nan
c5b77c5c85
- Introduced `onnx_moge2.py` for MoGe-2 ONNX inference with NumPy post-processing. - Added `onnx_ben2.py` for BEN2 ONNX inference with Pillow and NumPy preprocessing. - Created `onnx_da3.py` for Depth Anything 3 inference with ONNX Runtime and NumPy. - Implemented shared ONNX Runtime session helpers in `onnx_runtime.py`. - Developed unit tests for server functionalities in `test_server.py`, including model caching and job management. - Enhanced job server to handle frame extraction without requiring models.
55 lines
1.2 KiB
Python
55 lines
1.2 KiB
Python
from pathlib import Path
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import bpy
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from .blendjob import BlenderJobRuntime
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from .server import server
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DEFAULT_STORAGE_ROOT = Path.home() / ".snapform"
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def configured_storage_root():
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addon = bpy.context.preferences.addons.get(__package__)
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configured = getattr(getattr(addon, "preferences", None), "storage_root", "")
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if configured:
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return Path(bpy.path.abspath(configured))
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return DEFAULT_STORAGE_ROOT
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ENVIRONMENT = {
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"python": "3.12",
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"packages": [
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"numpy==2.4.2",
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"pillow==12.1.1",
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"av==17.0.0",
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"trimesh==4.11.2",
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"huggingface-hub==1.4.1",
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],
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"platform_packages": {
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"windows": ["onnxruntime-directml==1.24.4"],
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"default": ["onnxruntime==1.24.4"],
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},
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}
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def post_install(job_runtime):
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errors = []
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for model in ("MOGE2_VITS_NORMAL", "BEN2_BASE"):
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try:
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job_runtime.request("download-model", {"model": model})
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except RuntimeError as error:
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errors.append(str(error))
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if errors:
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raise RuntimeError("; ".join(errors))
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runtime = BlenderJobRuntime(
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server,
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storage_root=configured_storage_root,
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environment=ENVIRONMENT,
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post_install=post_install,
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)
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JobOperatorBase = runtime.JobOperatorBase
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