diff --git a/adv_control/control.py b/adv_control/control.py
index bf2429f..fc5b8a2 100644
--- a/adv_control/control.py
+++ b/adv_control/control.py
@@ -21,8 +21,8 @@ from .logger import logger
 
 
 class ControlNetAdvanced(ControlNet, AdvancedControlBase):
-    def __init__(self, control_model, timestep_keyframes: TimestepKeyframeGroup, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
-        super().__init__(control_model=control_model, global_average_pooling=global_average_pooling, device=device, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+    def __init__(self, control_model, timestep_keyframes: TimestepKeyframeGroup, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None):
+        super().__init__(control_model=control_model, global_average_pooling=global_average_pooling, compression_ratio=compression_ratio, latent_format=latent_format, device=device, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
         AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controlnet())
 
     def get_universal_weights(self) -> ControlWeights:
@@ -52,18 +52,28 @@ class ControlNetAdvanced(ControlNet, AdvancedControlBase):
         output_dtype = x_noisy.dtype
         # make cond_hint appropriate dimensions
         # TODO: change this to not require cond_hint upscaling every step when self.sub_idxs are present
-        if self.sub_idxs is not None or 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.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
             if self.cond_hint is not None:
                 del self.cond_hint
             self.cond_hint = None
+            compression_ratio = self.compression_ratio
+            if self.vae is not None:
+                compression_ratio *= self.vae.downscale_ratio
             # if self.cond_hint_original length greater or equal to real latent count, subdivide it before scaling
             if self.sub_idxs is not None:
                 actual_cond_hint_orig = self.cond_hint_original
                 if self.cond_hint_original.size(0) < self.full_latent_length:
                     actual_cond_hint_orig = extend_to_batch_size(tensor=actual_cond_hint_orig, batch_size=self.full_latent_length)
-                self.cond_hint = comfy.utils.common_upscale(actual_cond_hint_orig[self.sub_idxs], x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
+                self.cond_hint = comfy.utils.common_upscale(actual_cond_hint_orig[self.sub_idxs], x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, 'nearest-exact', "center")
             else:
-                self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
+                self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, 'nearest-exact', "center")
+            if self.vae is not None:
+                loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+                self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1))
+                comfy.model_management.load_models_gpu(loaded_models)
+            if self.latent_format is not None:
+                self.cond_hint = self.latent_format.process_in(self.cond_hint)
+            self.cond_hint = self.cond_hint.to(device=self.device, dtype=dtype)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to_extend(self.cond_hint, x_noisy.shape[0], batched_number)
 
@@ -71,10 +81,7 @@ class ControlNetAdvanced(ControlNet, AdvancedControlBase):
         self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number, dtype=dtype)
 
         context = cond.get('crossattn_controlnet', cond['c_crossattn'])
-        # uses 'y' in new ComfyUI update
         y = cond.get('y', None)
-        if y is None: # TODO: remove this in the future since no longer used by newest ComfyUI
-            y = cond.get('c_adm', None)
         if y is not None:
             y = y.to(dtype)
         timestep = self.model_sampling_current.timestep(t)
@@ -92,7 +99,7 @@ class ControlNetAdvanced(ControlNet, AdvancedControlBase):
     @staticmethod
     def from_vanilla(v: ControlNet, timestep_keyframe: TimestepKeyframeGroup=None) -> 'ControlNetAdvanced':
         return ControlNetAdvanced(control_model=v.control_model, timestep_keyframes=timestep_keyframe,
-                                  global_average_pooling=v.global_average_pooling, device=v.device, load_device=v.load_device, manual_cast_dtype=v.manual_cast_dtype)
+                                  global_average_pooling=v.global_average_pooling, compression_ratio=v.compression_ratio, latent_format=v.latent_format, device=v.device, load_device=v.load_device, manual_cast_dtype=v.manual_cast_dtype)
 
 
 class T2IAdapterAdvanced(T2IAdapter, AdvancedControlBase):