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https://github.com/comfyanonymous/ComfyUI.git
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c5e7b9cdaf
Adds opt-in process isolation for custom nodes using pyisolate's bwrap sandbox and JSON-RPC bridge. Each isolated node pack runs in its own child process with zero-copy tensor transfer via shared memory. Core infrastructure: - CLI flag --use-process-isolation to enable isolation - Host/child startup fencing via PYISOLATE_CHILD env var - Manifest-driven node discovery and extension loading - JSON-RPC bridge between host and child processes - Shared memory forensics for leak detection Proxy layer: - ModelPatcher, CLIP, VAE, and ModelSampling proxies - Host service proxies (folder_paths, model_management, progress, etc.) - Proxy base with automatic method forwarding Execution integration: - Extension wrapper with V3 hidden param mapping - Runtime helpers for isolated node execution - Host policy for node isolation decisions - Fenced sampler device handling and model ejection parity Serializers for cross-process data transfer: - File3D (GLB), PLY (structured + gaussian), NPZ (streaming frames), VIDEO (VideoFromFile + VideoFromComponents) serializers - data_type flag in SerializerRegistry for type-aware dispatch - Isolated get_temp_directory() fence New core save nodes: - SavePLY and SaveNPZ with comfytype registrations (Ply, Npz) DynamicVRAM compatibility: - comfy-aimdo early init gated by isolation fence Tests: - Integration and policy tests for isolation lifecycle - Manifest loader, host policy, proxy, and adapter unit tests Depends on: pyisolate >= 0.9.2
35 lines
1.0 KiB
Python
35 lines
1.0 KiB
Python
import os
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import folder_paths
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from comfy_api.latest import io
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from comfy_api.latest._util.ply_types import PLY
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class SavePLY(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="SavePLY",
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display_name="Save PLY",
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category="3d",
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is_output_node=True,
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inputs=[
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io.Ply.Input("ply"),
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io.String.Input("filename_prefix", default="pointcloud/ComfyUI"),
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],
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)
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@classmethod
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def execute(cls, ply: PLY, filename_prefix: str) -> io.NodeOutput:
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full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(
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filename_prefix, folder_paths.get_output_directory()
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)
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f = f"{filename}_{counter:05}_.ply"
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ply.save_to(os.path.join(full_output_folder, f))
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return io.NodeOutput(ui={"pointclouds": [{"filename": f, "subfolder": subfolder, "type": "output"}]})
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NODE_CLASS_MAPPINGS = {
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"SavePLY": SavePLY,
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}
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