Add a ControlNetApplyAdvanced node.

The controlnet can be applied to the positive or negative prompt only by
connecting it correctly.

comfy/samplers.py

@@ -607,7 +607,7 @@ class KSampler:
         for c in negative:
             create_cond_with_same_area_if_none(positive, c)
 
-        apply_empty_x_to_equal_area(positive, negative, 'control', lambda cond_cnets, x: cond_cnets[x])
+        apply_empty_x_to_equal_area(list(filter(lambda c: c[1].get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
         apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
 
         if self.model.is_adm():

nodes.py

@@ -602,9 +602,56 @@ class ControlNetApply:
             if 'control' in t[1]:
                 c_net.set_previous_controlnet(t[1]['control'])
             n[1]['control'] = c_net
+            n[1]['control_apply_to_uncond'] = True
             c.append(n)
         return (c, )
 
+
+class ControlNetApplyAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "control_net": ("CONTROL_NET", ),
+                             "image": ("IMAGE", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             }}
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    FUNCTION = "apply_controlnet"
+
+    CATEGORY = "conditioning"
+
+    def apply_controlnet(self, positive, negative, control_net, image, strength):
+        if strength == 0:
+            return (positive, negative)
+
+        control_hint = image.movedim(-1,1)
+        cnets = {}
+
+        out = []
+        for conditioning in [positive, negative]:
+            c = []
+            for t in conditioning:
+                d = t[1].copy()
+
+                prev_cnet = d.get('control', None)
+                if prev_cnet in cnets:
+                    c_net = cnets[prev_cnet]
+                else:
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength)
+                    c_net.set_previous_controlnet(prev_cnet)
+                    cnets[prev_cnet] = c_net
+
+                d['control'] = c_net
+                d['control_apply_to_uncond'] = False
+                n = [t[0], d]
+                c.append(n)
+            out.append(c)
+        return (out[0], out[1])
+
+
 class UNETLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -1449,6 +1496,7 @@ NODE_CLASS_MAPPINGS = {
     "StyleModelApply": StyleModelApply,
     "unCLIPConditioning": unCLIPConditioning,
     "ControlNetApply": ControlNetApply,
+    "ControlNetApplyAdvanced": ControlNetApplyAdvanced,
     "ControlNetLoader": ControlNetLoader,
     "DiffControlNetLoader": DiffControlNetLoader,
     "StyleModelLoader": StyleModelLoader,
@@ -1495,6 +1543,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ConditioningSetArea": "Conditioning (Set Area)",
     "ConditioningSetMask": "Conditioning (Set Mask)",
     "ControlNetApply": "Apply ControlNet",
+    "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)",
     # Latent
     "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
     "SetLatentNoiseMask": "Set Latent Noise Mask",