diff --git a/invokeai/app/invocations/control_adapter.py b/invokeai/app/invocations/control_adapter.py
new file mode 100644
index 0000000000..189c4baed5
--- /dev/null
+++ b/invokeai/app/invocations/control_adapter.py
@@ -0,0 +1,109 @@
+from builtins import bool, float
+from typing import Dict, List, Literal, Optional, Union
+
+from pydantic import BaseModel, Field, validator
+from invokeai.app.invocations.primitives import ImageField
+
+from ...backend.model_management import BaseModelType
+
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    FieldDescriptions,
+    InputField,
+    Input,
+    InvocationContext,
+    OutputField,
+    UIType,
+    tags,
+    title,
+)
+
+
+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
+
+
+class ControlOutput(BaseInvocationOutput):
+    """node output for ControlNet info"""
+
+    type: Literal["control_output"] = "control_output"
+
+    # Outputs
+    control: ControlField = OutputField(description=FieldDescriptions.control)
+
+
+@title("ControlNet")
+@tags("controlnet")
+class ControlNetInvocation(BaseInvocation):
+    """Collects ControlNet info to pass to other nodes"""
+
+    type: Literal["controlnet"] = "controlnet"
+
+    # 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,
+            ),
+        )