diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index 44433ec623..b07a406089 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -282,13 +282,9 @@ class TextToLatentsInvocation(BaseInvocation):
             control_models = []
             for control_info in control_list:
                 # handle control models
-                # FIXME: change this to dropdown menu
                 control_model = ControlNetModel.from_pretrained(control_info.control_model,
                                                                 torch_dtype=model.unet.dtype).to(model.device)
                 control_models.append(control_model)
-                # handle control images
-                # loading controlnet image (currently requires pre-processed image)
-                # control_image = prep_control_image(control_info.image)
                 control_image_field = control_info.image
                 input_image = context.services.images.get(control_image_field.image_type, control_image_field.image_name)
                 # FIXME: still need to test with different widths, heights, devices, dtypes
@@ -298,7 +294,6 @@ class TextToLatentsInvocation(BaseInvocation):
                 control_image = model.prepare_control_image(
                     image=input_image,
                     do_classifier_free_guidance=do_classifier_free_guidance,
-                    # do_classifier_free_guidance=True,
                     width=control_width_resize,
                     height=control_height_resize,
                     # batch_size=batch_size * num_images_per_prompt,
@@ -312,17 +307,11 @@ class TextToLatentsInvocation(BaseInvocation):
                                               begin_step_percent=control_info.begin_step_percent,
                                               end_step_percent=control_info.end_step_percent)
                 control_data.append(control_item)
-            # multi_control = MultiControlNetModel(control_models) # no longer need MultiControlNetModel
-            # model.control_model = multi_control
-            # model.control_model = control_models
+                # MultiControlNetModel has been refactored out, just need list[ControlNetData]
         return control_data
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         noise = context.services.latents.get(self.noise.latents_name)
-        # latents_shape = noise.shape
-        # assuming fixed dimensional scaling of 8:1 for image:latents
-        # control_height_resize = latents_shape[2] * 8
-        # control_width_resize = latents_shape[3] * 8
 
         # Get the source node id (we are invoking the prepared node)
         graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
@@ -337,8 +326,7 @@ class TextToLatentsInvocation(BaseInvocation):
         print("type of control input: ", type(self.control))
         control_data = self.prep_control_data(model=model, context=context, control_input=self.control,
                                               latents_shape=noise.shape,
-                                              do_classifier_free_guidance=(self.cfg_scale >= 1.0),
-                                              )
+                                              do_classifier_free_guidance=(self.cfg_scale >= 1.0))
 
         # TODO: Verify the noise is the right size
         result_latents, result_attention_map_saver = model.latents_from_embeddings(