diff --git a/control.py b/control.py
index e052a11..ff725dd 100644
--- a/control.py
+++ b/control.py
@@ -228,78 +228,6 @@ class ControlNetAdvanced(ControlBase):
                             o[i] += prev_val
         return out
 
-    def get_control_old(self, x_noisy, t, cond, batched_number):
-        control_prev = None
-        if self.previous_controlnet is not None:
-            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
-
-        if self.timestep_range is not None:
-            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
-                if control_prev is not None:
-                    return control_prev
-                else:
-                    return {}
-
-        output_dtype = x_noisy.dtype
-        if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
-            if self.cond_hint is not None:
-                del self.cond_hint
-            self.cond_hint = None
-            self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
-        if x_noisy.shape[0] != self.cond_hint.shape[0]:
-            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
-
-        if self.control_model.dtype == torch.float16:
-            precision_scope = torch.autocast
-        else:
-            precision_scope = contextlib.nullcontext
-
-        # TODO: select based on progress in diffusion
-        current_timestep_keyframe = self.timestep_keyframes[0]
-
-        with precision_scope(model_management.get_autocast_device(self.device)):
-            context = torch.cat(cond['c_crossattn'], 1)
-            y = cond.get('c_adm', None)
-            control = self.control_model(x=x_noisy, hint=self.cond_hint, timesteps=t, context=context, y=y)
-        out = {'middle':[], 'output': []}
-        autocast_enabled = torch.is_autocast_enabled()
-
-        for i in range(len(control)):
-            if i == (len(control) - 1):
-                key = 'middle'
-                index = 0
-            else:
-                key = 'output'
-                index = i
-            x = control[i]
-            if self.global_average_pooling:
-                x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
-
-            if current_timestep_keyframe.latent_keyframes is not None:
-                # get batch indeces to zero out, AKA latents that should not be influenced by ControlNet
-                indeces_to_zero = set(range(x.size()[0]//2))
-                for keyframe in current_timestep_keyframe.latent_keyframes:
-                    if keyframe.batch_index in indeces_to_zero:
-                        indeces_to_zero.remove(keyframe.batch_index)
-
-                # zero them out by multiplying by zero
-                for batch_index in indeces_to_zero:
-                    x[batch_index] *= 0.0
-                    x[(x.size()[0]//2) + batch_index] *= 0.0
-
-            x *= self.strength * self.weights[i]
-            if x.dtype != output_dtype and not autocast_enabled:
-                x = x.to(output_dtype)
-
-            if control_prev is not None and key in control_prev:
-                prev = control_prev[key][index]
-                if prev is not None:
-                    x += prev
-            out[key].append(x)
-        if control_prev is not None and 'input' in control_prev:
-            out['input'] = control_prev['input']
-        return out
-
     def copy(self):
         c = ControlNetAdvanced(self.control_model, self.timestep_keyframes, global_average_pooling=self.global_average_pooling)
         self.copy_to(c)