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More cleanup after rebasing to main.

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This commit is contained in:
user1 2023-08-30 18:29:06 -07:00
parent 0c17f8604f
commit 79db0e9e93
2 changed files with 5 additions and 95 deletions
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11
invokeai/app/invocations/control_adapter.py
View File

@ -15,8 +15,8 @@ from .baseinvocation import (
InvocationContext,
OutputField,
UIType,
tags,
title,
invocation,
invocation_output,
)
@ -28,7 +28,6 @@ CONTROLNET_RESIZE_VALUES = Literal[
"just_resize_simple",
]
class ControlNetModelField(BaseModel):
"""ControlNet model field"""
@ -61,7 +60,7 @@ class ControlField(BaseModel):
raise ValueError("Control weights must be within -1 to 2 range")
return v
@invocation_output("control_output")
class ControlOutput(BaseInvocationOutput):
"""node output for ControlNet info"""
@ -71,8 +70,8 @@ class ControlOutput(BaseInvocationOutput):
control: ControlField = OutputField(description=FieldDescriptions.control)
@title("ControlNet")
@tags("controlnet")
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet")
class ControlNetInvocation(BaseInvocation):
"""Collects ControlNet info to pass to other nodes"""

89
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -1,5 +1,4 @@
# Invocations for ControlNet image preprocessors
# initial implementation by Gregg Helt, 2023
# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
from builtins import bool, float
from typing import Dict, List, Literal, Optional, Union
@ -24,12 +23,9 @@ from controlnet_aux import (
)
from controlnet_aux.util import HWC3, ade_palette
from PIL import Image
from pydantic import BaseModel, Field, validator
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from ...backend.model_management import BaseModelType
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import (
BaseInvocation,
@ -41,91 +37,6 @@ from .baseinvocation import (
OutputField,
UIType,
invocation,
invocation_output,
)
CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
CONTROLNET_RESIZE_VALUES = Literal[
"just_resize",
"crop_resize",
"fill_resize",
"just_resize_simple",
]
class ControlNetModelField(BaseModel):
"""ControlNet model field"""
model_name: str = Field(description="Name of the ControlNet model")
base_model: BaseModelType = Field(description="Base model")
class ControlField(BaseModel):
image: ImageField = Field(description="The control image")
control_model: ControlNetModelField = Field(description="The ControlNet model to use")
control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
begin_step_percent: float = Field(
default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
)
end_step_percent: float = Field(
default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
)
control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
@validator("control_weight")
def validate_control_weight(cls, v):
"""Validate that all control weights in the valid range"""
if isinstance(v, list):
for i in v:
if i < -1 or i > 2:
raise ValueError("Control weights must be within -1 to 2 range")
else:
if v < -1 or v > 2:
raise ValueError("Control weights must be within -1 to 2 range")
return v
@invocation_output("control_output")
class ControlOutput(BaseInvocationOutput):
"""node output for ControlNet info"""
# Outputs
control: ControlField = OutputField(description=FieldDescriptions.control)
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet")
class ControlNetInvocation(BaseInvocation):
"""Collects ControlNet info to pass to other nodes"""
image: ImageField = InputField(description="The control image")
control_model: ControlNetModelField = InputField(
default="lllyasviel/sd-controlnet-canny", description=FieldDescriptions.controlnet_model, input=Input.Direct
)
control_weight: Union[float, List[float]] = InputField(
default=1.0, description="The weight given to the ControlNet", ui_type=UIType.Float
)
begin_step_percent: float = InputField(
default=0, ge=-1, le=2, description="When the ControlNet is first applied (% of total steps)"
)
end_step_percent: float = InputField(
default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
)
control_mode: CONTROLNET_MODE_VALUES = InputField(default="balanced", description="The control mode used")
resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
def invoke(self, context: InvocationContext) -> ControlOutput:
return ControlOutput(
control=ControlField(
image=self.image,
control_model=self.control_model,
control_weight=self.control_weight,
begin_step_percent=self.begin_step_percent,
end_step_percent=self.end_step_percent,
control_mode=self.control_mode,
resize_mode=self.resize_mode,
),
)