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New config file format
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space-nuko
parent
5c38958e49
commit
ae091ccf82
@ -1,36 +1,292 @@
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import os.path
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import pprint
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import argparse
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import yaml
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import folder_paths
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parser = argparse.ArgumentParser()
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class AbstractOptionInfo:
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name: str
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parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)")
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parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
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parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
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parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
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parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.")
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parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
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parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
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parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.")
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parser.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
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parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
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def add_argument(self, parser):
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pass
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attn_group = parser.add_mutually_exclusive_group()
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attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization instead of the sub-quadratic one. Ignored when xformers is used.")
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attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.")
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def get_arg_defaults(self, parser):
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pass
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parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
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def convert_to_args_array(self, value):
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pass
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vram_group = parser.add_mutually_exclusive_group()
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vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
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vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
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vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
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vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
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vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
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def convert_to_file_option(self, parser, args):
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pass
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parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
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parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
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parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")
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def save(self, yaml):
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pass
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args = parser.parse_args()
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def load(self, yaml):
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pass
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class OptionInfo(AbstractOptionInfo):
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def __init__(self, name, **parser_args):
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self.name = name
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self.parser_args = parser_args
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def __repr__(self):
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return f'OptionInfo(\'{self.name}\', {pprint.pformat(self.parser_args)})'
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def add_argument(self, parser):
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parser.add_argument(f"--{self.name}", **self.parser_args)
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def get_arg_defaults(self, parser):
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return { self.name: parser.get_default(self.name) }
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def convert_to_args_array(self, value):
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if value is not None:
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return [f"--{self.name}", str(value)]
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return []
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def convert_to_file_option(self, parser, args):
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return args.get(self.name, parser.get_default(self.name))
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def save(self, yaml):
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pass
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def load(self, yaml):
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pass
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class OptionInfoFlag(AbstractOptionInfo):
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def __init__(self, name, **parser_args):
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self.name = name
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self.parser_args = parser_args
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def __repr__(self):
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return f'OptionInfoFlag(\'{self.name}\', {pprint.pformat(self.parser_args)})'
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def add_argument(self, parser):
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parser.add_argument(f"--{self.name}", action="store_true", **self.parser_args)
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def get_arg_defaults(self, parser):
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return { self.name: parser.get_default(self.name) or False }
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def convert_to_args_array(self, value):
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if value:
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return [f"--{self.name}"]
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return []
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def convert_to_file_option(self, parser, args):
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return args.get(self.name, parser.get_default(self.name) or False)
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def save(self, yaml):
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pass
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def load(self, yaml):
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pass
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class OptionInfoEnum(AbstractOptionInfo):
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def __init__(self, name, options):
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self.name = name
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self.options = options
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def __repr__(self):
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return f'OptionInfoEnum(\'{self.name}\', {pprint.pformat(self.options)})'
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def add_argument(self, parser):
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group = parser.add_mutually_exclusive_group()
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for option in self.options:
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group.add_argument(f"--{option.option_name}", action="store_true", help=option.help)
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def get_arg_defaults(self, parser):
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return {} # treat as no flag in the group being passed
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def convert_to_args_array(self, file_value):
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for option in self.options:
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if option.name == file_value:
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return [f"--{option.option_name}"]
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return []
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def convert_to_file_option(self, parser, args):
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for option in self.options:
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if args.get(option.option_name) is True:
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return option.name
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return None
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def load_from_yaml(self, yaml):
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pass
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class OptionInfoEnumChoice:
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name: str
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option_name: str
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help: str
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def __init__(self, name, option_name=None, help=None):
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self.name = name
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if option_name is None:
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option_name = self.name
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self.option_name = option_name
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if help is None:
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help = ""
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self.help = help
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def __repr__(self):
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return f'OptionInfoEnumChoice(\'{self.name}\', \'{self.option_name}\', \'{self.help}\')'
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CONFIG_OPTIONS = [
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("network", [
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OptionInfo("listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0",
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help="Specify the IP address to listen on (default: 127.0.0.1). \
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If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)"),
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OptionInfo("port", type=int, default=8188, help="Set the listen port."),
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OptionInfo("enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*",
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help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'."),
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]),
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("files", [
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OptionInfo("extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append',
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help="Load one or more extra_model_paths.yaml files."),
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OptionInfo("output-directory", type=str, default=None, help="Set the ComfyUI output directory."),
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]),
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("behavior", [
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OptionInfoFlag("auto-launch",
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help="Automatically launch ComfyUI in the default browser."),
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OptionInfoFlag("dont-print-server",
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help="Don't print server output."),
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OptionInfoFlag("quick-test-for-ci",
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help="Quick test for CI."),
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OptionInfoFlag("windows-standalone-build",
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help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup)."),
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]),
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("pytorch", [
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OptionInfo("cuda-device", type=int, default=None, metavar="DEVICE_ID",
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help="Set the id of the cuda device this instance will use."),
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OptionInfoFlag("dont-upcast-attention",
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help="Disable upcasting of attention. Can boost speed but increase the chances of black images."),
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OptionInfoFlag("force-fp32",
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help="Force fp32 (If this makes your GPU work better please report it)."),
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OptionInfo("directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1,
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help="Use torch-directml."),
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OptionInfoEnum("cross-attention", [
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OptionInfoEnumChoice("split", option_name="use-split-cross-attention", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory."),
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OptionInfoEnumChoice("pytorch", option_name="use-pytorch-cross-attention", help="Used to force normal vram use if lowvram gets automatically enabled."),
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]),
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OptionInfoFlag("disable-xformers",
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help="Disable xformers."),
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OptionInfoEnum("vram", [
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OptionInfoEnumChoice("highvram", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory."),
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OptionInfoEnumChoice("normalvram", help="Used to force normal vram use if lowvram gets automatically enabled."),
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OptionInfoEnumChoice("lowvram", help="Split the unet in parts to use less vram."),
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OptionInfoEnumChoice("novram", help="When lowvram isn't enough."),
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OptionInfoEnumChoice("cpu", help="To use the CPU for everything (slow).")
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])
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])
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]
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def make_config_parser(option_infos):
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parser = argparse.ArgumentParser()
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for category, options in option_infos:
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for option in options:
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option.add_argument(parser)
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return parser
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def merge_dicts(dict1, dict2):
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for k in set(dict1.keys()).union(dict2.keys()):
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if k in dict1 and k in dict2:
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if isinstance(dict1[k], dict) and isinstance(dict2[k], dict):
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yield (k, dict(merge_dicts(dict1[k], dict2[k])))
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else:
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# If one of the values is not a dict, you can't continue merging it.
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# Value from second dict overrides one in first and we move on.
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yield (k, dict2[k])
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# Alternatively, replace this with exception raiser to alert you of value conflicts
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elif k in dict1:
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yield (k, dict1[k])
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else:
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yield (k, dict2[k])
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class ComfyConfigLoader:
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def __init__(self, config_path):
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self.config_path = config_path
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self.option_infos = CONFIG_OPTIONS
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self.parser = make_config_parser(self.option_infos)
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def get_arg_defaults(self):
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defaults = {}
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for category, options in self.option_infos:
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for option in options:
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arg_defaults = option.get_arg_defaults(self.parser)
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for k, v in arg_defaults.items():
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k = k.replace('-', '_')
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defaults[k] = v
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return defaults
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def load_from_file(self, yaml_path):
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with open(yaml_path, 'r') as stream:
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raw_config = yaml.safe_load(stream)
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config = {}
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root = raw_config.get("config", {})
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for category, options in self.option_infos:
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if category in root:
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from_file = root[category]
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known_args = []
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for k, v in from_file.items():
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option_info = next((o for o in options if o.name == k), None)
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if option_info is not None:
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known_args = option_info.convert_to_args_array(v)
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parsed, rest = self.parser.parse_known_args(known_args)
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for k, v in vars(parsed).items():
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k = k.replace("-", "_")
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config[k] = v
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if rest:
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print(f"Warning: unparsed args - {pprint.pformat(rest)}")
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return config
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def convert_args_to_options(self, args):
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config = {}
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import pprint; pprint.pp(args)
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for category, options in self.option_infos:
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d = {}
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for option in options:
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k = option.name.replace('-', '_')
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d[k] = option.convert_to_file_option(self.parser, args)
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config[category] = d
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return { "config": config }
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def save_config(self, args):
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options = self.convert_args_to_options(args)
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with open(self.config_path, 'w') as f:
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yaml.dump(options, f, default_flow_style=False, sort_keys=False)
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def get_cli_arguments(self):
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return vars(self.parser.parse_args())
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def parse_args(self):
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defaults = self.get_arg_defaults()
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if not os.path.isfile(self.config_path):
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print(f"Warning: no config file at path '{self.config_path}', creating it")
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config_options = {}
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else:
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config_options = self.load_from_file(self.config_path)
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config_options = dict(merge_dicts(defaults, config_options))
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self.save_config(defaults)
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cli_args = self.get_cli_arguments()
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args = dict(merge_dicts(config_options, cli_args))
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return argparse.Namespace(**args)
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config_loader = ComfyConfigLoader(folder_paths.default_config_path)
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args = config_loader.parse_args()
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if args.windows_standalone_build:
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args.auto_launch = True
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@ -6,6 +6,7 @@ supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors'])
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folder_names_and_paths = {}
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base_path = os.path.dirname(os.path.realpath(__file__))
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default_config_path = os.path.join(base_path, "config.yaml")
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models_dir = os.path.join(base_path, "models")
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folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_ckpt_extensions)
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folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"])
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