diff --git a/nodes.py b/nodes.py
index fef0374..17db14a 100644
--- a/nodes.py
+++ b/nodes.py
@@ -131,7 +131,7 @@ def gaussian_kernel(size, sigma):
     kernel = torch.exp(-(xx**2 + yy**2) / (2. * sigma**2))
     return kernel / kernel.sum()
 
-def blur_tensor(input_tensor,kernel_size = 7, sigma = 2.0):
+def blur_tensor(input_tensor, kernel_size = 7, sigma = 2.0):
     device = input_tensor.device
     x_coord = torch.arange(kernel_size) - (kernel_size - 1) / 2
     xy_grid = torch.square(x_coord.repeat(kernel_size).view(kernel_size, kernel_size)) + torch.square(x_coord.repeat(kernel_size).view(kernel_size, kernel_size).t())
@@ -737,7 +737,7 @@ class simpleDynamicCFG:
 class presetLoader:
     @classmethod
     def INPUT_TYPES(s):
-        presets_files = [pj.replace(".json","") for pj in os.listdir(json_preset_path) if ".json" in pj]
+        presets_files = [pj.replace(".json","") for pj in os.listdir(json_preset_path) if ".json" in pj and "do_not_delete" not in pj]
         presets_files = sorted(presets_files, key=str.lower)
         return {"required": {
                                 "model": ("MODEL",),
@@ -895,7 +895,7 @@ class simpleDynamicCFGExcellentattentionPatch:
         return {"required": {
                                 "model": ("MODEL",),
                                 "Auto_CFG": ("BOOLEAN", {"default": True}),
-                                "patch_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "patch_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 1.0, "round": 0.01}),
                                 "patch_cond":   ("BOOLEAN", {"default": True}),
                                 "patch_uncond": ("BOOLEAN", {"default": True}),
                                 "light_patch":  ("BOOLEAN", {"default": False}),
@@ -965,3 +965,80 @@ class simpleDynamicCFGExcellentattentionPatch:
         m = advcfg.patch(model, **patch_parameters)[0]
         
         return (m, parameters_as_string, )
+    
+class simpleDynamicCFGCustomAttentionPatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "model": ("MODEL",),
+                                "Auto_CFG": ("BOOLEAN", {"default": True}),
+                                "cond_mode" :   (["replace_by_custom","normal+(normal-custom_cond)*multiplier","normal+(normal-custom_uncond)*multiplier"],),
+                                "uncond_mode" : (["replace_by_custom","normal+(normal-custom_cond)*multiplier","normal+(normal-custom_uncond)*multiplier"],),
+                                "cond_diff_multiplier":   ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+                                "uncond_diff_multiplier": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+                                "uncond_sigma_end":       ("FLOAT", {"default": 1.0, "min":    0.0, "max": 10000, "step": 0.1, "round": 0.01}),
+                                "save_as_preset": ("BOOLEAN", {"default": False}),
+                                "preset_name":    ("STRING",  {"multiline": False}),
+                              },
+                              "optional":{
+                                  "attn_mod_for_positive_operation": ("ATTNMOD", {"forceInput": True}),
+                                  "attn_mod_for_negative_operation": ("ATTNMOD", {"forceInput": True}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("Model",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/automatic_cfg/experimental_attention_modifiers"
+
+    def patch(self, model, Auto_CFG, cond_mode, uncond_mode, cond_diff_multiplier, uncond_diff_multiplier, uncond_sigma_end, save_as_preset, preset_name,
+              attn_mod_for_positive_operation = [], attn_mod_for_negative_operation = []):
+                
+        with open(os.path.join(json_preset_path, "do_not_delete.json"), 'r', encoding='utf-8') as f:
+            patch_parameters = json.load(f)
+        
+        patch_parameters["cond_exp_value"]   = cond_diff_multiplier
+        patch_parameters["uncond_exp_value"] = uncond_diff_multiplier
+
+        if cond_mode != "replace_by_custom":
+            patch_parameters["disable_cond"] = False
+        if cond_mode == "normal+(normal-custom_cond)*multiplier":
+            patch_parameters["cond_exp_method"] = "subtract_attention_modifiers_input_using_cond"
+        elif cond_mode == "normal+(normal-custom_uncond)*multiplier":
+            patch_parameters["cond_exp_method"] = "subtract_attention_modifiers_input_using_uncond"
+
+        if uncond_mode != "replace_by_custom":
+            patch_parameters["uncond_sigma_start"] = 1000.0
+            patch_parameters["fake_uncond_exp"]    = False
+            patch_parameters["uncond_exp"]         = True
+        
+        if uncond_mode == "normal+(normal-custom_cond)*multiplier":
+            patch_parameters["uncond_exp_method"] = "subtract_attention_modifiers_input_using_cond"
+        elif uncond_mode == "normal+(normal-custom_uncond)*multiplier":
+            patch_parameters["uncond_exp_method"] = "subtract_attention_modifiers_input_using_uncond"
+
+        if cond_mode != "replace_by_custom" and attn_mod_for_positive_operation != []:
+            smallest_sigma = min([float(x['sigma_end']) for x in attn_mod_for_positive_operation])
+            patch_parameters["disable_cond_sigma_end"] = smallest_sigma
+            patch_parameters["cond_exp_sigma_end"]     = smallest_sigma
+
+        if uncond_mode != "replace_by_custom" and attn_mod_for_negative_operation != []:
+            smallest_sigma = min([float(x['sigma_end'])   for x in attn_mod_for_negative_operation])
+            patch_parameters["uncond_exp_sigma_end"] = smallest_sigma
+            patch_parameters["fake_uncond_start"]    = False
+        # else:
+        #     biggest_sigma = max([float(x['sigma_start']) for x in attn_mod_for_negative_operation])
+        #     patch_parameters["fake_uncond_sigma_start"] = biggest_sigma
+
+        patch_parameters["automatic_cfg"] = "hard" if Auto_CFG else "None"
+        patch_parameters['attention_modifiers_positive']      = attn_mod_for_positive_operation
+        patch_parameters['attention_modifiers_negative']      = attn_mod_for_negative_operation
+        patch_parameters['attention_modifiers_fake_negative'] = attn_mod_for_negative_operation
+        patch_parameters["uncond_sigma_end"]      = uncond_sigma_end
+        patch_parameters["fake_uncond_sigma_end"] = uncond_sigma_end
+        patch_parameters["save_as_preset"] = save_as_preset
+        patch_parameters["preset_name"]    = preset_name
+        
+        advcfg = advancedDynamicCFG()
+        m = advcfg.patch(model, **patch_parameters)[0]
+        
+        return (m, )
\ No newline at end of file