diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py
index 6cb9a73976..59b76da01b 100644
--- a/invokeai/app/invocations/controlnet_image_processors.py
+++ b/invokeai/app/invocations/controlnet_image_processors.py
@@ -34,7 +34,7 @@ from .image import ImageOutput, build_image_output, PILInvocationConfig
 class ControlField(BaseModel):
     image: ImageField = Field(default=None, description="processed image")
     control_model: Optional[str] = Field(default=None, description="control model used")
-    control_weight: Optional[float] = Field(default=None, description="weight given to controlnet")
+    control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
     begin_step_percent: float = Field(default=0, ge=0, le=1,
                                                 description="% of total steps at which controlnet is first applied")
     end_step_percent: float = Field(default=1, ge=0, le=1,
@@ -61,7 +61,7 @@ class ControlNetInvocation(BaseInvocation):
     # Inputs
     image: ImageField = Field(default=None, description="image to process")
     control_model: str = Field(default=None, description="control model to use")
-    control_weight: float = Field(default=0.5, ge=0, le=1, description="weight given to controlnet")
+    control_weight: float = Field(default=1.0, ge=0, le=1, description="weight given to controlnet")
     # TODO: add support in backend core for begin_step_percent, end_step_percent, guess_mode
     begin_step_percent: float = Field(default=0, ge=0, le=1,
                                         description="% of total steps at which controlnet is first applied")
diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index ad4adc6fbb..65f2bf22a9 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -260,24 +260,25 @@ class TextToLatentsInvocation(BaseInvocation):
         conditioning_data = self.get_conditioning_data(context, model)
         # print("type of control input: ", type(self.control))
         if self.control is None:
-            print("control input is None")
+            # print("control input is None")
             control_list = None
         elif isinstance(self.control, list) and len(self.control) == 0:
-            print("control input is empty list")
+            # print("control input is empty list")
             control_list = None
         elif isinstance(self.control, ControlField):
-            print("control input is ControlField")
+            # print("control input is ControlField")
             control_list = [self.control]
         elif isinstance(self.control, list) and len(self.control) > 0 and isinstance(self.control[0], ControlField):
-            print("control input is list[ControlField]")
+            # print("control input is list[ControlField]")
             control_list = self.control
         else:
-            print("input control is unrecognized:", type(self.control))
+            #print("input control is unrecognized:", type(self.control))
             control_list = None
 
         #if (self.control is None or (isinstance(self.control, list) and len(self.control)==0)):
         if (control_list is None):
             control_models = None
+            control_data = None
         # from above handling, any control that is not None should now be of type list[ControlField]
         else:
             # FIXME: add checks to skip entry if model or image is None
diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index 5fe8289741..ec2902e4d6 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -670,7 +670,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             else:
                 latent_control_input = latent_model_input
             # control_data should be type List[ControlNetData]
-
             # this loop covers both ControlNet (one ControlNetData in list)
             #      and MultiControlNet (multiple ControlNetData in list)
             for i, control_datum in enumerate(control_data):