0.15.1

README.md

@@ -137,15 +137,6 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 ![TransparentImage](./assets/TransparentImage.png)
 
 
-> Consistency Decoder
-
-[openai Consistency Decoder]( https://github.com/openai/consistencydecoder)
-
-![Consistency](./assets/consistency.png)
-After downloading the OpenAI VAE model, place it in the "model/vae" directory for use.
-https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt
-
-
 > FeatheredMask、SmoothMask
 
 Add edges to an image.
@@ -213,6 +204,11 @@ If you are using a venv, make sure you have it activated before installation and
 pip3 install -r requirements.txt
 ```
 
+#### 
+[comfyui-ultralytics-yolo](https://github.com/shadowcz007/comfyui-ultralytics-yolo)
+[comfyui-moondream](https://github.com/shadowcz007/comfyui-moondream)
+
+
 #### Chinese community
 访问 [www.mixcomfy.com](https://www.mixcomfy.com)，获得更多内测功能，关注微信公众号：Mixlab无界社区
 

__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)
@@ -222,7 +222,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
             # print(item)
             try:
                 x=item["data"]
-                if i==0:
+                # 管理员模式，读取全部数据
+                if i==0 or is_all:
                     apps.append({
                         "filename":item["filename"],
                         # "category":item['category'],
@@ -391,9 +392,6 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
         site = web.TCPSite(runner, address, port)
         await site.start()
 
-        PromptServer.instance.port=port
-
-
         import ssl
         crt, key = create_for_https()
         ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
@@ -496,7 +494,6 @@ async def mixlab_workflow_hander(request):
                     'status':'success',
                 }
             elif data['task']=='list':
-                # 暂时没有用到
                 result={
                     'data':get_workflows(),
                     'status':'success',
@@ -535,20 +532,19 @@ async def nodes_map_hander(request):
 # 导入节点
 from .nodes.PromptNode import EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
 from .nodes.ImageNode import SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
-from .nodes.Vae import VAELoader,VAEDecode
+# 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 CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+from .nodes.Utils import CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
 from .nodes.Mask import OutlineMask,FeatheredMask
-from .nodes.App import AppInfo,AppNode
+
 
 # 要导出的所有节点及其名称的字典
 # 注意：名称应全局唯一
 NODE_CLASS_MAPPINGS = {
     "AppInfo":AppInfo,
-    "AppNode":AppNode,
     "TESTNODE_":TESTNODE_,
     "RandomPrompt":RandomPrompt,
     "EmbeddingPrompt":EmbeddingPrompt,
@@ -579,9 +575,9 @@ NODE_CLASS_MAPPINGS = {
     "FaceToMask":FaceToMask,
     "AreaToMask":AreaToMask,
     "ImageCropByAlpha":ImageCropByAlpha,
-    "VAELoaderConsistencyDecoder":VAELoader,
+    # "VAELoaderConsistencyDecoder":VAELoader,
     "SaveImageToLocal":SaveImageToLocal,
-    "VAEDecodeConsistencyDecoder":VAEDecode,
+    # "VAEDecodeConsistencyDecoder":VAEDecode,
     "ScreenShare":ScreenShareNode,
     "FloatingVideo":FloatingVideo,
     "CLIPSeg_":CLIPSeg,

data/extension-node-map.json

@@ -4768,7 +4768,6 @@
             "ChinesePrompt_Mix",
             "3DImage",
             "AppInfo",
-            "AppNode",
             "IntNumber",
             "FloatSlider",
             "ResizeImage",
@@ -4812,8 +4811,6 @@
             "TextImage",
             "ResizeImageMixlab",
             "TransparentImage",
-            "VAEDecodeConsistencyDecoder",
-            "VAELoaderConsistencyDecoder",
             "TextToNumber",
             "TextInput_",
             "DynamicDelayProcessor",

nodes/App.py

@@ -1,397 +0,0 @@
-import os,sys
-# import re,random,json
-from PIL import Image
-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 os
-import json
-import datetime
-
-import folder_paths
-from server import PromptServer
-import importlib.util
-
-
-def is_installed(package):
-    try:
-        spec = importlib.util.find_spec(package)
-    except ModuleNotFoundError:
-        return False
-    return spec is not None
-
-
-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("*")
-
-app_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'../app'))
-
-# workflow  目录下的所有json
-def read_workflow_json_files_all(folder_path):
-    print('#read_workflow_json_files_all',folder_path)
-    json_files = []
-    for root, dirs, files in os.walk(folder_path):
-        for file in files:
-            if file.endswith('.json'):
-                json_files.append(os.path.join(root, file))
-
-    data = []
-    for file_path in json_files:
-        try:
-            with open(file_path) as json_file:
-                json_data = json.load(json_file)
-                creation_time = datetime.datetime.fromtimestamp(os.path.getctime(file_path))
-                numeric_timestamp = creation_time.timestamp()
-
-                option=os.path.basename(os.path.dirname(file_path))+'/'+os.path.basename(file_path)
-                if os.path.dirname(file_path) == folder_path:
-                    option=os.path.basename(file_path)
-
-                file_info = {
-                    'filename': os.path.basename(file_path),
-                    'category': os.path.dirname(file_path),
-                    'data': json_data,
-                    'date': numeric_timestamp,
-                    "option":option
-                }
-                data.append(file_info)
-        except Exception as e:
-            print(e)
-    
-    sorted_data = sorted(data, key=lambda x: x['date'], reverse=True)
-    return sorted_data
-
-def tensor2pil(image):
-    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-
-
-def create_temp_file(image):
-    output_dir = folder_paths.get_temp_directory()
-
-    (
-            full_output_folder,
-            filename,
-            counter,
-            subfolder,
-            _,
-        ) = folder_paths.get_save_image_path('tmp', output_dir)
-
-    
-    im=tensor2pil(image)
- 
-    image_file = f"{filename}_{counter:05}.png"
-     
-    image_path=os.path.join(full_output_folder, image_file)
-
-    im.save(image_path,compress_level=4)
-
-    return [{
-                "filename": image_file,
-                "subfolder": subfolder,
-                "type": "temp"
-                }]
-
-
-
-try:
-    if is_installed('websocket')==False:
-        import subprocess
-
-        # 安装
-        print('#pip install websocket-client')
-
-        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'websocket-client'], capture_output=True, text=True)
-
-        #检查命令执行结果
-        if result.returncode == 0:
-            print("#install success")
-            import websocket 
-        else:
-            print("#install error")
-        
-    else:
-        import websocket 
-        # NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
-
-except:
-    print("#websocket-client error")
-#This is an example that uses the websockets api to know when a prompt execution is done
-#Once the prompt execution is done it downloads the images using the /history endpoint
-
-import uuid
-import json
-import urllib.request
-import urllib.parse
-
-server_address = "127.0.0.1:8188"
-client_id = str(uuid.uuid4())
-
-def queue_prompt(prompt):
-    p = {"prompt": prompt, "client_id": client_id}
-    data = json.dumps(p).encode('utf-8')
-    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
-    return json.loads(urllib.request.urlopen(req).read())
-
-def get_image(filename, subfolder, folder_type):
-    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
-    url_values = urllib.parse.urlencode(data)
-    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
-        return response.read()
-
-def get_history(prompt_id):
-    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
-        return json.loads(response.read())
-
-def get_images(ws, prompt):
-    prompt_id = queue_prompt(prompt)['prompt_id']
-    output_images = {}
-    while True:
-        out = ws.recv()
-        if isinstance(out, str):
-            message = json.loads(out)
-            if message['type'] == 'executing':
-                data = message['data']
-                if data['node'] is None and data['prompt_id'] == prompt_id:
-                    break #Execution is done
-        else:
-            continue #previews are binary data
-
-    history = get_history(prompt_id)[prompt_id]
-    for o in history['outputs']:
-        for node_id in history['outputs']:
-            node_output = history['outputs'][node_id]
-            if 'images' in node_output:
-                images_output = []
-                for image in node_output['images']:
-                    image_data = get_image(image['filename'], image['subfolder'], image['type'])
-                    images_output.append(image_data)
-            output_images[node_id] = images_output
-
-    return output_images
-
-prompt_text = """
-{
-    "3": {
-        "class_type": "KSampler",
-        "inputs": {
-            "cfg": 8,
-            "denoise": 1,
-            "latent_image": [
-                "5",
-                0
-            ],
-            "model": [
-                "4",
-                0
-            ],
-            "negative": [
-                "7",
-                0
-            ],
-            "positive": [
-                "6",
-                0
-            ],
-            "sampler_name": "euler",
-            "scheduler": "normal",
-            "seed": 8566257,
-            "steps": 20
-        }
-    },
-    "4": {
-        "class_type": "CheckpointLoaderSimple",
-        "inputs": {
-            "ckpt_name": "v1-5-pruned-emaonly.ckpt"
-        }
-    },
-    "5": {
-        "class_type": "EmptyLatentImage",
-        "inputs": {
-            "batch_size": 1,
-            "height": 512,
-            "width": 512
-        }
-    },
-    "6": {
-        "class_type": "CLIPTextEncode",
-        "inputs": {
-            "clip": [
-                "4",
-                1
-            ],
-            "text": "masterpiece best quality girl"
-        }
-    },
-    "7": {
-        "class_type": "CLIPTextEncode",
-        "inputs": {
-            "clip": [
-                "4",
-                1
-            ],
-            "text": "bad hands"
-        }
-    },
-    "8": {
-        "class_type": "VAEDecode",
-        "inputs": {
-            "samples": [
-                "3",
-                0
-            ],
-            "vae": [
-                "4",
-                2
-            ]
-        }
-    },
-    "9": {
-        "class_type": "SaveImage",
-        "inputs": {
-            "filename_prefix": "ComfyUI",
-            "images": [
-                "8",
-                0
-            ]
-        }
-    }
-}
-"""
-
-# prompt = json.loads(prompt_text)
-# #set the text prompt for our positive CLIPTextEncode
-# prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
-
-# #set the seed for our KSampler node
-# prompt["3"]["inputs"]["seed"] = 5
-
-# ws = websocket.WebSocket()
-# ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
-# images = get_images(ws, prompt)
-
-# #Commented out code to display the output images:
-
-# # for node_id in images:
-# #     for image_data in images[node_id]:
-# #         from PIL import Image
-# #         import io
-# #         image = Image.open(io.BytesIO(image_data))
-# #         image.show()
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# app 配置节点
-class AppInfo:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "name": ("STRING",{"multiline": False,"default": "Mixlab-App","dynamicPrompts": False}),
-                    "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"]),"dynamicPrompts": False}),
-                    "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"]),"dynamicPrompts": False}),
-                             },
-
-                "optional":{
-                    "IMAGE": ("IMAGE",),
-                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
-                    "version":("INT", {
-                        "default": 1, 
-                        "min": 1, 
-                        "max": 10000, 
-                        "step": 1, 
-                        "display": "number"  
-                    }),
-                    "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
-                    "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
-                    "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
-                    "auto_save": (["enable","disable"],),
-                }
-
-                }
-    
-    RETURN_TYPES = ()
-    # RETURN_NAMES = ("IMAGE",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab"
-
-    OUTPUT_NODE = True
-    INPUT_IS_LIST = True
-    # OUTPUT_IS_LIST = (True,)
-
-    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
-        name=name[0]
-        
-        im=None
-        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，
-
-        input_ids=input_ids[0]
-        output_ids=output_ids[0]
-        description=description[0]
-        version=version[0]
-        share_prefix=share_prefix[0]
-        link=link[0]
-        category=category[0]
-        
-        # id=get_json_hash([name,im,input_ids,output_ids,description,version])
-
-        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": ()}
-    
-
-
-#  app可以当成节点运行
-class AppNode:
-   
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "name": ([x['option'] for x in read_workflow_json_files_all(app_path)],),
-                    "image":("IMAGE",),
-                             }
-                }
-    
-    RETURN_TYPES = (any_type,)
-    RETURN_NAMES = ("output",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab"
-
-    OUTPUT_NODE = True
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
-    
-
-    def run(self,name,input):
-        print('#app_path',input)
-        print(PromptServer.instance.port)
-
-        return (name,)
-    
-

nodes/ChatGPT.py

@@ -3,7 +3,12 @@ import time
 import urllib.error
 import re,json,os,string,random
 import folder_paths
+import hashlib
 
+def get_unique_hash(string):
+    hash_object = hashlib.sha1(string.encode())
+    unique_hash = hash_object.hexdigest()
+    return unique_hash
 
 def generate_random_string(length):
     letters = string.ascii_letters + string.digits
@@ -178,7 +183,7 @@ class ShowTextForGPT:
                 "text": ("STRING", {"forceInput": True,"dynamicPrompts": False}),
             },
             "optional":{ 
-                    "output_dir": ("STRING",{"default": "","multiline": True,"dynamicPrompts": False}), 
+                    "output_dir": ("STRING",{"forceInput": True,"default": "","multiline": True,"dynamicPrompts": False}), 
                 }
         }
 
@@ -190,16 +195,51 @@ class ShowTextForGPT:
 
     CATEGORY = "♾️Mixlab/GPT"
 
-    def run(self, text,output_dir):
-        output_dir=output_dir[0]
-        filename=generate_random_string(4)+'.txt'
+    def run(self, text,output_dir=[""]):
+
+        if len(output_dir)==1 and (output_dir[0]=='' or os.path.dirname(output_dir[0])==''):
+            t='\n'.join(text)
+            output_dir=[
+                os.path.join(folder_paths.get_temp_directory(),
+                             get_unique_hash(t)+'.txt'
+                             )
+            ]
+        elif len(output_dir)==1:
+            base=os.path.basename(output_dir[0])
+            t='\n'.join(text)
+            if base=='' or os.path.splitext(base)[1]=='':
+                base=get_unique_hash(t)+'.txt'
+            output_dir=[
+                os.path.join(output_dir[0],
+                             base
+                             )
+            ]
+        # elif len(output_dir)>1:
 
-        if output_dir=='':
-            output_dir = folder_paths.get_temp_directory()
         
-        save_to_dirpath=os.path.join(output_dir,filename)
-        with open(save_to_dirpath, 'w') as file:
-            file.write("\n".join(text))
+
+        if len(output_dir)==1 and len(text)>1:
+            output_dir=[output_dir[0] for _ in range(len(text))]
+        
+        for i in range(len(text)):
+
+            o_fp=output_dir[i]
+            dirp=os.path.dirname(o_fp)
+            if dirp=='':
+                dirp=folder_paths.get_temp_directory()
+                o_fp=os.path.join(folder_paths.get_temp_directory(),o_fp
+                             )
+
+            if not os.path.exists(dirp):
+                os.mkdir(dirp)
+
+            if not os.path.splitext(o_fp)[1].lower()=='.txt':
+                o_fp=o_fp+'.txt'
+
+            t=text[i]
+            with open(o_fp, 'w') as file:
+                file.write(t)
+
         # print(text)
         return {"ui": {"text": text}, "result": (text,)}
         

nodes/ClipInterrogator.py

@@ -77,8 +77,7 @@ if not os.path.exists(caption_model_path):
     caption_model_path='Salesforce/blip-image-captioning-base'
 
 cache_path=os.path.join(folder_paths.models_dir, "clip_interrogator")
-if not os.path.exists(cache_path):
-    os.mkdir(cache_path)
+
 
 # Tensor to PIL
 def tensor2pil(image):

nodes/ImageNode.py

@@ -380,6 +380,7 @@ def get_images_filepath(f,white_bg=False):
         for root, dirs, files in os.walk(f):
             for file in files:
                 file_path = os.path.join(root, file)
+                file_name=os.path.basename(file_path)
                 try:
                     imgs=load_image(file_path,white_bg)
                     for img in imgs:
@@ -387,6 +388,7 @@ def get_images_filepath(f,white_bg=False):
                             "image":img['image'],
                             "mask":img['mask'],
                             "file_path":file_path,
+                            "file_name":file_name,
                             "psd":len(imgs)>1
                         })
                 except:
@@ -394,12 +396,15 @@ def get_images_filepath(f,white_bg=False):
  
     elif os.path.isfile(f):
         try:
+            file_path = os.path.join(root, f)
+            file_name=os.path.basename(file_path)
             imgs=load_image(f,white_bg)
             for img in imgs:
                 images.append({
                     "image":img['image'],
                     "mask":img['mask'],
                     "file_path":file_path,
+                    "file_name":file_name,
                     "psd":len(imgs)>1
                 })
         except:
@@ -1059,14 +1064,15 @@ class LoadImagesFromPath:
                 }
             }
     
-    RETURN_TYPES = ('IMAGE','MASK','STRING',)
+    RETURN_TYPES = ('IMAGE','MASK','STRING','STRING',)
+    RETURN_NAMES = ("IMAGE","MASK","prompt_for_FloatingVideo","filepaths",)
 
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/Image"
 
     # INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,True,False,)
+    OUTPUT_IS_LIST = (True,True,False,True,)
   
     global watcher_folder
     watcher_folder=None
@@ -1102,10 +1108,12 @@ class LoadImagesFromPath:
 
         imgs=[]
         masks=[]
+        file_names=[]
 
         for im in sorted_files:
             imgs.append(im['image'])
             masks.append(im['mask'])
+            file_names.append(im['file_name'])
         
         # print('index_variable',index_variable)
         
@@ -1113,11 +1121,12 @@ class LoadImagesFromPath:
             if index_variable!=-1:
                 imgs=[imgs[index_variable]] if index_variable < len(imgs) else None
                 masks=[masks[index_variable]] if index_variable < len(masks) else None
+                file_names=[file_names[index_variable]] if index_variable < len(file_names) else None
         except Exception as e:
             print("发生了一个未知的错误：", str(e))
 
         # print('#prompt::::',prompt)
-        return  {"ui": {"seed": [1]}, "result":(imgs,masks,prompt,)}
+        return  {"ui": {"seed": [1]}, "result":(imgs,masks,prompt,file_names,)}
 
 
 # TODO 扩大选区的功能,重新输出mask
@@ -1620,6 +1629,18 @@ class NewLayer:
         return (layer_n,)
 
 
+
+def createMask(image,x,y,w,h):
+    mask = Image.new("L", image.size)
+    pixels = mask.load()
+    # 遍历指定区域的像素，将其设置为黑色（0 表示黑色）
+    for i in range(int(x), int(x + w)):
+        for j in range(int(y), int(y + h)):
+            pixels[i, j] = 255
+    # mask.save("mask.png")
+    return mask
+
+
 def splitImage(image, num):
     width, height = image.size
 
@@ -1688,8 +1709,8 @@ class SplitImage:
             }
                 }
     
-    RETURN_TYPES = ("_GRID","_GRID",)
-    RETURN_NAMES = ("grids","grid")
+    RETURN_TYPES = ("_GRID","_GRID","MASK",)
+    RETURN_NAMES = ("grids","grid","mask",)
 
     FUNCTION = "run"
 
@@ -1700,10 +1721,10 @@ class SplitImage:
 
     def run(self,image,num,seed):
         image=tensor2pil(image)
-
+        
         grids=splitImage(image,num)
 
-        if seed>=num:
+        if seed>num:
             num=int(seed / 500 * num)-1
         else:
             num=seed-1
@@ -1712,7 +1733,11 @@ class SplitImage:
 
         g=grids[num]
 
-        return (grids,g,)
+        x,y,w,h=g
+        mask=createMask(image, x,y,w,h)
+        mask=pil2tensor(mask)
+
+        return (grids,g,mask,)
 
 
 
@@ -1753,8 +1778,8 @@ class CenterImage:
             }
                 }
     
-    RETURN_TYPES = ("_GRID",)
-    RETURN_NAMES = ("grid",)
+    RETURN_TYPES = ("_GRID","MASK",)
+    RETURN_NAMES = ("grid","mask",)
 
     FUNCTION = "run"
 
@@ -1768,7 +1793,9 @@ class CenterImage:
 
         grid=centerImage((left,top,right,bottom),canvas)
 
-        return (grid,)
+        mask=createMask(canvas,left,top,canvas.width-left-right,canvas.height-top-bottom)
+
+        return (grid,pil2tensor(mask),)
 
 
 
@@ -2259,19 +2286,41 @@ class GetImageSize_:
         return {
             "required": {
                 "image": ("IMAGE",),
-            }
+            },
+             "optional":{
+                    "min_width":("INT", {
+                        "default": 512, 
+                        "min":1, #Minimum value
+                        "max": 2048, #Maximum value
+                        "step": 8, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    })
+                },
         }
 
-    RETURN_TYPES = ("INT", "INT")
-    RETURN_NAMES = ("width", "height")
+    RETURN_TYPES = ("INT", "INT","INT", "INT",)
+    RETURN_NAMES = ("width", "height","min_width", "min_height",)
 
     FUNCTION = "get_size"
 
     CATEGORY = "♾️Mixlab/Image"
 
-    def get_size(self, image):
+    def get_size(self, image,min_width):
         _, height, width, _ = image.shape
-        return (width, height)
+        
+        # 如果比min_widht,还小，则输出 min width
+        if min_width>width:
+            im=tensor2pil(image)
+            im=resize_image(im,'width',min_width,min_width,"white")
+            im=im.convert('RGB')
+
+            min_width,min_height=im.size
+
+        else:
+            min_width=width
+            min_height=height
+
+        return (width, height,min_width,min_height,)
 
 
 
@@ -2345,10 +2394,21 @@ class SaveImageToLocal:
         filename_prefix = os.path.basename(file_path)
         if file_path=='':
             filename_prefix="ComfyUI"
+        
+        filename_prefix, _ = os.path.splitext(filename_prefix)
+
+        _, extension = os.path.splitext(file_path)
+
+        if extension:
+            # 是文件名，需要处理
+            file_path=os.path.dirname(file_path)
+            # filename_prefix=
+
             
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
         
-        if not os.path.exists(file_path):
+    
+        if not os.path.exists(file_path) and not extension:
             # 使用os.makedirs函数创建新目录
             os.makedirs(file_path)
             print("目录已创建")
@@ -2393,6 +2453,7 @@ class SaveImageToLocal:
                 })
             
             else:
+
                 fp=os.path.join(file_path, file)
                 if os.path.exists(fp):
                     file = f"{filename}_{counter:05}_{generate_random_string(8)}.png"

nodes/Lama.py

@@ -42,9 +42,6 @@ else:
     _available=True
 
 
-lama_path=os.path.join(folder_paths.models_dir, "lama")
-if not os.path.exists(lama_path):
-    os.mkdir(lama_path)
  
 llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
 if not os.path.exists(llma_model_path):

nodes/Utils.py

@@ -1,9 +1,10 @@
 import os,platform
 import re,random,json
-
+from PIL import Image
+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 matplotlib.font_manager as fm
 import torch
 
 
@@ -64,7 +65,37 @@ def get_system_font_path():
 #     return hash_value
     
 
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+
+def create_temp_file(image):
+    output_dir = folder_paths.get_temp_directory()
+
+    (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('tmp', output_dir)
+
+    
+    im=tensor2pil(image)
  
+    image_file = f"{filename}_{counter:05}.png"
+     
+    image_path=os.path.join(full_output_folder, image_file)
+
+    im.save(image_path,compress_level=4)
+
+    return [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type": "temp"
+                }]
+
 def get_font_files(directory):
     font_files = {}
 
@@ -382,7 +413,6 @@ class AnyType(str):
     return False
 
 any_type = AnyType("*")
-
 import time
 
 class DynamicDelayProcessor:
@@ -460,6 +490,69 @@ class DynamicDelayProcessor:
         
 
 
+
+# app 配置节点
+class AppInfo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "name": ("STRING",{"multiline": False,"default": "Mixlab-App","dynamicPrompts": False}),
+                    "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"]),"dynamicPrompts": False}),
+                    "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"]),"dynamicPrompts": False}),
+                             },
+
+                "optional":{
+                    "IMAGE": ("IMAGE",),
+                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
+                    "version":("INT", {
+                        "default": 1, 
+                        "min": 1, 
+                        "max": 10000, 
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                    "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                    "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
+                    "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                    "auto_save": (["enable","disable"],),
+                }
+
+                }
+    
+    RETURN_TYPES = ()
+    # RETURN_NAMES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
+        name=name[0]
+        
+        im=None
+        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，
+
+        input_ids=input_ids[0]
+        output_ids=output_ids[0]
+        description=description[0]
+        version=version[0]
+        share_prefix=share_prefix[0]
+        link=link[0]
+        category=category[0]
+        
+        # id=get_json_hash([name,im,input_ids,output_ids,description,version])
+
+        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": ()}
+    
+
     
 
 

nodes/Vae.py

@@ -1,179 +0,0 @@
-# https://github.com/openai/consistencydecoder/blob/main/consistencydecoder/__init__.py
-
-import folder_paths
-from comfy import model_management
-
-import math
-import torch
-import numpy as np
-from PIL import Image 
-
-
-class ConsistencyDecoderWrapper:
-    def __init__(self, decoder):
-        self.decoder = decoder
-    def decode(self, x):
-        return self.decoder(x)
-
-def _extract_into_tensor(arr, timesteps, broadcast_shape):
-    
-    # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895    """
-    res = arr[timesteps].float()
-    dims_to_append = len(broadcast_shape) - len(res.shape)
-    return res[(...,) + (None,) * dims_to_append]
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L45
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return torch.tensor(betas)
-
-class ConsistencyDecoder:
-    def __init__(self, device="cuda:0", download_target=""):
-        self.n_distilled_steps = 64
-        # download_target = _download("https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt", download_root)
-        self.ckpt = torch.jit.load(download_target).to(device)
-        self.device = device 
-        sigma_data = 0.5
-        betas = betas_for_alpha_bar(
-            1024, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-        ).to(device)
-        alphas = 1.0 - betas
-        alphas_cumprod = torch.cumprod(alphas, dim=0)
-        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
-        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
-        sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
-        sigmas = torch.sqrt(1.0 / alphas_cumprod - 1)
-        self.c_skip = (
-            sqrt_recip_alphas_cumprod
-            * sigma_data**2
-            / (sigmas**2 + sigma_data**2)
-        )
-        self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5
-        self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5
-
-    @staticmethod
-    def round_timesteps(
-        timesteps, total_timesteps, n_distilled_steps, truncate_start=True
-    ):
-        with torch.no_grad():
-            space = torch.div(total_timesteps, n_distilled_steps, rounding_mode="floor")
-            rounded_timesteps = (
-                torch.div(timesteps, space, rounding_mode="floor") + 1
-            ) * space
-            if truncate_start:
-                rounded_timesteps[rounded_timesteps == total_timesteps] -= space
-            else:
-                rounded_timesteps[rounded_timesteps == total_timesteps] -= space
-                rounded_timesteps[rounded_timesteps == 0] += space
-            return rounded_timesteps
-
-    @staticmethod
-    def ldm_transform_latent(z, extra_scale_factor=1):
-        channel_means = [0.38862467, 0.02253063, 0.07381133, -0.0171294]
-        channel_stds = [0.9654121, 1.0440036, 0.76147926, 0.77022034]
-
-        if len(z.shape) != 4:
-            raise ValueError()
-
-        z = z * 0.18215
-        channels = [z[:, i] for i in range(z.shape[1])]
-
-        channels = [
-            extra_scale_factor * (c - channel_means[i]) / channel_stds[i]
-            for i, c in enumerate(channels)
-        ]
-        return torch.stack(channels, dim=1)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        features: torch.Tensor,
-        schedule=[1.0, 0.5],
-    ):
-        features = self.ldm_transform_latent(features)
-  
-        ts = self.round_timesteps(
-            torch.arange(0, 1024),
-            1024,
-            self.n_distilled_steps,
-            truncate_start=False,
-        )
-        shape = (
-            features.size(0),
-            3,
-            8 * features.size(2),
-            8 * features.size(3),
-        )
-        
-        x_start = torch.zeros(shape, device=features.device, dtype=features.dtype)
-        schedule_timesteps = [int((1024 - 1) * s) for s in schedule]
-        for i in schedule_timesteps:
-            t = ts[i].item()
-            t_ = torch.tensor([t] * features.shape[0]).to(self.device)
-            noise = torch.randn_like(x_start)
-            
-            x_start = (
-                _extract_into_tensor(self.sqrt_alphas_cumprod, t_, x_start.shape)
-                * x_start
-                + _extract_into_tensor(
-                    self.sqrt_one_minus_alphas_cumprod, t_, x_start.shape
-                )
-                * noise
-            )
-            c_in = _extract_into_tensor(self.c_in, t_, x_start.shape)
-            model_output = self.ckpt(c_in * x_start, t_, features=features)
-            B, C = x_start.shape[:2]
-            model_output, _ = torch.split(model_output, C, dim=1)
-            pred_xstart = (
-                _extract_into_tensor(self.c_out, t_, x_start.shape) * model_output
-                + _extract_into_tensor(self.c_skip, t_, x_start.shape) * x_start
-            ).clamp(-1, 1)
-            x_start = pred_xstart
-        return x_start
-
-
-
-class VAELoader:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "vae_name": (folder_paths.get_filename_list("vae"), )}}
-    RETURN_TYPES = ("VAE",)
-    FUNCTION = "load_vae"
-
-    CATEGORY = "♾️Mixlab/__TEST"
-
-    #TODO: scale factor?
-    def load_vae(self, vae_name):
-        vae_path = folder_paths.get_full_path("vae", vae_name)
-        device = 'cuda:0'
-        # print('device',device)
-        consistencyDecoder = ConsistencyDecoder(device=device,
-                                 download_target=vae_path) # Model size: 2.49 GB
-        vae = ConsistencyDecoderWrapper(consistencyDecoder)
-        return (vae,)
-    
-
-class VAEDecode:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "decode"
-
-    CATEGORY = "♾️Mixlab/__TEST"
-
-    def decode(self, vae, samples):
-        image = vae.decode(samples["samples"].to("cuda:0"))
-        image = image[0].cpu().numpy()
-        image = (image + 1.0) * 127.5
-        image = image.clip(0, 255).astype(np.uint8)
-        image = Image.fromarray(image.transpose(1, 2, 0))
-        image = image.convert("RGB")
-        image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image)[None,]
-        return (image, )

requirements.txt

@@ -5,4 +5,5 @@ opencv-python-headless
 matplotlib
 openai
 simple-lama-inpainting
-clip-interrogator==0.6.0
+clip-interrogator==0.6.0
+transformers>=4.36.0

web/index.html

@@ -643,7 +643,7 @@
             return true
         }
 
-
+        // 种子的处理
         function randomSeed(seed, data) {
             for (const id in data) {
                 if (data[id].inputs.seed != undefined
@@ -1316,6 +1316,7 @@
                         }
                     }), value);
 
+                    // 选择事件绑定
                     selectDom.addEventListener('change', e => {
                         e.preventDefault();
                         // console.log(selectDom.value)
@@ -1550,6 +1551,7 @@
 
         }
 
+        // 创建下拉选择
         function createSelect(options, defaultValue) {
             var selectElement = document.createElement("select");
             selectElement.className = "select"
@@ -1558,7 +1560,7 @@
             for (var i = 0; i < options.length; i++) {
                 var option = document.createElement("option");
                 option.value = options[i].value;
-                option.text = options[i].text;
+                option.innerText = options[i].text;
                 selectElement.appendChild(option);
                 // if(options[i].selected) 
             }
@@ -1569,6 +1571,7 @@
             return selectElement
         }
 
+        // 创建下拉选择 - 带说明
         function createSelectWithOptions(title, options, defaultValue) {
 
             const div = document.createElement("div");

web/javascript/app_mixlab.js

@@ -67,9 +67,13 @@ async function drawImageToCanvas (imageUrl) {
 }
 
 function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
-  const data = jsonData
-  const input = []
-  const output = []
+  // workflow
+  // const workflow=jsonData.workflow;
+  // const nodes=workflow.nodes;
+
+  const data = jsonData.output
+  let input = []
+  let output = []
   const seed = {}
 
   for (const id in data) {
@@ -77,7 +81,7 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
       let node = app.graph.getNodeById(id)
       if (inputIds.includes(id)) {
         // let node = app.graph.getNodeById(id)
-        let options = []
+        let options = {}
         // 模型
         try {
           if (node.type === 'CheckpointLoaderSimple') {
@@ -113,6 +117,15 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
         if (node.type == 'Color') {
         }
 
+        // loadImage的mask支持
+        if (node.type === 'LoadImage') {
+          let output = node.outputs.filter(ot => ot.type == 'MASK')[0]
+          if (output.links) {
+            // 有输出
+            options.hasMask = true
+          }
+        }
+
         input[inputIds.indexOf(id)] = {
           ...data[id],
           title: node.title,
@@ -138,6 +151,10 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
     }
   }
 
+  // 修复bug，当节点不存在时
+  input = input.filter(i => i)
+  output = output.filter(i => i)
+
   return { input, output, seed }
 }
 
@@ -211,8 +228,8 @@ async function save (json, download = false, showInfo = true) {
   try {
     let data = await app.graphToPrompt()
 
-    const { input, output, seed } = extractInputAndOutputData(
-      data.output,
+    let { input, output, seed } = extractInputAndOutputData(
+      data,
       inputIds,
       outputIds
     )
@@ -287,7 +304,7 @@ function getInputsAndOutputs () {
 }
 
 app.registerExtension({
-  name: 'Mixlab.app.AppInfo',
+  name: 'Mixlab.utils.AppInfo',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeType.comfyClass == 'AppInfo') {
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
@@ -436,67 +453,3 @@ api.addEventListener('executed', async ({ detail }) => {
     }
   }
 })
-
-app.registerExtension({
-  name: 'Mixlab.app.AppNode',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeType.comfyClass == 'AppNode') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = function () {
-        orig_nodeCreated?.apply(this, arguments)
-        console.log('#orig_nodeCreated', this)
-        let node=this;
-
-        const name = this.widgets.filter(w => w.name === 'name')[0]
-
-        name.callback = async e => {
-          let es = e.split('/')
-          let filename = '',
-            category = ''
-          if (es.length == 2) {
-            category = es[0]
-            filename = es[1]
-          } else if (es.length === 1) {
-            filename = e
-          }
-          const res = await api.fetchApi('/mixlab/workflow', {
-            method: 'POST',
-            headers: {
-              'Content-Type': 'application/json'
-            },
-            body: JSON.stringify({
-              task: 'my_app',
-              filename,
-              category
-            })
-          })
-
-          if (res.status !== 200) {
-            throw {
-              response: await res.json()
-            }
-          }
-          let result=await res.json()
-          const {app,output:workflow,}=result.data[0].data;
-          let input=app.input
-          console.log(input)
-
-          if(input.length==1){
-            let widget = node.inputs.filter(w => w.name === 'input')[0];
-            let sc=input[0];
-            LGraphCanvas.prototype._createNodeForInput(
-              node, //当前node
-              widget, //当前node里需要自动连线的widget
-              sc.class_type, //作为input的node type
-              'IMAGE' // 作为input的node的outputs的name. the input slot type of the target node
-            )
-          }
-
-        }
-
-        this.serialize_widgets = true //需要保存参数
-      }
-    }
-  },
-  async loadedGraphNode (node, app) {}
-})

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 = 'v0.15.0'
+const version = 'v0.15.1'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/gpt_mixlab.js

@@ -68,7 +68,7 @@ app.registerExtension({
           size: [128, 32], // a default size
           draw (ctx, node, width, y) {},
           computeSize (...args) {
-            return [128,32] // a method to compute the current size of the widget
+            return [128, 32] // a method to compute the current size of the widget
           },
           async serializeValue (nodeId, widgetIndex) {
             let data = getLocalData('_mixlab_api_key')
@@ -203,76 +203,82 @@ app.registerExtension({
 
 app.registerExtension({
   name: 'Mixlab.GPT.ShowTextForGPT',
-  async beforeRegisterNodeDef(nodeType, nodeData, app) {
-		if (nodeData.name === "ShowTextForGPT") {
-			function populate(text) {
-				if (this.widgets) {
-          
-					const pos = this.widgets.findIndex((w) => w.name === "text");
-					if (pos !== -1) {
-						for (let i = pos; i < this.widgets.length; i++) {
-							this.widgets[i].onRemove?.();
-						}
-						this.widgets.length = pos;
-					}
-				}
-        // console.log('ShowTextForGPT',text)
-				for (let list of text) {
-					const w = ComfyWidgets["STRING"](this, "text", ["STRING", { multiline: true }], app).widget;
-					w.inputEl.readOnly = true;
-					w.inputEl.style.opacity = 0.6;
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData.name === 'ShowTextForGPT') {
+      function populate (text) {
+        text = text.filter(t => t && t?.trim())
 
-          try {
-            let data=JSON.parse(list);
-            data=Array.from(data,d=>{
-              return {
-                ...d,
-                content:decodeURIComponent(d.content)
-              }
-            })
-            list=JSON.stringify(data,null,2)
-          } catch (error) {
-            // console.log(error)
+        if (this.widgets) {
+          // const pos = this.widgets.findIndex(w => w.name === 'text')
+          for (let i = 0; i < this.widgets.length; i++) {
+            if (this.widgets[i].name == 'show_text') this.widgets[i].onRemove?.()
           }
+          this.widgets.length = 1
+        }
+        // console.log('ShowTextForGPT',text)
+        for (let list of text) {
+          if (list) {
+            // console.log('#####', list)
+            const w = ComfyWidgets['STRING'](
+              this,
+              'show_text',
+              ['STRING', { multiline: true }],
+              app
+            ).widget
+            w.inputEl.readOnly = true
+            w.inputEl.style.opacity = 0.6
 
-					w.value =list;
-        
-				}
+            try {
+              if (typeof list != 'string') {
+                let data = JSON.parse(list)
+                data = Array.from(data, d => {
+                  return {
+                    ...d,
+                    content: decodeURIComponent(d.content)
+                  }
+                })
+                list = JSON.stringify(data, null, 2)
+              }
+            } catch (error) {
+              console.log(error)
+            }
+
+            w.value = list
+          }
+        }
         // console.log('ShowTextForGPT',this.widgets.length)
-				requestAnimationFrame(() => {
-					const sz = this.computeSize();
-					if (sz[0] < this.size[0]) {
-						sz[0] = this.size[0];
-					}
-					if (sz[1] < this.size[1]) {
-						sz[1] = this.size[1];
-					}
-					this.onResize?.(sz);
-					app.graph.setDirtyCanvas(true, false);
-				});
-			}
+        requestAnimationFrame(() => {
+          if (this) {
+            const sz = this.computeSize()
+            if (sz[0] < this.size[0]) {
+              sz[0] = this.size[0]
+            }
+            if (sz[1] < this.size[1]) {
+              sz[1] = this.size[1]
+            }
+            this.onResize?.(sz)
+            app.graph.setDirtyCanvas(true, false)
+          }
+        })
+      }
 
-			// When the node is executed we will be sent the input text, display this in the widget
-			const onExecuted = nodeType.prototype.onExecuted;
-			nodeType.prototype.onExecuted = function (message) {
-				onExecuted?.apply(this, arguments);
-        console.log('##',message.text)
-				populate.call(this, message.text);
-			};
+      // When the node is executed we will be sent the input text, display this in the widget
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        // console.log('##onExecuted', this, message)
+        if (message.text) populate.call(this, message.text)
+      }
 
-			const onConfigure = nodeType.prototype.onConfigure;
-			nodeType.prototype.onConfigure = function () {
-				onConfigure?.apply(this, arguments);
-				if (this.widgets_values?.length) {
-          
-					populate.call(this, this.widgets_values);
-				}
-			};
+      const onConfigure = nodeType.prototype.onConfigure
+      nodeType.prototype.onConfigure = function () {
+        onConfigure?.apply(this, arguments)
+        if (this.widgets_values?.length) {
+          populate.call(this, this.widgets_values)
+        }
+      }
 
       this.serialize_widgets = true //需要保存参数
-
-		}
-
-
-	},
+    }
+  }
 })

web/javascript/ui_mixlab.js

@@ -1,5 +1,6 @@
 import { app } from '../../../scripts/app.js'
 import { closeIcon } from './svg_icons.js'
+import { api } from '../../../scripts/api.js'
 
 import {
   GroupNodeConfig,
@@ -8,6 +9,180 @@ import {
 
 import { smart_init, addSmartMenu } from './smart_connect.js'
 
+let isScriptLoaded = {};
+
+function loadExternalScript(url) {
+  return new Promise((resolve, reject) => {
+    if (isScriptLoaded[url]) {
+      resolve();
+      return;
+    }
+
+    const script = document.createElement('script');
+    script.src = url;
+    script.onload = () => {
+      isScriptLoaded[url]= true;
+      resolve();
+    };
+    script.onerror = reject;
+    document.head.appendChild(script);
+  });
+}
+
+
+//
+
+function createChart(chartDom,nodes){
+
+  var myChart = echarts.init(chartDom);
+  var option;
+
+  console.log(nodes)
+  option = {
+    series: [
+      {
+        type: 'treemap',
+        data: [
+          {
+            name: 'nodeA',
+            value: 10,
+            children: Array.from(nodes,n=>{
+              return {
+                name:n.type,
+                value:n.count
+              }
+            })
+          },
+          
+        ]
+      }
+    ]
+  };
+  
+  option && myChart.setOption(option);
+}
+
+async function createNodesCharts () {
+  await loadExternalScript('/extensions/comfyui-mixlab-nodes/lib/echarts.min.js')
+  const templates = await loadTemplate()
+  var nodes = {}
+  Array.from(templates, t => {
+    let j = JSON.parse(t.data)
+    for (let node of j.nodes) {
+      if (!nodes[node.type]) nodes[node.type] = { type: node.type, count: 0 }
+      nodes[node.type].count++
+    }
+  })
+  nodes = Object.values(nodes).sort((a, b) => b.count - a.count)
+ 
+
+  const menu = document.querySelector('.comfy-menu')
+  const separator = document.createElement('div')
+  separator.style = `margin: 20px 0px;
+  width: 100%;
+  height: 1px;
+  background: var(--border-color);
+  `
+  menu.append(separator)
+
+  const appsButton = document.createElement('button')
+  appsButton.textContent = 'Nodes';
+
+  
+
+  appsButton.onclick = () => { 
+    let div = document.querySelector('#mixlab_apps')
+    if (!div) {
+      div = document.createElement('div')
+      div.id = 'mixlab_apps'
+      document.body.appendChild(div)
+
+      let btn = document.createElement('div')
+      btn.style = `display: flex;
+     width: calc(100% - 24px);
+     justify-content: space-between;
+     align-items: center;
+     padding: 0 12px;
+     height: 44px;`
+      let btnB = document.createElement('button')
+      let textB = document.createElement('p')
+      btn.appendChild(textB)
+      btn.appendChild(btnB)
+      textB.style.fontSize = '12px'
+      textB.innerText = `Nodes`
+
+      btnB.style = `float: right; border: none; color: var(--input-text);
+     background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
+      btnB.addEventListener('click', () => {
+        div.style.display = 'none'
+      })
+      btnB.innerText = 'X'
+
+      // 悬浮框拖动事件
+      div.addEventListener('mousedown', function (e) {
+        var startX = e.clientX
+        var startY = e.clientY
+        var offsetX = div.offsetLeft
+        var offsetY = div.offsetTop
+
+        function moveBox (e) {
+          var newX = e.clientX
+          var newY = e.clientY
+          var deltaX = newX - startX
+          var deltaY = newY - startY
+          div.style.left = offsetX + deltaX + 'px'
+          div.style.top = offsetY + deltaY + 'px'
+          localStorage.setItem(
+            'mixlab_app_pannel',
+            JSON.stringify({ x: div.style.left, y: div.style.top })
+          )
+        }
+
+        function stopMoving () {
+          document.removeEventListener('mousemove', moveBox)
+          document.removeEventListener('mouseup', stopMoving)
+        }
+
+        document.addEventListener('mousemove', moveBox)
+        document.addEventListener('mouseup', stopMoving)
+      })
+
+      div.appendChild(btn)
+
+      let chartDom = document.createElement('div')
+      chartDom.style=`height:80vh;width:450px`
+      chartDom.className='chart'
+      div.appendChild(chartDom)
+      
+    }
+    if (div.style.display == 'flex') {
+      div.style.display = 'none'
+    } else {
+      let pos = JSON.parse(
+        localStorage.getItem('mixlab_app_pannel') ||
+          JSON.stringify({ x: 0, y: 0 })
+      )
+
+      div.style = `
+      flex-direction: column;
+      align-items: end;
+      display:flex;
+      position: absolute; 
+      top: ${pos.y}; left: ${pos.x}; width: 450px; 
+      color: var(--descrip-text);
+      background-color: var(--comfy-menu-bg);
+      padding: 10px; 
+      border: 1px solid black;z-index: 999999999;padding-top: 0;`
+    };
+
+   
+    createChart(div.querySelector('.chart'),nodes)
+
+  }
+  menu.append(appsButton)
+
+}
+
 function copyNodeValues (src, dest) {
   // title
   dest.title = src.title
@@ -616,6 +791,39 @@ function createModal (url, markdown, title) {
   div.appendChild(bgElement)
 }
 
+const loadTemplate = async () => {
+  const id = 'Comfy.NodeTemplates'
+  const file = 'comfy.templates.json'
+
+  let templates = []
+  if (app.storageLocation === 'server') {
+    if (app.isNewUserSession) {
+      // New user so migrate existing templates
+      const json = localStorage.getItem(id)
+      if (json) {
+        templates = JSON.parse(json)
+      }
+      await api.storeUserData(file, json, { stringify: false })
+    } else {
+      const res = await api.getUserData(file)
+      if (res.status === 200) {
+        try {
+          templates = await res.json()
+        } catch (error) {}
+      } else if (res.status !== 404) {
+        console.error(res.status + ' ' + res.statusText)
+      }
+    }
+  } else {
+    const json = localStorage.getItem(id)
+    if (json) {
+      templates = JSON.parse(json)
+    }
+  }
+
+  return templates ?? []
+}
+
 app.registerExtension({
   name: 'Comfy.Mixlab.ui',
   init () {
@@ -665,7 +873,7 @@ app.registerExtension({
         }
       ]
 
-      opts = addSmartMenu(opts,node)
+      opts = addSmartMenu(opts, node)
 
       // if (node.type == 'CLIPTextEncode') {
       //   // 则出现 randomPrompt
@@ -706,16 +914,6 @@ app.registerExtension({
       const options = getGroupMenuOptions.apply(this, arguments) // start by calling the stored one
       node.setDirtyCanvas(true, true) // force a redraw of (foreground, background)
 
-      // templete
-      const key = 'Comfy.NodeTemplates'
-      let templates = localStorage.getItem(key)
-      if (templates) {
-        templates = JSON.parse(templates)
-      } else {
-        templates = []
-      }
-      const store = () => localStorage.setItem(key, JSON.stringify(templates))
-
       return [
         {
           content: 'Clone Group ♾️Mixlab', // with a name
@@ -773,7 +971,7 @@ app.registerExtension({
               localStorage.setItem('litegrapheditor_clipboard', old)
             }
 
-            clipboardAction(() => {
+            clipboardAction(async () => {
               let name = group.title + ' ♾️Mixlab'
               let nodes = group._nodes
 
@@ -786,6 +984,8 @@ app.registerExtension({
                 const nodeData = node.serialize()
 
                 let groupData = GroupNodeHandler.getGroupData(node)
+
+                // console.log('groupData',GroupNodeHandler.isGroupNode(node),groupData)
                 if (groupData) {
                   groupData = groupData.nodeData
                   if (!data.groupNodes) {
@@ -796,11 +996,32 @@ app.registerExtension({
                 }
               }
 
-              templates.push({
+              // templete
+              const store = async nt => {
+                const id = 'Comfy.NodeTemplates'
+                const file = 'comfy.templates.json'
+                let templates = await loadTemplate()
+                templates.push(nt)
+                if (app.storageLocation === 'server') {
+                  const ts = JSON.stringify(templates, undefined, 4)
+                  localStorage.setItem(id, ts) // Backwards compatibility
+                  try {
+                    await api.storeUserData(file, ts, {
+                      stringify: false
+                    })
+                  } catch (error) {
+                    console.error(error)
+                    alert(error.message)
+                  }
+                } else {
+                  localStorage.setItem(id, JSON.stringify(templates))
+                }
+              }
+              console.log('data', data)
+              store({
                 name,
                 data: JSON.stringify(data)
               })
-              store()
             })
           } // and the callback
         },
@@ -1173,6 +1394,8 @@ app.registerExtension({
         return options
       }
     }, 1000)
+
+    // createNodesCharts()
   },
   async loadedGraphNode (node, app) {
     // console.log(

web/javascript/utils_mixlab.js

@@ -1,8 +1,5 @@
 import { app } from '../../../scripts/app.js'
-import { api } from '../../../scripts/api.js'
-import { ComfyWidgets } from '../../../scripts/widgets.js'
-import { $el } from '../../../scripts/ui.js'
-import { addValueControlWidget } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js' 
 
 const getLocalData = key => {
   let data = {}