Upgrade v1.3.1 to ComfyRegistry

补充了用于选择lora模型的 setLoraName

README.ZH_CN.md

@@ -515,7 +515,6 @@ git clone https://github.com/yolain/ComfyUI-Easy-Use
 **Comfyui-Easy-Use** 是一个 GPL 许可的开源项目。为了项目取得更好、可持续的发展，我希望能够获得更多的支持。 如果我的自定义节点为您的一天增添了价值，请考虑喝杯咖啡来进一步补充能量！ 💖感谢您的支持，每一杯咖啡都是我创作的动力！
 
 - [BiliBili充电](https://space.bilibili.com/1840885116)
-- [爱发电](https://afdian.com/a/yolain)
 - [Wechat/Alipay](https://github.com/user-attachments/assets/803469bd-ed6a-4fab-932d-50e5088a2d03)
 
 感谢您的捐助，我将用这些费用来租用 GPU 或购买其他 GPT 服务，以便更好地调试和完善 ComfyUI-Easy-Use 功能

README.md

@@ -47,6 +47,12 @@ Double-click install.bat to install the required dependencies
 
 ## 📜 Changelog
 
+**v1.3.1**
+
+- Rewrite drawNodeWidget and fix the GroupNode preview issue.
+- Updated some features of XYPlot  by [mekinney](https://github.com/mekinney)
+- Add `easy seedList` node (It's useful for in loops)
+
 **v1.3.0**
 
 - Set loop nodes maximum number of inputs and outputs to 20
@@ -499,7 +505,6 @@ If my custom nodes has added value to your day, consider indulging in a coffee t
 💖You can support me in any of the following ways:
 
 - [BiliBili](https://space.bilibili.com/1840885116)
-- [Afdian](https://afdian.com/a/yolain)
 - [Wechat / Alipay](https://github.com/user-attachments/assets/803469bd-ed6a-4fab-932d-50e5088a2d03)
 
 ##  🌟Stargazers

__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.3.0"
+__version__ = "1.3.1"
 
 import yaml
 import json

locales/zh/nodeDefs.json

@@ -1756,6 +1756,35 @@
       }
     }
   },
+  "easy seedList": {
+    "display_name": "随机种列表",
+    "description": "可用于for循环的随机数种子列表，通过与easy forLoopStart节点的索引与easy indexAny节点相连接可实现在循环中使用不同种子值进行采样",
+    "inputs": {
+      "min_num": {
+        "name": "最小值"
+      },
+      "max_num": {
+        "name": "最大值"
+      },
+      "method": {
+        "name": "生成方式"
+      },
+      "total": {
+        "name": "总量"
+      },
+      "seed": {
+        "name": "列表序号"
+      }
+    },
+    "outputs": {
+      "0": {
+        "name": "随机种"
+      },
+      "1": {
+        "name": "总量"
+      }
+    }
+  },
   "easy globalSeed": {
     "display_name": "全局随机种",
     "inputs": {
@@ -6365,7 +6394,7 @@
           "name": "高度"
       },
       "scale": {
-        "name": "缩放洗漱"
+        "name": "缩放系数"
       },
       "flip_w/h": {
         "name": "翻转宽高"
@@ -6708,4 +6737,4 @@
       }
     }
   }
-}
+}

py/libs/loader.py

@@ -351,7 +351,7 @@ class easyLoader:
             lora_path = None
 
         if lora_path is not None:
-            log_node_info("Load LORA",f"{lora_name}: {model_strength}, {clip_strength}, LBW={lbw}, A={lbw_a}, B={lbw_b}")
+            log_node_info("Load LORA",f"{lora_name}: model={model_strength:.3f}, clip={clip_strength:.3f}, LBW={lbw}, A={lbw_a}, B={lbw_b}")
             if lbw:
                 lbw = lora["lbw"]
                 lbw_a = lora["lbw_a"]
@@ -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 uses 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)

py/libs/xyplot.py

@@ -8,6 +8,7 @@ from .log import log_node_warn
 from ..modules.layer_diffuse import LayerDiffuse
 from ..config import RESOURCES_DIR
 from nodes import CLIPTextEncode
+import pprint
 try:
     from comfy_extras.nodes_flux import FluxGuidance
 except:
@@ -52,7 +53,7 @@ class easyXYPlot():
 
         plot_image_vars[value_type] = value
         if value_type in ["seed", "Seeds++ Batch"]:
-            value_label = f"{value}"
+            value_label = f"seed: {value}"
         else:
             value_label = f"{value_type}: {value}"
 
@@ -63,7 +64,9 @@ class easyXYPlot():
             arr = value.split(',')
             model_name = os.path.basename(os.path.splitext(arr[0])[0])
             trigger_words = ' ' + arr[3] if value_type == 'Lora' and len(arr[3]) > 2 else ''
-            value_label = f"{model_name}{trigger_words}"
+            lora_weight = float(arr[1]) if value_type == 'Lora' and len(arr) > 1 else 0
+            lora_weight_desc = f"({lora_weight:.2f})" if lora_weight > 0 else ''
+            value_label = f"{model_name[:30]}{lora_weight_desc} {trigger_words}"
 
         if value_type in ["ModelMergeBlocks"]:
             if ":" in value:
@@ -118,24 +121,32 @@ class easyXYPlot():
 
     def calculate_background_dimensions(self):
         border_size = int((self.max_width // 8) * 1.5) if self.y_type != "None" or self.x_type != "None" else 0
+
         bg_width = self.num_cols * (self.max_width + self.grid_spacing) - self.grid_spacing + border_size * (
                     self.y_type != "None")
         bg_height = self.num_rows * (self.max_height + self.grid_spacing) - self.grid_spacing + border_size * (
                     self.x_type != "None")
+        
+        # Add space at the bottom of the image for common informaiton about the image
+        bg_height = bg_height + (border_size*2)
+#        print(f"Grid Size: width = {bg_width} height = {bg_height} border_size = {border_size}")
 
         x_offset_initial = border_size if self.y_type != "None" else 0
         y_offset = border_size if self.x_type != "None" else 0
 
         return bg_width, bg_height, x_offset_initial, y_offset
 
+
     def adjust_font_size(self, text, initial_font_size, label_width):
         font = self.get_font(initial_font_size, self.custom_font)
         text_width = font.getbbox(text)
+#        pprint.pp(f"Initial font size: {initial_font_size}, text: {text}, text_width: {text_width}")
         if text_width and text_width[2]:
             text_width = text_width[2]
 
         scaling_factor = 0.9
         if text_width > (label_width * scaling_factor):
+#            print(f"Adjusting font size from {initial_font_size} to fit text width {text_width} into label width {label_width} scaling_factor {scaling_factor}")
             return int(initial_font_size * (label_width / text_width) * scaling_factor)
         else:
             return initial_font_size
@@ -144,15 +155,22 @@ class easyXYPlot():
         _, _, width, height = d.textbbox((0, 0), text=text, font=font)
         return width, height
 
-    def create_label(self, img, text, initial_font_size, is_x_label=True, max_font_size=70, min_font_size=10):
-        label_width = img.width if is_x_label else img.height
+    def create_label(self, img, text, initial_font_size, is_x_label=True, max_font_size=70, min_font_size=10, label_width=0, label_height=0):
 
+        # if the label_width is specified, leave it along.  Otherwise do the old logic.
+        if label_width == 0:          
+            label_width = img.width if is_x_label else img.height
+
+        text_lines = text.split('\n')
+        longest_line = max(text_lines, key=len)
+                
         # Adjust font size
-        font_size = self.adjust_font_size(text, initial_font_size, label_width)
+        font_size = self.adjust_font_size(longest_line, initial_font_size, label_width)
         font_size = min(max_font_size, font_size)  # Ensure font isn't too large
         font_size = max(min_font_size, font_size)  # Ensure font isn't too small
 
-        label_height = int(font_size * 1.5) if is_x_label else font_size
+        if label_height == 0:
+            label_height = int(font_size * 1.5) if is_x_label else font_size
 
         label_bg = Image.new('RGBA', (label_width, label_height), color=(255, 255, 255, 0))
         d = ImageDraw.Draw(label_bg)
@@ -166,7 +184,7 @@ class easyXYPlot():
             text = text + '...'
 
         # Compute text width and height for multi-line text
-        text_lines = text.split('\n')
+  
         text_widths, text_heights = zip(*[self.textsize(d, line, font=font) for line in text_lines])
         max_text_width = max(text_widths)
         total_text_height = sum(text_heights)
@@ -195,8 +213,7 @@ 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'])
-
+        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":
@@ -347,17 +364,24 @@ class easyXYPlot():
 
             # Lora
             if self.x_type == "Lora" or self.y_type == "Lora":
+#                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}")
+
+                
                 if 'lora_stack' in plot_image_vars:
                     lora_stack = lora_stack + plot_image_vars['lora_stack']
-
+                
                 if lora_stack is not None and lora_stack != []:
                     for lora in lora_stack:
+                        # Each generation of the model, must use the reference to previously created model / clip objects.
+                        lora['model'] = model
+                        lora['clip'] = clip
                         model, clip = self.easyCache.load_lora(lora)
 
             # 提示词
@@ -464,6 +488,7 @@ class easyXYPlot():
                                                             plot_image_vars['negative_weight_interpretation'], w_max=1.0,
                                                             apply_to_pooled="enable", a1111_prompt_style=a1111_prompt_style, steps=steps)
 
+
         model = model if model is not None else plot_image_vars["model"]
         vae = vae if vae is not None else plot_image_vars["vae"]
         positive = positive if positive is not None else plot_image_vars["positive_cond"]
@@ -582,11 +607,10 @@ class easyXYPlot():
 
         return self.latents_plot
 
-    def plot_images_and_labels(self):
-        # Calculate the background dimensions
+    def plot_images_and_labels(self, plot_image_vars):
+                    
         bg_width, bg_height, x_offset_initial, y_offset = self.calculate_background_dimensions()
 
-        # Create the white background image
         background = Image.new('RGBA', (int(bg_width), int(bg_height)), color=(255, 255, 255, 255))
 
         output_image = []
@@ -618,4 +642,55 @@ class easyXYPlot():
 
             y_offset += img.height + self.grid_spacing
 
-        return (self.sampler.pil2tensor(background), output_image)
+        # lookup used models in the image
+        common_label = ""
+        # Update to add a function to do the heavy lifting. Parameters are plot_image_vars name, label to use, names of the axis, 
+
+        # pprint.pp(plot_image_vars)
+
+        # We don't process LORAs here because there can be multiple of them.
+        labels = [
+            {"id": "ckpt_name", "id_desc": "ckpt", "axis_type" : "Checkpoint"},
+            {"id": "vae_name", "id_desc": '', "axis_type" : "vae_name"},
+            {"id": "sampler_name", "id_desc": "sampler", "axis_type" : "Sampler"},
+            {"id": "scheduler", "id_desc": '', "axis_type" : "Scheduler"},
+            {"id": "steps", "id_desc": '', "axis_type" : "Steps"},
+            {"id": "Flux Guidance", "id_desc": 'guidance', "axis_type" : "Flux Guidance"},     
+            {"id": "seed", "id_desc": '', "axis_type" : "Seeds++ Batch"}
+        ]
+        
+        for item in labels:
+            # Only add the label if it's not one of the axis
+            # print(f"Checking item: {item['id']} axis_type {item['axis_type']} x_type: {self.x_type} y_type: {self.y_type}")
+            if self.x_type != item['axis_type'] and self.y_type != item['axis_type']:
+                common_label += self.add_common_label(item['id'], plot_image_vars, item['id_desc'])
+        common_label += f"\n"
+                
+        if plot_image_vars['lora_stack'] is not None and plot_image_vars['lora_stack'] != []:
+#            print(f"lora_stack: {plot_image_vars['lora_stack']}")
+            for lora in plot_image_vars['lora_stack']:
+
+                lora_name = lora['lora_name']
+                lora_weight = lora['model_strength']
+                if lora_name is not None and len(lora_name) > 0 and lora_weight > 0:
+                    common_label += f"LORA: {lora_name} weight: {lora_weight:.2f} \n"
+                    
+        common_label = common_label.strip()
+        
+        if len(common_label) > 0:
+            label_height = background.height - y_offset
+            label_bg = self.create_label(background, common_label, int(48 * background.width / 512), label_width=background.width, label_height=label_height)
+            label_x = (background.width - label_bg.width) // 2
+            label_y = y_offset
+#            print(f"Adding common label: {common_label} x = {label_x} y = {label_y}")
+            background.alpha_composite(label_bg, (label_x, label_y))
+
+        return (self.sampler.pil2tensor(background), output_image)
+
+    def add_common_label(self, tag, plot_image_vars, description = ''):
+        label = ''
+        if description == '': description = tag
+        if tag in plot_image_vars and plot_image_vars[tag] is not None and plot_image_vars[tag] != 'None':
+            label += f"{description}: {plot_image_vars[tag]} "
+#        print(f"add_common_label: {tag} description: {description} label: {label}" )
+        return label

py/nodes/image.py

@@ -1057,7 +1057,12 @@ class imageChooser(PreviewImage):
     images_in = torch.cat(kwargs.pop('images'))
     self.batch = images_in.shape[0]
     for x in kwargs: kwargs[x] = kwargs[x][0]
-    result = self.save_images(images=images_in, prompt=prompt)
+
+    try:
+      pnginfo = extra_pnginfo[0]
+    except:
+      pnginfo = None
+    result = self.save_images(images=images_in, prompt=prompt, extra_pnginfo=pnginfo)
 
     images = result['ui']['images']
     PromptServer.instance.send_sync("easyuse-image-choose", {"id": id, "urls": images})
@@ -1964,7 +1969,7 @@ class makeImageForICRepaint:
     b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF)
     return torch.cat((r, g, b), dim=-1)
 
-  def resize_image_and_mask(self, image, mask, w, h ):
+  def resize_image_and_mask(self, image, mask, w, h ,fit='fill'):
       ret_images = []
       ret_masks = []
       _mask = Image.new('L', size=(w, h), color='black')
@@ -1972,12 +1977,12 @@ class makeImageForICRepaint:
       if image is not None and len(image) > 0:
           for i in image:
               _image = tensor2pil(i).convert('RGB')
-              _image = fit_resize_image(_image, w, h, 'fill', Image.LANCZOS, '#000000')
+              _image = fit_resize_image(_image, w, h, fit, Image.LANCZOS, '#000000')
               ret_images.append(pil2tensor(_image))
       if mask is not None and len(mask) > 0:
           for m in mask:
               _mask = tensor2pil(m).convert('L')
-              _mask = fit_resize_image(_mask, w, h, 'fill', Image.LANCZOS).convert('L')
+              _mask = fit_resize_image(_mask, w, h, fit, Image.LANCZOS).convert('L')
               ret_masks.append(image2mask(_mask))
 
       if len(ret_images) > 0 and len(ret_masks) > 0:
@@ -2017,16 +2022,18 @@ class makeImageForICRepaint:
     image, mask, context_mask = None, None, None
 
     # resize
-    if img1_h != img2_h and img1_w != img2_w:
+    if img1_h != img2_h or img1_w != img2_w:
       width, height = img2_w, img2_h
-      if direction == 'left-right' and img1_h != img2_h:
-        scale_factor = img2_h / img1_h
-        width = round(img1_w * scale_factor)
-      elif direction == 'top-bottom' and img1_w != img2_w:
-        scale_factor = img2_w / img1_w
-        height = round(img1_h * scale_factor)
-
-      image_1, mask_1 = self.resize_image_and_mask(image_1, mask_1, width, height)
+      fit = 'crop'
+      if method != 'uniform width':
+        if direction == 'left-right' and img1_h != img2_h:
+          scale_factor = img2_h / img1_h
+          width = round(img1_w * scale_factor)
+        elif direction == 'top-bottom' and img1_w != img2_w:
+          scale_factor = img2_w / img1_w
+          height = round(img1_h * scale_factor)
+        fit = 'fill'
+      image_1, mask_1 = self.resize_image_and_mask(image_1, mask_1, width, height, fit)
 
     if mask_1 is None:
       mask_1 = torch.full((1, image_1.shape[1], image_1.shape[2]), 0, dtype=torch.float32, device="cpu")

py/nodes/logic.py

@@ -166,7 +166,7 @@ class Float:
     @classmethod
     def INPUT_TYPES(s):
         return {
-            "required": {"value": ("FLOAT", {"default": 0, "step": 0.01, "min": -999999, "max": 999999, })},
+            "required": {"value": ("FLOAT", {"default": 0, "step": 0.01, "min":-0xffffffffffffffff, "max":  0xffffffffffffffff, })},
         }
 
     RETURN_TYPES = ("FLOAT",)
@@ -175,7 +175,7 @@ class Float:
     CATEGORY = "EasyUse/Logic/Type"
 
     def execute(self, value):
-        return (value,)
+        return (round(value, 3),)
 
 
 # 浮点数范围
@@ -239,9 +239,9 @@ class RangeFloat:
         error_if_mismatched_list_args(locals())
         getcontext().prec = 12
 
-        start = [Decimal(s) for s in start]
-        stop = [Decimal(s) for s in stop]
-        step = [Decimal(s) for s in step]
+        start = [round(Decimal(s),2) for s in start]
+        stop = [round(Decimal(s),2) for s in stop]
+        step = [round(Decimal(s),2) for s in step]
 
         ranges = []
         range_sizes = []
@@ -573,17 +573,17 @@ class mathFloatOperation:
 
     def float_math_operation(self, a, b, operation):
         if operation == "add":
-            return (a + b,)
+            return (round(a + b,3),)
         elif operation == "subtract":
-            return (a - b,)
+            return (round(a - b,3),)
         elif operation == "multiply":
-            return (a * b,)
+            return (round(a * b,3),)
         elif operation == "divide":
-            return (a / b,)
+            return (round(a / b,3),)
         elif operation == "modulo":
-            return (a % b,)
+            return (round(a % b,3),)
         elif operation == "power":
-            return (a ** b,)
+            return (round(a ** b,3),)
 
 
 class mathStringOperation:
@@ -1607,8 +1607,10 @@ class saveText:
         if not os.path.exists(output_file_path):
             os.makedirs(output_file_path)
 
-        if not overwrite:
-            pass
+        if overwrite:
+            file_mode = "w"
+        else:
+            file_mode = "a"
 
         log_node_info("Save Text", f"Saving to {filepath}")
 
@@ -1617,13 +1619,13 @@ class saveText:
             for i in text.split("\n"):
                 text_list.append(i.strip())
 
-            with open(filepath, "w", newline="", encoding='utf-8') as csv_file:
+            with open(filepath, file_mode, newline="", encoding='utf-8') as csv_file:
                 csv_writer = csv.writer(csv_file)
                 # Write each line as a separate row in the CSV file
                 for line in text_list:
                     csv_writer.writerow([line])
         else:
-            with open(filepath, "w", newline="", encoding='utf-8') as text_file:
+            with open(filepath, file_mode, newline="", encoding='utf-8') as text_file:
                 for line in text:
                     text_file.write(line)
 

py/nodes/samplers.py

@@ -513,7 +513,7 @@ class samplerFull:
 
             samp_samples = {"samples": latents_plot}
 
-            images, image_list = sampleXYplot.plot_images_and_labels()
+            images, image_list = sampleXYplot.plot_images_and_labels(plot_image_vars)
 
             # Generate output_images
             output_images = torch.stack([tensor.squeeze() for tensor in image_list])

py/nodes/seed.py

@@ -1,4 +1,6 @@
 from ..config import MAX_SEED_NUM
+import hashlib
+import random
 
 class easySeed:
     @classmethod
@@ -19,6 +21,53 @@ class easySeed:
     def doit(self, seed=0, prompt=None, extra_pnginfo=None, my_unique_id=None):
         return seed,
 
+class seedList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "min_num": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM}),
+                "max_num": ("INT", {"default": MAX_SEED_NUM, "min": 0 }),
+                "method": (["random", "increment", "decrement"], {"default": "random"}),
+                "total": ("INT", {"default": 1, "min": 1, "max": 100000}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED_NUM,}),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"},
+        }
+
+    RETURN_TYPES = ("INT", "INT")
+    RETURN_NAMES = ("seed", "total")
+    FUNCTION = "doit"
+    DESCRIPTION = "Random number seed that can be used in a for loop, by connecting index and easy indexAny node to realize different seed values in the loop."
+
+    CATEGORY = "EasyUse/Seed"
+
+    def doit(self, min_num, max_num, method, total, seed=0, prompt=None, extra_pnginfo=None, my_unique_id=None):
+        random.seed(seed)
+
+        seed_list = []
+        if min_num > max_num:
+            min_num, max_num = max_num, min_num
+        for i in range(total):
+            if method == 'random':
+                s = random.randint(min_num, max_num)
+            elif method == 'increment':
+                s = min_num + i
+                if s > max_num:
+                    s = max_num
+            elif method == 'decrement':
+                s = max_num - i
+                if s < min_num:
+                    s = min_num
+            seed_list.append(s)
+        return seed_list, total
+
+    @classmethod
+    def IS_CHANGED(s, seed, **kwargs):
+        m = hashlib.sha256()
+        m.update(seed)
+        return m.digest().hex()
+
 # 全局随机种
 class globalSeed:
     @classmethod
@@ -46,10 +95,12 @@ class globalSeed:
 
 NODE_CLASS_MAPPINGS = {
     "easy seed": easySeed,
+    "easy seedList": seedList,
     "easy globalSeed": globalSeed,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "easy seed": "EasySeed",
+    "easy seedList": "EasySeedList",
     "easy globalSeed": "EasyGlobalSeed",
 }

py/nodes/util.py

@@ -106,6 +106,23 @@ class setControlName:
 
     def set_name(self, controlnet_name):
         return (controlnet_name,)
+    
+class setLoraName:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                "lora_name": (folder_paths.get_filename_list("loras"),),
+            }
+        }
+
+    RETURN_TYPES = (AlwaysEqualProxy('*'),)
+    RETURN_NAMES = ("lora_name",)
+    FUNCTION = "set_name"
+    CATEGORY = "EasyUse/Util"
+
+    def set_name(self, lora_name):
+        return (lora_name,)
 
 
 NODE_CLASS_MAPPINGS = {
@@ -113,6 +130,7 @@ NODE_CLASS_MAPPINGS = {
     "easy sliderControl": sliderControl,
     "easy ckptNames": setCkptName,
     "easy controlnetNames": setControlName,
+    "easy loraNames": setLoraName,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -120,4 +138,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "easy sliderControl": "Easy Slider Control",
     "easy ckptNames": "Ckpt Names",
     "easy controlnetNames": "ControlNet Names",
-}
+    "easy loraNames": "Lora Names",
+}

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-easy-use"
 description = "To enhance the usability of ComfyUI, optimizations and integrations have been implemented for several commonly used nodes."
-version = "1.3.0"
+version = "1.3.1"
 license = { file = "LICENSE" }
 dependencies = ["diffusers", "accelerate", "clip_interrogator>=0.6.0", "sentencepiece", "lark", "onnxruntime", "spandrel", "opencv-python", "matplotlib", "peft"]