Add compression_ratio to controlnet code.

comfy/controlnet.py

@@ -37,6 +37,7 @@ class ControlBase:
         self.timestep_percent_range = (0.0, 1.0)
         self.global_average_pooling = False
         self.timestep_range = None
+        self.compression_ratio = 8
 
         if device is None:
             device = comfy.model_management.get_torch_device()
@@ -158,11 +159,11 @@ class ControlNet(ControlBase):
             dtype = self.manual_cast_dtype
 
         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 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
-            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] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio, 'nearest-exact', "center").to(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)
 
@@ -456,12 +457,12 @@ class T2IAdapter(ControlBase):
                 else:
                     return None
 
-        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 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.control_input = None
             self.cond_hint = None
-            width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8)
+            width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio)
             self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device)
             if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
                 self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)