xinyun/ComfyUI
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Merge 6f4d889053b61b378d8d0c1fc1265b5bcfd674a2 into fd271dedfde6e192a1f1a025521070876e89e04a

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Chakib Benziane 2025-12-08 17:52:33 +08:00 committed by GitHub
commit 0d7a82ddbb
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2 changed files with 16 additions and 4 deletions
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1
comfy/cli_args.py
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@ -90,6 +90,7 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE"
parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
parser.add_argument("--total-ram", type=float, default=0, help="Maximum system RAM visible to comfy in GB (default 0: all)")
class LatentPreviewMethod(enum.Enum):
NoPreviews = "none"

19
comfy/model_management.py
View File

@ -192,8 +192,12 @@ def get_total_memory(dev=None, torch_total_too=False):
if dev is None:
dev = get_torch_device()
if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
mem_total = psutil.virtual_memory().total
if hasattr(dev, "type") and (dev.type == "cpu" or dev.type == "mps"):
mem_total = 0
if args.total_ram != 0:
mem_total = args.total_ram * 1024 * 1024
else:
mem_total = psutil.virtual_memory().total
mem_total_torch = mem_total
else:
if directml_enabled:
@ -236,8 +240,15 @@ def mac_version():
return None
total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
total_ram = psutil.virtual_memory().total / (1024 * 1024)
logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
total_ram = 0
if args.total_ram != 0:
total_ram = args.total_ram * (1024) # arg in GB
else:
total_ram = psutil.virtual_memory().total / (1024 * 1024)
logging.info(
"Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)
)
try:
logging.info("pytorch version: {}".format(torch_version))