test

2023-12-29 13:27:48 +08:00 · 2023-12-29 12:59:27 +08:00 · 2023-12-29 12:49:05 +08:00
73 changed files with 3388 additions and 19961 deletions
@@ -2,5 +2,4 @@ __pycache__/
 https/
 nodes/config.json
 workflow/my_workflow.json
-workflow/my_workflow_app.json
-app/*
+workflow/my_workflow_app.json
@@ -1,56 +1,32 @@
-> 适配了最新版comfyui的py3.11 ，torch 2.1.2+cu121
-
-> [Mixlab nodes discord](https://discord.gg/cXs9vZSqeK)
-
-#### 
-[comfyui-ultralytics-yolo](https://github.com/shadowcz007/comfyui-ultralytics-yolo)
-
-[comfyui-moondream](https://github.com/shadowcz007/comfyui-moondream)
-
-[comfyui-CLIPSeg](https://github.com/shadowcz007/comfyui-CLIPSeg)
-
-
-## 🚀🚗🚚🏃 Workflow-to-APP 
+## 
+v0.6.0 🚀🚗🚚🏃‍ Workflow-to-APP
 - 新增AppInfo节点，可以通过简单的配置，把workflow转变为一个Web APP。
- 支持多个web app 切换
- 发布为app的workflow，可以在右键里再次编辑了
- web app可以设置分类，在comfyui右键菜单可以编辑更新web app
-
-
- Support multiple web app switching.
 - Add the AppInfo node, which allows you to transform the workflow into a web app by simple configuration.
- The workflow, which is now released as an app, can also be edited again by right-clicking.
- The web app can be configured with categories, and the web app can be edited and updated in the right-click menu of ComfyUI.
-
-
-![](./assets/0-m-app.png)

 ![](./assets/appinfo-readme.png)
-
-![](./assets/appinfo-2.png)
-
 Example:
 - workflow
 ![APP info](./workflow/appinfo-workflow.svg)
-[text-to-image](./workflow/Text-to-Image-app.json)

 APP-JSON:
- [text-to-image](./example/Text-to-Image_3.json)
- [image-to-image](./example/Image-to-Image_2.json)
+- [text-to-image](./app/text-to-image_1_Wed%20Dec%2027%202023.json)
+- [image-to-image](./app/image-to-image_1_Wed%20Dec%2027%202023.json)
 - text-to-text

-> 暂时支持 9 种节点作为界面上的输入节点：Load Image、VHS_LoadVideo、CLIPTextEncode、PromptSlide、TextInput_、Color、FloatSlider、IntNumber、CheckpointLoaderSimple、LoraLoader
+> 暂时支持6种节点作为界面上的输入节点：Load Image、CLIPTextEncode、TextInput_、FloatSlider、CheckpointLoaderSimple、LoraLoader

-> 输出节点：PreviewImage 、SaveImage、ShowTextForGPT、VHS_VideoCombine、PromptImage
-
-> seed统一输入控件，支持：SamplerCustom、KSampler
-
-> [ps插件](https://github.com/shadowcz007/comfyui-ps-plugin)
+> 输出节点：PreviewImage 、SaveImage、ShowTextForGPT


-## 🏃🚗🚚🚀  Real-time Design
-> ScreenShareNode & FloatingVideoNode. Now comfyui supports capturing screen pixel streams from any software and can be used for LCM-Lora integration. Let's get started with implementation and design! 💻🌐
+### 3D
+![](./assets/3dimage.png)
+[workflow](./workflow/3D-workflow.json)

+
+### ScreenShareNode & FloatingVideoNode
+> Now comfyui supports capturing screen pixel streams from any software and can be used for LCM-Lora integration. Let's get started with implementation and design! 💻🌐
+
+> 
 ![screenshare](./assets/screenshare.png)

 https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43e-410a-ab3a-1952b7b4e7da
@@ -61,7 +37,6 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43

 !! Please use the address with HTTPS (https://127.0.0.1).

-
 ### SpeechRecognition & SpeechSynthesis
 ![f](./assets/audio-workflow.svg)

@@ -70,45 +45,11 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 ### GPT
 > Support for calling multiple GPTs.ChatGPT、ChatGLM3 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 .  Azure OpenAI:https://xxxx.openai.azure.com 

+
 ![gpt-workflow.svg](./assets/gpt-workflow.svg)

 [workflow-5](./workflow/5-gpt-workflow.json)

-
-## Prompt
-> PromptSlide
-![](./assets/prompt_weight.png)
-
-<!-- ![](./workflow/promptslide-appinfo-workflow.svg) -->
-
-> randomPrompt
-
-![randomPrompt](./assets/randomPrompt.png)
-
-> ClipInterrogator
-
-[add clip-interrogator](https://github.com/pharmapsychotic/clip-interrogator)
-
-> PromptImage & PromptSimplification,Assist in simplifying prompt words, comparing images and prompt word nodes.
-
-> ChinesePrompt && PromptGenerate，中文prompt节点，直接用中文书写你的prompt
-
-![](./assets/ChinesePrompt_workflow.svg)
-
-
-### Layers
-> A new layer class node has been added, allowing you to separate the image into layers. After merging the images, you can input the controlnet for further processing.
-
-![layers](./assets/layers-workflow.svg)
-
-![poster](./assets/poster-workflow.svg)
-
-
-### 3D
-![](./assets/3dimage.png)
-[workflow](./workflow/3D-workflow.json)
-
-
 ### LoadImagesFromLocal
 > Monitor changes to images in a local folder, and trigger real-time execution of workflows, supporting common image formats, especially PSD format, in conjunction with Photoshop. 

@@ -120,6 +61,13 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 > Conveniently load images from a fixed address on the internet to ensure that default images in the workflow can be executed.


+### Layers
+> A new layer class node has been added, allowing you to separate the image into layers. After merging the images, you can input the controlnet for further processing.
+
+![layers](./assets/layers-workflow.svg)
+
+![poster](./assets/poster-workflow.svg)
+
 ## Utils
 > The Color node provides a color picker for easy color selection, the Font node offers built-in font selection for use with TextImage to generate text images, and the DynamicDelayByText node allows delayed execution based on the length of the input text.

@@ -127,12 +75,6 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43

 - [Added DynamicDelayByText, enabling delayed execution based on input text length.](./workflow/audio-chatgpt-workflow.json)

- [使用CkptNames 对比不同的模型效果](./workflow/ckpts-image-workflow.json)
-
- [CkptNames compare the effects of different models.](./workflow/ckpts-image-workflow.json)
-
-
-
 ## Other Nodes

 ![main](./assets/all-workflow.svg)
@@ -140,13 +82,24 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43

 [workflow-1](./workflow/1-workflow.json)

+> randomPrompt

+![randomPrompt](./assets/randomPrompt.png)

 > TransparentImage

 ![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.
@@ -154,16 +107,6 @@ Add edges to an image.
 ![FeatheredMask](./assets/FlVou_Y6kaGWYoEj1Tn0aTd4AjMI.jpg)


-> LaMaInpainting
-
-from [simple-lama-inpainting](https://github.com/enesmsahin/simple-lama-inpainting)
-
-
-> rembgNode
-
-"briarmbg","u2net","u2netp","u2net_human_seg","u2net_cloth_seg","silueta","isnet-general-use","isnet-anime"
-
-

 ### Improvement 

@@ -178,16 +121,12 @@ An improvement has been made to directly redirect to GitHub to search for missin


 ### Models
+[Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : model/clipseg

-[Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:models/rembg
+<!-- ### Workflow
+[Workflow](./workflow.md) -->

-[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: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

 ## Installation

@@ -218,25 +157,21 @@ If you are using a venv, make sure you have it activated before installation and
 pip3 install -r requirements.txt
 ```

-
-
-
-
 #### 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



-
-#### 
-File / LoadImagesFromPath SaveImageToLocal LoadImagesFromURL
-
-
-
+#### Thanks:
+[ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)

 #### discussions:
 [discussions](https://github.com/shadowcz007/comfyui-mixlab-nodes/discussions)

+### TODO:
+- 音频播放节点：带可视化、支持多音轨、可配置音轨音量
+- vector https://github.com/GeorgLegato/stable-diffusion-webui-vectorstudio
+

 <picture>
  <source
@@ -4,7 +4,7 @@ import subprocess
 import importlib.util
 import sys,json
 import urllib
-import hashlib
+
 import datetime


@@ -79,13 +79,6 @@ install_openai()
 current_path = os.path.abspath(os.path.dirname(__file__))


-
-def calculate_md5(string):
-    encoded_string = string.encode()
-    md5_hash = hashlib.md5(encoded_string).hexdigest()
-    return md5_hash
-
-
 def create_key(key_p,crt_p):
    import OpenSSL
    # 生成自签名证书
@@ -133,38 +126,8 @@ def create_for_https():
    return (crt,key)


-
-# 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()
-                file_info = {
-                    'filename': os.path.basename(file_path),
-                    'category': os.path.dirname(file_path),
-                    'data': json_data,
-                    'date': numeric_timestamp
-                }
-                data.append(file_info)
-        except Exception as e:
-            print(e)
-    
-    sorted_data = sorted(data, key=lambda x: x['date'], reverse=True)
-    return sorted_data
-
 # workflow  
-def read_workflow_json_files(folder_path ):
+def read_workflow_json_files(folder_path):
    json_files = []
    for filename in os.listdir(folder_path):
        if filename.endswith('.json'):
@@ -192,121 +155,24 @@ 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)
    workflows=read_workflow_json_files(workflow_path)
    return workflows

-def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=False):
-    app_path=os.path.join(current_path, "app")
-    if not os.path.exists(app_path):
-        os.mkdir(app_path)
-
-    category_path=os.path.join(app_path,category)
-    if not os.path.exists(category_path):
-        os.mkdir(category_path)
-
-    apps=[]
-    if filename==None:
-
-        #TODO 支持目录内遍历 
-        if is_all:
-            data=read_workflow_json_files_all(category_path)
-        else:
-            data=read_workflow_json_files(category_path)
-        
-        i=0
-        for item in data:
-            # print(item)
-            try:
-                x=item["data"]
-                # 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'],
-                        "data":x,
-                        "date":item["date"],
-                    })
-                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,
-                        "data":{
-                            "app":{
-                                "category":category,
-                                "description":x['app']['description'],
-                                "filename":(x['app']['filename'] if 'filename' in x['app'] else "") ,
-                                "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"]
-                    })
-                i+=1
-            except Exception as e:
-                print("发生异常：", str(e))
-    else:
-        app_workflow_path=os.path.join(category_path, filename)
-        # print('app_workflow_path: ',app_workflow_path)
-        try:
-            with open(app_workflow_path) as json_file:
-                apps = [{
-                    'filename':filename,
-                    'data':json.load(json_file)
-                }]
-        except Exception as e:
-            print("发生异常：", str(e))
-        
-        if len(apps)==1 and category!='' and category!=None:
-            data=read_workflow_json_files(category_path)
-            
-            for item in data:
-                x=item["data"]
-                # 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,
-                        "data":{
-                            "app":{
-                                "category":category,
-                                "description":x['app']['description'],
-                                "filename":(x['app']['filename'] if 'filename' in x['app'] else "") ,
-                                "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"]
-                    })
-
-    return apps
+def get_my_workflow_for_app():
+    # print("#####path::", current_path)
+    workflow_path=os.path.join(current_path, "workflow/my_workflow_app.json")
+    print('workflow_path: ',workflow_path)
+    json_data={}
+    try:
+        with open(workflow_path) as json_file:
+            json_data = json.load(json_file)
+    except:
+        print('-')
+    return json_data

 def save_workflow_json(data):
    workflow_path=os.path.join(current_path, "workflow/my_workflow.json")
@@ -314,27 +180,11 @@ def save_workflow_json(data):
        json.dump(data, file)
    return workflow_path

-def save_workflow_for_app(data,filename="my_workflow_app.json",category=""):
-    app_path=os.path.join(current_path, "app")
-    if not os.path.exists(app_path):
-        os.mkdir(app_path)
-
-    category_path=os.path.join(app_path,category)
-    if not os.path.exists(category_path):
-        os.mkdir(category_path)
-    
-    app_workflow_path=os.path.join(category_path, filename)
- 
-    try:
-        output_str = json.dumps(data['output'])
-        data['app']['id']=calculate_md5(output_str)
-        # id=data['app']['id']
-    except Exception as e:
-        print("发生异常：", str(e))
-    
-    with open(app_workflow_path, 'w') as file:
+def save_workflow_for_app(data):
+    workflow_path=os.path.join(current_path, "workflow/my_workflow_app.json")
+    with open(workflow_path, 'w') as file:
        json.dump(data, file)
-    return filename
+    return workflow_path

 def get_nodes_map():
    # print("#####path::", current_path)
@@ -352,6 +202,8 @@ def get_nodes_map():
    return json_data


+
+
 # 保存原始的 get 方法
 _original_request = aiohttp.ClientSession._request

@@ -368,59 +220,33 @@ async def new_request(self, method, url, *args, **kwargs):

 # 应用 Monkey Patch
 aiohttp.ClientSession._request = new_request
-import socket
-
-async def check_port_available(address, port):
-    #检查端口是否可用
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
-        sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-        try:
-            sock.bind((address, port))
-            return True
-        except socket.error:
-            return False

 # https
 async def new_start(self, address, port, verbose=True, call_on_start=None):
-    try:
+        
+
        runner = web.AppRunner(self.app, access_log=None)
        await runner.setup()
-
-        if not await check_port_available(address, port):
-            raise RuntimeError(f"Port {port} is already in use.")
-
        site = web.TCPSite(runner, address, port)
        await site.start()

        import ssl
-        crt, key = create_for_https()
+        crt,key=create_for_https()
        ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
-        ssl_context.load_cert_chain(crt, key)
-
-        success = False
-        for i in range(10):  # 尝试最多10次
-            if await check_port_available(address, port + 1 + i):
-                https_port = port + 1 + i
-                site2 = web.TCPSite(runner, address, https_port, ssl_context=ssl_context)
-                await site2.start()
-                success = True
-                break
-
-        if not success:
-            raise RuntimeError(f"Ports {port + 1} to {port + 10} are all in use.")
+        ssl_context.load_cert_chain(crt,key)
+        site2 = web.TCPSite(runner, address, port+1,ssl_context=ssl_context)
+        await site2.start()

        if address == '':
            address = '0.0.0.0'
        if verbose:
+            # print('\033[91mMixlab Nodes: \033[93mLoaded\033[0m')
            print("\033[93mStarting server\n")
            print("\033[93mTo see the GUI go to: http://{}:{}".format(address, port))
-            print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
+            print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, port+1))
        if call_on_start is not None:
            call_on_start(address, port)

-    except Exception as e:
-        print(f"Error starting the server: {e}")
-
        # import webbrowser
        # if os.name == 'nt' and address == '0.0.0.0':
        #     address = '127.0.0.1'
@@ -431,7 +257,7 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
 PromptServer.start=new_start

 # 创建路由表
-routes = PromptServer.instance.routes
+routes = web.RouteTableDef()

@routes.post('/mixlab')
 async def mixlab_hander(request):
@@ -446,6 +272,10 @@ async def mixlab_hander(request):
            print(e)
    return web.json_response(data)

+# @routes.post('/test')
+# async def mixlab_hander(request):
+#     test_auto()
+#     return web.Response(text="test", status=200)

@routes.get('/mixlab/app')
 async def mixlab_app_handler(request):
@@ -471,26 +301,14 @@ async def mixlab_workflow_hander(request):
                    'file_path':file_path
                }
            elif data['task']=='save_app':
-                category=""
-                if "category" in data:
-                    category=data['category']
-                file_path=save_workflow_for_app(data['data'],data['filename'],category)
+                file_path=save_workflow_for_app(data['data'])
                result={
                    'status':'success',
                    'file_path':file_path
                }
            elif data['task']=='my_app':
-                filename=None
-                category=""
-                admin=False
-                if 'filename' in data:
-                    filename=data['filename']
-                if 'category' in data:
-                    category=data['category']
-                if 'admin' in data:
-                    admin=data['admin']
                result={
-                    'data':get_my_workflow_for_app(filename,category,admin),
+                    'data':get_my_workflow_for_app(),
                    'status':'success',
                }
            elif data['task']=='list':
@@ -517,6 +335,22 @@ async def nodes_map_hander(request):

    return web.json_response(result)

+# 把插件自定义的路由添加到comfyui server里
+def new_add_routes(self):
+        import nodes
+        self.app.add_routes(routes)
+        self.app.add_routes(self.routes)
+        for name, dir in nodes.EXTENSION_WEB_DIRS.items():
+            self.app.add_routes([
+                web.static('/extensions/' + urllib.parse.quote(name), dir, follow_symlinks=True),
+            ])
+        self.app.add_routes([
+            web.static('/', self.web_root, follow_symlinks=True),
+        ])
+
+PromptServer.add_routes=new_add_routes
+
+

 # 扩展api接口
 # from server import PromptServer
@@ -530,32 +364,22 @@ 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.PromptNode import RandomPrompt
+from .nodes.ImageNode import NoiseImage,TransparentImage,LoadImagesFromPath,LoadImagesFromURL,UploadImageForSMMS,ResizeImage,TextImage,SvgImage,Image3D,EmptyLayer,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
-
-from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
+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_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
-from .nodes.Mask import OutlineMask,FeatheredMask
-
+from .nodes.Utils import AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,GetImageSize_,MultiplicationNode
+from .nodes.ShareNode import ShareToWeibo

 # 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,
-    "TESTNODE_":TESTNODE_,
-    "TESTNODE_TOKEN":TESTNODE_TOKEN,
    "RandomPrompt":RandomPrompt,
-    # "LoraPrompt":LoraPrompt,
-    "EmbeddingPrompt":EmbeddingPrompt,
-    "PromptSlide":PromptSlide,
-    "PromptSimplification":PromptSimplification,
-    "PromptImage":PromptImage,
-    "MirroredImage":MirroredImage,
    "NoiseImage":NoiseImage,
-    "GradientImage":GradientImage,
    "TransparentImage":TransparentImage,
    "ResizeImageMixlab":ResizeImage,
    "LoadImagesFromPath":LoadImagesFromPath,
@@ -564,12 +388,8 @@ NODE_CLASS_MAPPINGS = {
    "EnhanceImage":EnhanceImage,
    "SvgImage":SvgImage,
    "3DImage":Image3D, 
-    "ImageColorTransfer":ImageColorTransfer,
    "ShowLayer":ShowLayer,
    "NewLayer":NewLayer,
-    "SplitImage":SplitImage,
-    "CenterImage":CenterImage,
-    "GridOutput":GridOutput,
    "MergeLayers":MergeLayers,
    "SplitLongMask":SplitLongMask,
    "FeatheredMask":FeatheredMask,
@@ -577,15 +397,15 @@ NODE_CLASS_MAPPINGS = {
    "FaceToMask":FaceToMask,
    "AreaToMask":AreaToMask,
    "ImageCropByAlpha":ImageCropByAlpha,
-    # "VAELoaderConsistencyDecoder":VAELoader,
-    "SaveImageToLocal":SaveImageToLocal,
-    # "VAEDecodeConsistencyDecoder":VAEDecode,
+    "VAELoaderConsistencyDecoder":VAELoader,
+    "VAEDecodeConsistencyDecoder":VAEDecode,
    "ScreenShare":ScreenShareNode,
    "FloatingVideo":FloatingVideo,
+    "CLIPSeg_":CLIPSeg,
+    "CombineMasks_":CombineMasks,
    "ChatGPTOpenAI":ChatGPTNode,
    "ShowTextForGPT":ShowTextForGPT,
    "CharacterInText":CharacterInText,
-    "TextSplitByDelimiter":TextSplitByDelimiter,
    "SpeechRecognition":SpeechRecognition,
    "SpeechSynthesis":SpeechSynthesis,
    "Color":ColorInput,
@@ -599,13 +419,8 @@ NODE_CLASS_MAPPINGS = {
    "GetImageSize_":GetImageSize_,
    "SwitchByIndex":SwitchByIndex,
    "LimitNumber":LimitNumber, 
-    "OutlineMask":OutlineMask,
-    "JoinWithDelimiter":JoinWithDelimiter,
-    "Seed_":CreateSeedNode,
-    "CkptNames_":CreateCkptNames,
-    "SamplerNames_":CreateSampler_names,
-    "LoraNames_":CreateLoraNames
-    # "LaMaInpainting":LaMaInpainting
+    "UploadImageForSMMS":UploadImageForSMMS,
+    "ShareToWeibo":ShareToWeibo
    # "GamePal":GamePal
 }

@@ -625,55 +440,14 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
    "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
    "3DImage":"3DImage ♾️Mixlab",
-    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
-    "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
-    "PromptSlide":"PromptSlide ♾️Mixlab",
-    "PromptGenerate_Mix":"PromptGenerate ♾️Mixlab",
-    "ChinesePrompt_Mix":"ChinesePrompt ♾️Mixlab",
-    "GamePal":"GamePal ♾️Mixlab",
-    "RembgNode_Mix":"Removebg",
-    "LoraNames_":"LoraName_TriggerWords.safetensors"
+    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab"
+
+    # "GamePal":"GamePal ♾️Mixlab"
 }

 # web ui的节点功能
 WEB_DIRECTORY = "./web"

 print('--------------')
-print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
-
-try:
-    from .nodes.Lama import LaMaInpainting
-    print('LaMaInpainting.available',LaMaInpainting.available)
-    if LaMaInpainting.available:
-        NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
-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 Exception as e:
-    print('ClipInterrogator.available',False,e)
-
-try:
-    from .nodes.TextGenerateNode import PromptGenerate,ChinesePrompt
-    print('PromptGenerate.available',PromptGenerate.available)
-    if PromptGenerate.available:
-        NODE_CLASS_MAPPINGS['PromptGenerate_Mix']=PromptGenerate
-    print('ChinesePrompt.available',ChinesePrompt.available)
-    if ChinesePrompt.available:
-        NODE_CLASS_MAPPINGS['ChinesePrompt_Mix']=ChinesePrompt
-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')
+print('\033[91mMixlab Nodes: \033[93mLoaded\033[0m')
+print('--------------')
@@ -1,30 +0,0 @@
-Jony Ive
-Dieter Rams
-Philippe Starck
-Karim Rashid
-Yves Béhar
-Marc Newson
-Naoto Fukasawa
-Jonathan Adler
-Patricia Urquiola
-Ross Lovegrove
-Tom Dixon
-Jasper Morrison
-Charles Eames
-Ray Eames
-Achille Castiglioni
-Ron Arad
-Konstantin Grcic
-Marcel Wanders
-Maarten Baas
-Stefan Sagmeister
-Ingo Maurer
-Hella Jongerius
-Sam Hecht
-Kim Colin
-Jaime Hayon
-Michael Anastassiades
-Nendo
-Oki Sato
-Matali Crasset
-Tokujin Yoshioka
@@ -1,10 +0,0 @@
-Chibi Anime Style
-Gakuen Anime Style
-Gekiga Anime Style
-Jidaimono Anime Style
-Kawaii Anime Style
-Mecha Anime Style
-Realistic Anime Style
-Semi-Realistic Anime Style
-Shoji Anime Style
-Kemonomimi Anime Style
@@ -1,23 +0,0 @@
-GoPro
-Drone
-polaroid
-black and white film
-Kodachrome
-shot on 8mm
-shot on 16mm
-shot on 35mm
-Microscopic
-Fisheye Lens
-Wide Angle
-Ultra-Wide Angle
-Panorama
-Short Exposure
-Long Exposure
-Double Exposure
-f2.8
-Depth of Field
-Soft Focus
-Deep Focus
-Shallow Focus
-Vanishing Point
-Vantage Point
@@ -1,30 +0,0 @@
-Elegant evening gown
-Casual jeans and t-shirt
-Formal black suit
-Stylish leather jacket
-Flowy bohemian dress
-Sporty tracksuit
-Chic little black dress
-Trendy ripped jeans
-Classic white button-down shirt
-Cozy oversized sweater
-Sophisticated tailored blazer
-Quirky patterned leggings
-Striped sailor top
-Polished knee-length skirt
-Vintage-inspired floral dress
-Edgy motorcycle jacket
-Preppy polo shirt
-Boho maxi skirt
-Professional pinstripe suit
-Relaxed denim shorts
-Glamorous sequined dress
-Athletic running shoes
-Formal bow tie
-Casual baseball cap
-Stylish fedora hat
-Warm woolen scarf
-Comfortable cotton socks
-Trendy ankle boots
-Cute summer sandals
-Cozy pajama set
@@ -1,30 +0,0 @@
-Happy
-Sad
-Angry
-Surprised
-Excited
-Worried
-Confused
-Disgusted
-Amused
-Bored
-Curious
-Embarrassed
-Frustrated
-Nervous
-Pleased
-Relieved
-Shy
-Tired
-Serious
-Silly
-Proud
-Grumpy
-Smug
-Sarcastic
-Flirty
-Skeptical
-Shocked
-Blissful
-Envious
-Mischievous
@@ -4761,29 +4761,19 @@
    ],
    "https://github.com/shadowcz007/comfyui-mixlab-nodes": [
        [
-            "GridOutput",
-            "SplitImage",
-            "PromptGenerate_Mix",
-            "JoinWithDelimiter",
-            "ChinesePrompt_Mix",
            "3DImage",
            "AppInfo",
            "IntNumber",
            "FloatSlider",
            "ResizeImage",
            "NoiseImage",
-            "PromptImage",
-            "SaveImageToLocal",
            "AreaToMask",
+            "CLIPSeg_",
            "CharacterInText",
            "ChatGPTOpenAI",
            "Color",
-            "Seed_",
-            "CkptNames_",
-            "SamplerNames_",
-            "LoraNames_",
+            "CombineMasks_",
            "EnhanceImage",
-            "GradientImage",
            "FaceToMask",
            "FeatheredMask",
            "FloatingVideo",
@@ -4793,11 +4783,7 @@
            "LoadImagesFromURL",
            "MergeLayers",
            "NewLayer",
-            "CenterImage",
            "RandomPrompt",
-            "PromptSlide",
-            "PromptSimplification",
-            "ClipInterrogator",
            "ScreenShare",
            "ShowLayer",
            "ShowTextForGPT",
@@ -4807,13 +4793,18 @@
            "SplitLongMask",
            "SvgImage",
            "TextImage",
-            "ResizeImageMixlab",
            "TransparentImage",
+            "VAEDecodeConsistencyDecoder",
+            "VAELoaderConsistencyDecoder",
            "TextToNumber",
            "TextInput_",
            "DynamicDelayProcessor",
-            "LaMaInpainting",
-            "Moondream"
+            "MultiplicationNode",
+            "ShareToWeibo",
+            "LimitNumber",
+            "SwitchByIndex",
+            "UploadImageForSMMS",
+            "GetImageSize_"
        ],
        {
            "title_aux": "comfyui-mixlab-nodes"
@@ -1,16 +0,0 @@
-Mood Lighting
-Moody Lighting
-Studio Lighting
-Cove Lighting
-Soft Lighting
-Hard Lighting
-Volumetric Lighting
-Low-Key Lighting
-High-Key Lighting
-Epic Light
-Rembrandt Lighting
-Contre-Jour
-Veiling Flare
-Crepuscular Rays
-Rays of Shimmering Light
-Godrays
@@ -1 +0,0 @@
-{}
@@ -1,132 +0,0 @@
-Aaron Siskind
-Alessio Albi
-Alfred Eisenstaedt
-Alfred Stieglitz
-Alyssa Monks
-André Kertész
-Andreas Gursky
-Andrew Wyeth
-Anne Geddes
-Annie Leibovitz
-Ansel Adams
-Arnold Newman
-August Sander
-Balthus
-Berenice Abbott
-Bill Brandt
-Bill Henson
-Brassaï (Gyula Halász)
-Brooke Shaden
-Bruce Davidson
-Bruce Weber
-Bunny Yeager
-Carleton Watkins
-Carrie Mae Weems
-Chuck Close
-Cindy Sherman
-Clarence H. White
-Claude Cahun
-Danny Lyon
-David LaChapelle
-Dawoud Bey
-Diane Arbus
-Don McCullin
-Dora Maar
-Dorothea Lange
-Duane Michals
-Eadweard Muybridge
-Edward Burtynsky
-Edward Curtis
-Edward Ruscha
-Edward Steichen
-Edward Weston
-Elliott Erwitt
-Ernst Haas
-Eugene Atget
-Fan Ho
-Francesca Woodman
-Frans Lanting
-Garry Winogrand
-Georges Melies
-Gerda Taro
-Gertrude Käsebier
-Gordon Parks
-Graciela Iturbide
-Gregory Crewdson
-Harold Edgerton
-Helen Levitt
-Helmut Newton
-Hendrik Kerstens
-Henri Cartier-Bresson
-Hugh Kretschmer
-Irving Penn
-Jacques Henri Lartigue
-James Nachtwey
-James Van Der Zee
-Jay Maisel
-Jerry Uelsmann
-Joel Peter Witkin
-Joel Sartore
-John Frederick William Herschel
-Josef Sudek
-Julia Margaret Cameron
-Karl Blossfeldt
-Larry Burrows
-László Moholy-Nagy (photography)
-Lee Jeffries
-Lewis Hine
-Lorna Simpson
-Lynsey Addario
-Margaret Bourke-White
-Mario Testino
-Martin Parr
-Martin Schoeller
-Mary Ellen Mark
-Mathew B. Brady
-Méret Oppenheim
-Meryl McMaster
-Mick Rock
-Miles Aldridge
-Minor Martin White
-Nan Goldin
-Nathan Wirth
-Olive Cotton
-Olivier Rousteing
-Patrick Demarchelier
-Paul Nicklen
-Paul Outerbridge
-Paul Strand
-Pete Souza
-Peter Dombrovskis
-Peter Henry Emerson
-Peter Lik
-Peter Lindbergh
-Philip-Lorca diCorcia
-Philippe Halsman
-Ralph Gibson
-Richard Avedon
-Robert Adams
-Robert Bechtle
-Robert Capa
-Robert Frank
-Robert Mapplethorpe
-Roger Fenton
-Ruth Bernhard
-Sally Mann
-Sebastião Salgado
-Shirin Neshat
-Stefan Gesell
-Steven Meisel
-Susan Meiselas
-Vivian Maier
-Vivian Maier
-Viviane Sassen
-Walker Evans
-Wes Anderson
-William Eggleston
-William Eugene Smith
-William Henry Fox Talbot
-Yinka Shonibare
-Yousuf Karsh
-Man Ray
-Robert Mapplethorpe
@@ -1,101 +0,0 @@
-Doctor
-Teacher
-Engineer
-Lawyer
-Accountant
-Nurse
-Architect
-Chef
-Pilot
-Scientist
-Artist
-Writer
-Musician
-Actor
-Photographer
-Police officer
-Firefighter
-Dentist
-Pharmacist
-Veterinarian
-Electrician
-Plumber
-Carpenter
-Mechanic
-Farmer
-Astronaut
-Athlete
-Journalist
-Politician
-Economist
-Psychologist
-Social worker
-Librarian
-Translator
-Salesperson
-Entrepreneur
-Financial advisor
-Graphic designer
-Web developer
-Marketing manager
-Human resources manager
-Project manager
-Event planner
-Fashion designer
-Interior decorator
-Real estate agent
-Archaeologist
-Biologist
-Chemist
-Geologist
-Physicist
-Mathematician
-Historian
-Geographer
-Economist
-Sociologist
-Anthropologist
-Archaeologist
-Linguist
-Philosopher
-Economist
-Sociologist
-Anthropologist
-Archaeologist
-Linguist
-Philosopher
-Geographer
-Historian
-Economist
-Sociologist
-Anthropologist
-Archaeologist
-Linguist
-Philosopher
-Geographer
-Historian
-Economist
-Sociologist
-Anthropologist
-Archaeologist
-Linguist
-Philosopher
-Geographer
-Historian
-Economist
-Sociologist
-Anthropologist
-Archaeologist
-Linguist
-Philosopher
-Geographer
-Historian
-Economist
-Sociologist
-Anthropologist
-Archaeologist
-Linguist
-Philosopher
-Geographer
-Historian
-                     #MixCopilot
@@ -1,58 +0,0 @@
-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
@@ -1,135 +0,0 @@
-Vintage
-Grain
-Sepia
-High Key
-Low Key
-High Dynamic Range
-Cross Process
-Radial Blur
-Infrared
-Lomo
-Photocopy
-Pencil Sketch
-Pop Art
-Orton
-Mosaic
-Selective Black and White
-Torn Paper
-Tilt-Shift
-Double Exposure
-Polaroid
-Liquid Ink
-Color Splash
-Sketch
-Water Drops
-Polarizer
-Chinese Painting
-Water Droplets
-Polarization
-Color Inversion
-Fish-eye
-Soft Focus
-Solarization
-Posterize
-Comic Book
-Duotone
-Gradient Map
-Edge Detection
-Oil Painting
-Reflection
-Mirror
-ASCII Art
-Glitch
-Time-Lapse
-Day to Night
-Surreal
-Black and White
-Sepia Tone
-Vintage Film
-Grainy Texture
-High Key Lighting
-Low Key Lighting
-Cross Processed Film
-Infrared Photography
-Photocopy
-Pencil Drawing
-Pop Art Filter
-Mosaic Filter
-Selective Desaturation
-Torn Paper
-Tilt-Shift Photography
-Double Exposure
-Polaroid Style Frame
-Water Drops Texture
-Polarizer
-Chinese Painting
-Water Droplets Texture
-Polarization
-Color Inversion
-Fish-eye Lens
-Soft Focus
-Solarize Filter
-Edge Detection
-Oil Painting
-Reflection
-Mirror Image
-Time-Lapse Photography
-Day to Night Transition
-Surreal Art Style
-Abstract Expressionism
-Acrylic Painting
-Anime
-Art Deco
-Biomorphic Abstraction
-Black and White Photograph
-Cartoon
-Charcoal Sketch
-Chibi Anime
-Chinese Painting
-Classicist Painting
-Collage
-Concept Art
-Cyberpunk
-Dada Art
-Digital Art
-Fantasy Art
-Fashion Art
-Fashion Sketch
-Fish-Eye lens Photograph
-Goth Art
-Graffiti
-Harlem Renaissance
-High Key Photograph
-Hyperrealist Pencil Sketch
-Impressionist Painting
-Josei Anime
-Long Exposure Photograph
-Low Key Photograph
-Macro Photograph
-Manga
-Metal Sculpture
-Mid Century Modern Illustration
-Mixed Media
-Modern Art
-Moe Anime
-Nihonga
-Origami
-Paper Mache
-Pen and Ink
-Pencil Sketch
-Photograph
-Photorealism
-Pinup Art
-Romanticist Painting
-Sci-Fi Art
-Semi Realistic Fantasy Art
-Semi Realistic Cyberpunk Art
-Shallow Depth of Field Photograph
-Steam Punk Art
-Stone Sculpture
-Superhero Comic
-Surrealist Art
-Tempura Painting
-Underground Comic
-Watercolor Painting
-Zulu Urban Art
@@ -24,7 +24,7 @@ class SpeechRecognition:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Audio"
+    CATEGORY = "♾️Mixlab/audio"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -48,7 +48,7 @@ class SpeechSynthesis:
    OUTPUT_NODE = True
    OUTPUT_IS_LIST = (True,)

-    CATEGORY = "♾️Mixlab/Audio"
+    CATEGORY = "♾️Mixlab/audio"

    def run(self, text):
        # print(session_history)
@@ -82,7 +82,7 @@ class GamePal:
    OUTPUT_NODE = True
    OUTPUT_IS_LIST = (False,)

-    CATEGORY = "♾️Mixlab/Audio"
+    CATEGORY = "♾️Mixlab/audio"

    def run(self, input_text,input_num,python_code):
        exec(python_code)
@@ -1,26 +1,7 @@
 import openai 
 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
-    return ''.join(random.choice(letters) for _ in range(length))
-
-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("*")
+import re,json

 #  判断是否是azure服务
 def is_azure_url(url):
@@ -92,13 +73,13 @@ class ChatGPTNode:
    def INPUT_TYPES(cls):
        return {
            "required": {
-                "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
-                "api_url":("URL", {"default": "", "multiline": True,"dynamicPrompts": False}),
-                "prompt": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                "api_key":("KEY", {"default": "", "multiline": True}),
+                "api_url":("URL", {"default": "", "multiline": True}),
+                "prompt": ("STRING", {"multiline": True}),
                "system_content": ("STRING", 
                                   {
                                       "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
-                                       "multiline": True,"dynamicPrompts": False
+                                       "multiline": True
                                       }),
                "model": (["gpt-3.5-turbo","gpt-35-turbo","gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-4-0613","gpt-4-1106-preview"], 
                          {"default": "gpt-3.5-turbo"}),
@@ -186,11 +167,8 @@ class ShowTextForGPT:
    def INPUT_TYPES(s):
        return {
            "required": {
-                "text": ("STRING", {"forceInput": True,"dynamicPrompts": False}),
-            },
-            "optional":{ 
-                    "output_dir": ("STRING",{"forceInput": True,"default": "","multiline": True,"dynamicPrompts": False}), 
-                }
+                "text": ("STRING", {"forceInput": True}),
+            }
        }

    INPUT_IS_LIST = True
@@ -201,63 +179,9 @@ class ShowTextForGPT:

    CATEGORY = "♾️Mixlab/GPT"

-    def run(self, text,output_dir=[""]):
-        
-        # 类型纠正
-        texts=[]
-        for t in text:
-            if not isinstance(t, str):
-                t = str(t)
-            texts.append(t)
-
-        text=texts
-
-        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 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)
+    def run(self, text):
+        # print(session_history)
        return {"ui": {"text": text}, "result": (text,)}
-        


 class CharacterInText:
@@ -265,8 +189,8 @@ class CharacterInText:
    def INPUT_TYPES(s):
        return {
            "required": {
-                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
-                 "character": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                 "text": ("STRING", {"multiline": True}),
+                 "character": ("STRING", {"multiline": True}),
                 "start_index": ("INT", {
                    "default": 1,
                    "min": 0, #Minimum value
@@ -287,58 +211,7 @@ class CharacterInText:

    def run(self, text,character,start_index):
        # print(text,character,start_index)
-        b=1 if character.lower() in text.lower() else 0
+        b=1 if character in text else 0
        
        return (b+start_index,)

-class TextSplitByDelimiter:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
-                "delimiter":(["newline","comma"],),
-                "start_index": ("INT", {
-                    "default": 0,
-                    "min": 0, #Minimum value
-                    "max": 1000, #Maximum value
-                    "step": 1, #Slider's step
-                    "display": "number" # Cosmetic only: display as "number" or "slider"
-                }),
-                 "skip_every": ("INT", {
-                    "default": 0,
-                    "min": 0, #Minimum value
-                    "max": 10, #Maximum value
-                    "step": 1, #Slider's step
-                    "display": "number" # Cosmetic only: display as "number" or "slider"
-                }),
-                "max_count": ("INT", {
-                    "default": 10,
-                    "min": 1, #Minimum value
-                    "max": 1000, #Maximum value
-                    "step": 1, #Slider's step
-                    "display": "number" # Cosmetic only: display as "number" or "slider"
-                }),
-            }
-        }
-
-    INPUT_IS_LIST = False
-    RETURN_TYPES = ("STRING",)
-    FUNCTION = "run"
-    # OUTPUT_NODE = True
-    OUTPUT_IS_LIST = (True,)
-
-    CATEGORY = "♾️Mixlab/GPT"
-
-    def run(self, text,delimiter,start_index,skip_every,max_count):
-        arr=[]
-        if delimiter=='newline':
-            arr = [line for line in text.split('\n') if line.strip()]
-        elif delimiter=='comma':
-            arr = [line for line in text.split(',') if line.strip()]
-        
-        arr= arr[start_index:start_index + max_count * (skip_every+1):(skip_every+1)]
-
-        return (arr,)
-
-
@@ -1,277 +0,0 @@
-import os,sys
-import folder_paths
-
-from PIL import Image
-import importlib.util
-
-import comfy.utils
-import numpy as np
-import json
-import torch
-import random
-
-
-# from clip_interrogator import Config, Interrogator
-
-
-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('clip_interrogator')==False:
-        import subprocess
-
-        # 安装
-        print('#pip install clip-interrogator==0.6.0')
-
-        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'clip-interrogator==0.6.0'], capture_output=True, text=True)
-
-        #检查命令执行结果
-        if result.returncode == 0:
-            print("#install success")
-            from clip_interrogator import Config, Interrogator
-            _available=True
-        else:
-            print("#install error")
-        
-    else:
-        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
-    caption_model = BlipForConditionalGeneration.from_pretrained(model_path, torch_dtype=dtype)
-    
-    caption_processor = AutoProcessor.from_pretrained(model_path)
-
-    caption_model.eval()
-    if not config.caption_offload:
-        caption_model = caption_model.to(config.device)
-    
-    return (caption_model,caption_processor)
-    
-
-
-caption_model_path=os.path.join(folder_paths.models_dir, "clip_interrogator/Salesforce/blip-image-captioning-base")
-if not os.path.exists(caption_model_path):
-    print(f"## clip_interrogator_model not found: {caption_model_path}, pls download from https://huggingface.co/Salesforce/blip-image-captioning-base")
-    caption_model_path='Salesforce/blip-image-captioning-base'
-
-cache_path=os.path.join(folder_paths.models_dir, "clip_interrogator")
-
-
-# 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 image_analysis_fn(ci,image):
-    image = image.convert('RGB')
-    image_features = ci.image_to_features(image)
-
-    top_mediums = ci.mediums.rank(image_features, 5)
-    top_artists = ci.artists.rank(image_features, 5)
-    top_movements = ci.movements.rank(image_features, 5)
-    top_trendings = ci.trendings.rank(image_features, 5)
-    top_flavors = ci.flavors.rank(image_features, 5)
-
-    medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))}
-    artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))}
-    movement_ranks = {movement: sim for movement, sim in zip(top_movements, ci.similarities(image_features, top_movements))}
-    trending_ranks = {trending: sim for trending, sim in zip(top_trendings, ci.similarities(image_features, top_trendings))}
-    flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))}
-    
-    return {
-        "medium_ranks":medium_ranks, 
-        "artist_ranks":artist_ranks, 
-        "movement_ranks":movement_ranks, 
-        "trending_ranks":trending_ranks, 
-        "flavor_ranks":flavor_ranks
-        }
-
-
-def generate_sentences(data):
-    sentences = []
-
-    # Get the length of data
-    data_length = len(data)
-
-    # Use a recursive function to handle variable-length data
-    def generate_recursive(index, current_sentence, current_score):
-        # Check if recursion is complete
-        if index == data_length:
-            sentences.append({"sentence": current_sentence, "score": current_score})
-            return
-
-        # Get the current level data
-        current_data = data[index]
-
-        # Iterate through the current level data
-        for phrase in current_data:
-            sentence = current_sentence + ("," if current_sentence.strip() else "") + phrase
-            score = current_score + current_data[phrase]
-            generate_recursive(index + 1, sentence, score)
-
-    # Start recursive generation of sentences
-    generate_recursive(0, "", 0)
-
-    # Sort the generated sentences by score in descending order
-    sentences.sort(key=lambda x: x["score"], reverse=True)
-
-    def get_random_elements(elements, num):
-        return random.sample(elements, num)
-
-    ps = get_random_elements(sentences, 5)
-    ps = [s["sentence"] for s in sorted(ps, key=lambda x: x["score"], reverse=True)]
-
-    return ps
-
-
-
-
-def image_to_prompt(ci,image, mode):
-    ci.config.chunk_size = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
-    ci.config.flavor_intermediate_count = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
-    image = image.convert('RGB')
-    if mode == 'best':
-        return ci.interrogate(image)
-    elif mode == 'classic':
-        return ci.interrogate_classic(image)
-    elif mode == 'fast':
-        return ci.interrogate_fast(image)
-    elif mode == 'negative':
-        return ci.interrogate_negative(image)
-
-# image = Image.open(image_path).convert('RGB')
-# ci = Interrogator(Config(clip_model_name="ViT-L-14/openai"))
-# print(ci.interrogate(image))
-
-
-class ClipInterrogator:
-
-    global _available
-    available=_available
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "image": ("IMAGE",),
-            "prompt_mode": (['fast','classic','best','negative'],),
-            "image_analysis": (["off","on"],), 
-                             },
-
-                # "optional":{
-                #     "output":("CLIPINTERROGATOR", {"multiline": True,"default": "", "dynamicPrompts": False})
-                # },
-
-                }
-    
-    RETURN_TYPES = ("STRING","STRING",)
-    RETURN_NAMES = ("prompt","random_samples",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Prompt"
-    OUTPUT_NODE = True
-    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,True,)
-    global ci
-    ci = None
-    def run(self,image,prompt_mode,image_analysis):
-        global ci
-
-        prompt_mode=prompt_mode[0]
-        analysis=image_analysis[0]
-
-        prompt_result=[]
-        analysis_result=[]
-
-        # 进度条
-        pbar = comfy.utils.ProgressBar(len(image)*(2 if analysis=='on' else 1))
-        
-        if ci==None:
-            config=Config(
-                clip_model_name="ViT-L-14/openai",
-                device="cuda" if torch.cuda.is_available() else "cpu",
-                download_cache=True,
-                clip_model_path=cache_path,
-                cache_path=cache_path
-                )
-            config.apply_low_vram_defaults()
-
-            caption_model,caption_processor=load_caption_model(caption_model_path,config)
-
-            config.caption_model= caption_model
-            config.caption_processor= caption_processor
-
-            ci = Interrogator(config)
-        # else:
-        #     simple_lama.model.to("cuda" if torch.cuda.is_available() else "cpu")
-
-        for i in range(len(image)):
-            im=image[i]
-
-            im=tensor2pil(im)
-            im=im.convert('RGB')
-
-            if analysis=='on':
-                analysis_res=image_analysis_fn(ci,im)
-                analysis_result.append( analysis_res )
-                pbar.update(1)
-
-            prompt=image_to_prompt(ci,im,prompt_mode)
-            pbar.update(1)
-            prompt_result.append(prompt)
-
-
-        # result.save("inpainted.png")
-        if ci.config.clip_offload and not ci.clip_offloaded:
-            ci.clip_model = ci.clip_model.to('cpu')
-            ci.clip_offloaded = True
-
-        if ci.config.caption_offload and not ci.caption_offloaded:
-            ci.caption_model = ci.caption_model.to('cpu')
-            ci.caption_offloaded = True
-
-        # analysis_result=[]
-        # items = app.graph.getNodeById(31).widgets[2].value["items"]
-        
-        random_samples=[]
-
-        for r in analysis_result:
-            random_sample = generate_sentences([r['medium_ranks'], r['artist_ranks'],r['movement_ranks'],r['trending_ranks'],r['flavor_ranks']])
-            for s in random_sample:
-                random_samples.append(s)
-        # print(len(random_samples))
-        # print('-----')
-        # print( random_samples)
-        return {
-            "ui":{
-                    "prompt": prompt_result,
-                    "analysis":analysis_result,
-                    "random_samples":random_samples
-                },
-            "result": (prompt_result,random_samples,)}
@@ -0,0 +1,272 @@
+#### Thanks:
+# [ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)
+
+from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+
+from PIL import Image
+import torch
+import torchvision.transforms as T
+import numpy as np
+
+from torchvision.transforms.functional import to_pil_image
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+
+
+import cv2
+
+from scipy.ndimage import gaussian_filter
+
+from typing import Optional, Tuple
+
+import warnings,os
+warnings.filterwarnings("ignore", category=UserWarning, module="torch")
+warnings.filterwarnings("ignore", category=UserWarning, module="safetensors")
+
+import folder_paths
+import logging
+
+logger = logging.getLogger('CLIPSeg nodes')
+
+clipseg_model_dir = os.path.join(folder_paths.models_dir, "clipseg")
+
+if not os.path.exists(clipseg_model_dir):
+    clipseg_model_dir='CIDAS/clipseg-rd64-refined'
+
+"""Helper methods for CLIPSeg nodes"""
+
+
+# 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 tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
+    """Convert a tensor to a numpy array and scale its values to 0-255."""
+    array = tensor.numpy().squeeze()
+    return (array * 255).astype(np.uint8)
+
+def numpy_to_tensor(array: np.ndarray) -> torch.Tensor:
+    """Convert a numpy array to a tensor and scale its values from 0-255 to 0-1."""
+    array = array.astype(np.float32) / 255.0
+    return torch.from_numpy(array)[None,]
+
+def apply_colormap(mask: torch.Tensor, colormap) -> np.ndarray:
+    """Apply a colormap to a tensor and convert it to a numpy array."""
+    colored_mask = colormap(mask.numpy())[:, :, :3]
+    return (colored_mask * 255).astype(np.uint8)
+
+def resize_image(image: np.ndarray, dimensions: Tuple[int, int]) -> np.ndarray:
+    """Resize an image to the given dimensions using linear interpolation."""
+    return cv2.resize(image, dimensions, interpolation=cv2.INTER_LINEAR)
+
+def overlay_image(background: np.ndarray, foreground: np.ndarray, alpha: float) -> np.ndarray:
+    """Overlay the foreground image onto the background with a given opacity (alpha)."""
+    return cv2.addWeighted(background, 1 - alpha, foreground, alpha, 0)
+
+def dilate_mask(mask: torch.Tensor, dilation_factor: float) -> torch.Tensor:
+    """Dilate a mask using a square kernel with a given dilation factor."""
+    kernel_size = int(dilation_factor * 2) + 1
+    kernel = np.ones((kernel_size, kernel_size), np.uint8)
+    mask_dilated = cv2.dilate(mask.numpy(), kernel, iterations=1)
+    return torch.from_numpy(mask_dilated)
+
+
+
+class CLIPSeg:
+
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        """
+            Return a dictionary which contains config for all input fields.
+            Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
+            Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
+            The type can be a list for selection.
+
+            Returns: `dict`:
+                - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
+                - Value input_fields (`dict`): Contains input fields config:
+                    * Key field_name (`string`): Name of a entry-point method's argument
+                    * Value field_config (`tuple`):
+                        + First value is a string indicate the type of field or a list for selection.
+                        + Secound value is a config for type "INT", "STRING" or "FLOAT".
+        """
+        return {"required":
+                    {
+                        "image": ("IMAGE",),
+                        "text": ("STRING", {"multiline": False}),
+                        
+                     },
+                "optional":
+                    {
+                        "blur": ("FLOAT", {"min": 0, "max": 15, "step": 0.1, "default": 7}),
+                        "threshold": ("FLOAT", {"min": 0, "max": 1, "step": 0.05, "default": 0.4}),
+                        "dilation_factor": ("INT", {"min": 0, "max": 10, "step": 1, "default": 4}),
+                    }
+                }
+
+    CATEGORY = "♾️Mixlab/mask"
+    RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
+    RETURN_NAMES = ("Mask","Heatmap Mask", "BW Mask")
+
+    # INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (False,False,False,)
+
+    FUNCTION = "segment_image"
+    def segment_image(self, image: torch.Tensor, text: str, blur: float, threshold: float, dilation_factor: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Create a segmentation mask from an image and a text prompt using CLIPSeg.
+
+        Args:
+            image (torch.Tensor): The image to segment.
+            text (str): The text prompt to use for segmentation.
+            blur (float): How much to blur the segmentation mask.
+            threshold (float): The threshold to use for binarizing the segmentation mask.
+            dilation_factor (int): How much to dilate the segmentation mask.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: The segmentation mask, the heatmap mask, and the binarized mask.
+        """
+            
+        # Convert the Tensor to a PIL image
+        image_np = image.numpy().squeeze()  # Remove the first dimension (batch size of 1)
+        # Convert the numpy array back to the original range (0-255) and data type (uint8)
+        image_np = (image_np * 255).astype(np.uint8)
+        # Create a PIL image from the numpy array
+        i = Image.fromarray(image_np, mode="RGB")
+
+        processor = CLIPSegProcessor.from_pretrained(clipseg_model_dir)
+        model = CLIPSegForImageSegmentation.from_pretrained(clipseg_model_dir)
+        
+        prompt = text
+        
+        input_prc = processor(text=prompt, images=i, padding="max_length", return_tensors="pt")
+        
+        # Predict the segemntation mask
+        with torch.no_grad():
+            outputs = model(**input_prc)
+        
+        tensor = torch.sigmoid(outputs[0]) # get the mask
+        
+        # Apply a threshold to the original tensor to cut off low values
+        thresh = threshold
+        tensor_thresholded = torch.where(tensor > thresh, tensor, torch.tensor(0, dtype=torch.float))
+
+        # Apply Gaussian blur to the thresholded tensor
+        sigma = blur
+        tensor_smoothed = gaussian_filter(tensor_thresholded.numpy(), sigma=sigma)
+        tensor_smoothed = torch.from_numpy(tensor_smoothed)
+
+        # Normalize the smoothed tensor to [0, 1]
+        mask_normalized = (tensor_smoothed - tensor_smoothed.min()) / (tensor_smoothed.max() - tensor_smoothed.min())
+
+        # Dilate the normalized mask
+        mask_dilated = dilate_mask(mask_normalized, dilation_factor)
+
+        # Convert the mask to a heatmap and a binary mask
+        heatmap = apply_colormap(mask_dilated, cm.viridis)
+        binary_mask = apply_colormap(mask_dilated, cm.Greys_r)
+
+        # Overlay the heatmap and binary mask on the original image
+        dimensions = (image_np.shape[1], image_np.shape[0])
+        heatmap_resized = resize_image(heatmap, dimensions)
+        binary_mask_resized = resize_image(binary_mask, dimensions)
+
+        alpha_heatmap, alpha_binary = 0.5, 1
+        overlay_heatmap = overlay_image(image_np, heatmap_resized, alpha_heatmap)
+        overlay_binary = overlay_image(image_np, binary_mask_resized, alpha_binary)
+
+        # Convert the numpy arrays to tensors
+        image_out_heatmap = numpy_to_tensor(overlay_heatmap)
+        image_out_binary = numpy_to_tensor(overlay_binary)
+
+        # Save or display the resulting binary mask
+        binary_mask_image = Image.fromarray(binary_mask_resized[..., 0])
+
+        # convert PIL image to numpy array
+        tensor_bw = binary_mask_image.convert("L")
+        tensor_bw=pil2tensor(tensor_bw)
+        # tensor_bw = np.array(tensor_bw).astype(np.float32) / 255.0
+        # tensor_bw = torch.from_numpy(tensor_bw)[None,]
+        # tensor_bw = tensor_bw.squeeze(0)[..., 0]
+
+        return (tensor_bw, image_out_heatmap, image_out_binary,)
+
+    #OUTPUT_NODE = False
+
+class CombineMasks:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                        "input_image": ("IMAGE", ),
+                        "mask_1": ("MASK", ), 
+                        "mask_2": ("MASK", ),
+                    },
+                "optional": 
+                    {
+                        "mask_3": ("MASK",), 
+                    },
+                }
+        
+    CATEGORY = "♾️Mixlab/mask"
+    RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
+    RETURN_NAMES = ("Combined Mask","Heatmap Mask", "BW Mask")
+
+    FUNCTION = "combine_masks"
+            
+    def combine_masks(self, input_image: torch.Tensor, mask_1: torch.Tensor, mask_2: torch.Tensor, mask_3: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """A method that combines two or three masks into one mask. Takes in tensors and returns the mask as a tensor, as well as the heatmap and binary mask as tensors."""
+        
+        # Combine masks
+        if mask_1 is not None:
+            mask_1 = mask_1.squeeze()
+        if mask_2 is not None:
+            mask_2 = mask_2.squeeze()
+        if mask_3 is not None:
+            mask_3 = mask_3.squeeze()
+        
+        print(mask_1.shape,mask_2.shape , mask_3.shape)
+        combined_mask = mask_1 + mask_2 + mask_3 if mask_3 is not None else mask_1 + mask_2
+        # print(combined_mask)
+
+        # Convert image and masks to numpy arrays
+        image_np = tensor_to_numpy(input_image)
+        heatmap = apply_colormap(combined_mask, cm.viridis)
+        binary_mask = apply_colormap(combined_mask, cm.Greys_r)
+        
+        # Resize heatmap and binary mask to match the original image dimensions
+        dimensions = (image_np.shape[1], image_np.shape[0])
+        print('heatmap',heatmap)
+        if dimensions is None or dimensions[0] == 0 or dimensions[1] == 0:
+            raise ValueError("Invalid dimensions")
+        
+        heatmap_resized = resize_image(heatmap, dimensions)
+        binary_mask_resized = resize_image(binary_mask, dimensions)
+
+        # Overlay the heatmap and binary mask onto the original image
+        alpha_heatmap, alpha_binary = 0.5, 1
+        overlay_heatmap = overlay_image(image_np, heatmap_resized, alpha_heatmap)
+        overlay_binary = overlay_image(image_np, binary_mask_resized, alpha_binary)
+
+        # Convert overlays to tensors
+        image_out_heatmap = numpy_to_tensor(overlay_heatmap)
+        image_out_binary = numpy_to_tensor(overlay_binary)
+
+        return combined_mask, image_out_heatmap, image_out_binary
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+# NODE_CLASS_MAPPINGS = {
+#     "CLIPSeg": CLIPSeg,
+#     "CombineSegMasks": CombineMasks,
+# }
@@ -1,120 +0,0 @@
-import os,sys
-import folder_paths
-
-from PIL import Image
-import importlib.util
-import numpy as np
-
-import torch
-
-global _available
-_available=False
-
-def is_installed(package):
-    try:
-        spec = importlib.util.find_spec(package)
-    except ModuleNotFoundError:
-        return False
-    return spec is not None
-
-if is_installed('simple_lama_inpainting')==False:
-    import subprocess
-    from packaging import version
-    
-    if version.parse(torch.__version__)>=version.parse('2.1'):
-        # 安装
-        print('#pip install simple_lama_inpainting')
-
-        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'simple_lama_inpainting'], capture_output=True, text=True)
-
-        #检查命令执行结果
-        if result.returncode == 0:
-            print("#install success")
-            from simple_lama_inpainting import SimpleLama
-            _available=True
-        else:
-            print("#install error")
-    else:
-        print('#pls check your torch version >= 2.1')
-
-else:
-    from simple_lama_inpainting import SimpleLama
-    _available=True
-
-
- 
-llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
-if not os.path.exists(llma_model_path):
-    os.environ['LAMA_MODEL']=''
-    print(f"## lama torchscript model not found: {llma_model_path},pls download from https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt")
-else:
-    os.environ['LAMA_MODEL'] = llma_model_path
-
-# 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)
-
-
-# simple_lama = SimpleLama()
-
-# img_path = "image.png"
-# mask_path = "mask.png"
-
-# image = Image.open(img_path)
-# mask = Image.open(mask_path).convert('L')
-
-# result = simple_lama(image, mask)
-# result.save("inpainted.png")
-
-
-class LaMaInpainting:
-    global _available
-    available=_available
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "image": ("IMAGE",),
-            "mask": ("MASK",),
-                             },
-
-            
-                }
-    
-    RETURN_TYPES = ("IMAGE",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Image"
-
-    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,)
-    global simple_lama
-    simple_lama = None
-    def run(self,image,mask):
-        global simple_lama
-
-        result=[]
-        if simple_lama==None:
-            simple_lama = SimpleLama()
-        else:
-            simple_lama.model.to("cuda" if torch.cuda.is_available() else "cpu")
-
-        for i in range(len(image)):
-            im=image[i]
-            ma=mask[i]
-            im=tensor2pil(im)
-            ma=tensor2pil(ma)
-            ma =ma.convert('L')
-            
-            res = simple_lama(im, ma)
-            res=pil2tensor(res)
-            result.append(res)
-        # result.save("inpainted.png")
-        if simple_lama.device=='cuda':
-            simple_lama.model.to('cpu')
-
-        return (result,)
@@ -1,171 +0,0 @@
- 
-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,)
@@ -1,90 +1,15 @@
 import random
 import comfy.utils
-import os
-import numpy as np
-from urllib import request, parse
-import folder_paths
-from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
-from PIL.PngImagePlugin import PngInfo
-
-import hashlib
-import requests
 import json
-
-
-# def queue_prompt(prompt_workflow):
-#     p = {"prompt": prompt_workflow}
-#     data = json.dumps(p).encode('utf-8')
-#     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
+from urllib import request, parse



-def load_json(file_path):
-    try:
-        with open(file_path, 'r') as json_file:
-            data = json.load(json_file)
-            return data
-    except FileNotFoundError:
-        print(f"File not found: {file_path}")
-        return None
-    except json.JSONDecodeError:
-        print(f"Error decoding JSON in file: {file_path}")
-        return None
-
-def save_json(data_dict, file_path):
-    try:
-        with open(file_path, 'w') as json_file:
-            json.dump(data_dict, json_file, indent=4)
-            print(f"Data saved to {file_path}")
-    except Exception as e:
-        print(f"Error saving JSON to file: {e}")
-
-# pysss的lora加载器
-# def get_model_version_info(hash_value):
-#     # http://127.0.0.1:1082
-#     proxies = {'http': 'http://127.0.0.1:1082', 'https': 'https://127.0.0.1:1082'}
-#     api_url = f"https://civitai.com/api/v1/model-versions/by-hash/{hash_value}"
-#     print(api_url)
-#     response = requests.get(api_url,proxies=proxies, verify=False)
-    
-#     if response.status_code == 200:
-#         return response.json()
-#     else:
-#         return None
-    
-# def calculate_sha256(file_path):
-#     sha256_hash = hashlib.sha256()
-#     with open(file_path, "rb") as f:
-#         for chunk in iter(lambda: f.read(4096), b""):
-#             sha256_hash.update(chunk)
-#     return sha256_hash.hexdigest()
-
-
-
-
-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("*")
+def queue_prompt(prompt_workflow):
+    p = {"prompt": prompt_workflow}
+    data = json.dumps(p).encode('utf-8')
+    req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
+    request.urlopen(req)    


 default_prompt1='''Swing
@@ -120,232 +45,6 @@ default_prompt1='''Swing
                                                '''
 default_prompt1="\n".join([p.strip() for p in default_prompt1.split('\n') if p.strip()!=''])

-
-def tensor2pil(image):
-    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-
-def addWeight(text, weight=1):
-    if weight == 1:
-        return text
-    else:
-        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
-    words = [word.strip() for word in sentence.split(",")]
-    
-    # 计算需要删除的单词数量
-    num_to_delete = len(words) - new_words_length
-    
-    words_to=[w for w in words]
-
-    # 逐个删除单词并存储在新列表中
-    new_words = []
-    for i in range(len(words)):
-        if num_to_delete > 0:
-            num_to_delete -= 1
-        else:
-            words_to.pop()
-            if len(words_to)>0:
-                new_words.append(", ".join(words_to))
-         
-    return new_words
-
-# # 测试方法
-# sentence = "a computer, a glass tablet with a keyboard on a dark background, 3d illustration, reflection, cgi 8k, clear glass, archaic, cut-away, white outline"
-# new_words_length = 5
-# result = prompt_delete_words(sentence, new_words_length)
-# print(result)
-
-class PromptImage:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = "PromptImage"
-        self.compress_level = 4
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompts": ("STRING", 
-                         {
-                            "multiline": True, 
-                            "default": '',
-                            "dynamicPrompts": False
-                          }),
-
-                "images": ("IMAGE",{"default": None}), 
-                 "save_to_image": (["enable", "disable"],),
-                }
-            }
-    
-    RETURN_TYPES = ()
-   
-    OUTPUT_NODE = True
-
-    INPUT_IS_LIST = True
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Prompt"
-
-    # 运行的函数
-    def run(self,prompts,images,save_to_image):
-        filename_prefix="mixlab_"
-        filename_prefix += self.prefix_append
-        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])
-        
-        results = list()
-
-        save_to_image=save_to_image[0]=='enable'
-
-        for index in range(len(images)):
-            res=[]
-            imgs=images[index]
-
-            for image in imgs:
-                img=tensor2pil(image)
-
-                metadata = None
-                if save_to_image:
-                    metadata = PngInfo()
-                    prompt_text=prompts[index]
-                    if prompt_text is not None:
-                        metadata.add_text("prompt_text", prompt_text)
-                    
-                file = f"{filename}_{index}_{counter:05}_.png"
-                img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
-                res.append({
-                    "filename": file,
-                    "subfolder": subfolder,
-                    "type": self.type
-                })
-                counter += 1
-            results.append(res)
-        
-        return { "ui": { "_images": results,"prompts":prompts } }
-
-
-
-
-class PromptSimplification:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": ("STRING", 
-                         {
-                            "multiline": True, 
-                            "default": '',
-                            "dynamicPrompts": False
-                          }),
-
-                "length":("INT", {"default": 5, "min": 1,"max":100, "step": 1, "display": "number"}),
-
-                # "min_value":("FLOAT", {
-                #         "default": -2, 
-                #         "min": -10, 
-                #         "max": 0xffffffffffffffff,
-                #         "step": 0.01, 
-                #         "display": "number"  
-                #     }),
-                # "max_value":("FLOAT", {
-                #         "default": 2, 
-                #         "min": -10, 
-                #         "max": 0xffffffffffffffff,
-                #         "step": 0.01, 
-                #         "display": "number"  
-                #     }),
-              
-                }
-            }
-    
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("prompts",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Prompt"
-
-    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,)
-    OUTPUT_NODE = True
-
-    # 运行的函数
-    def run(self,prompt,length):
-        length=length[0]
-        result=[]
-        for p in prompt:
-            nps=prompt_delete_words(p,length)
-            for n in nps:
-                result.append(n)
-
-        result= [elem.strip() for elem in result if elem.strip()]
-
-        return {"ui": {"prompts": result}, "result": (result,)}
-
-
-
-class PromptSlide:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                
-                "prompt_keyword": ("STRING", 
-                         {
-                            "multiline": False, 
-                            "default": '',
-                            "dynamicPrompts": False
-                          }),
-
-                "weight":("FLOAT", {"default": 1, "min": -3,"max": 3,
-                                                                "step": 0.01,
-                                                                "display": "slider"}),
-
-                # "min_value":("FLOAT", {
-                #         "default": -2, 
-                #         "min": -10, 
-                #         "max": 0xffffffffffffffff,
-                #         "step": 0.01, 
-                #         "display": "number"  
-                #     }),
-                # "max_value":("FLOAT", {
-                #         "default": 2, 
-                #         "min": -10, 
-                #         "max": 0xffffffffffffffff,
-                #         "step": 0.01, 
-                #         "display": "number"  
-                #     }),
-              
-                }
-            }
-    
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("prompt",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Prompt"
-
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
-    OUTPUT_NODE = False
-
-    # 运行的函数
-    def run(self,prompt_keyword,weight):
-        # if weight < min_value:
-        #     weight= min_value
-        # elif weight > max_value:
-        #     weight= max_value
-        p=addWeight(prompt_keyword,weight)
-        return (p,)
-
-
-
-
 class RandomPrompt:

    '''
@@ -371,10 +70,6 @@ class RandomPrompt:
                            "default": 'sticker, Cartoon, ``'
                          }),
                "random_sample": (["enable", "disable"],),
-                # "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
-                },
-            "optional":{
-                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                }
            }
    
@@ -384,15 +79,15 @@ class RandomPrompt:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Prompt"
+    CATEGORY = "♾️Mixlab/prompt"

    OUTPUT_IS_LIST = (True,)
    OUTPUT_NODE = True


    # 运行的函数
-    def run(self,max_count,mutable_prompt,immutable_prompt,random_sample,seed=0):
-        # print('#运行的函数',mutable_prompt,immutable_prompt,max_count,random_sample)
+    def run(self,max_count,mutable_prompt,immutable_prompt,random_sample):
+        print('#运行的函数',mutable_prompt,immutable_prompt,max_count,random_sample)
        
        # Split the text into an array of words
        words1 = mutable_prompt.split("\n")
@@ -411,11 +106,6 @@ class RandomPrompt:
            w1=w1.strip()
            for w2 in words2:
                w2=w2.strip()
-                if '``' not in w2:
-                    if w2=="":
-                        w2='``'
-                    else:
-                        w2=w2+',``'
                if w1!='' and w2!='':
                    prompts.append(w2.replace('``', w1))
                pbar.update(1)
@@ -429,148 +119,69 @@ class RandomPrompt:
        else:
            prompts = prompts[:min(max_count,len(prompts))]

-        prompts= [elem.strip() for elem in prompts if elem.strip()]
-
        # return (new_prompt)
        return {"ui": {"prompts": prompts}, "result": (prompts,)}


-# class LoraPrompt:
-#     @classmethod
-#     def INPUT_TYPES(s):
-#         return {
-#             "required": {
-#                 "lora_name":(sorted(folder_paths.get_filename_list("loras"), key=str.lower),),
-#                 "weight": ("FLOAT", {"default": 1, "min": -2, "max": 2,"step":0.01 ,"display": "slider"}),
-#                 "force_update": ("BOOLEAN", {"default": False}),
-#                 },
-                
-#             }
-    
-#     RETURN_TYPES = ("STRING","STRING",any_type)
-#     RETURN_NAMES = ("lora_name","prompt","tags",)
-
-#     FUNCTION = "run"
-
-#     CATEGORY = "♾️Mixlab/Prompt"
-
-#     OUTPUT_IS_LIST = (False,False,True,)
-#     # OUTPUT_NODE = True
-
-#     # 运行的函数
-#     def run(self,lora_name,weight,force_update=False):
-       
-#         # print('##LoraPrompt',__file__)
-#         # 从本地数据库读取
-#         json_tags_path = os.path.join(os.path.dirname(os.path.dirname(__file__)),r'data/loras_tags.json')
-
-#         if not os.path.exists(json_tags_path):
-#             save_json({},json_tags_path)
-
-#         lora_tags = load_json(json_tags_path)
-#         output_tags = lora_tags.get(lora_name, None) if lora_tags is not None else None
-#         if output_tags is not None:
-#             output_tags = ",".join(output_tags)
-#             print("trainedWords:",output_tags)
-#         else:
-#             output_tags = ""
-
-
-#         lora_path = folder_paths.get_full_path("loras", lora_name)
-#         if output_tags == "" or force_update:
-#             print("calculating lora hash")
-#             LORAsha256 = calculate_sha256(lora_path)
-#             print("requesting infos")
-#             model_info = get_model_version_info(LORAsha256)
-#             if model_info is not None:
-#                 if "trainedWords" in model_info:
-#                     print("tags found!")
-#                     if lora_tags is None:
-#                         lora_tags = {}
-#                     lora_tags[lora_name] = model_info["trainedWords"]
-#                     save_json(lora_tags,json_tags_path)
-#                     output_tags = ",".join(model_info["trainedWords"])
-#                     print("trainedWords:",output_tags)
-#             else:
-#                 print("No informations found.")
-#                 if lora_tags is None:
-#                     lora_tags = {}
-#                 lora_tags[lora_name] = []
-#                 save_json(lora_tags,json_tags_path)
-
-
-#         weight = round(weight, 3)
-#         prompt=[]
-#         for p in output_tags.split(','):
-            
-#             if weight!=1:
-#                 prompt.append('('+p+':'+str(weight)+')')
-#             else:
-#                 prompt.append(p)
-
-#         prompt=",".join(prompt)
-
-#         return (lora_name,prompt,output_tags.split(','),)



-class EmbeddingPrompt:
+class RunWorkflow:
    @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"}),
+                "workflow": ("STRING", {
+                            "multiline": False, 
+                            "default": ''
+                          }),
+                "prompt": ("STRING", {
+                            "multiline": False, 
+                            "default": ''
+                          }),
+                 "image": ("IMAGE",),
+                 "input_node": ("STRING", {
+                            "multiline": False, 
+                            "default": ''
+                          }),
+                 "output_node": ("STRING", {
+                            "multiline": False, 
+                            "default": ''
+                          }),
                },
                
            }
    
-    RETURN_TYPES = ("STRING",)
+    
+
+    RETURN_TYPES = ("IMAGE","STRING",)

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Prompt"
+    CATEGORY = "♾️Mixlab/workflow"
+
+    OUTPUT_IS_LIST = (True,)
+    OUTPUT_NODE = True

-    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+' ' 
+    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 (prompt,)
+        return  {"ui":{"images": []},"result": ([image],['text'],)}

-RETURN_TYPES = (any_type,)
-
-class JoinWithDelimiter:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-                    "text_list": (any_type,),
-                    "delimiter":(["newline","comma"],),
-                             },
-                }
-    
-    RETURN_TYPES = ("STRING",) 
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Prompt"
-
-    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,)
@@ -1,694 +0,0 @@
-import os,sys
-import folder_paths
-
-from PIL import Image
-import importlib.util
-
-import comfy.utils
-import numpy as np
-import torch
-
-from huggingface_hub import hf_hub_download
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.transforms.functional import normalize
-# BRIA-RMBG-1.4 / briarmbg.py
-class REBNCONV(nn.Module):
-    def __init__(self,in_ch=3,out_ch=3,dirate=1,stride=1):
-        super(REBNCONV,self).__init__()
-
-        self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate,stride=stride)
-        self.bn_s1 = nn.BatchNorm2d(out_ch)
-        self.relu_s1 = nn.ReLU(inplace=True)
-
-    def forward(self,x):
-
-        hx = x
-        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
-
-        return xout
-
-## upsample tensor 'src' to have the same spatial size with tensor 'tar'
-def _upsample_like(src,tar):
-
-    src = F.interpolate(src,size=tar.shape[2:],mode='bilinear')
-
-    return src
-
-
-### RSU-7 ###
-class RSU7(nn.Module):
-
-    def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
-        super(RSU7,self).__init__()
-
-        self.in_ch = in_ch
-        self.mid_ch = mid_ch
-        self.out_ch = out_ch
-
-        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) ## 1 -> 1/2
-
-        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
-        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
-        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
-        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
-        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
-        self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
-
-        self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
-
-        self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
-
-    def forward(self,x):
-        b, c, h, w = x.shape
-
-        hx = x
-        hxin = self.rebnconvin(hx)
-
-        hx1 = self.rebnconv1(hxin)
-        hx = self.pool1(hx1)
-
-        hx2 = self.rebnconv2(hx)
-        hx = self.pool2(hx2)
-
-        hx3 = self.rebnconv3(hx)
-        hx = self.pool3(hx3)
-
-        hx4 = self.rebnconv4(hx)
-        hx = self.pool4(hx4)
-
-        hx5 = self.rebnconv5(hx)
-        hx = self.pool5(hx5)
-
-        hx6 = self.rebnconv6(hx)
-
-        hx7 = self.rebnconv7(hx6)
-
-        hx6d =  self.rebnconv6d(torch.cat((hx7,hx6),1))
-        hx6dup = _upsample_like(hx6d,hx5)
-
-        hx5d =  self.rebnconv5d(torch.cat((hx6dup,hx5),1))
-        hx5dup = _upsample_like(hx5d,hx4)
-
-        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
-        hx4dup = _upsample_like(hx4d,hx3)
-
-        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
-        hx3dup = _upsample_like(hx3d,hx2)
-
-        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
-        hx2dup = _upsample_like(hx2d,hx1)
-
-        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
-
-        return hx1d + hxin
-
-
-### RSU-6 ###
-class RSU6(nn.Module):
-
-    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
-        super(RSU6,self).__init__()
-
-        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
-
-        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
-        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
-        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
-        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
-        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
-
-        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
-
-        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
-
-    def forward(self,x):
-
-        hx = x
-
-        hxin = self.rebnconvin(hx)
-
-        hx1 = self.rebnconv1(hxin)
-        hx = self.pool1(hx1)
-
-        hx2 = self.rebnconv2(hx)
-        hx = self.pool2(hx2)
-
-        hx3 = self.rebnconv3(hx)
-        hx = self.pool3(hx3)
-
-        hx4 = self.rebnconv4(hx)
-        hx = self.pool4(hx4)
-
-        hx5 = self.rebnconv5(hx)
-
-        hx6 = self.rebnconv6(hx5)
-
-
-        hx5d =  self.rebnconv5d(torch.cat((hx6,hx5),1))
-        hx5dup = _upsample_like(hx5d,hx4)
-
-        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
-        hx4dup = _upsample_like(hx4d,hx3)
-
-        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
-        hx3dup = _upsample_like(hx3d,hx2)
-
-        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
-        hx2dup = _upsample_like(hx2d,hx1)
-
-        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
-
-        return hx1d + hxin
-
-### RSU-5 ###
-class RSU5(nn.Module):
-
-    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
-        super(RSU5,self).__init__()
-
-        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
-
-        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
-        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
-        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
-        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
-
-        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
-
-        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
-
-    def forward(self,x):
-
-        hx = x
-
-        hxin = self.rebnconvin(hx)
-
-        hx1 = self.rebnconv1(hxin)
-        hx = self.pool1(hx1)
-
-        hx2 = self.rebnconv2(hx)
-        hx = self.pool2(hx2)
-
-        hx3 = self.rebnconv3(hx)
-        hx = self.pool3(hx3)
-
-        hx4 = self.rebnconv4(hx)
-
-        hx5 = self.rebnconv5(hx4)
-
-        hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
-        hx4dup = _upsample_like(hx4d,hx3)
-
-        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
-        hx3dup = _upsample_like(hx3d,hx2)
-
-        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
-        hx2dup = _upsample_like(hx2d,hx1)
-
-        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
-
-        return hx1d + hxin
-
-### RSU-4 ###
-class RSU4(nn.Module):
-
-    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
-        super(RSU4,self).__init__()
-
-        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
-
-        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
-        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
-        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
-
-        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
-
-        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
-        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
-
-    def forward(self,x):
-
-        hx = x
-
-        hxin = self.rebnconvin(hx)
-
-        hx1 = self.rebnconv1(hxin)
-        hx = self.pool1(hx1)
-
-        hx2 = self.rebnconv2(hx)
-        hx = self.pool2(hx2)
-
-        hx3 = self.rebnconv3(hx)
-
-        hx4 = self.rebnconv4(hx3)
-
-        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
-        hx3dup = _upsample_like(hx3d,hx2)
-
-        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
-        hx2dup = _upsample_like(hx2d,hx1)
-
-        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
-
-        return hx1d + hxin
-
-### RSU-4F ###
-class RSU4F(nn.Module):
-
-    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
-        super(RSU4F,self).__init__()
-
-        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
-
-        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
-        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
-        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
-
-        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
-
-        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
-        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
-        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
-
-    def forward(self,x):
-
-        hx = x
-
-        hxin = self.rebnconvin(hx)
-
-        hx1 = self.rebnconv1(hxin)
-        hx2 = self.rebnconv2(hx1)
-        hx3 = self.rebnconv3(hx2)
-
-        hx4 = self.rebnconv4(hx3)
-
-        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
-        hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
-        hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
-
-        return hx1d + hxin
-
-
-class myrebnconv(nn.Module):
-    def __init__(self, in_ch=3,
-                       out_ch=1,
-                       kernel_size=3,
-                       stride=1,
-                       padding=1,
-                       dilation=1,
-                       groups=1):
-        super(myrebnconv,self).__init__()
-
-        self.conv = nn.Conv2d(in_ch,
-                              out_ch,
-                              kernel_size=kernel_size,
-                              stride=stride,
-                              padding=padding,
-                              dilation=dilation,
-                              groups=groups)
-        self.bn = nn.BatchNorm2d(out_ch)
-        self.rl = nn.ReLU(inplace=True)
-
-    def forward(self,x):
-        return self.rl(self.bn(self.conv(x)))
-
-
-class BriaRMBG(nn.Module):
-
-    def __init__(self,in_ch=3,out_ch=1):
-        super(BriaRMBG,self).__init__()
-
-        self.conv_in = nn.Conv2d(in_ch,64,3,stride=2,padding=1)
-        self.pool_in = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.stage1 = RSU7(64,32,64)
-        self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.stage2 = RSU6(64,32,128)
-        self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.stage3 = RSU5(128,64,256)
-        self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.stage4 = RSU4(256,128,512)
-        self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.stage5 = RSU4F(512,256,512)
-        self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
-        self.stage6 = RSU4F(512,256,512)
-
-        # decoder
-        self.stage5d = RSU4F(1024,256,512)
-        self.stage4d = RSU4(1024,128,256)
-        self.stage3d = RSU5(512,64,128)
-        self.stage2d = RSU6(256,32,64)
-        self.stage1d = RSU7(128,16,64)
-
-        self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
-        self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
-        self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
-        self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
-        self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
-        self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
-
-        # self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
-
-    def forward(self,x):
-
-        hx = x
-
-        hxin = self.conv_in(hx)
-        #hx = self.pool_in(hxin)
-
-        #stage 1
-        hx1 = self.stage1(hxin)
-        hx = self.pool12(hx1)
-
-        #stage 2
-        hx2 = self.stage2(hx)
-        hx = self.pool23(hx2)
-
-        #stage 3
-        hx3 = self.stage3(hx)
-        hx = self.pool34(hx3)
-
-        #stage 4
-        hx4 = self.stage4(hx)
-        hx = self.pool45(hx4)
-
-        #stage 5
-        hx5 = self.stage5(hx)
-        hx = self.pool56(hx5)
-
-        #stage 6
-        hx6 = self.stage6(hx)
-        hx6up = _upsample_like(hx6,hx5)
-
-        #-------------------- decoder --------------------
-        hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
-        hx5dup = _upsample_like(hx5d,hx4)
-
-        hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
-        hx4dup = _upsample_like(hx4d,hx3)
-
-        hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
-        hx3dup = _upsample_like(hx3d,hx2)
-
-        hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
-        hx2dup = _upsample_like(hx2d,hx1)
-
-        hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
-
-
-        #side output
-        d1 = self.side1(hx1d)
-        d1 = _upsample_like(d1,x)
-
-        d2 = self.side2(hx2d)
-        d2 = _upsample_like(d2,x)
-
-        d3 = self.side3(hx3d)
-        d3 = _upsample_like(d3,x)
-
-        d4 = self.side4(hx4d)
-        d4 = _upsample_like(d4,x)
-
-        d5 = self.side5(hx5d)
-        d5 = _upsample_like(d5,x)
-
-        d6 = self.side6(hx6)
-        d6 = _upsample_like(d6,x)
-
-        return [F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)],[hx1d,hx2d,hx3d,hx4d,hx5d,hx6]
-    
-
-
-
-
-
-
-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 briarmbg_run(images=[]):
-    mroot=os.path.join(folder_paths.models_dir, "rembg")
-    m=os.path.join(mroot,'briarmbg.pth')
-    if os.path.exists(m)==False:
-        # 下载
-        m1=hf_hub_download("briaai/RMBG-1.4",
-                       local_dir=mroot,
-                       filename='model.pth',
-                       local_dir_use_symlinks=False,
-                       endpoint='https://hf-mirror.com')
-        os.rename(m1, m)
-    
-    net=BriaRMBG()
-    if torch.cuda.is_available():
-        net.load_state_dict(torch.load(m))
-        net=net.cuda()
-    else:
-        net.load_state_dict(torch.load(m,map_location="cpu"))
-    net.eval() 
-
-    masks=[]
-    rgba_images=[]
-    rgb_images=[]
-    for orig_image in images:
-
-        w,h = orig_im_size = orig_image.size
-
-        image = orig_image.convert('RGB')
-        model_input_size = (1024, 1024)
-        image = image.resize(model_input_size, Image.BILINEAR)
-
-        im_np = np.array(image)
-        im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1)
-        im_tensor = torch.unsqueeze(im_tensor,0)
-        im_tensor = torch.divide(im_tensor,255.0)
-        im_tensor = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0])
-        if torch.cuda.is_available():
-            im_tensor=im_tensor.cuda()
-
-        result=net(im_tensor)
-        result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
-        ma = torch.max(result)
-        mi = torch.min(result)
-        result = (result-mi)/(ma-mi)
-        im_array = (result*255).cpu().data.numpy().astype(np.uint8)
-        mask = Image.fromarray(np.squeeze(im_array))
-        # mask.save('test.png')
-        # mask=tensor2pil(result)
-        mask=mask.convert('L') 
-
-        masks.append(mask)
-
-        # rgba图
-        image_rgba =orig_image.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)
-    return (masks,rgba_images,rgb_images)   
-
-
-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": ([
-                            "briarmbg",
-                            "u2net",
-                            "u2netp",
-                            "u2net_human_seg",
-                            "u2net_cloth_seg",
-                            "silueta",
-                            "isnet-general-use",
-                            "isnet-anime",
-                            
-                            ],),
-            
-                             },
-                }
-    
-    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)
-
-        if model_name=='briarmbg':
-            masks,rgba_images,rgb_images=briarmbg_run(images)
-        else:
-            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,)
@@ -93,7 +93,7 @@ class ScreenShareNode:
    RETURN_NAMES = ("IMAGE","PROMPT","FLOAT","INT")
    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Image"
+    CATEGORY = "♾️Mixlab/image"

    # INPUT_IS_LIST = True
    OUTPUT_IS_LIST = (False,False,False,False)
@@ -118,7 +118,7 @@ class FloatingVideo:
    OUTPUT_NODE = True
    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Image"
+    CATEGORY = "♾️Mixlab/image"

    # INPUT_IS_LIST = True
    # OUTPUT_IS_LIST = (False,False,)
@@ -0,0 +1,37 @@
+
+import urllib.parse
+
+
+# 分享到微博
+class ShareToWeibo:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "title":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
+                "pic_url":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                "url":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = ()
+    # RETURN_NAMES = ("number",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/share"
+
+    INPUT_IS_LIST = False
+    OUTPUT_NODE = True
+    # OUTPUT_IS_LIST = ()
+
+    def run(self, title, pic_url, url):
+        encoded_title = urllib.parse.quote(title)
+        encoded_pic_url = urllib.parse.quote(pic_url)
+        encoded_url = urllib.parse.quote(url)
+        url = "https://service.weibo.com/share/share.php?title={}&pic={}&url={}".format(encoded_title,encoded_pic_url,encoded_url)
+        print(url)
+        return {"ui": {"url": [url]}, "result": ()}
+    
+
+
@@ -1,307 +0,0 @@
-from transformers import pipeline, set_seed,AutoTokenizer, AutoModelForSeq2SeqLM
-import random
-import re
-
-import os,sys
-import folder_paths
-
-# from PIL import Image
-import importlib.util
-
-import comfy.utils
-# import numpy as np
-import torch
-import random
-
-global _available
-_available=True
-
-
-text_generator_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/text2image-prompt-generator")
-if not os.path.exists(text_generator_model_path):
-    print(f"## text_generator_model not found: {text_generator_model_path}, pls download from https://huggingface.co/succinctly/text2image-prompt-generator/tree/main")
-    text_generator_model_path='succinctly/text2image-prompt-generator'
-
-zh_en_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/opus-mt-zh-en")
-if not os.path.exists(zh_en_model_path):
-    print(f"## zh_en_model not found: {zh_en_model_path}, pls download from https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main")
-    zh_en_model_path='Helsinki-NLP/opus-mt-zh-en'
-
-
-
-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([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)[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:
-        seed = random.randint(100, 1000000)
-    
-    set_seed(seed)
-
-    for count in range(6):    
-        sequences = text_pipe(input, max_length=random.randint(60, 90), num_return_sequences=8)
-        list = []
-        for sequence in sequences:
-            line = sequence['generated_text'].strip()
-            if line != input and len(line) > (len(input) + 4) and line.endswith((":", "-", "—")) is False:
-                list.append(line)
-
-        result = "\n".join(list)
-        result = re.sub('[^ ]+\.[^ ]+','', result)
-        result = result.replace("<", "").replace(">", "")
-        if result != "":
-            return result
-        if count == 5:
-            return result
-
-# 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:
-
-    global _available
-    available=_available
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-               "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
-               "generation": (["on","off"],{"default": "off"}), 
-                             },
-               
-                "optional":{
-                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
-                    
-                },
-
-                }
-    
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("prompt",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Prompt"
-    OUTPUT_NODE = True
-    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,)
-
-    global text_pipe,zh_en_model,zh_en_tokenizer
-
-    text_pipe= None
-    zh_en_model=None
-    zh_en_tokenizer=None 
-
-    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,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")
-        
-        text_pipe=pipeline('text-generation', model=text_generator_model_path,device="cuda" if torch.cuda.is_available() else "cpu")
-        
-        # text_pipe.model.to("cuda" if torch.cuda.is_available() else "cpu")
-
-        prompt_result=[]
-
-        # print('zh_en_model device',zh_en_model.device,text_pipe.model.device,torch.cuda.current_device() )
-        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_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
-                },
-            "result": (prompt_result,)}
-
-
-class PromptGenerate:
-
-    global _available
-    available=_available
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
-                             },
-
-                "optional":{
-                    "multiple": (["off","on"],), 
-                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
-                },
-
-                }
-    
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("prompt",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Prompt"
-    OUTPUT_NODE = True
-    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,)
-
-    global text_pipe
-
-    text_pipe= None
-    # 
-
-    def run(self,text,multiple,seed):
-        global text_pipe
-
-        seed=seed[0]
-
-        multiple=multiple[0]
-
-        # 进度条
-        pbar = comfy.utils.ProgressBar(len(text))
-        
-        text_pipe=pipeline('text-generation', model=text_generator_model_path,device="cuda" if torch.cuda.is_available() else "cpu")
-        
-        prompt_result=[]
-
-        for t in text:
-            prompt =text_generate(text_pipe,t,seed)
-            prompt = prompt.split("\n")
-            if multiple=='off':
-                prompt = [max(prompt, key=len)]
-            
-            for p in prompt:
-                prompt_result.append(p)
-            pbar.update(1)
-
-        text_pipe.model.to('cpu')
-
-        return {
-            "ui":{
-                    "prompt": prompt_result
-                },
-            "result": (prompt_result,)}
@@ -1,58 +1,10 @@
-import os,platform
-import re,random,json
+import os
+import re,random
 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 torch
-
-
-
-def recursive_search(directory, excluded_dir_names=None):
-    if not os.path.isdir(directory):
-        return [], {}
-
-    if excluded_dir_names is None:
-        excluded_dir_names = []
-
-    result = []
-    dirs = {directory: os.path.getmtime(directory)}
-    for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
-        subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
-        for file_name in filenames:
-            relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
-            result.append(relative_path)
-        for d in subdirs:
-            path = os.path.join(dirpath, d)
-            dirs[path] = os.path.getmtime(path)
-    return result, dirs
-
-def filter_files_extensions(files, extensions):
-    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))
-
-
-def get_system_font_path():
-    ps=[]
-    system = platform.system()
-    if system == "Windows":
-        ps.append(os.path.join(os.environ["WINDIR"], "Fonts"))
-    elif system == "Darwin":
-        ps.append(os.path.join("/Library", "Fonts"))
-    elif system == "Linux":
-        ps.append(os.path.join("/usr", "share", "fonts"))
-        ps.append(os.path.join("/usr", "local", "share", "fonts"))
-    ps=[p for p in ps if os.path.exists(p)]
-    file_paths=[]
-    for f in ps:
-        result, dirs=recursive_search(f)
-        for r in result:
-            file_paths.append(r)
-    file_paths=filter_files_extensions(file_paths,[".otf", ".ttf"])
-
-    return file_paths
-
-

 # import json
 # import hashlib
@@ -82,13 +34,13 @@ def create_temp_file(image):
        ) = folder_paths.get_save_image_path('tmp', output_dir)

    
-    im=tensor2pil(image)
+    image=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)
+    image.save(image_path,compress_level=4)

    return [{
                "filename": image_file,
@@ -108,14 +60,14 @@ def get_font_files(directory):

    # 尝试获取系统字体
    try:
-        font_paths = get_system_font_path()
-        for file in font_paths:
+        font_paths = fm.findSystemFonts()
+        for path in font_paths:
            try:
-                font_name = os.path.splitext(file)[0]
-                font_path = file
-                font_files[font_name] = os.path.abspath(font_path)
+                font_prop = fm.FontProperties(fname=path)
+                font_name = font_prop.get_name()
+                font_files[font_name] = path
            except Exception as e:
-                print(f"Error processing font {file}: {e}")
+                print(f"Error processing font {path}: {e}")
    except Exception as e:
        print(f"Error finding system fonts: {e}")

@@ -127,21 +79,6 @@ font_files = get_font_files(r_directory)
 # print(font_files)


-def flatten_list(nested_list):
-    flat_list = []
-    for item in nested_list:
-        if isinstance(item, list):
-            flat_list.extend(flatten_list(item))
-        else:
-            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
-
-
 class ColorInput:
    @classmethod
    def INPUT_TYPES(s):
@@ -151,23 +88,18 @@ class ColorInput:
                             },
                }
    
-    RETURN_TYPES = ("STRING","INT","INT","INT","FLOAT",)
-    RETURN_NAMES = ("hex","r","g","b","a",)
+    RETURN_TYPES = ("STRING",)
+    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/utils"

    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,False,False,False,False,)
+    OUTPUT_IS_LIST = (False,False,)

    def run(self,color):
-        h=color['hex']
-        r=color['r']
-        g=color['g']
-        b=color['b']
-        a=color['a']
-        return (h,r,g,b,a,)
+        return (color,)



@@ -185,13 +117,13 @@ class FontInput:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/utils"

    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
+    OUTPUT_IS_LIST = (False,False,)

    def run(self,font):
-        
+
        return (font_files[font],)
    
 class TextToNumber:
@@ -200,17 +132,14 @@ class TextToNumber:
        return {"required": { 
                    "text": ("STRING",{"multiline": False,"default": "1"}),
                    "random_number": (["enable", "disable"],),
-                    "max_num":("INT", {
-                        "default": 10, 
-                        "min":2, #Minimum value
+                    "number":("INT", {
+                        "default": 0, 
+                        "min": 0, #Minimum value
                        "max": 10000000000, #Maximum value
                        "step": 1, #Slider's step
                        "display": "number" # Cosmetic only: display as "number" or "slider"
                    }),
                             },
-                 "optional":{
-                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                }
                }
    
    RETURN_TYPES = ("INT",)
@@ -218,12 +147,12 @@ class TextToNumber:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/utils"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)

-    def run(self,text,random_number,max_num,seed=0):
+    def run(self,text,random_number,number):
        
        numbers = re.findall(r'\d+', text)
        result=0
@@ -232,7 +161,7 @@ class TextToNumber:
            # print(result)
        
        if random_number=='enable' and result>0:
-            result= random.randint(1, max_num)
+            result= random.randint(1, 10000000000)
        return {"ui": {"text": [text],"num":[result]}, "result": (result,)}
    

@@ -244,31 +173,10 @@ class FloatSlider:
                    "number":("FLOAT", {
                        "default": 0, 
                        "min": 0, #Minimum value
-                        "max": 0xffffffffffffffff, #Maximum value
+                        "max": 1, #Maximum value
                        "step": 0.001, #Slider's step
                        "display": "slider" # Cosmetic only: display as "number" or "slider"
                    }),
-                    "min_value":("FLOAT", {
-                        "default": 0, 
-                        "min": -0xffffffffffffffff, 
-                        "max": 0xffffffffffffffff,
-                        "step": 0.001, 
-                        "display": "number"  
-                    }),
-                    "max_value":("FLOAT", {
-                            "default": 1, 
-                            "min": -0xffffffffffffffff,
-                            "max": 0xffffffffffffffff,
-                            "step": 0.001, 
-                            "display": "number"  
-                        }),
-                    "step":("FLOAT", {
-                            "default": 0.001, 
-                            "min": -0xffffffffffffffff,
-                            "max": 0xffffffffffffffff,
-                            "step": 0.001, 
-                            "display": "number"  
-                        }),
                             },
                }
    
@@ -276,18 +184,14 @@ class FloatSlider:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/utils"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)

-    def run(self, number, min_value, max_value, step):
-        if number < min_value:
-            number = min_value
-        elif number > max_value:
-            number = max_value
-        scaled_number = (number - min_value) / (max_value - min_value)
-        return (scaled_number,)
+    def run(self,number):
+       
+        return (number,)
    
  
 class IntNumber:
@@ -298,30 +202,9 @@ class IntNumber:
                        "default": 0, 
                        "min": -1, #Minimum value
                        "max": 0xffffffffffffffff,
-                        "step": 1, 
-                        "display": "number"
+                        "step": 1, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
                    }),
-                    "min_value":("INT", {
-                        "default": 0, 
-                        "min": -0xffffffffffffffff, 
-                        "max": 0xffffffffffffffff,
-                        "step": 1, 
-                        "display": "number"  
-                    }),
-                    "max_value":("INT", {
-                            "default": 1, 
-                            "min": -0xffffffffffffffff,
-                            "max": 0xffffffffffffffff,
-                            "step": 1, 
-                            "display": "number"  
-                        }),
-                    "step":("INT", {
-                            "default": 1, 
-                            "min": -0xffffffffffffffff,
-                            "max": 0xffffffffffffffff,
-                            "step":1,
-                            "display": "number"  
-                        }),
                             },
                }
    
@@ -329,16 +212,13 @@ class IntNumber:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/utils"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)

-    def run(self,number,min_value,max_value,step):
-        if number < min_value:
-            number= min_value
-        elif number > max_value:
-            number= max_value
+    def run(self,number):
+       
        return (number,)

 class MultiplicationNode:
@@ -346,18 +226,11 @@ class MultiplicationNode:
    def INPUT_TYPES(s):
        return {"required": {
                    "numberA":(any_type,),
-                    "multiply_by":("FLOAT", {
+                    "numberB":("FLOAT", {
                        "default": 0, 
-                        "min": -2, #Minimum value
+                        "min": -1, #Minimum value
                        "max": 0xffffffffffffffff,
-                        "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
+                        "step": 0.1, #Slider's step
                        "display": "number" # Cosmetic only: display as "number" or "slider"
                    })
                             },
@@ -367,21 +240,21 @@ class MultiplicationNode:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/utils"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,False,)

-    def run(self,numberA,multiply_by,add_by):
-        b=int(numberA*multiply_by+add_by)
-        a=float(numberA*multiply_by+add_by)
+    def run(self,numberA,numberB):
+        b=int(numberA*numberB)
+        a=float(numberA*numberB)
        return (a,b,) 

 class TextInput:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
-                    "text": ("STRING",{"multiline": True,"default": ""})
+                    "text": ("STRING",{"multiline": True,"default": ""}),
                             },
                }
    
@@ -389,7 +262,7 @@ class TextInput:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/utils"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -430,7 +303,7 @@ class DynamicDelayProcessor:
            },
            "optional":{
                "any_input":(any_type,),
-                "delay_by_text":("STRING",{"multiline":True,"dynamicPrompts": False,}),
+                "delay_by_text":("STRING",{"multiline":True,}),
                "words_per_seconds":("FLOAT",{ "default":1.50,"min": 0.0,"max": 1000.00,"display":"Chars per second?"}),
                "replace_output": (["disable","enable"],),
                "replace_value":("INT",{ "default":-1,"min": 0,"max": 1000000,"display":"Replacement value"})
@@ -463,7 +336,7 @@ class DynamicDelayProcessor:
    RETURN_TYPES = (any_type,)
    RETURN_NAMES = ('output',)

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/utils"
    def run(self,any_input,delay_seconds,delay_by_text,words_per_seconds,replace_output,replace_value):
        # print(f"Delay text:",delay_by_text )
        # 获取开始时间戳
@@ -496,14 +369,14 @@ 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}),
+                    "name": ("STRING",{"multiline": False,"default": "Mixlab-App"}),
+                    "image": ("IMAGE",),
+                    "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"])}),
+                    "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"])}),
                             },

                "optional":{
-                    "IMAGE": ("IMAGE",),
-                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
+                    "description":("STRING",{"multiline": True,"default": ""}),
                    "version":("INT", {
                        "default": 1, 
                        "min": 1, 
@@ -511,50 +384,53 @@ class AppInfo:
                        "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",)
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("IMAGE",)

    FUNCTION = "run"

    CATEGORY = "♾️Mixlab"

+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
    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，
+    def run(self,name,image,input_ids,output_ids,description,version):

-        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]
+        im=create_temp_file(image)
        
        # 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": ()}
+        return {"ui": {"json": [name,im,input_ids,output_ids,description,version]}, "result": (image,)}
    

    

+class GetImageSize_:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("INT", "INT")
+    RETURN_NAMES = ("width", "height")
+
+    FUNCTION = "get_size"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    def get_size(self, image):
+        _, height, width, _ = image.shape
+        return (width, height)
+
+

 class SwitchByIndex:
    @classmethod
@@ -570,7 +446,6 @@ class SwitchByIndex:
                        "step": 1, 
                        "display": "number"  
                    }),
-                 "flat": (['off',"on"],),
            }
        }

@@ -579,32 +454,23 @@ class SwitchByIndex:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/utils"

    INPUT_IS_LIST = True
    OUTPUT_IS_LIST = (True,)

-    def run(self, A,B,index,flat):
-
-        flat=flat[0]
-
+    def run(self, A,B,index):
        C=[]
        index=index[0]
-
-        for a in A:    
+        for a in A:
            C.append(a)
        for b in B:
            C.append(b)
-
-        if flat=='on':
-            C=flatten_list(C)
-
        if index>-1:
            try:
                C=[C[index]]
            except Exception as e:
                C=[]
-        
        return (C,)


@@ -637,7 +503,7 @@ class LimitNumber:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/utils"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -660,208 +526,4 @@ class LimitNumber:
        return (nn,)


-
-class ListStatistics:
-    @staticmethod
-    def count_types(lst):
-        type_count = {}
-
-        for item in lst:
-            item_type = type(item).__name__
-            if item_type not in type_count:
-                type_count[item_type] = []
-
-            if item_type in ['dict', 'str', 'int', 'float']:
-                type_count[item_type].append(item)
-
-        return type_count
-
-# # 示例列表
-# my_list = [1, 'hello', {'name': 'John'}, 3.14, {'age': 25}, 'world', 10]
-
-# # 创建ListStatistics对象
-# list_stats = ListStatistics()
-
-# # 调用count_types方法进行统计
-# result = list_stats.count_types(my_list)
-
-# # 输出结果
-# for item_type, values in result.items():
-#     print(item_type + ':')
-#     for value in values:
-#         print(value)
-#     print('---')
-
-class TESTNODE_:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-            "ANY":(any_type,),                
-                              },
-                }
-    
-    RETURN_TYPES = (any_type,)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/__TEST"
-
-    OUTPUT_NODE = True
-    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,)
-
-    def run(self,ANY):
-        print(ANY)
-        # data=ANY
-        list_stats = ListStatistics()
-
-        # 调用count_types方法进行统计
-        result = list_stats.count_types(ANY)
-
-
-        return {"ui": {"data": result,"type":[str(type(ANY[0]))]}, "result": (ANY,)}
-
-
-class TESTNODE_TOKEN:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-            "text":("STRING", {"forceInput": True,}),
-            "clip": ("CLIP", )                 
-                              },
-                }
-    
-    RETURN_TYPES = ("STRING",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/__TEST"
-
-    OUTPUT_NODE = True
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
-
-    def run(self,text,clip=None):
-        # print(text)
-        
-        tokens = clip.tokenize(text)
-        
-        tokens=[v for v in tokens.values()][0][0]
-
-        tokens=json.dumps(tokens)
-        
-        return (tokens,)
-
-
-
-class CreateSeedNode:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-            }
-        }
-
-    RETURN_TYPES = ("INT",)
-    RETURN_NAMES = ("seed",)
-  
-    OUTPUT_NODE = True
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Utils"
-
-    def run(self, seed):
-        return (seed,)
-    
-
-class CreateCkptNames:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "ckpt_names": ("STRING",{"multiline": True,"default": "\n".join(folder_paths.get_filename_list("checkpoints")),"dynamicPrompts": False}),
-            }
-        }
-
-    RETURN_TYPES = (any_type,)
-    RETURN_NAMES = ("ckpt_names",)
-    
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (True,)
-
-    # OUTPUT_NODE = True
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Utils"
-
-    def run(self, ckpt_names):
-        ckpt_names=ckpt_names.split('\n')
-        ckpt_names = [name for name in ckpt_names if name.strip()]
-        return (ckpt_names,)
-
-
-class CreateLoraNames:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "lora_names": ("STRING",{"multiline": True,"default": "\n".join(folder_paths.get_filename_list("loras")),"dynamicPrompts": False}),
-            }
-        }
-
-    RETURN_TYPES = (any_type,"STRING",)
-    RETURN_NAMES = ("lora_names","prompt",)
-    
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (True,True,)
-
-    # OUTPUT_NODE = True
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Utils"
-
-    def run(self, lora_names):
-        lora_names=lora_names.split('\n')
-        lora_names = [name for name in lora_names if name.strip()]
-        prompts=[os.path.splitext(n)[0] for n in lora_names]
-        return (lora_names,prompts,)
-   
-
-
-class CreateSampler_names:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "sampler_names": ("STRING",{"multiline": True,"default": "\n".join(comfy.samplers.KSampler.SAMPLERS),"dynamicPrompts": False}),
-            }
-        }
-
-    RETURN_TYPES = (any_type,)
-    RETURN_NAMES = ("sampler_names",)
-    
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (True,)
-
-    # OUTPUT_NODE = True
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Utils"
-
-    def run(self, sampler_names):
-        sampler_names=sampler_names.split('\n')
-        sampler_names = [name for name in sampler_names if name.strip()]
-        return (sampler_names,)
+ 
@@ -0,0 +1,179 @@
+# 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, )
@@ -4,6 +4,4 @@ watchdog
 opencv-python-headless
 matplotlib
 openai
-simple-lama-inpainting
-clip-interrogator==0.6.0
-transformers>=4.36.0
+# playwright
@@ -196,9 +196,6 @@ app.registerExtension({
              }
              if (bg) {
                data.bg_image = await parseImage(bg)
-                if (!data.bg_image.match('data:image/')) {
-                  delete data.bg_image
-                }
              }

              if (material) {
@@ -67,65 +67,25 @@ async function drawImageToCanvas (imageUrl) {
 }

 function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
-  // workflow
-  // const workflow=jsonData.workflow;
-  // const nodes=workflow.nodes;
-
-  const data = jsonData.output
-  let input = []
-  let output = []
-  const seed = {}
+  const data = jsonData
+  const input = []
+  const output = []

  for (const id in data) {
    if (data.hasOwnProperty(id)) {
-      let node = app.graph.getNodeById(id)
      if (inputIds.includes(id)) {
-        // let node = app.graph.getNodeById(id)
-        let options = {}
+        let node = app.graph.getNodeById(id)
+        let options = []
        // 模型
        try {
          if (node.type === 'CheckpointLoaderSimple') {
            options = node.widgets.filter(w => w.name === 'ckpt_name')[0]
              .options.values
-          } else if (node.type === 'LoraLoader') {
-            options = node.widgets.filter(w => w.name === 'lora_name')[0]
-              .options.values
+          }else if(node.type === 'LoraLoader'){
+            options =node.widgets.filter(w=>w.name==='lora_name')[0].options.values
          }
        } catch (error) {}

-        if (node.type == 'IntNumber' || node.type == 'FloatSlider') {
-          // min max step
-          let [v, min, max, step] = Array.from(node.widgets, w => w.value)
-          options = { min, max, step }
-          // node.widgets.filter(w => w.type === 'number')[0].options
-        }
-
-        if (node.type == 'PromptSlide') {
-          // min max step
-          options = node.widgets.filter(w => w.type === 'slider')[0].options
-          // 备选的keywords清单
-          try {
-            let keywords = node.widgets.filter(w => w.name === 'upload')[0]
-              .value
-            keywords = JSON.parse(keywords)
-            options.keywords = keywords
-          } catch (error) {
-            console.log(error)
-          }
-        }
-
-        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,
@@ -135,27 +95,14 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
        // input.push()
      }
      if (outputIds.includes(id)) {
-        // let node = app.graph.getNodeById(id)
+        let node = app.graph.getNodeById(id)
        // output.push()
        output[outputIds.indexOf(id)] = { ...data[id], title: node.title, id }
      }
-
-      if (node.type === 'KSampler' || node.type == 'SamplerCustom') {
-        // seed 的类型收集
-        try {
-          seed[id] = node.widgets.filter(
-            w => w.name === 'seed' || w.name == 'noise_seed'
-          )[0].linkedWidgets[0].value
-        } catch (error) {}
-      }
    }
  }

-  // 修复bug，当节点不存在时
-  input = input.filter(i => i)
-  output = output.filter(i => i)
-
-  return { input, output, seed }
+  return { input, output }
 }

 function getUrl () {
@@ -165,16 +112,6 @@ function getUrl () {
  return url
 }

-const getLocalData = key => {
-  let data = {}
-  try {
-    data = JSON.parse(localStorage.getItem(key)) || {}
-  } catch (error) {
-    return {}
-  }
-  return data
-}
-
 async function save_app (json) {
  let url = getUrl()

@@ -182,9 +119,7 @@ async function save_app (json) {
    method: 'POST',
    body: JSON.stringify({
      data: json,
-      task: 'save_app',
-      filename: json.app.filename,
-      category: json.app.category
+      task: 'save_app'
    })
  })
  return await res.json()
@@ -206,13 +141,9 @@ function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
  }, 0)
 }

-async function save (json, download = false, showInfo = true) {
-  console.log('####SAVE', json[0])
+async function save (json, download = false) {
  const name = json[0],
    version = json[5],
-    share_prefix = json[6], //用于分享的功能扩展
-    link = json[7], //用于创建界面上的跳转链接
-    category = json[8] || '', //用于分类
    description = json[4],
    inputIds = json[2].split('\n').filter(f => f),
    outputIds = json[3].split('\n').filter(f => f)
@@ -228,8 +159,8 @@ async function save (json, download = false, showInfo = true) {
  try {
    let data = await app.graphToPrompt()

-    let { input, output, seed } = extractInputAndOutputData(
-      data,
+    const { input, output } = extractInputAndOutputData(
+      data.output,
      inputIds,
      outputIds
    )
@@ -239,12 +170,7 @@ async function save (json, download = false, showInfo = true) {
      description,
      version,
      input,
-      output,
-      seed, //控制是fixed 还是random
-      share_prefix,
-      link,
-      category,
-      filename: `${name}_${version}.json`
+      output
    }

    try {
@@ -252,54 +178,26 @@ async function save (json, download = false, showInfo = true) {
    } catch (error) {}
    // console.log(data.app)
    // let http_workflow = app.graph.serialize()
-    await save_app(data)
-    if (download) {
-      await downloadJsonFile(data, data.app.filename)
-    }

-    if (showInfo) {
-      let open = window.confirm(
-        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app?filename=${encodeURIComponent(
-          data.app.filename
-        )}&category=${encodeURIComponent(data.app.category)}`
+    if (download) {
+      await downloadJsonFile(
+        data,
+        `${data.app.name}_${data.app.version}_${new Date().toDateString()}.json`
      )
-      if (open)
-        window.open(
-          `${getUrl()}/mixlab/app?filename=${encodeURIComponent(
-            data.app.filename
-          )}&category=${encodeURIComponent(data.app.category)}`
-        )
+      let open = window.confirm(
+        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app?type=new`
+      )
+      if (open) window.open(`${getUrl()}/mixlab/app?type=new`)
+    } else {
+      await save_app(data)
+
+      let open = window.confirm(
+        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app`
+      )
+      if (open) window.open(`${getUrl()}/mixlab/app`)
    }
  } catch (error) {
-    console.log('###error', error)
-  }
-}
-
-function getInputsAndOutputs () {
-  const inputs =
-      `LoadImage VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
-        ' '
-      ),
-    outputs = `PreviewImage SaveImage ShowTextForGPT VHS_VideoCombine`.split(
-      ' '
-    )
-
-  let inputsId = [],
-    outputsId = []
-
-  for (let node of app.graph._nodes) {
-    if (inputs.includes(node.type)) {
-      inputsId.push(node.id)
-    }
-
-    if (outputs.includes(node.type)) {
-      outputsId.push(node.id)
-    }
-  }
-
-  return {
-    input: inputsId,
-    output: outputsId
+    console.log('###SpeechRecognition', error)
  }
 }

@@ -310,16 +208,7 @@ app.registerExtension({
      const orig_nodeCreated = nodeType.prototype.onNodeCreated
      nodeType.prototype.onNodeCreated = function () {
        orig_nodeCreated?.apply(this, arguments)
-        // console.log('#orig_nodeCreated', this)
-
-        // 自动计算workflow里哪些节点支持
-        let input_ids = this.widgets.filter(w => w.name == 'input_ids')[0],
-          output_ids = this.widgets.filter(w => w.name == 'output_ids')[0]
-
-        const { input, output } = getInputsAndOutputs()
-        input_ids.value = input.join('\n')
-        output_ids.value = output.join('\n')
-
+        // console.log(this)
        const widget = {
          type: 'div',
          name: 'AppInfoRun',
@@ -329,7 +218,7 @@ app.registerExtension({
              get_position_style(
                ctx,
                widget_width,
-                node.size[1] - widget_height,
+                node.widgets[4].last_y + 24,
                node.size[1]
              )
            )
@@ -347,7 +236,7 @@ app.registerExtension({
        widget.div = $el('div', {})

        const btn = document.createElement('button')
-        btn.innerText = 'Save & Open'
+        btn.innerText = 'Save For App'
        btn.style = style

        btn.addEventListener('click', () => {
@@ -357,7 +246,6 @@ app.registerExtension({
          } else {
            alert('Please run the workflow before saving')
            // app.queuePrompt(0, 1)
-            this.widgets.filter(w => w.name === 'version')[0].value += 1
          }
        })

@@ -373,7 +261,6 @@ app.registerExtension({
          } else {
            alert('Please run the workflow before saving')
            // app.queuePrompt(0, 1)
-            this.widgets.filter(w => w.name === 'version')[0].value += 1
          }
        })

@@ -390,22 +277,15 @@ app.registerExtension({
        }

        this.serialize_widgets = true //需要保存参数
-
-        window._mixlab_app_json = null
      }

      const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = function (message) {
+      nodeType.prototype.onExecuted = async function (message) {
        onExecuted?.apply(this, arguments)
-        console.log(message.json)
+        // console.log(this.widgets)
+
        window._mixlab_app_json = message.json
        try {
-          let a = this.widgets.filter(w => w.name === 'AppInfoRun')[0]
-          if (a) {
-            if (!a.value) a.value = 0
-            a.value += 1
-          }
-
          const div = this.widgets.filter(w => w.div)[0].div
          Array.from(
            div.querySelectorAll('button'),
@@ -414,42 +294,5 @@ app.registerExtension({
        } catch (error) {}
      }
    }
-  },
-  async loadedGraphNode (node, app) {
-    // console.log('#loadedGraphNode1111')
-    window._mixlab_app_json = null //切换workflow需要清空
-    if (node.type === 'AppInfo') {
-      let auto_save = node.widgets.filter(w => w.name == 'auto_save')[0]
-      if (auto_save) {
-        if (!['enable', 'disable'].includes(auto_save.value)) {
-          auto_save.value = 'enable'
-        }
-      }
-
-      // app.canvas.centerOnNode(node)
-      // app.canvas.setZoom(0.45)
-    }
-  }
-})
-
-api.addEventListener('execution_start', async ({ detail }) => {
-  console.log('#execution_start', detail)
-  window._mixlab_app_json = null
-})
-
-api.addEventListener('executed', async ({ detail }) => {
-  console.log('#executed', detail)
-  // window._mixlab_app_json=null;
-  const { output } = getInputsAndOutputs()
-  if (output.includes(parseInt(detail.node))) {
-    let appinfo = app.graph.findNodesByType('AppInfo')[0]
-    if (appinfo) {
-      let auto_save = appinfo.widgets.filter(w => w.name == 'auto_save')[0]
-      if (auto_save?.value === 'enable') {
-        // 自动保存
-        console.log('auto_save')
-        if (window._mixlab_app_json) save(window._mixlab_app_json, false, false)
-      }
-    }
  }
 })
@@ -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 = 'v0.17.0'
+const version = 'v0.6.0'

 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
  .then(response => response.json())
@@ -1,119 +0,0 @@
-import { app } from '../../../scripts/app.js'
-// import { api } from '../../../scripts/api.js'
-import { ComfyWidgets } from '../../../scripts/widgets.js'
-import { $el } from '../../../scripts/ui.js'
-
-function getRandomElements (arr, num) {
-  var result = []
-  var len = arr.length
-
-  for (var i = 0; i < num; i++) {
-    var randomIndex = Math.floor(Math.random() * len)
-    result.push(arr[randomIndex])
-  }
-
-  return result
-}
-
-const createPrompt = (node, prompts, items, sample) => {
-  const w = ComfyWidgets['STRING'](
-    node,
-    'text',
-    ['STRING', { multiline: true }],
-    app
-  ).widget
-  w.inputEl.readOnly = true
-  w.inputEl.style.opacity = 0.6
-
-  w.value = typeof prompts === 'string' ? prompts : prompts.join('\n\n')
-
-  const w2 = ComfyWidgets['STRING'](
-    node,
-    'text',
-    ['STRING', { multiline: true }],
-    app
-  ).widget
-  w2.inputEl.readOnly = true
-  w2.inputEl.style.opacity = 0.6
-
-  w2.value = typeof items === 'string' ? items : JSON.stringify(items, null, 2)
-
-  const w3 = ComfyWidgets['STRING'](
-    node,
-    'text',
-    ['STRING', { multiline: true }],
-    app
-  ).widget
-  w3.inputEl.readOnly = true
-  w3.inputEl.style.opacity = 0.6
-  w3.value = typeof sample === 'string' ? sample : sample.join('\n\n')
-}
-
-app.registerExtension({
-  name: 'Mixlab.prompt.ClipInterrogator',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeData.name === 'ClipInterrogator') {
-      function populate (prompts, items, random_samples) {
-        if (this.widgets) {
-          for (let i = 0; i < this.widgets.length; i++) {
-            if (this.widgets[i].type !== 'combo') this.widgets[i].onRemove?.()
-          }
-          this.widgets.length = 2
-        }
-
-        createPrompt(this, prompts, items, random_samples)
-
-        // console.log('ClipInterrogator', w, w2)
-        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)
-        })
-      }
-
-      // 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)
-        populate.call(
-          this,
-          message.prompt,
-          message.analysis,
-          message.random_samples
-        )
-      }
-
-      this.serialize_widgets = true //需要保存参数
-    }
-  },
-  async loadedGraphNode (node, app) {
-    // Fires every time a node is constructed
-    // You can modify widgets/add handlers/etc here
-
-    if (node.type === 'ClipInterrogator') {
-      try {
-        
-        let widgets_values = node.widgets_values
-        console.log(widgets_values )
-        try {
-          if (widgets_values[2] && widgets_values[3] && widgets_values[4])
-            createPrompt(
-              node,
-              widgets_values[2],
-              widgets_values[3],
-              widgets_values[4]
-            )
-        } catch (error) {
-          console.log(error)
-        }
-      } catch (error) {}
-    }
-  }
-})
@@ -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')
@@ -203,82 +203,75 @@ app.registerExtension({

 app.registerExtension({
  name: 'Mixlab.GPT.ShowTextForGPT',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeData.name === 'ShowTextForGPT') {
-      function populate (text) {
-        text = text.filter(t => t && t?.trim())
-
-        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
-        }
+  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) {
-          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
+				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;

-            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)
+          try {
+            let data=JSON.parse(list);
+            data=Array.from(data,d=>{
+              return {
+                ...d,
+                content:decodeURIComponent(d.content)
              }
-            } catch (error) {
-              console.log(error)
-            }
-
-            w.value = list
+            })
+            list=JSON.stringify(data,null,2)
+          } catch (error) {
+            // console.log(error)
          }
-        }
+
+					w.value =list;
+        
+				}
        // console.log('ShowTextForGPT',this.widgets.length)
-        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)
-          }
-        })
-      }
+				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);
+				});
+			}

-      // 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)
-      }
+			// 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);
+				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
-    }
-  }
+
+		}
+
+
+	},
 })
@@ -156,32 +156,6 @@ const parseSvg = async svgContent => {
  return { data, image: base64, svgElement }
 }

-
-function findImages(nodeId) {
-  // 检查当前节点是否有 imgs 字段
-  const n = app.graph.getNodeById(nodeId)
-  if (n.imgs) {
-    return n.imgs;
-  }
-
-  // 检查当前节点的 inputs 是否有 image 字段
-  if (n.inputs) {
-    for (let i = 0; i < n.inputs.length; i++) {
-      if (n.inputs[i].name==='image'||n.inputs[i].name==='images') {
-        // 获取新的 nodeId，并递归调用 findImages 函数
-        var linkId = n.inputs[i]?.link;
-        var origin_id = app.graph.links[linkId].origin_id
-        return findImages(origin_id);
-      }
-    }
-  }
-
-  // 如果没有找到 imgs 字段或者 image 字段，则返回 null
-  return null;
-}
-
- 
-
 async function setArea (cw, ch, topBase64, base64, data, fn) {
  let displayHeight = Math.round(window.screen.availHeight * 0.8)
  let div = document.createElement('div')
@@ -597,19 +571,15 @@ app.registerExtension({
              }
            }
            try {
-              console.log('this.inputs', this.id)
-              let imgs=findImages(this.id)
-              
-              // let topLinkId = this.inputs[0].link
-              // let topNodeId = app.graph.links[topLinkId].origin_id
-              let topIm = imgs[0]
+              console.log('this.inputs', this.inputs)
+              let topLinkId = this.inputs[0].link
+              let topNodeId = app.graph.links[topLinkId].origin_id
+              let topIm = app.graph.getNodeById(topNodeId).imgs[0]

              let linkId = this.inputs[3].link
              let nodeId = app.graph.links[linkId].origin_id
              // console.log(linkId,this.inputs)
-              let imgs2=findImages(nodeId)
-              let im = imgs2[0]
-              console.log(topIm,im)
+              let im = app.graph.getNodeById(nodeId).imgs[0]
              // let src = im.src
              setArea(
                im.naturalWidth,
@@ -619,9 +589,7 @@ app.registerExtension({
                data,
                updateValue
              )
-            } catch (error) {
-              console.log(error)
-            }
+            } catch (error) {}
          })
        }
      }
@@ -1331,13 +1331,7 @@ app.registerExtension({
          let w = 360,
            s = widget.preview.videoWidth / widget.preview.videoHeight,
            h = w / s || w
-          // 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'
-            )
-          }
+          console.log(h)

          const pipWindow = await documentPictureInPicture.requestWindow({
            width: w,
@@ -1806,19 +1800,15 @@ const updateUI = node => {
    pw.inputEl.title = `Total of ${prompts.length} prompts`
  } else {
    // 动态添加
-    // console.log('ComfyWidgets',ComfyWidgets.STRING(
-    //   node,
-    //   'prompts',
-    //   ['STRING', { multiline: true }]
-    // ))
-
-    // ComfyWidgets.STRING(this, "", ["", {default:this.properties.text, multiline: true}], app)
-
+    console.log('ComfyWidgets',ComfyWidgets.STRING(
+      node,
+      'prompts',
+      ['STRING', { multiline: true }]
+    ))
    const w = ComfyWidgets.STRING(
      node,
      'prompts',
-      ['STRING', { multiline: true }],
-      app
+      ['STRING', { multiline: true }]
    ).widget
    w.inputEl.readOnly = true
    w.inputEl.style.opacity = 0.6
@@ -2099,13 +2089,13 @@ const node = {
  name: 'RandomPrompt',
  async init (app) {
    // Any initial setup to run as soon as the page loads
-    // console.log('[logging]', 'extension init')
+    console.log('[logging]', 'extension init')

    if (window.location.href.match('/?')) {
      const { workflow } = getURLParameters(window.location.href)
      if (workflow)
        get_my_workflow().then(data => {
-          // console.log('#get_my_workflow', data)
+          console.log('#get_my_workflow', data)
          let my_workflow = data.filter(
            d => d.filename == 'my_workflow.json'
          )[0]
@@ -2141,15 +2131,10 @@ 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]
-        // console.log('RandomPrompt',max_count,node.widgets_values[0])
-      } catch (error) {
-        console.log(error)
-      }
-    }
+    // Fires for each node when loading/dragging/etc a workflow json or png
+    // If you break something in the backend and want to patch workflows in the frontend
+    // This is the place to do this
+    // console.log("[logging]", "loaded graph node: ", exportGraph(node.graph));
  },
  async nodeCreated (node) {
    if (node.type === 'RandomPrompt') {
@@ -2242,7 +2227,7 @@ const node = {
        const r = onExecuted?.apply?.(this, arguments)

        let prompts = message.prompts
-        // console.log('executed', message)
+        console.log('executed', message)
        // console.log('#RandomPrompt', this.widgets)
        const pw = this.widgets.filter(w => w.name === 'prompts')[0]

@@ -2253,7 +2238,7 @@ const node = {
        } else {
          // 动态添加
          const w = ComfyWidgets.STRING(
-            this,
+            node,
            'prompts',
            ['STRING', { multiline: true }],
            app
@@ -1,565 +0,0 @@
-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 PhotoSwipeLightbox from '/extensions/comfyui-mixlab-nodes/lib/photoswipe-lightbox.esm.min.js'
-function loadCSS (url) {
-  var link = document.createElement('link')
-  link.rel = 'stylesheet'
-  link.type = 'text/css'
-  link.href = url
-  document.getElementsByTagName('head')[0].appendChild(link)
-
-  // Create a style element
-  const style = document.createElement('style')
-  // Define the CSS rule for scrollbar width
-  const cssRule = `.pswp__custom-caption {
-    background: rgb(20 27 70);
-    font-size: 16px;
-    color: #fff;
-    width: calc(100% - 32px);
-    max-width: 980px;
-    padding: 2px 8px;
-    border-radius: 4px;
-    position: absolute;
-    left: 50%;
-    bottom: 16px;
-    transform: translateX(-50%);
-  }
-  .pswp__custom-caption a {
-    color: #fff;
-    text-decoration: underline;
-  }
-  .hidden-caption-content {
-    display: none;
-  }`
-  // Add the CSS rule to the style element
-  style.appendChild(document.createTextNode(cssRule))
-
-  // Append the style element to the document head
-  document.head.appendChild(style)
-}
-loadCSS('/extensions/comfyui-mixlab-nodes/lib/photoswipe.min.css')
-
-function initLightBox () {
-  const lightbox = new PhotoSwipeLightbox({
-    gallery: '.prompt_image_output',
-    children: 'a',
-    pswpModule: () =>
-      import('/extensions/comfyui-mixlab-nodes/lib/photoswipe.esm.min.js')
-  })
-
-  lightbox.on('uiRegister', function () {
-    lightbox.pswp.ui.registerElement({
-      name: 'custom-caption',
-      order: 9,
-      isButton: false,
-      appendTo: 'root',
-      html: 'Caption text',
-      onInit: (el, pswp) => {
-        lightbox.pswp.on('change', () => {
-          const currSlideElement = lightbox.pswp.currSlide.data.element
-          let captionHTML = ''
-          if (currSlideElement) {
-            const hiddenCaption = currSlideElement.querySelector(
-              '.hidden-caption-content'
-            )
-            if (hiddenCaption) {
-              // get caption from element with class hidden-caption-content
-              captionHTML = hiddenCaption.innerHTML
-            } else {
-              // get caption from alt attribute
-              captionHTML = currSlideElement
-                .querySelector('img')
-                .getAttribute('alt')
-            }
-          }
-          el.innerHTML = captionHTML || ''
-        })
-      }
-    })
-  })
-
-  lightbox.init()
-}
-
-function get_position_style (ctx, widget_width, y, node_height) {
-  const MARGIN = 4 // the margin around the html element
-
-  /* Create a transform that deals with all the scrolling and zooming */
-  const elRect = ctx.canvas.getBoundingClientRect()
-  const transform = new DOMMatrix()
-    .scaleSelf(
-      elRect.width / ctx.canvas.width,
-      elRect.height / ctx.canvas.height
-    )
-    .multiplySelf(ctx.getTransform())
-    .translateSelf(MARGIN, MARGIN + y)
-
-  return {
-    transformOrigin: '0 0',
-    transform: transform,
-    left: `0`,
-    top: `0`,
-    cursor: 'pointer',
-    position: 'absolute',
-    maxWidth: `${widget_width - MARGIN * 2}px`,
-    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
-    width: `${widget_width - MARGIN * 2 - 24}px`,
-    // height: `${node_height * 0.3 - MARGIN * 2}px`,
-    // background: '#EEEEEE',
-    paddingLeft: '12px',
-    display: 'flex',
-    flexDirection: 'row',
-    // alignItems: 'center',
-    justifyContent: 'space-between'
-  }
-}
-function createImage (url) {
-  let im = new Image()
-  return new Promise((res, rej) => {
-    im.onload = () => res(im)
-    im.src = url
-  })
-}
-
-async function fetchImage (url) {
-  try {
-    const response = await fetch(url)
-    const blob = await response.blob()
-
-    return blob
-  } catch (error) {
-    console.error('出现错误:', error)
-  }
-}
-
-const getLocalData = key => {
-  let data = {}
-  try {
-    data = JSON.parse(localStorage.getItem(key)) || {}
-  } catch (error) {
-    return {}
-  }
-  return data
-}
-
-const setLocalDataOfWin = (key, value) => {
-  localStorage.setItem(key, JSON.stringify(value))
-  // window[key] = value
-}
-
-const createSelect = (select, opts, targetWidget) => {
-  select.style.display = 'block'
-  let html = ''
-  let isMatch = false
-  for (const opt of opts) {
-    html += `<option value='${opt}' ${
-      targetWidget.value === opt ? 'selected' : ''
-    }>${opt}</option>`
-    if (targetWidget.value === opt) isMatch = true
-  }
-  select.innerHTML = html
-  if (!isMatch) targetWidget.value = opts[0]
-  // 添加change事件监听器
-  select.addEventListener('change', function () {
-    // 获取选中的选项的值
-    var selectedOption = select.options[select.selectedIndex].value
-    targetWidget.value = selectedOption
-    // console.log(widget,selectedOption)
-  })
-}
-
-app.registerExtension({
-  name: 'Mixlab.prompt.RandomPrompt',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeType.comfyClass == 'RandomPrompt') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = async function () {
-        orig_nodeCreated?.apply(this, arguments)
-         
-        const mutable_prompt = this.widgets.filter(
-          w => w.name == 'mutable_prompt'
-        )[0]
-        // console.log('PromptSlide nodeData', prompt_keyword)
-
-        const widget = {
-          type: 'div',
-          name: 'upload',
-          draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(
-              this.div.style,
-              get_position_style(ctx, widget_width, y, node.size[1])
-            )
-          }
-        }
-
-        widget.div = $el('div', {})
-
-        const btn = document.createElement('button')
-        btn.innerText = 'Upload Keywords'
-
-        btn.style = `cursor: pointer;
-        font-weight: 300;
-        margin: 2px; 
-        color: var(--descrip-text);
-        background-color: var(--comfy-input-bg);
-        border-radius: 8px;
-        border-color: var(--border-color);
-        border-style: solid;    height: 30px;min-width: 122px;
-       `
-
-        // const btn=document.createElement('button');
-        // btn.innerText='Upload'
-        btn.addEventListener('click', () => {
-          let inp = document.createElement('input')
-          inp.type = 'file'
-          inp.accept = '.txt'
-          inp.click()
-          inp.addEventListener('change', event => {
-            // 获取选择的文件
-            const file = event.target.files[0]
-            this.title = file.name.split('.')[0]
-
-            // console.log(file.name.split('.')[0])
-            // 创建文件读取器
-            const reader = new FileReader()
-
-            // 定义读取完成事件的回调函数
-            reader.onload = event => {
-              // 读取完成后的文本内容
-              const fileContent = event.target.result.split('\n')
-              const keywords = Array.from(fileContent, f => f.trim()).filter(
-                f => f
-              )
-              // 打印文件内容
-              //   console.log(keywords)
-
-              mutable_prompt.value = keywords.join('\n')
-
-              inp.remove()
-            }
-
-            // 以文本方式读取文件
-            reader.readAsText(file)
-          })
-        })
-
-        widget.div.appendChild(btn)
-        document.body.appendChild(widget.div)
-        this.addCustomWidget(widget)
-
-        const onRemoved = this.onRemoved
-        this.onRemoved = () => {
-          widget.div.remove()
-          return onRemoved?.()
-        }
-
-        if (this.onResize) {
-          this.onResize(this.size)
-        }
-
-        this.serialize_widgets = true //需要保存参数
-      }
-    }
-  },
-  async loadedGraphNode (node, app) {
-    if (node.type === 'RandomPrompt') {
-     
-    }
-  }
-})
-
-app.registerExtension({
-  name: 'Mixlab.prompt.PromptSlide',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeType.comfyClass == 'PromptSlide') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = async function () {
-        orig_nodeCreated?.apply(this, arguments)
-
-        const prompt_keyword = this.widgets.filter(
-          w => w.name == 'prompt_keyword'
-        )[0]
-        // console.log('PromptSlide nodeData', prompt_keyword)
-
-        const widget = {
-          type: 'div',
-          name: 'upload',
-          draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(
-              this.div.style,
-              get_position_style(ctx, widget_width, y, node.size[1])
-            )
-          }
-        }
-
-        widget.div = $el('div', {})
-
-        const btn = document.createElement('button')
-        btn.innerText = 'Upload Keywords'
-
-        btn.style = `cursor: pointer;
-        font-weight: 300;
-        margin: 2px; 
-        color: var(--descrip-text);
-        background-color: var(--comfy-input-bg);
-        border-radius: 8px;
-        border-color: var(--border-color);
-        border-style: solid;    height: 30px;min-width: 122px;
-       `
-
-        const select = document.createElement('select')
-        select.style = `display:none;cursor: pointer;
-        font-weight: 300;
-        margin: 2px; 
-        color: var(--descrip-text);
-        background-color: var(--comfy-input-bg);
-        border-radius: 8px;
-        border-color: var(--border-color);
-        border-style: solid;    height: 30px;min-width: 100px;
-       `
-        widget.select = select
-
-        // const btn=document.createElement('button');
-        // btn.innerText='Upload'
-        btn.addEventListener('click', () => {
-          let inp = document.createElement('input')
-          inp.type = 'file'
-          inp.accept = '.txt'
-          inp.click()
-          inp.addEventListener('change', event => {
-            // 获取选择的文件
-            const file = event.target.files[0]
-            this.title = file.name.split('.')[0]
-
-            // console.log(file.name.split('.')[0])
-            // 创建文件读取器
-            const reader = new FileReader()
-
-            // 定义读取完成事件的回调函数
-            reader.onload = event => {
-              // 读取完成后的文本内容
-              const fileContent = event.target.result.split('\n')
-              const keywords = Array.from(fileContent, f => f.trim()).filter(
-                f => f
-              )
-              // 打印文件内容
-              //   console.log(keywords)
-
-              widget.value = JSON.stringify(keywords)
-
-              // let ks = getLocalData(`_mixlab_PromptSlide`)
-              // ks[this.id] = keywords
-              // setLocalDataOfWin(`_mixlab_PromptSlide`, ks)
-
-              createSelect(select, keywords, prompt_keyword)
-
-              inp.remove()
-            }
-
-            // 以文本方式读取文件
-            reader.readAsText(file)
-          })
-        })
-
-        widget.div.appendChild(btn)
-        widget.div.appendChild(select)
-        document.body.appendChild(widget.div)
-        this.addCustomWidget(widget)
-
-        const onRemoved = this.onRemoved
-        this.onRemoved = () => {
-          widget.div.remove()
-          return onRemoved?.()
-        }
-
-        if (this.onResize) {
-          this.onResize(this.size)
-        }
-
-        this.serialize_widgets = true //需要保存参数
-      }
-    }
-  },
-  async loadedGraphNode (node, app) {
-    if (node.type === 'PromptSlide') {
-      try {
-        let prompt = node.widgets.filter(w => w.name === 'prompt_keyword')[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)
-        // console.log('keywords',keywords)
-        let widget = node.widgets.filter(w => w.select)[0]
-        if (keywords && keywords[0]) {
-          widget.select.style.display = 'block'
-          createSelect(widget.select, keywords, prompt)
-        }
-      } catch (error) {}
-    }
-  }
-})
-
-const _createResult = async (node, widget, message) => {
-  widget.div.innerHTML = ``
-
-  const width = node.size[0] * 0.5 - 12
-
-  let height_add = 0
-  
-  for (let index = 0; index < message._images.length; index++) {
-    const imgs = message._images[index]
-
-    for (const img of imgs) {
-      let url = api.apiURL(
-        `/view?filename=${encodeURIComponent(img.filename)}&type=${
-          img.type
-        }&subfolder=${
-          img.subfolder
-        }${app.getPreviewFormatParam()}${app.getRandParam()}`
-      )
-
-      let image = await createImage(url)
-
-      // 创建card
-      let div = document.createElement('div')
-      div.className = 'card'
-      div.draggable = true
-
-      div.ondragend = async event => {
-        console.log('拖动停止')
-        let url = div.querySelector('img').src
-
-        let blob = await fetchImage(url)
-
-        let imageNode = null
-        // No image node selected: add a new one
-        if (!imageNode) {
-          const newNode = LiteGraph.createNode('LoadImage')
-          newNode.pos = [...app.canvas.graph_mouse]
-          imageNode = app.graph.add(newNode)
-          app.graph.change()
-        }
-
-        // const blob = item.getAsFile();
-        imageNode.pasteFile(blob)
-      }
-
-      div.setAttribute('data-scale', image.naturalHeight / image.naturalWidth)
-
-      let h = (image.naturalHeight * width) / image.naturalWidth
-      if (index % 2 === 0) height_add += h
-      div.style = `width: ${width}px;height:${h}px;position: relative;margin: 4px;`
-
-      div.innerHTML = `<a href="${url}" 
-      data-pswp-width="${image.naturalWidth}" 
-      data-pswp-height="${image.naturalHeight}" 
-      target="_blank">
-      <img src="${url}" style='width: 100%' alt="${message.prompts[index]}"/>
-    </a>
-    <p style="position: absolute;
-            bottom: 0;
-            left: 0;
-            opacity: 0.6;
-            background-color: var(--comfy-input-bg); 
-            color: var(--descrip-text);
-            margin: 0;
-            font-size: 12px;
-            padding: 5px;
-            text-align: left;">${message.prompts[index]}</p>`
-      widget.div.appendChild(div)
-    }
-  }
-
-  node.size[1] = 98 + height_add
-}
-
-app.registerExtension({
-  name: 'Mixlab.prompt.PromptImage',
-
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeType.comfyClass == 'PromptImage') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = function () {
-        orig_nodeCreated?.apply(this, arguments)
-        console.log('#orig_nodeCreated', this)
-        const widget = {
-          type: 'div',
-          name: 'result',
-          draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(this.div.style, {
-              ...get_position_style(ctx, widget_width, y, node.size[1]),
-              flexWrap: 'wrap',
-              justifyContent: 'space-between',
-              // outline: '1px solid red',
-              paddingLeft: '0px',
-              width: widget_width + 'px'
-            })
-          }
-        }
-
-        widget.div = $el('div', {})
-        widget.div.className = 'prompt_image_output'
-
-        document.body.appendChild(widget.div)
-
-        this.addCustomWidget(widget)
-
-        initLightBox()
-
-        const onRemoved = this.onRemoved
-        this.onRemoved = () => {
-          widget.div.remove()
-          return onRemoved?.()
-        }
-
-        const onResize = this.onResize
-        this.onResize = function () {
-          // 缩放发生
-          // console.log('##缩放发生', this.size)
-          let w = this.size[0] * 0.5 - 12
-          Array.from(widget.div.querySelectorAll('.card'), card => {
-            card.style.width = `${w}px`
-            card.style.height = `${
-              w * parseFloat(card.getAttribute('data-scale'))
-            }px`
-          })
-          return onResize?.apply(this, arguments)
-        }
-
-        // this.serialize_widgets = true //需要保存参数
-      }
-
-      const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = async function (message) {
-        onExecuted?.apply(this, arguments)
-        console.log('#PromptImage', message.prompts, message._images)
-        // window._mixlab_app_json = message.json
-        try {
-          let widget = this.widgets.filter(w => w.name === 'result')[0]
-          widget.value = message
-          _createResult(this, widget, { ...message })
-        } catch (error) {
-          console.log(error)
-        }
-      }
-
-      this.serialize_widgets = true //需要保存参数
-    }
-  },
-  async loadedGraphNode (node, app) {
-    if (node.type === 'PromptImage') {
-      // await sleep(0)
-      let widget = node.widgets.filter(w => w.name === 'result')[0]
-      console.log('widget.value', widget.value)
-
-      initLightBox()
-
-      let cards = widget.div.querySelectorAll('.card')
-      if (cards.length == 0) node.size = [280, 120]
-      if(widget.value) _createResult(node, widget, widget.value)
-    }
-  }
-})
@@ -0,0 +1,110 @@
+import { app } from '../../../scripts/app.js'
+import { $el } from '../../../scripts/ui.js'
+import { api } from '../../../scripts/api.js'
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 12 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'row',
+    // alignItems: 'center',
+    justifyContent: 'flex-start'
+  }
+}
+
+app.registerExtension({
+  name: 'Mixlab.share.ShareToWeibo',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'ShareToWeibo') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        // console.log(this)
+        const widget = {
+          type: 'div',
+          name: 'ShareToWeiboBtn',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(
+                ctx,
+                widget_width,
+                node.widgets[2].last_y +16,
+                node.size[1]
+              )
+            )
+          }
+        }
+
+        const style = `
+        flex-direction: row;
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;
+        color: var(--descrip-text);`
+
+        widget.div = $el('div', {})
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Share'
+        btn.style = style
+
+        btn.addEventListener('click', () => {
+          if (window._mixlab_share_to_weibo)
+            window.open(window._mixlab_share_to_weibo)
+        })
+
+        document.body.appendChild(widget.div)
+        widget.div.appendChild(btn)
+
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = async function (message) {
+        onExecuted?.apply(this, arguments)
+        // console.log(this.widgets)
+
+        window._mixlab_share_to_weibo = message.url
+        try {
+          const div = this.widgets.filter(w => w.div)[0].div
+          Array.from(
+            div.querySelectorAll('button'),
+            b => (b.style.background = 'yellow')
+          )
+        } catch (error) {}
+      }
+    }
+  }
+})
@@ -1,302 +0,0 @@
-const smart_connect_config_input = [
-  {
-    node_type: 'CLIPTextEncode',
-    node_widget_name: 'text',
-    inputNodeName: 'RandomPrompt',
-    inputNode_output_name: 'STRING'
-  },
-  {
-    node_type: 'CLIPTextEncode',
-    node_widget_name: 'text',
-    inputNodeName: 'EmbeddingPrompt',
-    inputNode_output_name: 'STRING'
-  },
-  {
-    node_type: 'CLIPTextEncode',
-    node_widget_name: 'text',
-    inputNodeName: 'ChinesePrompt_Mix',
-    inputNode_output_name: 'prompt'
-  },
-  {
-    node_type: 'CheckpointLoaderSimple',
-    node_widget_name: 'ckpt_name',
-    inputNodeName: 'CkptNames_',
-    inputNode_output_name: 'ckpt_names'
-  },
-  {
-    node_type: 'KSampler',
-    node_widget_name: 'sampler_name',
-    inputNodeName: 'SamplerNames_',
-    inputNode_output_name: 'sampler_names'
-  },
-  {
-    node_type: 'LoraLoaderModelOnly',
-    node_widget_name: 'lora_name',
-    inputNodeName: 'LoraNames_',
-    inputNode_output_name: 'lora_names'
-  },
-  {
-    node_type: 'LoadLoRA',
-    node_widget_name: 'lora_name',
-    inputNodeName: 'LoraNames_',
-    inputNode_output_name: 'lora_names'
-  },
-  {
-    node_type: 'Moondream',
-    node_widget_name: 'image',
-    inputNodeName: 'LoadImage',
-    inputNode_output_name: 'IMAGE'
-  }
-]
-
-const smart_connect_config_output = [
-  {
-    node_type: 'LoadImage',
-    node_output_name: 'IMAGE',
-    outputNodeName: 'ClipInterrogator',
-    outputNode_input_name: 'image'
-  },
-  {
-    node_type: 'VAEDecode',
-    node_output_name: 'IMAGE',
-    outputNodeName: 'PromptImage',
-    outputNode_input_name: 'images'
-  },
-  {
-    node_type: 'VAEDecode',
-    node_output_name: 'IMAGE',
-    outputNodeName: 'PreviewImage',
-    outputNode_input_name: 'images'
-  },
-  {
-    node_type: 'VAEDecode',
-    node_output_name: 'IMAGE',
-    outputNodeName: 'SaveImage',
-    outputNode_input_name: 'images'
-  },
-  {
-    node_type: 'Moondream',
-    node_output_name: 'STRING',
-    outputNodeName: 'ShowTextForGPT',
-    outputNode_input_name: 'text'
-  }
-]
-
-// import {
-//   convertToInput,
-//   getConfig,
-//   isConvertableWidget
-// } from '../../../extensions/core/widgetInputs.js'
-
-const CONVERTED_TYPE = 'converted-widget'
-const GET_CONFIG = Symbol()
-
-function getConfig (widgetName) {
-  const { nodeData } = this.constructor
-  return (
-    nodeData?.input?.required[widgetName] ??
-    nodeData?.input?.optional?.[widgetName]
-  )
-}
-
-function hideWidget (node, widget, suffix = '') {
-  widget.origType = widget.type
-  widget.origComputeSize = widget.computeSize
-  widget.origSerializeValue = widget.serializeValue
-  widget.computeSize = () => [0, -4] // -4 is due to the gap litegraph adds between widgets automatically
-  widget.type = CONVERTED_TYPE + suffix
-  widget.serializeValue = () => {
-    // Prevent serializing the widget if we have no input linked
-    if (!node.inputs) {
-      return undefined
-    }
-    let node_input = node.inputs.find(i => i.widget?.name === widget.name)
-
-    if (!node_input || !node_input.link) {
-      return undefined
-    }
-    return widget.origSerializeValue
-      ? widget.origSerializeValue()
-      : widget.value
-  }
-
-  // Hide any linked widgets, e.g. seed+seedControl
-  if (widget.linkedWidgets) {
-    for (const w of widget.linkedWidgets) {
-      hideWidget(node, w, ':' + widget.name)
-    }
-  }
-}
-
-function convertToInput (node, widget, config) {
-  hideWidget(node, widget)
-
-  const type = config[0]
-
-  // Add input and store widget config for creating on primitive node
-  const sz = node.size
-  node.addInput(widget.name, type, {
-    widget: { name: widget.name, [GET_CONFIG]: () => config }
-  })
-
-  for (const widget of node.widgets) {
-    widget.last_y += LiteGraph.NODE_SLOT_HEIGHT
-  }
-
-  // Restore original size but grow if needed
-  node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])])
-}
-
-export function smart_init () {
-  LGraphCanvas.prototype._createNodeForInput = function (
-    node,
-    widget,
-    inputNodeName,
-    inputNode_slot
-  ) {
-    // console.log(node.pos)
-
-    // var widget = node.widgets.filter(w => w.name === node_widget_name)[0]
-    if (widget) {
-      // 如果有存在的，没有连线输出的，自动连，不新建
-      let input_node = null
-
-      Array.from(app.graph.findNodesByType(inputNodeName), n => {
-        var links = n.outputs.filter(o => o.name === inputNode_slot)[0].links
-        //  console.log(links)
-        if (!links || links?.length === 0) input_node = n
-      })
-      // 新建
-      if (!input_node) {
-        input_node = LiteGraph.createNode(inputNodeName)
-        input_node.pos = [node.pos[0] - node.size[0] - 24, node.pos[1] - 48]
-        app.canvas.graph.add(input_node, false)
-      } else {
-        input_node.pos = [node.pos[0] - node.size[0] - 24, node.pos[1] - 48]
-      }
-
-      const config = getConfig.call(node, widget.name) ?? [
-        widget.type,
-        widget.options || {}
-      ]
-      let node_slotType = config[0]
-      // 如果input没有，则创建
-      if (!node.inputs?.filter(inp => inp.name === widget.name)[0]||!node.inputs)
-        convertToInput(node, widget, config)
-      input_node.connectByType(inputNode_slot, node, node_slotType)
-    }
-  }
-
-  LGraphCanvas.prototype._createNodeForOutput = function (
-    node,
-    widget,
-    outputNodeName,
-    outputNode_slot
-  ) {
-    if (widget) {
-      let output_node
-      Array.from(app.graph.findNodesByType(outputNodeName), n => {
-        var links = n.inputs.filter(o => o.name === outputNode_slot)[0].links
-        //  console.log(links)
-        if (!links || links?.length === 0) output_node = n
-      })
-      console.log('output_node', output_node, widget.name)
-
-      if (!output_node) {
-        // 新建
-        output_node = LiteGraph.createNode(outputNodeName)
-        output_node.pos = [node.pos[0] + node.size[0] + 24, node.pos[1] - 48]
-        app.canvas.graph.add(output_node, false)
-      } else {
-        output_node.pos = [node.pos[0] + node.size[0] + 24, node.pos[1] - 48]
-      }
-
-      const config = getConfig.call(node, widget.name) ?? [
-        widget.type,
-        widget.options || {}
-      ]
-      let node_slotType = config[0]
-      console.log(node_slotType, output_node, outputNode_slot)
-      let type = output_node.inputs.filter(
-        inp => inp.name == outputNode_slot
-      )[0].type
-      node.connectByType(node_slotType, output_node, type)
-    }
-  }
-}
-
-export function addSmartMenu (options, node) {
-  let sopts = []
-
-  for (const sc of smart_connect_config_input) {
-    // 有智能推荐，则出现
-    if (node.type === sc.node_type) {
-      // console.log('smart',node)
-      // 则出现 randomPrompt
-      // CLIPTextEncode 的widget ，name== 'text'
-      let node_widget_name = sc.node_widget_name
-      let widget = node.widgets.filter(w => w.name === node_widget_name)[0]
-      if (!widget) {
-        // 控件没有，则查找inputs
-        widget = node.inputs.filter(w => w.name === node_widget_name)[0]
-      }
-
-      let isLinkNull = true
-      // 如果input里已经有，但是link为空
-      if (node.inputs?.filter(inp => inp.name === node_widget_name)[0]) {
-        isLinkNull =
-          node.inputs.filter(inp => inp.name === node_widget_name)[0].link ===
-          null
-      }
-
-      if (widget && isLinkNull) {
-        sopts.push({
-          content: sc.inputNodeName.split('_')[0] + '➡️',
-          callback: () => {
-            LGraphCanvas.prototype._createNodeForInput(
-              node, //当前node
-              widget, //当前node里需要自动连线的widget
-              sc.inputNodeName, //作为input的node type
-              sc.inputNode_output_name // 作为input的node的outputs的name. the input slot type of the target node
-            )
-          }
-        })
-      }
-    }
-  }
-
-  for (const sc of smart_connect_config_output) {
-    if (node.type === sc.node_type) {
-      let node_output_name = sc.node_output_name
-      const widget = node.outputs.filter(w => w.name === node_output_name)[0]
-
-      let isLinkNull = true
-      // 如果output里 link为空
-      if (node.outputs?.filter(inp => inp.name === node_output_name)[0]) {
-        isLinkNull =
-          node.outputs.filter(inp => inp.name === node_output_name)[0].links
-            ?.length === 0
-        if (!node.outputs.filter(inp => inp.name === node_output_name)[0].links)
-          isLinkNull = true
-      }
-
-      if (widget && isLinkNull) {
-        sopts.push({
-          content: '➡️' + sc.outputNodeName.split('_')[0],
-          callback: () => {
-            LGraphCanvas.prototype._createNodeForOutput(
-              node, //当前node
-              widget, //当前node里需要自动连线的widget
-              sc.outputNodeName, //作为input的node type
-              sc.outputNode_input_name // 作为input的node的outputs的name. the input slot type of the target node
-            )
-          }
-        })
-      }
-    }
-  }
-
-  if (sopts.length > 0) options = [...sopts, null, ...options]
-
-  return options
-}
@@ -1,5 +1,8 @@
 import { app } from '../../../scripts/app.js'
-import { $el } from '../../../scripts/ui.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";

 const getLocalData = key => {
  let data = {}
@@ -42,47 +45,8 @@ function get_position_style (ctx, widget_width, y, node_height) {
  }
 }

-function hexToRGBA (hexColor) {
-  var hex = hexColor.replace('#', '')
-  var r = parseInt(hex.substring(0, 2), 16)
-  var g = parseInt(hex.substring(2, 4), 16)
-  var b = parseInt(hex.substring(4, 6), 16)
-
-  // 获取透明度的十六进制值
-  var alphaHex = hex.substring(6)
-
-  // 将透明度的十六进制值转换为十进制值
-  var alpha = parseInt(alphaHex, 16) / 255
-
-  return [r, g, b, alpha]
-}
-
 app.registerExtension({
  name: 'Mixlab.utils.Color',
-  init () {
-    $el('link', {
-      rel: 'stylesheet',
-      href: '/extensions/comfyui-mixlab-nodes/lib/classic.min.css',
-      parent: document.head
-    })
-
-    $el('style', {
-      textContent: `
-      .pickr{
-        display: flex;
-        justify-content: center;
-        align-items: center;
-      }
-				.pickr .pcr-button {
-					width: 56px;
-          height: 56px;
-          outline: 1px solid white;
-				}
-				 
-			`,
-      parent: document.body
-    })
-  },
  async getCustomWidgets (app) {
    return {
      TCOLOR (node, inputName, inputData, app) {
@@ -96,17 +60,8 @@ app.registerExtension({
            return [128, 32] // a method to compute the current size of the widget
          },
          async serializeValue (nodeId, widgetIndex) {
-            // let data = getLocalData('_mixlab_utils_color')
-            // let hex = data[node.id] || '#000000'
-            let hex = widget.value || '#000000'
-            let [r, g, b, a] = hexToRGBA(hex)
-            return {
-              hex,
-              r,
-              g,
-              b,
-              a
-            }
+            let data = getLocalData('_mixlab_utils_color')
+            return data[node.id] || '#000000'
          }
        }
        //  widget.something = something;          // maybe adds stuff to it
@@ -122,7 +77,7 @@ app.registerExtension({
      nodeType.prototype.onNodeCreated = function () {
        orig_nodeCreated?.apply(this, arguments)

-        // console.log('Color nodeData', this.widgets)
+        console.log('Color nodeData', this.widgets)

        const widget = {
          type: 'div',
@@ -132,7 +87,6 @@ app.registerExtension({
              this.div.style,
              get_position_style(ctx, widget_width, 44, node.size[1])
            )
-            // console.log('draw',y,node.widgets[0].last_y)
          }
        }

@@ -140,9 +94,35 @@ app.registerExtension({

        document.body.appendChild(widget.div)

-        const inputDiv = () => {
+        const inputDiv = (key, placeholder, value) => {
          let div = document.createElement('div')
-          div.id = `color_picker_${this.id}`
+          const ip = document.createElement('input')
+          ip.type = 'color'
+          ip.className = `${'comfy-multiline-input'} ${placeholder}`
+          div.style = `display: flex;
+            align-items: center; 
+            margin: 6px 8px;
+            margin-top: 0;`
+          ip.placeholder = placeholder
+          ip.value = value
+
+          ip.style = `outline: none;
+            border: none;
+            padding: 4px;
+            width: 100%;cursor: pointer;
+            height: 32px;`
+          const label = document.createElement('label')
+          label.style = 'font-size: 10px;min-width:32px'
+          label.innerText = placeholder
+          div.appendChild(label)
+          div.appendChild(ip)
+
+          ip.addEventListener('change', () => {
+            let data = getLocalData(key)
+            data[this.id] = ip.value.trim()
+            localStorage.setItem(key, JSON.stringify(data))
+            // console.log(this.id, ip.value.trim())
+          })
          return div
        }

@@ -152,85 +132,10 @@ app.registerExtension({

        this.addCustomWidget(widget)

-        const pickr = Pickr.create({
-          el: `#${inputColor.id}`,
-          theme: 'classic', // or 'monolith', or 'nano'
-          // closeOnScroll: true,
-          default: '#000000',
-          swatches: [
-            'rgba(244, 67, 54, 1)',
-            'rgba(233, 30, 99, 0.95)',
-            'rgba(156, 39, 176, 0.9)',
-            'rgba(103, 58, 183, 0.85)',
-            'rgba(63, 81, 181, 0.8)',
-            'rgba(33, 150, 243, 0.75)',
-            'rgba(3, 169, 244, 0.7)',
-            'rgba(0, 188, 212, 0.7)',
-            'rgba(0, 150, 136, 0.75)',
-            'rgba(76, 175, 80, 0.8)',
-            'rgba(139, 195, 74, 0.85)',
-            'rgba(205, 220, 57, 0.9)',
-            'rgba(255, 235, 59, 0.95)',
-            'rgba(255, 193, 7, 1)'
-          ],
-
-          components: {
-            // Main components
-            preview: true,
-            opacity: true,
-            hue: true,
-            // Input / output Options
-            interaction: {
-              hex: true,
-              rgba: true,
-              hsla: true,
-              hsva: true,
-              cmyk: true,
-              input: true,
-              // clear: true,
-              save: true,
-              cancel: true
-            }
-          }
-        })
-
-        pickr
-          .on('save', (color, instance) => {
-            // console.log('Event: "save"', color.toHEXA().toString())
-            // let data = getLocalData('_mixlab_utils_color')
-            // data[this.id] = color.toHEXA().toString()
-            // localStorage.setItem('_mixlab_utils_color', JSON.stringify(data))
-
-            try {
-              let tc = this.widgets.filter(w => w.type == 'TCOLOR')[0]
-              tc.value = color.toHEXA().toString()
-            } catch (error) {}
-          })
-          .on('cancel', instance => {
-            pickr && pickr.hide()
-          })
-        this.pickr = pickr
-        const handleMouseWheel = () => {
-          try {
-            this.pickr && this.pickr.hide()
-          } catch (error) {}
-        }
-
-        document.addEventListener('wheel', handleMouseWheel)
-
        const onRemoved = this.onRemoved
        this.onRemoved = () => {
          inputColor.remove()
          widget.div.remove()
-
-          try {
-            this.pickr.destroyAndRemove()
-            this.pickr = null
-            document.removeEventListener('wheel', handleMouseWheel)
-          } catch (error) {
-            console.log(error)
-          }
-
          return onRemoved?.()
        }

@@ -243,119 +148,34 @@ app.registerExtension({
    // You can modify widgets/add handlers/etc here

    if (node.type === 'Color') {
-      try {
-        let TCOLOR = node.widgets.filter(w => w.type == 'TCOLOR')[0]
+      let widget = node.widgets.filter(w => w.div)[0]

-        setTimeout(() => node.pickr.setColor(TCOLOR.value || '#000000'), 1000)
-      } catch (error) {}
+      let data = getLocalData('_mixlab_utils_color')
+
+      let id = node.id
+
+      widget.div.querySelector('.Color').value = data[id] || '#000000'
    }
  }
 })

 app.registerExtension({
  name: 'Mixlab.utils.TextToNumber',
+
  async beforeRegisterNodeDef (nodeType, nodeData, app) {
    if (nodeType.comfyClass == 'TextToNumber') {
+
      const onExecuted = nodeType.prototype.onExecuted
      nodeType.prototype.onExecuted = function (message) {
        onExecuted?.apply(this, arguments)
-        const random_number = this.widgets.filter(
-          w => w.name === 'random_number'
-        )[0]
-        if (random_number.value === 'enable') {
-          const n = this.widgets.filter(w => w.name === 'number')[0]
-          n.value = message.num[0]
+        const random_number=this.widgets.filter(w=>w.name==='random_number')[0]
+        if(random_number.value==='enable'){
+          const n=this.widgets.filter(w=>w.name==='number')[0]
+          n.value=message.num[0]
        }
+        
        console.log('TextToNumber', random_number.value)
      }
    }
  }
 })
-
-const min_max = node => {
-  if(node.widgets){
-    const min_value = node.widgets.filter(w => w.name === 'min_value')[0]
-    const max_value = node.widgets.filter(w => w.name === 'max_value')[0]
-  
-    const number = node.widgets.filter(w => w.name === 'number')[0]
-    if (number) {
-      number.options.min = min_value.value
-      number.options.max = max_value.value
-  
-      number.value = Math.min(number.options.max, number.value)
-      number.value = Math.max(number.options.min, number.value)
-    }
-  
-    if (min_value)
-      min_value.callback = e => {
-        number.options.min = e
-        number.value = e
-      }
-    if (max_value)
-      max_value.callback = e => {
-        number.options.max = e
-        number.value = e
-      }
-  }
-  
-}
-
-app.registerExtension({
-  name: 'Mixlab.utils.FloatSlider',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-
-    if (nodeType.comfyClass == 'FloatSlider') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated;
-      nodeType.prototype.onNodeCreated = function () {
-        orig_nodeCreated?.apply(this, arguments)
-        min_max(this)
-      }
-    }
-
-   
-  },
-  async loadedGraphNode (node, app) {
-    if (node.type === 'FloatSlider') {
-      min_max(node)
-    }
-  }
-})
-app.registerExtension({
-  name: 'Mixlab.utils.IntNumber',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    
-    if (nodeType.comfyClass == 'IntNumber') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = function () {
-        orig_nodeCreated?.apply(this, arguments)
-        min_max(this)
-      }
-    }
-   
-  },
-  async loadedGraphNode (node, app) {
-    if (node.type === 'IntNumber') {
-      min_max(node)
-    }
-  }
-})
-
-
-app.registerExtension({
-  name: 'Mixlab.utils.TESTNODE_',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    
-    if (nodeType.comfyClass == 'TESTNODE_') {
-    
-      const onExecuted = nodeType.prototype.onExecuted;
-			nodeType.prototype.onExecuted = function (message) {
-				onExecuted?.apply(this, arguments);
-        console.log('##',message)
-				 
-			};
-
-
-    }
-   
-  }, 
-})
@@ -60,7 +60,30 @@ app.registerExtension({
          type: inputData[0], // the type, CHEESE
          name: inputName, // the name, slice
          size: [128, 24], // a default size
-          draw (ctx, node, width, y) {},
+          draw (ctx, node, width, y) {
+            // // 绘制文件图标的函数
+            // function drawFileIcon () {
+            //   // 清空画布
+            //   // ctx.clearRect(0, 0, canvas.width, canvas.height)
+
+            //   // 绘制文件外框
+            //   ctx.fillStyle = '#000'
+            //   ctx.fillRect(5, 5, 40, 40)
+
+            //   // 绘制文件夹图标
+            //   ctx.fillStyle = '#f00'
+            //   ctx.fillRect(10, 15, 30, 20)
+
+            //   // 绘制监听符号
+            //   ctx.beginPath()
+            //   ctx.arc(30, 35, 5, 0, 2 * Math.PI)
+            //   ctx.fillStyle = '#00f'
+            //   ctx.fill()
+            // }
+
+            // // 调用绘制函数
+            // drawFileIcon()
+          },
          computeSize (...args) {
            return [128, 24] // a method to compute the current size of the widget
          },
@@ -101,19 +124,11 @@ app.registerExtension({
      nodeType.prototype.onNodeCreated = function () {
        orig_nodeCreated?.apply(this, arguments)

-        // 虚拟的widget，用于更新节点，让其每次都运行
-        const widget = {
-          type: 'div',
-          name: 'seed',
-          draw (ctx, node, widget_width, y, widget_height) {}
-        }
-
-        this.addCustomWidget(widget)
+        console.log('watch widtget', this.widgets)

        const watcher = this.widgets.filter(w => w.name == 'watcher')[0]

        watcher.callback = () => {
-          console.log('watcher', watcher.value)
          if (watcher.value === 'enable') {
            if (window._mixlab_watcher_t)
              clearInterval(window._mixlab_watcher_t)
@@ -125,7 +140,7 @@ app.registerExtension({
                  window._mixlab_file_path_watcher = json.event_type
                  // widget.card.innerText = window._mixlab_file_path_watcher || ''
                  //运行
-                  // document.querySelector('#queue-button').click()
+                  document.querySelector('#queue-button').click()
                }
              })
            }, 1000)
@@ -147,59 +162,15 @@ app.registerExtension({
          window._mixlab_file_path_watcher = json.event_type
        })

+        /*
+                Add the widget, make sure we clean up nicely, and we do not want to be serialized!
+                */
+        // this.addCustomWidget(widget)
        this.onRemoved = function () {
          // widget.card.remove()
        }
        this.serialize_widgets = true
      }
-
-      const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = function (message) {
-        onExecuted?.apply(this, arguments)
-        console.log(message)
-        try {
-          let seed = this.widgets.filter(w => w.name === 'seed')[0]
-          if (seed) {
-            if (!seed.value) seed.value = 0
-            seed.value += 1
-          }
-        } catch (error) {}
-      }
-    }
-  },
-  async loadedGraphNode (node, app) {
-    if (node.type === 'LoadImagesFromPath') {
-      const watcher = node.widgets.filter(w => w.name == 'watcher')[0]
-      if (watcher) {
-        if (watcher.value === 'enable') {
-          if (window._mixlab_watcher_t) clearInterval(window._mixlab_watcher_t)
-          window._mixlab_watcher_t = setInterval(() => {
-            // 上次路径填充
-            getConfig().then(json => {
-              console.log(json.event_type)
-              if (json.event_type != window._mixlab_file_path_watcher) {
-                window._mixlab_file_path_watcher = json.event_type
-                // widget.card.innerText = window._mixlab_file_path_watcher || ''
-                //运行
-                document.querySelector('#queue-button').click()
-              }
-            })
-          }, 1000)
-        } else {
-          if (window._mixlab_watcher_t) {
-            clearInterval(window._mixlab_watcher_t)
-          }
-          window._mixlab_watcher_t = null
-        }
-      }
-
-      try {
-        let seed = node.widgets.filter(w => w.name === 'seed')[0]
-        if (seed) {
-          if (!seed.value) seed.value = 0
-          seed.value += 1
-        }
-      } catch (error) {}
    }
  }
 })
@@ -1 +0,0 @@
-/*! PhotoSwipe main CSS by Dmytro Semenov | photoswipe.com */.pswp{--pswp-bg:#000;--pswp-placeholder-bg:#222;--pswp-root-z-index:100000;--pswp-preloader-color:rgba(79, 79, 79, 0.4);--pswp-preloader-color-secondary:rgba(255, 255, 255, 0.9);--pswp-icon-color:#fff;--pswp-icon-color-secondary:#4f4f4f;--pswp-icon-stroke-color:#4f4f4f;--pswp-icon-stroke-width:2px;--pswp-error-text-color:var(--pswp-icon-color)}.pswp{position:fixed;top:0;left:0;width:100%;height:100%;z-index:var(--pswp-root-z-index);display:none;touch-action:none;outline:0;opacity:.003;contain:layout style size;-webkit-tap-highlight-color:transparent}.pswp:focus{outline:0}.pswp *{box-sizing:border-box}.pswp img{max-width:none}.pswp--open{display:block}.pswp,.pswp__bg{transform:translateZ(0);will-change:opacity}.pswp__bg{opacity:.005;background:var(--pswp-bg)}.pswp,.pswp__scroll-wrap{overflow:hidden}.pswp__bg,.pswp__container,.pswp__content,.pswp__img,.pswp__item,.pswp__scroll-wrap,.pswp__zoom-wrap{position:absolute;top:0;left:0;width:100%;height:100%}.pswp__img,.pswp__zoom-wrap{width:auto;height:auto}.pswp--click-to-zoom.pswp--zoom-allowed .pswp__img{cursor:-webkit-zoom-in;cursor:-moz-zoom-in;cursor:zoom-in}.pswp--click-to-zoom.pswp--zoomed-in .pswp__img{cursor:move;cursor:-webkit-grab;cursor:-moz-grab;cursor:grab}.pswp--click-to-zoom.pswp--zoomed-in .pswp__img:active{cursor:-webkit-grabbing;cursor:-moz-grabbing;cursor:grabbing}.pswp--no-mouse-drag.pswp--zoomed-in .pswp__img,.pswp--no-mouse-drag.pswp--zoomed-in .pswp__img:active,.pswp__img{cursor:-webkit-zoom-out;cursor:-moz-zoom-out;cursor:zoom-out}.pswp__button,.pswp__container,.pswp__counter,.pswp__img{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.pswp__item{z-index:1;overflow:hidden}.pswp__hidden{display:none!important}.pswp__content{pointer-events:none}.pswp__content>*{pointer-events:auto}.pswp__error-msg-container{display:grid}.pswp__error-msg{margin:auto;font-size:1em;line-height:1;color:var(--pswp-error-text-color)}.pswp .pswp__hide-on-close{opacity:.005;will-change:opacity;transition:opacity var(--pswp-transition-duration) cubic-bezier(.4,0,.22,1);z-index:10;pointer-events:none}.pswp--ui-visible .pswp__hide-on-close{opacity:1;pointer-events:auto}.pswp__button{position:relative;display:block;width:50px;height:60px;padding:0;margin:0;overflow:hidden;cursor:pointer;background:0 0;border:0;box-shadow:none;opacity:.85;-webkit-appearance:none;-webkit-touch-callout:none}.pswp__button:active,.pswp__button:focus,.pswp__button:hover{transition:none;padding:0;background:0 0;border:0;box-shadow:none;opacity:1}.pswp__button:disabled{opacity:.3;cursor:auto}.pswp__icn{fill:var(--pswp-icon-color);color:var(--pswp-icon-color-secondary)}.pswp__icn{position:absolute;top:14px;left:9px;width:32px;height:32px;overflow:hidden;pointer-events:none}.pswp__icn-shadow{stroke:var(--pswp-icon-stroke-color);stroke-width:var(--pswp-icon-stroke-width);fill:none}.pswp__icn:focus{outline:0}.pswp__img--with-bg,div.pswp__img--placeholder{background:var(--pswp-placeholder-bg)}.pswp__top-bar{position:absolute;left:0;top:0;width:100%;height:60px;display:flex;flex-direction:row;justify-content:flex-end;z-index:10;pointer-events:none!important}.pswp__top-bar>*{pointer-events:auto;will-change:opacity}.pswp__button--close{margin-right:6px}.pswp__button--arrow{position:absolute;top:0;width:75px;height:100px;top:50%;margin-top:-50px}.pswp__button--arrow:disabled{display:none;cursor:default}.pswp__button--arrow .pswp__icn{top:50%;margin-top:-30px;width:60px;height:60px;background:0 0;border-radius:0}.pswp--one-slide .pswp__button--arrow{display:none}.pswp--touch .pswp__button--arrow{visibility:hidden}.pswp--has_mouse .pswp__button--arrow{visibility:visible}.pswp__button--arrow--prev{right:auto;left:0}.pswp__button--arrow--next{right:0}.pswp__button--arrow--next .pswp__icn{left:auto;right:14px;transform:scale(-1,1)}.pswp__button--zoom{display:none}.pswp--zoom-allowed .pswp__button--zoom{display:block}.pswp--zoomed-in .pswp__zoom-icn-bar-v{display:none}.pswp__preloader{position:relative;overflow:hidden;width:50px;height:60px;margin-right:auto}.pswp__preloader .pswp__icn{opacity:0;transition:opacity .2s linear;animation:pswp-clockwise .6s linear infinite}.pswp__preloader--active .pswp__icn{opacity:.85}@keyframes pswp-clockwise{0%{transform:rotate(0)}100%{transform:rotate(360deg)}}.pswp__counter{height:30px;margin-top:15px;margin-inline-start:20px;font-size:14px;line-height:30px;color:var(--pswp-icon-color);text-shadow:1px 1px 3px var(--pswp-icon-color-secondary);opacity:.85}.pswp--one-slide .pswp__counter{display:none}
@@ -1,736 +0,0 @@
-{
-  "last_node_id": 29,
-  "last_link_id": 32,
-  "nodes": [
-    {
-      "id": 7,
-      "type": "CLIPTextEncode",
-      "pos": [
-        108,
-        316
-      ],
-      "size": {
-        "0": 425.27801513671875,
-        "1": 180.6060791015625
-      },
-      "flags": {},
-      "order": 8,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "clip",
-          "type": "CLIP",
-          "link": 16
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            6
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "CLIPTextEncode"
-      },
-      "widgets_values": [
-        "text, watermark"
-      ]
-    },
-    {
-      "id": 18,
-      "type": "ControlNetApply",
-      "pos": [
-        485,
-        792
-      ],
-      "size": {
-        "0": 317.4000244140625,
-        "1": 98
-      },
-      "flags": {},
-      "order": 10,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "conditioning",
-          "type": "CONDITIONING",
-          "link": 22,
-          "slot_index": 0
-        },
-        {
-          "name": "control_net",
-          "type": "CONTROL_NET",
-          "link": 18,
-          "slot_index": 1
-        },
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "link": 26
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            21
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ControlNetApply"
-      },
-      "widgets_values": [
-        1
-      ]
-    },
-    {
-      "id": 6,
-      "type": "CLIPTextEncode",
-      "pos": [
-        117,
-        95
-      ],
-      "size": {
-        "0": 422.84503173828125,
-        "1": 164.31304931640625
-      },
-      "flags": {},
-      "order": 7,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "clip",
-          "type": "CLIP",
-          "link": 15
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            22
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "CLIPTextEncode"
-      },
-      "widgets_values": [
-        "a future city,buiding,future,magic,under water"
-      ]
-    },
-    {
-      "id": 15,
-      "type": "LoraLoader",
-      "pos": [
-        116,
-        -113
-      ],
-      "size": {
-        "0": 315,
-        "1": 126
-      },
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
-          "type": "MODEL",
-          "link": 13
-        },
-        {
-          "name": "clip",
-          "type": "CLIP",
-          "link": 14
-        }
-      ],
-      "outputs": [
-        {
-          "name": "MODEL",
-          "type": "MODEL",
-          "links": [
-            12,
-            23
-          ],
-          "shape": 3,
-          "slot_index": 0
-        },
-        {
-          "name": "CLIP",
-          "type": "CLIP",
-          "links": [
-            15,
-            16
-          ],
-          "shape": 3,
-          "slot_index": 1
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoraLoader"
-      },
-      "widgets_values": [
-        "lcm-lora-sdv1-5.safetensors",
-        1,
-        1
-      ]
-    },
-    {
-      "id": 4,
-      "type": "CheckpointLoaderSimple",
-      "pos": [
-        -380,
-        185
-      ],
-      "size": {
-        "0": 315,
-        "1": 98
-      },
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "MODEL",
-          "type": "MODEL",
-          "links": [
-            13
-          ],
-          "slot_index": 0
-        },
-        {
-          "name": "CLIP",
-          "type": "CLIP",
-          "links": [
-            14
-          ],
-          "slot_index": 1
-        },
-        {
-          "name": "VAE",
-          "type": "VAE",
-          "links": [
-            8
-          ],
-          "slot_index": 2
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "CheckpointLoaderSimple"
-      },
-      "widgets_values": [
-        "deliberate_v2.safetensors"
-      ]
-    },
-    {
-      "id": 19,
-      "type": "DiffControlNetLoader",
-      "pos": [
-        40,
-        792
-      ],
-      "size": {
-        "0": 367.8165283203125,
-        "1": 58.083831787109375
-      },
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
-          "type": "MODEL",
-          "link": 23,
-          "slot_index": 0
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONTROL_NET",
-          "type": "CONTROL_NET",
-          "links": [
-            18
-          ],
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "DiffControlNetLoader"
-      },
-      "widgets_values": [
-        "control_v11f1p_sd15_depth.pth"
-      ]
-    },
-    {
-      "id": 5,
-      "type": "EmptyLatentImage",
-      "pos": [
-        65,
-        573
-      ],
-      "size": {
-        "0": 315,
-        "1": 106
-      },
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "LATENT",
-          "type": "LATENT",
-          "links": [
-            2
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "EmptyLatentImage"
-      },
-      "widgets_values": [
-        512,
-        512,
-        1
-      ]
-    },
-    {
-      "id": 25,
-      "type": "LeReS-DepthMapPreprocessor",
-      "pos": [
-        47,
-        904
-      ],
-      "size": {
-        "0": 369.6000061035156,
-        "1": 130
-      },
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "link": 31
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            26,
-            27
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LeReS-DepthMapPreprocessor"
-      },
-      "widgets_values": [
-        0.1,
-        0,
-        "disable",
-        512
-      ]
-    },
-    {
-      "id": 3,
-      "type": "KSampler",
-      "pos": [
-        1100,
-        -97
-      ],
-      "size": {
-        "0": 315,
-        "1": 262
-      },
-      "flags": {},
-      "order": 11,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
-          "type": "MODEL",
-          "link": 12,
-          "slot_index": 0
-        },
-        {
-          "name": "positive",
-          "type": "CONDITIONING",
-          "link": 21
-        },
-        {
-          "name": "negative",
-          "type": "CONDITIONING",
-          "link": 6
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-        {
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Author	SHA1	Message	Date
shadowcz007	3191cf2af0	test	2023-12-29 13:27:48 +08:00
shadowcz007	7d3e1ae945	test	2023-12-29 12:59:27 +08:00
shadowcz007	bfced5b4da	test	2023-12-29 12:49:05 +08:00
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