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 d644874c17..c047fa9beb 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -265,24 +265,25 @@ 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 0e685e5a9a..b5e686ec64 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -670,15 +670,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             else:
                 latent_control_input = latent_model_input
             # control_data should be type List[ControlNetData]
-            # this loop covers both ControlNet (1 ControlNetData in list)
+            # this loop covers both ControlNet (one ControlNetData in list)
             #      and MultiControlNet (multiple ControlNetData in list)
             for i, control_datum in enumerate(control_data):
                 # print("controlnet", i, "==>", type(control_datum))
                 first_control_step = math.floor(control_datum.begin_step_percent * total_step_count)
                 last_control_step = math.ceil(control_datum.end_step_percent * total_step_count)
-                # apply_control_this_step = step_index >= first_control_step and step_index <= last_control_step
+                # only apply controlnet if current step is within the controlnet's begin/end step range
                 if step_index >= first_control_step and step_index <= last_control_step:
-                    print("running controlnet", i, "for step", step_index)
+                    # print("running controlnet", i, "for step", step_index)
                     down_samples, mid_sample = control_datum.model(
                         sample=latent_control_input,
                         timestep=timestep,
@@ -709,8 +709,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             conditioning_data.guidance_scale,
             step_index=step_index,
             total_step_count=total_step_count,
-            down_block_additional_residuals=down_block_res_samples,
-            mid_block_additional_residual=mid_block_res_sample,
+            down_block_additional_residuals=down_block_res_samples,  # from controlnet(s)
+            mid_block_additional_residual=mid_block_res_sample,      # from controlnet(s)
         )
 
         # compute the previous noisy sample x_t -> x_t-1