Use embedded public hand assets and allow model estimates without login

README.md

@@ -51,7 +51,10 @@ not apply. Aria Gen1 image rotation is explicit. The annotated copy retains ever
 source frame, writes 156D actions and native SOMA parameters, and masks frames
 outside directly observed bimanual spans of at least 30 frames. This recipe has
 no motion infiller and needs no MANO files. Model assets come from a pinned,
-checksummed private runtime release using the signed-in user's read access.
+checksummed package embedded in the public worker image. The Space build fetches
+the large weights from public upstream sources; Jobs need no model-repository access.
+Browsing and estimating public datasets need no login. Sign in to launch HF Jobs
+or access private datasets.
 The worker image and asset identities are verified before native inference.
 
 The measured development reference is 39.41–41.04 input FPS on RTX 5090 over ten

flora-worker-image.json

@@ -31,7 +31,7 @@
     "flora_batcher.py": "38e03e2032af8d5f3ed34980cc4d89927fe70c9742aa2ce13f4d0ccdd6fba727",
     "flora_speed_entry.py": "192b18d75b9d876c2a65d88de340c8cea7c8da90543d0510b5ee6865d123d524",
     "studio_runtime.py": "2b393bf7fafcdead638ad1cea35e49df8c6edce725f5545b76fd68e0a8fedd80",
-    "pipeline-version.json": "c42cab00f33cbe53582b0ad56f384d453cbb747eef6db7ec1ba91803bc469681",
+    "pipeline-version.json": "fa74c6924bcb98c81dd90cfd05ad78209b6ca89bbb0b92e1e451ca14549b6f23",
     "flora_vision.py": "a5a4231abf7b615164c40986df5f87047f745a23c55d86385d88f2a09e70f1e8"
   },
   "acceptance": {
@@ -99,6 +99,7 @@
     },
     "inference_or_benchmark_rerun": false,
     "core_change_scope": "Hand package only; other src files byte-identical to accepted pipeline bd4c312dac38068440edf59e764ba0c68ab7ce0b",
-    "pipeline_revision": "850bb584c545ebe9bfcefdcd9b02391cba1c4638"
+    "pipeline_revision": "33c4ca366114b8717bb5df6ed4f412387af12dfe",
+    "public_asset_distribution": "Only the hand asset provisioning helper changed; all Flora and execution-engine source bytes remain identical to the accepted release."
   }
 }

hand-pipeline-catalog.json

@@ -44,5 +44,5 @@
     "soma": "https://huggingface.co/nvidia/SOMA-X",
     "runtime": "https://github.com/NVlabs/GEM-X/blob/20555befe2fbbe5b2015e1fff7ff214dc18ea9d6/ATTRIBUTIONS.md"
   },
-  "lerobot_revision": "850bb584c545ebe9bfcefdcd9b02391cba1c4638"
+  "lerobot_revision": "33c4ca366114b8717bb5df6ed4f412387af12dfe"
 }

hand-worker-image.json

@@ -2,13 +2,15 @@
   "recipe": "egohand-soma-v3-checkpoint-overlap-v4",
   "build_verified": true,
   "image_repo": "finerobotics/annotation-hands-worker",
-  "image_revision": "775ee404b1c327b6f952561f761c5be27d455122",
+  "image_revision": "a42cb4f1194cf843c14ad3a8b4689b7303ffc36e",
   "image": "hf.co/spaces/finerobotics/annotation-hands-worker",
-  "image_version_sha256": "96757674746f24b8e2031e6d0ca656b8d24681eb6493a2e826edbbd735dbaf6f",
+  "image_version_sha256": "f151a235a263afe5c3e3c5e7ab16ab7d15f16927edb9fb0a1666f81d9eb537d3",
   "assets": {
-    "repo": "finerobotics/egohand-soma-runtime",
-    "revision": "cb082150aed8612fd50a6a78a0e37deebf24b6c3",
-    "manifest_sha256": "f6fb26f2f1c2d26a4622a67c5e775701564e22fe869180df6c2803de00580c91"
+    "repo": "finerobotics/annotation-hands-worker",
+    "repo_type": "space",
+    "revision": "a42cb4f1194cf843c14ad3a8b4689b7303ffc36e",
+    "manifest_sha256": "f6fb26f2f1c2d26a4622a67c5e775701564e22fe869180df6c2803de00580c91",
+    "embedded": true
   },
   "backend": {
     "root": "/opt/camera-adapter",
@@ -50,5 +52,13 @@
     "calibration_mode": "estimate-moge",
     "metric_accuracy_validated": false,
     "compute_flavor": "h200"
+  },
+  "distribution_validation": {
+    "previous_gpu_accepted_image_revision": "775ee404b1c327b6f952561f761c5be27d455122",
+    "model_manifest_unchanged": true,
+    "native_runtime_unchanged": true,
+    "public_source_downloads": true,
+    "all_model_files_verified_at_build": true,
+    "new_gpu_inference_run": false
   }
 }

hand_actions.py

@@ -95,15 +95,11 @@ def catalog():
 
 
 def prepare_hands(selection, camera, options, token):
-    """Validate calibration, orientation, compute and asset access before quoting."""
-    from huggingface_hub import HfApi
-
+    """Validate calibration, orientation and compute without a model-access login."""
     options = options or {}
     compute = options.get("compute", "h200")
     if compute not in COMPUTE:
         raise ValueError("Choose a supported HF Jobs GPU")
-    if not token:
-        raise ValueError("Sign in with Hugging Face to use the hand runtime assets")
     feature = selection["info"]["features"][camera]
     names = feature.get("names") or ["height", "width", "channel"]
     shape = dict(zip(names, feature["shape"], strict=True))
@@ -130,12 +126,6 @@ def prepare_hands(selection, camera, options, token):
             "Choose a source dataset whose actions have not already been replaced by hand annotations"
         )
     profile = runtime_configuration()
-    try:
-        HfApi(token=token).model_info(profile["assets"]["repo"], revision=profile["assets"]["revision"])
-    except Exception as exc:
-        raise ValueError(
-            "Your Hugging Face login needs read access to the private EgoHand/SOMA runtime assets"
-        ) from exc
     return {
         "compute": compute,
         "calibration": calibration,

pipeline-source.tar.gz

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ba5218df544d3a6491b97a10cb36506bec38ffddff40ce51a8947560ff52eee4
-size 1869569
+oid sha256:41ba290372651fd9d16c9403ec36d8adc1c90313ae89ec9cd623319340ba9663
+size 1869708

pipeline-version.json

@@ -1,6 +1,6 @@
 {
-  "git_commit": "850bb584c545ebe9bfcefdcd9b02391cba1c4638",
-  "sha256": "ba5218df544d3a6491b97a10cb36506bec38ffddff40ce51a8947560ff52eee4",
+  "git_commit": "33c4ca366114b8717bb5df6ed4f412387af12dfe",
+  "sha256": "41ba290372651fd9d16c9403ec36d8adc1c90313ae89ec9cd623319340ba9663",
   "preset": "abc_phases_schema_with_original_window_recovery",
   "preset_date": "2026-09-25",
   "engine": "lerobot.annotations.runtime",

test_hand_actions.py

@@ -217,13 +217,21 @@ def test_launch_uses_only_hf_gpu_image_and_private_dataset(monkeypatch):
     assert api.run_job.call_count == 1
 
 
-def test_hand_quote_requires_measured_metadata_and_access_before_dispatch(monkeypatch):
+@pytest.mark.parametrize("token", [None, "user-token"])
+def test_hand_quote_validates_metadata_without_model_access_login(monkeypatch, token):
     import hand_actions
 
     camera = "observation.images.ego"
-    calibration = dict(
-        model="pinhole", width=64, height=48, fx=50.0, fy=51.0, cx=32.0, cy=24.0, distortion=[]
-    )
+    calibration = {
+        "model": "pinhole",
+        "width": 64,
+        "height": 48,
+        "fx": 50.0,
+        "fy": 51.0,
+        "cx": 32.0,
+        "cy": 24.0,
+        "distortion": [],
+    }
     selection = {
         "episodes": [{"length": 90}],
         "info": {
@@ -239,20 +247,20 @@ def test_hand_quote_requires_measured_metadata_and_access_before_dispatch(monkey
             }
         },
     }
-    profile = {"assets": {"repo": "private/hand-runtime", "revision": "a" * 40}}
+    profile = {"assets": {"repo": "public/hand-runtime", "repo_type": "space", "revision": "a" * 40}}
     monkeypatch.setattr(hand_actions, "runtime_configuration", lambda: profile)
     api = Mock()
     monkeypatch.setattr("huggingface_hub.HfApi", lambda **kwargs: api)
     quote = hand_actions.prepare_hands(
-        selection, camera, {"compute": "h200", "orientation": "dataset"}, "user-token"
+        selection, camera, {"compute": "h200", "orientation": "dataset"}, token
     )
     assert quote["calibration"] == calibration and quote["orientation"] == "aria-gen1-cw90"
     assert quote["timing"]["seconds"] is None
-    api.model_info.assert_called_once_with("private/hand-runtime", revision="a" * 40)
+    api.model_info.assert_not_called()
     selection["info"]["features"][camera]["info"].pop("camera.intrinsics")
     with pytest.raises(ValueError, match="measured"):
         hand_actions.prepare_hands(selection, camera, {}, "user-token")
-    assert api.model_info.call_count == 1
+    api.model_info.assert_not_called()
     quote = hand_actions.prepare_hands(selection, camera, {"calibration_mode": "estimate-moge"}, "user-token")
     assert quote["calibration"]["method"] == "moge-v2-fov-median-v1"
     assert "fx" not in quote["calibration"]