diff --git a/py/libs/loader.py b/py/libs/loader.py
index 4f0cccd..ab5db57 100644
--- a/py/libs/loader.py
+++ b/py/libs/loader.py
@@ -432,10 +432,13 @@ class easyLoader:
         clip_vision = None
         lora_stack = []
 
+        # Check for model override
         can_load_lora = True
         # 判断是否存在 模型或Lora叠加xyplot, 若存在优先缓存第一个模型
+        # Determine whether there is a model or Lora overlapping xyplot, and if there is, prioritize caching the first model.
         xy_model_id = next((x for x in prompt if str(prompt[x]["class_type"]) in ["easy XYInputs: ModelMergeBlocks",
                                                                                   "easy XYInputs: Checkpoint"]), None)
+        # This will find nodes that aren't actively connected to anything, and skip loading lora's for them.
         xy_lora_id = next((x for x in prompt if str(prompt[x]["class_type"]) == "easy XYInputs: Lora"), None)
         if xy_lora_id is not None:
             can_load_lora = False
@@ -461,6 +464,7 @@ class easyLoader:
 
         if optional_lora_stack is not None and can_load_lora:
             for lora in optional_lora_stack:
+                # This is a subtle bit of code because it saves the model created by the last call, and passes it to the next call.
                 lora = {"lora_name": lora[0], "model": model, "clip": clip, "model_strength": lora[1],
                         "clip_strength": lora[2]}
                 model, clip = self.load_lora(lora)
diff --git a/py/libs/xyplot.py b/py/libs/xyplot.py
index a807172..a10311e 100644
--- a/py/libs/xyplot.py
+++ b/py/libs/xyplot.py
@@ -211,11 +211,7 @@ class easyXYPlot():
         clip = clip if clip is not None else plot_image_vars["clip"]
         steps = plot_image_vars['steps'] if "steps" in plot_image_vars else 1
 
-        sd_version = get_sd_version(plot_image_vars['model'])
-
-#        print(f"sample_plot_image x_type: {self.x_type} y_type: {self.y_type} x_value: {x_value} y_value: {y_value}") 
-          
-  
+        sd_version = get_sd_version(plot_image_vars['model'])          
         # 高级用法
         if plot_image_vars["x_node_type"] == "advanced" or plot_image_vars["y_node_type"] == "advanced":
             if self.x_type == "Seeds++ Batch" or self.y_type == "Seeds++ Batch":
@@ -369,25 +365,21 @@ class easyXYPlot():
 #                print(f"Lora: {x_value} {y_value}")
                 model = model if model is not None else plot_image_vars["model"]
                 clip = clip if clip is not None else plot_image_vars["clip"]
-
                 xy_values = x_value if self.x_type == "Lora" else y_value
                 lora_name, lora_model_strength, lora_clip_strength, _ = xy_values.split(",")
                 lora_stack = [{"lora_name": lora_name, "model": model, "clip" :clip, "model_strength": float(lora_model_strength), "clip_strength": float(lora_clip_strength)}]
                 
 #                print(f"new_lora_stack: {new_lora_stack}")
 
-#                optional_lora_stack = plot_image_vars['lora_stack']
-#                print(f"optional_lora_stack: {optional_lora_stack}")
-#                if optional_lora_stack is not None and optional_lora_stack != []:
-#                    for lora in optional_lora_stack:
-#                        print(f"Lora: {lora}")
                 
                 if 'lora_stack' in plot_image_vars:
                     lora_stack = lora_stack + plot_image_vars['lora_stack']
-#                print(f"new_lora_stack: {new_lora_stack}")
                 
                 if lora_stack is not None and lora_stack != []:
                     for lora in lora_stack:
+                        # Use the updated model and clip, for the next lora load.
+                        lora['model'] = model
+                        lora['clip'] = clip
                         model, clip = self.easyCache.load_lora(lora)
 
             # 提示词
@@ -494,9 +486,6 @@ class easyXYPlot():
                                                             plot_image_vars['negative_weight_interpretation'], w_max=1.0,
                                                             apply_to_pooled="enable", a1111_prompt_style=a1111_prompt_style, steps=steps)
 
-#        print(f"plotting image")
-#        if model is not None:
-#            pprint.pp(f"Model: {model}")
 
         model = model if model is not None else plot_image_vars["model"]
         vae = vae if vae is not None else plot_image_vars["vae"]