294 lines
9.3 KiB
Python
294 lines
9.3 KiB
Python
# (c) TenserTensor || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
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import dataclasses
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import re
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from dataclasses import dataclass
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from typing import override
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import torch
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from comfy.clip_vision import Output
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from comfy_api.latest import IO, ComfyExtension, Input
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from .nodes_workflow import TTWorkflowSettings, WorkflowSettings
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CATEGORY = "TenserTensor/Context"
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@dataclass
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class TTContext():
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"""
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Shared context object passed between TenserTensor context nodes.
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Holds all pipeline state as optional fields.
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All fields default to None and are populated incrementally as the workflow progresses.
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Use create() to instantiate from kwargs, and set_attr/get_attr for safe field access.
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Workflow config fields are accessible via set_workflow_config_attr/get_workflow_config_attr.
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"""
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cfg: IO.Float | None = None
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clip: IO.Clip | None = None
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control_net: IO.ControlNet | None = None
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conditioning: IO.Conditioning | None = None
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guider: IO.Guider | None = None
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image: IO.Image | None = None
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latent: IO.Latent | None = None
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mask: IO.Mask | None = None
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model: IO.Model | None = None
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negative: IO.Conditioning | None = None
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positive: IO.Conditioning | None = None
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rnd_noise: IO.Noise | None = None
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sampler: IO.Sampler | None = None
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seed: IO.Int | None = None
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sigmas: IO.Sigmas | None = None
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steps: IO.Int | None = None
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vae: IO.Vae | None = None
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workflow_config: WorkflowSettings | None = None
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sampler_name: IO.String | None = None
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scheduler: IO.String | None = None
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@classmethod
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def create(cls, **kwargs):
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ctx = cls()
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for field in dataclasses.fields(ctx):
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ctx.set_attr(field.name, kwargs.get(field.name, None))
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return ctx
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def set_attr(self, key, attr):
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if hasattr(self, key):
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setattr(self, key, attr)
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else:
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self.set_workflow_config_attr(key, attr)
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def get_attr(self, key, default=None):
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if hasattr(self, key):
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val = getattr(self, key)
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return val if val is not None else default
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else:
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self.get_workflow_config_attr(key, default)
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def update_attrs(self, **kwargs):
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for key in kwargs.keys():
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if hasattr(self, key):
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val = kwargs.get(key)
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if val is not None:
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setattr(self, key, val)
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def set_workflow_config_attr(self, key, attr):
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if self.workflow_config:
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self.workflow_config.set_attr(key, attr)
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else:
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raise AttributeError(f"ERROR: Workflow Config is not set in context")
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def get_workflow_config_attr(self, key, default=None):
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if self.workflow_config:
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return self.workflow_config.get_attr(key, default)
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else:
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raise AttributeError(f"ERROR: Workflow Config is not set in context")
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def update_workflow_config_attrs(self, **kwargs):
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if self.workflow_config:
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for key in kwargs.keys():
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if hasattr(self.workflow_config, key):
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val = kwargs.get(key)
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if val is not None:
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self.workflow_config.set_attr(key, val)
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else:
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raise AttributeError(f"ERROR: Workflow Config is not set in context")
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@IO.comfytype(io_type="TT_CONTEXT")
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class Context:
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Type = TTContext
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class Input(IO.Input):
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def __init__(self, id: str, **kwargs):
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super().__init__(id, **kwargs)
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class Output(IO.Output):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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class ContextNode(IO.ComfyNode):
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"""
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Abstract base class for all context nodes.
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Handles context creation, delegation to handle(), and building return tuple.
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Subclasses should set INPUT_TYPES_DICT, RETURN_TYPES, RETURN_NAMES from a Schema,
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and override handle() to implement node-specific logic.
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"""
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REQUIRED = ()
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OPTIONAL = ()
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RETURNS = ()
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F_TYPES = {}
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@classmethod
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def fill_field_types(cls):
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cls.F_TYPES = {f.name: f.type.__args__[0] for f in dataclasses.fields(TTContext)}
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@classmethod
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def inputs(cls) -> list[Input]:
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def get_input(input_name, optional):
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match input_name:
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case "context":
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return Context.Input(input_name, optional=optional)
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case _:
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f_type = cls.F_TYPES[input_name]
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if f_type in (IO.Boolean, IO.Int, IO.Float, IO.String,):
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return f_type.Input(input_name, optional=optional, force_input=True)
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else:
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return f_type.Input(input_name, optional=optional)
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input_schema = []
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for f_name in cls.REQUIRED:
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input_schema.append(get_input(f_name, optional=False))
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for f_name in cls.OPTIONAL:
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input_schema.append(get_input(f_name, optional=True))
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return input_schema
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@classmethod
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def outputs(cls) -> list[Output]:
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output_schema = []
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for f_name in cls.RETURNS if len(cls.RETURNS) > 0 else cls.OPTIONAL:
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output_schema.append(
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Context.Output(display_name="CONTEXT")
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if f_name == "context"
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else cls.F_TYPES[f_name].Output(display_name=f_name.upper())
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)
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return output_schema
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@classmethod
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def node_output(cls, context) -> IO.NodeOutput:
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args = [context]
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for f_name in cls.RETURNS if len(cls.RETURNS) > 0 else cls.OPTIONAL:
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if f_name != "context":
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wf = context.get_attr("workflow_config")
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if hasattr(wf, f_name):
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args.append(wf.get_attr(f_name))
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else:
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args.append(context.get_attr(f_name))
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return IO.NodeOutput(*args)
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@classmethod
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def display_name(cls):
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return re.sub(r'(?<=[a-z])(?=[A-Z])', ' ', cls.__name__.replace("TT_", "TT ").replace("Node", ""))
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@classmethod
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def define_schema(cls) -> IO.Schema:
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if len(cls.F_TYPES) == 0:
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cls.fill_field_types()
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return IO.Schema(
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node_id=cls.__name__,
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display_name=cls.display_name(),
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category=CATEGORY,
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inputs=cls.inputs(),
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outputs=cls.outputs(),
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)
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@classmethod
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def handle(cls, context, **kwargs):
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workflow_config = kwargs.get("workflow_config")
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kwargs["workflow_config"] = (
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TTWorkflowSettings.create(**kwargs)
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if not workflow_config
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else workflow_config.update_attrs(**kwargs)
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)
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context.update_attrs(**kwargs)
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return (context, kwargs,)
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@classmethod
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def execute(cls, context=None, **kwargs) -> IO.NodeOutput:
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if not context:
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context = TTContext.create(**kwargs)
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cls.handle(context, **kwargs)
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else:
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context, kwargs = cls.handle(context, **kwargs)
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return cls.node_output(context)
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class TT_BaseContextNode(ContextNode):
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"""Base context node. Accepts model, clip, vae, latent and optional workflow_config."""
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REQUIRED = ("model", "clip", "vae", "latent")
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OPTIONAL = ("workflow_config",)
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RETURNS = ("context",)
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class TT_BaseContextFlux2Node(ContextNode):
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"""
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Base context node for FLUX2 workflows. Accepts model, clip, vae, latent, sampler, noise, and sigmas.
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Optionally accepts workflow_config. Returns context.
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"""
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REQUIRED = ("model", "clip", "vae", "latent", "sampler", "rnd_noise", "sigmas",)
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OPTIONAL = ("workflow_config",)
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RETURNS = ("context",)
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class TT_BaseContextPassthroughNode(ContextNode):
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"""Passes context through without modification."""
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REQUIRED = ("context",)
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OPTIONAL = ()
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RETURNS = ("context",)
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class TT_ContextNode(ContextNode):
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"""General-purpose context node for FLUX and SDXL workflows. All fields are optional."""
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REQUIRED = ()
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OPTIONAL = (
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"context", "workflow_config", "model", "clip", "vae", "latent", "positive", "negative", "image",
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"seed", "steps", "cfg",
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)
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RETURNS = ()
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class TT_ContextFlux2Node(ContextNode):
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"""Context node for FLUX2 workflows. All fields are optional."""
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REQUIRED = ()
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OPTIONAL = (
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"context", "workflow_config", "model", "clip", "vae", "latent", "conditioning", "guider", "rnd_noise",
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"sampler", "sigmas", "image", "mask", "seed", "steps", "cfg",
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)
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RETURNS = ()
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# ==============================================================================
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# V3 entrypoint — registers context nodes with ComfyUI
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# ==============================================================================
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class ContextExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[IO.ComfyNode]]:
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return [
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TT_BaseContextNode,
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TT_BaseContextFlux2Node,
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TT_BaseContextPassthroughNode,
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TT_ContextNode,
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TT_ContextFlux2Node,
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]
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async def comfy_entrypoint() -> ContextExtension:
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return ContextExtension()
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# ==============================================================================
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# Re-exports for backward compatibility
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# ==============================================================================
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__all__ = [
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"TT_BaseContextNode",
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"TT_BaseContextFlux2Node",
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"TT_BaseContextPassthroughNode",
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"TT_ContextNode",
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"TT_ContextFlux2Node",
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]
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