader(CachedDataset(args.manifest, val_rows), batch_size=1) if val_rows else None
+    model = Student(train_data.grid, args.width).to(device)
+    initialization = None
+    if getattr(args, "init_from", None):
+        initial_path = Path(args.init_from)
+        state = torch.load(initial_path, map_location="cpu", weights_only=True)
+        if state.get("format") != 1 or state["config"]["width"] != args.width:
+            raise ValueError("Initialization requires format=1 and matching --width")
+        # Coordinates are deterministic buffers, not learned weights. Rebuild them
+        # for the target grid while preserving every learned encoder/decoder weight.
+        learned = {k: v for k, v in state["model"].items() if k not in ("centers", "coords")}
+        result = model.load_state_dict(learned, strict=False)
+        if set(result.missing_keys) != {"centers", "coords"} or result.unexpected_keys:
+            raise ValueError("Checkpoint architecture mismatch")
+        initialization = {"sha256": hashlib.sha256(initial_path.read_bytes()).hexdigest(),
+                          "config": state["config"], "step": state["step"],
+                          "optimizer": "fresh AdamW; weights-only post-training"}
+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)
+    run = Path(args.out)
+    if run.exists() and any(run.iterdir()):
+        raise ValueError("Run directory must be empty; choose a new --out directory")
+    run.mkdir(parents=True, exist_ok=True)
+    meta = {"synthetic_only": all(r["source"] == "synthetic" for r in rows),
+            "contains_synthetic": synthetic, "device": str(device), "torch": str(torch.__version__),
+            "seed": args.seed, "train_ids": [r["id"] for r in train_rows], "val_ids": [r["id"] for r in val_rows],
+            "parameters": sum(p.numel() for p in model.parameters()),
+            "teachers": sorted({v for r in rows for v in r["teachers"].values() if v}),
+            "validation": "held-out objects" if val_rows else "none; training overfit test only",
+            "dataset": str(Path(args.manifest).resolve()),
+            "manifest_sha256": hashlib.sha256(Path(args.manifest).read_bytes()).hexdigest(),
+            "initialization": initialization, "steps_requested": args.steps,
+            "learning_rate": args.lr, "batch_size": args.batch_size,
+            "validation_fraction": args.validation_fraction}
+    if val_loader:
+        meta["baseline_validation"] = evaluate(model, val_loader, device)
+    (run / "run.json").write_text(json.dumps(meta, indent=2))
+    print(json.dumps(meta), flush=True)
+    step, best, started = 0, float("inf"), time.monotonic()
+    iterator = iter(loader)
+    try:
+        while step < args.steps:
+            try:
+                images, target = next(iterator)
+            except StopIteration:
+                iterator = iter(loader)
+                images, target = next(iterator)
+            model.train()
+            optimizer.zero_grad(set_to_none=True)
+            pred = model(images.to(device))
+            loss, terms = distillation_loss(pred, on_device(target, device), model.grid)
+            if not torch.isfinite(loss):
+                raise RuntimeError("Nonfinite training loss; stopping")
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.)
+            optimizer.step()
+            step += 1
+            if step == 1 or step % args.eval_every == 0 or step == args.steps:
+                record = {"step": step, "seconds": round(time.monotonic()-started, 3),
+                          "train_loss": float(loss.detach()), **{k: float(v.detach()) for k, v in terms.items()}}
+                if val_loader:
+                    record["validation"] = evaluate(model, val_loader, device)
+                    score = record["validation"]["loss"]
+                    if score < best:
+                        best = score
+                        save_checkpoint(run / "best.pt", model, optimizer, step, meta)
+                with (run / "metrics.jsonl").open("a") as handle:
+                    handle.write(json.dumps(record) + "\n")
+                print(json.dumps(record), flush=True)
+                save_checkpoint(run / "last.pt", model, optimizer, step, meta)
+    except KeyboardInterrupt:
+        print("Interrupted; saving completed optimizer steps.", flush=True)
+    finally:
+        if step:
+            save_checkpoint(run / "last.pt", model, optimizer, step, meta)
+    return meta
+
+
+@torch.no_grad()
+def generate(checkpoint, image, out, device="auto", threshold=.5, max_splats=32768):
+    if not 0 < threshold < 1 or max_splats < 1:
+        raise ValueError("Threshold must be in (0,1) and max-splats positive")
+    device = device_for(device)
+    state = torch.load(checkpoint, map_location="cpu", weights_only=True)
+    if state.get("format") != 1 or state.get("step", 0) < 1:
+        raise ValueError("Expected a trained image3d-student checkpoint")
+    model = Student(**state["config"])
+    model.load_state_dict(state["model"])
+    model.to(device).eval()
+    if device.type == "mps":
+        torch.mps.synchronize()
+    started = time.monotonic()
+    pred = model(image_tensor(image)[None].to(device))
+    score = pred["occ"][0, :, 0].sigmoid()
+    valid = torch.nonzero(score >= threshold).flatten()
+    if len(valid) == 0:
+        raise ValueError("No occupied cells above threshold. Train longer or inspect validation; no fabricated fallback splats were exported.")
+    valid = valid[torch.argsort(score[valid], descending=True)[:max_splats]]
+    values = {k: pred[k][0, valid].cpu().numpy() for k in ("xyz", "rgb", "scale", "quat", "opacity")}
+    elapsed = time.monotonic() - started
+    splats = Splats(**values)
+    out = Path(out)
+    if out.exists() or out.with_suffix(".json").exists():
+        raise ValueError("Output already exists; choose a new filename")
+    write_ply(out, splats)
+    report = {"gaussians": len(valid), "seconds_preprocess_and_forward": elapsed,
+              "device": str(device), "checkpoint_step": state["step"], "training": state["metadata"],
+              "note": ("Synthetic-trained output is a pipeline demonstration, not a pretrained image-to-3D result."
+                       if state["metadata"].get("contains_synthetic") else "Experimental cached-target student; inspect unseen-object quality.")}
+    out.with_suffix(".json").write_text(json.dumps(report, indent=2))
+    return report

src/image3d_student/model.py

@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2026 Lucasgitguy-co
+import math
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .data import grid_centers
+
+
+class Student(nn.Module):
+    """Small image-conditioned coordinate MLP; fixed cells avoid unordered point regression.
+
+    15 Gaussian outputs + 2 auxiliary geometry logits. This is a coarse baseline,
+    not an architecture expected to reproduce million-splat teacher detail.
+    """
+    def __init__(self, grid=16, width=128):
+        super().__init__()
+        if grid not in (8, 16, 32) or width < 16:
+            raise ValueError("Unsupported grid or width")
+        self.config = {"grid": grid, "width": width}
+        self.grid = grid
+        blocks, channels = [], 3
+        for out in (24, 48, 96, 128):
+            blocks.extend([nn.Conv2d(channels, out, 3, stride=2, padding=1), nn.GroupNorm(8, out), nn.SiLU()])
+            channels = out
+        self.encoder = nn.Sequential(*blocks, nn.AdaptiveAvgPool2d(4), nn.Flatten(),
+                                     nn.Linear(128*4*4, width), nn.SiLU())
+        centers = torch.from_numpy(grid_centers(grid))
+        freq = 2**torch.arange(4, dtype=torch.float32) * math.pi * 2
+        angles = centers[..., None] * freq
+        self.register_buffer("centers", centers)
+        self.register_buffer("coords", torch.cat([centers, angles.sin().flatten(1), angles.cos().flatten(1)], dim=1))
+        self.query = nn.Sequential(nn.Linear(27, width), nn.SiLU())
+        self.decoder = nn.Sequential(nn.Linear(width, width), nn.SiLU(), nn.Linear(width, width), nn.SiLU(), nn.Linear(width, 17))
+        with torch.no_grad():
+            self.decoder[-1].bias.zero_()
+            self.decoder[-1].bias[0] = -2
+
+    def forward(self, images):
+        features = self.encoder(images)
+        raw = self.decoder(features[:, None, :] + self.query(self.coords)[None])
+        quat = raw[..., 10:14] + raw.new_tensor([1, 0, 0, 0])
+        return {"occ": raw[..., :1], "xyz": self.centers[None] + raw[..., 1:4].tanh() / (2*self.grid),
+                "rgb": raw[..., 4:7].sigmoid(),
+                "scale": (raw[..., 7:10].clamp(-8, 5) - math.log(self.grid*3)).exp(),
+                "quat": F.normalize(quat, dim=-1, eps=1e-8), "opacity": raw[..., 14:15].sigmoid(),
+                "geometry": raw[..., 15:17]}
+
+
+def balanced_bce(logits, target):
+    error = F.binary_cross_entropy_with_logits(logits, target, reduction="none")
+    positive = (error * target).sum() / target.sum().clamp_min(1)
+    negative = (error * (1-target)).sum() / (1-target).sum().clamp_min(1)
+    return (positive + negative) / 2
+
+
+def distillation_loss(pred, target, grid):
+    mask = target["occ"]
+    def masked(value):
+        return (value * mask).sum() / (mask.sum().clamp_min(1) * value.shape[-1])
+    terms = {"occupancy": balanced_bce(pred["occ"], mask),
+             "position": masked(((pred["xyz"] - target["xyz"]) * grid).square()),
+             "colour": masked((pred["rgb"] - target["rgb"].clamp(0, 1)).square()),
+             "scale": masked((pred["scale"].log() - target["scale"].clamp_min(1e-8).log()).square()),
+             "rotation": masked(1 - (pred["quat"] * target["quat"]).sum(-1, keepdim=True).square().clamp(0, 1)),
+             "opacity": masked((pred["opacity"] - target["opacity"]).square())}
+    geometry = pred["geometry"].sum() * 0
+    for i in range(2):
+        available = target["geometry_valid"][:, i] > 0
+        if available.any():
+            geometry = geometry + balanced_bce(pred["geometry"][available, :, i:i+1], target["geometry"][available, :, i:i+1])
+    terms["geometry"] = geometry
+    total = terms["occupancy"] + terms["position"] + 2*terms["colour"] + .1*terms["scale"] + .1*terms["rotation"] + terms["opacity"] + .25*geometry
+    return total, terms

src/image3d_student/splats.py

@@ -0,0 +1,108 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2026 Lucasgitguy-co
+"""Activated Gaussian attributes and standard degree-zero 3DGS PLY IO.
+
+Coordinates are preserved as supplied. Idol's *exported PLY* uses its y-up frame;
+we do not apply Idol's internal z-up conversion a second time.
+"""
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+from plyfile import PlyData, PlyElement
+
+C0 = 0.28209479177387814
+
+
+@dataclass
+class Splats:
+    xyz: np.ndarray
+    rgb: np.ndarray
+    scale: np.ndarray
+    quat: np.ndarray  # w,x,y,z
+    opacity: np.ndarray
+
+    def __post_init__(self):
+        n = len(self.xyz)
+        if n == 0:
+            raise ValueError("No Gaussians: refusing to write or train on an empty object.")
+        for name, width in [("xyz", 3), ("rgb", 3), ("scale", 3), ("quat", 4), ("opacity", 1)]:
+            a = np.asarray(getattr(self, name), dtype=np.float32).reshape(-1, width)
+            if a.shape != (n, width) or not np.isfinite(a).all():
+                raise ValueError(f"Invalid {name} shape or nonfinite values")
+            setattr(self, name, a)
+        if (self.scale <= 0).any():
+            raise ValueError("Gaussian scales must be positive")
+        if (self.opacity < 0).any() or (self.opacity > 1).any():
+            raise ValueError("Opacity must lie in [0,1]")
+        norm = np.linalg.norm(self.quat, axis=1, keepdims=True)
+        if (norm < 1e-8).any():
+            raise ValueError("Zero quaternion")
+        self.quat = self.quat / norm
+
+    def take(self, indices):
+        return Splats(**{k: getattr(self, k)[indices] for k in self.__dataclass_fields__})
+
+
+def read_ply(path):
+    v = PlyData.read(str(path))["vertex"].data
+    required = ["x", "y", "z", "f_dc_0", "f_dc_1", "f_dc_2", "opacity",
+                "scale_0", "scale_1", "scale_2", "rot_0", "rot_1", "rot_2", "rot_3"]
+    if not set(required).issubset(v.dtype.names):
+        raise ValueError("Expected a Gaussian-splat PLY, not a mesh or ordinary point-cloud PLY")
+    col = lambda names: np.column_stack([v[k] for k in names]).astype(np.float32)
+    return Splats(col(["x", "y", "z"]), 0.5 + C0 * col([f"f_dc_{i}" for i in range(3)]),
+                  np.exp(np.clip(col([f"scale_{i}" for i in range(3)]), -20, 5)),
+                  col([f"rot_{i}" for i in range(4)]),
+                  1 / (1 + np.exp(-np.clip(col(["opacity"]), -30, 30))))
+
+
+def write_ply(path, splats):
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    names = ["x", "y", "z", "nx", "ny", "nz", "f_dc_0", "f_dc_1", "f_dc_2", "opacity",
+             "scale_0", "scale_1", "scale_2", "rot_0", "rot_1", "rot_2", "rot_3"]
+    opacity = np.clip(splats.opacity, 1e-6, 1 - 1e-6)
+    values = np.concatenate([splats.xyz, np.zeros_like(splats.xyz), (splats.rgb - 0.5) / C0,
+                             np.log(opacity / (1 - opacity)), np.log(splats.scale), splats.quat], axis=1)
+    table = np.empty(len(values), dtype=[(k, "f4") for k in names])
+    for i, name in enumerate(names):
+        table[name] = values[:, i]
+    PlyData([PlyElement.describe(table, "vertex")], text=False).write(str(path))
+
+
+def rotation_matrices(q):
+    w, x, y, z = q.T
+    return np.stack([1-2*(y*y+z*z), 2*(x*y-z*w), 2*(x*z+y*w),
+                     2*(x*y+z*w), 1-2*(x*x+z*z), 2*(y*z-x*w),
+                     2*(x*z-y*w), 2*(y*z+x*w), 1-2*(x*x+y*y)], axis=-1).reshape(-1, 3, 3)
+
+
+def preview(splats, size=256, yaw=0.0):
+    """CPU orthographic Gaussian preview; debugging only, not an evaluation renderer."""
+    from PIL import Image
+    c, s = np.cos(yaw), np.sin(yaw)
+    view = np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]], dtype=np.float32)
+    xyz = splats.xyz @ view.T
+    rot = rotation_matrices(splats.quat)
+    cov = (rot * (splats.scale ** 2)[:, None, :]) @ rot.transpose(0, 2, 1)
+    cov = view[None] @ cov @ view.T[None]
+    pixels = size / 1.25
+    canvas = np.zeros((size, size, 3), dtype=np.float32)
+    # Back to front, camera on +z. Image y points down.
+    for i in np.argsort(xyz[:, 2]):
+        center = np.array([xyz[i, 0], -xyz[i, 1]]) * pixels + size / 2
+        flip = np.diag([1., -1.])
+        covariance = flip @ cov[i, :2, :2] @ flip * pixels ** 2 + np.eye(2) * 0.3
+        radius = min(size, int(np.ceil(3 * np.sqrt(np.linalg.eigvalsh(covariance).max()))))
+        lo = np.maximum(0, np.floor(center - radius).astype(int))
+        hi = np.minimum(size, np.ceil(center + radius + 1).astype(int))
+        if np.any(hi <= lo):
+            continue
+        yy, xx = np.mgrid[lo[1]:hi[1], lo[0]:hi[0]]
+        delta = np.stack([xx - center[0], yy - center[1]], axis=-1)
+        exponent = np.einsum("...i,ij,...j->...", delta, np.linalg.inv(covariance), delta)
+        alpha = (splats.opacity[i, 0] * np.exp(-0.5 * exponent)).clip(0, .99)[..., None]
+        patch = canvas[lo[1]:hi[1], lo[0]:hi[0]]
+        patch[:] = patch * (1-alpha) + np.clip(splats.rgb[i], 0, 1) * alpha
+    return Image.fromarray((canvas.clip(0, 1) * 255).astype(np.uint8))

src/image3d_student/teacher_idol.py

@@ -0,0 +1,141 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2026 Lucasgitguy-co
+"""Experimental, staged Idol inference for Apple MPS.
+
+Upstream revision: 4702a7f00850e34b6e83a2170cc123c3e9570744.
+No weights are bundled. Full-weight inference has NOT been verified by this project.
+"""
+import gc
+import json
+import os
+import shutil
+from pathlib import Path
+
+import numpy as np
+import torch
+from PIL import Image
+
+from .engine import device_for
+
+
+def patch_attention(layers, query_chunk=128):
+    """Bound attention working memory by splitting only the query dimension.
+
+    Each query still sees ALL keys/values. Does not split or change softmax's key axis.
+    Disable CUDA-specific optimized sampling and avoid complex RoPE tensors on MPS.
+    """
+    if query_chunk < 1:
+        raise ValueError("query_chunk must be positive")
+    from torch.nn import functional as F
+
+    def attention(*args):
+        q, k, v = args[0].unbind(dim=2) if len(args) == 1 else args
+        q, k, v = (t.permute(0, 2, 1, 3) for t in (q, k, v))
+        chunks = [F.scaled_dot_product_attention(q[:, :, start:start+query_chunk], k, v)
+                  for start in range(0, q.shape[2], query_chunk)]
+        return torch.cat(chunks, dim=2).permute(0, 2, 1, 3)
+
+    layers.scaled_dot_product_attention = attention
+    layers.RePo3DRotaryEmbedding.real_valued = True
+    return attention
+
+
+def release(device):
+    gc.collect()
+    if device.type == "mps":
+        torch.mps.synchronize()
+        torch.mps.empty_cache()
+
+
+@torch.no_grad()
+def run_idol(args):
+    if args.steps < 1 or args.gaussians < 32768 or args.gaussians > 1048576 or args.gaussians % 32:
+        raise ValueError("Steps must be positive; Idol requires 32768..1048576 Gaussians, divisible by 32")
+    device = device_for(args.device)
+    cache = Path(args.cache).resolve()
+    cache.mkdir(parents=True, exist_ok=True)
+    os.environ["HF_HOME"] = str(cache)
+    os.environ["HF_HUB_OFFLINE"] = "0" if args.allow_download else "1"
+    os.environ["ATTN_BACKEND"] = "sdpa"
+    if args.allow_download and shutil.disk_usage(cache).free < 16 * 1024**3:
+        raise ValueError("Less than 16 GiB disk space available. This conservative download guard leaves room for teacher weights, encoders and temporary files. Free space, or use an existing complete cache without --allow-download.")
+    try:
+        from huggingface_hub import hf_hub_download, snapshot_download
+        from safetensors.torch import load_file
+        from tensordict import TensorDict
+        from idol.conditioning import ImageEncoder, require_encoder_access
+        from idol.models import BiRefNet, GaussianFixedlenDecoder, LatentSeqMMFlowModel, OctreeProbabilityFixedlenDecoder, layers
+        from idol.preprocess import CROP_MARGIN, preprocess_image, has_real_alpha
+        from idol.sampler import sample_flow
+        from idol.camera import decode_camera
+    except ImportError as exc:
+        raise RuntimeError("Install the optional teacher dependencies: python -m pip install '.[teacher]'") from exc
+    out = Path(args.out)
+    if out.exists():
+        raise ValueError("Teacher output directory already exists; choose a new --out")
+    # Local-only by default. Hugging Face authentication is never written to project files.
+    config_path = hf_hub_download(args.weights, "config.json", local_files_only=not args.allow_download)
+    config = json.loads(Path(config_path).read_text())
+    encoders = config["image_encoders"]
+    require_encoder_access([encoders["dinov3"], encoders["flux2_vae"]])
+    weights = Path(snapshot_download(args.weights, allow_patterns=["config.json", "*.safetensors"],
+                                    local_files_only=not args.allow_download))
+    patch_attention(layers, args.query_chunk)
+    torch.manual_seed(args.seed)
+
+    def load(model, name):
+        state = load_file(str(weights / f"{name}.safetensors"), device="cpu")
+        model.load_state_dict(state, strict=True)
+        del state
+        return model.eval().requires_grad_(False).to(device)
+
+    print("Experimental Idol MPS adapter: full-weight compatibility and quality remain unverified.", flush=True)
+    with Image.open(args.image) as opened:
+        image = opened.copy()
+    background = None
+    if not has_real_alpha(image):
+        print("Stage 1: background removal", flush=True)
+        background = load(BiRefNet(), "birefnet")
+    prepared = preprocess_image(image, background, config["image_size"], CROP_MARGIN[args.mode])
+    del background
+    release(device)
+    pixels = torch.from_numpy(np.array(prepared)).permute(2, 0, 1).float()[None].div(255).to(device)
+    print("Stage 2: image conditioning", flush=True)
+    encoder = ImageEncoder(encoders["dinov3"], encoders["flux2_vae"], device="cpu")
+    # Both encoders initially reside on CPU; only the active encoder is moved to MPS.
+    encoder.dinov3.to(device)
+    feature1 = encoder._dinov3_tokens(pixels)
+    del encoder.dinov3
+    release(device)
+    encoder.flux2_vae.to(device)
+    feature2 = encoder._flux2_tokens(pixels)
+    del encoder, pixels
+    release(device)
+    cond = TensorDict({"feature1": feature1, "feature2": feature2}, batch_size=1, device=device)
+    print("Stage 3: latent sampling", flush=True)
+    model = load(LatentSeqMMFlowModel(**config["denoiser"]["args"]), "denoiser")
+    noise = TensorDict({"latent": torch.randn(1, model.q_token_length, model.in_channels, device=device),
+                        "camera": torch.randn(1, 1, model.cam_channels, device=device)}, batch_size=1, device=device)
+    result = sample_flow(model, noise, cond=cond, neg_cond=cond.apply(torch.zeros_like), steps=args.steps,
+                         rescale_t=3., cfg_strength=3., verbose=True, batch_cfg=False)
+    latent, camera = result["latent"], decode_camera(result["camera"][0, 0])
+    del model, noise, cond, feature1, feature2, result
+    release(device)
+    print("Stage 4: octree decoding", flush=True)
+    model = load(OctreeProbabilityFixedlenDecoder(**config["decoder"]["args"]), "decoder")
+    per_anchor = config["decoder_gs"]["args"]["representation_config"]["num_gaussians"]
+    anchors = OctreeProbabilityFixedlenDecoder.sample(model, latent, num_points=args.gaussians // per_anchor,
+                                                     level=config["max_voxel_level"])
+    del model
+    release(device)
+    print("Stage 5: Gaussian decoding", flush=True)
+    model = load(GaussianFixedlenDecoder(**config["decoder_gs"]["args"]), "decoder_gs")
+    gaussian = model.to_representation(anchors, model(anchors, latent))[0]
+    out.mkdir(parents=True)
+    gaussian.save_ply(str(out / "teacher.ply"))
+    prepared.save(out / "input.png")
+    (out / "metadata.json").write_text(json.dumps({"teacher": args.weights, "steps": args.steps,
+        "gaussians": args.gaussians, "seed": args.seed, "device": str(device), "camera": camera,
+        "sdk_revision": "4702a7f00850e34b6e83a2170cc123c3e9570744", "weights_snapshot": weights.name,
+        "coordinate_frame": "Idol exported PLY (y-up)", "adapter": "experimental staged MPS"}, indent=2))
+    print(f"Teacher output saved to {out}", flush=True)

train-mac.sh

@@ -0,0 +1,44 @@
+#!/usr/bin/env bash
+set -euo pipefail
+cd "$(dirname "$0")"
+if [[ "${1:-}" != "--publish" && -n "${1:-}" ]]; then
+  echo 'Usage: bash train-mac.sh [--publish]' >&2
+  exit 2
+fi
+FORMA3D_RUNTIME_DIR="${FORMA3D_RUNTIME_DIR:-$HOME/Library/Application Support/Forma3D}"
+FORMA3D_PYTHON="$FORMA3D_RUNTIME_DIR/venv/bin/python"
+if [[ ! -x "$FORMA3D_PYTHON" ]]; then
+  bash setup-mac.sh
+fi
+# Never depend on the caller's PATH, aliases, activation, or editable .pth files.
+"$FORMA3D_PYTHON" -m image3d_student.cli doctor
+if [[ ! -f base/examples/synthetic-smoke/synthetic-only.pt ]]; then
+  "$FORMA3D_RUNTIME_DIR/venv/bin/hf" download Lucasgitguy-co/image3d-student \
+    examples/synthetic-smoke/synthetic-only.pt \
+    --revision 1738cdfaf1bb940bf576a96d11b742ce014b9139 --local-dir base
+fi
+"$FORMA3D_PYTHON" - <<'PY'
+from pathlib import Path
+import hashlib
+path = Path("base/examples/synthetic-smoke/synthetic-only.pt")
+expected = "dd1a1719541c5603125e7f9379b0ad8ede20b7e9f2816a04e9120244cdbf528e"
+if hashlib.sha256(path.read_bytes()).hexdigest() != expected:
+    raise SystemExit("Base checkpoint checksum mismatch; restore the pinned original checkpoint.")
+print("Verified local base checkpoint; no Hugging Face connection required for training.")
+PY
+if [[ -s data/forma3d-v1/manifest.jsonl && -s data/forma3d-v1/test-manifest.jsonl ]]; then
+  echo 'Using completed dataset. Training will verify the cached sample hashes.'
+else
+  "$FORMA3D_PYTHON" scripts/prepare_hf.py --out data/forma3d-v1 --cache data/downloads \
+    --scenario 64 --objaverse 32 --objaverse-shards 8 --grid 16 --views 4 --resume \
+    --max-file-mb 16 --max-download-mb 2048
+fi
+"$FORMA3D_PYTHON" -m image3d_student.cli train --manifest data/forma3d-v1/manifest.jsonl \
+  --init-from base/examples/synthetic-smoke/synthetic-only.pt \
+  --out runs/forma3d-v1 --device mps --width 64 --steps 5000 \
+  --batch-size 2 --lr 0.0001 --eval-every 100 --validation-fraction 0.2 --seed 42
+if [[ "${1:-}" == "--publish" ]]; then set -- --publish; else set --; fi
+"$FORMA3D_PYTHON" scripts/release.py --run runs/forma3d-v1 --data data/forma3d-v1 \
+  --base base/examples/synthetic-smoke/synthetic-only.pt \
+  --release release/forma3d-v1 --device mps --repo Lucasgitguy-co/forma3d-tiny \
+  "$@"

training.json

@@ -0,0 +1,366 @@
+{
+  "synthetic_only": false,
+  "contains_synthetic": false,
+  "device": "mps",
+  "torch": "2.15.0",
+  "seed": 42,
+  "train_ids": [
+    "e72c70c3a1e70f61edd5-v0",
+    "e72c70c3a1e70f61edd5-v1",
+    "e72c70c3a1e70f61edd5-v2",
+    "e72c70c3a1e70f61edd5-v3",
+    "0d291e312da4267be597-v0",
+    "0d291e312da4267be597-v1",
+    "0d291e312da4267be597-v2",
+    "0d291e312da4267be597-v3",
+    "84e4d421b6e5a54babac-v0",
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+    "f008965c2682f5284b4a-v2",
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