diff --git a/nodes.py b/nodes.py
index 0a3016e..c6caf63 100644
--- a/nodes.py
+++ b/nodes.py
@@ -36,42 +36,3 @@ class simpleDynamicCFG:
         m.set_model_sampler_cfg_function(linear_cfg, disable_cfg1_optimization=True)
         return (m, )
 
-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 = 0.41903709829879954/model.model.latent_format.scale_factor
-        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, )