Update index.html

修复3d image的bug，未上传bg图也可以运行了

.gitignore

@@ -1,4 +1,6 @@
 __pycache__/
 https/
 nodes/config.json
-workflow/my_workflow.json
+workflow/my_workflow.json
+workflow/my_workflow_app.json
+app/*

README.md

@@ -1,11 +1,44 @@
-## 
+> 适配了最新版comfyui的py3.11 ，torch 2.1.2+cu121
+
+## 🚀🚗🚚🏃 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-text
+
+> 暂时支持8种节点作为界面上的输入节点：Load Image、CLIPTextEncode、PromptSlide、TextInput_、Color、FloatSlider、IntNumber、CheckpointLoaderSimple、LoraLoader
+
+> 输出节点：PreviewImage 、SaveImage、ShowTextForGPT、VHS_VideoCombine
+
+
+## 🏃🚗🚚🚀  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! 💻🌐
 
 ![screenshare](./assets/screenshare.png)
 
-
-### 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! 💻🌐
-
 https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43e-410a-ab3a-1952b7b4e7da
 
 
@@ -15,73 +48,79 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 !! Please use the address with HTTPS (https://127.0.0.1).
 
 
-### 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. Q: Translate into English
-
-![watch](./assets/4-loadfromlocal-watcher-workflow.svg)
-
-[workflow-4](./workflow/4-loadfromlocal-watcher-workflow.json)
+### SpeechRecognition & SpeechSynthesis
+![f](./assets/audio-workflow.svg)
 
+[Voice + Real-time Face Swap Workflow](./workflow/语音+实时换脸workflow.json)
 
 ### GPT
-> ChatGPT、ChatGLM3 , Some code provided by rui.
-
+> 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)
 
 
-## Installation
+## Prompt
+> PromptSlide
+![](./assets/prompt_weight.png)
 
-manually install, simply clone the repo into the custom_nodes directory with this command:
+![](./workflow/promptslide-appinfo-workflow.svg)
 
-```
-cd ComfyUI/custom_nodes
+> randomPrompt
 
-git clone https://github.com/shadowcz007/comfyui-mixlab-nodes.git
+![randomPrompt](./assets/randomPrompt.png)
 
-```
+> ClipInterrogator
 
-Install the requirements:
+[add clip-interrogator](https://github.com/pharmapsychotic/clip-interrogator)
 
-run directly:
-```
-cd ComfyUI_Mixlab
-install.bat
-```
+### 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.
 
-or install the requirements using:
-```
-../../../python_embeded/python.exe -s -m pip install -r requirements.txt
-```
+![layers](./assets/layers-workflow.svg)
 
-If you are using a venv, make sure you have it activated before installation and use:
-```
-pip3 install -r requirements.txt
-```
+![poster](./assets/poster-workflow.svg)
 
 
+### 3D
+![](./assets/3dimage.png)
+[workflow](./workflow/3D-workflow.json)
 
-## Nodes
+
+### 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. 
+
+![watch](./assets/4-loadfromlocal-watcher-workflow.svg)
+
+[workflow-4](./workflow/4-loadfromlocal-watcher-workflow.json)
+
+### LoadImagesFromURL
+> Conveniently load images from a fixed address on the internet to ensure that default images in the workflow can be executed.
+
+
+## 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.
+
+- [添加了DynamicDelayByText功能，可以根据输入文本的长度进行延迟执行。](./workflow/audio-chatgpt-workflow.json)
+
+- [Added DynamicDelayByText, enabling delayed execution based on input text length.](./workflow/audio-chatgpt-workflow.json)
+
+
+## Other Nodes
 
 ![main](./assets/all-workflow.svg)
 ![main2](./assets/detect-face-all.png)
 
 [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)
@@ -98,18 +137,73 @@ Add edges to an image.
 ![FeatheredMask](./assets/FlVou_Y6kaGWYoEj1Tn0aTd4AjMI.jpg)
 
 
+> LaMaInpainting
+
+from [simple-lama-inpainting](https://github.com/enesmsahin/simple-lama-inpainting)
+
 
 ### Improvement 
+
+- Add "help" option to the context menu for each node.
+- Add "Nodes Map" option to the global context menu.
+
 An improvement has been made to directly redirect to GitHub to search for missing nodes when loading the graph.
 
+![help](./assets/help.png)
+
 ![node-not-found](./assets/node-not-found.png)
 
 
+### Update 
+v0.8.0 🚀🚗🚚🏃‍ LaMaInpainting
+- 新增 LaMaInpainting
+- 优化color节点的输出
+- 修复高清显示屏上定位节点不准的情况
+
+- Add LaMaInpainting
+- Optimize the output of the color node
+- Fix the issue of inaccurate positioning node on high-definition display screens
+
+
 ### Models
-[Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : model/clipseg
+[Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : models/clipseg
+
+[Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : models/lama
+
+[Download Salesforce\blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base
+
+## Installation
+
+manually install, simply clone the repo into the custom_nodes directory with this command:
+
+```
+cd ComfyUI/custom_nodes
+
+git clone https://github.com/shadowcz007/comfyui-mixlab-nodes.git
+
+```
+
+Install the requirements:
+
+run directly:
+```
+cd ComfyUI/custom_nodes/comfyui-mixlab-nodes
+install.bat
+```
+
+or install the requirements using:
+```
+../../../python_embeded/python.exe -s -m pip install -r requirements.txt
+```
+
+If you are using a venv, make sure you have it activated before installation and use:
+```
+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
 
-<!-- ### Workflow
-[Workflow](./workflow.md) -->
 
 #### Thanks:
 [ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)
@@ -118,5 +212,26 @@ An improvement has been made to directly redirect to GitHub to search for missin
 [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
+    media="(prefers-color-scheme: dark)"
+    srcset="
+      https://api.star-history.com/svg?repos=shadowcz007/comfyui-mixlab-nodes&type=Date&theme=dark
+    "
+  />
+  <source
+    media="(prefers-color-scheme: light)"
+    srcset="
+      https://api.star-history.com/svg?repos=shadowcz007/comfyui-mixlab-nodes&type=Date
+    "
+  />
+  <img
+    alt="Star History Chart"
+    src="https://api.star-history.com/svg?repos=shadowcz007/comfyui-mixlab-nodes&type=Date"
+  />
+</picture>
+

__init__.py

@@ -4,7 +4,7 @@ import subprocess
 import importlib.util
 import sys,json
 import urllib
-
+import hashlib
 import datetime
 
 
@@ -64,9 +64,28 @@ except ImportError:
     sys.exit()
 
 
+
+def install_openai():
+    # Helper function to install the OpenAI module if not already installed
+    try:
+        importlib.import_module('openai')
+    except ImportError:
+        import pip
+        pip.main(['install', 'openai'])
+
+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
@@ -114,8 +133,38 @@ 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'):
@@ -150,14 +199,163 @@ def get_workflows():
     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"]
+                if i==0:
+                    apps.append({
+                        "filename":item["filename"],
+                        # "category":item['category'],
+                        "data":x,
+                        "date":item["date"],
+                    })
+                else:
+                    category=''
+                    if 'category' in x['app']:
+                        category=x['app']['category']
+                    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'],
+                            }
+                        },
+                        "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=''
+                    if 'category' in x['app']:
+                        category=x['app']['category']
+                    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'],
+                            }
+                        },
+                        "date":item["date"]
+                    })
+
+    return apps
+
 def save_workflow_json(data):
     workflow_path=os.path.join(current_path, "workflow/my_workflow.json")
     with open(workflow_path, 'w') as file:
         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:
+        json.dump(data, file)
+    return filename
+
+def get_nodes_map():
+    # print("#####path::", current_path)
+    data_path=os.path.join(current_path, "data")
+    print('data_path: ',data_path)
+    # if not os.path.exists(data_path):
+    #     # 使用mkdir()方法创建新目录
+    #     os.mkdir(data_path)
+    json_data={}
+    nodes_map=os.path.join(current_path, "data/extension-node-map.json")
+    if os.path.exists(nodes_map):
+        with open(nodes_map) as json_file:
+            json_data = json.load(json_file)
+
+    return json_data
+
+
+# 保存原始的 get 方法
+_original_request = aiohttp.ClientSession._request
+
+# 定义新的 get 方法
+async def new_request(self, method, url, *args, **kwargs):
+   # 检查环境变量以确定是否使用代理
+    proxy = os.environ.get('HTTP_PROXY') or os.environ.get('HTTPS_PROXY') or os.environ.get('http_proxy') or os.environ.get('https_proxy') 
+    # print('Proxy Config:',proxy)
+    if proxy and 'proxy' not in kwargs:
+        kwargs['proxy'] = proxy
+        print('Use Proxy:',proxy)
+    # 调用原始的 _request 方法
+    return await _original_request(self, method, url, *args, **kwargs)
+
+# 应用 Monkey Patch
+aiohttp.ClientSession._request = new_request
+
 # https
 async def new_start(self, address, port, verbose=True, call_on_start=None):
+        
+
         runner = web.AppRunner(self.app, access_log=None)
         await runner.setup()
         site = web.TCPSite(runner, address, port)
@@ -205,6 +403,18 @@ async def mixlab_hander(request):
             print(e)
     return web.json_response(data)
 
+
+@routes.get('/mixlab/app')
+async def mixlab_app_handler(request):
+    html_file = os.path.join(current_path, "web/index.html")
+    if os.path.exists(html_file):
+        with open(html_file, 'r', encoding='utf-8', errors='ignore') as f:
+            html_data = f.read()
+            return web.Response(text=html_data, content_type='text/html')
+    else:
+        return web.Response(text="HTML file not found", status=404)
+    
+
 @routes.post('/mixlab/workflow')
 async def mixlab_workflow_hander(request):
     data = await request.json()
@@ -217,6 +427,29 @@ async def mixlab_workflow_hander(request):
                     'status':'success',
                     '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)
+                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),
+                    'status':'success',
+                }
             elif data['task']=='list':
                 result={
                     'data':get_workflows(),
@@ -227,6 +460,21 @@ async def mixlab_workflow_hander(request):
 
     return web.json_response(result)
 
+@routes.post('/mixlab/nodes_map')
+async def nodes_map_hander(request):
+    data = await request.json()
+    result={}
+    try: 
+        result={
+            'data':get_nodes_map(),
+            'status':'success',
+                }
+    except Exception as e:
+            print(e)
+
+    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)
@@ -255,20 +503,37 @@ PromptServer.add_routes=new_add_routes
 
 
 # 导入节点
-from .nodes.PromptNode import RandomPrompt
-from .nodes.ImageNode import TransparentImage,LoadImagesFromPath,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.PromptNode import RandomPrompt,PromptSlide
+from .nodes.ImageNode import NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
 from .nodes.Clipseg import CLIPSeg,CombineMasks
-from .nodes.ChatGPT import ChatGPTNode,SessionHistory
+from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
+from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
+from .nodes.Utils import AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,GetImageSize_,MultiplicationNode
+from .nodes.Lama import LaMaInpainting
+from .nodes.ClipInterrogator import ClipInterrogator
 
 # 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,
     "RandomPrompt":RandomPrompt,
+    "PromptSlide":PromptSlide,
+    "ClipInterrogator":ClipInterrogator,
+    "NoiseImage":NoiseImage,
+    "GradientImage":GradientImage,
     "TransparentImage":TransparentImage,
+    "ResizeImageMixlab":ResizeImage,
     "LoadImagesFromPath":LoadImagesFromPath,
+    "LoadImagesFromURL":LoadImagesFromURL,
+    "TextImage":TextImage,
     "EnhanceImage":EnhanceImage,
+    "SvgImage":SvgImage,
+    "3DImage":Image3D, 
+    "ShowLayer":ShowLayer,
+    "NewLayer":NewLayer,
+    "MergeLayers":MergeLayers,
     "SplitLongMask":SplitLongMask,
     "FeatheredMask":FeatheredMask,
     "SmoothMask":SmoothMask,
@@ -281,25 +546,52 @@ NODE_CLASS_MAPPINGS = {
     "FloatingVideo":FloatingVideo,
     "CLIPSeg_":CLIPSeg,
     "CombineMasks_":CombineMasks,
-    "ChatGPT":ChatGPTNode,
-    "SessionHistory":SessionHistory
+    "ChatGPTOpenAI":ChatGPTNode,
+    "ShowTextForGPT":ShowTextForGPT,
+    "CharacterInText":CharacterInText,
+    "SpeechRecognition":SpeechRecognition,
+    "SpeechSynthesis":SpeechSynthesis,
+    "Color":ColorInput,
+    "FloatSlider":FloatSlider,
+    "IntNumber":IntNumber,
+    "TextInput_":TextInput,
+    "Font":FontInput,
+    "TextToNumber":TextToNumber,
+    "DynamicDelayProcessor":DynamicDelayProcessor,
+    "MultiplicationNode":MultiplicationNode,
+    "GetImageSize_":GetImageSize_,
+    "SwitchByIndex":SwitchByIndex,
+    "LimitNumber":LimitNumber, 
+    "LaMaInpainting":LaMaInpainting
+    # "GamePal":GamePal
 }
 
 # 一个包含节点友好/可读的标题的字典
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "RandomPrompt": "Random Prompt #Mixlab",
+    "AppInfo":"AppInfo ♾️Mixlab",
+    "ResizeImageMixlab":"ResizeImage ♾️Mixlab",
+    "RandomPrompt": "Random Prompt ♾️Mixlab",
     "SplitLongMask":"Splitting a long image into sections",
     "VAELoaderConsistencyDecoder":"Consistency Decoder Loader",
     "VAEDecodeConsistencyDecoder":"Consistency Decoder Decode",
-    "ScreenShare":"ScreenShare #Mixlab",
-    "FloatingVideo":"FloatingVideo #Mixlab",
-    "ChatGPT":"ChatGPT #Mixlab",
-    "SessionHistory":"SessionHistory #Mixlab"
+    "ScreenShare":"ScreenShare ♾️Mixlab",
+    "FloatingVideo":"FloatingVideo ♾️Mixlab",
+    "ChatGPTOpenAI":"ChatGPT ♾️Mixlab",
+    "ShowTextForGPT":"ShowTextForGPT ♾️Mixlab",
+    "MergeLayers":"MergeLayers ♾️Mixlab",
+    "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
+    "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
+    "3DImage":"3DImage ♾️Mixlab",
+    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
+    "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
+    "PromptSlide":"PromptSlide ♾️Mixlab"
+
+    # "GamePal":"GamePal ♾️Mixlab"
 }
 
 # web ui的节点功能
 WEB_DIRECTORY = "./web"
 
 print('--------------')
-print('\033[91mMixlab Nodes: \033[93mLoaded\033[0m')
+print('\033[91m ### Mixlab Nodes: \033[93mLoaded\033[0m')
 print('--------------')

data/anime.txt

@@ -0,0 +1,10 @@
+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

data/camera-and-lens.txt

@@ -0,0 +1,23 @@
+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

data/lighting.txt

@@ -0,0 +1,16 @@
+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

data/photographers.txt

@@ -0,0 +1,132 @@
+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

data/styles.txt

@@ -0,0 +1,135 @@
+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

nodes/Audio.py

@@ -0,0 +1,100 @@
+
+
+
+
+class SpeechRecognition:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "upload":("AUDIOINPUTMIX",),   },
+                "optional":{
+                    "start_by":("INT", {
+                        "default": 0, 
+                        "min": 0, #Minimum value
+                        "max": 2048, #Maximum value
+                        "step": 1, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                   
+                }
+                }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/audio"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,upload,start_by):
+        return {"ui": {"start_by": [start_by]}, "result": (upload,)}
+
+
+class SpeechSynthesis:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "text": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "♾️Mixlab/audio"
+
+    def run(self, text):
+        # print(session_history)
+        return {"ui": {"text": text}, "result": (text,)}
+    
+
+#
+class GamePal:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_text": ("STRING",{"multiline": True,"default": ""}),
+            },
+            "optional": {
+                 
+                "input_num": ("INT",{
+                                "default":100, 
+                                "min": -1, #Minimum value
+                                "max": 0xffffffffffffffff, #Maximum value
+                                "step": 1, #Slider's step
+                                "display": "slider" # Cosmetic only: display as "number" or "slider"
+                                }), 
+                "python_code": ("STRING",{"multiline": True,"default": "result= 1 if 'Mixlab' in input_text else 0"}),
+            }
+        }
+
+    INPUT_IS_LIST = False
+    RETURN_TYPES = ("INT",)
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (False,)
+
+    CATEGORY = "♾️Mixlab/audio"
+
+    def run(self, input_text,input_num,python_code):
+        exec(python_code)
+        res=None
+        try:
+            # 可能会引发异常的代码
+            res=result
+        except:
+            # 处理异常的代码
+            print('')
+
+        print(res)
+
+        # print(session_history)
+        return {"ui": {"text": [input_text],"num":[input_num]}, "result": (res,)}

nodes/ChatGPT.py

@@ -46,7 +46,7 @@ def chat(client, model_name,messages ):
             except (urllib.error.HTTPError, openai.OpenAIError) as ex:
                 if try_count >= 3:
                     raise ex
-                time.sleep(5)
+                time.sleep(3)
                 continue
 
         finish_reason = response.choices[0].finish_reason
@@ -66,7 +66,7 @@ class ChatGPTNode:
     def __init__(self):
         # self.__client = OpenAI()
         self.session_history = []  # 用于存储会话历史的列表
-        self.seed=0
+        # self.seed=0
         self.system_content="You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible."
 
     @classmethod
@@ -75,24 +75,29 @@ class ChatGPTNode:
             "required": {
                 "api_key":("KEY", {"default": "", "multiline": True}),
                 "api_url":("URL", {"default": "", "multiline": True}),
-                "prompt": ("STRING", {"default": "", "multiline": True}),
+                "prompt": ("STRING", {"multiline": True,"dynamicPrompts": False}),
                 "system_content": ("STRING", 
                                    {
                                        "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
-                                       "multiline": True
+                                       "multiline": True,"dynamicPrompts": False
                                        }),
-                "model": (["gpt-3.5-turbo", "gpt-3.5-turbo-16k-0613", "gpt-4-0613","gpt-4-1106-preview"], 
+                "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"}),
-                "seed": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
+                "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
+            },
+             "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
             },
         }
 
-    RETURN_TYPES = ("STRING","STRING",)
-    RETURN_NAMES = ("STRING","session_history",)
+    RETURN_TYPES = ("STRING","STRING","STRING",)
+    RETURN_NAMES = ("text","messages","session_history",)
     FUNCTION = "generate_contextual_text"
-    CATEGORY = "Mixlab/GPT"
+    CATEGORY = "♾️Mixlab/GPT"
     INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,False,)
+    OUTPUT_IS_LIST = (False,False,False,)
 
     
     def generate_contextual_text(self,
@@ -101,18 +106,13 @@ class ChatGPTNode:
                                  prompt, 
                                  system_content,
                                    model, 
-                                   seed):
-        print(api_key,
-                                 api_url, 
-                                 prompt, 
-                                 system_content,
-                                   model, 
-                                   seed)
+                                   seed,context_size,unique_id = None, extra_pnginfo=None):
+        # print(api_key!='',api_url,prompt,system_content,model,seed)
         # 可以选择保留会话历史以维持上下文记忆
         # 或者在此处清除会话历史 self.session_history.clear()
-        if seed!=self.seed:
-            self.seed=seed
-            self.session_history=[]
+        # if seed!=self.seed:
+        #     self.seed=seed
+        #     self.session_history=[]
         
         # 把系统信息和初始信息添加到会话历史中
         if system_content:
@@ -129,21 +129,45 @@ class ChatGPTNode:
 
         # 把用户的提示添加到会话历史中
         # 调用API时传递整个会话历史
-        messages=[{"role": "system", "content": self.system_content}]+self.session_history+[{"role": "user", "content": prompt}]
+
+        def crop_list_tail(lst, size):
+            if size >= len(lst):
+                return lst
+            elif size==0:
+                return []
+            else:
+                return lst[-size:]
+            
+        session_history=crop_list_tail(self.session_history,context_size)
+
+        messages=[{"role": "system", "content": self.system_content}]+session_history+[{"role": "user", "content": prompt}]
         response_content = chat(client,model,messages)
         
         self.session_history=self.session_history+[{"role": "user", "content": prompt}]+[{'role':'assistant',"content":response_content}]
+
+
+        # if unique_id and extra_pnginfo and "workflow" in extra_pnginfo[0]:
+        #     workflow = extra_pnginfo[0]["workflow"]
+        #     node = next((x for x in workflow["nodes"] if str(x["id"]) == unique_id[0]), None)
+        #     if node:
+        #         node["widgets_values"] = ["",
+        #                          api_url, 
+        #                          prompt, 
+        #                          system_content,
+        #                            model,
+        #                            seed,
+        #                            context_size]
         
-        return (response_content,json.dumps(self.session_history, indent=4),)
+        return (response_content,json.dumps(messages, indent=4),json.dumps(self.session_history, indent=4),)
 
 
 
-class SessionHistory:
+class ShowTextForGPT:
     @classmethod
     def INPUT_TYPES(s):
         return {
             "required": {
-                "session_history": ("STRING", {"forceInput": True}),
+                "text": ("STRING", {"forceInput": True,"dynamicPrompts": False}),
             }
         }
 
@@ -153,8 +177,41 @@ class SessionHistory:
     OUTPUT_NODE = True
     OUTPUT_IS_LIST = (True,)
 
-    CATEGORY = "Mixlab/GPT"
+    CATEGORY = "♾️Mixlab/GPT"
 
-    def run(self, session_history):
+    def run(self, text):
         # print(session_history)
-        return {"ui": {"text": session_history}, "result": (session_history,)}
+        return {"ui": {"text": text}, "result": (text,)}
+
+
+class CharacterInText:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                 "character": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                 "start_index": ("INT", {
+                    "default": 1,
+                    "min": 0, #Minimum value
+                    "max": 1024, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+            }
+        }
+
+    INPUT_IS_LIST = False
+    RETURN_TYPES = ("INT",)
+    FUNCTION = "run"
+    # OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (False,)
+
+    CATEGORY = "♾️Mixlab/GPT"
+
+    def run(self, text,character,start_index):
+        # print(text,character,start_index)
+        b=1 if character in text else 0
+        
+        return (b+start_index,)
+

nodes/ClipInterrogator.py

@@ -0,0 +1,158 @@
+import os
+import folder_paths
+
+from PIL import Image
+import comfy.utils
+import numpy as np
+import json
+import torch
+
+from transformers import AutoProcessor, BlipForConditionalGeneration
+
+from clip_interrogator import Config, Interrogator
+
+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")
+
+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(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, artist_ranks, movement_ranks, trending_ranks, flavor_ranks
+
+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:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "prompt_mode": (['fast','classic','best','negative'],),
+            "image_analysis": (["off","on"],), 
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING","STRING",)
+    RETURN_NAMES = ("prompt","analysis",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/prompt"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (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(ci,im)
+                analysis_result.append(json.dumps(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
+
+        return {"ui":{"prompt": prompt_result,"analysis":analysis_result},"result": (prompt_result,analysis_result,)}

nodes/Clipseg.py

@@ -1,3 +1,6 @@
+#### Thanks:
+# [ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)
+
 from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
 
 from PIL import Image
@@ -28,10 +31,20 @@ logger = logging.getLogger('CLIPSeg nodes')
 clipseg_model_dir = os.path.join(folder_paths.models_dir, "clipseg")
 
 if not os.path.exists(clipseg_model_dir):
+    print(f"## clipseg model not found: {clipseg_model_dir},pls download from https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main")
     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()
@@ -88,7 +101,7 @@ class CLIPSeg:
         return {"required":
                     {
                         "image": ("IMAGE",),
-                        "text": ("STRING", {"multiline": False}),
+                        "text": ("STRING", {"multiline": False,"dynamicPrompts": False}),
                         
                      },
                 "optional":
@@ -99,12 +112,12 @@ class CLIPSeg:
                     }
                 }
 
-    CATEGORY = "Mixlab/mask"
+    CATEGORY = "♾️Mixlab/mask"
     RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
     RETURN_NAMES = ("Mask","Heatmap Mask", "BW Mask")
 
     # INPUT_IS_LIST = True
-    # OUTPUT_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]:
@@ -177,12 +190,13 @@ class CLIPSeg:
         binary_mask_image = Image.fromarray(binary_mask_resized[..., 0])
 
         # convert PIL image to numpy array
-        tensor_bw = binary_mask_image.convert("RGB")
-        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]
+        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
+        return (tensor_bw, image_out_heatmap, image_out_binary,)
 
     #OUTPUT_NODE = False
 
@@ -204,7 +218,7 @@ class CombineMasks:
                     },
                 }
         
-    CATEGORY = "Mixlab/mask"
+    CATEGORY = "♾️Mixlab/mask"
     RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
     RETURN_NAMES = ("Combined Mask","Heatmap Mask", "BW Mask")
 
@@ -232,7 +246,7 @@ class CombineMasks:
         
         # Resize heatmap and binary mask to match the original image dimensions
         dimensions = (image_np.shape[1], image_np.shape[0])
-        print('heatmap',heatmap)
+        # print('heatmap',heatmap)
         if dimensions is None or dimensions[0] == 0 or dimensions[1] == 0:
             raise ValueError("Invalid dimensions")
         
@@ -252,7 +266,7 @@ class CombineMasks:
 
 # 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,
-}
+# NODE_CLASS_MAPPINGS = {
+#     "CLIPSeg": CLIPSeg,
+#     "CombineSegMasks": CombineMasks,
+# }

nodes/Lama.py

@@ -0,0 +1,84 @@
+import os
+import folder_paths
+from simple_lama_inpainting import SimpleLama
+from PIL import Image
+
+import numpy as np
+
+import torch
+
+
+ 
+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:
+    @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,)

nodes/PromptNode.py

@@ -4,12 +4,11 @@ import json
 from urllib import request, parse
 
 
-
-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)    
+# 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
@@ -45,6 +44,73 @@ default_prompt1='''Swing
                                                 '''
 default_prompt1="\n".join([p.strip() for p in default_prompt1.split('\n') if p.strip()!=''])
 
+
+def addWeight(text, weight=1):
+    if weight == 1:
+        return text
+    else:
+        return f"({text}:{round(weight,2)})"
+
+
+
+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:
 
     '''
@@ -79,7 +145,7 @@ class RandomPrompt:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/prompt"
+    CATEGORY = "♾️Mixlab/prompt"
 
     OUTPUT_IS_LIST = (True,)
     OUTPUT_NODE = True
@@ -87,7 +153,7 @@ class RandomPrompt:
 
     # 运行的函数
     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)
+        # 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")
@@ -106,6 +172,8 @@ class RandomPrompt:
             w1=w1.strip()
             for w2 in words2:
                 w2=w2.strip()
+                if '``' not in w2:
+                    w2=w2+',``'
                 if w1!='' and w2!='':
                     prompts.append(w2.replace('``', w1))
                 pbar.update(1)
@@ -126,62 +194,62 @@ class RandomPrompt:
 
 
 
-class RunWorkflow:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "workflow": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
-                "prompt": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
-                 "image": ("IMAGE",),
-                 "input_node": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
-                 "output_node": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
-                },
+# class RunWorkflow:
+#     @classmethod
+#     def INPUT_TYPES(s):
+#         return {
+#             "required": {
+#                 "workflow": ("STRING", {
+#                             "multiline": False, 
+#                             "default": ''
+#                           }),
+#                 "prompt": ("STRING", {
+#                             "multiline": False, 
+#                             "default": ''
+#                           }),
+#                  "image": ("IMAGE",),
+#                  "input_node": ("STRING", {
+#                             "multiline": False, 
+#                             "default": ''
+#                           }),
+#                  "output_node": ("STRING", {
+#                             "multiline": False, 
+#                             "default": ''
+#                           }),
+#                 },
                 
-            }
+#             }
     
     
 
-    RETURN_TYPES = ("IMAGE","STRING",)
+#     RETURN_TYPES = ("IMAGE","STRING",)
 
-    FUNCTION = "run"
+#     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/workflow"
+#     CATEGORY = "♾️Mixlab/workflow"
 
-    OUTPUT_IS_LIST = (True,)
-    OUTPUT_NODE = True
+#     OUTPUT_IS_LIST = (True,)
+#     OUTPUT_NODE = True
 
 
-    # 运行的函数
-    def run(self,workflow,prompt,image,input_node,output_node):
-        print('#运行的函数',prompt,image,input_node,output_node)
-        workflow=json.loads(workflow)
-        input_node=input_node.split(".")
-        workflow[input_node[0]][input_node[1]][input_node[2]]=prompt
+#     # 运行的函数
+#     def run(self,workflow,prompt,image,input_node,output_node):
+#         print('#运行的函数',prompt,image,input_node,output_node)
+#         workflow=json.loads(workflow)
+#         input_node=input_node.split(".")
+#         workflow[input_node[0]][input_node[1]][input_node[2]]=prompt
 
-        workflow_new={}
-        # 遍历，seed设为随机
-        for key, value in workflow.items():
-            if 'inputs' in value:
-                if 'seed' in value['inputs']:
-                    value['inputs']['seed']= random.randint(1, 18446744073709551614)
-            workflow_new[key]=value
+#         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]])
+#         queue_prompt(workflow_new)
+#         print('#运行的函数',workflow_new[input_node[0]])
 
-        # return (new_prompt)
-        return  {"ui":{"images": []},"result": ([image],['text'],)}
+#         # return (new_prompt)
+#         return  {"ui":{"images": []},"result": ([image],['text'],)}
 

nodes/ScreenShareNode.py

@@ -79,27 +79,31 @@ class ScreenShareNode:
     def INPUT_TYPES(s):
         return { "required":{
             "image_base64": ("CHEESE",),
+            "refresh_rate": ("INT", {"default": 500, "min": 0,"step": 50, "max": 0xffffffffffffffff}),
         },
           "optional":{
               "prompt": ("PROMPT",),
+              "slide": ("SLIDE",),
+              "seed": ("SEED",),
+              
             #   "seed": ("INT", {"default": 1, "min": 0, "max": 0xffffffffffffffff}),
                 } }
     
-    RETURN_TYPES = ('IMAGE','STRING')
-
+    RETURN_TYPES = ('IMAGE','STRING','FLOAT',"INT")
+    RETURN_NAMES = ("IMAGE","PROMPT","FLOAT","INT")
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/image"
+    CATEGORY = "♾️Mixlab/image"
 
     # INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (False,False,False)
+    OUTPUT_IS_LIST = (False,False,False,False)
   
     # 运行的函数
-    def run(self,image_base64,prompt):
+    def run(self,image_base64,refresh_rate ,prompt,slide,seed):
         im,mask=base64_save(image_base64)
         # print('##########prompt',prompt)
-        return (im,prompt)
-    
+        return {"ui":{"refresh_rate": [refresh_rate]},"result": (im,prompt,slide,seed,)}
+
 
 class FloatingVideo:
     @classmethod
@@ -114,7 +118,7 @@ class FloatingVideo:
     OUTPUT_NODE = True
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/image"
+    CATEGORY = "♾️Mixlab/image"
 
     # INPUT_IS_LIST = True
     # OUTPUT_IS_LIST = (False,False,)
@@ -137,3 +141,15 @@ class FloatingVideo:
 
         return { "ui": { "images_": results } }
 
+
+
+# class SildeNode:
+#     CATEGORY = "quicknodes"
+#     @classmethod    
+#     def INPUT_TYPES(s):
+#         return { "required":{} }
+#     RETURN_TYPES = ()
+#     RETURN_NAMES = ()
+#     FUNCTION = "func"
+#     def func(self):
+#         return ()

nodes/Utils.py

@@ -0,0 +1,612 @@
+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 json
+# import hashlib
+
+
+# def get_json_hash(json_content):
+#     json_string = json.dumps(json_content, sort_keys=True)
+#     hash_object = hashlib.sha256(json_string.encode())
+#     hash_value = hash_object.hexdigest()
+#     return hash_value
+    
+
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+
+def create_temp_file(image):
+    output_dir = folder_paths.get_temp_directory()
+
+    (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('tmp', output_dir)
+
+    
+    im=tensor2pil(image)
+ 
+    image_file = f"{filename}_{counter:05}.png"
+     
+    image_path=os.path.join(full_output_folder, image_file)
+
+    im.save(image_path,compress_level=4)
+
+    return [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type": "temp"
+                }]
+
+def get_font_files(directory):
+    font_files = {}
+
+    # 从指定目录加载字体
+    for file in os.listdir(directory):
+        if file.endswith('.ttf') or file.endswith('.otf'):
+            font_name = os.path.splitext(file)[0]
+            font_path = os.path.join(directory, file)
+            font_files[font_name] = os.path.abspath(font_path)
+
+    # 尝试获取系统字体
+    try:
+        font_paths = fm.findSystemFonts()
+        for path in font_paths:
+            try:
+                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 {path}: {e}")
+    except Exception as e:
+        print(f"Error finding system fonts: {e}")
+
+    return font_files
+
+r_directory = os.path.join(os.path.dirname(__file__), '../assets/')
+
+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:
+            flat_list.append(item)
+    return flat_list
+
+
+class ColorInput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+             
+                    "color":("TCOLOR",), 
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING","INT","INT","INT","FLOAT",)
+    RETURN_NAMES = ("hex","r","g","b","a",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,False,False,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,)
+
+
+
+class FontInput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+             
+                    "font": (list(font_files.keys()),),
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING",)
+    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,font):
+        
+        return (font_files[font],)
+    
+class TextToNumber:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "text": ("STRING",{"multiline": False,"default": "1"}),
+                    "random_number": (["enable", "disable"],),
+                    "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"
+                    }),
+                             },
+                }
+    
+    RETURN_TYPES = ("INT",)
+    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,text,random_number,number):
+        
+        numbers = re.findall(r'\d+', text)
+        result=0
+        for n in numbers:
+            result = int(n)
+            # print(result)
+        
+        if random_number=='enable' and result>0:
+            result= random.randint(1, 10000000000)
+        return {"ui": {"text": [text],"num":[result]}, "result": (result,)}
+    
+
+   
+class FloatSlider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "number":("FLOAT", {
+                        "default": 0, 
+                        "min": 0, #Minimum 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"  
+                        }),
+                             },
+                }
+    
+    RETURN_TYPES = ("FLOAT",) 
+
+    FUNCTION = "run"
+
+    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
+        return (number,)
+    
+  
+class IntNumber:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "number":("INT", {
+                        "default": 0, 
+                        "min": -1, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 1, 
+                        "display": "number"
+                    }),
+                    "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"  
+                        }),
+                             },
+                }
+    
+    RETURN_TYPES = ("INT",) 
+
+    FUNCTION = "run"
+
+    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
+        return (number,)
+
+class MultiplicationNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "numberA":(any_type,),
+                    "numberB":("FLOAT", {
+                        "default": 0, 
+                        "min": -1, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.1, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    })
+                             },
+                }
+    
+    RETURN_TYPES = ("FLOAT","INT",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,False,)
+
+    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": ""}),
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,text):
+       
+        return (text,)
+    
+# 接收一个值，然后根据字符串或数值长度计算延迟时间，用户可以自定义延迟"字/s"，延迟之后将转化
+
+import comfy.samplers
+import folder_paths
+ 
+# import time
+
+
+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 time
+
+class DynamicDelayProcessor:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        # print("print INPUT_TYPES",cls)
+        return {
+            "required":{
+                    "delay_seconds":("INT",{
+                    "default":1,
+                    "min": 0,
+                    "max": 1000000,
+                    }),
+            },
+            "optional":{
+                "any_input":(any_type,),
+                "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"})
+                }
+            }
+  
+    @classmethod
+    def calculate_words_length(cls,text):
+        chinese_char_pattern = re.compile(r'[\u4e00-\u9fff]')
+        english_word_pattern = re.compile(r'\b[a-zA-Z]+\b')
+        number_pattern = re.compile(r'\b[0-9]+\b')
+
+        words_length = 0
+        for segment in text.split():
+            if chinese_char_pattern.search(segment):
+                # 中文字符，每个字符计为 1
+                words_length += len(segment)
+            elif number_pattern.match(segment):
+                # 数字，每个字符计为 1
+                words_length += len(segment)
+            elif english_word_pattern.match(segment):
+                # 英文单词，整个单词计为 1
+                words_length += 1
+
+        return words_length
+
+
+
+    FUNCTION = "run"
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ('output',)
+
+    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
+        start_time = time.time()
+
+        # 计算延迟时间
+        delay_time = delay_seconds
+        if delay_by_text and isinstance(delay_by_text, str) and words_per_seconds > 0:
+            words_length = self.calculate_words_length(delay_by_text)
+            print(f"Delay text: {delay_by_text}, Length: {words_length}")
+            delay_time += words_length / words_per_seconds
+            
+        # 延迟执行
+        print(f"延迟执行: {delay_time}")
+        time.sleep(delay_time)
+
+        # 获取结束时间戳并计算间隔
+        end_time = time.time()
+        elapsed_time = end_time - start_time
+        print(f"实际延迟时间: {elapsed_time} 秒")        
+
+        # 根据 replace_output 决定输出值
+        return (max(0, replace_value),) if replace_output == "enable" else (any_input,)
+        
+
+
+
+# app 配置节点
+class AppInfo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "name": ("STRING",{"multiline": False,"default": "Mixlab-App","dynamicPrompts": False}),
+                    "image": ("IMAGE",),
+                    "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"]),"dynamicPrompts": False}),
+                    "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"]),"dynamicPrompts": False}),
+                             },
+
+                "optional":{
+                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
+                    "version":("INT", {
+                        "default": 1, 
+                        "min": 1, 
+                        "max": 10000, 
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                    "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                    "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
+                    "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                }
+
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self,name,image,input_ids,output_ids,description,version,share_prefix,link,category):
+        name=name[0]
+        im=image[0][0]
+        # image [img,] img[batch,w,h,a] 列表里面是batch，
+
+        input_ids=input_ids[0]
+        output_ids=output_ids[0]
+        description=description[0]
+        version=version[0]
+        share_prefix=share_prefix[0]
+        link=link[0]
+        category=category[0]
+
+        #TODO batch 的方式需要处理
+        im=create_temp_file(im)
+        
+        # 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": (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
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "A":(any_type,),
+                "B":(any_type,),
+                "index":("INT", {
+                        "default": -1, 
+                        "min": -1, 
+                        "max": 1000, 
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                 "flat": (['off',"on"],),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("C",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self, A,B,index,flat):
+
+        flat=flat[0]
+
+        C=[]
+        index=index[0]
+        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,)
+
+
+
+class LimitNumber:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "number":(any_type,),
+                "min_value":("INT", {
+                        "default": 0, 
+                        "min": 0, 
+                        "max": 0xffffffffffffffff,
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                "max_value":("INT", {
+                        "default": 1, 
+                        "min": 1, 
+                        "max": 0xffffffffffffffff,
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("number",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self, number, min_value, max_value):
+        nn=number
+
+        if isinstance(number, int):
+            min_value=int(min_value)
+            max_value=int(max_value)
+        if isinstance(number, float):
+            min_value=float(min_value)
+            max_value=float(max_value)
+
+        if number < min_value:
+            nn= min_value
+        elif number > max_value:
+            nn= max_value
+        
+        return (nn,)
+

nodes/Vae.py

@@ -145,7 +145,7 @@ class VAELoader:
     RETURN_TYPES = ("VAE",)
     FUNCTION = "load_vae"
 
-    CATEGORY = "Mixlab/ConsistencyDecoder"
+    CATEGORY = "♾️Mixlab/_test"
 
     #TODO: scale factor?
     def load_vae(self, vae_name):
@@ -165,7 +165,7 @@ class VAEDecode:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
 
-    CATEGORY = "Mixlab/ConsistencyDecoder"
+    CATEGORY = "♾️Mixlab/_test"
 
     def decode(self, vae, samples):
         image = vae.decode(samples["samples"].to("cuda:0"))

requirements.txt

@@ -3,4 +3,6 @@ pyOpenSSL
 watchdog
 opencv-python-headless
 matplotlib
-openai
+openai
+simple-lama-inpainting
+clip-interrogator==0.6.0

web/javascript/3d_mixlab.js

@@ -0,0 +1,630 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { $el } from '../../../scripts/ui.js'
+
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+function getContentTypeFromBase64 (base64Data) {
+  const regex = /^data:(.+);base64,/
+  const matches = base64Data.match(regex)
+  if (matches && matches.length >= 2) {
+    return matches[1]
+  }
+  return null
+}
+function base64ToBlobFromURL (base64URL, contentType) {
+  return fetch(base64URL).then(response => response.blob())
+}
+const setLocalDataOfWin = (key, value) => {
+  localStorage.setItem(key, JSON.stringify(value))
+  // window[key] = value
+}
+async function uploadImage (blob, fileType = '.svg', filename) {
+  // const blob = await (await fetch(src)).blob();
+  const body = new FormData()
+  body.append(
+    'image',
+    new File([blob], (filename || new Date().getTime()) + fileType)
+  )
+
+  const resp = await api.fetchApi('/upload/image', {
+    method: 'POST',
+    body
+  })
+
+  // console.log(resp)
+  let data = await resp.json()
+  let { name, subfolder } = data
+  let src = api.apiURL(
+    `/view?filename=${encodeURIComponent(
+      name
+    )}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+  )
+
+  return src
+}
+
+function createImage (url) {
+  let im = new Image()
+  return new Promise((res, rej) => {
+    im.onload = () => res(im)
+    im.src = url
+  })
+}
+
+const parseImage = url => {
+  return new Promise((res, rej) => {
+    fetch(url)
+      .then(response => response.blob())
+      .then(blob => {
+        const reader = new FileReader()
+        reader.onloadend = () => {
+          const base64data = reader.result
+          res(base64data)
+          // 在这里可以将base64数据用于进一步处理或显示图片
+        }
+        reader.readAsDataURL(blob)
+      })
+      .catch(error => {
+        console.log('发生错误:', error)
+      })
+  })
+}
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+async function extractMaterial (
+  modelViewerVariants,
+  selectMaterial,
+  material_img
+) {
+  // 材质
+  const materialsNames = []
+  for (
+    let index = 0;
+    index < modelViewerVariants.model.materials.length;
+    index++
+  ) {
+    let m = modelViewerVariants.model.materials[index]
+    let thumbUrl
+    try {
+      thumbUrl =
+        await m.pbrMetallicRoughness.baseColorTexture.texture.source.createThumbnail(
+          1024,
+          1024
+        )
+    } catch (error) {}
+    if (thumbUrl)
+      materialsNames.push({
+        value: m.name,
+        text: `#${index} ${m.name}`,
+        index,
+        thumbUrl
+      })
+  }
+
+  selectMaterial.innerHTML = ''
+  material_img.innerHTML = ''
+
+  for (let index = 0; index < materialsNames.length; index++) {
+    const name = materialsNames[index]
+    const option = document.createElement('option')
+    option.value = name.thumbUrl
+    option.textContent = name.text
+    option.setAttribute('data-index', index)
+    selectMaterial.appendChild(option)
+    let img = new Image()
+    img.src = name.thumbUrl
+    // img.setAttribute('data-index',name.index)
+    img.style.width = '40px'
+    material_img.appendChild(img)
+    if (index == 0) {
+      material_img.setAttribute('src', name.thumbUrl)
+    }
+  }
+}
+
+async function changeMaterial (
+  modelViewerVariants,
+  targetMaterial,
+  newImageUrl
+) {
+  const targetTexture = await modelViewerVariants.createTexture(newImageUrl)
+  // 用图片创建纹理
+  targetMaterial.pbrMetallicRoughness.baseColorTexture.setTexture(targetTexture)
+}
+
+app.registerExtension({
+  name: 'Mixlab.3D.3DImage',
+  async getCustomWidgets (app) {
+    return {
+      THREED (node, inputName, inputData, app) {
+        // console.log('##node', node, inputName, inputData)
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          size: [128, 88], // a default size
+          draw (ctx, node, width, y) {},
+          computeSize (...args) {
+            return [128, 88] // a method to compute the current size of the widget
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let d = getLocalData('_mixlab_3d_image')
+            // console.log('serializeValue', node)
+            if (d && d[node.id]) {
+              let { url, bg, material } = d[node.id]
+              let data = {}
+              if (url) {
+                data.image = await parseImage(url)
+              }
+              if (bg) {
+                data.bg_image = await parseImage(bg)
+                if (!data.bg_image.match('data:image/')) {
+                  delete data.bg_image
+                }
+              }
+
+              if (material) {
+                data.material = await parseImage(material)
+              }
+
+              return JSON.parse(JSON.stringify(data))
+            } else {
+              return {}
+            }
+          }
+        }
+        node.addCustomWidget(widget)
+        return widget
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == '3DImage') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const uploadWidget = this.widgets.filter(w => w.name == 'upload')[0]
+
+        const widget = {
+          type: 'div',
+          name: 'upload-preview',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 88, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+        widget.div.style.width = `120px`
+
+        document.body.appendChild(widget.div)
+
+        const inputDiv = (key, placeholder, preview) => {
+          let div = document.createElement('div')
+          const ip = document.createElement('input')
+          ip.type = 'file'
+          ip.className = `${'comfy-multiline-input'} ${placeholder}`
+          div.style = `display: flex;
+            align-items: center; 
+            margin: 6px 8px;
+            margin-top: 0;`
+          ip.placeholder = placeholder
+          // ip.value = value
+
+          ip.style = `outline: none;
+            border: none;
+            padding: 4px;
+            width: 60%;cursor: pointer;
+            height: 32px;`
+          const label = document.createElement('label')
+          label.style = 'font-size: 10px;min-width:32px'
+          label.innerText = placeholder
+          div.appendChild(label)
+          div.appendChild(ip)
+
+          let that = this,
+            filename = new Date().getTime()
+
+          ip.addEventListener('change', async event => {
+            const file = event.target.files[0]
+            const reader = new FileReader()
+            filename = new Date().getTime()
+            // 读取文件内容
+            reader.onload = async e => {
+              const fileURL = URL.createObjectURL(file)
+              // console.log('文件URL: ', fileURL)
+              let html = `<model-viewer  src="${fileURL}" 
+                min-field-of-view="0deg" max-field-of-view="180deg"
+                 shadow-intensity="1" 
+                 camera-controls 
+                 touch-action="pan-y">
+                 
+                 <div class="controls">
+                  <div>Variant: <select class="variant"></select></div>
+                  <div>Material: <select class="material"></select></div>
+                  <div>Material: <div class="material_img"> </div></div>
+                  <div><button class="bg">BG</button></div>
+                  <div><button class="export">Export GLB</button></div>
+                  
+                </div></model-viewer>`
+
+              preview.innerHTML = html
+              if (that.size[1] < 400) {
+                that.setSize([that.size[0], that.size[1] + 300])
+                app.canvas.draw(true, true)
+              }
+
+              const modelViewerVariants = preview.querySelector('model-viewer')
+              const select = preview.querySelector('.variant')
+              const selectMaterial = preview.querySelector('.material')
+              const material_img = preview.querySelector('.material_img')
+              const bg = preview.querySelector('.bg')
+              const exportGLB = preview.querySelector('.export')
+
+              if (modelViewerVariants) {
+                modelViewerVariants.style.width = `${that.size[0] - 24}px`
+                modelViewerVariants.style.height = `${that.size[1] - 48}px`
+              }
+
+              modelViewerVariants.addEventListener('load', async () => {
+                const names = modelViewerVariants.availableVariants
+
+                // 变量
+                for (const name of names) {
+                  const option = document.createElement('option')
+                  option.value = name
+                  option.textContent = name
+                  select.appendChild(option)
+                }
+                // Adds a default option.
+                if (names.length === 0) {
+                  const option = document.createElement('option')
+                  option.value = 'default'
+                  option.textContent = 'Default'
+                  select.appendChild(option)
+                }
+
+                // 材质
+                extractMaterial(
+                  modelViewerVariants,
+                  selectMaterial,
+                  material_img
+                )
+              })
+
+              let timer = null
+              const delay = 500 // 延迟时间，单位为毫秒
+
+              async function checkCameraChange () {
+                let dd = getLocalData(key)
+                let base64Data = modelViewerVariants.toDataURL()
+
+                const contentType = getContentTypeFromBase64(base64Data)
+
+                const blob = await base64ToBlobFromURL(base64Data, contentType)
+
+                //  const fileBlob = new Blob([e.target.result], { type: file.type });
+                let url = await uploadImage(blob, '.png')
+                // console.log(url)
+
+                let bg_blob = await base64ToBlobFromURL(
+                  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mN88uXrPQAFwwK/6xJ6CQAAAABJRU5ErkJggg=='
+                )
+                let url_bg = await uploadImage(bg_blob, '.png')
+                // console.log('url_bg',url_bg)
+
+                if (!dd[that.id]) {
+                  dd[that.id] = { url, bg: url_bg }
+                } else {
+                  dd[that.id] = { ...dd[that.id], url }
+                }
+
+                //  材质贴图
+                let thumbUrl = material_img.getAttribute('src')
+                if (thumbUrl) {
+                  let tb = await base64ToBlobFromURL(thumbUrl)
+                  let tUrl = await uploadImage(tb, '.png')
+                  // console.log('材质贴图', tUrl, thumbUrl)
+                  dd[that.id].material = tUrl
+                }
+
+                setLocalDataOfWin(key, dd)
+              }
+
+              function startTimer () {
+                if (timer) clearTimeout(timer)
+                timer = setTimeout(checkCameraChange, delay)
+              }
+
+              modelViewerVariants.addEventListener('camera-change', startTimer)
+
+              select.addEventListener('input', async event => {
+                modelViewerVariants.variantName =
+                  event.target.value === 'default' ? null : event.target.value
+                // 材质
+                await extractMaterial(
+                  modelViewerVariants,
+                  selectMaterial,
+                  material_img
+                )
+                checkCameraChange()
+              })
+
+              selectMaterial.addEventListener('input', event => {
+                // console.log(selectMaterial.value)
+                material_img.setAttribute('src', selectMaterial.value)
+
+                if (selectMaterial.getAttribute('data-new-material')) {
+                  let index =
+                    ~~selectMaterial.selectedOptions[0].getAttribute(
+                      'data-index'
+                    )
+                  changeMaterial(
+                    modelViewerVariants,
+                    modelViewerVariants.model.materials[index],
+                    selectMaterial.getAttribute('data-new-material')
+                  )
+                }
+
+                checkCameraChange()
+              })
+
+              bg.addEventListener('click', () => {
+                // 创建一个input元素
+                var input = document.createElement('input')
+                input.type = 'file'
+
+                // 监听input的change事件
+                input.addEventListener('change', function () {
+                  // 获取上传的文件
+                  var file = input.files[0]
+
+                  // 创建一个FileReader对象来读取文件
+                  var reader = new FileReader()
+
+                  // 监听FileReader的load事件
+                  reader.addEventListener('load', async () => {
+                    let base64 = reader.result
+                    // 将读取的文件内容设置为div的背景
+                    preview.style.backgroundImage = 'url(' + base64 + ')'
+
+                    const contentType = getContentTypeFromBase64(base64)
+
+                    const blob = await base64ToBlobFromURL(base64, contentType)
+
+                    //  const fileBlob = new Blob([e.target.result], { type: file.type });
+                    let bg_url = await uploadImage(blob, '.png')
+                    let bg_img = await createImage(base64)
+
+                    let dd = getLocalData(key)
+                    // console.log(dd[that.id],bg_url)
+                    if (!dd[that.id]) dd[that.id] = { url: '', bg: bg_url }
+                    dd[that.id] = {
+                      ...dd[that.id],
+                      bg: bg_url,
+                      bg_w: bg_img.naturalWidth,
+                      bg_h: bg_img.naturalHeight
+                    }
+
+                    setLocalDataOfWin(key, dd)
+
+                    // 更新尺寸
+                    let w = that.size[0] - 24,
+                      h = (w * bg_img.naturalHeight) / bg_img.naturalWidth
+
+                    if (modelViewerVariants) {
+                      modelViewerVariants.style.width = `${w}px`
+                      modelViewerVariants.style.height = `${h}px`
+                    }
+                    preview.style.width = `${w}px`
+                  })
+
+                  // 读取文件
+                  reader.readAsDataURL(file)
+                })
+
+                // 触发input的点击事件
+                input.click()
+              })
+
+              exportGLB.addEventListener('click', async () => {
+                const glTF = await modelViewerVariants.exportScene()
+                const file = new File([glTF], 'export.glb')
+                const link = document.createElement('a')
+                link.download = file.name
+                link.href = URL.createObjectURL(file)
+                link.click()
+              })
+
+              uploadWidget.value = await uploadWidget.serializeValue()
+
+              // 更新尺寸
+              let dd = getLocalData(key)
+              // console.log(dd[that.id],bg_url)
+              if (dd[that.id]) {
+                const { bg_w, bg_h } = dd[that.id]
+                if (bg_h && bg_w) {
+                  let w = that.size[0] - 24,
+                    h = (w * bg_h) / bg_w
+
+                  if (modelViewerVariants) {
+                    modelViewerVariants.style.width = `${w}px`
+                    modelViewerVariants.style.height = `${h}px`
+                  }
+                  preview.style.width = `${w}px`
+                }
+              }
+            }
+
+            // 以文本形式读取文件
+            reader.readAsDataURL(file)
+          })
+          return div
+        }
+
+        let preview = document.createElement('div')
+        preview.className = 'preview'
+        preview.style = `margin-top: 12px;display: flex;
+          justify-content: center;
+          align-items: center;background-repeat: no-repeat;background-size: contain;`
+
+        let upload = inputDiv('_mixlab_3d_image', '3D Model', preview)
+
+        widget.div.appendChild(upload)
+        widget.div.appendChild(preview)
+        this.addCustomWidget(widget)
+
+        const onResize = this.onResize
+        let that = this
+        this.onResize = function () {
+          let modelViewerVariants = preview.querySelector('model-viewer')
+
+          // 更新尺寸
+          let dd = getLocalData('_mixlab_3d_image')
+          // console.log(dd[that.id],bg_url)
+          if (dd[that.id]) {
+            const { bg_w, bg_h } = dd[that.id]
+            if (bg_h && bg_w) {
+              let w = that.size[0] - 24,
+                h = (w * bg_h) / bg_w
+
+              if (modelViewerVariants) {
+                modelViewerVariants.style.width = `${w}px`
+                modelViewerVariants.style.height = `${h}px`
+              }
+              preview.style.width = `${w}px`
+            }
+          }
+
+          return onResize?.apply(this, arguments)
+        }
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          upload.remove()
+          preview.remove()
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+        // this.isVirtualNode = true
+        this.serialize_widgets = false //需要保存参数
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        const r = onExecuted?.apply?.(this, arguments)
+
+        let div = this.widgets.filter(d => d.div)[0]?.div
+        console.log('Test', this.widgets)
+
+        let material = message.material[0]
+        if (material) {
+          const { filename, subfolder, type } = material
+          let src = api.apiURL(
+            `/view?filename=${encodeURIComponent(
+              filename
+            )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+          )
+
+          const modelViewerVariants = div.querySelector('model-viewer')
+
+          const selectMaterial = div.querySelector('.material')
+
+          let index =
+            ~~selectMaterial.selectedOptions[0].getAttribute('data-index')
+
+          selectMaterial.setAttribute('data-new-material', src)
+
+          changeMaterial(
+            modelViewerVariants,
+            modelViewerVariants.model.materials[index],
+            src
+          )
+        }
+
+        this.onResize?.(this.size)
+
+        return r
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    // Fires every time a node is constructed
+    // You can modify widgets/add handlers/etc here
+    const sleep = (t = 1000) => {
+      return new Promise((res, rej) => {
+        setTimeout(() => res(1), t)
+      })
+    }
+    if (node.type === '3DImage') {
+      // await sleep(0)
+      let widget = node.widgets.filter(w => w.name === 'upload-preview')[0]
+
+      let dd = getLocalData('_mixlab_3d_image')
+
+      let id = node.id
+      // console.log('3dImage load', node.widgets[0], node.widgets)
+      if (!dd[id]) return
+
+      let { url, bg } = dd[id]
+      if (!url) return
+      // let base64 = await parseImage(url)
+
+      let pre = widget.div.querySelector('.preview')
+      pre.style.width = `${node.size[0]}px`
+      pre.innerHTML = `
+        ${url ? `<img src="${url}" style="width:100%"/>` : ''}
+        `
+      pre.style.backgroundImage = 'url(' + bg + ')'
+
+      const uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
+      uploadWidget.value = await uploadWidget.serializeValue()
+    }
+  }
+})

web/javascript/app_mixlab.js

@@ -0,0 +1,351 @@
+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'
+  }
+}
+
+async function drawImageToCanvas (imageUrl) {
+  var canvas = document.createElement('canvas')
+  var ctx = canvas.getContext('2d')
+  var img = new Image()
+
+  await new Promise((resolve, reject) => {
+    img.onload = function () {
+      var scaleFactor = 320 / img.width
+      var canvasWidth = img.width * scaleFactor
+      var canvasHeight = img.height * scaleFactor
+
+      canvas.width = canvasWidth
+      canvas.height = canvasHeight
+
+      ctx.drawImage(img, 0, 0, canvasWidth, canvasHeight)
+
+      resolve()
+    }
+
+    img.onerror = function () {
+      reject(new Error('Failed to load image'))
+    }
+
+    img.src = imageUrl
+  })
+
+  var base64 = canvas.toDataURL('image/jpeg')
+  // console.log(base64); // 输出Base64数据
+  return base64
+  // 可以在这里执行其他操作，比如将Base64数据保存到服务器或显示在页面上
+}
+
+function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
+  const data = jsonData
+  const input = []
+  const output = []
+  const seed = {}
+
+  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 = []
+        // 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
+          }
+        } 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
+          let ks = getLocalData(`_mixlab_PromptSlide`)
+          let keywords = ks[id]
+          // console.log('keywords',keywords)
+          if (keywords && keywords[0]) {
+            options.keywords = keywords
+          }
+        }
+
+        if(node.type=='Color'){
+          
+        }
+
+        input[inputIds.indexOf(id)] = {
+          ...data[id],
+          title: node.title,
+          id,
+          options
+        }
+        // input.push()
+      }
+      if (outputIds.includes(id)) {
+        // let node = app.graph.getNodeById(id)
+        // output.push()
+        output[outputIds.indexOf(id)] = { ...data[id], title: node.title, id }
+      }
+
+      if (node.type === 'KSampler') {
+        // seed 的类型收集
+        try {
+          seed[id] = node.widgets.filter(
+            w => w.name === 'seed'
+          )[0].linkedWidgets[0].value
+        } catch (error) {}
+      }
+    }
+  }
+
+  return { input, output, seed }
+}
+
+function getUrl () {
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+  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()
+
+  const res = await fetch(`${url}/mixlab/workflow`, {
+    method: 'POST',
+    body: JSON.stringify({
+      data: json,
+      task: 'save_app',
+      filename: json.app.filename,
+      category: json.app.category
+    })
+  })
+  return await res.json()
+}
+
+function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
+  const dataString = JSON.stringify(jsonData)
+  const blob = new Blob([dataString], { type: 'application/json' })
+  const url = URL.createObjectURL(blob)
+
+  const link = document.createElement('a')
+  link.href = url
+  link.download = fileName
+  link.click()
+
+  // 释放URL对象
+  setTimeout(() => {
+    URL.revokeObjectURL(url)
+  }, 0)
+}
+
+async function save (json, download = false) {
+  const name = json[0],
+    version = json[5],
+    share_prefix = json[6], //用于分享的功能扩展
+    link = json[7], //用于创建界面上的跳转链接
+    category = json[8] || '', //用于分类
+    description = json[4],
+    inputIds = json[2].split('\n').filter(f => f),
+    outputIds = json[3].split('\n').filter(f => f)
+
+  const iconData = json[1][0]
+  let { filename, subfolder, type } = iconData
+  let iconUrl = api.apiURL(
+    `/view?filename=${encodeURIComponent(
+      filename
+    )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+  )
+
+  try {
+    let data = await app.graphToPrompt()
+
+    const { input, output, seed } = extractInputAndOutputData(
+      data.output,
+      inputIds,
+      outputIds
+    )
+
+    data.app = {
+      name,
+      description,
+      version,
+      input,
+      output,
+      seed, //控制是fixed 还是random
+      share_prefix,
+      link,
+      category,
+      filename: `${name}_${version}.json`
+    }
+
+    try {
+      data.app.icon = await drawImageToCanvas(iconUrl)
+    } catch (error) {}
+    // console.log(data.app)
+    // let http_workflow = app.graph.serialize()
+    await save_app(data)
+    if (download) {
+      await downloadJsonFile(data, data.app.filename)
+    }
+    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=${encodeURIComponent(data.app.category)}`
+    )
+    if (open)
+      window.open(
+        `${getUrl()}/mixlab/app?filename=${encodeURIComponent(
+          data.app.filename
+        )}&category=${encodeURIComponent(data.app.category)}`
+      )
+  } catch (error) {
+    console.log('###error', error)
+  }
+}
+
+app.registerExtension({
+  name: 'Mixlab.utils.AppInfo',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'AppInfo') {
+      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: 'AppInfoRun',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(
+                ctx,
+                widget_width,
+                node.size[1] - widget_height,
+                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 = 'Save & Open'
+        btn.style = style
+
+        btn.addEventListener('click', () => {
+          //   console.log('hahhah')
+          if (window._mixlab_app_json) {
+            save(window._mixlab_app_json)
+          } else {
+            alert('Please run the workflow before saving')
+            // app.queuePrompt(0, 1)
+            this.widgets.filter(w => w.name === 'version')[0].value += 1
+          }
+        })
+
+        const download = document.createElement('button')
+        download.innerText = 'Download For App'
+        download.style = style
+        download.style.marginLeft = '12px'
+
+        download.addEventListener('click', () => {
+          //   console.log('hahhah')
+          if (window._mixlab_app_json) {
+            save(window._mixlab_app_json, true)
+          } else {
+            alert('Please run the workflow before saving')
+            // app.queuePrompt(0, 1)
+            this.widgets.filter(w => w.name === 'version')[0].value += 1
+          }
+        })
+
+        document.body.appendChild(widget.div)
+        widget.div.appendChild(btn)
+        widget.div.appendChild(download)
+
+        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 = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log(message.json)
+        window._mixlab_app_json = message.json
+        try {
+          const div = this.widgets.filter(w => w.div)[0].div
+          Array.from(
+            div.querySelectorAll('button'),
+            b => (b.style.background = 'yellow')
+          )
+        } catch (error) {}
+      }
+    }
+  }
+})

web/javascript/audio_mixlab.js

@@ -0,0 +1,398 @@
+import { app } from '../../../scripts/app.js'
+// import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+
+function speakText (text) {
+  const speechMsg = new SpeechSynthesisUtterance()
+  speechMsg.text = text
+
+  // 语音合成结束时触发的事件
+  speechMsg.onend = function (event) {
+    console.log('语音播放结束')
+    window._mixlab_speech_synthesis_onend = true
+  }
+
+  // 语音合成错误时触发的事件
+  speechMsg.onerror = function (event) {
+    console.error('语音播放错误:', event.error)
+  }
+
+  // 使用浏览器默认语音合成器进行语音播放
+  speechSynthesis.speak(speechMsg)
+}
+
+// 调用方法，将文字转换为语音播放
+// speakText('Hello, how are you?');
+//                      #MixCopilot
+
+const start = (element, id, startBtn, node) => {
+  startBtn.className = 'loading_mixlab'
+
+  window.recognition = new webkitSpeechRecognition()
+
+  window.recognition.continuous = true
+  window.recognition.interimResults = true
+  window.recognition.lang = navigator.language
+
+  let timeoutId, intervalId
+
+  window.recognition.onstart = () => {
+    console.log('开始语音输入', window._mixlab_speech_synthesis_onend)
+    window._mixlab_speech_synthesis_onend = false
+  }
+
+  window.recognition.onresult = function (event) {
+    const result = event.results[event.results.length - 1][0].transcript
+    console.log('识别结果：', result)
+    element.value = result
+
+    let data = getLocalData('_mixlab_speech_recognition')
+    data[id] = result.trim()
+    localStorage.setItem('_mixlab_speech_recognition', JSON.stringify(data))
+
+    if (timeoutId) clearTimeout(timeoutId)
+
+    if (!window.recognition) return
+
+    timeoutId = setTimeout(function () {
+      console.log('结果传递：：', result)
+
+      // 把数据发送到chatgpt的输入prompt里
+      try {
+        const sendToId = node.widgets.filter(
+          w => w.name === 'Send to ChatGPT #'
+        )[0].value
+        app.graph
+          .getNodeById(sendToId)
+          .widgets.filter(w => w.name === 'prompt')[0].value = result
+      } catch (error) {}
+
+      setTimeout(() => app.queuePrompt(0, 1), 100)
+      window.recognition?.stop()
+      window.recognition = null
+      startBtn.className = ''
+      startBtn.innerText = 'START'
+
+      timeoutId = null
+
+      intervalId = setInterval(() => {
+        if (
+          app.ui.lastQueueSize === 0 &&
+          !window.recognition &&
+          window._mixlab_speech_synthesis_onend
+        ) {
+          start(element, id, startBtn, node)
+          startBtn.innerText = 'STOP'
+          if (intervalId) {
+            clearInterval(intervalId)
+          }
+        }
+      }, 2200)
+    }, 2000)
+  }
+
+  window.recognition.onend = function () {
+    console.log('语音输入结束')
+  }
+
+  window.recognition.onspeechend = function () {
+    console.log('onspeechend')
+  }
+
+  window.recognition.onerror = function (event) {
+    console.log('Error occurred in recognition: ' + event.error)
+  }
+
+  window.recognition.start()
+}
+
+app.registerExtension({
+  name: 'Mixlab.audio.SpeechRecognition',
+  async getCustomWidgets (app) {
+    return {
+      AUDIOINPUTMIX (node, inputName, inputData, app) {
+        // console.log('##node', node)
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          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
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let data = getLocalData('_mixlab_speech_recognition')
+            return data[node.id] || 'Hello Mixlab'
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'SpeechRecognition') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const sendTo = ComfyWidgets.INT(
+          this,
+          'Send to ChatGPT #',
+          ['INT', { default: 0 }],
+          app
+        )
+        // console.log('sendTo',sendTo)
+
+        const widget = {
+          type: 'div',
+          name: 'chatgptdiv',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 78, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        const inputDiv = (key, placeholder) => {
+          let div = document.createElement('div')
+          const startBtn = document.createElement('button')
+
+          const textArea = document.createElement('textarea')
+          textArea.placeholder = 'speak text'
+          // sendTo.type='range';
+          // sendTo.min=0;
+          // sendTo.max=2000;
+          // sendTo.step=1;
+          // sendTo.className='comfy-multiline-input'
+
+          textArea.className = `${'comfy-multiline-input'} ${placeholder}`
+
+          textArea.style = `margin-top: 14px;
+          height: 44px;`
+
+          div.style = `flex-direction: column;
+          display: flex;
+          margin: 0px 8px 6px;`
+
+          startBtn.style = `
+          margin-top:48px;
+          background-color: var(--comfy-input-bg);
+          border-radius: 8px;
+          border-color: var(--border-color);
+          border-style: solid;
+          color: var(--descrip-text);
+          `
+
+          startBtn.innerText = 'START'
+
+          div.appendChild(startBtn)
+          // div.appendChild(sendTo);
+          div.appendChild(textArea)
+
+          startBtn.addEventListener('click', () => {
+            if (window.recognition) {
+              window.recognition.stop()
+              window.recognition = null
+              startBtn.innerText = 'START'
+              startBtn.className = ''
+            } else {
+              start(textArea, this.id, startBtn, this)
+              startBtn.innerText = 'STOP'
+            }
+          })
+
+          // sendTo.addEventListener('change',()=>{
+          //   console.log(sendTo.value)
+          // })
+
+          return div
+        }
+
+        let inputAudio = inputDiv('_mixlab_speech_recognition', 'audio')
+        widget.div.appendChild(inputAudio)
+
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          inputAudio.remove()
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+
+      // const onGraphConfigured=nodeType.prototype.onGraphConfigured;
+      // nodeType.prototype.onGraphConfigured = function (message) {
+      //   onGraphConfigured?.apply(this, arguments)
+      //   console.log('###SpeechRecognition onGraphConfigured',this,message)
+      // }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        // console.log('this.widgets', this.widgets)
+
+        try {
+          // 是否根据start by 开启
+          let open = message.start_by[0] > 0
+          if (open) {
+            const div = this.widgets.filter(w => w.name == 'chatgptdiv')[0].div
+            const startBtn = div.querySelector('button')
+            let textArea = div.querySelector('textarea')
+            if (open && !window.recognition) {
+              start(textArea, this.id, startBtn, this)
+              startBtn.innerText = 'STOP'
+            } else if (!open && window.recognition) {
+              window.recognition.stop()
+              window.recognition = null
+              startBtn.innerText = 'START'
+              startBtn.className = ''
+            }
+          }
+        } catch (error) {
+          console.log('###SpeechRecognition', error)
+        }
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'SpeechRecognition') {
+      let data = getLocalData('_mixlab_speech_recognition')
+      // console.log('_mixlab_speech_recognition', node )
+      let div = node.widgets.filter(f => f.type === 'div')[0]
+      if (div && data[node.id]) {
+        div.div.querySelector('textarea').value = data[node.id]
+      }
+
+      try {
+        let open = node.widgets_values[1] > 0
+        if (open) {
+          const div = node.widgets.filter(w => w.name == 'chatgptdiv')[0].div
+          const startBtn = div.querySelector('button')
+          let textArea = div.querySelector('textarea')
+          if (open && !window.recognition) {
+            start(textArea, node.id, startBtn, node)
+            startBtn.innerText = 'STOP'
+          } else if (!open && window.recognition) {
+            window.recognition.stop()
+            window.recognition = null
+            startBtn.innerText = 'START'
+            startBtn.className = ''
+          }
+        }
+      } catch (error) {
+        console.log('###SpeechRecognition', error)
+      }
+    }
+  }
+})
+
+app.registerExtension({
+  name: 'Mixlab.audio.SpeechSynthesis',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData.name === 'SpeechSynthesis') {
+      function populate (text) {
+        // console.log('SpeechSynthesis',this.widgets)
+
+        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
+          }
+        }
+
+        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
+          w.value = list
+        }
+
+        speakText(text.join('\n'))
+
+        // console.log('ShowTextForGPT',this.widgets.length)
+        requestAnimationFrame(() => {
+          const sz = this.computeSize()
+          if (sz[0] < this.size[0]) {
+            sz[0] = this.size[0]
+          }
+          if (sz[1] < this.size[1]) {
+            sz[1] = this.size[1]
+          }
+          this.onResize?.(sz)
+          app.graph.setDirtyCanvas(true, false)
+        })
+      }
+
+      // 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)
+      }
+
+      this.serialize_widgets = true //需要保存参数
+    }
+  }
+})

web/javascript/checkVersion_mixlab.js

@@ -3,20 +3,24 @@ 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 = 'v0.2.5'
+const version = 'v0.10.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())
   .then(data => {
     const latestVersion = data.tag_name
     console.log('Latest release version:', latestVersion)
-    // if (latestVersion === localStorage.getItem('_mixlab_nodes_vesion')) return
-    if (latestVersion != version) {
-      // localStorage.setItem('_mixlab_nodes_vesion', latestVersion)
+    if (
+      latestVersion &&
+      latestVersion === localStorage.getItem('_mixlab_nodes_vesion')
+    )
+      return
+    if (latestVersion && latestVersion != version) {
+      localStorage.setItem('_mixlab_nodes_vesion', latestVersion)
       app.ui.dialog.show(`<h4 style="font-size: 18px;">${repoName} <br>
       Latest release version: ${latestVersion}</h4>
       <p>Please proceed to the official repository to download the latest version.</p>
-      <a style="    color: #2196F3;
+      <a style="color: #2196F3;
       font-size: 18px;
       font-weight: 800;
       letter-spacing: 2px;

web/javascript/gpt_mixlab.js

@@ -46,22 +46,33 @@ function get_position_style (ctx, widget_width, y, node_height) {
   }
 }
 
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+
 app.registerExtension({
-  name: 'Mixlab.GPT.ChatGPT',
+  name: 'Mixlab.GPT.ChatGPTOpenAI',
   async getCustomWidgets (app) {
     return {
       KEY (node, inputName, inputData, app) {
-        // console.log('node', inputName, inputData[0])
+        console.log('##inputData', inputData)
         const widget = {
           type: inputData[0], // the type, CHEESE
           name: inputName, // the name, slice
-          size: [128, 24], // a default size
+          size: [128, 32], // a default size
           draw (ctx, node, width, y) {},
           computeSize (...args) {
-            return [128, 24] // 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) {
-            return localStorage.getItem('_mixlab_api_key') || ''
+            let data = getLocalData('_mixlab_api_key')
+            return data[node.id] || 'by Mixlab'
           }
         }
         //  widget.something = something;          // maybe adds stuff to it
@@ -73,15 +84,16 @@ app.registerExtension({
         const widget = {
           type: inputData[0], // the type, CHEESE
           name: inputName, // the name, slice
-          size: [128, 24], // a default size
+          size: [128, 32], // a default size
           draw (ctx, node, width, y) {
             // a method to draw the widget (ctx is a CanvasRenderingContext2D)
           },
           computeSize (...args) {
-            return [128, 24] // 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) {
-            return localStorage.getItem('_mixlab_api_url') || ''
+            let data = getLocalData('_mixlab_api_url')
+            return data[node.id] || 'https://api.openai.com/v1'
           }
         }
         //  widget.something = something;          // maybe adds stuff to it
@@ -90,22 +102,21 @@ app.registerExtension({
       }
     }
   },
+
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    // console.log(nodeType.comfyClass)
-    if (nodeType.comfyClass == 'ChatGPT') {
+    if (nodeType.comfyClass == 'ChatGPTOpenAI') {
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
 
-        console.log('ChatGPT widtget', this.widgets)
-
         const api_key = this.widgets.filter(w => w.name == 'api_key')[0]
         const api_url = this.widgets.filter(w => w.name == 'api_url')[0]
-        console.log('api_key', api_key, api_url)
+
+        console.log('ChatGPTOpenAI nodeData', this.widgets)
 
         const widget = {
           type: 'div',
-          name: 'chatgpt div',
+          name: 'chatgptdiv',
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
@@ -118,87 +129,116 @@ app.registerExtension({
 
         document.body.appendChild(widget.div)
 
-        const inputKey = document.createElement('input'),
-          inputUrl = document.createElement('input')
-        inputKey.type = 'text'
-        inputUrl.type = 'text'
+        const inputDiv = (key, placeholder) => {
+          let div = document.createElement('div')
+          const ip = document.createElement('input')
+          ip.type = placeholder === 'Key' ? 'password' : 'text'
+          ip.className = `${'comfy-multiline-input'} ${placeholder}`
+          div.style = `display: flex;
+          align-items: center; 
+          margin: 6px 8px;
+          margin-top: 0;`
+          ip.placeholder = placeholder
+          ip.value = placeholder
 
-        inputKey.placeholder = 'Key'
-        inputUrl.placeholder = 'URL'
+          ip.style = `margin-left: 24px;
+          outline: none;
+          border: none;
+          padding: 4px;width: 100%;`
+          const label = document.createElement('label')
+          label.style = 'font-size: 10px;min-width:32px'
+          label.innerText = placeholder
+          div.appendChild(label)
+          div.appendChild(ip)
 
-        inputKey.style = `margin:4px 48px;`
-        inputUrl.style = `margin:4px 48px`
+          ip.addEventListener('change', () => {
+            let data = getLocalData(key)
+            data[this.id] = ip.value.trim()
+            localStorage.setItem(key, JSON.stringify(data))
+            console.log(this.id, key)
+          })
+          return div
+        }
 
-        inputUrl.value = localStorage.getItem('_mixlab_api_url') || ''
-        inputKey.value = localStorage.getItem('_mixlab_api_key') || ''
+        let inputKey = inputDiv('_mixlab_api_key', 'Key')
+        let inputUrl = inputDiv('_mixlab_api_url', 'URL')
 
         widget.div.appendChild(inputKey)
         widget.div.appendChild(inputUrl)
 
-        inputKey.addEventListener('change', () => {
-          api_key.serializeValue = () => inputKey.value || ''
-          localStorage.setItem('_mixlab_api_key', inputKey.value)
-        })
-
-        inputUrl.addEventListener('change', () => {
-          api_url.serializeValue = () => inputUrl.value || ''
-          localStorage.setItem('_mixlab_api_url', inputUrl.value)
-        })
-
-        /*
-                Add the widget, make sure we clean up nicely, and we do not want to be serialized!
-                */
         this.addCustomWidget(widget)
-        this.onRemoved = function () {
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          inputUrl.remove()
+          inputKey.remove()
           widget.div.remove()
+          return onRemoved?.()
         }
-        this.serialize_widgets = false
+
+        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 === 'ChatGPTOpenAI') {
+      let widget = node.widgets.filter(w => w.div)[0]
+
+      let apiKey = getLocalData('_mixlab_api_key'),
+        url = getLocalData('_mixlab_api_url')
+
+      let id = node.id
+
+      // console.log('ChatGPTOpenAI serialize_widgets', this)
+
+      widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
+      widget.div.querySelector('.URL').value =
+        url[id] || 'https://api.openai.com/v1'
+    }
   }
 })
 
-
-
 app.registerExtension({
-	name: "Mixlab.GPT.SessionHistory",
-	async beforeRegisterNodeDef(nodeType, nodeData, app) {
-		if (nodeData.name === "SessionHistory") {
+  name: 'Mixlab.GPT.ShowTextForGPT',
+  async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === "ShowTextForGPT") {
 			function populate(text) {
 				if (this.widgets) {
- 
-					// const pos = this.widgets.findIndex((w) => w.name === "text");
-					// if (pos !== -1) {
-					// 	for (let i = pos; i < this.widgets.length; i++) {
-					// 		this.widgets[i].onRemove?.();
-					// 	}
-					// 	this.widgets.length = pos;
-					// }
-                   
-                    for (let i = 0; i < this.widgets.length; i++) {
-                        this.widgets[i].onRemove?.();
-                    }
-                    this.widgets.length = 0;
+          
+					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('SessionHistory',this.widgets,text)
-
-				for (const list of text) {
+        // console.log('ShowTextForGPT',text)
+				for (let list of text) {
 					const w = ComfyWidgets["STRING"](this, "text", ["STRING", { multiline: true }], app).widget;
 					w.inputEl.readOnly = true;
 					w.inputEl.style.opacity = 0.6;
 
-                    let res=list;
+          try {
+            let data=JSON.parse(list);
+            data=Array.from(data,d=>{
+              return {
+                ...d,
+                content:decodeURIComponent(d.content)
+              }
+            })
+            list=JSON.stringify(data,null,2)
+          } catch (error) {
+            // console.log(error)
+          }
 
-                    try {
-                        res=JSON.stringify(JSON.parse(list),null,2)
-                    } catch (error) {
-                        // console.log(list)
-                    }
-
-					w.value = res;
+					w.value =list;
+        
 				}
-
+        // console.log('ShowTextForGPT',this.widgets.length)
 				requestAnimationFrame(() => {
 					const sz = this.computeSize();
 					if (sz[0] < this.size[0]) {
@@ -223,9 +263,15 @@ app.registerExtension({
 			nodeType.prototype.onConfigure = function () {
 				onConfigure?.apply(this, arguments);
 				if (this.widgets_values?.length) {
+          
 					populate.call(this, this.widgets_values);
 				}
 			};
+
+      this.serialize_widgets = true //需要保存参数
+
 		}
+
+
 	},
-});
+})

web/javascript/image_mixlab.js

@@ -0,0 +1,444 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+async function uploadImage (blob, fileType = '.svg', filename) {
+  // const blob = await (await fetch(src)).blob();
+  const body = new FormData()
+  body.append(
+    'image',
+    new File([blob], (filename || new Date().getTime()) + fileType)
+  )
+
+  const resp = await api.fetchApi('/upload/image', {
+    method: 'POST',
+    body
+  })
+
+  // console.log(resp)
+  let data = await resp.json()
+  let { name, subfolder } = data
+  let src = api.apiURL(
+    `/view?filename=${encodeURIComponent(
+      name
+    )}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+  )
+
+  return src
+}
+
+function base64ToBlobFromURL (base64URL, contentType) {
+  return fetch(base64URL).then(response => response.blob())
+}
+
+function getContentTypeFromBase64 (base64Data) {
+  const regex = /^data:(.+);base64,/
+  const matches = base64Data.match(regex)
+  if (matches && matches.length >= 2) {
+    return matches[1]
+  }
+  return null
+}
+
+// 示例用法
+// const base64Data = 'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAAAAAAAD/...'; // 替换为实际的base64图片数据
+// const contentType = getContentTypeFromBase64(base64Data);
+// console.log(contentType);
+
+// // 示例用法
+// const base64Data = '...'; // 替换为实际的base64图片数据
+// const contentType = 'image/jpeg'; // 替换为实际的图片类型
+
+// const blob = base64ToBlob(base64Data, contentType);
+// console.log(blob);
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+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
+}
+
+function createImage (url) {
+  let im = new Image()
+  return new Promise((res, rej) => {
+    im.onload = () => res(im)
+    im.src = url
+  })
+}
+
+const parseImage = url => {
+  return new Promise((res, rej) => {
+    fetch(url)
+      .then(response => response.blob())
+      .then(blob => {
+        const reader = new FileReader()
+        reader.onloadend = () => {
+          const base64data = reader.result
+          res(base64data)
+          // 在这里可以将base64数据用于进一步处理或显示图片
+        }
+        reader.readAsDataURL(blob)
+      })
+      .catch(error => {
+        console.log('发生错误:', error)
+      })
+  })
+}
+
+const parseSvg = async svgContent => {
+  let scale = 2
+  // 创建一个临时的DOM元素来解析SVG
+  const tempContainer = document.createElement('div')
+  tempContainer.innerHTML = svgContent
+
+  // 提取SVG元素
+  const svgElement = tempContainer.querySelector('svg')
+  if (!svgElement) return
+  // 获取SVG中 rect元素
+  var rectElements = svgElement?.querySelectorAll('rect') || []
+  // console.log(rectElements,svgElement)
+  // 定义一个数组来存储处理后的数据
+  var data = []
+
+  Array.from(rectElements, (rectElement, i) => {
+    // 获取rect元素的属性值
+    var x = ~~(rectElement.getAttribute('x') || 0)
+    var y = ~~(rectElement.getAttribute('y') || 0)
+    var width = ~~rectElement.getAttribute('width')
+    var height = ~~rectElement.getAttribute('height')
+    // console.log('rectElements',rectElement,x,y,width,height)
+    if (x != undefined && y != undefined && width && height) {
+      // 创建一个新的canvas元素
+      var canvas = document.createElement('canvas')
+      canvas.width = width
+      canvas.height = height
+      var context = canvas.getContext('2d')
+
+      // 填充颜色到canvas
+      var fill = rectElement.getAttribute('fill')
+      context.fillStyle = fill
+      context.fillRect(0, 0, width, height)
+
+      // 将canvas转换为base64格式
+      var base64 = canvas.toDataURL()
+
+      // 将数据转化为指定的JSON格式
+
+      var rectData = {
+        x: parseInt(x),
+        y: parseInt(y),
+        width: parseInt(width),
+        height: parseInt(height),
+        z_index: i + 1,
+        scale_option: 'width',
+        image: base64,
+        mask: base64,
+        type: 'base64',
+        _t: 'rect'
+      }
+
+      // 将处理后的数据添加到数组中
+      data.push(rectData)
+    }
+  })
+
+  var svgWidth = svgElement.getAttribute('width')
+  var svgHeight = svgElement.getAttribute('height')
+
+  if (!(svgWidth && svgHeight)) {
+    // viewBox
+    let viewBox = svgElement.viewBox.baseVal
+
+    svgWidth = viewBox.width
+    svgHeight = viewBox.height
+  } else {
+    try {
+      svgWidth = ~~svgWidth.replace('px', '')
+      svgHeight = ~~svgHeight.replace('px', '')
+    } catch (error) {}
+  }
+
+  // 创建一个新的canvas元素
+  var canvas = document.createElement('canvas')
+  canvas.width = svgWidth
+  canvas.height = svgHeight
+  var context = canvas.getContext('2d')
+  // 绘制SVG到canvas
+  var svgString = new XMLSerializer().serializeToString(svgElement)
+  var DOMURL = window.URL || window.webkitURL || window
+
+  var svgBlob = new Blob([svgString], { type: 'image/svg+xml;charset=utf-8' })
+  var url = DOMURL.createObjectURL(svgBlob)
+
+  let img = await createImage(url)
+  context.drawImage(img, 0, 0)
+
+  let base64 = canvas.toDataURL()
+
+  var rectData = {
+    x: 0,
+    y: 0,
+    width: parseInt(svgWidth),
+    height: parseInt(svgHeight),
+    z_index: 0,
+    scale_option: 'width',
+    image: base64,
+    mask: base64,
+    type: 'base64',
+    _t: 'canvas'
+  }
+  data.push(rectData)
+
+  // 打印处理后的数据
+  console.log('layers', { data, image: base64, svgElement })
+  return { data, image: base64, svgElement }
+}
+
+function exportModelViewerImage (
+  modelViewer,
+  width,
+  height,
+  format = 'image/png',
+  quality = 1.0
+) {
+  const canvas = document.createElement('canvas')
+  canvas.width = width
+  canvas.height = height
+  const context = canvas.getContext('2d')
+
+  return new Promise((resolve, reject) => {
+    context.drawImage(modelViewer, 0, 0, width, height)
+
+    resolve(canvas.toDataURL(format, quality))
+  })
+}
+
+app.registerExtension({
+  name: 'Mixlab.image.SvgImage',
+  async getCustomWidgets (app) {
+    return {
+      SVG (node, inputName, inputData, app) {
+        // console.log('##node', node, inputName, inputData)
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          size: [128, 88], // a default size
+          draw (ctx, node, width, y) {},
+          computeSize (...args) {
+            return [128, 88] // a method to compute the current size of the widget
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let d = getLocalData('_mixlab_svg_image')
+            // console.log('serializeValue',d)
+            if (d) {
+              let url = d[node.id]
+              let dt = await fetch(url)
+              let svgStr = await dt.text()
+              const { data, image } = (await parseSvg(svgStr)) || {}
+              // console.log(data, image)
+              return JSON.parse(JSON.stringify({ data, image }))
+            } else {
+              return
+            }
+          }
+        }
+
+        // console.log('##node',node.serialize)
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+
+        return widget // and returns it.
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'SvgImage') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const uploadWidget = this.widgets.filter(w => w.name == 'upload')[0]
+        // console.log('SvgImage nodeData',await uploadWidget.serializeValue())
+
+        const widget = {
+          type: 'div',
+          name: 'upload-preview',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 44, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        const inputDiv = (key, placeholder, svgContainer) => {
+          let div = document.createElement('div')
+          const ip = document.createElement('input')
+          ip.type = 'file'
+          ip.className = `${'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: 60%;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)
+
+          let that = this
+
+          ip.addEventListener('change', event => {
+            const file = event.target.files[0]
+            const reader = new FileReader()
+
+            // 读取文件内容
+            reader.onload = async e => {
+              const svgContent = e.target.result
+
+              var blob = new Blob([svgContent], { type: 'image/svg+xml' })
+              let url = await uploadImage(blob)
+              // console.log(url)
+              const { svgElement, data, image } = await parseSvg(svgContent)
+              // 将提取的SVG元素显示在页面上
+              let dd = getLocalData(key)
+              dd[that.id] = url
+              setLocalDataOfWin(key, dd)
+              // console.log(this.id, ip.value.trim())
+
+              svgElement.style = `width: 90%;padding: 5%;height: auto;`
+              // 将提取的SVG元素显示在页面上
+
+              svgContainer.innerHTML = ''
+              svgContainer.appendChild(svgElement)
+              let h = ~~getComputedStyle(svgElement).height.replace('px', '')
+              if (that.size && that.size[1] < h) {
+                that.setSize([that.size[0], that.size[1] + h])
+                app.canvas.draw(true, true)
+              }
+              // console.log(that.size,~~getComputedStyle(svgElement).height.replace('px',''))
+
+              uploadWidget.value = await uploadWidget.serializeValue()
+            }
+
+            // 以文本形式读取文件
+            reader.readAsText(file)
+          })
+          return div
+        }
+
+        let svg = document.createElement('div')
+        svg.className = 'preview'
+        svg.style = `background:#eee;margin-top: 12px;`
+
+        let upload = inputDiv('_mixlab_svg_image', 'Svg', svg)
+
+        widget.div.appendChild(upload)
+        widget.div.appendChild(svg)
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          upload.remove()
+          svg.remove()
+          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) {
+    // Fires every time a node is constructed
+    // You can modify widgets/add handlers/etc here
+    const sleep = (t = 1000) => {
+      return new Promise((res, rej) => {
+        setTimeout(() => res(1), t)
+      })
+    }
+    if (node.type === 'SvgImage') {
+      // await sleep(0)
+      let widget = node.widgets.filter(w => w.name === 'upload-preview')[0]
+
+      let dd = getLocalData('_mixlab_svg_image')
+
+      let id = node.id
+      console.log('SvgImage load', node.widgets[0], node.widgets)
+      if (!dd[id]) return
+      let dt = await fetch(dd[id])
+      let svgStr = await dt.text()
+
+      const { svgElement, data, image } = await parseSvg(svgStr)
+      svgElement.style = `width: 90%;padding: 5%;height:auto`
+      // 将提取的SVG元素显示在页面上
+
+      widget.div.querySelector('.preview').innerHTML = ''
+      widget.div.querySelector('.preview').appendChild(svgElement)
+
+      const uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
+      uploadWidget.value = await uploadWidget.serializeValue()
+    }
+  }
+})

web/javascript/layer_mixlab.js

@@ -0,0 +1,611 @@
+import { app } from '../../../scripts/app.js'
+// import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    // flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+
+function createImage (url) {
+  let im = new Image()
+  return new Promise((res, rej) => {
+    im.onload = () => res(im)
+    im.src = url
+  })
+}
+
+const parseSvg = async svgContent => {
+  // 创建一个临时的DOM元素来解析SVG
+  const tempContainer = document.createElement('div')
+  tempContainer.innerHTML = svgContent
+
+  // 提取SVG元素
+  const svgElement = tempContainer.querySelector('svg')
+  if (!svgElement) return
+  // 获取SVG中 rect元素
+  var rectElements = svgElement?.querySelectorAll('rect') || []
+  // console.log(rectElements,svgElement)
+  // 定义一个数组来存储处理后的数据
+  var data = []
+
+  Array.from(rectElements, (rectElement, i) => {
+    // 获取rect元素的属性值
+    var x = ~~(rectElement.getAttribute('x') || 0)
+    var y = ~~(rectElement.getAttribute('y') || 0)
+    var width = ~~rectElement.getAttribute('width')
+    var height = ~~rectElement.getAttribute('height')
+    // console.log('rectElements',rectElement,x,y,width,height)
+    if (x != undefined && y != undefined && width && height) {
+      // 创建一个新的canvas元素
+      var canvas = document.createElement('canvas')
+      canvas.width = width
+      canvas.height = height
+      var context = canvas.getContext('2d')
+
+      // 填充颜色到canvas
+      var fill = rectElement.getAttribute('fill')
+      context.fillStyle = fill
+      context.fillRect(0, 0, width, height)
+
+      // 将canvas转换为base64格式
+      var base64 = canvas.toDataURL()
+
+      // 将数据转化为指定的JSON格式
+
+      var rectData = {
+        x: parseInt(x),
+        y: parseInt(y),
+        width: parseInt(width),
+        height: parseInt(height),
+        z_index: i + 1,
+        scale_option: 'width',
+        image: base64,
+        mask: base64,
+        type: 'base64',
+        _t: 'rect'
+      }
+
+      // 将处理后的数据添加到数组中
+      data.push(rectData)
+    }
+  })
+
+  var svgWidth = svgElement.getAttribute('width')
+  var svgHeight = svgElement.getAttribute('height')
+
+  if (!(svgWidth && svgHeight)) {
+    // viewBox
+    let viewBox = svgElement.viewBox.baseVal
+
+    svgWidth = viewBox.width
+    svgHeight = viewBox.height
+  }
+
+  // 创建一个新的canvas元素
+  var canvas = document.createElement('canvas')
+  canvas.width = svgWidth
+  canvas.height = svgHeight
+  var context = canvas.getContext('2d')
+  // 绘制SVG到canvas
+  var svgString = new XMLSerializer().serializeToString(svgElement)
+  var DOMURL = window.URL || window.webkitURL || window
+
+  var svgBlob = new Blob([svgString], { type: 'image/svg+xml;charset=utf-8' })
+  var url = DOMURL.createObjectURL(svgBlob)
+
+  let img = await createImage(url)
+  context.drawImage(img, 0, 0)
+
+  let base64 = canvas.toDataURL()
+
+  var rectData = {
+    x: 0,
+    y: 0,
+    width: parseInt(svgWidth),
+    height: parseInt(svgHeight),
+    z_index: 0,
+    scale_option: 'width',
+    image: base64,
+    mask: base64,
+    type: 'base64',
+    _t: 'canvas'
+  }
+  data.push(rectData)
+
+  // 打印处理后的数据
+  console.log('layers', { data, image: base64, svgElement })
+  return { data, image: base64, svgElement }
+}
+
+async function setArea (cw, ch, topBase64, base64, data, fn) {
+  let displayHeight = Math.round(window.screen.availHeight * 0.8)
+  let div = document.createElement('div')
+  div.innerHTML = `
+    <div id='ml_overlay' style='position: absolute;top:0;background: #251f1fc4;
+    height: 100vh;
+    z-index:999999;
+    width: 100%;'>
+      <img id='ml_video' style='position: absolute; 
+      height: ${displayHeight}px;user-select: none; 
+      -webkit-user-drag: none;
+      outline: 2px solid #eaeaea;
+      box-shadow: 8px 9px 17px #575757;' />
+      <div id='ml_selection' style='position: absolute; 
+      border: 2px dashed red;
+      pointer-events: none;
+      background-image: url("${topBase64}");
+            background-repeat: no-repeat;
+            background-size: cover;
+            '></div>
+      <div class="mx_close"> X </div>
+    </div>`
+  // document.body.querySelector('#ml_overlay')
+  document.body.appendChild(div)
+
+  // let canvas = document.createElement('canvas')
+  // canvas.width = cw
+  // canvas.height = ch
+
+  let img = div.querySelector('#ml_video')
+  // let overlay = div.querySelector('#ml_overlay')
+  let selection = div.querySelector('#ml_selection')
+  let close = div.querySelector('.mx_close')
+  let startX, startY, endX, endY
+  let start = false
+  let setDone = false
+  // Set video source
+  img.src = base64
+  // canvas.toDataURL();
+  close.style = `cursor: pointer;
+  position: fixed;
+  left: 12px;
+  top: 12px;
+  z-index: 99999999;
+  background: black;
+  width: 44px;
+  height: 44px;
+  text-align: center;
+  line-height: 44px;`
+
+  // init area
+  // const data = getSetAreaData()
+  let x = 0,
+    y = 0,
+    width = (cw * displayHeight) / ch,
+    height = displayHeight
+
+  let imgWidth = cw
+  let imgHeight = ch
+
+  if (data && data.width > 0 && data.height > 0) {
+    // 相同尺寸窗口，恢复选区
+    x = (width * data.x) / imgWidth
+    y = (height * data.y) / imgHeight
+    width = (width * data.width) / imgWidth
+    height = (height * data.height) / imgHeight
+  }
+
+  selection.style.left = x + 'px'
+  selection.style.top = y + 'px'
+  selection.style.width = width + 'px'
+  selection.style.height = height + 'px'
+
+  // Add mouse events
+  img.addEventListener('mousedown', startSelection)
+  img.addEventListener('mousemove', updateSelection)
+  img.addEventListener('mouseup', endSelection)
+
+  const removeDiv = () => {
+    div.remove()
+    close.removeEventListener('click', removeDiv)
+    img.removeEventListener('mousedown', startSelection)
+    img.removeEventListener('mousemove', updateSelection)
+    img.removeEventListener('mouseup', endSelection)
+    img.removeEventListener('mousedown', setDoneCheck)
+  }
+  close.addEventListener('click', removeDiv)
+
+  const setDoneCheck = event => {
+    console.log(setDone)
+    if (setDone) {
+      img.addEventListener('mousedown', startSelection)
+      img.addEventListener('mousemove', updateSelection)
+      img.addEventListener('mouseup', endSelection)
+      setDone = false
+      start = false
+      startX = event.clientX
+      startY = event.clientY
+    }
+  }
+  img.addEventListener('mousedown', setDoneCheck)
+
+  function remove () {
+    img.removeEventListener('mousedown', startSelection)
+    img.removeEventListener('mousemove', updateSelection)
+    img.removeEventListener('mouseup', endSelection)
+    setDone = true
+    // div.remove()
+  }
+
+  function startSelection (event) {
+    if (start == false) {
+      startX = event.clientX
+      startY = event.clientY
+      updateSelection(event)
+      start = true
+    } else {
+    }
+  }
+
+  function updateSelection (event) {
+    endX = event.clientX
+    endY = event.clientY
+
+    // Calculate width, height, and coordinates
+    let width = Math.abs(endX - startX)
+    let height = Math.abs(endY - startY)
+    let left = Math.min(startX, endX)
+    let top = Math.min(startY, endY)
+
+    // Set selection style
+    selection.style.left = left + 'px'
+    selection.style.top = top + 'px'
+    selection.style.width = width + 'px'
+    selection.style.height = height + 'px'
+  }
+
+  function endSelection (event) {
+    endX = event.clientX
+    endY = event.clientY
+
+    // 获取img元素的真实宽度和高度
+    let imgWidth = img.naturalWidth
+    let imgHeight = img.naturalHeight
+
+    // 换算起始坐标
+    let realStartX = (startX / img.offsetWidth) * imgWidth
+    let realStartY = (startY / img.offsetHeight) * imgHeight
+
+    // 换算起始坐标
+    let realEndX = (endX / img.offsetWidth) * imgWidth
+    let realEndY = (endY / img.offsetHeight) * imgHeight
+
+    startX = realStartX
+    startY = realStartY
+    endX = realEndX
+    endY = realEndY
+    // Calculate width, height, and coordinates
+    let width = Math.round(Math.abs(endX - startX))
+    let height = Math.round(Math.abs(endY - startY))
+    let left = Math.round(Math.min(startX, endX))
+    let top = Math.round(Math.min(startY, endY))
+
+    if (width <= 0 && height <= 0) return remove()
+
+    if (fn) fn(left, top, width, height)
+
+    remove()
+  }
+}
+
+app.registerExtension({
+  name: 'Mixlab.layer.ShowLayer',
+  async getCustomWidgets (app) {
+    return {
+      EDIT (node, inputName, inputData, app) {
+        //   console.log('EditLayer##node', node,inputName, inputData)
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          size: [128, 44], // a default size
+          draw (ctx, node, widget_width, y, widget_height) {
+            // console.log('EditLayer', this)
+            if (this.input)
+              Object.assign(
+                this.input.style,
+                get_position_style(ctx, widget_width, 32, node.size[1])
+              )
+          },
+          computeSize (...args) {
+            return [128, 44] // a method to compute the current size of the widget
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let d = getLocalData('_mixlab_edit_layer')
+            // console.log('EditLayer',d[node.id])
+            return d[node.id]
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'ShowLayer') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const findNode = nodeId => {
+          let node = app.graph._nodes_by_id[nodeId]
+          if (node?.type == 'Reroute') {
+            let linkId = node.inputs.filter(i => i.type == '*')[0].link
+            nodeId = app.graph.links.filter(link => link.id == linkId)[0]
+              ?.origin_id
+            return findNode(nodeId)
+          } else {
+            return nodeId
+          }
+        }
+
+        // 获取layers数据
+        const getLayers = async () => {
+          console.log(
+            'getLayers1',
+            this.inputs.filter(ip => ip.name === 'layers')
+          )
+          let linkId = this.inputs.filter(ip => ip.name === 'layers')[0].link
+          let nodeId = app.graph.links?.filter(link => link.id == linkId)[0]
+            ?.origin_id
+
+          if (nodeId) {
+            nodeId = findNode(nodeId)
+          }
+
+          //   let node = app.graph._nodes_by_id[nodeId]
+          //   if (node?.type == 'Reroute') {
+          //     linkId = node.inputs[0].link
+          //     nodeId = app.graph.links.filter(link => link.id == linkId)[0]
+          //       ?.origin_id
+          //   }
+
+          let d = getLocalData('_mixlab_svg_image')
+          console.log('test', d[nodeId])
+
+          if (d[nodeId]) {
+            let url = d[nodeId]
+            let dt = await fetch(url)
+
+            let svgStr = await dt.text()
+
+            const { data } = (await parseSvg(svgStr)) || {}
+            console.log('fetch', data)
+            return data
+          } else {
+            return []
+          }
+        }
+
+        // 修改layers数据
+        const setLayer = async (editIndex, layers = null) => {
+          //   let editIndex = 0
+          let lys = layers || (await getLayers())
+          let layer = lys[editIndex]
+          //   console.log(layer)
+
+          const updateValue = name => {
+            const x = this.widgets.filter(w => w.name == name)[0]
+            x.value = layer[name]
+          }
+          if (layer) {
+            Array.from(['x', 'y', 'width', 'height', 'z_index'], n =>
+              updateValue(n)
+            )
+          }
+        }
+
+        let that = this
+        const save_edit_layer_index = i => {
+          let data = getLocalData('_mixlab_edit_layer')
+          data[that.id] = i
+          localStorage.setItem('_mixlab_edit_layer', JSON.stringify(data))
+        }
+
+        await setLayer(0)
+        save_edit_layer_index(0)
+
+        const edit = this.widgets.filter(w => w.name == 'edit')[0]
+
+        edit.input = $el('div', {})
+        edit.input.style = `
+        display: flex;
+        flex-direction:row;
+        align-items: center;
+        margin-top: 0;`
+
+        const ip = $el('input', {})
+        ip.className = 'comfy-multiline-input'
+        ip.type = 'number'
+        ip.min = 0
+        ip.step = 1
+        ip.max = Math.max(0, (await getLayers()).length - 1)
+        // ip.className = `${'comfy-multiline-input'} `
+
+        ip.value = 0
+
+        ip.style = `
+        background-color: var(--comfy-input-bg);
+        color: var(--input-text);
+        outline: none;
+          border: none;
+          padding: 4px;
+          width: 60%;
+          cursor: pointer;
+          height: 24px;`
+        const label = document.createElement('label')
+        label.style = 'font-size: 10px;min-width:32px'
+        label.innerText = 'Layer Index'
+        edit.input.appendChild(label)
+        edit.input.appendChild(ip)
+
+        document.body.appendChild(edit.input)
+
+        ip.addEventListener('click', async event => {
+          console.log(await getLayers())
+          ip.max = Math.max(0, (await getLayers()).length - 1)
+        })
+
+        ip.addEventListener('change', async event => {
+          let index = ~~ip.value
+          let lys = await getLayers()
+          await setLayer(index, lys)
+          app.graph.setDirtyCanvas(true, true)
+          save_edit_layer_index(index)
+        })
+
+        // console.log('EditLayer nodeData', edit)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          edit.input.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+
+        this.serialize_widgets = false //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 === 'SvgImage') {
+      let widget = node.widgets.filter(w => w.div)[0]
+      let data = getLocalData('_mixlab_svg_image')
+      let id = node.id
+
+      // widget.div.querySelector('.Svg').value = data[id] || '#000000'
+    }
+  }
+})
+
+app.registerExtension({
+  name: 'Mixlab.layer.NewLayer',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData.name === 'NewLayer') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        let b = this.widgets.filter(w => w.type === 'button')[0]
+        // const [w, h, base64] = canvas
+
+        if (!b) {
+          const updateValue = (x1, y1, w1, h1) => {
+            if (this.widgets) {
+              for (const widget of this.widgets) {
+                if (widget.name === 'x') {
+                  widget.value = x1
+                }
+                if (widget.name === 'y') {
+                  widget.value = y1
+                }
+                if (widget.name === 'width') {
+                  widget.value = w1
+                }
+                if (widget.name === 'height') {
+                  widget.value = h1
+                }
+              }
+            }
+          }
+
+          this.addWidget('button', 'Set Area', '', () => {
+            let data = {}
+            for (const widget of this.widgets) {
+              if (widget.name === 'x') {
+                data.x = widget.value
+              }
+              if (widget.name === 'y') {
+                data.y = widget.value
+              }
+              if (widget.name === 'width') {
+                data.width = widget.value
+              }
+              if (widget.name === 'height') {
+                data.height = widget.value
+              }
+            }
+            try {
+              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 im = app.graph.getNodeById(nodeId).imgs[0]
+              // let src = im.src
+              setArea(
+                im.naturalWidth,
+                im.naturalHeight,
+                topIm.src,
+                im.src,
+                data,
+                updateValue
+              )
+            } catch (error) {}
+          })
+        }
+      }
+
+      const onRemoved = this.onRemoved
+      this.onRemoved = () => {
+        // let b = this.widgets.filter(w => w.type === 'button')[0];
+
+        return onRemoved?.()
+      }
+
+      if (this.onResize) {
+        this.onResize(this.size)
+      }
+
+      this.serialize_widgets = true //需要保存参数
+    }
+  }
+})

web/javascript/prompt_mixlab.js

@@ -0,0 +1,204 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    paddingLeft: '12px',
+    display: 'flex',
+    flexDirection: 'row',
+    // alignItems: 'center',
+    justifyContent: 'space-between'
+  }
+}
+
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+
+const setLocalDataOfWin = (key, value) => {
+  localStorage.setItem(key, JSON.stringify(value))
+  // window[key] = value
+}
+
+const createSelect = (select, opts, targetWidget) => {
+  select.style.display = 'block'
+  let html = ''
+  let isMatch = false
+  for (const opt of opts) {
+    html += `<option value='${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.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 = 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 keywords = ks[node.id]
+        // console.log('keywords',keywords)
+        let widget = node.widgets.filter(w => w.select)[0]
+        if (keywords && keywords[0]) {
+          // let widget = node.widgets.filter(w => w.select)[0]
+          // console.log('select',widget,widget.value)
+          widget.select.style.display = 'block'
+          createSelect(widget.select, keywords, prompt)
+        }
+      } catch (error) {}
+    }
+  }
+})

web/javascript/svg_icons.js

@@ -0,0 +1 @@
+export const closeIcon = '<svg xmlns="http://www.w3.org/2000/svg" height="24" viewBox="0 -960 960 960" width="24"><path d="m256-200-56-56 224-224-224-224 56-56 224 224 224-224 56 56-224 224 224 224-56 56-224-224-224 224Z"/></svg>'

web/javascript/ui.js

@@ -1,88 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-async function getCustomnodeMappings (mode = 'url') {
-  // mode = "local";
-  let api_host = `${window.location.hostname}:${window.location.port}`
-  let api_base = ''
-  let url = `${window.location.protocol}//${api_host}${api_base}`
-
-  const response = await fetch(`${url}/customnode/getmappings?mode=${mode}`)
-  const data = await response.json()
-
-  let nodes = {}
-  try {
-    for (let url in data) {
-      let n = data[url]
-      for (let node of n[0]) {
-        nodes[node] = { url, title: n[1].title_aux }
-      }
-    }
-  } catch (error) {}
-
-  return nodes
-}
-
-const missingNodeGithub = (missingNodeTypes, nodesMap) => {
-  let ts = {}
-
-  Array.from(new Set(missingNodeTypes), n => {
-    if (nodesMap[n]) {
-      let title = nodesMap[n].title
-      if (!ts[title]) {
-        ts[title] = {
-          title,
-          nodes: {},
-          url: nodesMap[n].url
-        }
-      }
-      ts[title].nodes[n] = 1
-    } else {
-      ts[n] = {
-        title: n,
-        nodes: {},
-        url: `https://github.com/search?q=${n}&type=code`
-      }
-      ts[n].nodes[n] = 1
-    }
-  })
-
-  return Array.from(Object.values(ts), n => {
-    const url = n.url
-    return `<li style="color: white;
-   background: black;
-   padding: 8px;
-   font-size: 12px;">${n.title}<a href="${url}" target="_blank"> 🔗</a></li>`
-  })
-}
-
-app.showMissingNodesError = async function (
-  missingNodeTypes,
-  hasAddedNodes = true
-) {
-  const nodesMap = await getCustomnodeMappings()
-  console.log('#nodesMap', nodesMap)
-  //   console.log('###MIXLAB', missingNodeTypes, hasAddedNodes)
-  this.ui.dialog.show(
-    `When loading the graph, the following node types were not found: <ul>${missingNodeGithub(
-      missingNodeTypes,
-      nodesMap
-    ).join('')}</ul>${
-      hasAddedNodes
-        ? 'Nodes that have failed to load will show as red on the graph.'
-        : ''
-    }`
-  )
-  this.logging.addEntry('Comfy.App', 'warn', {
-    MissingNodes: missingNodeTypes
-  })
-}
-
-// app.ui.dialog.show = function (html) {
-//   console.log('###MIXLAB', html)
-//   if (typeof html === 'string') {
-//     this.textElement.innerHTML = html
-//   } else {
-//     this.textElement.replaceChildren(html)
-//   }
-//   this.element.style.display = 'flex'
-// }

web/javascript/utils_mixlab.js

@@ -0,0 +1,278 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+import { addValueControlWidget } from '../../../scripts/widgets.js'
+
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+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) {
+        // console.log('##node', node)
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          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
+          },
+          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
+            }
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'Color') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        // console.log('Color nodeData', this.widgets)
+
+        const widget = {
+          type: 'div',
+          name: 'input_color',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 44, node.size[1])
+            )
+            // console.log('draw',y,node.widgets[0].last_y)
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        const inputDiv = () => {
+          let div = document.createElement('div')
+          div.id = `color_picker_${this.id}`
+          return div
+        }
+
+        let inputColor = inputDiv('_mixlab_utils_color', 'Color', '#000000')
+
+        widget.div.appendChild(inputColor)
+
+        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?.()
+        }
+
+        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 === 'Color') {
+      try {
+        let TCOLOR = node.widgets.filter(w => w.type == 'TCOLOR')[0]
+
+        setTimeout(() => node.pickr.setColor(TCOLOR.value || '#000000'), 1000)
+      } catch (error) {}
+    }
+  }
+})
+
+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]
+        }
+
+        console.log('TextToNumber', random_number.value)
+      }
+    }
+  }
+})

web/javascript/watcher_mixlab.js

@@ -169,7 +169,7 @@ app.registerExtension({
         this.onRemoved = function () {
           // widget.card.remove()
         }
-        this.serialize_widgets = false
+        this.serialize_widgets = true
       }
     }
   }

web/lib/photoswipe.min.css

@@ -0,0 +1 @@
+/*! 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}

web/main.css

@@ -1,3 +1,24 @@
 ::-webkit-scrollbar {
-    width: 2px;
-  }
+  width: 2px;
+}
+
+@keyframes loading_mixlab {
+  0% {
+    background-color: green;
+  }
+
+  50% {
+    background-color: lightgreen;
+  }
+
+  100% {
+    background-color: green;
+  }
+}
+
+.loading_mixlab {
+  background-color: green;
+  animation-name: loading_mixlab;
+  animation-duration: 2s;
+  animation-iteration-count: infinite;
+}

workflow/3-FloatVideo-workflow.json

@@ -1,6 +1,6 @@
 {
-  "last_node_id": 22,
-  "last_link_id": 48,
+  "last_node_id": 23,
+  "last_link_id": 51,
   "nodes": [
     {
       "id": 7,
@@ -105,7 +105,7 @@
         {
           "name": "text",
           "type": "STRING",
-          "link": 48,
+          "link": 51,
           "widget": {
             "name": "text"
           }
@@ -295,7 +295,7 @@
         "Node name for S&R": "KSampler"
       },
       "widgets_values": [
-        1115769645491668,
+        482859286431021,
         "randomize",
         4,
         1.6,
@@ -316,13 +316,13 @@
         "1": 246
       },
       "flags": {},
-      "order": 6,
+      "order": 7,
       "mode": 0,
       "inputs": [
         {
           "name": "images",
           "type": "IMAGE",
-          "link": 46
+          "link": 50
         }
       ],
       "properties": {
@@ -445,13 +445,13 @@
         "1": 58
       },
       "flags": {},
-      "order": 7,
+      "order": 6,
       "mode": 0,
       "inputs": [
         {
           "name": "image",
           "type": "IMAGE",
-          "link": 47
+          "link": 49
         }
       ],
       "outputs": [
@@ -472,45 +472,6 @@
         512
       ]
     },
-    {
-      "id": 22,
-      "type": "ScreenShare",
-      "pos": [
-        -63,
-        483
-      ],
-      "size": {
-        "0": 315,
-        "1": 98
-      },
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            46,
-            47
-          ],
-          "shape": 3,
-          "slot_index": 0
-        },
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": [
-            48
-          ],
-          "shape": 3,
-          "slot_index": 1
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ScreenShare"
-      }
-    },
     {
       "id": 20,
       "type": "FloatingVideo",
@@ -534,7 +495,69 @@
       ],
       "properties": {
         "Node name for S&R": "FloatingVideo"
-      }
+      },
+      "widgets_values": [
+        null
+      ]
+    },
+    {
+      "id": 23,
+      "type": "ScreenShare",
+      "pos": [
+        -65,
+        446
+      ],
+      "size": [
+        312.78457519531213,
+        606.2132135620109
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            49,
+            50
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "PROMPT",
+          "type": "STRING",
+          "links": [
+            51
+          ],
+          "shape": 3,
+          "slot_index": 1
+        },
+        {
+          "name": "FLOAT",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ScreenShare"
+      },
+      "widgets_values": [
+        null,
+        2018,
+        null,
+        null,
+        null,
+        null
+      ]
     }
   ],
   "links": [
@@ -787,24 +810,24 @@
       "CLIP"
     ],
     [
-      46,
-      22,
-      0,
-      2,
-      0,
-      "IMAGE"
-    ],
-    [
-      47,
-      22,
+      49,
+      23,
       0,
       18,
       0,
       "IMAGE"
     ],
     [
-      48,
-      22,
+      50,
+      23,
+      0,
+      2,
+      0,
+      "IMAGE"
+    ],
+    [
+      51,
+      23,
       1,
       8,
       1,

workflow/5-gpt-workflow.json

@@ -1,6 +1,6 @@
 {
-  "last_node_id": 34,
-  "last_link_id": 35,
+  "last_node_id": 47,
+  "last_link_id": 46,
   "nodes": [
     {
       "id": 27,
@@ -14,7 +14,7 @@
         "1": 164.31304931640625
       },
       "flags": {},
-      "order": 7,
+      "order": 5,
       "mode": 0,
       "inputs": [
         {
@@ -25,7 +25,7 @@
         {
           "name": "text",
           "type": "STRING",
-          "link": 35,
+          "link": 46,
           "widget": {
             "name": "text"
           }
@@ -136,7 +136,7 @@
         "Node name for S&R": "KSampler"
       },
       "widgets_values": [
-        264070254513477,
+        971428736321335,
         "randomize",
         15,
         8,
@@ -294,26 +294,26 @@
       }
     },
     {
-      "id": 33,
-      "type": "SessionHistory",
+      "id": 44,
+      "type": "ShowTextForGPT",
       "pos": [
-        1300,
-        430
-      ],
-      "size": [
-        580.1640810546876,
-        223.1105875244142
+        1171,
+        425
       ],
+      "size": {
+        "0": 432.46002197265625,
+        "1": 264.40771484375
+      },
       "flags": {},
       "order": 6,
       "mode": 0,
       "inputs": [
         {
-          "name": "session_history",
+          "name": "text",
           "type": "STRING",
-          "link": 33,
+          "link": 44,
           "widget": {
-            "name": "session_history"
+            "name": "text"
           }
         }
       ],
@@ -322,38 +322,37 @@
           "name": "STRING",
           "type": "STRING",
           "links": null,
-          "shape": 6,
-          "slot_index": 0
+          "shape": 6
         }
       ],
       "properties": {
-        "Node name for S&R": "SessionHistory"
+        "Node name for S&R": "ShowTextForGPT"
       },
       "widgets_values": [
-        "[\n  {\n    \"role\": \"user\",\n    \"content\": \"黑客与画家\"\n  },\n  {\n    \"role\": \"assistant\",\n    \"content\": \"\\\"Hacker and Painter\\\" is a popular Chinese comic series written and illustrated by Cheng先辈， which has gained a large following worldwide.\\n\\nThe series revolves around the lives of two main characters, a young hacker and an artist, who embark on a journey of self-discovery and personal growth. Set in a futuristic, dystopian world, the story combines elements of science fiction, technology, and action with a touch of humor and satire.\\n\\nThe art style in \\\"Hacker and Painter\\\" is a mix of sci-fi and traditional Chinese ink painting, with bold lines, vibrant colors, and intricate details. Cheng先辈 employs a unique painting technique, using watercolor washes and ink to create a dynamic, expressive, and visually striking visual style.\\n\\nThe artist's use of photography and various camera angles adds an extra layer of depth to the storytelling, allowing the viewer to experience the world from different perspectives and gain a deeper understanding of the characters' emotions and motivations.\\n\\nLighting plays a significant role in the series, with the use of dramatic, contrasting light sources adding to the intensity and emotion of various scenes. Cheng先辈 expertly utilizes lighting to enhance the mood and setting, drawing the audience into the world of \\\"Hacker and Painter\\\" and making them feel like they are part of the action.\\n\\nOverall, \\\"Hacker and Painter\\\" is a visually stunning and thought-provoking series that combines the best of science fiction, action, and art, making it a must-read for fans of all ages.\"\n  }\n]"
+        "[\n    {\n        \"role\": \"system\",\n        \"content\": \"You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.\"\n    },\n    {\n        \"role\": \"user\",\n        \"content\": \"\"\n    }\n]"
       ]
     },
     {
-      "id": 34,
-      "type": "SessionHistory",
+      "id": 43,
+      "type": "ShowTextForGPT",
       "pos": [
-        1300,
-        230
-      ],
-      "size": [
-        562.8610810546875,
-        125.32558752441412
+        1166,
+        740
       ],
+      "size": {
+        "0": 424.0079650878906,
+        "1": 430.6391296386719
+      },
       "flags": {},
-      "order": 5,
+      "order": 7,
       "mode": 0,
       "inputs": [
         {
-          "name": "session_history",
+          "name": "text",
           "type": "STRING",
-          "link": 34,
+          "link": 45,
           "widget": {
-            "name": "session_history"
+            "name": "text"
           }
         }
       ],
@@ -361,100 +360,114 @@
         {
           "name": "STRING",
           "type": "STRING",
-          "links": [
-            35
-          ],
-          "shape": 6,
-          "slot_index": 0
+          "links": null,
+          "shape": 6
         }
       ],
       "properties": {
-        "Node name for S&R": "SessionHistory"
+        "Node name for S&R": "ShowTextForGPT"
       },
       "widgets_values": [
-        "\"Hacker and Painter\" is a popular Chinese comic series written and illustrated by Cheng先辈， which has gained a large following worldwide.\n\nThe series revolves around the lives of two main characters, a young hacker and an artist, who embark on a journey of self-discovery and personal growth. Set in a futuristic, dystopian world, the story combines elements of science fiction, technology, and action with a touch of humor and satire.\n\nThe art style in \"Hacker and Painter\" is a mix of sci-fi and traditional Chinese ink painting, with bold lines, vibrant colors, and intricate details. Cheng先辈 employs a unique painting technique, using watercolor washes and ink to create a dynamic, expressive, and visually striking visual style.\n\nThe artist's use of photography and various camera angles adds an extra layer of depth to the storytelling, allowing the viewer to experience the world from different perspectives and gain a deeper understanding of the characters' emotions and motivations.\n\nLighting plays a significant role in the series, with the use of dramatic, contrasting light sources adding to the intensity and emotion of various scenes. Cheng先辈 expertly utilizes lighting to enhance the mood and setting, drawing the audience into the world of \"Hacker and Painter\" and making them feel like they are part of the action.\n\nOverall, \"Hacker and Painter\" is a visually stunning and thought-provoking series that combines the best of science fiction, action, and art, making it a must-read for fans of all ages."
+        "[\n    {\n        \"role\": \"user\",\n        \"content\": \"\"\n    },\n    {\n        \"role\": \"assistant\",\n        \"content\": \"I'm sorry, I'm ChatGLM3-6B, not ChatGPT. I am a language model jointly trained by Tsinghua University KEG Lab and Zhipu AI Company.\"\n    }\n]"
       ]
     },
     {
-      "id": 32,
-      "type": "PrimitiveNode",
+      "id": 45,
+      "type": "ShowTextForGPT",
       "pos": [
-        1314,
-        1075
+        1092,
+        262
       ],
       "size": {
-        "0": 571.9803466796875,
-        "1": 100.6700210571289
-      },
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": [
-            32
-          ],
-          "widget": {
-            "name": "prompt"
-          }
-        }
-      ],
-      "title": "prompt",
-      "properties": {},
-      "widgets_values": [
-        "黑客与画家"
-      ]
-    },
-    {
-      "id": 10,
-      "type": "ChatGPT",
-      "pos": [
-        1300,
-        720
-      ],
-      "size": {
-        "0": 586.7649536132812,
-        "1": 301.5857849121094
+        "0": 635.8358154296875,
+        "1": 101.46092224121094
       },
       "flags": {},
       "order": 4,
       "mode": 0,
       "inputs": [
         {
-          "name": "prompt",
+          "name": "text",
           "type": "STRING",
-          "link": 32,
+          "link": 43,
           "widget": {
-            "name": "prompt"
-          },
-          "slot_index": 0
+            "name": "text"
+          }
         }
       ],
       "outputs": [
         {
           "name": "STRING",
           "type": "STRING",
+          "links": null,
+          "shape": 6
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ShowTextForGPT"
+      },
+      "widgets_values": [
+        "I'm sorry, I'm ChatGLM3-6B, not ChatGPT. I am a language model jointly trained by Tsinghua University KEG Lab and Zhipu AI Company."
+      ]
+    },
+    {
+      "id": 47,
+      "type": "ChatGPTOpenAI",
+      "pos": [
+        585,
+        306
+      ],
+      "size": {
+        "0": 400,
+        "1": 342
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
           "links": [
-            34
+            43,
+            46
           ],
           "shape": 3,
           "slot_index": 0
         },
         {
-          "name": "JSON",
+          "name": "messages",
           "type": "STRING",
           "links": [
-            33
+            44
           ],
           "shape": 3,
           "slot_index": 1
+        },
+        {
+          "name": "session_history",
+          "type": "STRING",
+          "links": [
+            45
+          ],
+          "shape": 3,
+          "slot_index": 2
         }
       ],
       "properties": {
-        "Node name for S&R": "ChatGPT"
-      }
+        "Node name for S&R": "ChatGPTOpenAI"
+      },
+      "widgets_values": [
+        null,
+        null,
+        "",
+        "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.",
+        "gpt-3.5-turbo-16k",
+        2220,
+        "randomize",
+        1,
+        null
+      ]
     }
   ],
   "links": [
@@ -579,32 +592,32 @@
       "IMAGE"
     ],
     [
-      32,
-      32,
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workflow/Text-to-Image-app.json

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+}

workflow/workflow.md

@@ -1,2 +0,0 @@
-![workflow](./assets/City_Snapshot_00005_.png)
-![workflow](./assets/挖掘机_00076_1.png)

workflow/语音+实时换脸workflow.json

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