mirror of
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
102 lines
3.7 KiB
Python
102 lines
3.7 KiB
Python
import os
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import importlib.util
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from comfy.cli_args import args, PerformanceFeature
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import subprocess
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#Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
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def get_gpu_names():
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if os.name == 'nt':
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import ctypes
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# Define necessary C structures and types
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class DISPLAY_DEVICEA(ctypes.Structure):
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_fields_ = [
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('cb', ctypes.c_ulong),
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('DeviceName', ctypes.c_char * 32),
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('DeviceString', ctypes.c_char * 128),
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('StateFlags', ctypes.c_ulong),
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('DeviceID', ctypes.c_char * 128),
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('DeviceKey', ctypes.c_char * 128)
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]
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# Load user32.dll
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user32 = ctypes.windll.user32
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# Call EnumDisplayDevicesA
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def enum_display_devices():
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device_info = DISPLAY_DEVICEA()
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device_info.cb = ctypes.sizeof(device_info)
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device_index = 0
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gpu_names = set()
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while user32.EnumDisplayDevicesA(None, device_index, ctypes.byref(device_info), 0):
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device_index += 1
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gpu_names.add(device_info.DeviceString.decode('utf-8'))
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return gpu_names
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return enum_display_devices()
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else:
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gpu_names = set()
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out = subprocess.check_output(['nvidia-smi', '-L'])
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for l in out.split(b'\n'):
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if len(l) > 0:
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gpu_names.add(l.decode('utf-8').split(' (UUID')[0])
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return gpu_names
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blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M",
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"GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620",
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"Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000",
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"Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000",
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"GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M",
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"GeForce GTX 1650", "GeForce GTX 1630", "Tesla M4", "Tesla M6", "Tesla M10", "Tesla M40", "Tesla M60"
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}
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def cuda_malloc_supported():
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try:
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names = get_gpu_names()
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except:
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names = set()
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for x in names:
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if "NVIDIA" in x:
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for b in blacklist:
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if b in x:
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return False
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return True
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version = ""
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try:
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torch_spec = importlib.util.find_spec("torch")
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for folder in torch_spec.submodule_search_locations:
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ver_file = os.path.join(folder, "version.py")
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if os.path.isfile(ver_file):
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spec = importlib.util.spec_from_file_location("torch_version_import", ver_file)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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version = module.__version__
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except:
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pass
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if not args.cuda_malloc:
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try:
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if int(version[0]) >= 2 and "+cu" in version: # enable by default for torch version 2.0 and up only on cuda torch
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if PerformanceFeature.AutoTune not in args.fast: # Autotune has issues with cuda malloc
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args.cuda_malloc = cuda_malloc_supported()
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except:
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pass
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if args.disable_cuda_malloc:
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args.cuda_malloc = False
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if args.cuda_malloc:
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env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None)
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if env_var is None:
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env_var = "backend:cudaMallocAsync"
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elif not args.use_process_isolation:
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env_var += ",backend:cudaMallocAsync"
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os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var
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def get_torch_version_noimport():
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return str(version)
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