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
 
 
@@ -14,60 +47,80 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 !! Please use the address with HTTPS (https://127.0.0.1).
 
-## Installation
 
-manually install, simply clone the repo into the custom_nodes directory with this command:
+### SpeechRecognition & SpeechSynthesis
+![f](./assets/audio-workflow.svg)
 
-```
-cd ComfyUI/custom_nodes
+[Voice + Real-time Face Swap Workflow](./workflow/语音+实时换脸workflow.json)
 
-git clone https://github.com/shadowcz007/comfyui-mixlab-nodes.git
+### GPT
+> Support for calling multiple GPTs.ChatGPT、ChatGLM3 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 .  Azure OpenAI:https://xxxx.openai.azure.com 
 
-```
+![gpt-workflow.svg](./assets/gpt-workflow.svg)
 
-Install the requirements:
-
-run directly:
-```
-cd ComfyUI_Mixlab
-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
-```
+[workflow-5](./workflow/5-gpt-workflow.json)
 
 
+## Prompt
+> PromptSlide
+![](./assets/prompt_weight.png)
 
-## Nodes
-
-![main](./assets/all.png)
-![main2](./assets/detect-face-all.png)
-
-[workflow-1](./workflow/1-workflow.json)
+![](./workflow/promptslide-appinfo-workflow.svg)
 
 > randomPrompt
 
 ![randomPrompt](./assets/randomPrompt.png)
 
+> ClipInterrogator
+
+[add clip-interrogator](https://github.com/pharmapsychotic/clip-interrogator)
+
+### Layers
+> A new layer class node has been added, allowing you to separate the image into layers. After merging the images, you can input the controlnet for further processing.
+
+![layers](./assets/layers-workflow.svg)
+
+![poster](./assets/poster-workflow.svg)
+
+
+### 3D
+![](./assets/3dimage.png)
+[workflow](./workflow/3D-workflow.json)
+
+
+### LoadImagesFromLocal
+> Monitor changes to images in a local folder, and trigger real-time execution of workflows, supporting common image formats, especially PSD format, in conjunction with Photoshop. 
+
+![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)
+
+
+
 > TransparentImage
 
 ![TransparentImage](./assets/TransparentImage.png)
 
 
-
-> LoadImagesFromLocal
-
-![watch](./assets/load-watch.png)
-
-[workflow-4](./workflow/4-loadfromlocal-watcher-workflow.json)
-
 > Consistency Decoder
 
 [openai Consistency Decoder]( https://github.com/openai/consistencydecoder)
@@ -84,11 +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)
@@ -97,5 +212,26 @@ Add edges to an image.
 [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)
@@ -255,19 +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,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,
@@ -279,22 +545,53 @@ NODE_CLASS_MAPPINGS = {
     "ScreenShare":ScreenShareNode,
     "FloatingVideo":FloatingVideo,
     "CLIPSeg_":CLIPSeg,
-    "CombineMasks_":CombineMasks
+    "CombineMasks_":CombineMasks,
+    "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 #Example Node",
+    "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"
+    "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

@@ -0,0 +1,217 @@
+import openai 
+import time
+import urllib.error
+import re,json
+
+#  判断是否是azure服务
+def is_azure_url(url):
+    pattern = r'.*\.azure\.com$'
+    if re.match(pattern, url):
+        return True
+    else:
+        return False
+
+def azure_client(key,url):
+    client = openai.AzureOpenAI(
+        api_key=key,
+    # https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning
+    api_version="2023-07-01-preview",
+    # https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource
+    azure_endpoint=url
+    )
+    return client
+
+def openai_client(key,url):
+    client = openai.OpenAI(
+    api_key=key,
+    base_url=url
+    )
+    return client
+
+
+
+def chat(client, model_name,messages ):
+
+        try_count = 0
+        while True:
+            try_count += 1
+            try:
+                response = client.chat.completions.create(
+                    model=model_name,
+                    messages=messages
+                )
+                break
+            except openai.AuthenticationError as ex:
+                raise ex
+            except (urllib.error.HTTPError, openai.OpenAIError) as ex:
+                if try_count >= 3:
+                    raise ex
+                time.sleep(3)
+                continue
+
+        finish_reason = response.choices[0].finish_reason
+        if finish_reason != "stop":
+            raise RuntimeError("API finished with unexpected reason: " + finish_reason)
+
+        content=""
+        try:
+            content=response.choices[0].message.content
+        except:
+            content=response.choices[0].delta['content']
+
+        return content
+
+
+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.system_content="You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible."
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_key":("KEY", {"default": "", "multiline": True}),
+                "api_url":("URL", {"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,"dynamicPrompts": False
+                                       }),
+                "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": 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","STRING",)
+    RETURN_NAMES = ("text","messages","session_history",)
+    FUNCTION = "generate_contextual_text"
+    CATEGORY = "♾️Mixlab/GPT"
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,False,False,)
+
+    
+    def generate_contextual_text(self,
+                                 api_key,
+                                 api_url, 
+                                 prompt, 
+                                 system_content,
+                                   model, 
+                                   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=[]
+        
+        # 把系统信息和初始信息添加到会话历史中
+        if system_content:
+            self.system_content=system_content
+            # self.session_history=[]
+            # self.session_history.append({"role": "system", "content": system_content})
+        
+        # 
+        if is_azure_url(api_url):
+            client=azure_client(api_key,api_url)
+        else:
+            client=openai_client(api_key,api_url)
+            print('openai url')
+
+        # 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
+
+        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(messages, indent=4),json.dumps(self.session_history, indent=4),)
+
+
+
+class ShowTextForGPT:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "text": ("STRING", {"forceInput": True,"dynamicPrompts": False}),
+            }
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "♾️Mixlab/GPT"
+
+    def run(self, text):
+        # print(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

@@ -24,9 +24,42 @@ def base64_save(base64_data):
     return (image,mask)
 
 
+# # 把白色部分处理成黑色
+# def convert_to_bw(image):
+#     # 读取图片
+#     # image = Image.open(image_path)
+    
+#     # 获取图片的宽度和高度
+#     width, height = image.size
+    
+#     # 遍历图片的每个像素点
+#     for x in range(width):
+#         for y in range(height):
+#             # 获取当前像素点的RGB值
+#             r, g, b = image.getpixel((x, y))
+            
+#             # 判断当前像素点是否为白色
+#             if r == 255 and g == 255 and b == 255:
+#                 # 将白色部分处理成黑色
+#                 image.putpixel((x, y), (0, 0, 0))
+#             else:
+#                 # 将非白色部分处理成白色
+#                 image.putpixel((x, y), (255, 255, 255))
+    
+#     # 转换为黑白图
+#     mask = image.convert("L")
+    
+#     # # 保存处理后的图片
+#     # image.save("black_white_image.jpg")
+    
+#     # print("图片处理完成！")
+#     return mask
+
+
 def load_image(i,white_bg=False):
     # i = Image.open(fp)
     image = i.convert("RGB")
+
     image = np.array(image).astype(np.float32) / 255.0
     image = torch.from_numpy(image)[None,]
     if 'A' in i.getbands():
@@ -46,27 +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','MASK','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,mask,prompt)
-    
+        return {"ui":{"refresh_rate": [refresh_rate]},"result": (im,prompt,slide,seed,)}
+
 
 class FloatingVideo:
     @classmethod
@@ -81,7 +118,7 @@ class FloatingVideo:
     OUTPUT_NODE = True
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/image"
+    CATEGORY = "♾️Mixlab/image"
 
     # INPUT_IS_LIST = True
     # OUTPUT_IS_LIST = (False,False,)
@@ -104,3 +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"))

nodes/Watcher.py

@@ -48,7 +48,7 @@ class FolderWatcher:
             config['folder_path']=folder_path
             save_to_json(config_json,config)
 
-        # self.observer = Observer()
+        self.observer = None
         self.event_handler = self._create_event_handler()
         self.status = "Not started"
         self.event_type='-'
@@ -97,11 +97,12 @@ class FolderWatcher:
         print('Listening')
 
     def stop(self):
-        self.observer.stop()
-        self.observer.join()
-        self.observer=None
-        self.status = "Stopped"
-        self.event_type='-'
+        if self.observer!=None:
+            self.observer.stop()
+            self.observer.join()
+            self.observer=None
+            self.status = "Stopped"
+            self.event_type='-'
         print('Stopped')
     
   

requirements.txt

@@ -2,4 +2,7 @@ numpy
 pyOpenSSL
 watchdog
 opencv-python-headless
-matplotlib
+matplotlib
+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

@@ -3,21 +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='v0.2.2'
-
+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!=version){
-
+    console.log('Latest release version:', 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: ${latestVersion}</h4>
       <p>Please proceed to the official repository to download the latest version.</p>
-      <a style="    color: #2196F3;
+      <a style="color: #2196F3;
       font-size: 18px;
       font-weight: 800;
       letter-spacing: 2px;
@@ -25,12 +28,12 @@ fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   href="https://github.com/shadowcz007/comfyui-mixlab-nodes/releases/">https://github.com/shadowcz007/comfyui-mixlab-nodes/releases</a>
       `)
 
-        // window.alert(
-        //     `Please proceed to the official repository to download the latest version.https://github.com/shadowcz007/comfyui-mixlab-nodes/releases/`
-        //   )
-        //   window.open(
-        //     'https://github.com/shadowcz007/comfyui-mixlab-nodes/releases/'
-        //   )
+      // window.alert(
+      //     `Please proceed to the official repository to download the latest version.https://github.com/shadowcz007/comfyui-mixlab-nodes/releases/`
+      //   )
+      //   window.open(
+      //     'https://github.com/shadowcz007/comfyui-mixlab-nodes/releases/'
+      //   )
     }
   })
   .catch(error => {

web/javascript/gpt_mixlab.js

@@ -0,0 +1,277 @@
+import { app } from '../../../scripts/app.js'
+// import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+async function getConfig () {
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+
+  const res = await fetch(`${url}/mixlab`, {
+    method: 'POST'
+  })
+  return await res.json()
+}
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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
+}
+
+app.registerExtension({
+  name: 'Mixlab.GPT.ChatGPTOpenAI',
+  async getCustomWidgets (app) {
+    return {
+      KEY (node, inputName, inputData, app) {
+        console.log('##inputData', inputData)
+        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_api_key')
+            return data[node.id] || 'by Mixlab'
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      },
+      URL (node, inputName, inputData, app) {
+        // console.log('node', inputName, inputData[0])
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          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, 32] // a method to compute the current size of the widget
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let data = getLocalData('_mixlab_api_url')
+            return data[node.id] || 'https://api.openai.com/v1'
+          }
+        }
+        //  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 == 'ChatGPTOpenAI') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        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('ChatGPTOpenAI nodeData', this.widgets)
+
+        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, api_key.y, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        const inputDiv = (key, placeholder) => {
+          let div = document.createElement('div')
+          const ip = document.createElement('input')
+          ip.type = placeholder === 'Key' ? 'password' : 'text'
+          ip.className = `${'comfy-multiline-input'} ${placeholder}`
+          div.style = `display: flex;
+          align-items: center; 
+          margin: 6px 8px;
+          margin-top: 0;`
+          ip.placeholder = placeholder
+          ip.value = placeholder
+
+          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)
+
+          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
+        }
+
+        let inputKey = inputDiv('_mixlab_api_key', 'Key')
+        let inputUrl = inputDiv('_mixlab_api_url', 'URL')
+
+        widget.div.appendChild(inputKey)
+        widget.div.appendChild(inputUrl)
+
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          inputUrl.remove()
+          inputKey.remove()
+          widget.div.remove()
+          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 === '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.ShowTextForGPT',
+  async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === "ShowTextForGPT") {
+			function populate(text) {
+				if (this.widgets) {
+          
+					const pos = this.widgets.findIndex((w) => w.name === "text");
+					if (pos !== -1) {
+						for (let i = pos; i < this.widgets.length; i++) {
+							this.widgets[i].onRemove?.();
+						}
+						this.widgets.length = pos;
+					}
+				}
+        // console.log('ShowTextForGPT',text)
+				for (let list of text) {
+					const w = ComfyWidgets["STRING"](this, "text", ["STRING", { multiline: true }], app).widget;
+					w.inputEl.readOnly = true;
+					w.inputEl.style.opacity = 0.6;
+
+          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)
+          }
+
+					w.value =list;
+        
+				}
+        // console.log('ShowTextForGPT',this.widgets.length)
+				requestAnimationFrame(() => {
+					const sz = this.computeSize();
+					if (sz[0] < this.size[0]) {
+						sz[0] = this.size[0];
+					}
+					if (sz[1] < this.size[1]) {
+						sz[1] = this.size[1];
+					}
+					this.onResize?.(sz);
+					app.graph.setDirtyCanvas(true, false);
+				});
+			}
+
+			// When the node is executed we will be sent the input text, display this in the widget
+			const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = function (message) {
+				onExecuted?.apply(this, arguments);
+				populate.call(this, message.text);
+			};
+
+			const onConfigure = nodeType.prototype.onConfigure;
+			nodeType.prototype.onConfigure = function () {
+				onConfigure?.apply(this, arguments);
+				if (this.widgets_values?.length) {
+          
+					populate.call(this, this.widgets_values);
+				}
+			};
+
+      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

@@ -1,39 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-const missingNodeGithub = missingNodeTypes => {
-  return Array.from(
-    new Set(missingNodeTypes)
-  ,n=>{
-    const url = `https://github.com/search?q=${n}&type=code`;
-
-   return `<li style="color: white;
-   background: black;
-   padding: 8px;
-   font-size: 12px;">${n}<a href="${url}" target="_blank"> 🔗</a></li>`;
-  })
-}
-
-app.showMissingNodesError = function (missingNodeTypes, hasAddedNodes = true) {
-//   console.log('###MIXLAB', missingNodeTypes, hasAddedNodes)
-  this.ui.dialog.show(
-    `When loading the graph, the following node types were not found: <ul>${ missingNodeGithub(missingNodeTypes)
-      .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

@@ -14,7 +14,9 @@ async function getConfig () {
   return await res.json()
 }
 
-if(!window._mixlab_screen_prompt) window._mixlab_screen_prompt="beautiful scenery nature glass bottle landscape,under water"
+if (!window._mixlab_screen_prompt)
+  window._mixlab_screen_prompt =
+    'beautiful scenery nature glass bottle landscape,under water'
 
 function get_position_style (ctx, widget_width, y, node_height) {
   const MARGIN = 4 // the margin around the html element
@@ -59,7 +61,28 @@ app.registerExtension({
           name: inputName, // the name, slice
           size: [128, 24], // a default size
           draw (ctx, node, width, y) {
-            // a method to draw the widget (ctx is a CanvasRenderingContext2D)
+            // // 绘制文件图标的函数
+            // function drawFileIcon () {
+            //   // 清空画布
+            //   // ctx.clearRect(0, 0, canvas.width, canvas.height)
+
+            //   // 绘制文件外框
+            //   ctx.fillStyle = '#000'
+            //   ctx.fillRect(5, 5, 40, 40)
+
+            //   // 绘制文件夹图标
+            //   ctx.fillStyle = '#f00'
+            //   ctx.fillRect(10, 15, 30, 20)
+
+            //   // 绘制监听符号
+            //   ctx.beginPath()
+            //   ctx.arc(30, 35, 5, 0, 2 * Math.PI)
+            //   ctx.fillStyle = '#00f'
+            //   ctx.fill()
+            // }
+
+            // // 调用绘制函数
+            // drawFileIcon()
           },
           computeSize (...args) {
             return [128, 24] // a method to compute the current size of the widget
@@ -101,11 +124,7 @@ app.registerExtension({
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
 
-         
-        // console.log(
-        //   'watch widtget',
-        //   this.widgets.filter(w => w.name == 'watcher')[0]
-        // )
+        console.log('watch widtget', this.widgets)
 
         const watcher = this.widgets.filter(w => w.name == 'watcher')[0]
 
@@ -133,7 +152,6 @@ app.registerExtension({
           }
         }
 
-      
         // 上次路径填充
         getConfig().then(json => {
           let w = this.widgets.filter(w => w.name == 'file_path')[0]
@@ -142,7 +160,6 @@ app.registerExtension({
           }
           // console.log(json.event_type)
           window._mixlab_file_path_watcher = json.event_type
-          
         })
 
         /*
@@ -152,7 +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": 21,
-  "last_link_id": 45,
+  "last_node_id": 23,
+  "last_link_id": 51,
   "nodes": [
     {
       "id": 7,
@@ -14,7 +14,7 @@
         "1": 200
       },
       "flags": {},
-      "order": 7,
+      "order": 4,
       "mode": 0,
       "inputs": [
         {
@@ -89,12 +89,12 @@
         504,
         33
       ],
-      "size": [
-        400,
-        200
-      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
       "flags": {},
-      "order": 6,
+      "order": 8,
       "mode": 0,
       "inputs": [
         {
@@ -105,7 +105,7 @@
         {
           "name": "text",
           "type": "STRING",
-          "link": 43,
+          "link": 51,
           "widget": {
             "name": "text"
           }
@@ -295,7 +295,7 @@
         "Node name for S&R": "KSampler"
       },
       "widgets_values": [
-        613900833686415,
+        482859286431021,
         "randomize",
         4,
         1.6,
@@ -311,94 +311,24 @@
         338,
         786
       ],
-      "size": [
-        210,
-        246
-      ],
+      "size": {
+        "0": 210,
+        "1": 246
+      },
       "flags": {},
-      "order": 4,
+      "order": 7,
       "mode": 0,
       "inputs": [
         {
           "name": "images",
           "type": "IMAGE",
-          "link": 37
+          "link": 50
         }
       ],
       "properties": {
         "Node name for S&R": "PreviewImage"
       }
     },
-    {
-      "id": 19,
-      "type": "ScreenShare",
-      "pos": [
-        -55,
-        512
-      ],
-      "size": {
-        "0": 325.3117370605469,
-        "1": 459.7692565917969
-      },
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            37,
-            42
-          ],
-          "shape": 3,
-          "slot_index": 0
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": null,
-          "shape": 3
-        },
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": [
-            43
-          ],
-          "shape": 3,
-          "slot_index": 2
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ScreenShare"
-      }
-    },
-    {
-      "id": 20,
-      "type": "FloatingVideo",
-      "pos": [
-        2041,
-        277
-      ],
-      "size": [
-        315,
-        106
-      ],
-      "flags": {},
-      "order": 12,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 41
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "FloatingVideo"
-      }
-    },
     {
       "id": 6,
       "type": "LoraLoader",
@@ -411,7 +341,7 @@
         "1": 126
       },
       "flags": {},
-      "order": 8,
+      "order": 5,
       "mode": 0,
       "inputs": [
         {
@@ -463,7 +393,7 @@
         "1": 98
       },
       "flags": {},
-      "order": 3,
+      "order": 2,
       "mode": 0,
       "outputs": [
         {
@@ -515,13 +445,13 @@
         "1": 58
       },
       "flags": {},
-      "order": 5,
+      "order": 6,
       "mode": 0,
       "inputs": [
         {
           "name": "image",
           "type": "IMAGE",
-          "link": 42
+          "link": 49
         }
       ],
       "outputs": [
@@ -541,6 +471,93 @@
       "widgets_values": [
         512
       ]
+    },
+    {
+      "id": 20,
+      "type": "FloatingVideo",
+      "pos": [
+        1928,
+        295
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 41
+        }
+      ],
+      "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": [
@@ -744,14 +761,6 @@
       0,
       "LATENT"
     ],
-    [
-      37,
-      19,
-      0,
-      2,
-      0,
-      "IMAGE"
-    ],
     [
       38,
       16,
@@ -784,22 +793,6 @@
       0,
       "IMAGE"
     ],
-    [
-      42,
-      19,
-      0,
-      18,
-      0,
-      "IMAGE"
-    ],
-    [
-      43,
-      19,
-      2,
-      8,
-      1,
-      "STRING"
-    ],
     [
       44,
       6,
@@ -815,6 +808,30 @@
       6,
       1,
       "CLIP"
+    ],
+    [
+      49,
+      23,
+      0,
+      18,
+      0,
+      "IMAGE"
+    ],
+    [
+      50,
+      23,
+      0,
+      2,
+      0,
+      "IMAGE"
+    ],
+    [
+      51,
+      23,
+      1,
+      8,
+      1,
+      "STRING"
     ]
   ],
   "groups": [],

workflow/5-gpt-workflow.json

@@ -0,0 +1,631 @@
+{
+  "last_node_id": 47,
+  "last_link_id": 46,
+  "nodes": [
+    {
+      "id": 27,
+      "type": "CLIPTextEncode",
+      "pos": [
+        1961.178268896482,
+        527.6060791015625
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 24
+        },
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 46,
+          "widget": {
+            "name": "text"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            21
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"
+      ]
+    },
+    {
+      "id": 28,
+      "type": "CLIPTextEncode",
+      "pos": [
+        1958.7452854980445,
+        266
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 25
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
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workflow/6-gpt-all-workflow.json

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workflow/Prompt-weight-workflow.json

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workflow/workflow.md

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-![workflow](./assets/City_Snapshot_00005_.png)
-![workflow](./assets/挖掘机_00076_1.png)

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