ControlNetApply now stacks.

It can be used to apply multiple control nets at the same time.

comfy/sd.py

@@ -334,8 +334,13 @@ class ControlNet:
         self.cond_hint = None
         self.strength = 1.0
         self.device = device
+        self.previous_controlnet = None
 
     def get_control(self, x_noisy, t, cond_txt):
+        control_prev = None
+        if self.previous_controlnet is not None:
+            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond_txt)
+
         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:
@@ -354,10 +359,15 @@ class ControlNet:
             self.control_model = model_management.unload_if_low_vram(self.control_model)
         out = []
         autocast_enabled = torch.is_autocast_enabled()
-        for x in control:
+
+        for i in range(len(control)):
+            x = control[i]
             x *= self.strength
             if x.dtype != output_dtype and not autocast_enabled:
                 x = x.to(output_dtype)
+
+            if control_prev is not None:
+                x += control_prev[i]
             out.append(x)
         return out
 
@@ -366,7 +376,13 @@ class ControlNet:
         self.strength = strength
         return self
 
+    def set_previous_controlnet(self, controlnet):
+        self.previous_controlnet = controlnet
+        return self
+
     def cleanup(self):
+        if self.previous_controlnet is not None:
+            self.previous_controlnet.cleanup()
         if self.cond_hint is not None:
             del self.cond_hint
             self.cond_hint = None
@@ -377,6 +393,13 @@ class ControlNet:
         c.strength = self.strength
         return c
 
+    def get_control_models(self):
+        out = []
+        if self.previous_controlnet is not None:
+            out += self.previous_controlnet.get_control_models()
+        out.append(self.control_model)
+        return out
+
 def load_controlnet(ckpt_path):
     controlnet_data = load_torch_file(ckpt_path)
     pth_key = 'control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight'

nodes.py

@@ -252,7 +252,10 @@ class ControlNetApply:
         print(control_hint.shape)
         for t in conditioning:
             n = [t[0], t[1].copy()]
-            n[1]['control'] = control_net.copy().set_cond_hint(control_hint, strength)
+            c_net = control_net.copy().set_cond_hint(control_hint, strength)
+            if 'control' in t[1]:
+                c_net.set_previous_controlnet(t[1]['control'])
+            n[1]['control'] = c_net
             c.append(n)
         return (c, )
 
@@ -510,7 +513,10 @@ def common_ksampler(device, model, seed, steps, cfg, sampler_name, scheduler, po
             control_nets += [p[1]['control']]
         negative_copy += [[t] + n[1:]]
 
-    model_management.load_controlnet_gpu(list(map(lambda a: a.control_model, control_nets)))
+    control_net_models = []
+    for x in control_nets:
+        control_net_models += x.get_control_models()
+    model_management.load_controlnet_gpu(control_net_models)
 
     if sampler_name in comfy.samplers.KSampler.SAMPLERS:
         sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise)