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101 lines
4.2 KiB
Python
101 lines
4.2 KiB
Python
from pydantic import BaseModel, Field, field_validator, model_validator
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from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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invocation,
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invocation_output,
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)
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from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, OutputField
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from invokeai.app.invocations.model import ModelIdentifierField
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from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES
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from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType
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class FluxControlNetField(BaseModel):
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image: ImageField = Field(description="The control image")
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control_model: ModelIdentifierField = Field(description="The ControlNet model to use")
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control_weight: float | list[float] = Field(default=1, description="The weight given to the ControlNet")
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begin_step_percent: float = Field(
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default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
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)
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end_step_percent: float = Field(
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default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
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)
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resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
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instantx_control_mode: int | None = Field(default=-1, description=FieldDescriptions.instantx_control_mode)
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@field_validator("control_weight")
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@classmethod
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def validate_control_weight(cls, v: float | list[float]) -> float | list[float]:
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validate_weights(v)
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return v
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@model_validator(mode="after")
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def validate_begin_end_step_percent(self):
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validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
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return self
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@invocation_output("flux_controlnet_output")
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class FluxControlNetOutput(BaseInvocationOutput):
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"""FLUX ControlNet info"""
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control: FluxControlNetField = OutputField(description=FieldDescriptions.control)
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@invocation(
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"flux_controlnet",
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title="FLUX ControlNet",
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tags=["controlnet", "flux"],
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category="conditioning",
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version="1.0.0",
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)
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class FluxControlNetInvocation(BaseInvocation):
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"""Collect FLUX ControlNet info to pass to other nodes."""
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image: ImageField = InputField(description="The control image")
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control_model: ModelIdentifierField = InputField(
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description=FieldDescriptions.controlnet_model,
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ui_model_base=BaseModelType.Flux,
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ui_model_type=ModelType.ControlNet,
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)
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control_weight: float | list[float] = InputField(
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default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
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)
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begin_step_percent: float = InputField(
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default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
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)
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end_step_percent: float = InputField(
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default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
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)
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resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
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# Note: We default to -1 instead of None, because in the workflow editor UI None is not currently supported.
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instantx_control_mode: int | None = InputField(default=-1, description=FieldDescriptions.instantx_control_mode)
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@field_validator("control_weight")
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@classmethod
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def validate_control_weight(cls, v: float | list[float]) -> float | list[float]:
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validate_weights(v)
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return v
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@model_validator(mode="after")
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def validate_begin_end_step_percent(self):
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validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
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return self
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def invoke(self, context: InvocationContext) -> FluxControlNetOutput:
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return FluxControlNetOutput(
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control=FluxControlNetField(
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image=self.image,
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control_model=self.control_model,
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control_weight=self.control_weight,
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begin_step_percent=self.begin_step_percent,
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end_step_percent=self.end_step_percent,
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resize_mode=self.resize_mode,
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instantx_control_mode=self.instantx_control_mode,
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),
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)
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