diff --git a/nodes.py b/nodes.py
index 48965f6..49ebf4a 100644
--- a/nodes.py
+++ b/nodes.py
@@ -35,4 +35,43 @@ class simpleDynamicCFG:
         m = model.clone()
         m.set_model_sampler_cfg_function(linear_cfg, disable_cfg1_optimization=True)
         return (m, )
-    
\ No newline at end of file
+
+class simpleDynamicCFGperChannelMultiplier:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL",),
+                             "dynamic_intensity_channel_1": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step":0.01, "round": 0.01}),
+                             "dynamic_intensity_channel_2": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step":0.01, "round": 0.01}),
+                             "dynamic_intensity_channel_3": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step":0.01, "round": 0.01}),
+                             "dynamic_intensity_channel_4": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step":0.01, "round": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches"
+
+    def patch(self, model, dynamic_intensity_channel_1,dynamic_intensity_channel_2,dynamic_intensity_channel_3,dynamic_intensity_channel_4):
+        base_target = 24.7/7.677542686462402
+        top_k = 0.25
+        dynamic_channels = [dynamic_intensity_channel_1,dynamic_intensity_channel_2,dynamic_intensity_channel_3,dynamic_intensity_channel_4]
+        def linear_cfg(args):
+            cond = args["cond"]
+            cond_scale = args["cond_scale"]
+            intensity_target = cond_scale/8
+            uncond = args["uncond"]
+            input_x = args["input"]
+            denoised_tmp = input_x-(uncond + 8*(cond - uncond))
+            for b in range(len(denoised_tmp)):
+                for c in range(len(denoised_tmp[b])):
+                    channel = denoised_tmp[b][c]
+                    max_values = torch.topk(channel, k=int(len(channel)*top_k), largest=True).values
+                    min_values = torch.topk(channel, k=int(len(channel)*top_k), largest=False).values
+                    max_val = torch.mean(max_values).item()
+                    min_val = torch.mean(min_values).item()
+                    denoised_range = (max_val+abs(min_val))/2
+                    tmp_scale = 2*base_target*intensity_target/denoised_range*dynamic_channels[c]
+                    denoised_tmp[b][c] = uncond[b][c] + tmp_scale * (cond[b][c] - uncond[b][c])
+            return denoised_tmp
+        m = model.clone()
+        m.set_model_sampler_cfg_function(linear_cfg, disable_cfg1_optimization=True)
+        return (m, )