diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index 0e1f7e1a5f..74952e7896 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -334,72 +334,6 @@ class TextToLatentsInvocation(BaseInvocation):
             multi_control = MultiControlNetModel(control_models)
             model.control_model = multi_control
 
-        print("type of control input: ", type(self.control))
-
-        if (self.control is None):
-            control_model = None
-            control_image_field = None
-            control_weight = None
-        else:
-            control_model_name = self.control.control_model
-            control_image_field = self.control.image
-            control_weight = self.control.control_weight
-
-        # # loading controlnet model
-        # if (self.control_model is None or self.control_model==''):
-        #     control_model = None
-        # else:
-            # FIXME: change this to dropdown menu?
-            # FIXME: generalize so don't have to hardcode torch_dtype and device
-            control_model = ControlNetModel.from_pretrained(control_model_name,
-                                                            torch_dtype=torch.float16).to("cuda")
-        model.control_model = control_model
-
-        # loading controlnet image (currently requires pre-processed image)
-        control_image = (
-            None if control_image_field is None
-            else context.services.images.get(
-                control_image_field.image_type, control_image_field.image_name
-            )
-        )
-
-        latents_shape = noise.shape
-        control_height_resize = latents_shape[2] * 8
-        control_width_resize = latents_shape[3] * 8
-
-        # copied from old backend/txt2img.py
-        # FIXME: still need to test with different widths, heights, devices, dtypes
-        #        and add in batch_size, num_images_per_prompt?
-        if control_image is not None:
-            if isinstance(control_model, ControlNetModel):
-                control_image = model.prepare_control_image(
-                    image=control_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,
-                    # num_images_per_prompt=num_images_per_prompt,
-                    device=control_model.device,
-                    dtype=control_model.dtype,
-                )
-            elif isinstance(control_model, MultiControlNetModel):
-                images = []
-                for image_ in control_image:
-                    image_ = model.prepare_control_image(
-                        image=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,
-                        # num_images_per_prompt=num_images_per_prompt,
-                        device=control_model.device,
-                        dtype=control_model.dtype,
-                    )
-                    images.append(image_)
-                control_image = images
-
         # TODO: Verify the noise is the right size
         result_latents, result_attention_map_saver = model.latents_from_embeddings(
             latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)),