Controlnet refactor.

comfy/cldm/cldm.py

@@ -289,7 +289,8 @@ class ControlNet(nn.Module):
 
         guided_hint = self.input_hint_block(hint, emb, context)
 
-        outs = []
+        out_output = []
+        out_middle = []
 
         hs = []
         if self.num_classes is not None:
@@ -304,10 +305,10 @@ class ControlNet(nn.Module):
                 guided_hint = None
             else:
                 h = module(h, emb, context)
-            outs.append(zero_conv(h, emb, context))
+            out_output.append(zero_conv(h, emb, context))
 
         h = self.middle_block(h, emb, context)
-        outs.append(self.middle_block_out(h, emb, context))
+        out_middle.append(self.middle_block_out(h, emb, context))
 
-        return outs
+        return {"middle": out_middle, "output": out_output}
 

comfy/controlnet.py

@@ -89,27 +89,12 @@ class ControlBase:
             return self.previous_controlnet.inference_memory_requirements(dtype)
         return 0
 
-    def control_merge(self, control_input, control_output, control_prev, output_dtype):
+    def control_merge(self, control, control_prev, output_dtype):
         out = {'input':[], 'middle':[], 'output': []}
 
-        if control_input is not None:
-            for i in range(len(control_input)):
-                key = 'input'
-                x = control_input[i]
-                if x is not None:
-                    x *= self.strength
-                    if x.dtype != output_dtype:
-                        x = x.to(output_dtype)
-                out[key].insert(0, x)
-
-        if control_output is not None:
+        for key in control:
+            control_output = control[key]
             for i in range(len(control_output)):
-                if i == (len(control_output) - 1):
-                    key = 'middle'
-                    index = 0
-                else:
-                    key = 'output'
-                    index = i
                 x = control_output[i]
                 if x is not None:
                     if self.global_average_pooling:
@@ -120,6 +105,7 @@ class ControlBase:
                         x = x.to(output_dtype)
 
                 out[key].append(x)
+
         if control_prev is not None:
             for x in ['input', 'middle', 'output']:
                 o = out[x]
@@ -182,7 +168,7 @@ class ControlNet(ControlBase):
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 
         control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)
-        return self.control_merge(None, control, control_prev, output_dtype)
+        return self.control_merge(control, control_prev, output_dtype)
 
     def copy(self):
         c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
@@ -490,12 +476,11 @@ class T2IAdapter(ControlBase):
             self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
             self.t2i_model.cpu()
 
-        control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))
-        mid = None
-        if self.t2i_model.xl == True:
-            mid = control_input[-1:]
-            control_input = control_input[:-1]
-        return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)
+        control_input = {}
+        for k in self.control_input:
+            control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k]))
+
+        return self.control_merge(control_input, control_prev, x_noisy.dtype)
 
     def copy(self):
         c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm)

comfy/ldm/cascade/controlnet.py

@@ -90,4 +90,4 @@ class ControlNet(nn.Module):
         proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)]
         for i, idx in enumerate(self.proj_blocks):
             proj_outputs[idx] = self.projections[i](x)
-        return proj_outputs
+        return {"input": proj_outputs[::-1]}

comfy/t2i_adapter/adapter.py

@@ -153,7 +153,13 @@ class Adapter(nn.Module):
                 features.append(None)
             features.append(x)
 
-        return features
+        features = features[::-1]
+
+        if self.xl:
+            return {"input": features[1:], "middle": features[:1]}
+        else:
+            return {"input": features}
+
 
 
 class LayerNorm(nn.LayerNorm):
@@ -290,4 +296,4 @@ class Adapter_light(nn.Module):
             features.append(None)
             features.append(x)
 
-        return features
+        return {"input": features[::-1]}