mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
from builtins import float
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from typing import List, Literal, Optional, Union
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from pydantic import BaseModel, Field, root_validator, validator
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from invokeai.app.invocations.primitives import ImageField
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from ...backend.model_management import BaseModelType
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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FieldDescriptions,
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Input,
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InputField,
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InvocationContext,
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OutputField,
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UIType,
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invocation,
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invocation_output,
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)
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CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
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CONTROLNET_RESIZE_VALUES = Literal[
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"just_resize",
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"crop_resize",
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"fill_resize",
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"just_resize_simple",
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]
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class ControlNetModelField(BaseModel):
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"""ControlNet model field"""
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model_name: str = Field(description="Name of the ControlNet model")
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base_model: BaseModelType = Field(description="Base model")
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class ControlField(BaseModel):
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image: ImageField = Field(description="The control image")
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control_model: ControlNetModelField = Field(description="The ControlNet model to use")
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control_weight: Union[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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control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
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resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
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@validator("control_weight")
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def validate_control_weight(cls, v):
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"""Validate that all control weights in the valid range"""
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if isinstance(v, list):
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for i in v:
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if i < -1 or i > 2:
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raise ValueError("Control weights must be within -1 to 2 range")
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else:
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if v < -1 or v > 2:
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raise ValueError("Control weights must be within -1 to 2 range")
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return v
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@invocation_output("control_output")
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class ControlOutput(BaseInvocationOutput):
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"""node output for ControlNet info"""
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# Outputs
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control: ControlField = OutputField(description=FieldDescriptions.control)
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@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.0.0")
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class ControlNetInvocation(BaseInvocation):
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"""Collects ControlNet info to pass to other nodes"""
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# Inputs
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image: ImageField = InputField(description="The control image")
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control_model: ControlNetModelField = InputField(
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default="lllyasviel/sd-controlnet-canny", description=FieldDescriptions.controlnet_model, input=Input.Direct
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)
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control_weight: Union[float, List[float]] = InputField(
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default=1.0, description="The weight given to the ControlNet", ui_type=UIType.Float
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)
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begin_step_percent: float = InputField(
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default=0, ge=-1, le=2, 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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control_mode: CONTROLNET_MODE_VALUES = InputField(default="balanced", description="The control mode used")
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resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
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def invoke(self, context: InvocationContext) -> ControlOutput:
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return ControlOutput(
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control=ControlField(
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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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control_mode=self.control_mode,
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resize_mode=self.resize_mode,
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),
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)
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