Revert ControlNetInvocation changes.

invokeai/app/invocations/control_adapter.py

@@ -1,104 +0,0 @@
-from builtins import float
-from typing import List, Literal, Optional, Union
-
-from pydantic import BaseModel, Field, root_validator, validator
-
-from invokeai.app.invocations.primitives import ImageField
-
-from ...backend.model_management import BaseModelType
-from .baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    FieldDescriptions,
-    Input,
-    InputField,
-    InvocationContext,
-    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", version="1.0.0")
-class ControlNetInvocation(BaseInvocation):
-    """Collects ControlNet info to pass to other nodes"""
-
-    # Inputs
-    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,
-            ),
-        )

invokeai/app/invocations/controlnet_image_processors.py

@@ -1,7 +1,8 @@
 # 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, Optional
+from typing import Dict, List, Literal, Optional, Union
 
 import cv2
 import numpy as np
@@ -23,11 +24,105 @@ 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, FieldDescriptions, InputField, InvocationContext, invocation
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    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", version="1.0.0")
+class ControlNetInvocation(BaseInvocation):
+    """Collects ControlNet info to pass to other nodes"""
+
+    image: ImageField = InputField(description="The control image")
+    control_model: ControlNetModelField = InputField(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,
+            ),
+        )
 
 
 @invocation(

invokeai/app/invocations/latent.py

@@ -64,7 +64,7 @@ from .baseinvocation import (
     invocation_output,
 )
 from .compel import ConditioningField
-from .control_adapter import ControlField
+from .controlnet_image_processors import ControlField
 from .model import ModelInfo, UNetField, VaeField
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())

invokeai/app/invocations/metadata.py

@@ -11,7 +11,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.control_adapter import ControlField
+from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 

invokeai/app/invocations/onnx.py

@@ -13,7 +13,12 @@ from pydantic import BaseModel, Field, validator
 from tqdm import tqdm
 
 from invokeai.app.invocations.metadata import CoreMetadata
-from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
+from invokeai.app.invocations.primitives import (
+    ConditioningField,
+    ConditioningOutput,
+    ImageField,
+    ImageOutput,
+)
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend import BaseModelType, ModelType, SubModelType
 
@@ -25,8 +30,8 @@ from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     FieldDescriptions,
-    InputField,
     Input,
+    InputField,
     InvocationContext,
     OutputField,
     UIComponent,
@@ -34,8 +39,14 @@ from .baseinvocation import (
     invocation,
     invocation_output,
 )
-from .control_adapter import ControlField
+from .controlnet_image_processors import ControlField
-from .latent import SAMPLER_NAME_VALUES, LatentsField, LatentsOutput, build_latents_output, get_scheduler
+from .latent import (
+    SAMPLER_NAME_VALUES,
+    LatentsField,
+    LatentsOutput,
+    build_latents_output,
+    get_scheduler,
+)
 from .model import ClipField, ModelInfo, UNetField, VaeField
 
 ORT_TO_NP_TYPE = {
@@ -95,9 +106,10 @@ class ONNXPromptInvocation(BaseInvocation):
                     print(f'Warn: trigger: "{trigger}" not found')
             if loras or ti_list:
                 text_encoder.release_session()
-            with ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras), ONNXModelPatcher.apply_ti(
+            with (
-                orig_tokenizer, text_encoder, ti_list
+                ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras),
-            ) as (tokenizer, ti_manager):
+                ONNXModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager),
+            ):
                 text_encoder.create_session()
 
                 # copy from