v0.14.0

发布新节点splitImage & gridoutput，用于分割图片和随机摆放元素
修复若干bug

README.md

@@ -67,7 +67,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 > PromptSlide
 ![](./assets/prompt_weight.png)
 
-![](./workflow/promptslide-appinfo-workflow.svg)
+<!-- ![](./workflow/promptslide-appinfo-workflow.svg) -->
 
 > randomPrompt
 
@@ -164,13 +164,16 @@ An improvement has been made to directly redirect to GitHub to search for missin
 
 
 ### Models
+
+[Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:models/rembg
+
 [Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : models/clipseg
 
 [Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : models/lama
 
 [Download Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base
 
-[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:text_generator/text2image-prompt-generator
+[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:prompt_generator/text2image-prompt-generator
 
 [Download Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main),move to:prompt_generator/opus-mt-zh-en
 

__init__.py

@@ -192,7 +192,7 @@ def read_workflow_json_files(folder_path ):
 def get_workflows():
     # print("#####path::", current_path)
     workflow_path=os.path.join(current_path, "workflow")
-    print('workflow_path: ',workflow_path)
+    # print('workflow_path: ',workflow_path)
     if not os.path.exists(workflow_path):
         # 使用mkdir()方法创建新目录
         os.mkdir(workflow_path)
@@ -231,8 +231,14 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                     })
                 else:
                     category=''
+                    input=None
+                    output=None
                     if 'category' in x['app']:
                         category=x['app']['category']
+                    if 'input' in x['app']:
+                        input=x['app']['input']
+                    if 'output' in x['app']:
+                        output=x['app']['output']
                     apps.append({
                         "filename":item["filename"],
                         "category":category,
@@ -244,6 +250,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                                 "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
                                 "name":x['app']['name'],
                                 "version":x['app']['version'],
+                                "input":input,
+                                "output":output
                             }
                         },
                         "date":item["date"]
@@ -271,8 +279,14 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                 # print(apps[0]['filename'] ,item["filename"])
                 if apps[0]['filename']!=item["filename"]:
                     category=''
+                    input=None
+                    output=None
                     if 'category' in x['app']:
                         category=x['app']['category']
+                    if 'input' in x['app']:
+                        input=x['app']['input']
+                    if 'output' in x['app']:
+                        output=x['app']['output']
                     apps.append({
                         "filename":item["filename"],
                         # "category":category,
@@ -284,6 +298,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                                 "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
                                 "name":x['app']['name'],
                                 "version":x['app']['version'],
+                                "input":input,
+                                "output":output
                             }
                         },
                         "date":item["date"]
@@ -534,15 +550,15 @@ PromptServer.add_routes=new_add_routes
 
 
 # 导入节点
-from .nodes.PromptNode import RandomPrompt,PromptSlide,PromptSimplification,PromptImage
-from .nodes.ImageNode import GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.PromptNode import EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
+from .nodes.ImageNode import SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
 from .nodes.Clipseg import CLIPSeg,CombineMasks
 from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
 from .nodes.Utils import TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
-
+from .nodes.Mask import OutlineMask,FeatheredMask
 
 
 # 要导出的所有节点及其名称的字典
@@ -551,6 +567,7 @@ NODE_CLASS_MAPPINGS = {
     "AppInfo":AppInfo,
     "TESTNODE_":TESTNODE_,
     "RandomPrompt":RandomPrompt,
+    "EmbeddingPrompt":EmbeddingPrompt,
     "PromptSlide":PromptSlide,
     "PromptSimplification":PromptSimplification,
     "PromptImage":PromptImage,
@@ -568,6 +585,8 @@ NODE_CLASS_MAPPINGS = {
     "ImageColorTransfer":ImageColorTransfer,
     "ShowLayer":ShowLayer,
     "NewLayer":NewLayer,
+    "SplitImage":SplitImage,
+    "GridOutput":GridOutput,
     "MergeLayers":MergeLayers,
     "SplitLongMask":SplitLongMask,
     "FeatheredMask":FeatheredMask,
@@ -597,6 +616,8 @@ NODE_CLASS_MAPPINGS = {
     "GetImageSize_":GetImageSize_,
     "SwitchByIndex":SwitchByIndex,
     "LimitNumber":LimitNumber, 
+    "OutlineMask":OutlineMask,
+    "JoinWithDelimiter":JoinWithDelimiter
     # "LaMaInpainting":LaMaInpainting
     # "GamePal":GamePal
 }
@@ -622,7 +643,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "PromptSlide":"PromptSlide ♾️Mixlab",
     "PromptGenerate_Mix":"PromptGenerate ♾️Mixlab",
     "ChinesePrompt_Mix":"ChinesePrompt ♾️Mixlab",
-    "GamePal":"GamePal ♾️Mixlab"
+    "GamePal":"GamePal ♾️Mixlab",
+    "RembgNode_Mix":"Removebg"
 }
 
 # web ui的节点功能
@@ -636,16 +658,16 @@ try:
     print('LaMaInpainting.available',LaMaInpainting.available)
     if LaMaInpainting.available:
         NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
-except:
-    print('LaMaInpainting.available',False)
+except Exception as e:
+    print('LaMaInpainting.available',False,e)
 
 try:
     from .nodes.ClipInterrogator import ClipInterrogator
     print('ClipInterrogator.available',ClipInterrogator.available)
     if ClipInterrogator.available:
         NODE_CLASS_MAPPINGS['ClipInterrogator']=ClipInterrogator
-except:
-    print('ClipInterrogator.available',False)
+except Exception as e:
+    print('ClipInterrogator.available',False,e)
 
 try:
     from .nodes.TextGenerateNode import PromptGenerate,ChinesePrompt
@@ -655,8 +677,15 @@ try:
     print('ChinesePrompt.available',ChinesePrompt.available)
     if ChinesePrompt.available:
         NODE_CLASS_MAPPINGS['ChinesePrompt_Mix']=ChinesePrompt
-except:
-    print('TextGenerateNode.available',False)
- 
+except Exception as e:
+    print('TextGenerateNode.available',False,e)
+
+try:
+    from .nodes.RembgNode import RembgNode_
+    print('RembgNode_.available',RembgNode_.available)
+    if RembgNode_.available:
+        NODE_CLASS_MAPPINGS['RembgNode_Mix']=RembgNode_
+except Exception as e:
+    print('RembgNode_.available',False,e)
 
 print('\033[93m -------------- \033[0m')

data/extension-node-map.json

@@ -4761,7 +4761,10 @@
     ],
     "https://github.com/shadowcz007/comfyui-mixlab-nodes": [
         [
+            "GridOutput",
+            "SplitImage",
             "PromptGenerate_Mix",
+            "JoinWithDelimiter",
             "ChinesePrompt_Mix",
             "3DImage",
             "AppInfo",

data/space.txt

@@ -0,0 +1,58 @@
+Residential space
+Apartment building
+Villa
+Bungalow
+Condominium
+Commercial space
+Shopping mall
+Supermarket
+Restaurant
+Store
+Market
+Office space
+Office building
+Office
+Meeting room
+Co-working space
+Educational space
+School
+University
+Training institution
+Library
+Laboratory
+Medical space
+Hospital
+Clinic
+Pharmacy
+Nursing home
+Rehabilitation center
+Cultural space
+Museum
+Library
+Theater
+Concert hall
+Gallery
+Sports space
+Sports stadium
+Gym
+Swimming pool
+Basketball court
+Football field
+Transportation space
+Airport
+Train station
+Subway station
+Bus stop
+Parking lot
+Public space
+Park
+Square
+Street
+Pedestrian street
+Community center
+Industrial space
+Factory
+Warehouse
+Production workshop
+Mine
+Power plant

nodes/ClipInterrogator.py

@@ -24,6 +24,7 @@ def is_installed(package):
         return False
     return spec is not None
 
+
 try:
     if is_installed('clip_interrogator')==False:
         import subprocess
@@ -36,20 +37,25 @@ try:
         #检查命令执行结果
         if result.returncode == 0:
             print("#install success")
-            from transformers import AutoProcessor, BlipForConditionalGeneration
             from clip_interrogator import Config, Interrogator
             _available=True
         else:
             print("#install error")
         
     else:
-        from transformers import AutoProcessor, BlipForConditionalGeneration
         from clip_interrogator import Config, Interrogator
         _available=True
 
 except:
     _available=False
 
+try:
+    from transformers import AutoProcessor, BlipForConditionalGeneration
+except:
+    _available=False
+    print('pls check  transformers.__version__>=4.36.0:: AutoProcessor, BlipForConditionalGeneration')
+
+
 
 def load_caption_model(model_path,config,t='blip-base'):
     dtype=torch.float16 if config.device == 'cuda' else torch.float32

nodes/ImageNode.py

@@ -14,6 +14,8 @@ import math
 from .Watcher import FolderWatcher
 
 
+
+
 FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
 
 MAX_RESOLUTION=8192
@@ -70,6 +72,48 @@ def color_transfer(source,target):
 
 
 
+# 组合
+def create_big_image(image_folder, image_count):
+    # 计算行数和列数
+    rows = math.ceil(math.sqrt(image_count))
+    cols = math.ceil(image_count / rows)
+
+    # 获取每个小图的尺寸
+    small_width = 100
+    small_height = 100
+
+    # 计算大图的尺寸
+    big_width = small_width * cols
+    big_height = small_height * rows
+
+    # 创建一个新的大图
+    big_image = Image.new('RGB', (big_width, big_height))
+
+    # 获取所有图片文件的路径
+    image_files = [f for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))]
+
+    # 遍历所有图片文件
+    for i, image_file in enumerate(image_files):
+        # 打开图片并调整大小
+        image = Image.open(os.path.join(image_folder, image_file))
+        image = image.resize((small_width, small_height))
+
+        # 计算当前小图的位置
+        row = i // cols
+        col = i % cols
+        x = col * small_width
+        y = row * small_height
+
+        # 将小图粘贴到大图上
+        big_image.paste(image, (x, y))
+
+    return big_image
+
+# # 调用方法并保存大图
+# image_folder = 'path/to/folder/containing/images'
+# image_count = 100
+# big_image = create_big_image(image_folder, image_count)
+# big_image.save('path/to/save/big_image.jpg')
 
 
 
@@ -249,6 +293,11 @@ def generate_gradient_image(width, height, start_color_hex, end_color_hex):
 # gradient_image = generate_gradient_image(width, height, start_color_hex, end_color_hex)
 # gradient_image.save('gradient_image.png')
 
+def rgb_to_hex(rgb):
+    r, g, b = rgb
+    hex_color = "#{:02x}{:02x}{:02x}".format(r, g, b)
+    return hex_color
+
 
 # 读取不了分层
 def load_psd(image):
@@ -390,7 +439,8 @@ def get_average_color_image(image):
 
     im = Image.new("RGB", (image.width, image.height), (average_red, average_green, average_blue))
 
-    return im
+    hex=rgb_to_hex((average_red, average_green, average_blue))
+    return (im,hex)
 
 
 
@@ -797,79 +847,6 @@ class SmoothMask:
 
 
 
-class FeatheredMask:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-                "required": {
-                                "mask": ("MASK",),
-                                "start_offset":("INT", {"default": 1, 
-                                                        "min": -150, 
-                                                        "max": 150, 
-                                                        "step": 1,
-                                                        "display": "slider"}),
-                                "feathering_weight":("FLOAT", {"default": 0.1,
-                                                                "min": 0.0,
-                                                                "max": 1,
-                                                                "step": 0.1,
-                                                                "display": "slider"})
-                            }
-            }
-    
-    RETURN_TYPES = ('MASK',)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Mask"
-
-    OUTPUT_IS_LIST = (False,)
-  
-    # 运行的函数
-    def run(self,mask,start_offset, feathering_weight):
-        # print(mask.shape,mask.size())
-        
-        image=tensor2pil(mask)
-
-        # Open the image using PIL
-        image = image.convert("L")
-        if start_offset>0:
-            image=ImageOps.invert(image)
-
-        # Convert the image to a numpy array
-        image_np = np.array(image)
-
-        # Use Canny edge detection to get black contours
-        edges = cv2.Canny(image_np, 30, 150)
-
-        for i in range(0,abs(start_offset)):
-            # int(100*feathering_weight)
-            a=int(abs(start_offset)*0.1*i)
-            # Dilate the black contours to make them wider
-            kernel = np.ones((a, a), np.uint8)
-
-            dilated_edges = cv2.dilate(edges, kernel, iterations=1)
-            # dilated_edges = cv2.erode(edges, kernel, iterations=1)
-            # Smooth the dilated edges using Gaussian blur
-            smoothed_edges = cv2.GaussianBlur(dilated_edges, (5, 5), 0)
-
-            # Adjust the feathering weight
-            feathering_weight = max(0, min(feathering_weight, 1))
-
-            # Blend the smoothed edges with the original image to achieve feathering effect
-            image_np = cv2.addWeighted(image_np, 1, smoothed_edges, feathering_weight, feathering_weight)
-
-        # Convert the result back to PIL image
-        result_image = Image.fromarray(np.uint8(image_np))
-        result_image=result_image.convert("L")
-
-        if start_offset>0:
-            result_image=ImageOps.invert(result_image)
-        
-        mask=pil2tensor(result_image)
-        # print(mask.shape,mask.size())
-        return mask
-
-
 
 
 class SplitLongMask:
@@ -939,7 +916,7 @@ class TransparentImage:
 
     # 运行的函数
     def run(self,images,masks,invert,save,filename_prefix,prompt=None, extra_pnginfo=None):
-        print('TransparentImage',images.shape,images.size())
+        # print('TransparentImage',images.shape,images.size(),masks.shape,masks.size())
         # print(masks.shape,masks.size())
 
         ui_images=[]
@@ -949,11 +926,16 @@ class TransparentImage:
         masks_new=[]
         nh=masks.shape[0]//count
 
-        #INPUT_IS_LIST = False, 一个batch传进来
-        if nh*count==masks.shape[0]:
-            masks_new=split_mask_by_new_height(masks,nh)
+        masks_new=masks
+
+        if images.shape[0]==masks.shape[0] and  images.shape[1]==masks.shape[1] and  images.shape[2]==masks.shape[2]:
+            print('TransparentImage',images.shape,images.size(),masks.shape,masks.size())
         else:
-            masks_new=split_mask_by_new_height(masks,masks.shape[0])
+            #INPUT_IS_LIST = False, 一个batch传进来
+            if nh*count==masks.shape[0]:
+                masks_new=split_mask_by_new_height(masks,nh)
+            else:
+                masks_new=split_mask_by_new_height(masks,masks.shape[0])
 
 
         is_save=True if save=='yes' else False
@@ -1616,6 +1598,112 @@ class NewLayer:
 
         return (layer_n,)
 
+
+def splitImage(image, num):
+    width, height = image.size
+
+    num_rows = int(num ** 0.5)
+    num_cols = int(num / num_rows)
+    
+    grid_width = width // num_cols
+    grid_height = height // num_rows
+
+    grid_coordinates = []
+    for i in range(num_rows):
+        for j in range(num_cols):
+            x = j * grid_width
+            y = i * grid_height
+            grid_coordinates.append((x, y, grid_width, grid_height))
+
+    return grid_coordinates
+
+# # 读取图片
+# image = Image.open("path_to_your_image.jpg")
+
+# # 定义要切割的区域数量
+# num = 9
+
+# # 切割图片
+# grid_coordinates = splitImage(image, num)
+
+# # 输出切割区域坐标
+# for i, coordinates in enumerate(grid_coordinates):
+#     print(f"Region {i + 1}: x={coordinates[0]}, y={coordinates[1]}, width={coordinates[2]}, height={coordinates[3]}")
+
+
+class SplitImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return { 
+            "required": { 
+                "image": ("IMAGE",), 
+                "num": ("INT",{
+                    "default": 4, 
+                    "min": 1, #Minimum value
+                    "max": 500, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                "seed": ("INT",{
+                    "default": 4, 
+                    "min": 1, #Minimum value
+                    "max": 500, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+            }
+                }
+    
+    RETURN_TYPES = ("_GRID","_GRID",)
+    RETURN_NAMES = ("grids","grid")
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    INPUT_IS_LIST = False
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,image,num,seed):
+        image=tensor2pil(image)
+
+        grids=splitImage(image,num)
+
+        if seed>=num:
+            num=int(seed / 500 * num)-1
+        else:
+            num=seed-1
+
+        num=max(0,num) 
+
+        g=grids[num]
+
+        return (grids,g,)
+
+
+class GridOutput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return { 
+            "required": {
+                "grid": ("_GRID",)
+            }
+                }
+    
+    RETURN_TYPES = ("INT","INT","INT","INT",)
+    RETURN_NAMES = ("x","y","width","height",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    INPUT_IS_LIST = False
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,grid):
+        x,y,w,h=grid
+        return (x,y,w,h,)
+
 class ShowLayer:
     @classmethod
     def INPUT_TYPES(s):
@@ -1973,15 +2061,15 @@ class ResizeImage:
             }
                 }
     
-    RETURN_TYPES = ("IMAGE","IMAGE")
-    RETURN_NAMES = ("image","average_image",)
+    RETURN_TYPES = ("IMAGE","IMAGE","STRING",)
+    RETURN_NAMES = ("image","average_image","average_hex",)
 
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/Image"
 
     INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,True,)
+    OUTPUT_IS_LIST = (True,True,True,)
 
     def run(self,width,height,scale_option,image=None,average_color=['on'],fill_color=["#FFFFFF"]):
         
@@ -1993,16 +2081,18 @@ class ResizeImage:
 
         imgs=[]
         average_images=[]
+        hexs=[]
 
         if image==None:
             im=create_noisy_image(w,h,"RGB")
-            a_im=get_average_color_image(im)
+            a_im,hex=get_average_color_image(im)
             
             im=pil2tensor(im)
             imgs.append(im)
 
             a_im=pil2tensor(a_im)
             average_images.append(a_im)
+            hexs.append(hex)
         else:
             for ims in image:
                 for im in ims:
@@ -2010,15 +2100,16 @@ class ResizeImage:
                     im=resize_image(im,scale_option,w,h,fill_color)
                     im=im.convert('RGB')
 
-                    a_im=get_average_color_image(im)
+                    a_im,hex=get_average_color_image(im)
 
                     im=pil2tensor(im)
                     imgs.append(im)
 
                     a_im=pil2tensor(a_im)
                     average_images.append(a_im)
+                    hexs.append(hex)
         
-        return (imgs,average_images,)
+        return (imgs,average_images,hexs,)
 
 
 class MirroredImage:
@@ -2120,3 +2211,4 @@ class ImageColorTransfer:
         return (res,)
 
 
+

nodes/Mask.py

@@ -0,0 +1,171 @@
+ 
+import scipy.ndimage
+import torch
+
+from nodes import MAX_RESOLUTION
+
+import numpy as np 
+# from PIL import Image, ImageDraw
+from PIL import Image, ImageOps 
+
+from comfy.cli_args import args
+import cv2
+
+
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+def grow(mask, expand, tapered_corners):
+    c = 0 if tapered_corners else 1
+    kernel = np.array([[c, 1, c],
+                            [1, 1, 1],
+                            [c, 1, c]])
+    mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
+    out = []
+    for m in mask:
+        output = m.numpy()
+        for _ in range(abs(expand)):
+            if expand < 0:
+                output = scipy.ndimage.grey_erosion(output, footprint=kernel)
+            else:
+                output = scipy.ndimage.grey_dilation(output, footprint=kernel)
+        output = torch.from_numpy(output)
+        out.append(output)
+    return torch.stack(out, dim=0)
+
+def combine(destination, source, x, y):
+    output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
+    source = source.reshape((-1, source.shape[-2], source.shape[-1]))
+
+    left, top = (x, y,)
+    right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
+    visible_width, visible_height = (right - left, bottom - top,)
+
+    source_portion = source[:, :visible_height, :visible_width]
+    destination_portion = destination[:, top:bottom, left:right]
+ 
+    #operation == "subtract":
+    output[:, top:bottom, left:right] = destination_portion - source_portion
+        
+    output = torch.clamp(output, 0.0, 1.0)
+
+    return output
+
+class OutlineMask:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                                "mask": ("MASK",),
+                                "outline_width":("INT", {"default": 10,"min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                                "tapered_corners": ("BOOLEAN", {"default": True}),
+                            }
+            }
+    
+    RETURN_TYPES = ('MASK',)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Mask"
+
+    # 运行的函数
+    def run(self, mask, outline_width, tapered_corners):
+
+        m1=grow(mask,outline_width,tapered_corners)
+        m2=grow(mask,-outline_width,tapered_corners)
+
+        m3=combine(m1,m2,0,0)
+
+        return (m3,)
+    
+
+
+
+class FeatheredMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                                "mask": ("MASK",),
+                                "start_offset":("INT", {"default": 1, 
+                                                        "min": -150, 
+                                                        "max": 150, 
+                                                        "step": 1,
+                                                        "display": "slider"}),
+                                "feathering_weight":("FLOAT", {"default": 0.1,
+                                                                "min": 0.0,
+                                                                "max": 1,
+                                                                "step": 0.1,
+                                                                "display": "slider"})
+                            }
+            }
+    
+    RETURN_TYPES = ('MASK',)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Mask"
+
+    OUTPUT_IS_LIST = (True,)
+  
+    # 运行的函数
+    def run(self,mask,start_offset, feathering_weight):
+        # print(mask.shape,mask.size())
+        
+        num,_,_=mask.size()
+
+        masks=[]
+
+        for i in range(num):
+            mm=mask[i]
+            image=tensor2pil(mm)
+
+            # Open the image using PIL
+            image = image.convert("L")
+            if start_offset>0:
+                image=ImageOps.invert(image)
+
+            # Convert the image to a numpy array
+            image_np = np.array(image)
+
+            # Use Canny edge detection to get black contours
+            edges = cv2.Canny(image_np, 30, 150)
+
+            for i in range(0,abs(start_offset)):
+                # int(100*feathering_weight)
+                a=int(abs(start_offset)*0.1*i)
+                # Dilate the black contours to make them wider
+                kernel = np.ones((a, a), np.uint8)
+
+                dilated_edges = cv2.dilate(edges, kernel, iterations=1)
+                # dilated_edges = cv2.erode(edges, kernel, iterations=1)
+                # Smooth the dilated edges using Gaussian blur
+                smoothed_edges = cv2.GaussianBlur(dilated_edges, (5, 5), 0)
+
+                # Adjust the feathering weight
+                feathering_weight = max(0, min(feathering_weight, 1))
+
+                # Blend the smoothed edges with the original image to achieve feathering effect
+                image_np = cv2.addWeighted(image_np, 1, smoothed_edges, feathering_weight, feathering_weight)
+
+            # Convert the result back to PIL image
+            result_image = Image.fromarray(np.uint8(image_np))
+            result_image=result_image.convert("L")
+
+            if start_offset>0:
+                result_image=ImageOps.invert(result_image)
+            
+            result_image=result_image.convert("L")
+            mt=pil2tensor(result_image)
+            masks.append(mt)
+         
+            # print( mt.size())
+        return (masks,)

nodes/PromptNode.py

@@ -12,6 +12,30 @@ from PIL.PngImagePlugin import PngInfo
 #     req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
 #     request.urlopen(req)    
 
+embeddings_path=os.path.join(folder_paths.models_dir, "embeddings")
+
+def get_files_with_extension(directory, extension):
+    file_list = []
+    for root, dirs, files in os.walk(directory):
+        for file in files:
+            if file.endswith(extension):
+                file_name = os.path.splitext(file)[0]
+                file_list.append(file_name)
+    return file_list
+
+def join_with_(text_list,delimiter):
+    joined_text = delimiter.join(text_list)
+    return joined_text
+
+
+class AnyType(str):
+  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+
+  def __ne__(self, __value: object) -> bool:
+    return False
+
+any_type = AnyType("*")
+
 
 default_prompt1='''Swing
                                                 Slide
@@ -54,7 +78,7 @@ def addWeight(text, weight=1):
     if weight == 1:
         return text
     else:
-        return f"({text}:{round(weight,2)})"
+        return f"({text}:{round(weight,3)})"
 
 def prompt_delete_words(sentence, new_words_length):
     # 使用逗号分割句子，并去除空格
@@ -297,7 +321,9 @@ class RandomPrompt:
                             "default": 'sticker, Cartoon, ``'
                           }),
                 "random_sample": (["enable", "disable"],),
-                }
+                # "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
+                },
+           
             }
     
     
@@ -334,7 +360,10 @@ class RandomPrompt:
             for w2 in words2:
                 w2=w2.strip()
                 if '``' not in w2:
-                    w2=w2+',``'
+                    if w2=="":
+                        w2='``'
+                    else:
+                        w2=w2+',``'
                 if w1!='' and w2!='':
                     prompts.append(w2.replace('``', w1))
                 pbar.update(1)
@@ -357,62 +386,64 @@ class RandomPrompt:
 
 
 
-# class RunWorkflow:
-#     @classmethod
-#     def INPUT_TYPES(s):
-#         return {
-#             "required": {
-#                 "workflow": ("STRING", {
-#                             "multiline": False, 
-#                             "default": ''
-#                           }),
-#                 "prompt": ("STRING", {
-#                             "multiline": False, 
-#                             "default": ''
-#                           }),
-#                  "image": ("IMAGE",),
-#                  "input_node": ("STRING", {
-#                             "multiline": False, 
-#                             "default": ''
-#                           }),
-#                  "output_node": ("STRING", {
-#                             "multiline": False, 
-#                             "default": ''
-#                           }),
-#                 },
+
+class EmbeddingPrompt:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "embedding":(get_files_with_extension(embeddings_path,'.pt'),),
+                "weight": ("FLOAT", {"default": 1, "min": -2, "max": 2,"step":0.01 ,"display": "slider"}),
+                },
                 
-#             }
+            }
     
+    RETURN_TYPES = ("STRING",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+
+    OUTPUT_IS_LIST = (False,)
+    # OUTPUT_NODE = True
+
+    # 运行的函数
+    def run(self,embedding,weight):
+        weight = round(weight, 3)
+        prompt='embedding:'+embedding
+        if weight!=1:
+            prompt='('+prompt+':'+str(weight)+')'
+        prompt=" "+prompt+' ' 
+        # return (new_prompt)
+        return (prompt,)
+
+RETURN_TYPES = (any_type,)
+
+class JoinWithDelimiter:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "text_list": (any_type,),
+                    "delimiter":(["newline","comma"],),
+                             },
+                }
     
+    RETURN_TYPES = ("STRING",) 
 
-#     RETURN_TYPES = ("IMAGE","STRING",)
+    FUNCTION = "run"
 
-#     FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/Prompt"
 
-#     CATEGORY = "♾️Mixlab/workflow"
-
-#     OUTPUT_IS_LIST = (True,)
-#     OUTPUT_NODE = True
-
-
-#     # 运行的函数
-#     def run(self,workflow,prompt,image,input_node,output_node):
-#         print('#运行的函数',prompt,image,input_node,output_node)
-#         workflow=json.loads(workflow)
-#         input_node=input_node.split(".")
-#         workflow[input_node[0]][input_node[1]][input_node[2]]=prompt
-
-#         workflow_new={}
-#         # 遍历，seed设为随机
-#         for key, value in workflow.items():
-#             if 'inputs' in value:
-#                 if 'seed' in value['inputs']:
-#                     value['inputs']['seed']= random.randint(1, 18446744073709551614)
-#             workflow_new[key]=value
-
-#         queue_prompt(workflow_new)
-#         print('#运行的函数',workflow_new[input_node[0]])
-
-#         # return (new_prompt)
-#         return  {"ui":{"images": []},"result": ([image],['text'],)}
+    INPUT_IS_LIST = True # 当true的时候，输入时list，当false的时候，如果输入是list，则会自动包一层for循环调用
+    OUTPUT_IS_LIST = (False,)
 
+    def run(self,text_list,delimiter):
+        delimiter=delimiter[0]
+        if delimiter =='newline':
+            delimiter='\n'
+        elif delimiter=='comma':
+            delimiter=','
+        t=''
+        if isinstance(text_list, list):
+            t=join_with_(text_list,delimiter)
+        return (t,)

nodes/RembgNode.py

@@ -0,0 +1,164 @@
+import os,sys
+import folder_paths
+
+from PIL import Image
+import importlib.util
+
+import comfy.utils
+import numpy as np
+import torch
+
+
+U2NET_HOME=os.path.join(folder_paths.models_dir, "rembg")
+os.environ["U2NET_HOME"] = U2NET_HOME
+
+global _available
+_available=False
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+
+try:
+    if is_installed('rembg')==False:
+        import subprocess
+
+        # 安装
+        print('#pip install rembg[gpu]')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'rembg[gpu]'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0:
+            print("#install success")
+            from rembg import new_session, remove
+            _available=True
+        else:
+            print("#install error")
+        
+    else:
+        from rembg import new_session, remove
+        _available=True
+
+except:
+    _available=False
+
+
+def run_bg(model_name= "unet",images=[]):
+    # model_name = "unet" # "isnet-general-use"
+    rembg_session = new_session(model_name)
+    masks=[]
+    rgba_images=[]
+    rgb_images=[]
+    # 进度条
+    pbar = comfy.utils.ProgressBar(len(images) )
+    for img in images:
+        # use the post_process_mask argument to post process the mask to get better results.
+        mask = remove(img, session=rembg_session,only_mask=True,post_process_mask=True)
+        # mask=mask.convert('L')
+        # masks.append(mask)
+        if model_name=="u2net_cloth_seg":
+            width, original_height = mask.size
+            num_slices = original_height // img.height
+            for i in range(num_slices):
+                top = i * img.height
+                bottom = (i + 1) * img.height
+                slice_image = mask.crop((0, top, width, bottom))
+                slice_mask=slice_image.convert('L')
+                masks.append(slice_mask)
+
+                # rgba图
+                image_rgba = img.convert("RGBA")
+                image_rgba.putalpha(slice_mask)
+                rgba_images.append(image_rgba)
+
+                #rgb
+                rgb_image = Image.new("RGB", image_rgba.size, (0, 0, 0))
+                rgb_image.paste(image_rgba, mask=image_rgba.split()[3])
+                rgb_images.append(rgb_image)
+
+        else:
+            mask=mask.convert('L')    
+            # mask.save(output_path)
+            masks.append(mask)
+
+            # rgba图
+            image_rgba = img.convert("RGBA")
+            image_rgba.putalpha(mask)
+            rgba_images.append(image_rgba)
+
+            #rgb
+            rgb_image = Image.new("RGB", image_rgba.size, (0, 0, 0))
+            rgb_image.paste(image_rgba, mask=image_rgba.split()[3])
+            rgb_images.append(rgb_image)
+            
+        pbar.update(1)
+    return (masks,rgba_images,rgb_images)
+
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+class RembgNode_:
+
+    global _available
+    available=_available
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "model_name": (["u2net",
+                            "u2netp",
+                            "u2net_human_seg",
+                            "u2net_cloth_seg",
+                            "silueta",
+                            "isnet-general-use",
+                            "isnet-anime",
+                            # "sam"
+                            ],),
+            
+                             },
+                }
+    
+    RETURN_TYPES = ("MASK","IMAGE","RGBA",)
+    RETURN_NAMES = ("masks","images","RGBAs")
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Mask"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,True,True,)
+  
+    def run(self,image,model_name):
+        # 兼容list输入和batch输入
+
+        model_name=model_name[0]
+
+        images=[]
+        
+        for ims in image:
+            for im in ims:
+                im=tensor2pil(im)
+                images.append(im)
+
+        masks,rgba_images,rgb_images=run_bg(model_name,images)
+
+        masks=[pil2tensor(m) for m in masks]
+
+        rgba_images=[pil2tensor(m) for m in rgba_images]
+
+        rgb_images=[pil2tensor(m) for m in rgb_images]
+
+        return (masks,rgb_images,rgba_images,)

nodes/TextGenerateNode.py

@@ -6,7 +6,7 @@ import os,sys
 import folder_paths
 
 # from PIL import Image
-# import importlib.util
+import importlib.util
 
 import comfy.utils
 # import numpy as np
@@ -29,18 +29,50 @@ if not os.path.exists(zh_en_model_path):
 
 
 
-def translate(zh_en_tokenizer,zh_en_model,texts):
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+
+try:
+    if is_installed('sentencepiece')==False:
+        import subprocess
+
+        # 安装
+        print('#pip install sentencepiece')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'sentencepiece'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0 and is_installed('sentencepiece'):
+            print("#install success")
+            _available=True
+        else:
+            print("#install error")
+            _available=False
+        
+    else:
+        _available=True
+
+except:
+    _available=False
+
+
+
+def translate(zh_en_tokenizer,zh_en_model,text):
     with torch.no_grad():
-        encoded = zh_en_tokenizer(texts, return_tensors="pt")
+        encoded = zh_en_tokenizer([text], return_tensors="pt")
         encoded.to(zh_en_model.device)
         sequences = zh_en_model.generate(**encoded)
-        return zh_en_tokenizer.batch_decode(sequences, skip_special_tokens=True)
+        return zh_en_tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
 
 # input = "青春不能回头，所以青春没有终点。 ——《火影忍者》"
 # print(input, translate(input))
 
 
-
 def text_generate(text_pipe,input,seed=None):
     
     if seed==None:
@@ -66,6 +98,55 @@ def text_generate(text_pipe,input,seed=None):
 
 # input = "Youth can't turn back, so there's no end to youth."
 # print(input, text_generate(input))
+
+    
+import re
+
+def correct_prompt_syntax(prompt):
+
+    print("input prompt",prompt)
+    corrected_elements = []
+    # 处理成统一的英文标点
+    prompt = prompt.replace('（', '(').replace('）', ')').replace('，', ',').replace(';', ',').replace('。', '.').replace('：',':')
+    # 删除多余的空格
+    prompt = re.sub(r'\s+', ' ', prompt).strip()
+
+    # 分词
+    prompt_elements = prompt.split(',')
+
+    for element in prompt_elements:
+        element = element.strip()
+
+        # 处理空元素
+        if not element:
+            continue
+
+        # 检查并处理圆括号、方括号、尖括号
+        if element[0] in '([':
+            corrected_element = balance_brackets(element, '(', ')') if element[0] == '(' else balance_brackets(element, '[', ']')
+        elif element[0] == '<':
+            corrected_element = balance_brackets(element, '<', '>')
+        else:
+            # 删除开头的右括号或右方括号
+            corrected_element = element.lstrip(')]')
+
+        corrected_elements.append(corrected_element)
+
+    # 重组修正后的prompt
+    corrected_prompt = ', '.join(corrected_elements)
+    print("output prompt",corrected_prompt)
+    return corrected_prompt
+
+def balance_brackets(element, open_bracket, close_bracket):
+    open_brackets_count = element.count(open_bracket)
+    close_brackets_count = element.count(close_bracket)
+    return element + close_bracket * (open_brackets_count - close_brackets_count)
+
+# # 示例使用
+# test_prompt = "((middle-century castles)), [forsaken: 0.8], (mystery dragons: 1.3, mist forests, sunsets, quiet; (((dummy)), [fisting city: 0.5] background, radiant, soft and flavoured,] promising mountains, ((starry: 1.6), [[crowds], [middle-century castle: urban landscapes of the future: 0.5], [yellow: bright sun: 0.7], overlooking"
+# corrected_prompt = correct_prompt_syntax(test_prompt)
+# print(corrected_prompt)
+
         
         
 class ChinesePrompt:
@@ -76,12 +157,13 @@ class ChinesePrompt:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-            "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
-            
+               "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
+               "generation": (["on","off"],{"default": "off"}), 
                              },
-
+               
                 "optional":{
                     "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
+                    
                 },
 
                 }
@@ -102,17 +184,20 @@ class ChinesePrompt:
     zh_en_model=None
     zh_en_tokenizer=None 
 
-    def run(self,text,seed):
+    def run(self,text,seed,generation):
         global text_pipe,zh_en_model,zh_en_tokenizer
 
         seed=seed[0]
+        generation=generation[0]
 
         # 进度条
         pbar = comfy.utils.ProgressBar(len(text)+1)
-
+        
+        texts = [correct_prompt_syntax(t) for t in text]
+        print('correct_prompt_syntax::',texts)
         if zh_en_model==None:
             zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval()
-            zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path)
+            zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path,padding=True, truncation=True)
         
         zh_en_model.to("cuda" if torch.cuda.is_available() else "cpu")
             # zh_en_tokenizer.to("cuda" if torch.cuda.is_available() else "cpu")
@@ -124,24 +209,31 @@ class ChinesePrompt:
         prompt_result=[]
 
         # print('zh_en_model device',zh_en_model.device,text_pipe.model.device,torch.cuda.current_device() )
-        en_text=translate(zh_en_tokenizer,zh_en_model,text)
+        en_texts=[]
+        for t in texts:
+            en_text=translate(zh_en_tokenizer,zh_en_model,t)
+            en_texts.append(en_text)
+
         zh_en_model.to('cpu')
+        print("test en_text",en_texts)
         # en_text.to("cuda" if torch.cuda.is_available() else "cpu")
 
         pbar.update(1)
-        for t in en_text:
-            prompt =text_generate(text_pipe,t,seed)
-            # 多条，还是单条
-            lines = prompt.split("\n")
-            longest_line = max(lines, key=len)
-            # print(longest_line)
-            prompt_result.append(longest_line)
+        for t in en_texts:
+            if generation=='on':
+                prompt =text_generate(text_pipe,t,seed)
+                # 多条，还是单条
+                lines = prompt.split("\n")
+                longest_line = max(lines, key=len)
+                # print(longest_line)
+                prompt_result.append(longest_line)
+            else:
+                prompt_result.append(t)
             pbar.update(1)
 
         text_pipe.model.to('cpu')
+        prompt_result = [correct_prompt_syntax(p) for p in prompt_result]
         
-        
-
         return {
             "ui":{
                     "prompt": prompt_result

nodes/Utils.py

@@ -5,7 +5,7 @@ import numpy as np
 # FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
 import folder_paths
 import matplotlib.font_manager as fm
-
+import torch
 
 def recursive_search(directory, excluded_dir_names=None):
     if not os.path.isdir(directory):
@@ -131,7 +131,12 @@ def flatten_list(nested_list):
         if isinstance(item, list):
             flat_list.extend(flatten_list(item))
         else:
-            flat_list.append(item)
+            if torch.is_tensor(item):
+                print('item.shape',item.shape)
+                for i in range(item.shape[0]):
+                    flat_list.append(item[i:i + 1, ...])
+            else:
+                flat_list.append(item)
     return flat_list
 
 
@@ -336,11 +341,18 @@ class MultiplicationNode:
     def INPUT_TYPES(s):
         return {"required": {
                     "numberA":(any_type,),
-                    "numberB":("FLOAT", {
+                    "multiply_by":("FLOAT", {
                         "default": 0, 
-                        "min": -1, #Minimum value
+                        "min": -2, #Minimum value
                         "max": 0xffffffffffffffff,
-                        "step": 0.1, #Slider's step
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                     "add_by":("FLOAT", {
+                        "default": 0, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
                         "display": "number" # Cosmetic only: display as "number" or "slider"
                     })
                              },
@@ -355,9 +367,9 @@ class MultiplicationNode:
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,)
 
-    def run(self,numberA,numberB):
-        b=int(numberA*numberB)
-        a=float(numberA*numberB)
+    def run(self,numberA,multiply_by,add_by):
+        b=int(numberA*multiply_by+add_by)
+        a=float(numberA*multiply_by+add_by)
         return (a,b,) 
 
 class TextInput:
@@ -485,7 +497,7 @@ class AppInfo:
                              },
 
                 "optional":{
-                    "LOGO": ("IMAGE",),
+                    "IMAGE": ("IMAGE",),
                     "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
                     "version":("INT", {
                         "default": 1, 
@@ -513,12 +525,12 @@ class AppInfo:
     INPUT_IS_LIST = True
     # OUTPUT_IS_LIST = (True,)
 
-    def run(self,name,input_ids,output_ids,LOGO,description,version,share_prefix,link,category,auto_save):
+    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
         name=name[0]
         
         im=None
-        if LOGO:
-            im=LOGO[0][0]
+        if IMAGE:
+            im=IMAGE[0][0]
             #TODO batch 的方式需要处理
             im=create_temp_file(im)
         # image [img,] img[batch,w,h,a] 列表里面是batch，
@@ -573,7 +585,8 @@ class SwitchByIndex:
 
         C=[]
         index=index[0]
-        for a in A:
+
+        for a in A:    
             C.append(a)
         for b in B:
             C.append(b)

requirements.txt

@@ -4,5 +4,5 @@ watchdog
 opencv-python-headless
 matplotlib
 openai
-# simple-lama-inpainting
-# clip-interrogator==0.6.0
+simple-lama-inpainting
+clip-interrogator==0.6.0

web/index.html

@@ -391,14 +391,14 @@
             <a class="link" href="https://www.mixcomfy.com" target="_blank">ComfyUI中文爱好者社区推荐</a>
         </div>
 
-        <a target="_blank" href="https://github.com/shadowcz007/comfyui-mixlab-nodes" style="text-decoration: none;
+        <a target="_blank" href="https://www.mixcomfy.com/blog/" style="text-decoration: none;
         color: black;font-size:12px">
             <svg height="32" aria-hidden="true" viewBox="0 0 16 16" version="1.1" width="32" data-view-component="true"
                 class="octicon octicon-mark-github v-align-middle color-fg-default">
                 <path
                     d="M8 0c4.42 0 8 3.58 8 8a8.013 8.013 0 0 1-5.45 7.59c-.4.08-.55-.17-.55-.38 0-.27.01-1.13.01-2.2 0-.75-.25-1.23-.54-1.48 1.78-.2 3.65-.88 3.65-3.95 0-.88-.31-1.59-.82-2.15.08-.2.36-1.02-.08-2.12 0 0-.67-.22-2.2.82-.64-.18-1.32-.27-2-.27-.68 0-1.36.09-2 .27-1.53-1.03-2.2-.82-2.2-.82-.44 1.1-.16 1.92-.08 2.12-.51.56-.82 1.28-.82 2.15 0 3.06 1.86 3.75 3.64 3.95-.23.2-.44.55-.51 1.07-.46.21-1.61.55-2.33-.66-.15-.24-.6-.83-1.23-.82-.67.01-.27.38.01.53.34.19.73.9.82 1.13.16.45.68 1.31 2.69.94 0 .67.01 1.3.01 1.49 0 .21-.15.45-.55.38A7.995 7.995 0 0 1 0 8c0-4.42 3.58-8 8-8Z">
                 </path>
-            </svg> Code
+            </svg> Community
 
         </a>
     </div>
@@ -761,19 +761,44 @@
             } catch (error) {
                 isURL = false;
             }
+
+            let isAElement = undefined;
+            try {
+                let div = document.createElement('div');
+                div.innerHTML = link;
+                let a = div.querySelector('a');
+                if (a.href) {
+                    new URL(a.href);
+                    isAElement = div.innerHTML;
+                }
+            } catch (error) {
+
+            }
             // new URL(link)
-            if (isURL) {
+            if (isURL || isAElement) {
                 const linkBtn = document.createElement('button');
-                // linkBtn.href = link;
-                linkBtn.innerText = 'go to'
+
+                if (isURL) {
+                    // linkBtn.href = link;
+                    linkBtn.innerText = 'go to'
+                } else if (isAElement) {
+                    linkBtn.innerHTML = isAElement
+                }
+
                 action.appendChild(linkBtn)
                 linkBtn.style.marginLeft = '18px';
                 linkBtn.addEventListener('click', e => {
-                    e.preventDefault();
-                    window.open(link);
+
+                    if (isURL) {
+                        e.preventDefault();
+                        window.open(link);
+                    }
+                    //   if(isURL)  window.open(link);
                 })
+
             }
 
+
             const output_card = document.createElement("div");
             output_card.className = 'output_card'
             container.appendChild(output_card)
@@ -1826,7 +1851,7 @@
                                     p.className = 'prompt_image'
                                     p.innerText = prompt;
                                     a.appendChild(p)
-                                    img.alt=prompt
+                                    img.alt = prompt
                                 }
 
                                 // imgDiv.parentElement.appendChild(a);

web/javascript/checkVersion_mixlab.js

@@ -3,7 +3,7 @@ import { app } from '../../../scripts/app.js'
 const repoOwner = 'shadowcz007' // 替换为仓库的所有者
 const repoName = 'comfyui-mixlab-nodes' // 替换为仓库的名称
 
-const version = 'v0.12.0'
+const version = 'v0.14.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/main_mixlab.js

@@ -1331,7 +1331,13 @@ app.registerExtension({
           let w = 360,
             s = widget.preview.videoWidth / widget.preview.videoHeight,
             h = w / s || w
-          console.log(h)
+          // console.log(h)
+
+          if (!window.documentPictureInPicture) {
+            window.alert(
+              'This feature is available only in secure contexts (HTTPS), in some or all supporting browsers. https://developer.mozilla.org/en-US/docs/Web/API/Document_Picture-in-Picture_API'
+            )
+          }
 
           const pipWindow = await documentPictureInPicture.requestWindow({
             width: w,
@@ -2137,8 +2143,8 @@ const node = {
   loadedGraphNode (node, app) {
     if (node.type === 'RandomPrompt') {
       try {
-        let max_count = node.widgets.filter(w => w.name === "max_count")[0];
-        max_count.value= node.widgets_values[0]
+        let max_count = node.widgets.filter(w => w.name === 'max_count')[0]
+        max_count.value = node.widgets_values[0]
         // console.log('RandomPrompt',max_count,node.widgets_values[0])
       } catch (error) {
         console.log(error)

web/javascript/prompt_mixlab.js

@@ -19,7 +19,7 @@ function loadCSS (url) {
     font-size: 16px;
     color: #fff;
     width: calc(100% - 32px);
-    max-width: 400px;
+    max-width: 980px;
     padding: 2px 8px;
     border-radius: 4px;
     position: absolute;
@@ -178,8 +178,7 @@ app.registerExtension({
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = async function () {
         orig_nodeCreated?.apply(this, arguments)
-        name
-
+         
         const mutable_prompt = this.widgets.filter(
           w => w.name == 'mutable_prompt'
         )[0]
@@ -267,16 +266,7 @@ app.registerExtension({
   },
   async loadedGraphNode (node, app) {
     if (node.type === 'RandomPrompt') {
-      // try {
-      //   let mutable_prompt = node.widgets.filter(w => w.name === 'mutable_prompt')[0]
-      //   // let ks = getLocalData(`_mixlab_PromptSlide`)
-      //   let uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
-      //   // console.log('##widget', uploadWidget.value)
-      //   let keywords = JSON.parse(uploadWidget.value)
-      //   if (keywords && keywords[0]) {
-      //     mutable_prompt.value=keywords.join('\n')
-      //   }
-      // } catch (error) {}
+     
     }
   }
 })

web/javascript/ui_mixlab.js

@@ -739,8 +739,9 @@ app.registerExtension({
         if (category === '0') {
           apps_opts.push(
             ...Array.from(apps_map[category], a => {
+              // console.log('#1级',a)
               return {
-                content: a.name,
+                content: `${a.name}_${a.version}`,
                 has_submenu: false,
                 callback: async () => {
                   try {
@@ -768,8 +769,9 @@ app.registerExtension({
             disabled: false,
             submenu: {
               options: Array.from(apps_map[category], a => {
+                // console.log('#二级',a)
                 return {
-                  content: a.name,
+                  content: `${a.name}_${a.version}`,
                   callback: async () => {
                     try {
                       let item = (await get_my_app(a.filename, a.category))[0]