Update App.py

publish routes without having to replicate add_routes

.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

@@ -1,18 +1,46 @@
-## 
+> 适配了最新版comfyui的py3.11 ，torch 2.1.2+cu121
-v0.4.0 🚀🚗🚚🏃‍
-- Add "help" option to the context menu for each node.
-- Add "find the node" option to the global context menu.
-- Optimize the 3D Image node and add workflow.
 
-### 3D
+> [discord](https://discord.gg/cXs9vZSqeK)
-![](./assets/3dimage.png)
+
-[workflow](./workflow/3D-workflow.json)
+## 🚀🚗🚚🏃 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
 
 
-### ScreenShareNode & FloatingVideoNode
+- Support multiple web app switching.
-> 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! 💻🌐
+- 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、PromptImage
+
+> seed统一输入控件，支持：SamplerCustom、KSampler
+
+
+## 🏃🚗🚚🚀  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)
 
 https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43e-410a-ab3a-1952b7b4e7da
@@ -23,6 +51,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 !! Please use the address with HTTPS (https://127.0.0.1).
 
+
 ### SpeechRecognition & SpeechSynthesis
 ![f](./assets/audio-workflow.svg)
 
@@ -31,17 +60,30 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 ### GPT
 > Support for calling multiple GPTs.ChatGPT、ChatGLM3 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 .  Azure OpenAI:https://xxxx.openai.azure.com 
 
-
 ![gpt-workflow.svg](./assets/gpt-workflow.svg)
 
 [workflow-5](./workflow/5-gpt-workflow.json)
 
-### 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)
+## Prompt
+> PromptSlide
+![](./assets/prompt_weight.png)
 
-[workflow-4](./workflow/4-loadfromlocal-watcher-workflow.json)
+<!-- ![](./workflow/promptslide-appinfo-workflow.svg) -->
+
+> randomPrompt
+
+![randomPrompt](./assets/randomPrompt.png)
+
+> ClipInterrogator
+
+[add clip-interrogator](https://github.com/pharmapsychotic/clip-interrogator)
+
+> PromptImage & PromptSimplification,Assist in simplifying prompt words, comparing images and prompt word nodes.
+
+> ChinesePrompt && PromptGenerate，中文prompt节点，直接用中文书写你的prompt
+
+![](./assets/ChinesePrompt_workflow.svg)
 
 
 ### Layers
@@ -51,6 +93,36 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 ![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)
+
+- [使用CkptNames 对比不同的模型效果](./workflow/ckpts-image-workflow.json)
+
+- [CkptNames compare the effects of different models.](./workflow/ckpts-image-workflow.json)
+
+
+
 ## Other Nodes
 
 ![main](./assets/all-workflow.svg)
@@ -58,9 +130,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 [workflow-1](./workflow/1-workflow.json)
 
-> randomPrompt
 
-![randomPrompt](./assets/randomPrompt.png)
 
 > TransparentImage
 
@@ -83,11 +153,15 @@ 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 "find the node" option to the global context menu.
+- 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.
 
@@ -97,12 +171,18 @@ An improvement has been made to directly redirect to GitHub to search for missin
 
 
 ### Models
-[Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : model/clipseg
 
-<!-- ### Workflow
+[Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:models/rembg
-[Workflow](./workflow.md) -->
 
+[Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : models/clipseg
 
+[Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : models/lama
+
+[Download Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base
+
+[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:prompt_generator/text2image-prompt-generator
+
+[Download Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main),move to:prompt_generator/opus-mt-zh-en
 
 ## Installation
 
@@ -137,16 +217,12 @@ pip3 install -r requirements.txt
 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
 
 
-
 #### Thanks:
 [ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)
 
 #### discussions:
 [discussions](https://github.com/shadowcz007/comfyui-mixlab-nodes/discussions)
 
-### TODO:
-- 音频播放节点：带可视化、支持多音轨、可配置音轨音量
-- vector https://github.com/GeorgLegato/stable-diffusion-webui-vectorstudio
 
 
 <picture>

__init__.py

@@ -4,7 +4,7 @@ import subprocess
 import importlib.util
 import sys,json
 import urllib
-
+import hashlib
 import datetime
 
 
@@ -79,6 +79,13 @@ 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
@@ -126,8 +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'):
@@ -155,19 +192,148 @@ def read_workflow_json_files(folder_path):
 def get_workflows():
     # print("#####path::", current_path)
     workflow_path=os.path.join(current_path, "workflow")
-    print('workflow_path: ',workflow_path)
+    # print('##workflow_path: ',workflow_path)
     if not os.path.exists(workflow_path):
         # 使用mkdir()方法创建新目录
         os.mkdir(workflow_path)
     workflows=read_workflow_json_files(workflow_path)
     return workflows
 
+def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=False):
+    app_path=os.path.join(current_path, "app")
+    if not os.path.exists(app_path):
+        os.mkdir(app_path)
+
+    category_path=os.path.join(app_path,category)
+    if not os.path.exists(category_path):
+        os.mkdir(category_path)
+
+    apps=[]
+    if filename==None:
+
+        #TODO 支持目录内遍历 
+        if is_all:
+            data=read_workflow_json_files_all(category_path)
+        else:
+            data=read_workflow_json_files(category_path)
+        
+        i=0
+        for item in data:
+            # print(item)
+            try:
+                x=item["data"]
+                if i==0:
+                    apps.append({
+                        "filename":item["filename"],
+                        # "category":item['category'],
+                        "data":x,
+                        "date":item["date"],
+                    })
+                else:
+                    category=''
+                    input=None
+                    output=None
+                    if 'category' in x['app']:
+                        category=x['app']['category']
+                    if 'input' in x['app']:
+                        input=x['app']['input']
+                    if 'output' in x['app']:
+                        output=x['app']['output']
+                    apps.append({
+                        "filename":item["filename"],
+                        "category":category,
+                        "data":{
+                            "app":{
+                                "category":category,
+                                "description":x['app']['description'],
+                                "filename":(x['app']['filename'] if 'filename' in x['app'] else "") ,
+                                "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
+                                "name":x['app']['name'],
+                                "version":x['app']['version'],
+                                "input":input,
+                                "output":output
+                            }
+                        },
+                        "date":item["date"]
+                    })
+                i+=1
+            except Exception as e:
+                print("发生异常：", str(e))
+    else:
+        app_workflow_path=os.path.join(category_path, filename)
+        # print('app_workflow_path: ',app_workflow_path)
+        try:
+            with open(app_workflow_path) as json_file:
+                apps = [{
+                    'filename':filename,
+                    'data':json.load(json_file)
+                }]
+        except Exception as e:
+            print("发生异常：", str(e))
+        
+        if len(apps)==1 and category!='' and category!=None:
+            data=read_workflow_json_files(category_path)
+            
+            for item in data:
+                x=item["data"]
+                # print(apps[0]['filename'] ,item["filename"])
+                if apps[0]['filename']!=item["filename"]:
+                    category=''
+                    input=None
+                    output=None
+                    if 'category' in x['app']:
+                        category=x['app']['category']
+                    if 'input' in x['app']:
+                        input=x['app']['input']
+                    if 'output' in x['app']:
+                        output=x['app']['output']
+                    apps.append({
+                        "filename":item["filename"],
+                        # "category":category,
+                        "data":{
+                            "app":{
+                                "category":category,
+                                "description":x['app']['description'],
+                                "filename":(x['app']['filename'] if 'filename' in x['app'] else "") ,
+                                "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
+                                "name":x['app']['name'],
+                                "version":x['app']['version'],
+                                "input":input,
+                                "output":output
+                            }
+                        },
+                        "date":item["date"]
+                    })
+
+    return apps
+
 def 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)
@@ -201,33 +367,62 @@ async def new_request(self, method, url, *args, **kwargs):
 
 # 应用 Monkey Patch
 aiohttp.ClientSession._request = new_request
+import socket
+
+async def check_port_available(address, port):
+    #检查端口是否可用
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+        sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        try:
+            sock.bind((address, port))
+            return True
+        except socket.error:
+            return False
 
 # https
 async def new_start(self, address, port, verbose=True, call_on_start=None):
-        
+    try:
-
         runner = web.AppRunner(self.app, access_log=None)
         await runner.setup()
+
+        if not await check_port_available(address, port):
+            raise RuntimeError(f"Port {port} is already in use.")
+
         site = web.TCPSite(runner, address, port)
         await site.start()
 
+        PromptServer.instance.port=port
+
+
         import ssl
-        crt,key=create_for_https()
+        crt, key = create_for_https()
         ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
-        ssl_context.load_cert_chain(crt,key)
+        ssl_context.load_cert_chain(crt, key)
-        site2 = web.TCPSite(runner, address, port+1,ssl_context=ssl_context)
+
-        await site2.start()
+        success = False
+        for i in range(10):  # 尝试最多10次
+            if await check_port_available(address, port + 1 + i):
+                https_port = port + 1 + i
+                site2 = web.TCPSite(runner, address, https_port, ssl_context=ssl_context)
+                await site2.start()
+                success = True
+                break
+
+        if not success:
+            raise RuntimeError(f"Ports {port + 1} to {port + 10} are all in use.")
 
         if address == '':
             address = '0.0.0.0'
         if verbose:
-            # print('\033[91mMixlab Nodes: \033[93mLoaded\033[0m')
             print("\033[93mStarting server\n")
             print("\033[93mTo see the GUI go to: http://{}:{}".format(address, port))
-            print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, port+1))
+            print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
         if call_on_start is not None:
             call_on_start(address, port)
 
+    except Exception as e:
+        print(f"Error starting the server: {e}")
+
         # import webbrowser
         # if os.name == 'nt' and address == '0.0.0.0':
         #     address = '127.0.0.1'
@@ -238,7 +433,7 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
 PromptServer.start=new_start
 
 # 创建路由表
-routes = web.RouteTableDef()
+routes = PromptServer.instance.routes
 
 @routes.post('/mixlab')
 async def mixlab_hander(request):
@@ -253,6 +448,18 @@ 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()
@@ -265,7 +472,31 @@ 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':
+                # 暂时没有用到
                 result={
                     'data':get_workflows(),
                     'status':'success',
@@ -289,21 +520,6 @@ async def nodes_map_hander(request):
 
     return web.json_response(result)
 
-def new_add_routes(self):
-        import nodes
-        self.app.add_routes(routes)
-        self.app.add_routes(self.routes)
-        for name, dir in nodes.EXTENSION_WEB_DIRS.items():
-            self.app.add_routes([
-                web.static('/extensions/' + urllib.parse.quote(name), dir, follow_symlinks=True),
-            ])
-        self.app.add_routes([
-            web.static('/', self.web_root, follow_symlinks=True),
-        ])
-
-PromptServer.add_routes=new_add_routes
-
-
 
 # 扩展api接口
 # from server import PromptServer
@@ -317,29 +533,45 @@ PromptServer.add_routes=new_add_routes
 
 
 # 导入节点
-from .nodes.PromptNode import RandomPrompt
+from .nodes.PromptNode import EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
-from .nodes.ImageNode import TransparentImage,LoadImagesFromPath,ResizeImage,TextImage,SvgImage,Image3D,EmptyLayer,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.ImageNode import SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.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 ColorInput,FontInput,TextToNumber,DynamicDelayProcessor
+from .nodes.Utils import CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
-
+from .nodes.Mask import OutlineMask,FeatheredMask
+from .nodes.App import AppInfo,AppNode
 
 # 要导出的所有节点及其名称的字典
 # 注意：名称应全局唯一
 NODE_CLASS_MAPPINGS = {
+    "AppInfo":AppInfo,
+    "AppNode":AppNode,
+    "TESTNODE_":TESTNODE_,
     "RandomPrompt":RandomPrompt,
+    "EmbeddingPrompt":EmbeddingPrompt,
+    "PromptSlide":PromptSlide,
+    "PromptSimplification":PromptSimplification,
+    "PromptImage":PromptImage,
+    "MirroredImage":MirroredImage,
+    "NoiseImage":NoiseImage,
+    "GradientImage":GradientImage,
     "TransparentImage":TransparentImage,
     "ResizeImageMixlab":ResizeImage,
     "LoadImagesFromPath":LoadImagesFromPath,
+    "LoadImagesFromURL":LoadImagesFromURL,
     "TextImage":TextImage,
     "EnhanceImage":EnhanceImage,
     "SvgImage":SvgImage,
     "3DImage":Image3D, 
+    "ImageColorTransfer":ImageColorTransfer,
     "ShowLayer":ShowLayer,
     "NewLayer":NewLayer,
+    "SplitImage":SplitImage,
+    "CenterImage":CenterImage,
+    "GridOutput":GridOutput,
     "MergeLayers":MergeLayers,
     "SplitLongMask":SplitLongMask,
     "FeatheredMask":FeatheredMask,
@@ -348,6 +580,7 @@ NODE_CLASS_MAPPINGS = {
     "AreaToMask":AreaToMask,
     "ImageCropByAlpha":ImageCropByAlpha,
     "VAELoaderConsistencyDecoder":VAELoader,
+    "SaveImageToLocal":SaveImageToLocal,
     "VAEDecodeConsistencyDecoder":VAEDecode,
     "ScreenShare":ScreenShareNode,
     "FloatingVideo":FloatingVideo,
@@ -359,15 +592,30 @@ NODE_CLASS_MAPPINGS = {
     "SpeechRecognition":SpeechRecognition,
     "SpeechSynthesis":SpeechSynthesis,
     "Color":ColorInput,
+    "FloatSlider":FloatSlider,
+    "IntNumber":IntNumber,
+    "TextInput_":TextInput,
     "Font":FontInput,
     "TextToNumber":TextToNumber,
-    "DynamicDelayProcessor":DynamicDelayProcessor
+    "DynamicDelayProcessor":DynamicDelayProcessor,
+    "MultiplicationNode":MultiplicationNode,
+    "GetImageSize_":GetImageSize_,
+    "SwitchByIndex":SwitchByIndex,
+    "LimitNumber":LimitNumber, 
+    "OutlineMask":OutlineMask,
+    "JoinWithDelimiter":JoinWithDelimiter,
+    "Seed_":CreateSeedNode,
+    "CkptNames_":CreateCkptNames,
+    "SamplerNames_":CreateSampler_names,
+    "LoraNames_":CreateLoraNames
+    # "LaMaInpainting":LaMaInpainting
     # "GamePal":GamePal
 }
 
 # 一个包含节点友好/可读的标题的字典
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "ResizeImageMixlab":"ResizeImage",
+    "AppInfo":"AppInfo ♾️Mixlab",
+    "ResizeImageMixlab":"ResizeImage ♾️Mixlab",
     "RandomPrompt": "Random Prompt ♾️Mixlab",
     "SplitLongMask":"Splitting a long image into sections",
     "VAELoaderConsistencyDecoder":"Consistency Decoder Loader",
@@ -380,14 +628,55 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
     "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
     "3DImage":"3DImage ♾️Mixlab",
-    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab"
+    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
-
+    "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
-    # "GamePal":"GamePal ♾️Mixlab"
+    "PromptSlide":"PromptSlide ♾️Mixlab",
+    "PromptGenerate_Mix":"PromptGenerate ♾️Mixlab",
+    "ChinesePrompt_Mix":"ChinesePrompt ♾️Mixlab",
+    "GamePal":"GamePal ♾️Mixlab",
+    "RembgNode_Mix":"Removebg",
+    "LoraNames_":"LoraName_TriggerWords.safetensors"
 }
 
 # 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')
-print('--------------')
+
+try:
+    from .nodes.Lama import LaMaInpainting
+    print('LaMaInpainting.available',LaMaInpainting.available)
+    if LaMaInpainting.available:
+        NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
+except Exception as e:
+    print('LaMaInpainting.available',False,e)
+
+try:
+    from .nodes.ClipInterrogator import ClipInterrogator
+    print('ClipInterrogator.available',ClipInterrogator.available)
+    if ClipInterrogator.available:
+        NODE_CLASS_MAPPINGS['ClipInterrogator']=ClipInterrogator
+except Exception as e:
+    print('ClipInterrogator.available',False,e)
+
+try:
+    from .nodes.TextGenerateNode import PromptGenerate,ChinesePrompt
+    print('PromptGenerate.available',PromptGenerate.available)
+    if PromptGenerate.available:
+        NODE_CLASS_MAPPINGS['PromptGenerate_Mix']=PromptGenerate
+    print('ChinesePrompt.available',ChinesePrompt.available)
+    if ChinesePrompt.available:
+        NODE_CLASS_MAPPINGS['ChinesePrompt_Mix']=ChinesePrompt
+except Exception as e:
+    print('TextGenerateNode.available',False,e)
+
+try:
+    from .nodes.RembgNode import RembgNode_
+    print('RembgNode_.available',RembgNode_.available)
+    if RembgNode_.available:
+        NODE_CLASS_MAPPINGS['RembgNode_Mix']=RembgNode_
+except Exception as e:
+    print('RembgNode_.available',False,e)
+
+print('\033[93m -------------- \033[0m')

data/Product-designer.txt

@@ -0,0 +1,30 @@
+Jony Ive
+Dieter Rams
+Philippe Starck
+Karim Rashid
+Yves Béhar
+Marc Newson
+Naoto Fukasawa
+Jonathan Adler
+Patricia Urquiola
+Ross Lovegrove
+Tom Dixon
+Jasper Morrison
+Charles Eames
+Ray Eames
+Achille Castiglioni
+Ron Arad
+Konstantin Grcic
+Marcel Wanders
+Maarten Baas
+Stefan Sagmeister
+Ingo Maurer
+Hella Jongerius
+Sam Hecht
+Kim Colin
+Jaime Hayon
+Michael Anastassiades
+Nendo
+Oki Sato
+Matali Crasset
+Tokujin Yoshioka

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/clothes.txt

@@ -0,0 +1,30 @@
+Elegant evening gown
+Casual jeans and t-shirt
+Formal black suit
+Stylish leather jacket
+Flowy bohemian dress
+Sporty tracksuit
+Chic little black dress
+Trendy ripped jeans
+Classic white button-down shirt
+Cozy oversized sweater
+Sophisticated tailored blazer
+Quirky patterned leggings
+Striped sailor top
+Polished knee-length skirt
+Vintage-inspired floral dress
+Edgy motorcycle jacket
+Preppy polo shirt
+Boho maxi skirt
+Professional pinstripe suit
+Relaxed denim shorts
+Glamorous sequined dress
+Athletic running shoes
+Formal bow tie
+Casual baseball cap
+Stylish fedora hat
+Warm woolen scarf
+Comfortable cotton socks
+Trendy ankle boots
+Cute summer sandals
+Cozy pajama set

data/expressions.txt

@@ -0,0 +1,30 @@
+Happy
+Sad
+Angry
+Surprised
+Excited
+Worried
+Confused
+Disgusted
+Amused
+Bored
+Curious
+Embarrassed
+Frustrated
+Nervous
+Pleased
+Relieved
+Shy
+Tired
+Serious
+Silly
+Proud
+Grumpy
+Smug
+Sarcastic
+Flirty
+Skeptical
+Shocked
+Blissful
+Envious
+Mischievous

data/extension-node-map.json

@@ -4761,26 +4761,46 @@
     ],
     "https://github.com/shadowcz007/comfyui-mixlab-nodes": [
         [
+            "GridOutput",
+            "SplitImage",
+            "PromptGenerate_Mix",
+            "JoinWithDelimiter",
+            "ChinesePrompt_Mix",
             "3DImage",
+            "AppInfo",
+            "AppNode",
+            "IntNumber",
+            "FloatSlider",
+            "ResizeImage",
+            "NoiseImage",
+            "PromptImage",
+            "SaveImageToLocal",
             "AreaToMask",
-            "CLIPSeg",
             "CLIPSeg_",
             "CharacterInText",
             "ChatGPTOpenAI",
             "Color",
+            "Seed_",
+            "CkptNames_",
+            "SamplerNames_",
+            "LoraNames_",
             "CombineMasks_",
-            "CombineSegMasks",
-            "EmptyLayer",
             "EnhanceImage",
+            "GradientImage",
             "FaceToMask",
             "FeatheredMask",
             "FloatingVideo",
             "Font",
             "ImageCropByAlpha",
             "LoadImagesFromPath",
+            "LoadImagesFromURL",
             "MergeLayers",
             "NewLayer",
+            "CenterImage",
             "RandomPrompt",
+            "PromptSlide",
+            "PromptSimplification",
+            "ClipInterrogator",
             "ScreenShare",
             "ShowLayer",
             "ShowTextForGPT",
@@ -4790,12 +4810,18 @@
             "SplitLongMask",
             "SvgImage",
             "TextImage",
+            "ResizeImageMixlab",
             "TransparentImage",
             "VAEDecodeConsistencyDecoder",
-            "VAELoaderConsistencyDecoder"
+            "VAELoaderConsistencyDecoder",
+            "TextToNumber",
+            "TextInput_",
+            "DynamicDelayProcessor",
+            "LaMaInpainting",
+            "Moondream"
         ],
         {
-            "title_aux": "comfyui-mixlab-nodes [WIP]"
+            "title_aux": "comfyui-mixlab-nodes"
         }
     ],
     "https://github.com/shiimizu/ComfyUI_smZNodes": [

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/profession.txt

@@ -0,0 +1,101 @@
+Doctor
+Teacher
+Engineer
+Lawyer
+Accountant
+Nurse
+Architect
+Chef
+Pilot
+Scientist
+Artist
+Writer
+Musician
+Actor
+Photographer
+Police officer
+Firefighter
+Dentist
+Pharmacist
+Veterinarian
+Electrician
+Plumber
+Carpenter
+Mechanic
+Farmer
+Astronaut
+Athlete
+Journalist
+Politician
+Economist
+Psychologist
+Social worker
+Librarian
+Translator
+Salesperson
+Entrepreneur
+Financial advisor
+Graphic designer
+Web developer
+Marketing manager
+Human resources manager
+Project manager
+Event planner
+Fashion designer
+Interior decorator
+Real estate agent
+Archaeologist
+Biologist
+Chemist
+Geologist
+Physicist
+Mathematician
+Historian
+Geographer
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+                     #MixCopilot

data/space.txt

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

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/App.py

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

nodes/Audio.py

@@ -24,7 +24,7 @@ class SpeechRecognition:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/audio"
+    CATEGORY = "♾️Mixlab/Audio"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -48,7 +48,7 @@ class SpeechSynthesis:
     OUTPUT_NODE = True
     OUTPUT_IS_LIST = (True,)
 
-    CATEGORY = "♾️Mixlab/audio"
+    CATEGORY = "♾️Mixlab/Audio"
 
     def run(self, text):
         # print(session_history)
@@ -82,7 +82,7 @@ class GamePal:
     OUTPUT_NODE = True
     OUTPUT_IS_LIST = (False,)
 
-    CATEGORY = "♾️Mixlab/audio"
+    CATEGORY = "♾️Mixlab/Audio"
 
     def run(self, input_text,input_num,python_code):
         exec(python_code)

nodes/ChatGPT.py

@@ -1,7 +1,15 @@
 import openai 
 import time
 import urllib.error
-import re,json
+import re,json,os,string,random
+import folder_paths
+
+
+def generate_random_string(length):
+    letters = string.ascii_letters + string.digits
+    return ''.join(random.choice(letters) for _ in range(length))
+
+
 
 #  判断是否是azure服务
 def is_azure_url(url):
@@ -73,17 +81,17 @@ class ChatGPTNode:
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "api_key":("KEY", {"default": "", "multiline": True}),
+                "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
-                "api_url":("URL", {"default": "", "multiline": True}),
+                "api_url":("URL", {"default": "", "multiline": True,"dynamicPrompts": False}),
-                "prompt": ("STRING", {"multiline": True}),
+                "prompt": ("STRING", {"multiline": True,"dynamicPrompts": False}),
                 "system_content": ("STRING", 
                                    {
                                        "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
-                                       "multiline": True
+                                       "multiline": True,"dynamicPrompts": False
                                        }),
                 "model": (["gpt-3.5-turbo","gpt-35-turbo","gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-4-0613","gpt-4-1106-preview"], 
                           {"default": "gpt-3.5-turbo"}),
-                "seed": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
                 "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
             },
              "hidden": {
@@ -167,8 +175,11 @@ class ShowTextForGPT:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "text": ("STRING", {"forceInput": True}),
+                "text": ("STRING", {"forceInput": True,"dynamicPrompts": False}),
-            }
+            },
+            "optional":{ 
+                    "output_dir": ("STRING",{"default": "","multiline": True,"dynamicPrompts": False}), 
+                }
         }
 
     INPUT_IS_LIST = True
@@ -179,9 +190,19 @@ class ShowTextForGPT:
 
     CATEGORY = "♾️Mixlab/GPT"
 
-    def run(self, text):
+    def run(self, text,output_dir):
-        # print(session_history)
+        output_dir=output_dir[0]
+        filename=generate_random_string(4)+'.txt'
+
+        if output_dir=='':
+            output_dir = folder_paths.get_temp_directory()
+        
+        save_to_dirpath=os.path.join(output_dir,filename)
+        with open(save_to_dirpath, 'w') as file:
+            file.write("\n".join(text))
+        # print(text)
         return {"ui": {"text": text}, "result": (text,)}
+        
 
 
 class CharacterInText:
@@ -189,8 +210,8 @@ class CharacterInText:
     def INPUT_TYPES(s):
         return {
             "required": {
-                 "text": ("STRING", {"multiline": True}),
+                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
-                 "character": ("STRING", {"multiline": True}),
+                 "character": ("STRING", {"multiline": True,"dynamicPrompts": False}),
                  "start_index": ("INT", {
                     "default": 1,
                     "min": 0, #Minimum value

nodes/ClipInterrogator.py

@@ -0,0 +1,278 @@
+import os,sys
+import folder_paths
+
+from PIL import Image
+import importlib.util
+
+import comfy.utils
+import numpy as np
+import json
+import torch
+import random
+
+
+# from clip_interrogator import Config, Interrogator
+
+
+global _available
+_available=False
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+
+try:
+    if is_installed('clip_interrogator')==False:
+        import subprocess
+
+        # 安装
+        print('#pip install clip-interrogator==0.6.0')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'clip-interrogator==0.6.0'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0:
+            print("#install success")
+            from clip_interrogator import Config, Interrogator
+            _available=True
+        else:
+            print("#install error")
+        
+    else:
+        from clip_interrogator import Config, Interrogator
+        _available=True
+
+except:
+    _available=False
+
+try:
+    from transformers import AutoProcessor, BlipForConditionalGeneration
+except:
+    _available=False
+    print('pls check  transformers.__version__>=4.36.0:: AutoProcessor, BlipForConditionalGeneration')
+
+
+
+def load_caption_model(model_path,config,t='blip-base'):
+    dtype=torch.float16 if config.device == 'cuda' else torch.float32
+    caption_model = BlipForConditionalGeneration.from_pretrained(model_path, torch_dtype=dtype)
+    
+    caption_processor = AutoProcessor.from_pretrained(model_path)
+
+    caption_model.eval()
+    if not config.caption_offload:
+        caption_model = caption_model.to(config.device)
+    
+    return (caption_model,caption_processor)
+    
+
+
+caption_model_path=os.path.join(folder_paths.models_dir, "clip_interrogator/Salesforce/blip-image-captioning-base")
+if not os.path.exists(caption_model_path):
+    print(f"## clip_interrogator_model not found: {caption_model_path}, pls download from https://huggingface.co/Salesforce/blip-image-captioning-base")
+    caption_model_path='Salesforce/blip-image-captioning-base'
+
+cache_path=os.path.join(folder_paths.models_dir, "clip_interrogator")
+if not os.path.exists(cache_path):
+    os.mkdir(cache_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)
+
+
+def image_analysis_fn(ci,image):
+    image = image.convert('RGB')
+    image_features = ci.image_to_features(image)
+
+    top_mediums = ci.mediums.rank(image_features, 5)
+    top_artists = ci.artists.rank(image_features, 5)
+    top_movements = ci.movements.rank(image_features, 5)
+    top_trendings = ci.trendings.rank(image_features, 5)
+    top_flavors = ci.flavors.rank(image_features, 5)
+
+    medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))}
+    artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))}
+    movement_ranks = {movement: sim for movement, sim in zip(top_movements, ci.similarities(image_features, top_movements))}
+    trending_ranks = {trending: sim for trending, sim in zip(top_trendings, ci.similarities(image_features, top_trendings))}
+    flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))}
+    
+    return {
+        "medium_ranks":medium_ranks, 
+        "artist_ranks":artist_ranks, 
+        "movement_ranks":movement_ranks, 
+        "trending_ranks":trending_ranks, 
+        "flavor_ranks":flavor_ranks
+        }
+
+
+def generate_sentences(data):
+    sentences = []
+
+    # Get the length of data
+    data_length = len(data)
+
+    # Use a recursive function to handle variable-length data
+    def generate_recursive(index, current_sentence, current_score):
+        # Check if recursion is complete
+        if index == data_length:
+            sentences.append({"sentence": current_sentence, "score": current_score})
+            return
+
+        # Get the current level data
+        current_data = data[index]
+
+        # Iterate through the current level data
+        for phrase in current_data:
+            sentence = current_sentence + ("," if current_sentence.strip() else "") + phrase
+            score = current_score + current_data[phrase]
+            generate_recursive(index + 1, sentence, score)
+
+    # Start recursive generation of sentences
+    generate_recursive(0, "", 0)
+
+    # Sort the generated sentences by score in descending order
+    sentences.sort(key=lambda x: x["score"], reverse=True)
+
+    def get_random_elements(elements, num):
+        return random.sample(elements, num)
+
+    ps = get_random_elements(sentences, 5)
+    ps = [s["sentence"] for s in sorted(ps, key=lambda x: x["score"], reverse=True)]
+
+    return ps
+
+
+
+
+def image_to_prompt(ci,image, mode):
+    ci.config.chunk_size = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+    ci.config.flavor_intermediate_count = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+    image = image.convert('RGB')
+    if mode == 'best':
+        return ci.interrogate(image)
+    elif mode == 'classic':
+        return ci.interrogate_classic(image)
+    elif mode == 'fast':
+        return ci.interrogate_fast(image)
+    elif mode == 'negative':
+        return ci.interrogate_negative(image)
+
+# image = Image.open(image_path).convert('RGB')
+# ci = Interrogator(Config(clip_model_name="ViT-L-14/openai"))
+# print(ci.interrogate(image))
+
+
+class ClipInterrogator:
+
+    global _available
+    available=_available
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "prompt_mode": (['fast','classic','best','negative'],),
+            "image_analysis": (["off","on"],), 
+                             },
+
+                # "optional":{
+                #     "output":("CLIPINTERROGATOR", {"multiline": True,"default": "", "dynamicPrompts": False})
+                # },
+
+                }
+    
+    RETURN_TYPES = ("STRING","STRING",)
+    RETURN_NAMES = ("prompt","random_samples",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,True,)
+    global ci
+    ci = None
+    def run(self,image,prompt_mode,image_analysis):
+        global ci
+
+        prompt_mode=prompt_mode[0]
+        analysis=image_analysis[0]
+
+        prompt_result=[]
+        analysis_result=[]
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(image)*(2 if analysis=='on' else 1))
+        
+        if ci==None:
+            config=Config(
+                clip_model_name="ViT-L-14/openai",
+                device="cuda" if torch.cuda.is_available() else "cpu",
+                download_cache=True,
+                clip_model_path=cache_path,
+                cache_path=cache_path
+                )
+            config.apply_low_vram_defaults()
+
+            caption_model,caption_processor=load_caption_model(caption_model_path,config)
+
+            config.caption_model= caption_model
+            config.caption_processor= caption_processor
+
+            ci = Interrogator(config)
+        # else:
+        #     simple_lama.model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        for i in range(len(image)):
+            im=image[i]
+
+            im=tensor2pil(im)
+            im=im.convert('RGB')
+
+            if analysis=='on':
+                analysis_res=image_analysis_fn(ci,im)
+                analysis_result.append( analysis_res )
+                pbar.update(1)
+
+            prompt=image_to_prompt(ci,im,prompt_mode)
+            pbar.update(1)
+            prompt_result.append(prompt)
+
+
+        # result.save("inpainted.png")
+        if ci.config.clip_offload and not ci.clip_offloaded:
+            ci.clip_model = ci.clip_model.to('cpu')
+            ci.clip_offloaded = True
+
+        if ci.config.caption_offload and not ci.caption_offloaded:
+            ci.caption_model = ci.caption_model.to('cpu')
+            ci.caption_offloaded = True
+
+        # analysis_result=[]
+        # items = app.graph.getNodeById(31).widgets[2].value["items"]
+        
+        random_samples=[]
+
+        for r in analysis_result:
+            random_sample = generate_sentences([r['medium_ranks'], r['artist_ranks'],r['movement_ranks'],r['trending_ranks'],r['flavor_ranks']])
+            for s in random_sample:
+                random_samples.append(s)
+        # print(len(random_samples))
+        # print('-----')
+        # print( random_samples)
+        return {
+            "ui":{
+                    "prompt": prompt_result,
+                    "analysis":analysis_result,
+                    "random_samples":random_samples
+                },
+            "result": (prompt_result,random_samples,)}

nodes/Clipseg.py

@@ -31,10 +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()
@@ -91,23 +101,23 @@ class CLIPSeg:
         return {"required":
                     {
                         "image": ("IMAGE",),
-                        "text": ("STRING", {"multiline": False}),
+                        "text": ("STRING", {"multiline": False,"dynamicPrompts": False}),
                         
                      },
                 "optional":
                     {
-                        "blur": ("FLOAT", {"min": 0, "max": 15, "step": 0.1, "default": 7}),
+                        "blur": ("FLOAT", {"min": 0, "max": 15, "step": 0.1, "default": 3}),
-                        "threshold": ("FLOAT", {"min": 0, "max": 1, "step": 0.05, "default": 0.4}),
+                        "threshold": ("FLOAT", {"min": 0, "max": 1, "step": 0.05, "default": 0.3}),
                         "dilation_factor": ("INT", {"min": 0, "max": 10, "step": 1, "default": 4}),
                     }
                 }
 
-    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]:
@@ -180,12 +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 = binary_mask_image.convert("L")
-        tensor_bw = np.array(tensor_bw).astype(np.float32) / 255.0
+        tensor_bw=pil2tensor(tensor_bw)
-        tensor_bw = torch.from_numpy(tensor_bw)[None,]
+        # tensor_bw = np.array(tensor_bw).astype(np.float32) / 255.0
-        tensor_bw = tensor_bw.squeeze(0)[..., 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
 
@@ -207,7 +218,7 @@ class CombineMasks:
                     },
                 }
         
-    CATEGORY = "♾️Mixlab/mask"
+    CATEGORY = "♾️Mixlab/Mask"
     RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
     RETURN_NAMES = ("Combined Mask","Heatmap Mask", "BW Mask")
 
@@ -235,7 +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")
         

nodes/Lama.py

@@ -0,0 +1,123 @@
+import os,sys
+import folder_paths
+
+from PIL import Image
+import importlib.util
+import numpy as np
+
+import torch
+
+global _available
+_available=False
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+if is_installed('simple_lama_inpainting')==False:
+    import subprocess
+    from packaging import version
+    
+    if version.parse(torch.__version__)>=version.parse('2.1'):
+        # 安装
+        print('#pip install simple_lama_inpainting')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'simple_lama_inpainting'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0:
+            print("#install success")
+            from simple_lama_inpainting import SimpleLama
+            _available=True
+        else:
+            print("#install error")
+    else:
+        print('#pls check your torch version >= 2.1')
+
+else:
+    from simple_lama_inpainting import SimpleLama
+    _available=True
+
+
+lama_path=os.path.join(folder_paths.models_dir, "lama")
+if not os.path.exists(lama_path):
+    os.mkdir(lama_path)
+ 
+llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
+if not os.path.exists(llma_model_path):
+    os.environ['LAMA_MODEL']=''
+    print(f"## lama torchscript model not found: {llma_model_path},pls download from https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt")
+else:
+    os.environ['LAMA_MODEL'] = llma_model_path
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+# simple_lama = SimpleLama()
+
+# img_path = "image.png"
+# mask_path = "mask.png"
+
+# image = Image.open(img_path)
+# mask = Image.open(mask_path).convert('L')
+
+# result = simple_lama(image, mask)
+# result.save("inpainted.png")
+
+
+class LaMaInpainting:
+    global _available
+    available=_available
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "mask": ("MASK",),
+                             },
+
+            
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Image"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+    global simple_lama
+    simple_lama = None
+    def run(self,image,mask):
+        global simple_lama
+
+        result=[]
+        if simple_lama==None:
+            simple_lama = SimpleLama()
+        else:
+            simple_lama.model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        for i in range(len(image)):
+            im=image[i]
+            ma=mask[i]
+            im=tensor2pil(im)
+            ma=tensor2pil(ma)
+            ma =ma.convert('L')
+            
+            res = simple_lama(im, ma)
+            res=pil2tensor(res)
+            result.append(res)
+        # result.save("inpainted.png")
+        if simple_lama.device=='cuda':
+            simple_lama.model.to('cpu')
+
+        return (result,)

nodes/Mask.py

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

nodes/PromptNode.py

@@ -1,15 +1,40 @@
 import random
 import comfy.utils
-import json
+import os
+import numpy as np
 from urllib import request, parse
+import folder_paths
+from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
+from PIL.PngImagePlugin import PngInfo
+# def queue_prompt(prompt_workflow):
+#     p = {"prompt": prompt_workflow}
+#     data = json.dumps(p).encode('utf-8')
+#     req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
+#     request.urlopen(req)    
+
+embeddings_path=os.path.join(folder_paths.models_dir, "embeddings")
+
+def get_files_with_extension(directory, extension):
+    file_list = []
+    for root, dirs, files in os.walk(directory):
+        for file in files:
+            if file.endswith(extension):
+                file_name = os.path.splitext(file)[0]
+                file_list.append(file_name)
+    return file_list
+
+def join_with_(text_list,delimiter):
+    joined_text = delimiter.join(text_list)
+    return joined_text
 
 
+class AnyType(str):
+  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
 
-def queue_prompt(prompt_workflow):
+  def __ne__(self, __value: object) -> bool:
-    p = {"prompt": prompt_workflow}
+    return False
-    data = json.dumps(p).encode('utf-8')
+
-    req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
+any_type = AnyType("*")
-    request.urlopen(req)    
 
 
 default_prompt1='''Swing
@@ -45,6 +70,232 @@ default_prompt1='''Swing
                                                 '''
 default_prompt1="\n".join([p.strip() for p in default_prompt1.split('\n') if p.strip()!=''])
 
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+def addWeight(text, weight=1):
+    if weight == 1:
+        return text
+    else:
+        return f"({text}:{round(weight,3)})"
+
+def prompt_delete_words(sentence, new_words_length):
+    # 使用逗号分割句子，并去除空格
+    words = [word.strip() for word in sentence.split(",")]
+    
+    # 计算需要删除的单词数量
+    num_to_delete = len(words) - new_words_length
+    
+    words_to=[w for w in words]
+
+    # 逐个删除单词并存储在新列表中
+    new_words = []
+    for i in range(len(words)):
+        if num_to_delete > 0:
+            num_to_delete -= 1
+        else:
+            words_to.pop()
+            if len(words_to)>0:
+                new_words.append(", ".join(words_to))
+         
+    return new_words
+
+# # 测试方法
+# sentence = "a computer, a glass tablet with a keyboard on a dark background, 3d illustration, reflection, cgi 8k, clear glass, archaic, cut-away, white outline"
+# new_words_length = 5
+# result = prompt_delete_words(sentence, new_words_length)
+# print(result)
+
+class PromptImage:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = "PromptImage"
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompts": ("STRING", 
+                         {
+                            "multiline": True, 
+                            "default": '',
+                            "dynamicPrompts": False
+                          }),
+
+                "images": ("IMAGE",{"default": None}), 
+                 "save_to_image": (["enable", "disable"],),
+                }
+            }
+    
+    RETURN_TYPES = ()
+   
+    OUTPUT_NODE = True
+
+    INPUT_IS_LIST = True
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+
+    # 运行的函数
+    def run(self,prompts,images,save_to_image):
+        filename_prefix="mixlab_"
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+            filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+        
+        results = list()
+
+        save_to_image=save_to_image[0]=='enable'
+
+        for index in range(len(images)):
+            res=[]
+            imgs=images[index]
+
+            for image in imgs:
+                img=tensor2pil(image)
+
+                metadata = None
+                if save_to_image:
+                    metadata = PngInfo()
+                    prompt_text=prompts[index]
+                    if prompt_text is not None:
+                        metadata.add_text("prompt_text", prompt_text)
+                    
+                file = f"{filename}_{index}_{counter:05}_.png"
+                img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
+                res.append({
+                    "filename": file,
+                    "subfolder": subfolder,
+                    "type": self.type
+                })
+                counter += 1
+            results.append(res)
+        
+        return { "ui": { "_images": results,"prompts":prompts } }
+
+
+
+
+class PromptSimplification:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": ("STRING", 
+                         {
+                            "multiline": True, 
+                            "default": '',
+                            "dynamicPrompts": False
+                          }),
+
+                "length":("INT", {"default": 5, "min": 1,"max":100, "step": 1, "display": "number"}),
+
+                # "min_value":("FLOAT", {
+                #         "default": -2, 
+                #         "min": -10, 
+                #         "max": 0xffffffffffffffff,
+                #         "step": 0.01, 
+                #         "display": "number"  
+                #     }),
+                # "max_value":("FLOAT", {
+                #         "default": 2, 
+                #         "min": -10, 
+                #         "max": 0xffffffffffffffff,
+                #         "step": 0.01, 
+                #         "display": "number"  
+                #     }),
+              
+                }
+            }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompts",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+    OUTPUT_NODE = True
+
+    # 运行的函数
+    def run(self,prompt,length):
+        length=length[0]
+        result=[]
+        for p in prompt:
+            nps=prompt_delete_words(p,length)
+            for n in nps:
+                result.append(n)
+
+        result= [elem.strip() for elem in result if elem.strip()]
+
+        return {"ui": {"prompts": result}, "result": (result,)}
+
+
+
+class PromptSlide:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                
+                "prompt_keyword": ("STRING", 
+                         {
+                            "multiline": False, 
+                            "default": '',
+                            "dynamicPrompts": False
+                          }),
+
+                "weight":("FLOAT", {"default": 1, "min": -3,"max": 3,
+                                                                "step": 0.01,
+                                                                "display": "slider"}),
+
+                # "min_value":("FLOAT", {
+                #         "default": -2, 
+                #         "min": -10, 
+                #         "max": 0xffffffffffffffff,
+                #         "step": 0.01, 
+                #         "display": "number"  
+                #     }),
+                # "max_value":("FLOAT", {
+                #         "default": 2, 
+                #         "min": -10, 
+                #         "max": 0xffffffffffffffff,
+                #         "step": 0.01, 
+                #         "display": "number"  
+                #     }),
+              
+                }
+            }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+    OUTPUT_NODE = False
+
+    # 运行的函数
+    def run(self,prompt_keyword,weight):
+        # if weight < min_value:
+        #     weight= min_value
+        # elif weight > max_value:
+        #     weight= max_value
+        p=addWeight(prompt_keyword,weight)
+        return (p,)
+
+
+
+
 class RandomPrompt:
 
     '''
@@ -70,6 +321,10 @@ class RandomPrompt:
                             "default": 'sticker, Cartoon, ``'
                           }),
                 "random_sample": (["enable", "disable"],),
+                # "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
+                },
+            "optional":{
+                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                 }
             }
     
@@ -79,15 +334,15 @@ class RandomPrompt:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/prompt"
+    CATEGORY = "♾️Mixlab/Prompt"
 
     OUTPUT_IS_LIST = (True,)
     OUTPUT_NODE = True
 
 
     # 运行的函数
-    def run(self,max_count,mutable_prompt,immutable_prompt,random_sample):
+    def run(self,max_count,mutable_prompt,immutable_prompt,random_sample,seed=0):
-        print('#运行的函数',mutable_prompt,immutable_prompt,max_count,random_sample)
+        # 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 +361,11 @@ class RandomPrompt:
             w1=w1.strip()
             for w2 in words2:
                 w2=w2.strip()
+                if '``' not in w2:
+                    if w2=="":
+                        w2='``'
+                    else:
+                        w2=w2+',``'
                 if w1!='' and w2!='':
                     prompts.append(w2.replace('``', w1))
                 pbar.update(1)
@@ -119,6 +379,8 @@ class RandomPrompt:
         else:
             prompts = prompts[:min(max_count,len(prompts))]
 
+        prompts= [elem.strip() for elem in prompts if elem.strip()]
+
         # return (new_prompt)
         return {"ui": {"prompts": prompts}, "result": (prompts,)}
 
@@ -126,62 +388,64 @@ class RandomPrompt:
 
 
 
-class RunWorkflow:
+
+class EmbeddingPrompt:
     @classmethod
     def INPUT_TYPES(s):
         return {
             "required": {
-                "workflow": ("STRING", {
+                "embedding":(get_files_with_extension(embeddings_path,'.pt'),),
-                            "multiline": False, 
+                "weight": ("FLOAT", {"default": 1, "min": -2, "max": 2,"step":0.01 ,"display": "slider"}),
-                            "default": ''
-                          }),
-                "prompt": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
-                 "image": ("IMAGE",),
-                 "input_node": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
-                 "output_node": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
                 },
                 
             }
     
-    
+    RETURN_TYPES = ("STRING",)
-
-    RETURN_TYPES = ("IMAGE","STRING",)
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/workflow"
+    CATEGORY = "♾️Mixlab/Prompt"
-
-    OUTPUT_IS_LIST = (True,)
-    OUTPUT_NODE = True
 
+    OUTPUT_IS_LIST = (False,)
+    # OUTPUT_NODE = True
 
     # 运行的函数
-    def run(self,workflow,prompt,image,input_node,output_node):
+    def run(self,embedding,weight):
-        print('#运行的函数',prompt,image,input_node,output_node)
+        weight = round(weight, 3)
-        workflow=json.loads(workflow)
+        prompt='embedding:'+embedding
-        input_node=input_node.split(".")
+        if weight!=1:
-        workflow[input_node[0]][input_node[1]][input_node[2]]=prompt
+            prompt='('+prompt+':'+str(weight)+')'
-
+        prompt=" "+prompt+' ' 
-        workflow_new={}
-        # 遍历，seed设为随机
-        for key, value in workflow.items():
-            if 'inputs' in value:
-                if 'seed' in value['inputs']:
-                    value['inputs']['seed']= random.randint(1, 18446744073709551614)
-            workflow_new[key]=value
-
-        queue_prompt(workflow_new)
-        print('#运行的函数',workflow_new[input_node[0]])
-
         # return (new_prompt)
-        return  {"ui":{"images": []},"result": ([image],['text'],)}
+        return (prompt,)
 
+RETURN_TYPES = (any_type,)
+
+class JoinWithDelimiter:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "text_list": (any_type,),
+                    "delimiter":(["newline","comma"],),
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+
+    INPUT_IS_LIST = True # 当true的时候，输入时list，当false的时候，如果输入是list，则会自动包一层for循环调用
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,text_list,delimiter):
+        delimiter=delimiter[0]
+        if delimiter =='newline':
+            delimiter='\n'
+        elif delimiter=='comma':
+            delimiter=','
+        t=''
+        if isinstance(text_list, list):
+            t=join_with_(text_list,delimiter)
+        return (t,)

nodes/RembgNode.py

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

nodes/ScreenShareNode.py

@@ -93,7 +93,7 @@ class ScreenShareNode:
     RETURN_NAMES = ("IMAGE","PROMPT","FLOAT","INT")
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     # INPUT_IS_LIST = True
     OUTPUT_IS_LIST = (False,False,False,False)
@@ -118,7 +118,7 @@ class FloatingVideo:
     OUTPUT_NODE = True
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     # INPUT_IS_LIST = True
     # OUTPUT_IS_LIST = (False,False,)

nodes/TextGenerateNode.py

@@ -0,0 +1,307 @@
+from transformers import pipeline, set_seed,AutoTokenizer, AutoModelForSeq2SeqLM
+import random
+import re
+
+import os,sys
+import folder_paths
+
+# from PIL import Image
+import importlib.util
+
+import comfy.utils
+# import numpy as np
+import torch
+import random
+
+global _available
+_available=True
+
+
+text_generator_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/text2image-prompt-generator")
+if not os.path.exists(text_generator_model_path):
+    print(f"## text_generator_model not found: {text_generator_model_path}, pls download from https://huggingface.co/succinctly/text2image-prompt-generator/tree/main")
+    text_generator_model_path='succinctly/text2image-prompt-generator'
+
+zh_en_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/opus-mt-zh-en")
+if not os.path.exists(zh_en_model_path):
+    print(f"## zh_en_model not found: {zh_en_model_path}, pls download from https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main")
+    zh_en_model_path='Helsinki-NLP/opus-mt-zh-en'
+
+
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+
+try:
+    if is_installed('sentencepiece')==False:
+        import subprocess
+
+        # 安装
+        print('#pip install sentencepiece')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'sentencepiece'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0 and is_installed('sentencepiece'):
+            print("#install success")
+            _available=True
+        else:
+            print("#install error")
+            _available=False
+        
+    else:
+        _available=True
+
+except:
+    _available=False
+
+
+
+def translate(zh_en_tokenizer,zh_en_model,text):
+    with torch.no_grad():
+        encoded = zh_en_tokenizer([text], return_tensors="pt")
+        encoded.to(zh_en_model.device)
+        sequences = zh_en_model.generate(**encoded)
+        return zh_en_tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
+
+# input = "青春不能回头，所以青春没有终点。 ——《火影忍者》"
+# print(input, translate(input))
+
+
+def text_generate(text_pipe,input,seed=None):
+    
+    if seed==None:
+        seed = random.randint(100, 1000000)
+    
+    set_seed(seed)
+
+    for count in range(6):    
+        sequences = text_pipe(input, max_length=random.randint(60, 90), num_return_sequences=8)
+        list = []
+        for sequence in sequences:
+            line = sequence['generated_text'].strip()
+            if line != input and len(line) > (len(input) + 4) and line.endswith((":", "-", "—")) is False:
+                list.append(line)
+
+        result = "\n".join(list)
+        result = re.sub('[^ ]+\.[^ ]+','', result)
+        result = result.replace("<", "").replace(">", "")
+        if result != "":
+            return result
+        if count == 5:
+            return result
+
+# input = "Youth can't turn back, so there's no end to youth."
+# print(input, text_generate(input))
+
+    
+import re
+
+def correct_prompt_syntax(prompt):
+
+    print("input prompt",prompt)
+    corrected_elements = []
+    # 处理成统一的英文标点
+    prompt = prompt.replace('（', '(').replace('）', ')').replace('，', ',').replace(';', ',').replace('。', '.').replace('：',':')
+    # 删除多余的空格
+    prompt = re.sub(r'\s+', ' ', prompt).strip()
+
+    # 分词
+    prompt_elements = prompt.split(',')
+
+    for element in prompt_elements:
+        element = element.strip()
+
+        # 处理空元素
+        if not element:
+            continue
+
+        # 检查并处理圆括号、方括号、尖括号
+        if element[0] in '([':
+            corrected_element = balance_brackets(element, '(', ')') if element[0] == '(' else balance_brackets(element, '[', ']')
+        elif element[0] == '<':
+            corrected_element = balance_brackets(element, '<', '>')
+        else:
+            # 删除开头的右括号或右方括号
+            corrected_element = element.lstrip(')]')
+
+        corrected_elements.append(corrected_element)
+
+    # 重组修正后的prompt
+    corrected_prompt = ', '.join(corrected_elements)
+    print("output prompt",corrected_prompt)
+    return corrected_prompt
+
+def balance_brackets(element, open_bracket, close_bracket):
+    open_brackets_count = element.count(open_bracket)
+    close_brackets_count = element.count(close_bracket)
+    return element + close_bracket * (open_brackets_count - close_brackets_count)
+
+# # 示例使用
+# test_prompt = "((middle-century castles)), [forsaken: 0.8], (mystery dragons: 1.3, mist forests, sunsets, quiet; (((dummy)), [fisting city: 0.5] background, radiant, soft and flavoured,] promising mountains, ((starry: 1.6), [[crowds], [middle-century castle: urban landscapes of the future: 0.5], [yellow: bright sun: 0.7], overlooking"
+# corrected_prompt = correct_prompt_syntax(test_prompt)
+# print(corrected_prompt)
+
+        
+        
+class ChinesePrompt:
+
+    global _available
+    available=_available
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+               "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
+               "generation": (["on","off"],{"default": "off"}), 
+                             },
+               
+                "optional":{
+                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
+                    
+                },
+
+                }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    global text_pipe,zh_en_model,zh_en_tokenizer
+
+    text_pipe= None
+    zh_en_model=None
+    zh_en_tokenizer=None 
+
+    def run(self,text,seed,generation):
+        global text_pipe,zh_en_model,zh_en_tokenizer
+
+        seed=seed[0]
+        generation=generation[0]
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(text)+1)
+        
+        texts = [correct_prompt_syntax(t) for t in text]
+        print('correct_prompt_syntax::',texts)
+        if zh_en_model==None:
+            zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval()
+            zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path,padding=True, truncation=True)
+        
+        zh_en_model.to("cuda" if torch.cuda.is_available() else "cpu")
+            # zh_en_tokenizer.to("cuda" if torch.cuda.is_available() else "cpu")
+        
+        text_pipe=pipeline('text-generation', model=text_generator_model_path,device="cuda" if torch.cuda.is_available() else "cpu")
+        
+        # text_pipe.model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        prompt_result=[]
+
+        # print('zh_en_model device',zh_en_model.device,text_pipe.model.device,torch.cuda.current_device() )
+        en_texts=[]
+        for t in texts:
+            en_text=translate(zh_en_tokenizer,zh_en_model,t)
+            en_texts.append(en_text)
+
+        zh_en_model.to('cpu')
+        print("test en_text",en_texts)
+        # en_text.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        pbar.update(1)
+        for t in en_texts:
+            if generation=='on':
+                prompt =text_generate(text_pipe,t,seed)
+                # 多条，还是单条
+                lines = prompt.split("\n")
+                longest_line = max(lines, key=len)
+                # print(longest_line)
+                prompt_result.append(longest_line)
+            else:
+                prompt_result.append(t)
+            pbar.update(1)
+
+        text_pipe.model.to('cpu')
+        prompt_result = [correct_prompt_syntax(p) for p in prompt_result]
+        
+        return {
+            "ui":{
+                    "prompt": prompt_result
+                },
+            "result": (prompt_result,)}
+
+
+class PromptGenerate:
+
+    global _available
+    available=_available
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
+                             },
+
+                "optional":{
+                    "multiple": (["off","on"],), 
+                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
+                },
+
+                }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    global text_pipe
+
+    text_pipe= None
+    # 
+
+    def run(self,text,multiple,seed):
+        global text_pipe
+
+        seed=seed[0]
+
+        multiple=multiple[0]
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(text))
+        
+        text_pipe=pipeline('text-generation', model=text_generator_model_path,device="cuda" if torch.cuda.is_available() else "cpu")
+        
+        prompt_result=[]
+
+        for t in text:
+            prompt =text_generate(text_pipe,t,seed)
+            prompt = prompt.split("\n")
+            if multiple=='off':
+                prompt = [max(prompt, key=len)]
+            
+            for p in prompt:
+                prompt_result.append(p)
+            pbar.update(1)
+
+        text_pipe.model.to('cpu')
+
+        return {
+            "ui":{
+                    "prompt": prompt_result
+                },
+            "result": (prompt_result,)}

nodes/Utils.py

@@ -1,9 +1,70 @@
-import os
+import os,platform
-import re,random
+import re,random,json
+
 # FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
+import folder_paths
+# import matplotlib.font_manager as fm
+import torch
 
-import matplotlib.font_manager as fm
 
+
+def recursive_search(directory, excluded_dir_names=None):
+    if not os.path.isdir(directory):
+        return [], {}
+
+    if excluded_dir_names is None:
+        excluded_dir_names = []
+
+    result = []
+    dirs = {directory: os.path.getmtime(directory)}
+    for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
+        subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
+        for file_name in filenames:
+            relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+            result.append(relative_path)
+        for d in subdirs:
+            path = os.path.join(dirpath, d)
+            dirs[path] = os.path.getmtime(path)
+    return result, dirs
+
+def filter_files_extensions(files, extensions):
+    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))
+
+
+def get_system_font_path():
+    ps=[]
+    system = platform.system()
+    if system == "Windows":
+        ps.append(os.path.join(os.environ["WINDIR"], "Fonts"))
+    elif system == "Darwin":
+        ps.append(os.path.join("/Library", "Fonts"))
+    elif system == "Linux":
+        ps.append(os.path.join("/usr", "share", "fonts"))
+        ps.append(os.path.join("/usr", "local", "share", "fonts"))
+    ps=[p for p in ps if os.path.exists(p)]
+    file_paths=[]
+    for f in ps:
+        result, dirs=recursive_search(f)
+        for r in result:
+            file_paths.append(r)
+    file_paths=filter_files_extensions(file_paths,[".otf", ".ttf"])
+
+    return file_paths
+
+
+
+# import json
+# import hashlib
+
+
+# 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 get_font_files(directory):
     font_files = {}
 
@@ -16,14 +77,14 @@ def get_font_files(directory):
 
     # 尝试获取系统字体
     try:
-        font_paths = fm.findSystemFonts()
+        font_paths = get_system_font_path()
-        for path in font_paths:
+        for file in font_paths:
             try:
-                font_prop = fm.FontProperties(fname=path)
+                font_name = os.path.splitext(file)[0]
-                font_name = font_prop.get_name()
+                font_path = file
-                font_files[font_name] = path
+                font_files[font_name] = os.path.abspath(font_path)
             except Exception as e:
-                print(f"Error processing font {path}: {e}")
+                print(f"Error processing font {file}: {e}")
     except Exception as e:
         print(f"Error finding system fonts: {e}")
 
@@ -35,6 +96,21 @@ font_files = get_font_files(r_directory)
 # print(font_files)
 
 
+def flatten_list(nested_list):
+    flat_list = []
+    for item in nested_list:
+        if isinstance(item, list):
+            flat_list.extend(flatten_list(item))
+        else:
+            if torch.is_tensor(item):
+                print('item.shape',item.shape)
+                for i in range(item.shape[0]):
+                    flat_list.append(item[i:i + 1, ...])
+            else:
+                flat_list.append(item)
+    return flat_list
+
+
 class ColorInput:
     @classmethod
     def INPUT_TYPES(s):
@@ -44,18 +120,23 @@ class ColorInput:
                              },
                 }
     
-    RETURN_TYPES = ("STRING",)
+    RETURN_TYPES = ("STRING","INT","INT","INT","FLOAT",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_NAMES = ("hex","r","g","b","a",)
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,False,)
+    OUTPUT_IS_LIST = (False,False,False,False,False,)
 
     def run(self,color):
-        return (color,)
+        h=color['hex']
+        r=color['r']
+        g=color['g']
+        b=color['b']
+        a=color['a']
+        return (h,r,g,b,a,)
 
 
 
@@ -73,13 +154,13 @@ class FontInput:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,False,)
+    OUTPUT_IS_LIST = (False,)
 
     def run(self,font):
-
+        
         return (font_files[font],)
     
 class TextToNumber:
@@ -88,14 +169,17 @@ class TextToNumber:
         return {"required": { 
                     "text": ("STRING",{"multiline": False,"default": "1"}),
                     "random_number": (["enable", "disable"],),
-                    "number":("INT", {
+                    "max_num":("INT", {
-                        "default": 0, 
+                        "default": 10, 
-                        "min": 0, #Minimum value
+                        "min":2, #Minimum value
                         "max": 10000000000, #Maximum value
                         "step": 1, #Slider's step
                         "display": "number" # Cosmetic only: display as "number" or "slider"
                     }),
                              },
+                 "optional":{
+                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                }
                 }
     
     RETURN_TYPES = ("INT",)
@@ -103,12 +187,12 @@ class TextToNumber:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
 
-    def run(self,text,random_number,number):
+    def run(self,text,random_number,max_num,seed=0):
         
         numbers = re.findall(r'\d+', text)
         result=0
@@ -117,9 +201,172 @@ class TextToNumber:
             # print(result)
         
         if random_number=='enable' and result>0:
-            result= random.randint(1, 10000000000)
+            result= random.randint(1, max_num)
         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": 0xffffffffffffffff, #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
+        scaled_number = (number - min_value) / (max_value - min_value)
+        return (scaled_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,),
+                    "multiply_by":("FLOAT", {
+                        "default": 0, 
+                        "min": -2, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                     "add_by":("FLOAT", {
+                        "default": 0, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "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,multiply_by,add_by):
+        b=int(numberA*multiply_by+add_by)
+        a=float(numberA*multiply_by+add_by)
+        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
@@ -135,13 +382,14 @@ class AnyType(str):
     return False
 
 any_type = AnyType("*")
+
 import time
 
 class DynamicDelayProcessor:
 
     @classmethod
     def INPUT_TYPES(cls):
-        print("print INPUT_TYPES",cls)
+        # print("print INPUT_TYPES",cls)
         return {
             "required":{
                     "delay_seconds":("INT",{
@@ -152,7 +400,7 @@ class DynamicDelayProcessor:
             },
             "optional":{
                 "any_input":(any_type,),
-                "delay_by_text":("STRING",{"multiline":True,}),
+                "delay_by_text":("STRING",{"multiline":True,"dynamicPrompts": False,}),
                 "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"})
@@ -185,7 +433,7 @@ class DynamicDelayProcessor:
     RETURN_TYPES = (any_type,)
     RETURN_NAMES = ('output',)
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
     def run(self,any_input,delay_seconds,delay_by_text,words_per_seconds,replace_output,replace_value):
         # print(f"Delay text:",delay_by_text )
         # 获取开始时间戳
@@ -210,3 +458,283 @@ class DynamicDelayProcessor:
         # 根据 replace_output 决定输出值
         return (max(0, replace_value),) if replace_output == "enable" else (any_input,)
         
+
+
+    
+
+
+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,)
+
+
+
+class ListStatistics:
+    @staticmethod
+    def count_types(lst):
+        type_count = {}
+
+        for item in lst:
+            item_type = type(item).__name__
+            if item_type not in type_count:
+                type_count[item_type] = []
+
+            if item_type in ['dict', 'str', 'int', 'float']:
+                type_count[item_type].append(item)
+
+        return type_count
+
+# # 示例列表
+# my_list = [1, 'hello', {'name': 'John'}, 3.14, {'age': 25}, 'world', 10]
+
+# # 创建ListStatistics对象
+# list_stats = ListStatistics()
+
+# # 调用count_types方法进行统计
+# result = list_stats.count_types(my_list)
+
+# # 输出结果
+# for item_type, values in result.items():
+#     print(item_type + ':')
+#     for value in values:
+#         print(value)
+#     print('---')
+
+class TESTNODE_:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "ANY":(any_type,), },
+                }
+    
+    RETURN_TYPES = (any_type,)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/__TEST"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self,ANY):
+        print(ANY)
+        # data=ANY
+        list_stats = ListStatistics()
+
+        # 调用count_types方法进行统计
+        result = list_stats.count_types(ANY)
+            
+        return {"ui": {"data": result,"type":[str(type(ANY[0]))]}, "result": (ANY,)}
+
+
+
+class CreateSeedNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            }
+        }
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("seed",)
+  
+    OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, seed):
+        return (seed,)
+    
+
+class CreateCkptNames:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "ckpt_names": ("STRING",{"multiline": True,"default": "\n".join(folder_paths.get_filename_list("checkpoints")),"dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("ckpt_names",)
+    
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,)
+
+    # OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, ckpt_names):
+        ckpt_names=ckpt_names.split('\n')
+        ckpt_names = [name for name in ckpt_names if name.strip()]
+        return (ckpt_names,)
+
+
+class CreateLoraNames:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "lora_names": ("STRING",{"multiline": True,"default": "\n".join(folder_paths.get_filename_list("loras")),"dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = (any_type,"STRING",)
+    RETURN_NAMES = ("lora_names","prompt",)
+    
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,True,)
+
+    # OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, lora_names):
+        lora_names=lora_names.split('\n')
+        lora_names = [name for name in lora_names if name.strip()]
+        prompts=[os.path.splitext(n)[0] for n in lora_names]
+        return (lora_names,prompts,)
+   
+
+
+class CreateSampler_names:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "sampler_names": ("STRING",{"multiline": True,"default": "\n".join(comfy.samplers.KSampler.SAMPLERS),"dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("sampler_names",)
+    
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,)
+
+    # OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, sampler_names):
+        sampler_names=sampler_names.split('\n')
+        sampler_names = [name for name in sampler_names if name.strip()]
+        return (sampler_names,)

nodes/Vae.py

@@ -145,7 +145,7 @@ class VAELoader:
     RETURN_TYPES = ("VAE",)
     FUNCTION = "load_vae"
 
-    CATEGORY = "♾️Mixlab/_test"
+    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/_test"
+    CATEGORY = "♾️Mixlab/__TEST"
 
     def decode(self, vae, samples):
         image = vae.decode(samples["samples"].to("cuda:0"))

requirements.txt

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

web/javascript/3d_mixlab.js

@@ -187,20 +187,25 @@ app.registerExtension({
           },
           async serializeValue (nodeId, widgetIndex) {
             let d = getLocalData('_mixlab_3d_image')
-            console.log('serializeValue', node)
+            // console.log('serializeValue', node)
             if (d && d[node.id]) {
               let { url, bg, material } = d[node.id]
-              let base64 = await parseImage(url)
+              let data = {}
-              let bg_base64 = await parseImage(bg)
+              if (url) {
-              let material_base64 = await parseImage(material)
+                data.image = await parseImage(url)
+              }
+              if (bg) {
+                data.bg_image = await parseImage(bg)
+                if (!data.bg_image.match('data:image/')) {
+                  delete data.bg_image
+                }
+              }
 
-              return JSON.parse(
+              if (material) {
-                JSON.stringify({
+                data.material = await parseImage(material)
-                  image: base64,
+              }
-                  bg_image: bg_base64,
+
-                  material: material_base64
+              return JSON.parse(JSON.stringify(data))
-                })
-              )
             } else {
               return {}
             }
@@ -281,6 +286,8 @@ app.registerExtension({
                   <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
@@ -294,6 +301,7 @@ app.registerExtension({
               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`
@@ -341,21 +349,26 @@ app.registerExtension({
                 let url = await uploadImage(blob, '.png')
                 // console.log(url)
 
-                //  材质贴图
-                let thumbUrl = material_img.getAttribute('src')
-                let tb = await base64ToBlobFromURL(thumbUrl)
-                let tUrl = await uploadImage(tb, '.png')
-
-                // console.log(tUrl)
-
                 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])
+                if (!dd[that.id]) {
-                  dd[that.id] = { url, bg: url_bg, material: tUrl }
+                  dd[that.id] = { url, bg: url_bg }
-                dd[that.id] = { ...dd[that.id], url, material: tUrl }
+                } 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)
               }
@@ -367,11 +380,11 @@ app.registerExtension({
 
               modelViewerVariants.addEventListener('camera-change', startTimer)
 
-              select.addEventListener('input', event => {
+              select.addEventListener('input', async event => {
                 modelViewerVariants.variantName =
                   event.target.value === 'default' ? null : event.target.value
                 // 材质
-                extractMaterial(
+                await extractMaterial(
                   modelViewerVariants,
                   selectMaterial,
                   material_img
@@ -456,6 +469,15 @@ app.registerExtension({
                 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

web/javascript/app_mixlab.js

@@ -0,0 +1,502 @@
+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
+          try {
+            let keywords = node.widgets.filter(w => w.name === 'upload')[0]
+              .value
+            keywords = JSON.parse(keywords)
+            options.keywords = keywords
+          } catch (error) {
+            console.log(error)
+          }
+        }
+
+        if (node.type == 'Color') {
+        }
+
+        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' || node.type == 'SamplerCustom') {
+        // seed 的类型收集
+        try {
+          seed[id] = node.widgets.filter(
+            w => w.name === 'seed' || w.name == 'noise_seed'
+          )[0].linkedWidgets[0].value
+        } catch (error) {}
+      }
+    }
+  }
+
+  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(`${url}/mixlab/workflow`, {
+    method: 'POST',
+    body: JSON.stringify({
+      data: json,
+      task: 'save_app',
+      filename: json.app.filename,
+      category: json.app.category
+    })
+  })
+  return await res.json()
+}
+
+function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
+  const dataString = JSON.stringify(jsonData)
+  const blob = new Blob([dataString], { type: 'application/json' })
+  const url = URL.createObjectURL(blob)
+
+  const link = document.createElement('a')
+  link.href = url
+  link.download = fileName
+  link.click()
+
+  // 释放URL对象
+  setTimeout(() => {
+    URL.revokeObjectURL(url)
+  }, 0)
+}
+
+async function save (json, download = false, showInfo = true) {
+  console.log('####SAVE', json[0])
+  const name = json[0],
+    version = json[5],
+    share_prefix = json[6], //用于分享的功能扩展
+    link = json[7], //用于创建界面上的跳转链接
+    category = json[8] || '', //用于分类
+    description = json[4],
+    inputIds = json[2].split('\n').filter(f => f),
+    outputIds = json[3].split('\n').filter(f => f)
+
+  const iconData = json[1][0]
+  let { filename, subfolder, type } = iconData
+  let iconUrl = api.apiURL(
+    `/view?filename=${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)
+    }
+
+    if (showInfo) {
+      let open = window.confirm(
+        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app?filename=${encodeURIComponent(
+          data.app.filename
+        )}&category=${encodeURIComponent(data.app.category)}`
+      )
+      if (open)
+        window.open(
+          `${getUrl()}/mixlab/app?filename=${encodeURIComponent(
+            data.app.filename
+          )}&category=${encodeURIComponent(data.app.category)}`
+        )
+    }
+  } catch (error) {
+    console.log('###error', error)
+  }
+}
+
+function getInputsAndOutputs () {
+  const inputs =
+      `LoadImage CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
+        ' '
+      ),
+    outputs = `PreviewImage SaveImage ShowTextForGPT VHS_VideoCombine`.split(
+      ' '
+    )
+
+  let inputsId = [],
+    outputsId = []
+
+  for (let node of app.graph._nodes) {
+    if (inputs.includes(node.type)) {
+      inputsId.push(node.id)
+    }
+
+    if (outputs.includes(node.type)) {
+      outputsId.push(node.id)
+    }
+  }
+
+  return {
+    input: inputsId,
+    output: outputsId
+  }
+}
+
+app.registerExtension({
+  name: 'Mixlab.app.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)
+
+        // 自动计算workflow里哪些节点支持
+        let input_ids = this.widgets.filter(w => w.name == 'input_ids')[0],
+          output_ids = this.widgets.filter(w => w.name == 'output_ids')[0]
+
+        const { input, output } = getInputsAndOutputs()
+        input_ids.value = input.join('\n')
+        output_ids.value = output.join('\n')
+
+        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
+
+        window._mixlab_app_json = null
+      }
+
+      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 {
+          let a = this.widgets.filter(w => w.name === 'AppInfoRun')[0]
+          if (a) {
+            if (!a.value) a.value = 0
+            a.value += 1
+          }
+
+          const div = this.widgets.filter(w => w.div)[0].div
+          Array.from(
+            div.querySelectorAll('button'),
+            b => (b.style.background = 'yellow')
+          )
+        } catch (error) {}
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    // console.log('#loadedGraphNode1111')
+    window._mixlab_app_json = null //切换workflow需要清空
+    if (node.type === 'AppInfo') {
+      let auto_save = node.widgets.filter(w => w.name == 'auto_save')[0]
+      if (auto_save) {
+        if (!['enable', 'disable'].includes(auto_save.value)) {
+          auto_save.value = 'enable'
+        }
+      }
+
+      // app.canvas.centerOnNode(node)
+      // app.canvas.setZoom(0.45)
+    }
+  }
+})
+
+api.addEventListener('execution_start', async ({ detail }) => {
+  console.log('#execution_start', detail)
+  window._mixlab_app_json = null
+})
+
+api.addEventListener('executed', async ({ detail }) => {
+  console.log('#executed', detail)
+  // window._mixlab_app_json=null;
+  const { output } = getInputsAndOutputs()
+  if (output.includes(parseInt(detail.node))) {
+    let appinfo = app.graph.findNodesByType('AppInfo')[0]
+    if (appinfo) {
+      let auto_save = appinfo.widgets.filter(w => w.name == 'auto_save')[0]
+      if (auto_save?.value === 'enable') {
+        // 自动保存
+        console.log('auto_save')
+        if (window._mixlab_app_json) save(window._mixlab_app_json, false, false)
+      }
+    }
+  }
+})
+
+app.registerExtension({
+  name: 'Mixlab.app.AppNode',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'AppNode') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        console.log('#orig_nodeCreated', this)
+        let node=this;
+
+        const name = this.widgets.filter(w => w.name === 'name')[0]
+
+        name.callback = async e => {
+          let es = e.split('/')
+          let filename = '',
+            category = ''
+          if (es.length == 2) {
+            category = es[0]
+            filename = es[1]
+          } else if (es.length === 1) {
+            filename = e
+          }
+          const res = await api.fetchApi('/mixlab/workflow', {
+            method: 'POST',
+            headers: {
+              'Content-Type': 'application/json'
+            },
+            body: JSON.stringify({
+              task: 'my_app',
+              filename,
+              category
+            })
+          })
+
+          if (res.status !== 200) {
+            throw {
+              response: await res.json()
+            }
+          }
+          let result=await res.json()
+          const {app,output:workflow,}=result.data[0].data;
+          let input=app.input
+          console.log(input)
+
+          if(input.length==1){
+            let widget = node.inputs.filter(w => w.name === 'input')[0];
+            let sc=input[0];
+            LGraphCanvas.prototype._createNodeForInput(
+              node, //当前node
+              widget, //当前node里需要自动连线的widget
+              sc.class_type, //作为input的node type
+              'IMAGE' // 作为input的node的outputs的name. the input slot type of the target node
+            )
+          }
+
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {}
+})

web/javascript/checkVersion_mixlab.js

@@ -3,7 +3,7 @@ import { app } from '../../../scripts/app.js'
 const repoOwner = 'shadowcz007' // 替换为仓库的所有者
 const repoName = 'comfyui-mixlab-nodes' // 替换为仓库的名称
 
-const version = 'v0.4.2'
+const version = 'v0.15.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/clipInterrogator_mixlab.js

@@ -0,0 +1,119 @@
+import { app } from '../../../scripts/app.js'
+// import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+function getRandomElements (arr, num) {
+  var result = []
+  var len = arr.length
+
+  for (var i = 0; i < num; i++) {
+    var randomIndex = Math.floor(Math.random() * len)
+    result.push(arr[randomIndex])
+  }
+
+  return result
+}
+
+const createPrompt = (node, prompts, items, sample) => {
+  const w = ComfyWidgets['STRING'](
+    node,
+    'text',
+    ['STRING', { multiline: true }],
+    app
+  ).widget
+  w.inputEl.readOnly = true
+  w.inputEl.style.opacity = 0.6
+
+  w.value = typeof prompts === 'string' ? prompts : prompts.join('\n\n')
+
+  const w2 = ComfyWidgets['STRING'](
+    node,
+    'text',
+    ['STRING', { multiline: true }],
+    app
+  ).widget
+  w2.inputEl.readOnly = true
+  w2.inputEl.style.opacity = 0.6
+
+  w2.value = typeof items === 'string' ? items : JSON.stringify(items, null, 2)
+
+  const w3 = ComfyWidgets['STRING'](
+    node,
+    'text',
+    ['STRING', { multiline: true }],
+    app
+  ).widget
+  w3.inputEl.readOnly = true
+  w3.inputEl.style.opacity = 0.6
+  w3.value = typeof sample === 'string' ? sample : sample.join('\n\n')
+}
+
+app.registerExtension({
+  name: 'Mixlab.prompt.ClipInterrogator',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData.name === 'ClipInterrogator') {
+      function populate (prompts, items, random_samples) {
+        if (this.widgets) {
+          for (let i = 0; i < this.widgets.length; i++) {
+            if (this.widgets[i].type !== 'combo') this.widgets[i].onRemove?.()
+          }
+          this.widgets.length = 2
+        }
+
+        createPrompt(this, prompts, items, random_samples)
+
+        // console.log('ClipInterrogator', w, w2)
+        requestAnimationFrame(() => {
+          const sz = this.computeSize()
+          if (sz[0] < this.size[0]) {
+            sz[0] = this.size[0]
+          }
+          if (sz[1] < this.size[1]) {
+            sz[1] = this.size[1]
+          }
+          this.onResize?.(sz)
+          app.graph.setDirtyCanvas(true, false)
+        })
+      }
+
+      // When the node is executed we will be sent the input text, display this in the widget
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        // console.log('##', message)
+        populate.call(
+          this,
+          message.prompt,
+          message.analysis,
+          message.random_samples
+        )
+      }
+
+      this.serialize_widgets = true //需要保存参数
+    }
+  },
+  async loadedGraphNode (node, app) {
+    // Fires every time a node is constructed
+    // You can modify widgets/add handlers/etc here
+
+    if (node.type === 'ClipInterrogator') {
+      try {
+        
+        let widgets_values = node.widgets_values
+        console.log(widgets_values )
+        try {
+          if (widgets_values[2] && widgets_values[3] && widgets_values[4])
+            createPrompt(
+              node,
+              widgets_values[2],
+              widgets_values[3],
+              widgets_values[4]
+            )
+        } catch (error) {
+          console.log(error)
+        }
+      } catch (error) {}
+    }
+  }
+})

web/javascript/gpt_mixlab.js

@@ -256,6 +256,7 @@ app.registerExtension({
 			const onExecuted = nodeType.prototype.onExecuted;
 			nodeType.prototype.onExecuted = function (message) {
 				onExecuted?.apply(this, arguments);
+        console.log('##',message.text)
 				populate.call(this, message.text);
 			};
 

web/javascript/image_mixlab.js

@@ -193,6 +193,11 @@ const parseSvg = async svgContent => {
 
     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
@@ -356,7 +361,7 @@ app.registerExtension({
               setLocalDataOfWin(key, dd)
               // console.log(this.id, ip.value.trim())
 
-              svgElement.style = `width: 90%;padding: 5%;`
+              svgElement.style = `width: 90%;padding: 5%;height: auto;`
               // 将提取的SVG元素显示在页面上
 
               svgContainer.innerHTML = ''
@@ -401,7 +406,9 @@ app.registerExtension({
 
         this.serialize_widgets = true //需要保存参数
       }
-    }
+    };
+
+ 
   },
   async loadedGraphNode (node, app) {
     // Fires every time a node is constructed
@@ -424,7 +431,7 @@ app.registerExtension({
       let svgStr = await dt.text()
 
       const { svgElement, data, image } = await parseSvg(svgStr)
-      svgElement.style = `width: 90%;padding: 5%;`
+      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 = ''
@@ -432,7 +439,6 @@ app.registerExtension({
 
       const uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
       uploadWidget.value = await uploadWidget.serializeValue()
- 
     }
   }
-})
+})

web/javascript/layer_mixlab.js

@@ -156,8 +156,34 @@ const parseSvg = async svgContent => {
   return { data, image: base64, svgElement }
 }
 
-async function setArea (cw, ch, base64, data, fn) {
+
-  let displayHeight = Math.round(window.screen.availHeight * 0.6)
+function findImages(nodeId) {
+  // 检查当前节点是否有 imgs 字段
+  const n = app.graph.getNodeById(nodeId)
+  if (n.imgs) {
+    return n.imgs;
+  }
+
+  // 检查当前节点的 inputs 是否有 image 字段
+  if (n.inputs) {
+    for (let i = 0; i < n.inputs.length; i++) {
+      if (n.inputs[i].name==='image'||n.inputs[i].name==='images') {
+        // 获取新的 nodeId，并递归调用 findImages 函数
+        var linkId = n.inputs[i]?.link;
+        var origin_id = app.graph.links[linkId].origin_id
+        return findImages(origin_id);
+      }
+    }
+  }
+
+  // 如果没有找到 imgs 字段或者 image 字段，则返回 null
+  return null;
+}
+
+ 
+
+async function setArea (cw, ch, topBase64, base64, data, fn) {
+  let displayHeight = Math.round(window.screen.availHeight * 0.8)
   let div = document.createElement('div')
   div.innerHTML = `
     <div id='ml_overlay' style='position: absolute;top:0;background: #251f1fc4;
@@ -170,8 +196,13 @@ async function setArea (cw, ch, base64, data, fn) {
       outline: 2px solid #eaeaea;
       box-shadow: 8px 9px 17px #575757;' />
       <div id='ml_selection' style='position: absolute; 
-      border: 2px dashed red; 
+      border: 2px dashed red;
-      pointer-events: none;'></div>
+      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)
@@ -181,13 +212,25 @@ async function setArea (cw, ch, base64, data, fn) {
   // canvas.height = ch
 
   let img = div.querySelector('#ml_video')
-  let overlay = div.querySelector('#ml_overlay')
+  // 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()
@@ -216,14 +259,37 @@ async function setArea (cw, ch, base64, data, fn) {
   img.addEventListener('mousedown', startSelection)
   img.addEventListener('mousemove', updateSelection)
   img.addEventListener('mouseup', endSelection)
-  overlay.addEventListener('click', remove)
 
-  function remove () {
+  const removeDiv = () => {
-    overlay.removeEventListener('click', remove)
+    div.remove()
+    close.removeEventListener('click', removeDiv)
     img.removeEventListener('mousedown', startSelection)
     img.removeEventListener('mousemove', updateSelection)
     img.removeEventListener('mouseup', endSelection)
-    div.remove()
+    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) {
@@ -531,13 +597,31 @@ app.registerExtension({
               }
             }
             try {
+              console.log('this.inputs', this.id)
+              let imgs=findImages(this.id)
+              
+              // let topLinkId = this.inputs[0].link
+              // let topNodeId = app.graph.links[topLinkId].origin_id
+              let topIm = imgs[0]
+
               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 imgs2=findImages(nodeId)
-              let src = im.src
+              let im = imgs2[0]
-              setArea(im.naturalWidth, im.naturalHeight, src, data, updateValue)
+              console.log(topIm,im)
-            } catch (error) {}
+              // let src = im.src
+              setArea(
+                im.naturalWidth,
+                im.naturalHeight,
+                topIm.src,
+                im.src,
+                data,
+                updateValue
+              )
+            } catch (error) {
+              console.log(error)
+            }
           })
         }
       }

web/javascript/main_mixlab.js

@@ -1331,7 +1331,13 @@ app.registerExtension({
           let w = 360,
             s = widget.preview.videoWidth / widget.preview.videoHeight,
             h = w / s || w
-          console.log(h)
+          // console.log(h)
+
+          if (!window.documentPictureInPicture) {
+            window.alert(
+              'This feature is available only in secure contexts (HTTPS), in some or all supporting browsers. https://developer.mozilla.org/en-US/docs/Web/API/Document_Picture-in-Picture_API'
+            )
+          }
 
           const pipWindow = await documentPictureInPicture.requestWindow({
             width: w,
@@ -1800,6 +1806,14 @@ const updateUI = node => {
     pw.inputEl.title = `Total of ${prompts.length} prompts`
   } else {
     // 动态添加
+    // console.log('ComfyWidgets',ComfyWidgets.STRING(
+    //   node,
+    //   'prompts',
+    //   ['STRING', { multiline: true }]
+    // ))
+
+    // ComfyWidgets.STRING(this, "", ["", {default:this.properties.text, multiline: true}], app)
+
     const w = ComfyWidgets.STRING(
       node,
       'prompts',
@@ -2085,13 +2099,13 @@ const node = {
   name: 'RandomPrompt',
   async init (app) {
     // Any initial setup to run as soon as the page loads
-    console.log('[logging]', 'extension init')
+    // console.log('[logging]', 'extension init')
 
     if (window.location.href.match('/?')) {
       const { workflow } = getURLParameters(window.location.href)
       if (workflow)
         get_my_workflow().then(data => {
-          console.log('#get_my_workflow', data)
+          // console.log('#get_my_workflow', data)
           let my_workflow = data.filter(
             d => d.filename == 'my_workflow.json'
           )[0]
@@ -2127,10 +2141,15 @@ const node = {
     // }
   },
   loadedGraphNode (node, app) {
-    // Fires for each node when loading/dragging/etc a workflow json or png
+    if (node.type === 'RandomPrompt') {
-    // If you break something in the backend and want to patch workflows in the frontend
+      try {
-    // This is the place to do this
+        let max_count = node.widgets.filter(w => w.name === 'max_count')[0]
-    // console.log("[logging]", "loaded graph node: ", exportGraph(node.graph));
+        max_count.value = node.widgets_values[0]
+        // console.log('RandomPrompt',max_count,node.widgets_values[0])
+      } catch (error) {
+        console.log(error)
+      }
+    }
   },
   async nodeCreated (node) {
     if (node.type === 'RandomPrompt') {
@@ -2223,7 +2242,7 @@ const node = {
         const r = onExecuted?.apply?.(this, arguments)
 
         let prompts = message.prompts
-        console.log('executed', message)
+        // console.log('executed', message)
         // console.log('#RandomPrompt', this.widgets)
         const pw = this.widgets.filter(w => w.name === 'prompts')[0]
 
@@ -2234,7 +2253,7 @@ const node = {
         } else {
           // 动态添加
           const w = ComfyWidgets.STRING(
-            node,
+            this,
             'prompts',
             ['STRING', { multiline: true }],
             app

web/javascript/prompt_mixlab.js

@@ -0,0 +1,566 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+import PhotoSwipeLightbox from '/extensions/comfyui-mixlab-nodes/lib/photoswipe-lightbox.esm.min.js'
+function loadCSS (url) {
+  var link = document.createElement('link')
+  link.rel = 'stylesheet'
+  link.type = 'text/css'
+  link.href = url
+  document.getElementsByTagName('head')[0].appendChild(link)
+
+  // Create a style element
+  const style = document.createElement('style')
+  // Define the CSS rule for scrollbar width
+  const cssRule = `.pswp__custom-caption {
+    background: rgb(20 27 70);
+    font-size: 16px;
+    color: #fff;
+    width: calc(100% - 32px);
+    max-width: 980px;
+    padding: 2px 8px;
+    border-radius: 4px;
+    position: absolute;
+    left: 50%;
+    bottom: 16px;
+    transform: translateX(-50%);
+  }
+  .pswp__custom-caption a {
+    color: #fff;
+    text-decoration: underline;
+  }
+  .hidden-caption-content {
+    display: none;
+  }`
+  // Add the CSS rule to the style element
+  style.appendChild(document.createTextNode(cssRule))
+
+  // Append the style element to the document head
+  document.head.appendChild(style)
+}
+loadCSS('/extensions/comfyui-mixlab-nodes/lib/photoswipe.min.css')
+
+function initLightBox () {
+  const lightbox = new PhotoSwipeLightbox({
+    gallery: '.prompt_image_output',
+    children: 'a',
+    pswpModule: () =>
+      import('/extensions/comfyui-mixlab-nodes/lib/photoswipe.esm.min.js')
+  })
+
+  lightbox.on('uiRegister', function () {
+    lightbox.pswp.ui.registerElement({
+      name: 'custom-caption',
+      order: 9,
+      isButton: false,
+      appendTo: 'root',
+      html: 'Caption text',
+      onInit: (el, pswp) => {
+        lightbox.pswp.on('change', () => {
+          const currSlideElement = lightbox.pswp.currSlide.data.element
+          let captionHTML = ''
+          if (currSlideElement) {
+            const hiddenCaption = currSlideElement.querySelector(
+              '.hidden-caption-content'
+            )
+            if (hiddenCaption) {
+              // get caption from element with class hidden-caption-content
+              captionHTML = hiddenCaption.innerHTML
+            } else {
+              // get caption from alt attribute
+              captionHTML = currSlideElement
+                .querySelector('img')
+                .getAttribute('alt')
+            }
+          }
+          el.innerHTML = captionHTML || ''
+        })
+      }
+    })
+  })
+
+  lightbox.init()
+}
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2 - 24}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    paddingLeft: '12px',
+    display: 'flex',
+    flexDirection: 'row',
+    // alignItems: 'center',
+    justifyContent: 'space-between'
+  }
+}
+function createImage (url) {
+  let im = new Image()
+  return new Promise((res, rej) => {
+    im.onload = () => res(im)
+    im.src = url
+  })
+}
+
+async function fetchImage (url) {
+  try {
+    const response = await fetch(url)
+    const blob = await response.blob()
+
+    return blob
+  } catch (error) {
+    console.error('出现错误:', error)
+  }
+}
+
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+
+const setLocalDataOfWin = (key, value) => {
+  localStorage.setItem(key, JSON.stringify(value))
+  // window[key] = value
+}
+
+const createSelect = (select, opts, targetWidget) => {
+  select.style.display = 'block'
+  let html = ''
+  let isMatch = false
+  for (const opt of opts) {
+    html += `<option value='${opt}' ${
+      targetWidget.value === opt ? 'selected' : ''
+    }>${opt}</option>`
+    if (targetWidget.value === opt) isMatch = true
+  }
+  select.innerHTML = html
+  if (!isMatch) targetWidget.value = opts[0]
+  // 添加change事件监听器
+  select.addEventListener('change', function () {
+    // 获取选中的选项的值
+    var selectedOption = select.options[select.selectedIndex].value
+    targetWidget.value = selectedOption
+    // console.log(widget,selectedOption)
+  })
+}
+
+app.registerExtension({
+  name: 'Mixlab.prompt.RandomPrompt',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'RandomPrompt') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+         
+        const mutable_prompt = this.widgets.filter(
+          w => w.name == 'mutable_prompt'
+        )[0]
+        // console.log('PromptSlide nodeData', prompt_keyword)
+
+        const widget = {
+          type: 'div',
+          name: 'upload',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, y, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Upload Keywords'
+
+        btn.style = `cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;    height: 30px;min-width: 122px;
+       `
+
+        // const btn=document.createElement('button');
+        // btn.innerText='Upload'
+        btn.addEventListener('click', () => {
+          let inp = document.createElement('input')
+          inp.type = 'file'
+          inp.accept = '.txt'
+          inp.click()
+          inp.addEventListener('change', event => {
+            // 获取选择的文件
+            const file = event.target.files[0]
+            this.title = file.name.split('.')[0]
+
+            // console.log(file.name.split('.')[0])
+            // 创建文件读取器
+            const reader = new FileReader()
+
+            // 定义读取完成事件的回调函数
+            reader.onload = event => {
+              // 读取完成后的文本内容
+              const fileContent = event.target.result.split('\n')
+              const keywords = Array.from(fileContent, f => f.trim()).filter(
+                f => f
+              )
+              // 打印文件内容
+              //   console.log(keywords)
+
+              mutable_prompt.value = keywords.join('\n')
+
+              inp.remove()
+            }
+
+            // 以文本方式读取文件
+            reader.readAsText(file)
+          })
+        })
+
+        widget.div.appendChild(btn)
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'RandomPrompt') {
+     
+    }
+  }
+})
+
+app.registerExtension({
+  name: 'Mixlab.prompt.PromptSlide',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'PromptSlide') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const prompt_keyword = this.widgets.filter(
+          w => w.name == 'prompt_keyword'
+        )[0]
+        // console.log('PromptSlide nodeData', prompt_keyword)
+
+        const widget = {
+          type: 'div',
+          name: 'upload',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, y, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Upload Keywords'
+
+        btn.style = `cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;    height: 30px;min-width: 122px;
+       `
+
+        const select = document.createElement('select')
+        select.style = `display:none;cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;    height: 30px;min-width: 100px;
+       `
+        widget.select = select
+
+        // const btn=document.createElement('button');
+        // btn.innerText='Upload'
+        btn.addEventListener('click', () => {
+          let inp = document.createElement('input')
+          inp.type = 'file'
+          inp.accept = '.txt'
+          inp.click()
+          inp.addEventListener('change', event => {
+            // 获取选择的文件
+            const file = event.target.files[0]
+            this.title = file.name.split('.')[0]
+
+            // console.log(file.name.split('.')[0])
+            // 创建文件读取器
+            const reader = new FileReader()
+
+            // 定义读取完成事件的回调函数
+            reader.onload = event => {
+              // 读取完成后的文本内容
+              const fileContent = event.target.result.split('\n')
+              const keywords = Array.from(fileContent, f => f.trim()).filter(
+                f => f
+              )
+              // 打印文件内容
+              //   console.log(keywords)
+
+              widget.value = JSON.stringify(keywords)
+
+              // let ks = getLocalData(`_mixlab_PromptSlide`)
+              // ks[this.id] = keywords
+              // setLocalDataOfWin(`_mixlab_PromptSlide`, ks)
+
+              createSelect(select, keywords, prompt_keyword)
+
+              inp.remove()
+            }
+
+            // 以文本方式读取文件
+            reader.readAsText(file)
+          })
+        })
+
+        widget.div.appendChild(btn)
+        widget.div.appendChild(select)
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'PromptSlide') {
+      try {
+        let prompt = node.widgets.filter(w => w.name === 'prompt_keyword')[0]
+        // let ks = getLocalData(`_mixlab_PromptSlide`)
+        let uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
+        // console.log('##widget', uploadWidget.value)
+        let keywords = JSON.parse(uploadWidget.value)
+        // console.log('keywords',keywords)
+        let widget = node.widgets.filter(w => w.select)[0]
+        if (keywords && keywords[0]) {
+          widget.select.style.display = 'block'
+          createSelect(widget.select, keywords, prompt)
+        }
+      } catch (error) {}
+    }
+  }
+})
+
+const _createResult = async (node, widget, message) => {
+  widget.div.innerHTML = ``
+
+  const width = node.size[0] * 0.5 - 12
+
+  let height_add = 0
+
+  for (let index = 0; index < message._images.length; index++) {
+    const imgs = message._images[index]
+
+    for (const img of imgs) {
+      let url = api.apiURL(
+        `/view?filename=${encodeURIComponent(img.filename)}&type=${
+          img.type
+        }&subfolder=${
+          img.subfolder
+        }${app.getPreviewFormatParam()}${app.getRandParam()}`
+      )
+
+      let image = await createImage(url)
+
+      // 创建card
+      let div = document.createElement('div')
+      div.className = 'card'
+      div.draggable = true
+
+      div.ondragend = async event => {
+        console.log('拖动停止')
+        let url = div.querySelector('img').src
+
+        let blob = await fetchImage(url)
+
+        let imageNode = null
+        // No image node selected: add a new one
+        if (!imageNode) {
+          const newNode = LiteGraph.createNode('LoadImage')
+          newNode.pos = [...app.canvas.graph_mouse]
+          imageNode = app.graph.add(newNode)
+          app.graph.change()
+        }
+
+        // const blob = item.getAsFile();
+        imageNode.pasteFile(blob)
+      }
+
+      div.setAttribute('data-scale', image.naturalHeight / image.naturalWidth)
+
+      let h = (image.naturalHeight * width) / image.naturalWidth
+      if (index % 2 === 0) height_add += h
+      div.style = `width: ${width}px;height:${h}px;position: relative;margin: 4px;`
+
+      div.innerHTML = `<a href="${url}" 
+      data-pswp-width="${image.naturalWidth}" 
+      data-pswp-height="${image.naturalHeight}" 
+      target="_blank">
+      <img src="${url}" style='width: 100%' alt="${message.prompts[index]}"/>
+    </a>
+    <p style="position: absolute;
+            bottom: 0;
+            left: 0;
+            opacity: 0.6;
+            background-color: var(--comfy-input-bg); 
+            color: var(--descrip-text);
+            margin: 0;
+            font-size: 12px;
+            padding: 5px;
+            text-align: left;">${message.prompts[index]}</p>`
+      widget.div.appendChild(div)
+    }
+  }
+
+  node.size[1] = 98 + height_add
+}
+
+app.registerExtension({
+  name: 'Mixlab.prompt.PromptImage',
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'PromptImage') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        console.log('#orig_nodeCreated', this)
+        const widget = {
+          type: 'div',
+          name: 'result',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(this.div.style, {
+              ...get_position_style(ctx, widget_width, y, node.size[1]),
+              flexWrap: 'wrap',
+              justifyContent: 'space-between',
+              // outline: '1px solid red',
+              paddingLeft: '0px',
+              width: widget_width + 'px'
+            })
+          }
+        }
+
+        widget.div = $el('div', {})
+        widget.div.className = 'prompt_image_output'
+
+        document.body.appendChild(widget.div)
+
+        this.addCustomWidget(widget)
+
+        initLightBox()
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        const onResize = this.onResize
+        this.onResize = function () {
+          // 缩放发生
+          // console.log('##缩放发生', this.size)
+          let w = this.size[0] * 0.5 - 12
+          Array.from(widget.div.querySelectorAll('.card'), card => {
+            card.style.width = `${w}px`
+            card.style.height = `${
+              w * parseFloat(card.getAttribute('data-scale'))
+            }px`
+          })
+          return onResize?.apply(this, arguments)
+        }
+
+        // this.serialize_widgets = true //需要保存参数
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = async function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log('#PromptImage', message.prompts, message._images)
+        // window._mixlab_app_json = message.json
+        try {
+          let widget = this.widgets.filter(w => w.name === 'result')[0]
+          widget.value = message
+          _createResult(this, widget, { ...message })
+        } catch (error) {
+          console.log(error)
+        }
+      }
+
+      this.serialize_widgets = true //需要保存参数
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'PromptImage') {
+      // await sleep(0)
+      let widget = node.widgets.filter(w => w.name === 'result')[0]
+      console.log('widget.value', widget.value)
+
+      initLightBox()
+
+      let cards = widget.div.querySelectorAll('.card')
+      if (cards.length == 0) node.size = [280, 120]
+
+      _createResult(node, widget, widget.value)
+    }
+  }
+})

web/javascript/smart_connect.js

@@ -0,0 +1,302 @@
+const smart_connect_config_input = [
+  {
+    node_type: 'CLIPTextEncode',
+    node_widget_name: 'text',
+    inputNodeName: 'RandomPrompt',
+    inputNode_output_name: 'STRING'
+  },
+  {
+    node_type: 'CLIPTextEncode',
+    node_widget_name: 'text',
+    inputNodeName: 'EmbeddingPrompt',
+    inputNode_output_name: 'STRING'
+  },
+  {
+    node_type: 'CLIPTextEncode',
+    node_widget_name: 'text',
+    inputNodeName: 'ChinesePrompt_Mix',
+    inputNode_output_name: 'prompt'
+  },
+  {
+    node_type: 'CheckpointLoaderSimple',
+    node_widget_name: 'ckpt_name',
+    inputNodeName: 'CkptNames_',
+    inputNode_output_name: 'ckpt_names'
+  },
+  {
+    node_type: 'KSampler',
+    node_widget_name: 'sampler_name',
+    inputNodeName: 'SamplerNames_',
+    inputNode_output_name: 'sampler_names'
+  },
+  {
+    node_type: 'LoraLoaderModelOnly',
+    node_widget_name: 'lora_name',
+    inputNodeName: 'LoraNames_',
+    inputNode_output_name: 'lora_names'
+  },
+  {
+    node_type: 'LoadLoRA',
+    node_widget_name: 'lora_name',
+    inputNodeName: 'LoraNames_',
+    inputNode_output_name: 'lora_names'
+  },
+  {
+    node_type: 'Moondream',
+    node_widget_name: 'image',
+    inputNodeName: 'LoadImage',
+    inputNode_output_name: 'IMAGE'
+  }
+]
+
+const smart_connect_config_output = [
+  {
+    node_type: 'LoadImage',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'ClipInterrogator',
+    outputNode_input_name: 'image'
+  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'PromptImage',
+    outputNode_input_name: 'images'
+  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'PreviewImage',
+    outputNode_input_name: 'images'
+  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'SaveImage',
+    outputNode_input_name: 'images'
+  },
+  {
+    node_type: 'Moondream',
+    node_output_name: 'STRING',
+    outputNodeName: 'ShowTextForGPT',
+    outputNode_input_name: 'text'
+  }
+]
+
+// import {
+//   convertToInput,
+//   getConfig,
+//   isConvertableWidget
+// } from '../../../extensions/core/widgetInputs.js'
+
+const CONVERTED_TYPE = 'converted-widget'
+const GET_CONFIG = Symbol()
+
+function getConfig (widgetName) {
+  const { nodeData } = this.constructor
+  return (
+    nodeData?.input?.required[widgetName] ??
+    nodeData?.input?.optional?.[widgetName]
+  )
+}
+
+function hideWidget (node, widget, suffix = '') {
+  widget.origType = widget.type
+  widget.origComputeSize = widget.computeSize
+  widget.origSerializeValue = widget.serializeValue
+  widget.computeSize = () => [0, -4] // -4 is due to the gap litegraph adds between widgets automatically
+  widget.type = CONVERTED_TYPE + suffix
+  widget.serializeValue = () => {
+    // Prevent serializing the widget if we have no input linked
+    if (!node.inputs) {
+      return undefined
+    }
+    let node_input = node.inputs.find(i => i.widget?.name === widget.name)
+
+    if (!node_input || !node_input.link) {
+      return undefined
+    }
+    return widget.origSerializeValue
+      ? widget.origSerializeValue()
+      : widget.value
+  }
+
+  // Hide any linked widgets, e.g. seed+seedControl
+  if (widget.linkedWidgets) {
+    for (const w of widget.linkedWidgets) {
+      hideWidget(node, w, ':' + widget.name)
+    }
+  }
+}
+
+function convertToInput (node, widget, config) {
+  hideWidget(node, widget)
+
+  const type = config[0]
+
+  // Add input and store widget config for creating on primitive node
+  const sz = node.size
+  node.addInput(widget.name, type, {
+    widget: { name: widget.name, [GET_CONFIG]: () => config }
+  })
+
+  for (const widget of node.widgets) {
+    widget.last_y += LiteGraph.NODE_SLOT_HEIGHT
+  }
+
+  // Restore original size but grow if needed
+  node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])])
+}
+
+export function smart_init () {
+  LGraphCanvas.prototype._createNodeForInput = function (
+    node,
+    widget,
+    inputNodeName,
+    inputNode_slot
+  ) {
+    // console.log(node.pos)
+
+    // var widget = node.widgets.filter(w => w.name === node_widget_name)[0]
+    if (widget) {
+      // 如果有存在的，没有连线输出的，自动连，不新建
+      let input_node = null
+
+      Array.from(app.graph.findNodesByType(inputNodeName), n => {
+        var links = n.outputs.filter(o => o.name === inputNode_slot)[0].links
+        //  console.log(links)
+        if (!links || links?.length === 0) input_node = n
+      })
+      // 新建
+      if (!input_node) {
+        input_node = LiteGraph.createNode(inputNodeName)
+        input_node.pos = [node.pos[0] - node.size[0] - 24, node.pos[1] - 48]
+        app.canvas.graph.add(input_node, false)
+      } else {
+        input_node.pos = [node.pos[0] - node.size[0] - 24, node.pos[1] - 48]
+      }
+
+      const config = getConfig.call(node, widget.name) ?? [
+        widget.type,
+        widget.options || {}
+      ]
+      let node_slotType = config[0]
+      // 如果input没有，则创建
+      if (!node.inputs?.filter(inp => inp.name === widget.name)[0]||!node.inputs)
+        convertToInput(node, widget, config)
+      input_node.connectByType(inputNode_slot, node, node_slotType)
+    }
+  }
+
+  LGraphCanvas.prototype._createNodeForOutput = function (
+    node,
+    widget,
+    outputNodeName,
+    outputNode_slot
+  ) {
+    if (widget) {
+      let output_node
+      Array.from(app.graph.findNodesByType(outputNodeName), n => {
+        var links = n.inputs.filter(o => o.name === outputNode_slot)[0].links
+        //  console.log(links)
+        if (!links || links?.length === 0) output_node = n
+      })
+      console.log('output_node', output_node, widget.name)
+
+      if (!output_node) {
+        // 新建
+        output_node = LiteGraph.createNode(outputNodeName)
+        output_node.pos = [node.pos[0] + node.size[0] + 24, node.pos[1] - 48]
+        app.canvas.graph.add(output_node, false)
+      } else {
+        output_node.pos = [node.pos[0] + node.size[0] + 24, node.pos[1] - 48]
+      }
+
+      const config = getConfig.call(node, widget.name) ?? [
+        widget.type,
+        widget.options || {}
+      ]
+      let node_slotType = config[0]
+      console.log(node_slotType, output_node, outputNode_slot)
+      let type = output_node.inputs.filter(
+        inp => inp.name == outputNode_slot
+      )[0].type
+      node.connectByType(node_slotType, output_node, type)
+    }
+  }
+}
+
+export function addSmartMenu (options, node) {
+  let sopts = []
+
+  for (const sc of smart_connect_config_input) {
+    // 有智能推荐，则出现
+    if (node.type === sc.node_type) {
+      // console.log('smart',node)
+      // 则出现 randomPrompt
+      // CLIPTextEncode 的widget ，name== 'text'
+      let node_widget_name = sc.node_widget_name
+      let widget = node.widgets.filter(w => w.name === node_widget_name)[0]
+      if (!widget) {
+        // 控件没有，则查找inputs
+        widget = node.inputs.filter(w => w.name === node_widget_name)[0]
+      }
+
+      let isLinkNull = true
+      // 如果input里已经有，但是link为空
+      if (node.inputs?.filter(inp => inp.name === node_widget_name)[0]) {
+        isLinkNull =
+          node.inputs.filter(inp => inp.name === node_widget_name)[0].link ===
+          null
+      }
+
+      if (widget && isLinkNull) {
+        sopts.push({
+          content: sc.inputNodeName.split('_')[0] + '➡️',
+          callback: () => {
+            LGraphCanvas.prototype._createNodeForInput(
+              node, //当前node
+              widget, //当前node里需要自动连线的widget
+              sc.inputNodeName, //作为input的node type
+              sc.inputNode_output_name // 作为input的node的outputs的name. the input slot type of the target node
+            )
+          }
+        })
+      }
+    }
+  }
+
+  for (const sc of smart_connect_config_output) {
+    if (node.type === sc.node_type) {
+      let node_output_name = sc.node_output_name
+      const widget = node.outputs.filter(w => w.name === node_output_name)[0]
+
+      let isLinkNull = true
+      // 如果output里 link为空
+      if (node.outputs?.filter(inp => inp.name === node_output_name)[0]) {
+        isLinkNull =
+          node.outputs.filter(inp => inp.name === node_output_name)[0].links
+            ?.length === 0
+        if (!node.outputs.filter(inp => inp.name === node_output_name)[0].links)
+          isLinkNull = true
+      }
+
+      if (widget && isLinkNull) {
+        sopts.push({
+          content: '➡️' + sc.outputNodeName.split('_')[0],
+          callback: () => {
+            LGraphCanvas.prototype._createNodeForOutput(
+              node, //当前node
+              widget, //当前node里需要自动连线的widget
+              sc.outputNodeName, //作为input的node type
+              sc.outputNode_input_name // 作为input的node的outputs的name. the input slot type of the target node
+            )
+          }
+        })
+      }
+    }
+  }
+
+  if (sopts.length > 0) options = [...sopts, null, ...options]
+
+  return options
+}

web/javascript/utils_mixlab.js

@@ -2,7 +2,7 @@ import { app } from '../../../scripts/app.js'
 import { api } from '../../../scripts/api.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
-import { addValueControlWidget } from "../../../scripts/widgets.js";
+import { addValueControlWidget } from '../../../scripts/widgets.js'
 
 const getLocalData = key => {
   let data = {}
@@ -45,8 +45,47 @@ function get_position_style (ctx, widget_width, y, node_height) {
   }
 }
 
+function hexToRGBA (hexColor) {
+  var hex = hexColor.replace('#', '')
+  var r = parseInt(hex.substring(0, 2), 16)
+  var g = parseInt(hex.substring(2, 4), 16)
+  var b = parseInt(hex.substring(4, 6), 16)
+
+  // 获取透明度的十六进制值
+  var alphaHex = hex.substring(6)
+
+  // 将透明度的十六进制值转换为十进制值
+  var alpha = parseInt(alphaHex, 16) / 255
+
+  return [r, g, b, alpha]
+}
+
 app.registerExtension({
   name: 'Mixlab.utils.Color',
+  init () {
+    $el('link', {
+      rel: 'stylesheet',
+      href: '/extensions/comfyui-mixlab-nodes/lib/classic.min.css',
+      parent: document.head
+    })
+
+    $el('style', {
+      textContent: `
+      .pickr{
+        display: flex;
+        justify-content: center;
+        align-items: center;
+      }
+				.pickr .pcr-button {
+					width: 56px;
+          height: 56px;
+          outline: 1px solid white;
+				}
+				 
+			`,
+      parent: document.body
+    })
+  },
   async getCustomWidgets (app) {
     return {
       TCOLOR (node, inputName, inputData, app) {
@@ -60,8 +99,17 @@ app.registerExtension({
             return [128, 32] // a method to compute the current size of the widget
           },
           async serializeValue (nodeId, widgetIndex) {
-            let data = getLocalData('_mixlab_utils_color')
+            // let data = getLocalData('_mixlab_utils_color')
-            return data[node.id] || '#000000'
+            // 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
@@ -77,7 +125,7 @@ app.registerExtension({
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
 
-        console.log('Color nodeData', this.widgets)
+        // console.log('Color nodeData', this.widgets)
 
         const widget = {
           type: 'div',
@@ -87,6 +135,7 @@ app.registerExtension({
               this.div.style,
               get_position_style(ctx, widget_width, 44, node.size[1])
             )
+            // console.log('draw',y,node.widgets[0].last_y)
           }
         }
 
@@ -94,35 +143,9 @@ app.registerExtension({
 
         document.body.appendChild(widget.div)
 
-        const inputDiv = (key, placeholder, value) => {
+        const inputDiv = () => {
           let div = document.createElement('div')
-          const ip = document.createElement('input')
+          div.id = `color_picker_${this.id}`
-          ip.type = 'color'
-          ip.className = `${'comfy-multiline-input'} ${placeholder}`
-          div.style = `display: flex;
-            align-items: center; 
-            margin: 6px 8px;
-            margin-top: 0;`
-          ip.placeholder = placeholder
-          ip.value = value
-
-          ip.style = `outline: none;
-            border: none;
-            padding: 4px;
-            width: 100%;cursor: pointer;
-            height: 32px;`
-          const label = document.createElement('label')
-          label.style = 'font-size: 10px;min-width:32px'
-          label.innerText = placeholder
-          div.appendChild(label)
-          div.appendChild(ip)
-
-          ip.addEventListener('change', () => {
-            let data = getLocalData(key)
-            data[this.id] = ip.value.trim()
-            localStorage.setItem(key, JSON.stringify(data))
-            // console.log(this.id, ip.value.trim())
-          })
           return div
         }
 
@@ -132,10 +155,85 @@ app.registerExtension({
 
         this.addCustomWidget(widget)
 
+        const pickr = Pickr.create({
+          el: `#${inputColor.id}`,
+          theme: 'classic', // or 'monolith', or 'nano'
+          // closeOnScroll: true,
+          default: '#000000',
+          swatches: [
+            'rgba(244, 67, 54, 1)',
+            'rgba(233, 30, 99, 0.95)',
+            'rgba(156, 39, 176, 0.9)',
+            'rgba(103, 58, 183, 0.85)',
+            'rgba(63, 81, 181, 0.8)',
+            'rgba(33, 150, 243, 0.75)',
+            'rgba(3, 169, 244, 0.7)',
+            'rgba(0, 188, 212, 0.7)',
+            'rgba(0, 150, 136, 0.75)',
+            'rgba(76, 175, 80, 0.8)',
+            'rgba(139, 195, 74, 0.85)',
+            'rgba(205, 220, 57, 0.9)',
+            'rgba(255, 235, 59, 0.95)',
+            'rgba(255, 193, 7, 1)'
+          ],
+
+          components: {
+            // Main components
+            preview: true,
+            opacity: true,
+            hue: true,
+            // Input / output Options
+            interaction: {
+              hex: true,
+              rgba: true,
+              hsla: true,
+              hsva: true,
+              cmyk: true,
+              input: true,
+              // clear: true,
+              save: true,
+              cancel: true
+            }
+          }
+        })
+
+        pickr
+          .on('save', (color, instance) => {
+            // console.log('Event: "save"', color.toHEXA().toString())
+            // let data = getLocalData('_mixlab_utils_color')
+            // data[this.id] = color.toHEXA().toString()
+            // localStorage.setItem('_mixlab_utils_color', JSON.stringify(data))
+
+            try {
+              let tc = this.widgets.filter(w => w.type == 'TCOLOR')[0]
+              tc.value = color.toHEXA().toString()
+            } catch (error) {}
+          })
+          .on('cancel', instance => {
+            pickr && pickr.hide()
+          })
+        this.pickr = pickr
+        const handleMouseWheel = () => {
+          try {
+            this.pickr && this.pickr.hide()
+          } catch (error) {}
+        }
+
+        document.addEventListener('wheel', handleMouseWheel)
+
         const onRemoved = this.onRemoved
         this.onRemoved = () => {
           inputColor.remove()
           widget.div.remove()
+
+          try {
+            this.pickr.destroyAndRemove()
+            this.pickr = null
+            document.removeEventListener('wheel', handleMouseWheel)
+          } catch (error) {
+            console.log(error)
+          }
+
           return onRemoved?.()
         }
 
@@ -148,34 +246,119 @@ app.registerExtension({
     // You can modify widgets/add handlers/etc here
 
     if (node.type === 'Color') {
-      let widget = node.widgets.filter(w => w.div)[0]
+      try {
+        let TCOLOR = node.widgets.filter(w => w.type == 'TCOLOR')[0]
 
-      let data = getLocalData('_mixlab_utils_color')
+        setTimeout(() => node.pickr.setColor(TCOLOR.value || '#000000'), 1000)
-
+      } catch (error) {}
-      let id = node.id
-
-      widget.div.querySelector('.Color').value = data[id] || '#000000'
     }
   }
 })
 
 app.registerExtension({
   name: 'Mixlab.utils.TextToNumber',
-
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeType.comfyClass == 'TextToNumber') {
-
       const onExecuted = nodeType.prototype.onExecuted
       nodeType.prototype.onExecuted = function (message) {
         onExecuted?.apply(this, arguments)
-        const random_number=this.widgets.filter(w=>w.name==='random_number')[0]
+        const random_number = this.widgets.filter(
-        if(random_number.value==='enable'){
+          w => w.name === 'random_number'
-          const n=this.widgets.filter(w=>w.name==='number')[0]
+        )[0]
-          n.value=message.num[0]
+        if (random_number.value === 'enable') {
+          const n = this.widgets.filter(w => w.name === 'number')[0]
+          n.value = message.num[0]
         }
-        
         console.log('TextToNumber', random_number.value)
       }
     }
   }
 })
+
+const min_max = node => {
+  if(node.widgets){
+    const min_value = node.widgets.filter(w => w.name === 'min_value')[0]
+    const max_value = node.widgets.filter(w => w.name === 'max_value')[0]
+  
+    const number = node.widgets.filter(w => w.name === 'number')[0]
+    if (number) {
+      number.options.min = min_value.value
+      number.options.max = max_value.value
+  
+      number.value = Math.min(number.options.max, number.value)
+      number.value = Math.max(number.options.min, number.value)
+    }
+  
+    if (min_value)
+      min_value.callback = e => {
+        number.options.min = e
+        number.value = e
+      }
+    if (max_value)
+      max_value.callback = e => {
+        number.options.max = e
+        number.value = e
+      }
+  }
+  
+}
+
+app.registerExtension({
+  name: 'Mixlab.utils.FloatSlider',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+
+    if (nodeType.comfyClass == 'FloatSlider') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated;
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        min_max(this)
+      }
+    }
+
+   
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'FloatSlider') {
+      min_max(node)
+    }
+  }
+})
+app.registerExtension({
+  name: 'Mixlab.utils.IntNumber',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    
+    if (nodeType.comfyClass == 'IntNumber') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        min_max(this)
+      }
+    }
+   
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'IntNumber') {
+      min_max(node)
+    }
+  }
+})
+
+
+app.registerExtension({
+  name: 'Mixlab.utils.TESTNODE_',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    
+    if (nodeType.comfyClass == 'TESTNODE_') {
+    
+      const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = function (message) {
+				onExecuted?.apply(this, arguments);
+        console.log('##',message)
+				 
+			};
+
+
+    }
+   
+  }, 
+})

web/javascript/watcher_mixlab.js

@@ -60,30 +60,7 @@ app.registerExtension({
           type: inputData[0], // the type, CHEESE
           name: inputName, // the name, slice
           size: [128, 24], // a default size
-          draw (ctx, node, width, y) {
+          draw (ctx, node, width, y) {},
-            // // 绘制文件图标的函数
-            // function drawFileIcon () {
-            //   // 清空画布
-            //   // ctx.clearRect(0, 0, canvas.width, canvas.height)
-
-            //   // 绘制文件外框
-            //   ctx.fillStyle = '#000'
-            //   ctx.fillRect(5, 5, 40, 40)
-
-            //   // 绘制文件夹图标
-            //   ctx.fillStyle = '#f00'
-            //   ctx.fillRect(10, 15, 30, 20)
-
-            //   // 绘制监听符号
-            //   ctx.beginPath()
-            //   ctx.arc(30, 35, 5, 0, 2 * Math.PI)
-            //   ctx.fillStyle = '#00f'
-            //   ctx.fill()
-            // }
-
-            // // 调用绘制函数
-            // drawFileIcon()
-          },
           computeSize (...args) {
             return [128, 24] // a method to compute the current size of the widget
           },
@@ -124,11 +101,19 @@ app.registerExtension({
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
 
-        console.log('watch widtget', this.widgets)
+        // 虚拟的widget，用于更新节点，让其每次都运行
+        const widget = {
+          type: 'div',
+          name: 'seed',
+          draw (ctx, node, widget_width, y, widget_height) {}
+        }
+
+        this.addCustomWidget(widget)
 
         const watcher = this.widgets.filter(w => w.name == 'watcher')[0]
 
         watcher.callback = () => {
+          console.log('watcher', watcher.value)
           if (watcher.value === 'enable') {
             if (window._mixlab_watcher_t)
               clearInterval(window._mixlab_watcher_t)
@@ -140,7 +125,7 @@ app.registerExtension({
                   window._mixlab_file_path_watcher = json.event_type
                   // widget.card.innerText = window._mixlab_file_path_watcher || ''
                   //运行
-                  document.querySelector('#queue-button').click()
+                  // document.querySelector('#queue-button').click()
                 }
               })
             }, 1000)
@@ -162,15 +147,59 @@ app.registerExtension({
           window._mixlab_file_path_watcher = json.event_type
         })
 
-        /*
-                Add the widget, make sure we clean up nicely, and we do not want to be serialized!
-                */
-        // this.addCustomWidget(widget)
         this.onRemoved = function () {
           // widget.card.remove()
         }
         this.serialize_widgets = true
       }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log(message)
+        try {
+          let seed = this.widgets.filter(w => w.name === 'seed')[0]
+          if (seed) {
+            if (!seed.value) seed.value = 0
+            seed.value += 1
+          }
+        } catch (error) {}
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'LoadImagesFromPath') {
+      const watcher = node.widgets.filter(w => w.name == 'watcher')[0]
+      if (watcher) {
+        if (watcher.value === 'enable') {
+          if (window._mixlab_watcher_t) clearInterval(window._mixlab_watcher_t)
+          window._mixlab_watcher_t = setInterval(() => {
+            // 上次路径填充
+            getConfig().then(json => {
+              console.log(json.event_type)
+              if (json.event_type != window._mixlab_file_path_watcher) {
+                window._mixlab_file_path_watcher = json.event_type
+                // widget.card.innerText = window._mixlab_file_path_watcher || ''
+                //运行
+                document.querySelector('#queue-button').click()
+              }
+            })
+          }, 1000)
+        } else {
+          if (window._mixlab_watcher_t) {
+            clearInterval(window._mixlab_watcher_t)
+          }
+          window._mixlab_watcher_t = null
+        }
+      }
+
+      try {
+        let seed = node.widgets.filter(w => w.name === 'seed')[0]
+        if (seed) {
+          if (!seed.value) seed.value = 0
+          seed.value += 1
+        }
+      } catch (error) {}
     }
   }
 })

web/lib/photoswipe.min.css

@@ -0,0 +1 @@
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workflow/2-screeshare.json

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+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 2
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
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+          "type": "IMAGE",
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+      "order": 2,
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+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
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+            32
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+          "shape": 3,
+          "slot_index": 0
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+          "shape": 3
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+        {
+          "name": "FLOAT",
+          "type": "FLOAT",
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+          "type": "INT",
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+    [
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+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

workflow/3-FloatVideo-workflow.json

@@ -1,6 +1,6 @@
 {
-  "last_node_id": 23,
+  "last_node_id": 22,
-  "last_link_id": 51,
+  "last_link_id": 48,
   "nodes": [
     {
       "id": 7,
@@ -105,7 +105,7 @@
         {
           "name": "text",
           "type": "STRING",
-          "link": 51,
+          "link": 48,
           "widget": {
             "name": "text"
           }
@@ -295,7 +295,7 @@
         "Node name for S&R": "KSampler"
       },
       "widgets_values": [
-        482859286431021,
+        644769503212755,
         "randomize",
         4,
         1.6,
@@ -316,19 +316,47 @@
         "1": 246
       },
       "flags": {},
-      "order": 7,
+      "order": 6,
       "mode": 0,
       "inputs": [
         {
           "name": "images",
           "type": "IMAGE",
-          "link": 50
+          "link": 46
         }
       ],
       "properties": {
         "Node name for S&R": "PreviewImage"
       }
     },
+    {
+      "id": 20,
+      "type": "FloatingVideo",
+      "pos": [
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+        277
+      ],
+      "size": [
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+        58
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+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 41
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "FloatingVideo"
+      },
+      "widgets_values": [
+        null
+      ]
+    },
     {
       "id": 6,
       "type": "LoraLoader",
@@ -445,13 +473,13 @@
         "1": 58
       },
       "flags": {},
-      "order": 6,
+      "order": 7,
       "mode": 0,
       "inputs": [
         {
           "name": "image",
           "type": "IMAGE",
-          "link": 49
+          "link": 47
         }
       ],
       "outputs": [
@@ -473,43 +501,15 @@
       ]
     },
     {
-      "id": 20,
+      "id": 22,
-      "type": "FloatingVideo",
+      "type": "ScreenShare",
       "pos": [
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+        -111,
-        295
+        427
       ],
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         315,
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workflow/Prompt-weight-workflow.json

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workflow/Text-to-Image-app.json

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workflow/audio-chatgpt-workflow.json

@@ -42,7 +42,7 @@
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       },
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-        "如果您想购买工程车，您需要考虑以下几个因素：\n\n1. 预算：确定您有多少钱可以用来购买工程车。\n\n2. 用途：确定您购买工程车的目的，这将帮助您选择合适的车型和配置。\n\n3. 品牌和型号：选择一个知名品牌和适合您需求的型号。\n\n4. 购买地点：考虑在哪个地区购买，这将影响价格和可用性。\n\n5. 保养和维修：考虑车辆的保养和维修成本，以确保购买后的维护成本合理。\n\n请告诉我您需要更多的哪方面的信息，我将能更好地帮助您。"
+        "作为一个人工智能助手，我没有性别，也无法进行社交互动。我的主要任务是为用户提供帮助和信息。请问有什么问题我可以为您解答吗？"
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workflow/promptslide-appinfo-workflow.svg

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workflow/workflow.md

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