Update index.html

修复3d image的bug，未上传bg图也可以运行了

.gitignore

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

README.md

@@ -1,30 +1,42 @@
-## 
-v0.5.0 🚀🚗🚚🏃‍
-- Added video composition support to the MergeLayers.
-- Enhanced visual selection support for the NewLayer node.
-- Introduced the NoiseImage node and ResizeImage node.
-- Improved compatibility for TextImage with line breaks.
-- Optimized the 3DImage node to export textures for modification.
-- [Added DynamicDelayByText, enabling delayed execution based on input text length.](./workflow/audio-chatgpt-workflow.json)
+> 适配了最新版comfyui的py3.11 ，torch 2.1.2+cu121
+
+## 🚀🚗🚚🏃 Workflow-to-APP 
+- 新增AppInfo节点，可以通过简单的配置，把workflow转变为一个Web APP。
+- 支持多个web app 切换
+- 发布为app的workflow，可以在右键里再次编辑了
+- web app可以设置分类，在comfyui右键菜单可以编辑更新web app
 
 
-- 为MergeLayers添加了视频合成功能。
-- NewLayer节点增加了视觉选择支持。
-- 添加了NoiseImage节点和ResizeImage节点。
-- 支持带有换行的文本图像。
-- 对3D节点进行了优化，支持导出纹理以进行修改。
-- [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)
+- Support multiple web app switching.
+- Add the AppInfo node, which allows you to transform the workflow into a web app by simple configuration.
+- The workflow, which is now released as an app, can also be edited again by right-clicking.
+- The web app can be configured with categories, and the web app can be edited and updated in the right-click menu of ComfyUI.
 
 
-### 3D
-![](./assets/3dimage.png)
-[workflow](./workflow/3D-workflow.json)
+![](./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
 
 
-### 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! 💻🌐
+## 🏃🚗🚚🚀  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
@@ -35,6 +47,7 @@ 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)
 
@@ -43,18 +56,24 @@ 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)
 
 ### Layers
 > A new layer class node has been added, allowing you to separate the image into layers. After merging the images, you can input the controlnet for further processing.
@@ -63,9 +82,29 @@ 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)
 
 
 ## Other Nodes
@@ -75,9 +114,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 [workflow-1](./workflow/1-workflow.json)
 
-> randomPrompt
 
-![randomPrompt](./assets/randomPrompt.png)
 
 > TransparentImage
 
@@ -100,11 +137,15 @@ 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.
 
@@ -113,13 +154,23 @@ An improvement has been made to directly redirect to GitHub to search for missin
 ![node-not-found](./assets/node-not-found.png)
 
 
+### Update 
+v0.8.0 🚀🚗🚚🏃‍ LaMaInpainting
+- 新增 LaMaInpainting
+- 优化color节点的输出
+- 修复高清显示屏上定位节点不准的情况
+
+- Add LaMaInpainting
+- Optimize the output of the color node
+- Fix the issue of inaccurate positioning node on high-definition display screens
+
+
 ### Models
-[Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : model/clipseg
-
-<!-- ### Workflow
-[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
 
 ## Installation
 
@@ -154,7 +205,6 @@ 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)
 

__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'):
@@ -162,12 +199,125 @@ def get_workflows():
     workflows=read_workflow_json_files(workflow_path)
     return workflows
 
+def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=False):
+    app_path=os.path.join(current_path, "app")
+    if not os.path.exists(app_path):
+        os.mkdir(app_path)
+
+    category_path=os.path.join(app_path,category)
+    if not os.path.exists(category_path):
+        os.mkdir(category_path)
+
+    apps=[]
+    if filename==None:
+
+        #TODO 支持目录内遍历 
+        if is_all:
+            data=read_workflow_json_files_all(category_path)
+        else:
+            data=read_workflow_json_files(category_path)
+        
+        i=0
+        for item in data:
+            # print(item)
+            try:
+                x=item["data"]
+                if i==0:
+                    apps.append({
+                        "filename":item["filename"],
+                        # "category":item['category'],
+                        "data":x,
+                        "date":item["date"],
+                    })
+                else:
+                    category=''
+                    if 'category' in x['app']:
+                        category=x['app']['category']
+                    apps.append({
+                        "filename":item["filename"],
+                        "category":category,
+                        "data":{
+                            "app":{
+                                "category":category,
+                                "description":x['app']['description'],
+                                "filename":(x['app']['filename'] if 'filename' in x['app'] else "") ,
+                                "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
+                                "name":x['app']['name'],
+                                "version":x['app']['version'],
+                            }
+                        },
+                        "date":item["date"]
+                    })
+                i+=1
+            except Exception as e:
+                print("发生异常：", str(e))
+    else:
+        app_workflow_path=os.path.join(category_path, filename)
+        # print('app_workflow_path: ',app_workflow_path)
+        try:
+            with open(app_workflow_path) as json_file:
+                apps = [{
+                    'filename':filename,
+                    'data':json.load(json_file)
+                }]
+        except Exception as e:
+            print("发生异常：", str(e))
+        
+        if len(apps)==1 and category!='' and category!=None:
+            data=read_workflow_json_files(category_path)
+            
+            for item in data:
+                x=item["data"]
+                # print(apps[0]['filename'] ,item["filename"])
+                if apps[0]['filename']!=item["filename"]:
+                    category=''
+                    if 'category' in x['app']:
+                        category=x['app']['category']
+                    apps.append({
+                        "filename":item["filename"],
+                        # "category":category,
+                        "data":{
+                            "app":{
+                                "category":category,
+                                "description":x['app']['description'],
+                                "filename":(x['app']['filename'] if 'filename' in x['app'] else "") ,
+                                "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
+                                "name":x['app']['name'],
+                                "version":x['app']['version'],
+                            }
+                        },
+                        "date":item["date"]
+                    })
+
+    return apps
+
 def save_workflow_json(data):
     workflow_path=os.path.join(current_path, "workflow/my_workflow.json")
     with open(workflow_path, 'w') as file:
         json.dump(data, file)
     return workflow_path
 
+def save_workflow_for_app(data,filename="my_workflow_app.json",category=""):
+    app_path=os.path.join(current_path, "app")
+    if not os.path.exists(app_path):
+        os.mkdir(app_path)
+
+    category_path=os.path.join(app_path,category)
+    if not os.path.exists(category_path):
+        os.mkdir(category_path)
+    
+    app_workflow_path=os.path.join(category_path, filename)
+ 
+    try:
+        output_str = json.dumps(data['output'])
+        data['app']['id']=calculate_md5(output_str)
+        # id=data['app']['id']
+    except Exception as e:
+        print("发生异常：", str(e))
+    
+    with open(app_workflow_path, 'w') as file:
+        json.dump(data, file)
+    return filename
 
 def get_nodes_map():
     # print("#####path::", current_path)
@@ -253,6 +403,18 @@ async def mixlab_hander(request):
             print(e)
     return web.json_response(data)
 
+
+@routes.get('/mixlab/app')
+async def mixlab_app_handler(request):
+    html_file = os.path.join(current_path, "web/index.html")
+    if os.path.exists(html_file):
+        with open(html_file, 'r', encoding='utf-8', errors='ignore') as f:
+            html_data = f.read()
+            return web.Response(text=html_data, content_type='text/html')
+    else:
+        return web.Response(text="HTML file not found", status=404)
+    
+
 @routes.post('/mixlab/workflow')
 async def mixlab_workflow_hander(request):
     data = await request.json()
@@ -265,6 +427,29 @@ async def mixlab_workflow_hander(request):
                     'status':'success',
                     'file_path':file_path
                 }
+            elif data['task']=='save_app':
+                category=""
+                if "category" in data:
+                    category=data['category']
+                file_path=save_workflow_for_app(data['data'],data['filename'],category)
+                result={
+                    'status':'success',
+                    'file_path':file_path
+                }
+            elif data['task']=='my_app':
+                filename=None
+                category=""
+                admin=False
+                if 'filename' in data:
+                    filename=data['filename']
+                if 'category' in data:
+                    category=data['category']
+                if 'admin' in data:
+                    admin=data['admin']
+                result={
+                    'data':get_my_workflow_for_app(filename,category,admin),
+                    'status':'success',
+                }
             elif data['task']=='list':
                 result={
                     'data':get_workflows(),
@@ -289,6 +474,7 @@ async def nodes_map_hander(request):
 
     return web.json_response(result)
 
+# 把插件自定义的路由添加到comfyui server里
 def new_add_routes(self):
         import nodes
         self.app.add_routes(routes)
@@ -317,24 +503,30 @@ PromptServer.add_routes=new_add_routes
 
 
 # 导入节点
-from .nodes.PromptNode import RandomPrompt
-from .nodes.ImageNode import NoiseImage,TransparentImage,LoadImagesFromPath,ResizeImage,TextImage,SvgImage,Image3D,EmptyLayer,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.PromptNode import RandomPrompt,PromptSlide
+from .nodes.ImageNode import NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
 from .nodes.Clipseg import CLIPSeg,CombineMasks
 from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import ColorInput,FontInput,TextToNumber,DynamicDelayProcessor
-
+from .nodes.Utils import AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,GetImageSize_,MultiplicationNode
+from .nodes.Lama import LaMaInpainting
+from .nodes.ClipInterrogator import ClipInterrogator
 
 # 要导出的所有节点及其名称的字典
 # 注意：名称应全局唯一
 NODE_CLASS_MAPPINGS = {
+    "AppInfo":AppInfo,
     "RandomPrompt":RandomPrompt,
+    "PromptSlide":PromptSlide,
+    "ClipInterrogator":ClipInterrogator,
     "NoiseImage":NoiseImage,
+    "GradientImage":GradientImage,
     "TransparentImage":TransparentImage,
     "ResizeImageMixlab":ResizeImage,
     "LoadImagesFromPath":LoadImagesFromPath,
+    "LoadImagesFromURL":LoadImagesFromURL,
     "TextImage":TextImage,
     "EnhanceImage":EnhanceImage,
     "SvgImage":SvgImage,
@@ -360,15 +552,24 @@ 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, 
+    "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",
@@ -381,7 +582,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
     "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
     "3DImage":"3DImage ♾️Mixlab",
-    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab"
+    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
+    "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
+    "PromptSlide":"PromptSlide ♾️Mixlab"
 
     # "GamePal":"GamePal ♾️Mixlab"
 }
@@ -390,5 +593,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
 WEB_DIRECTORY = "./web"
 
 print('--------------')
-print('\033[91mMixlab Nodes: \033[93mLoaded\033[0m')
+print('\033[91m ### Mixlab Nodes: \033[93mLoaded\033[0m')
 print('--------------')

data/anime.txt

@@ -0,0 +1,10 @@
+Chibi Anime Style
+Gakuen Anime Style
+Gekiga Anime Style
+Jidaimono Anime Style
+Kawaii Anime Style
+Mecha Anime Style
+Realistic Anime Style
+Semi-Realistic Anime Style
+Shoji Anime Style
+Kemonomimi Anime Style

data/camera-and-lens.txt

@@ -0,0 +1,23 @@
+GoPro
+Drone
+polaroid
+black and white film
+Kodachrome
+shot on 8mm
+shot on 16mm
+shot on 35mm
+Microscopic
+Fisheye Lens
+Wide Angle
+Ultra-Wide Angle
+Panorama
+Short Exposure
+Long Exposure
+Double Exposure
+f2.8
+Depth of Field
+Soft Focus
+Deep Focus
+Shallow Focus
+Vanishing Point
+Vantage Point

data/extension-node-map.json

@@ -4762,25 +4762,31 @@
     "https://github.com/shadowcz007/comfyui-mixlab-nodes": [
         [
             "3DImage",
+            "AppInfo",
+            "IntNumber",
+            "FloatSlider",
+            "ResizeImage",
+            "NoiseImage",
             "AreaToMask",
-            "CLIPSeg",
             "CLIPSeg_",
             "CharacterInText",
             "ChatGPTOpenAI",
             "Color",
             "CombineMasks_",
-            "CombineSegMasks",
-            "EmptyLayer",
             "EnhanceImage",
+            "GradientImage",
             "FaceToMask",
             "FeatheredMask",
             "FloatingVideo",
             "Font",
             "ImageCropByAlpha",
             "LoadImagesFromPath",
+            "LoadImagesFromURL",
             "MergeLayers",
             "NewLayer",
             "RandomPrompt",
+            "PromptSlide",
+            "ClipInterrogator",
             "ScreenShare",
             "ShowLayer",
             "ShowTextForGPT",
@@ -4790,12 +4796,17 @@
             "SplitLongMask",
             "SvgImage",
             "TextImage",
+            "ResizeImageMixlab",
             "TransparentImage",
             "VAEDecodeConsistencyDecoder",
-            "VAELoaderConsistencyDecoder"
+            "VAELoaderConsistencyDecoder",
+            "TextToNumber",
+            "TextInput_",
+            "DynamicDelayProcessor",
+            "LaMaInpainting"
         ],
         {
-            "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/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/ChatGPT.py

@@ -75,15 +75,15 @@ class ChatGPTNode:
             "required": {
                 "api_key":("KEY", {"default": "", "multiline": True}),
                 "api_url":("URL", {"default": "", "multiline": True}),
-                "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": {
@@ -167,7 +167,7 @@ class ShowTextForGPT:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "text": ("STRING", {"forceInput": True}),
+                "text": ("STRING", {"forceInput": True,"dynamicPrompts": False}),
             }
         }
 
@@ -189,8 +189,8 @@ class CharacterInText:
     def INPUT_TYPES(s):
         return {
             "required": {
-                 "text": ("STRING", {"multiline": True}),
-                 "character": ("STRING", {"multiline": True}),
+                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                 "character": ("STRING", {"multiline": True,"dynamicPrompts": False}),
                  "start_index": ("INT", {
                     "default": 1,
                     "min": 0, #Minimum value

nodes/ClipInterrogator.py

@@ -0,0 +1,158 @@
+import os
+import folder_paths
+
+from PIL import Image
+import comfy.utils
+import numpy as np
+import json
+import torch
+
+from transformers import AutoProcessor, BlipForConditionalGeneration
+
+from clip_interrogator import Config, Interrogator
+
+def load_caption_model(model_path,config,t='blip-base'):
+    dtype=torch.float16 if config.device == 'cuda' else torch.float32
+    caption_model = BlipForConditionalGeneration.from_pretrained(model_path, torch_dtype=dtype)
+    
+    caption_processor = AutoProcessor.from_pretrained(model_path)
+
+    caption_model.eval()
+    if not config.caption_offload:
+        caption_model = caption_model.to(config.device)
+    
+    return (caption_model,caption_processor)
+    
+
+
+caption_model_path=os.path.join(folder_paths.models_dir, "clip_interrogator/Salesforce/blip-image-captioning-base")
+if not os.path.exists(caption_model_path):
+    print(f"## clip_interrogator_model not found: {caption_model_path}, pls download from https://huggingface.co/Salesforce/blip-image-captioning-base")
+
+cache_path=os.path.join(folder_paths.models_dir, "clip_interrogator")
+
+
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+def image_analysis(ci,image):
+    image = image.convert('RGB')
+    image_features = ci.image_to_features(image)
+
+    top_mediums = ci.mediums.rank(image_features, 5)
+    top_artists = ci.artists.rank(image_features, 5)
+    top_movements = ci.movements.rank(image_features, 5)
+    top_trendings = ci.trendings.rank(image_features, 5)
+    top_flavors = ci.flavors.rank(image_features, 5)
+
+    medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))}
+    artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))}
+    movement_ranks = {movement: sim for movement, sim in zip(top_movements, ci.similarities(image_features, top_movements))}
+    trending_ranks = {trending: sim for trending, sim in zip(top_trendings, ci.similarities(image_features, top_trendings))}
+    flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))}
+    
+    return medium_ranks, artist_ranks, movement_ranks, trending_ranks, flavor_ranks
+
+def image_to_prompt(ci,image, mode):
+    ci.config.chunk_size = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+    ci.config.flavor_intermediate_count = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+    image = image.convert('RGB')
+    if mode == 'best':
+        return ci.interrogate(image)
+    elif mode == 'classic':
+        return ci.interrogate_classic(image)
+    elif mode == 'fast':
+        return ci.interrogate_fast(image)
+    elif mode == 'negative':
+        return ci.interrogate_negative(image)
+
+# image = Image.open(image_path).convert('RGB')
+# ci = Interrogator(Config(clip_model_name="ViT-L-14/openai"))
+# print(ci.interrogate(image))
+
+
+class ClipInterrogator:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "prompt_mode": (['fast','classic','best','negative'],),
+            "image_analysis": (["off","on"],), 
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING","STRING",)
+    RETURN_NAMES = ("prompt","analysis",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/prompt"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+    global ci
+    ci = None
+    def run(self,image,prompt_mode,image_analysis):
+        global ci
+
+        prompt_mode=prompt_mode[0]
+        analysis=image_analysis[0]
+
+        prompt_result=[]
+        analysis_result=[]
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(image)*(2 if analysis=='on' else 1))
+        
+        if ci==None:
+            config=Config(
+                clip_model_name="ViT-L-14/openai",
+                device="cuda" if torch.cuda.is_available() else "cpu",
+                download_cache=True,
+                clip_model_path=cache_path,
+                cache_path=cache_path
+                )
+            config.apply_low_vram_defaults()
+
+            caption_model,caption_processor=load_caption_model(caption_model_path,config)
+
+            config.caption_model= caption_model
+            config.caption_processor= caption_processor
+
+            ci = Interrogator(config)
+        # else:
+        #     simple_lama.model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        for i in range(len(image)):
+            im=image[i]
+
+            im=tensor2pil(im)
+            im=im.convert('RGB')
+
+            if analysis=='on':
+                analysis_res=image_analysis(ci,im)
+                analysis_result.append(json.dumps(analysis_res))
+                pbar.update(1)
+
+            prompt=image_to_prompt(ci,im,prompt_mode)
+            pbar.update(1)
+            prompt_result.append(prompt)
+
+
+        # result.save("inpainted.png")
+        if ci.config.clip_offload and not ci.clip_offloaded:
+            ci.clip_model = ci.clip_model.to('cpu')
+            ci.clip_offloaded = True
+
+        if ci.config.caption_offload and not ci.caption_offloaded:
+            ci.caption_model = ci.caption_model.to('cpu')
+            ci.caption_offloaded = True
+
+        return {"ui":{"prompt": prompt_result,"analysis":analysis_result},"result": (prompt_result,analysis_result,)}

nodes/Clipseg.py

@@ -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()
@@ -91,7 +101,7 @@ class CLIPSeg:
         return {"required":
                     {
                         "image": ("IMAGE",),
-                        "text": ("STRING", {"multiline": False}),
+                        "text": ("STRING", {"multiline": False,"dynamicPrompts": False}),
                         
                      },
                 "optional":
@@ -107,7 +117,7 @@ class CLIPSeg:
     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 = np.array(tensor_bw).astype(np.float32) / 255.0
-        tensor_bw = torch.from_numpy(tensor_bw)[None,]
-        tensor_bw = tensor_bw.squeeze(0)[..., 0]
+        tensor_bw = binary_mask_image.convert("L")
+        tensor_bw=pil2tensor(tensor_bw)
+        # tensor_bw = np.array(tensor_bw).astype(np.float32) / 255.0
+        # tensor_bw = torch.from_numpy(tensor_bw)[None,]
+        # tensor_bw = tensor_bw.squeeze(0)[..., 0]
 
-        return tensor_bw, image_out_heatmap, image_out_binary
+        return (tensor_bw, image_out_heatmap, image_out_binary,)
 
     #OUTPUT_NODE = False
 
@@ -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/ImageNode.py

@@ -1,5 +1,7 @@
 import numpy as np
+import requests
 import torch
+# from PIL import Image, ImageDraw
 from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
 from PIL.PngImagePlugin import PngInfo
 import base64,os,random
@@ -154,6 +156,54 @@ def get_not_transparent_area(image):
     return (x, y, w, h)
 
 
+
+
+def generate_gradient_image(width, height, start_color_hex, end_color_hex):
+    image = Image.new('RGBA', (width, height))
+    draw = ImageDraw.Draw(image)
+
+    if len(start_color_hex) == 7:
+        start_color_hex += "FF"
+    if len(end_color_hex) == 7:
+        end_color_hex += "FF"
+
+    start_color_hex = start_color_hex.lstrip("#")
+    end_color_hex = end_color_hex.lstrip("#")
+
+    # 将十六进制颜色代码转换为RGBA元组，包括透明度
+    start_color = tuple(int(start_color_hex[i:i+2], 16) for i in (0, 2, 4, 6))
+    end_color = tuple(int(end_color_hex[i:i+2], 16) for i in (0, 2, 4, 6))
+
+    for y in range(height):
+        # 计算当前行的颜色
+        r = int(start_color[0] + (end_color[0] - start_color[0]) * y / height)
+        g = int(start_color[1] + (end_color[1] - start_color[1]) * y / height)
+        b = int(start_color[2] + (end_color[2] - start_color[2]) * y / height)
+        a = int(start_color[3] + (end_color[3] - start_color[3]) * y / height)
+
+        # 绘制当前行的渐变色
+        draw.line((0, y, width, y), fill=(r, g, b, a))
+
+    # Create a mask from the image's alpha channel
+    mask = image.split()[-1]
+
+    # Convert the mask to a black and white image
+    mask = mask.convert('L')
+
+    image=image.convert('RGB')
+
+    return (image, mask)
+
+# 示例用法
+# width = 500
+# height = 200
+# start_color_hex = 'FF0000FF'  # 红色，完全不透明
+# end_color_hex = '0000FFFF'  # 蓝色，完全不透明
+
+# gradient_image = generate_gradient_image(width, height, start_color_hex, end_color_hex)
+# gradient_image.save('gradient_image.png')
+
+
 # 读取不了分层
 def load_psd(image):
     layers=[]
@@ -197,6 +247,24 @@ def load_image(fp,white_bg=False):
         
     return images
 
+def load_image_and_mask_from_url(url, timeout=10):
+    # Load the image from the URL
+    response = requests.get(url, timeout=timeout)
+
+    content_type = response.headers.get('Content-Type')
+    
+    image = Image.open(BytesIO(response.content))
+
+    # Create a mask from the image's alpha channel
+    mask = image.convert('RGBA').split()[-1]
+
+    # Convert the mask to a black and white image
+    mask = mask.convert('L')
+
+    image=image.convert('RGB')
+
+    return (image, mask)
+
 
 # 获取图片s
 def get_images_filepath(f,white_bg=False):
@@ -235,10 +303,59 @@ def get_images_filepath(f,white_bg=False):
 
     return images
 
+
+
+def get_average_color_image(image):
+    # 打开图片
+    # image = Image.open(image_path)
+
+    # 将图片转换为RGB模式
+    image = image.convert("RGB")
+
+    # 获取图片的像素值
+    pixel_data = image.load()
+
+    # 初始化颜色总和和像素数量
+    total_red = 0
+    total_green = 0
+    total_blue = 0
+    pixel_count = 0
+
+    # 遍历图片的每个像素
+    for i in range(image.width):
+        for j in range(image.height):
+            # 获取像素的RGB值
+            r, g, b = pixel_data[i, j]
+
+            # 累加颜色值
+            total_red += r
+            total_green += g
+            total_blue += b
+
+            # 像素数量加1
+            pixel_count += 1
+
+    # 计算平均颜色值
+    average_red = int(total_red // pixel_count)
+    average_green = int(total_green // pixel_count)
+    average_blue = int(total_blue // pixel_count)
+
+    # 返回平均颜色值
+
+    im = Image.new("RGB", (image.width, image.height), (average_red, average_green, average_blue))
+
+    return im
+
+
+
 # 创建噪声图像
-def create_noisy_image(width, height, mode="RGB", noise_level=128):
+def create_noisy_image(width, height, mode="RGB", noise_level=128, background_color="#FFFFFF"):
+    
+    background_rgb = tuple(int(background_color[i:i+2], 16) for i in (1, 3, 5))
+    image = Image.new(mode, (width, height), background_rgb)
+
     # 创建空白图像
-    image = Image.new(mode, (width, height))
+    # image = Image.new(mode, (width, height))
 
     # 遍历每个像素，并随机设置像素值
     pixels = image.load()
@@ -399,26 +516,42 @@ def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option)
     return bg_image
 
 
-def resize_image(layer_image,scale_option,width,height):
+
+def resize_image(layer_image, scale_option, width, height,color="white"):
     layer_image = layer_image.convert("RGB")
+    original_width, original_height = layer_image.size
+    
     if scale_option == "height":
-        # 按照高度比例缩放
-        original_width, original_height = layer_image.size
+        # Scale image based on height
         scale = height / original_height
         new_width = int(original_width * scale)
         layer_image = layer_image.resize((new_width, height))
+        
     elif scale_option == "width":
-        # 按照宽度比例缩放
-        original_width, original_height = layer_image.size
+        # Scale image based on width
         scale = width / original_width
         new_height = int(original_height * scale)
         layer_image = layer_image.resize((width, new_height))
+        
     elif scale_option == "overall":
-        # 整体缩放
+        # Scale image overall
         layer_image = layer_image.resize((width, height))
+        
+    elif scale_option == "center":
+        # Scale image to minimum of width and height, center it, and fill extra area with black
+        scale = min(width / original_width, height / original_height)
+        new_width = int(original_width * scale)
+        new_height = int(original_height * scale)
+        resized_image = Image.new("RGB", (width, height), color=color)
+        resized_image.paste(layer_image.resize((new_width, new_height)), ((width - new_width) // 2, (height - new_height) // 2))
+        resized_image=resized_image.convert("RGB")
+        return resized_image
+    
     return layer_image
 
 
+
+
 # def generate_text_image(text_list, font_path, font_size, text_color, vertical=True, spacing=0):
 #     # Load Chinese font
 #     font = ImageFont.truetype(font_path, font_size)
@@ -487,6 +620,7 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s
         y = 0
         for line in lines:
             for char in line:
+                #print('char',char)
                 char_coordinates.append((x, y))
                 x += font_size + spacing
             y += font_size + spacing
@@ -495,10 +629,10 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s
     # 3. Calculate image width and height
     if layout == "vertical":
         width = (len(lines) * (font_size + spacing)) - spacing
-        height = (len(max(lines, key=len)) * (font_size + spacing)) - spacing
+        height = ((len(max(lines, key=len))+1) * (font_size + spacing)) + spacing
     else:
         width = (len(max(lines, key=len)) * (font_size + spacing)) - spacing
-        height = (len(lines) * (font_size + spacing)) - spacing
+        height = ((len(lines)-1) * (font_size + spacing)) + font_size
 
     # 4. Draw each character on the image
     image = Image.new('RGBA', (width, height), (255, 255, 255,0))
@@ -794,7 +928,10 @@ class TransparentImage:
         # result 里输出给下个节点的数据 
         # print('TransparentImage',len(images_rgb))
         return {"ui":{"images": ui_images,"image_paths":image_paths},"result": (image_paths,images_rgb,images_rgba)}
-        
+
+
+
+
 
 class EnhanceImage:
     @classmethod
@@ -816,20 +953,27 @@ class EnhanceImage:
 
     CATEGORY = "♾️Mixlab/image"
 
-    INPUT_IS_LIST = False
+    INPUT_IS_LIST = True
 
-    OUTPUT_IS_LIST = (False,)
+    OUTPUT_IS_LIST = (True,)
   
     # 运行的函数
     def run(self,image,contrast):
-        # print('EnhanceImage',image.shape)
-        image=tensor2pil(image)
-     
-        image=enhance_depth_map(image,contrast)
+        # print('EnhanceImage',len(image),image[0].shape)
+        contrast=contrast[0]
+        res=[]
+        for ims in image:
+            for im in ims:
 
-        image=pil2tensor(image)
+                image=tensor2pil(im)
+            
+                image=enhance_depth_map(image,contrast)
+
+                image=pil2tensor(image)
+
+                res.append(image)
            
-        return (image,)
+        return (res,)
 
 
 
@@ -850,7 +994,7 @@ class LoadImagesFromPath:
     def INPUT_TYPES(s):
         return {
                 "required": {
-                                "file_path": ("STRING",{"multiline": False,"default": ""}),
+                                "file_path": ("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
                             },
                 "optional":{
                     "white_bg": (["disable","enable"],),
@@ -869,7 +1013,7 @@ class LoadImagesFromPath:
                 }
             }
     
-    RETURN_TYPES = ('IMAGE','MASK','STRING')
+    RETURN_TYPES = ('IMAGE','MASK','STRING',)
 
     FUNCTION = "run"
 
@@ -902,6 +1046,11 @@ class LoadImagesFromPath:
 
         images=get_images_filepath(file_path,white_bg=='enable')
 
+        # 当开启了监听，则取最新的，第一个文件
+        if watcher=='enable':
+            index_variable=0
+            newest_files='enable'
+
         # 排序
         sorted_files = sorted(images, key=lambda x: os.path.getmtime(x['file_path']), reverse=(newest_files=='enable'))
 
@@ -913,9 +1062,13 @@ class LoadImagesFromPath:
             masks.append(im['mask'])
         
         # print('index_variable',index_variable)
-        if index_variable!=-1:
-            imgs=[imgs[index_variable]] if index_variable < len(imgs) else None
-            masks=[masks[index_variable]] if index_variable < len(masks) else None
+        
+        try:
+            if index_variable!=-1:
+                imgs=[imgs[index_variable]] if index_variable < len(imgs) else None
+                masks=[masks[index_variable]] if index_variable < len(masks) else None
+        except Exception as e:
+            print("发生了一个未知的错误：", str(e))
 
         # print('#prompt::::',prompt)
         return (imgs,masks,prompt,)
@@ -962,8 +1115,8 @@ class TextImage:
     def INPUT_TYPES(s):
         return {"required": { 
             
-                    "text": ("STRING",{"multiline": True,"default": "龍馬精神迎新歲"}),
-                    "font_path": ("STRING",{"multiline": False,"default": FONT_PATH}),
+                    "text": ("STRING",{"multiline": True,"default": "龍馬精神迎新歲","dynamicPrompts": False}),
+                    "font_path": ("STRING",{"multiline": False,"default": FONT_PATH,"dynamicPrompts": False}),
                     "font_size": ("INT",{
                                 "default":100, 
                                 "min": 100, #Minimum value
@@ -973,17 +1126,17 @@ class TextImage:
                                 }), 
                     "spacing": ("INT",{
                                 "default":12, 
-                                "min": 1, #Minimum value
+                                "min": -200, #Minimum value
                                 "max": 200, #Maximum value
                                 "step": 1, #Slider's step
                                 "display": "number" # Cosmetic only: display as "number" or "slider"
                                 }), 
-                    "text_color":("STRING",{"multiline": False,"default": "#000000"}),
+                    "text_color":("STRING",{"multiline": False,"default": "#000000","dynamicPrompts": False}),
                     "vertical":("BOOLEAN", {"default": True},),
                              },
                 }
     
-    RETURN_TYPES = ("IMAGE","MASK")
+    RETURN_TYPES = ("IMAGE","MASK",)
     # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
 
     FUNCTION = "run"
@@ -1004,6 +1157,62 @@ class TextImage:
 
         return (img,mask,)
 
+class LoadImagesFromURL:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "url": ("STRING",{"multiline": True,"default": "https://","dynamicPrompts": False}),
+                             },
+                }
+    
+    RETURN_TYPES = ("IMAGE","MASK",)
+    RETURN_NAMES = ("images","masks",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/image"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,True,)
+
+
+    global urls_image
+    urls_image={}
+
+    def run(self,url):
+        global urls_image
+        print(urls_image)
+        def filter_http_urls(urls):
+            filtered_urls = []
+            for url in urls.split('\n'):
+                if url.startswith('http'):
+                    filtered_urls.append(url)
+            return filtered_urls
+
+        filtered_urls = filter_http_urls(url)
+
+        images=[]
+        masks=[]
+
+        for img_url in filtered_urls:
+            try:
+                if img_url in urls_image:
+                    img,mask=urls_image[img_url]
+                else:
+                    img,mask=load_image_and_mask_from_url(img_url)
+                    urls_image[img_url]=(img,mask)
+
+                img1=pil2tensor(img)
+                mask1=pil2tensor(mask)
+
+                images.append(img1)
+                masks.append(mask1)
+            except Exception as e:
+                print("发生了一个未知的错误：", str(e))
+            
+        return (images,masks,)
+
+
 
 
 class SvgImage:
@@ -1463,6 +1672,80 @@ class MergeLayers:
     
 
 
+class GradientImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                "width": ("INT",{
+                    "default": 512, 
+                    "min": 1, # 最小值
+                    "max": 8192, # 最大值
+                    "step": 1, # 间隔
+                    "display": "number" # 控件类型： 输入框 number、滑块 slider
+                }),
+                "height": ("INT",{
+                    "default": 512, 
+                    "min": 1,
+                    "max": 8192,
+                    "step": 1,
+                    "display": "number"
+                }),
+                "start_color_hex": ("STRING",{"multiline": False,"default": "#FFFFFF","dynamicPrompts": False}),
+                "end_color_hex": ("STRING",{"multiline": False,"default": "#000000","dynamicPrompts": False}),
+                },
+                }
+    
+    # 输出的数据类型
+    RETURN_TYPES = ("IMAGE","MASK",)
+
+    # 运行时方法名称
+    FUNCTION = "run"
+
+    # 右键菜单目录
+    CATEGORY = "♾️Mixlab/image"
+
+    # 输入是否为列表
+    INPUT_IS_LIST = False
+
+    # 输出是否为列表
+    OUTPUT_IS_LIST = (False,False,)
+
+    def run(self,width,height,start_color_hex, end_color_hex):
+
+        im,mask=generate_gradient_image(width, height, start_color_hex, end_color_hex)
+
+        #获取临时目录：temp
+        output_dir = folder_paths.get_temp_directory()
+
+        (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('tmp_', output_dir)
+        
+        image_file = f"{filename}_{counter:05}.png"
+
+        image_path=os.path.join(full_output_folder, image_file)
+        # 保存图片
+        im.save(image_path,compress_level=6)
+
+        # 把PIL数据类型转为tensor
+        im=pil2tensor(im)
+
+        mask=pil2tensor(mask)
+
+        # 定义ui字段，数据将回传到web前端的 nodeType.prototype.onExecuted
+        # result是节点的输出
+        return {"ui":{"images": [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type":"temp"
+            }]},"result": (im,mask,)}
+
+
+
 class NoiseImage:
     @classmethod
     def INPUT_TYPES(s):
@@ -1488,7 +1771,7 @@ class NoiseImage:
                     "step": 1,
                     "display": "slider"
                 }),
-
+                "color_hex": ("STRING",{"multiline": False,"default": "#FFFFFF","dynamicPrompts": False}),
                 },
                 }
     
@@ -1507,9 +1790,9 @@ class NoiseImage:
     # 输出是否为列表
     OUTPUT_IS_LIST = (False,)
 
-    def run(self,width,height,noise_level):
+    def run(self,width,height,noise_level,color_hex):
         # 创建噪声图像
-        im=create_noisy_image(width,height,"RGB",noise_level)
+        im=create_noisy_image(width,height,"RGB",noise_level,color_hex)
         
         #获取临时目录：temp
         output_dir = folder_paths.get_temp_directory()
@@ -1558,38 +1841,59 @@ class ResizeImage:
                     "step": 1, #Slider's step
                     "display": "number" # Cosmetic only: display as "number" or "slider"
                 }),
-                "scale_option": (["width","height",'overall'],),
+                "scale_option": (["width","height",'overall','center'],),
 
                              },
 
             "optional":{
                     "image": ("IMAGE",),
+                    "average_color": (["on",'off'],),
+                    "fill_color":("STRING",{"multiline": False,"default": "#FFFFFF","dynamicPrompts": False}),
             }
                 }
     
-    RETURN_TYPES = ("IMAGE",)
+    RETURN_TYPES = ("IMAGE","IMAGE")
+    RETURN_NAMES = ("image","average_image",)
 
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/image"
 
     INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (False,)
+    OUTPUT_IS_LIST = (True,True,)
 
-    def run(self,width,height,scale_option,image=None):
+    def run(self,width,height,scale_option,image=None,average_color=['on'],fill_color=["#FFFFFF"]):
         
         w=width[0]
         h=height[0]
         scale_option=scale_option[0]
+        average_color=average_color[0]
+        fill_color=fill_color[0]
+
+        imgs=[]
+        average_images=[]
 
         if image==None:
             im=create_noisy_image(w,h,"RGB")
-        else:
-            im=image[0]
-            im=tensor2pil(im)
-            im=resize_image(im,scale_option,w,h)
-            im=im.convert('RGB')
+            a_im=get_average_color_image(im)
+            
+            im=pil2tensor(im)
+            imgs.append(im)
 
-        im=pil2tensor(im)
+            a_im=pil2tensor(a_im)
+            average_images.append(a_im)
+        else:
+            for im in image:
+                im=tensor2pil(im)
+                im=resize_image(im,scale_option,w,h,fill_color)
+                im=im.convert('RGB')
+
+                a_im=get_average_color_image(im)
+
+                im=pil2tensor(im)
+                imgs.append(im)
+
+                a_im=pil2tensor(a_im)
+                average_images.append(a_im)
         
-        return (im,)
+        return (imgs,average_images,)

nodes/Lama.py

@@ -0,0 +1,84 @@
+import os
+import folder_paths
+from simple_lama_inpainting import SimpleLama
+from PIL import Image
+
+import numpy as np
+
+import torch
+
+
+ 
+llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
+if not os.path.exists(llma_model_path):
+    os.environ['LAMA_MODEL']=''
+    print(f"## lama torchscript model not found: {llma_model_path},pls download from https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt")
+else:
+    os.environ['LAMA_MODEL'] = llma_model_path
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+# simple_lama = SimpleLama()
+
+# img_path = "image.png"
+# mask_path = "mask.png"
+
+# image = Image.open(img_path)
+# mask = Image.open(mask_path).convert('L')
+
+# result = simple_lama(image, mask)
+# result.save("inpainted.png")
+
+
+class LaMaInpainting:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "mask": ("MASK",),
+                             },
+
+            
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/image"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+    global simple_lama
+    simple_lama = None
+    def run(self,image,mask):
+        global simple_lama
+
+        result=[]
+        if simple_lama==None:
+            simple_lama = SimpleLama()
+        else:
+            simple_lama.model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        for i in range(len(image)):
+            im=image[i]
+            ma=mask[i]
+            im=tensor2pil(im)
+            ma=tensor2pil(ma)
+            ma =ma.convert('L')
+            
+            res = simple_lama(im, ma)
+            res=pil2tensor(res)
+            result.append(res)
+        # result.save("inpainted.png")
+        if simple_lama.device=='cuda':
+            simple_lama.model.to('cpu')
+
+        return (result,)

nodes/PromptNode.py

@@ -4,12 +4,11 @@ import json
 from urllib import request, parse
 
 
-
-def queue_prompt(prompt_workflow):
-    p = {"prompt": prompt_workflow}
-    data = json.dumps(p).encode('utf-8')
-    req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
-    request.urlopen(req)    
+# def queue_prompt(prompt_workflow):
+#     p = {"prompt": prompt_workflow}
+#     data = json.dumps(p).encode('utf-8')
+#     req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
+#     request.urlopen(req)    
 
 
 default_prompt1='''Swing
@@ -45,6 +44,73 @@ default_prompt1='''Swing
                                                 '''
 default_prompt1="\n".join([p.strip() for p in default_prompt1.split('\n') if p.strip()!=''])
 
+
+def addWeight(text, weight=1):
+    if weight == 1:
+        return text
+    else:
+        return f"({text}:{round(weight,2)})"
+
+
+
+class PromptSlide:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                
+                "prompt_keyword": ("STRING", 
+                         {
+                            "multiline": False, 
+                            "default": '',
+                            "dynamicPrompts": False
+                          }),
+
+                "weight":("FLOAT", {"default": 1, "min": -3,"max": 3,
+                                                                "step": 0.01,
+                                                                "display": "slider"}),
+
+                # "min_value":("FLOAT", {
+                #         "default": -2, 
+                #         "min": -10, 
+                #         "max": 0xffffffffffffffff,
+                #         "step": 0.01, 
+                #         "display": "number"  
+                #     }),
+                # "max_value":("FLOAT", {
+                #         "default": 2, 
+                #         "min": -10, 
+                #         "max": 0xffffffffffffffff,
+                #         "step": 0.01, 
+                #         "display": "number"  
+                #     }),
+              
+                }
+            }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/prompt"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+    OUTPUT_NODE = False
+
+    # 运行的函数
+    def run(self,prompt_keyword,weight):
+        # if weight < min_value:
+        #     weight= min_value
+        # elif weight > max_value:
+        #     weight= max_value
+        p=addWeight(prompt_keyword,weight)
+        return (p,)
+
+
+
+
 class RandomPrompt:
 
     '''
@@ -87,7 +153,7 @@ class RandomPrompt:
 
     # 运行的函数
     def run(self,max_count,mutable_prompt,immutable_prompt,random_sample):
-        print('#运行的函数',mutable_prompt,immutable_prompt,max_count,random_sample)
+        # print('#运行的函数',mutable_prompt,immutable_prompt,max_count,random_sample)
         
         # Split the text into an array of words
         words1 = mutable_prompt.split("\n")
@@ -106,6 +172,8 @@ class RandomPrompt:
             w1=w1.strip()
             for w2 in words2:
                 w2=w2.strip()
+                if '``' not in w2:
+                    w2=w2+',``'
                 if w1!='' and w2!='':
                     prompts.append(w2.replace('``', w1))
                 pbar.update(1)
@@ -126,62 +194,62 @@ class RandomPrompt:
 
 
 
-class RunWorkflow:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "workflow": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
-                "prompt": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
-                 "image": ("IMAGE",),
-                 "input_node": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
-                 "output_node": ("STRING", {
-                            "multiline": False, 
-                            "default": ''
-                          }),
-                },
+# class RunWorkflow:
+#     @classmethod
+#     def INPUT_TYPES(s):
+#         return {
+#             "required": {
+#                 "workflow": ("STRING", {
+#                             "multiline": False, 
+#                             "default": ''
+#                           }),
+#                 "prompt": ("STRING", {
+#                             "multiline": False, 
+#                             "default": ''
+#                           }),
+#                  "image": ("IMAGE",),
+#                  "input_node": ("STRING", {
+#                             "multiline": False, 
+#                             "default": ''
+#                           }),
+#                  "output_node": ("STRING", {
+#                             "multiline": False, 
+#                             "default": ''
+#                           }),
+#                 },
                 
-            }
+#             }
     
     
 
-    RETURN_TYPES = ("IMAGE","STRING",)
+#     RETURN_TYPES = ("IMAGE","STRING",)
 
-    FUNCTION = "run"
+#     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/workflow"
+#     CATEGORY = "♾️Mixlab/workflow"
 
-    OUTPUT_IS_LIST = (True,)
-    OUTPUT_NODE = True
+#     OUTPUT_IS_LIST = (True,)
+#     OUTPUT_NODE = True
 
 
-    # 运行的函数
-    def run(self,workflow,prompt,image,input_node,output_node):
-        print('#运行的函数',prompt,image,input_node,output_node)
-        workflow=json.loads(workflow)
-        input_node=input_node.split(".")
-        workflow[input_node[0]][input_node[1]][input_node[2]]=prompt
+#     # 运行的函数
+#     def run(self,workflow,prompt,image,input_node,output_node):
+#         print('#运行的函数',prompt,image,input_node,output_node)
+#         workflow=json.loads(workflow)
+#         input_node=input_node.split(".")
+#         workflow[input_node[0]][input_node[1]][input_node[2]]=prompt
 
-        workflow_new={}
-        # 遍历，seed设为随机
-        for key, value in workflow.items():
-            if 'inputs' in value:
-                if 'seed' in value['inputs']:
-                    value['inputs']['seed']= random.randint(1, 18446744073709551614)
-            workflow_new[key]=value
+#         workflow_new={}
+#         # 遍历，seed设为随机
+#         for key, value in workflow.items():
+#             if 'inputs' in value:
+#                 if 'seed' in value['inputs']:
+#                     value['inputs']['seed']= random.randint(1, 18446744073709551614)
+#             workflow_new[key]=value
 
-        queue_prompt(workflow_new)
-        print('#运行的函数',workflow_new[input_node[0]])
+#         queue_prompt(workflow_new)
+#         print('#运行的函数',workflow_new[input_node[0]])
 
-        # return (new_prompt)
-        return  {"ui":{"images": []},"result": ([image],['text'],)}
+#         # return (new_prompt)
+#         return  {"ui":{"images": []},"result": ([image],['text'],)}
 

nodes/Utils.py

@@ -1,9 +1,53 @@
 import os
 import re,random
+from PIL import Image
+import numpy as np
 # FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
-
+import folder_paths
 import matplotlib.font_manager as fm
 
+# import json
+# import hashlib
+
+
+# def get_json_hash(json_content):
+#     json_string = json.dumps(json_content, sort_keys=True)
+#     hash_object = hashlib.sha256(json_string.encode())
+#     hash_value = hash_object.hexdigest()
+#     return hash_value
+    
+
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+
+def create_temp_file(image):
+    output_dir = folder_paths.get_temp_directory()
+
+    (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('tmp', output_dir)
+
+    
+    im=tensor2pil(image)
+ 
+    image_file = f"{filename}_{counter:05}.png"
+     
+    image_path=os.path.join(full_output_folder, image_file)
+
+    im.save(image_path,compress_level=4)
+
+    return [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type": "temp"
+                }]
+
 def get_font_files(directory):
     font_files = {}
 
@@ -35,6 +79,16 @@ font_files = get_font_files(r_directory)
 # print(font_files)
 
 
+def flatten_list(nested_list):
+    flat_list = []
+    for item in nested_list:
+        if isinstance(item, list):
+            flat_list.extend(flatten_list(item))
+        else:
+            flat_list.append(item)
+    return flat_list
+
+
 class ColorInput:
     @classmethod
     def INPUT_TYPES(s):
@@ -44,18 +98,23 @@ class ColorInput:
                              },
                 }
     
-    RETURN_TYPES = ("STRING",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_TYPES = ("STRING","INT","INT","INT","FLOAT",)
+    RETURN_NAMES = ("hex","r","g","b","a",)
 
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/utils"
 
     INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,False,)
+    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,)
 
 
 
@@ -76,10 +135,10 @@ class FontInput:
     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:
@@ -120,6 +179,161 @@ class TextToNumber:
             result= random.randint(1, 10000000000)
         return {"ui": {"text": [text],"num":[result]}, "result": (result,)}
     
+
+   
+class FloatSlider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "number":("FLOAT", {
+                        "default": 0, 
+                        "min": 0, #Minimum value
+                        "max": 1, #Maximum value
+                        "step": 0.001, #Slider's step
+                        "display": "slider" # Cosmetic only: display as "number" or "slider"
+                    }),
+                    "min_value":("FLOAT", {
+                        "default": 0, 
+                        "min": -0xffffffffffffffff, 
+                        "max": 0xffffffffffffffff,
+                        "step": 0.001, 
+                        "display": "number"  
+                    }),
+                    "max_value":("FLOAT", {
+                            "default": 1, 
+                            "min": -0xffffffffffffffff,
+                            "max": 0xffffffffffffffff,
+                            "step": 0.001, 
+                            "display": "number"  
+                        }),
+                    "step":("FLOAT", {
+                            "default": 0.001, 
+                            "min": -0xffffffffffffffff,
+                            "max": 0xffffffffffffffff,
+                            "step": 0.001, 
+                            "display": "number"  
+                        }),
+                             },
+                }
+    
+    RETURN_TYPES = ("FLOAT",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,number,min_value,max_value,step):
+        if number < min_value:
+            number= min_value
+        elif number > max_value:
+            number= max_value
+        return (number,)
+    
+  
+class IntNumber:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "number":("INT", {
+                        "default": 0, 
+                        "min": -1, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 1, 
+                        "display": "number"
+                    }),
+                    "min_value":("INT", {
+                        "default": 0, 
+                        "min": -0xffffffffffffffff, 
+                        "max": 0xffffffffffffffff,
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                    "max_value":("INT", {
+                            "default": 1, 
+                            "min": -0xffffffffffffffff,
+                            "max": 0xffffffffffffffff,
+                            "step": 1, 
+                            "display": "number"  
+                        }),
+                    "step":("INT", {
+                            "default": 1, 
+                            "min": -0xffffffffffffffff,
+                            "max": 0xffffffffffffffff,
+                            "step":1, 
+                            "display": "number"  
+                        }),
+                             },
+                }
+    
+    RETURN_TYPES = ("INT",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,number,min_value,max_value,step):
+        if number < min_value:
+            number= min_value
+        elif number > max_value:
+            number= max_value
+        return (number,)
+
+class MultiplicationNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "numberA":(any_type,),
+                    "numberB":("FLOAT", {
+                        "default": 0, 
+                        "min": -1, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.1, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    })
+                             },
+                }
+    
+    RETURN_TYPES = ("FLOAT","INT",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,False,)
+
+    def run(self,numberA,numberB):
+        b=int(numberA*numberB)
+        a=float(numberA*numberB)
+        return (a,b,) 
+
+class TextInput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "text": ("STRING",{"multiline": True,"default": ""}),
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,text):
+       
+        return (text,)
+    
 # 接收一个值，然后根据字符串或数值长度计算延迟时间，用户可以自定义延迟"字/s"，延迟之后将转化
 
 import comfy.samplers
@@ -141,7 +355,7 @@ class DynamicDelayProcessor:
 
     @classmethod
     def INPUT_TYPES(cls):
-        print("print INPUT_TYPES",cls)
+        # print("print INPUT_TYPES",cls)
         return {
             "required":{
                     "delay_seconds":("INT",{
@@ -210,3 +424,189 @@ class DynamicDelayProcessor:
         # 根据 replace_output 决定输出值
         return (max(0, replace_value),) if replace_output == "enable" else (any_input,)
         
+
+
+
+# app 配置节点
+class AppInfo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "name": ("STRING",{"multiline": False,"default": "Mixlab-App","dynamicPrompts": False}),
+                    "image": ("IMAGE",),
+                    "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"]),"dynamicPrompts": False}),
+                    "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"]),"dynamicPrompts": False}),
+                             },
+
+                "optional":{
+                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
+                    "version":("INT", {
+                        "default": 1, 
+                        "min": 1, 
+                        "max": 10000, 
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                    "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                    "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
+                    "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                }
+
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self,name,image,input_ids,output_ids,description,version,share_prefix,link,category):
+        name=name[0]
+        im=image[0][0]
+        # image [img,] img[batch,w,h,a] 列表里面是batch，
+
+        input_ids=input_ids[0]
+        output_ids=output_ids[0]
+        description=description[0]
+        version=version[0]
+        share_prefix=share_prefix[0]
+        link=link[0]
+        category=category[0]
+
+        #TODO batch 的方式需要处理
+        im=create_temp_file(im)
+        
+        # id=get_json_hash([name,im,input_ids,output_ids,description,version])
+
+        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": (image,)}
+    
+
+    
+
+class GetImageSize_:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("INT", "INT")
+    RETURN_NAMES = ("width", "height")
+
+    FUNCTION = "get_size"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    def get_size(self, image):
+        _, height, width, _ = image.shape
+        return (width, height)
+
+
+
+class SwitchByIndex:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "A":(any_type,),
+                "B":(any_type,),
+                "index":("INT", {
+                        "default": -1, 
+                        "min": -1, 
+                        "max": 1000, 
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                 "flat": (['off',"on"],),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("C",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self, A,B,index,flat):
+
+        flat=flat[0]
+
+        C=[]
+        index=index[0]
+        for a in A:
+            C.append(a)
+        for b in B:
+            C.append(b)
+
+        if flat=='on':
+            C=flatten_list(C)
+
+        if index>-1:
+            try:
+                C=[C[index]]
+            except Exception as e:
+                C=[]
+        return (C,)
+
+
+
+class LimitNumber:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "number":(any_type,),
+                "min_value":("INT", {
+                        "default": 0, 
+                        "min": 0, 
+                        "max": 0xffffffffffffffff,
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                "max_value":("INT", {
+                        "default": 1, 
+                        "min": 1, 
+                        "max": 0xffffffffffffffff,
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("number",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self, number, min_value, max_value):
+        nn=number
+
+        if isinstance(number, int):
+            min_value=int(min_value)
+            max_value=int(max_value)
+        if isinstance(number, float):
+            min_value=float(min_value)
+            max_value=float(max_value)
+
+        if number < min_value:
+            nn= min_value
+        elif number > max_value:
+            nn= max_value
+        
+        return (nn,)
+

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

@@ -196,6 +196,9 @@ app.registerExtension({
               }
               if (bg) {
                 data.bg_image = await parseImage(bg)
+                if (!data.bg_image.match('data:image/')) {
+                  delete data.bg_image
+                }
               }
 
               if (material) {

web/javascript/app_mixlab.js

@@ -0,0 +1,351 @@
+import { app } from '../../../scripts/app.js'
+import { $el } from '../../../scripts/ui.js'
+import { api } from '../../../scripts/api.js'
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 12 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'row',
+    // alignItems: 'center',
+    justifyContent: 'flex-start'
+  }
+}
+
+async function drawImageToCanvas (imageUrl) {
+  var canvas = document.createElement('canvas')
+  var ctx = canvas.getContext('2d')
+  var img = new Image()
+
+  await new Promise((resolve, reject) => {
+    img.onload = function () {
+      var scaleFactor = 320 / img.width
+      var canvasWidth = img.width * scaleFactor
+      var canvasHeight = img.height * scaleFactor
+
+      canvas.width = canvasWidth
+      canvas.height = canvasHeight
+
+      ctx.drawImage(img, 0, 0, canvasWidth, canvasHeight)
+
+      resolve()
+    }
+
+    img.onerror = function () {
+      reject(new Error('Failed to load image'))
+    }
+
+    img.src = imageUrl
+  })
+
+  var base64 = canvas.toDataURL('image/jpeg')
+  // console.log(base64); // 输出Base64数据
+  return base64
+  // 可以在这里执行其他操作，比如将Base64数据保存到服务器或显示在页面上
+}
+
+function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
+  const data = jsonData
+  const input = []
+  const output = []
+  const seed = {}
+
+  for (const id in data) {
+    if (data.hasOwnProperty(id)) {
+      let node = app.graph.getNodeById(id)
+      if (inputIds.includes(id)) {
+        // let node = app.graph.getNodeById(id)
+        let options = []
+        // 模型
+        try {
+          if (node.type === 'CheckpointLoaderSimple') {
+            options = node.widgets.filter(w => w.name === 'ckpt_name')[0]
+              .options.values
+          } else if (node.type === 'LoraLoader') {
+            options = node.widgets.filter(w => w.name === 'lora_name')[0]
+              .options.values
+          }
+        } catch (error) {}
+
+        if (node.type == 'IntNumber' || node.type == 'FloatSlider') {
+          // min max step
+          let [v, min, max, step] = Array.from(node.widgets, w => w.value)
+          options = { min, max, step }
+          // node.widgets.filter(w => w.type === 'number')[0].options
+        }
+
+        if (node.type == 'PromptSlide') {
+          // min max step
+          options = node.widgets.filter(w => w.type === 'slider')[0].options
+          // 备选的keywords清单
+          let ks = getLocalData(`_mixlab_PromptSlide`)
+          let keywords = ks[id]
+          // console.log('keywords',keywords)
+          if (keywords && keywords[0]) {
+            options.keywords = keywords
+          }
+        }
+
+        if(node.type=='Color'){
+          
+        }
+
+        input[inputIds.indexOf(id)] = {
+          ...data[id],
+          title: node.title,
+          id,
+          options
+        }
+        // input.push()
+      }
+      if (outputIds.includes(id)) {
+        // let node = app.graph.getNodeById(id)
+        // output.push()
+        output[outputIds.indexOf(id)] = { ...data[id], title: node.title, id }
+      }
+
+      if (node.type === 'KSampler') {
+        // seed 的类型收集
+        try {
+          seed[id] = node.widgets.filter(
+            w => w.name === 'seed'
+          )[0].linkedWidgets[0].value
+        } catch (error) {}
+      }
+    }
+  }
+
+  return { input, output, seed }
+}
+
+function getUrl () {
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+  return url
+}
+
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+
+async function save_app (json) {
+  let url = getUrl()
+
+  const res = await fetch(`${url}/mixlab/workflow`, {
+    method: 'POST',
+    body: JSON.stringify({
+      data: json,
+      task: 'save_app',
+      filename: json.app.filename,
+      category: json.app.category
+    })
+  })
+  return await res.json()
+}
+
+function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
+  const dataString = JSON.stringify(jsonData)
+  const blob = new Blob([dataString], { type: 'application/json' })
+  const url = URL.createObjectURL(blob)
+
+  const link = document.createElement('a')
+  link.href = url
+  link.download = fileName
+  link.click()
+
+  // 释放URL对象
+  setTimeout(() => {
+    URL.revokeObjectURL(url)
+  }, 0)
+}
+
+async function save (json, download = false) {
+  const name = json[0],
+    version = json[5],
+    share_prefix = json[6], //用于分享的功能扩展
+    link = json[7], //用于创建界面上的跳转链接
+    category = json[8] || '', //用于分类
+    description = json[4],
+    inputIds = json[2].split('\n').filter(f => f),
+    outputIds = json[3].split('\n').filter(f => f)
+
+  const iconData = json[1][0]
+  let { filename, subfolder, type } = iconData
+  let iconUrl = api.apiURL(
+    `/view?filename=${encodeURIComponent(
+      filename
+    )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+  )
+
+  try {
+    let data = await app.graphToPrompt()
+
+    const { input, output, seed } = extractInputAndOutputData(
+      data.output,
+      inputIds,
+      outputIds
+    )
+
+    data.app = {
+      name,
+      description,
+      version,
+      input,
+      output,
+      seed, //控制是fixed 还是random
+      share_prefix,
+      link,
+      category,
+      filename: `${name}_${version}.json`
+    }
+
+    try {
+      data.app.icon = await drawImageToCanvas(iconUrl)
+    } catch (error) {}
+    // console.log(data.app)
+    // let http_workflow = app.graph.serialize()
+    await save_app(data)
+    if (download) {
+      await downloadJsonFile(data, data.app.filename)
+    }
+    let open = window.confirm(
+      `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app?filename=${encodeURIComponent(
+        data.app.filename
+      )}&category=${encodeURIComponent(data.app.category)}`
+    )
+    if (open)
+      window.open(
+        `${getUrl()}/mixlab/app?filename=${encodeURIComponent(
+          data.app.filename
+        )}&category=${encodeURIComponent(data.app.category)}`
+      )
+  } catch (error) {
+    console.log('###error', error)
+  }
+}
+
+app.registerExtension({
+  name: 'Mixlab.utils.AppInfo',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'AppInfo') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        console.log('#orig_nodeCreated', this)
+        const widget = {
+          type: 'div',
+          name: 'AppInfoRun',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(
+                ctx,
+                widget_width,
+                node.size[1] - widget_height,
+                node.size[1]
+              )
+            )
+          }
+        }
+
+        const style = `
+        flex-direction: row;
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;
+        color: var(--descrip-text);`
+
+        widget.div = $el('div', {})
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Save & Open'
+        btn.style = style
+
+        btn.addEventListener('click', () => {
+          //   console.log('hahhah')
+          if (window._mixlab_app_json) {
+            save(window._mixlab_app_json)
+          } else {
+            alert('Please run the workflow before saving')
+            // app.queuePrompt(0, 1)
+            this.widgets.filter(w => w.name === 'version')[0].value += 1
+          }
+        })
+
+        const download = document.createElement('button')
+        download.innerText = 'Download For App'
+        download.style = style
+        download.style.marginLeft = '12px'
+
+        download.addEventListener('click', () => {
+          //   console.log('hahhah')
+          if (window._mixlab_app_json) {
+            save(window._mixlab_app_json, true)
+          } else {
+            alert('Please run the workflow before saving')
+            // app.queuePrompt(0, 1)
+            this.widgets.filter(w => w.name === 'version')[0].value += 1
+          }
+        })
+
+        document.body.appendChild(widget.div)
+        widget.div.appendChild(btn)
+        widget.div.appendChild(download)
+
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log(message.json)
+        window._mixlab_app_json = message.json
+        try {
+          const div = this.widgets.filter(w => w.div)[0].div
+          Array.from(
+            div.querySelectorAll('button'),
+            b => (b.style.background = 'yellow')
+          )
+        } catch (error) {}
+      }
+    }
+  }
+})

web/javascript/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 = 'v0.5.2'
+const version = 'v0.10.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/image_mixlab.js

@@ -406,7 +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

web/javascript/layer_mixlab.js

@@ -156,8 +156,8 @@ 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)
+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 +170,13 @@ 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; 
-      pointer-events: none;'></div>
+      border: 2px dashed red;
+      pointer-events: none;
+      background-image: url("${topBase64}");
+            background-repeat: no-repeat;
+            background-size: cover;
+            '></div>
+      <div class="mx_close"> X </div>
     </div>`
   // document.body.querySelector('#ml_overlay')
   document.body.appendChild(div)
@@ -181,13 +186,25 @@ 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 +233,37 @@ 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 () {
-    overlay.removeEventListener('click', remove)
+  const removeDiv = () => {
+    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) {
@@ -531,12 +571,24 @@ app.registerExtension({
               }
             }
             try {
+              console.log('this.inputs', this.inputs)
+              let topLinkId = this.inputs[0].link
+              let topNodeId = app.graph.links[topLinkId].origin_id
+              let topIm = app.graph.getNodeById(topNodeId).imgs[0]
+
               let linkId = this.inputs[3].link
               let nodeId = app.graph.links[linkId].origin_id
               // console.log(linkId,this.inputs)
               let im = app.graph.getNodeById(nodeId).imgs[0]
-              let src = im.src
-              setArea(im.naturalWidth, im.naturalHeight, src, data, updateValue)
+              // let src = im.src
+              setArea(
+                im.naturalWidth,
+                im.naturalHeight,
+                topIm.src,
+                im.src,
+                data,
+                updateValue
+              )
             } catch (error) {}
           })
         }

web/javascript/main_mixlab.js

@@ -1800,15 +1800,19 @@ 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 }]
-    ))
+    // 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',
-      ['STRING', { multiline: true }]
+      ['STRING', { multiline: true }],
+      app
     ).widget
     w.inputEl.readOnly = true
     w.inputEl.style.opacity = 0.6
@@ -2089,13 +2093,13 @@ 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]
@@ -2131,10 +2135,15 @@ const node = {
     // }
   },
   loadedGraphNode (node, app) {
-    // Fires for each node when loading/dragging/etc a workflow json or png
-    // If you break something in the backend and want to patch workflows in the frontend
-    // This is the place to do this
-    // console.log("[logging]", "loaded graph node: ", exportGraph(node.graph));
+    if (node.type === 'RandomPrompt') {
+      try {
+        let max_count = node.widgets.filter(w => w.name === "max_count")[0];
+        max_count.value= node.widgets_values[0]
+        // console.log('RandomPrompt',max_count,node.widgets_values[0])
+      } catch (error) {
+        console.log(error)
+      }
+    }
   },
   async nodeCreated (node) {
     if (node.type === 'RandomPrompt') {
@@ -2227,7 +2236,7 @@ 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]
 
@@ -2238,7 +2247,7 @@ 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 +1,204 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2 - 24}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    paddingLeft: '12px',
+    display: 'flex',
+    flexDirection: 'row',
+    // alignItems: 'center',
+    justifyContent: 'space-between'
+  }
+}
+
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+
+const setLocalDataOfWin = (key, value) => {
+  localStorage.setItem(key, JSON.stringify(value))
+  // window[key] = value
+}
+
+const createSelect = (select, opts, targetWidget) => {
+  select.style.display = 'block'
+  let html = ''
+  let isMatch = false
+  for (const opt of opts) {
+    html += `<option value='${opt}' ${
+      targetWidget.value === opt ? 'selected' : ''
+    }>${opt}</option>`
+    if (targetWidget.value === opt) isMatch = true
+  }
+  select.innerHTML = html
+  if (!isMatch) targetWidget.value = opts[0]
+  // 添加change事件监听器
+  select.addEventListener('change', function () {
+    // 获取选中的选项的值
+    var selectedOption = select.options[select.selectedIndex].value
+    targetWidget.value = selectedOption
+    // console.log(widget,selectedOption)
+  })
+}
+
+app.registerExtension({
+  name: 'Mixlab.prompt.PromptSlide',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'PromptSlide') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const prompt_keyword = this.widgets.filter(
+          w => w.name == 'prompt_keyword'
+        )[0]
+        // console.log('PromptSlide nodeData', prompt_keyword)
+
+        const widget = {
+          type: 'div',
+          name: 'upload',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, y, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Upload Keywords'
+
+        btn.style = `cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;    height: 30px;min-width: 122px;
+       `
+
+        const select = document.createElement('select')
+        select.style = `display:none;cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;    height: 30px;min-width: 100px;
+       `
+        widget.select = select
+
+        // const btn=document.createElement('button');
+        // btn.innerText='Upload'
+        btn.addEventListener('click', () => {
+          let inp = document.createElement('input')
+          inp.type = 'file'
+          inp.accept = '.txt'
+          inp.click()
+          inp.addEventListener('change', event => {
+            // 获取选择的文件
+            const file = event.target.files[0];
+            this.title=file.name.split('.')[0];
+
+            // console.log(file.name.split('.')[0])
+            // 创建文件读取器
+            const reader = new FileReader()
+
+            // 定义读取完成事件的回调函数
+            reader.onload = event => {
+              // 读取完成后的文本内容
+              const fileContent = event.target.result.split('\n')
+              const keywords = Array.from(fileContent, f => f.trim()).filter(
+                f => f
+              )
+              // 打印文件内容
+              //   console.log(keywords)
+
+              // widget.value = keywords
+              let ks = getLocalData(`_mixlab_PromptSlide`)
+              ks[this.id] = keywords
+              setLocalDataOfWin(`_mixlab_PromptSlide`, ks)
+
+              createSelect(select, keywords, prompt_keyword)
+
+              inp.remove()
+            }
+
+            // 以文本方式读取文件
+            reader.readAsText(file)
+          })
+        })
+
+        widget.div.appendChild(btn)
+        widget.div.appendChild(select)
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'PromptSlide') {
+      try {
+        let prompt = node.widgets.filter(w => w.name === 'prompt_keyword')[0]
+        let ks = getLocalData(`_mixlab_PromptSlide`)
+
+        let keywords = ks[node.id]
+        // console.log('keywords',keywords)
+        let widget = node.widgets.filter(w => w.select)[0]
+        if (keywords && keywords[0]) {
+          // let widget = node.widgets.filter(w => w.select)[0]
+          // console.log('select',widget,widget.value)
+          widget.select.style.display = 'block'
+          createSelect(widget.select, keywords, prompt)
+        }
+      } catch (error) {}
+    }
+  }
+})

web/javascript/ui_mixlab.js

@@ -48,6 +48,40 @@ async function get_nodes_map () {
   return await res.json()
 }
 
+function get_url () {
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+  return url
+}
+
+async function get_my_app (filename = null, category = '') {
+  let url = get_url()
+  const res = await fetch(`${url}/mixlab/workflow`, {
+    method: 'POST',
+    body: JSON.stringify({
+      task: 'my_app',
+      filename,
+      category,
+      admin: true
+    })
+  })
+  let result = await res.json()
+  let data = []
+  try {
+    for (const res of result.data) {
+      let { app, workflow } = res.data
+      if (app.filename)
+        data.push({
+          ...app,
+          data: workflow,
+          date: res.date
+        })
+    }
+  } catch (error) {}
+  return data
+}
+
 function loadCSS (url) {
   var link = document.createElement('link')
   link.rel = 'stylesheet'
@@ -633,7 +667,7 @@ app.registerExtension({
             }
 
             clipboardAction(() => {
-              let name = group.title+' ♾️Mixlab'
+              let name = group.title + ' ♾️Mixlab'
               let nodes = group._nodes
 
               app.canvas.copyToClipboard(nodes)
@@ -667,164 +701,357 @@ app.registerExtension({
         ...options
       ] // and return the options
     }
+    LGraphCanvas.prototype.centerOnNode = function (node) {
+      // console.log(node)
+      var dpr = window.devicePixelRatio || 1 // 获取设备像素比
+      this.ds.offset[0] =
+        -node.pos[0] -
+        node.size[0] * 0.5 +
+        (this.canvas.width * 0.5) / (this.ds.scale * dpr) // 考虑设备像素比
+      this.ds.offset[1] =
+        -node.pos[1] -
+        node.size[1] * 0.5 +
+        (this.canvas.height * 0.5) / (this.ds.scale * dpr) // 考虑设备像素比
+      this.setDirty(true, true)
+    }
   },
-  async setup () {
-    // Add canvas menu options
-    const orig = LGraphCanvas.prototype.getCanvasMenuOptions
-    LGraphCanvas.prototype.getCanvasMenuOptions = function () {
-      const options = orig.apply(this, arguments)
+  setup () {
+    setTimeout(async () => {
+      // Add canvas menu options
+      const orig = LGraphCanvas.prototype.getCanvasMenuOptions
 
-      options.push(null, {
-        content: `Find ♾️Mixlab`,
-        disabled: false, // or a function determining whether to disable
-        callback: async () => {
-          nodesMap =
-            nodesMap && Object.keys(nodesMap).length > 0
-              ? nodesMap
-              : await getCustomnodeMappings('url')
+      const apps = await get_my_app()
 
-          const nodesDiv = document.createDocumentFragment()
-          const nodes = (await app.graphToPrompt()).output
+      let apps_map = { '0': [] }
 
-          // console.log('[Mixlab]', 'loaded graph node: ', app)
-          let div =
-            document.querySelector('#mixlab_find_the_node') ||
-            document.createElement('div')
-          div.id = 'mixlab_find_the_node'
-          div.style = `
-              flex-direction: column;
-              align-items: end;
-              display:flex;position: absolute; 
-              top: 50px; left: 50px; width: 200px; 
-              color: var(--descrip-text);
-              background-color: var(--comfy-menu-bg);
-              padding: 10px; 
-              border: 1px solid black;z-index: 999999999;padding-top: 0;`
+      for (const app of apps) {
+        if (app.category) {
+          if (!apps_map[app.category]) apps_map[app.category] = []
+          apps_map[app.category].push(app)
+        } else {
+          apps_map['0'].push(app)
+        }
+      }
 
-          div.innerHTML = ''
-
-          let btn = document.createElement('div')
-          btn.style = `display: flex;
-            width: calc(100% - 24px);
-            justify-content: space-between;
-            align-items: center;
-            padding: 0 12px;
-            height: 32px;`
-          let btnB = document.createElement('button')
-          let textB = document.createElement('p')
-          btn.appendChild(textB)
-          btn.appendChild(btnB)
-          textB.innerText = `Find The Node`
-
-          btnB.style = `float: right; border: none; color: var(--input-text);
-            background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
-          btnB.addEventListener('click', () => {
-            div.style.display = 'none'
-          })
-          btnB.innerText = 'X'
-
-          // 悬浮框拖动事件
-          div.addEventListener('mousedown', function (e) {
-            var startX = e.clientX
-            var startY = e.clientY
-            var offsetX = div.offsetLeft
-            var offsetY = div.offsetTop
-
-            function moveBox (e) {
-              var newX = e.clientX
-              var newY = e.clientY
-              var deltaX = newX - startX
-              var deltaY = newY - startY
-              div.style.left = offsetX + deltaX + 'px'
-              div.style.top = offsetY + deltaY + 'px'
-            }
-
-            function stopMoving () {
-              document.removeEventListener('mousemove', moveBox)
-              document.removeEventListener('mouseup', stopMoving)
-            }
-
-            document.addEventListener('mousemove', moveBox)
-            document.addEventListener('mouseup', stopMoving)
-          })
-
-          div.appendChild(btn)
-
-          const updateNodes = (ns, nd) => {
-            for (let nodeId in ns) {
-              let n = ns[nodeId].class_type
-              const { url, title } = nodesMap[n]
-              let d = document.createElement('button')
-              d.style = `text-align: left;margin:6px;color: var(--input-text);
-                background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
-              d.addEventListener('click', () => {
-                const node = app.graph.getNodeById(nodeId)
-                if (!node) return
-                app.canvas.centerOnNode(node)
-                app.canvas.setZoom(1)
-              })
-              d.addEventListener('mouseover', async () => {
-                // console.log('mouseover')
-                let n = (await app.graphToPrompt()).output
-                if (!deepEqual(n, ns)) {
-                  nd.innerHTML = ''
-                  updateNodes(n, nd)
+      let apps_opts = []
+      for (const category in apps_map) {
+        console.log('category',typeof(category))
+        if (category === '0') {
+          apps_opts.push(
+            ...Array.from(apps_map[category], a => {
+              return {
+                content: a.name,
+                has_submenu: false,
+                callback: async () => {
+                  try {
+                    let item = (await get_my_app(a.filename))[0]
+                    if (item) {
+                      // console.log(item.data)
+                      app.loadGraphData(item.data)
+                      setTimeout(() => {
+                        const node = app.graph._nodes_in_order[0]
+                        if (!node) return
+                        app.canvas.centerOnNode(node)
+                        app.canvas.setZoom(0.5)
+                      }, 1000)
+                    }
+                  } catch (error) {}
                 }
-              })
-
-              d.innerHTML = `
-                <span>${'#' + nodeId} ${n}</span>
-                <a href="${url}" target="_blank" style="text-decoration: none;">🔗</a>
-                `
-              d.title = title
-
-              nd.appendChild(d)
-            }
-          }
-
-          let nodesDivv = document.createElement('div')
-
-          for (let nodeId in nodes) {
-            let n = nodes[nodeId].class_type
-            const { url, title } = nodesMap[n]
-            let d = document.createElement('button')
-            d.style = `text-align: left;margin:6px;color: var(--input-text);
-              background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
-            d.addEventListener('click', () => {
-              const node = app.graph.getNodeById(nodeId)
-              if (!node) return
-              app.canvas.centerOnNode(node)
-              app.canvas.setZoom(1)
-            })
-            d.addEventListener('mouseover', async () => {
-              console.log('mouseover')
-              let n = (await app.graphToPrompt()).output
-              if (!deepEqual(n, nodes)) {
-                nodesDivv.innerHTML = ''
-                updateNodes(n, nodesDivv)
               }
             })
-
-            d.innerHTML = `
-              <span>${'#' + nodeId} ${n}</span>
-              <a href="${url}" target="_blank" style="text-decoration: none;">🔗</a>
-              `
-            d.title = title
-
-            nodesDiv.appendChild(d)
-          }
-
-          nodesDivv.appendChild(nodesDiv)
-          nodesDivv.style = `overflow: scroll;
-            height: 70vh;width: 100%;`
-
-          div.appendChild(nodesDivv)
-
-          if (!document.querySelector('#mixlab_find_the_node'))
-            document.body.appendChild(div)
+          )
+        } else {
+          // 二级
+          apps_opts.push({
+            content:  '🚀 '+category,
+            has_submenu: true,
+            disabled: false,
+            submenu: {
+              options: Array.from(apps_map[category], a => {
+                return {
+                  content: a.name,
+                  callback: async () => {
+                    try {
+                      let item = (await get_my_app(a.filename, a.category))[0]
+                      if (item) {
+                        // console.log(item.data)
+                        app.loadGraphData(item.data)
+                        setTimeout(() => {
+                          const node = app.graph._nodes_in_order[0]
+                          if (!node) return
+                          app.canvas.centerOnNode(node)
+                          app.canvas.setZoom(0.5)
+                        }, 1000)
+                      }
+                    } catch (error) {}
+                  }
+                }
+              })
+            }
+          })
         }
-      })
-      return options
-    }
+      }
+
+      // console.log('apps',apps_map, apps_opts,apps)
+      LGraphCanvas.prototype.getCanvasMenuOptions = function () {
+        const options = orig.apply(this, arguments)
+
+        options.push(
+          null,
+          {
+            content: `Nodes Map ♾️Mixlab`,
+            disabled: false,
+            callback: async () => {
+              nodesMap =
+                nodesMap && Object.keys(nodesMap).length > 0
+                  ? nodesMap
+                  : await getCustomnodeMappings('url')
+
+              const nodesDiv = document.createDocumentFragment()
+              const nodes = (await app.graphToPrompt()).output
+
+              // console.log('[Mixlab]', 'loaded graph node: ', app)
+              let div =
+                document.querySelector('#mixlab_find_the_node') ||
+                document.createElement('div')
+              div.id = 'mixlab_find_the_node'
+              div.style = `
+               flex-direction: column;
+               align-items: end;
+               display:flex;position: absolute; 
+               top: 50px; left: 50px; width: 200px; 
+               color: var(--descrip-text);
+               background-color: var(--comfy-menu-bg);
+               padding: 10px; 
+               border: 1px solid black;z-index: 999999999;padding-top: 0;`
+
+              div.innerHTML = ''
+
+              let btn = document.createElement('div')
+              btn.style = `display: flex;
+             width: calc(100% - 24px);
+             justify-content: space-between;
+             align-items: center;
+             padding: 0 12px;
+             height: 44px;`
+              let btnB = document.createElement('button')
+              let textB = document.createElement('p')
+              btn.appendChild(textB)
+              btn.appendChild(btnB)
+              textB.style.fontSize = '12px'
+              textB.innerText = `Locate and navigate nodes ♾️Mixlab`
+
+              btnB.style = `float: right; border: none; color: var(--input-text);
+             background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
+              btnB.addEventListener('click', () => {
+                div.style.display = 'none'
+              })
+              btnB.innerText = 'X'
+
+              // 悬浮框拖动事件
+              div.addEventListener('mousedown', function (e) {
+                var startX = e.clientX
+                var startY = e.clientY
+                var offsetX = div.offsetLeft
+                var offsetY = div.offsetTop
+
+                function moveBox (e) {
+                  var newX = e.clientX
+                  var newY = e.clientY
+                  var deltaX = newX - startX
+                  var deltaY = newY - startY
+                  div.style.left = offsetX + deltaX + 'px'
+                  div.style.top = offsetY + deltaY + 'px'
+                }
+
+                function stopMoving () {
+                  document.removeEventListener('mousemove', moveBox)
+                  document.removeEventListener('mouseup', stopMoving)
+                }
+
+                document.addEventListener('mousemove', moveBox)
+                document.addEventListener('mouseup', stopMoving)
+              })
+
+              div.appendChild(btn)
+
+              const updateNodes = (ns, nd) => {
+                let appInfoNodes = {}
+                try {
+                  let appInfo = app.graph._nodes.filter(
+                    n => n.type === 'AppInfo'
+                  )[0]
+                  if (appInfo) {
+                    appInfoNodes[appInfo.id] = 2
+                    for (const id of appInfo.widgets[1].value.split('\n')) {
+                      if (id && id.trim() && parseInt(id)) {
+                        appInfoNodes[id] = 0
+                      }
+                    }
+                    for (const id of app.graph._nodes
+                      .filter(n => n.type === 'AppInfo')[0]
+                      .widgets[2].value.split('\n')) {
+                      if (id && id.trim() && parseInt(id)) {
+                        appInfoNodes[id] = 1
+                      }
+                    }
+                  }
+                } catch (error) {
+                  console.log(error)
+                }
+
+                for (let nodeId in ns) {
+                  let n = ns[nodeId].title || ns[nodeId].class_type
+                  if (nodesMap[n]) {
+                    const { url, title } = nodesMap[n]
+                    let d = document.createElement('button')
+                    d.style = `text-align: left;
+                    margin:6px;
+                    color: var(--input-text);
+                   background-color: var(--comfy-input-bg); 
+                   border-color: ${
+                     appInfoNodes[nodeId] >= 0
+                       ? appInfoNodes[nodeId] === 1
+                         ? 'blue'
+                         : 'red'
+                       : 'var(--border-color)'
+                   };
+                   cursor: pointer;`
+
+                    if (appInfoNodes[nodeId] === 2) {
+                      // appinfo
+                      d.style.backgroundColor = '#326328'
+                      d.style.color = '#ffffff'
+                      d.style.borderColor = 'transparent'
+                    }
+
+                    d.addEventListener('click', () => {
+                      // console.log('node')
+                      const node = app.graph.getNodeById(nodeId)
+
+                      if (!node) return
+                      app.canvas.centerOnNode(node)
+                      app.canvas.setZoom(1)
+                    })
+                    d.addEventListener('mouseover', async () => {
+                      // console.log('mouseover')
+                      let n = (await app.graphToPrompt()).output
+                      if (!deepEqual(n, ns)) {
+                        nd.innerHTML = ''
+                        updateNodes(n, nd)
+                      }
+                    })
+
+                    d.innerHTML = `
+                   <span>${'#' + nodeId} ${n}</span>
+                   <a href="${url}" target="_blank" style="text-decoration: none;">🔗</a>
+                   `
+                    d.title = title
+
+                    nd.appendChild(d)
+                  }
+                }
+              }
+
+              let nodesDivv = document.createElement('div')
+              let appInfoNodes = {}
+              try {
+                let appInfo = app.graph._nodes.filter(
+                  n => n.type === 'AppInfo'
+                )[0]
+                if (appInfo) {
+                  appInfoNodes[appInfo.id] = 2
+                  for (const id of appInfo.widgets[1].value.split('\n')) {
+                    if (id && id.trim() && parseInt(id)) {
+                      appInfoNodes[id] = 0
+                    }
+                  }
+                  for (const id of app.graph._nodes
+                    .filter(n => n.type === 'AppInfo')[0]
+                    .widgets[2].value.split('\n')) {
+                    if (id && id.trim() && parseInt(id)) {
+                      appInfoNodes[id] = 1
+                    }
+                  }
+                }
+              } catch (error) {
+                console.log(error)
+              }
+
+              for (let nodeId in nodes) {
+                let n = nodes[nodeId].class_type
+                if (nodesMap[n]) {
+                  const { url, title: _title } = nodesMap[n]
+                  let title = app.graph.getNodeById(nodeId).title || _title
+                  let d = document.createElement('button')
+                  d.style = `text-align: left;
+                  margin:6px;
+                  color: var(--input-text);
+                 background-color: var(--comfy-input-bg); 
+                 border-color: ${
+                   appInfoNodes[nodeId] >= 0
+                     ? appInfoNodes[nodeId] === 1
+                       ? 'blue'
+                       : 'red'
+                     : 'var(--border-color)'
+                 };
+                 cursor: pointer;`
+
+                  if (appInfoNodes[nodeId] === 2) {
+                    // appinfo
+                    d.style.backgroundColor = '#326328'
+                    d.style.color = '#ffffff'
+                    d.style.borderColor = 'transparent'
+                  }
+
+                  d.addEventListener('click', () => {
+                    console.log('click')
+                    const node = app.graph.getNodeById(nodeId)
+                    if (!node) return
+                    app.canvas.centerOnNode(node)
+                    app.canvas.setZoom(1)
+                  })
+                  d.addEventListener('mouseover', async () => {
+                    // console.log('mouseover')
+                    let n = (await app.graphToPrompt()).output
+                    if (!deepEqual(n, nodes)) {
+                      nodesDivv.innerHTML = ''
+                      updateNodes(n, nodesDivv)
+                    }
+                  })
+
+                  d.innerHTML = `
+                 <span>${'#' + nodeId} ${title}</span>
+                 <a href="${url}" target="_blank" style="text-decoration: none;">🔗</a>
+                 `
+                  d.title = n
+
+                  nodesDiv.appendChild(d)
+                }
+              }
+
+              nodesDivv.appendChild(nodesDiv)
+              nodesDivv.style = `overflow: scroll;
+             height: 70vh;width: 100%;`
+
+              div.appendChild(nodesDivv)
+
+              if (!document.querySelector('#mixlab_find_the_node'))
+                document.body.appendChild(div)
+            }
+          },
+          {
+            content: 'Workflow App ♾️Mixlab',
+            has_submenu: true,
+            disabled: false,
+            submenu: {
+              options:apps_opts
+            }
+          }
+        )
+
+        return options
+      }
+    }, 1000)
   }
 })

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')
-            return data[node.id] || '#000000'
+            // let data = getLocalData('_mixlab_utils_color')
+            // let hex = data[node.id] || '#000000'
+            let hex = widget.value || '#000000'
+            let [r, g, b, a] = hexToRGBA(hex)
+            return {
+              hex,
+              r,
+              g,
+              b,
+              a
+            }
           }
         }
         //  widget.something = something;          // maybe adds stuff to it
@@ -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')
-          ip.type = 'color'
-          ip.className = `${'comfy-multiline-input'} ${placeholder}`
-          div.style = `display: flex;
-            align-items: center; 
-            margin: 6px 8px;
-            margin-top: 0;`
-          ip.placeholder = placeholder
-          ip.value = value
-
-          ip.style = `outline: none;
-            border: none;
-            padding: 4px;
-            width: 100%;cursor: pointer;
-            height: 32px;`
-          const label = document.createElement('label')
-          label.style = 'font-size: 10px;min-width:32px'
-          label.innerText = placeholder
-          div.appendChild(label)
-          div.appendChild(ip)
-
-          ip.addEventListener('change', () => {
-            let data = getLocalData(key)
-            data[this.id] = ip.value.trim()
-            localStorage.setItem(key, JSON.stringify(data))
-            // console.log(this.id, ip.value.trim())
-          })
+          div.id = `color_picker_${this.id}`
           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?.()
         }
 
@@ -148,13 +246,11 @@ 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')
-
-      let id = node.id
-
-      widget.div.querySelector('.Color').value = data[id] || '#000000'
+        setTimeout(() => node.pickr.setColor(TCOLOR.value || '#000000'), 1000)
+      } catch (error) {}
     }
   }
 })
@@ -164,16 +260,17 @@ app.registerExtension({
 
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeType.comfyClass == 'TextToNumber') {
-
       const onExecuted = nodeType.prototype.onExecuted
       nodeType.prototype.onExecuted = function (message) {
         onExecuted?.apply(this, arguments)
-        const random_number=this.widgets.filter(w=>w.name==='random_number')[0]
-        if(random_number.value==='enable'){
-          const n=this.widgets.filter(w=>w.name==='number')[0]
-          n.value=message.num[0]
+        const random_number = this.widgets.filter(
+          w => w.name === 'random_number'
+        )[0]
+        if (random_number.value === 'enable') {
+          const n = this.widgets.filter(w => w.name === 'number')[0]
+          n.value = message.num[0]
         }
-        
+
         console.log('TextToNumber', random_number.value)
       }
     }

web/lib/photoswipe.min.css

@@ -0,0 +1 @@
+/*! PhotoSwipe main CSS by Dmytro Semenov | photoswipe.com */.pswp{--pswp-bg:#000;--pswp-placeholder-bg:#222;--pswp-root-z-index:100000;--pswp-preloader-color:rgba(79, 79, 79, 0.4);--pswp-preloader-color-secondary:rgba(255, 255, 255, 0.9);--pswp-icon-color:#fff;--pswp-icon-color-secondary:#4f4f4f;--pswp-icon-stroke-color:#4f4f4f;--pswp-icon-stroke-width:2px;--pswp-error-text-color:var(--pswp-icon-color)}.pswp{position:fixed;top:0;left:0;width:100%;height:100%;z-index:var(--pswp-root-z-index);display:none;touch-action:none;outline:0;opacity:.003;contain:layout style size;-webkit-tap-highlight-color:transparent}.pswp:focus{outline:0}.pswp *{box-sizing:border-box}.pswp img{max-width:none}.pswp--open{display:block}.pswp,.pswp__bg{transform:translateZ(0);will-change:opacity}.pswp__bg{opacity:.005;background:var(--pswp-bg)}.pswp,.pswp__scroll-wrap{overflow:hidden}.pswp__bg,.pswp__container,.pswp__content,.pswp__img,.pswp__item,.pswp__scroll-wrap,.pswp__zoom-wrap{position:absolute;top:0;left:0;width:100%;height:100%}.pswp__img,.pswp__zoom-wrap{width:auto;height:auto}.pswp--click-to-zoom.pswp--zoom-allowed .pswp__img{cursor:-webkit-zoom-in;cursor:-moz-zoom-in;cursor:zoom-in}.pswp--click-to-zoom.pswp--zoomed-in .pswp__img{cursor:move;cursor:-webkit-grab;cursor:-moz-grab;cursor:grab}.pswp--click-to-zoom.pswp--zoomed-in .pswp__img:active{cursor:-webkit-grabbing;cursor:-moz-grabbing;cursor:grabbing}.pswp--no-mouse-drag.pswp--zoomed-in .pswp__img,.pswp--no-mouse-drag.pswp--zoomed-in .pswp__img:active,.pswp__img{cursor:-webkit-zoom-out;cursor:-moz-zoom-out;cursor:zoom-out}.pswp__button,.pswp__container,.pswp__counter,.pswp__img{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.pswp__item{z-index:1;overflow:hidden}.pswp__hidden{display:none!important}.pswp__content{pointer-events:none}.pswp__content>*{pointer-events:auto}.pswp__error-msg-container{display:grid}.pswp__error-msg{margin:auto;font-size:1em;line-height:1;color:var(--pswp-error-text-color)}.pswp .pswp__hide-on-close{opacity:.005;will-change:opacity;transition:opacity var(--pswp-transition-duration) cubic-bezier(.4,0,.22,1);z-index:10;pointer-events:none}.pswp--ui-visible .pswp__hide-on-close{opacity:1;pointer-events:auto}.pswp__button{position:relative;display:block;width:50px;height:60px;padding:0;margin:0;overflow:hidden;cursor:pointer;background:0 0;border:0;box-shadow:none;opacity:.85;-webkit-appearance:none;-webkit-touch-callout:none}.pswp__button:active,.pswp__button:focus,.pswp__button:hover{transition:none;padding:0;background:0 0;border:0;box-shadow:none;opacity:1}.pswp__button:disabled{opacity:.3;cursor:auto}.pswp__icn{fill:var(--pswp-icon-color);color:var(--pswp-icon-color-secondary)}.pswp__icn{position:absolute;top:14px;left:9px;width:32px;height:32px;overflow:hidden;pointer-events:none}.pswp__icn-shadow{stroke:var(--pswp-icon-stroke-color);stroke-width:var(--pswp-icon-stroke-width);fill:none}.pswp__icn:focus{outline:0}.pswp__img--with-bg,div.pswp__img--placeholder{background:var(--pswp-placeholder-bg)}.pswp__top-bar{position:absolute;left:0;top:0;width:100%;height:60px;display:flex;flex-direction:row;justify-content:flex-end;z-index:10;pointer-events:none!important}.pswp__top-bar>*{pointer-events:auto;will-change:opacity}.pswp__button--close{margin-right:6px}.pswp__button--arrow{position:absolute;top:0;width:75px;height:100px;top:50%;margin-top:-50px}.pswp__button--arrow:disabled{display:none;cursor:default}.pswp__button--arrow .pswp__icn{top:50%;margin-top:-30px;width:60px;height:60px;background:0 0;border-radius:0}.pswp--one-slide .pswp__button--arrow{display:none}.pswp--touch .pswp__button--arrow{visibility:hidden}.pswp--has_mouse .pswp__button--arrow{visibility:visible}.pswp__button--arrow--prev{right:auto;left:0}.pswp__button--arrow--next{right:0}.pswp__button--arrow--next .pswp__icn{left:auto;right:14px;transform:scale(-1,1)}.pswp__button--zoom{display:none}.pswp--zoom-allowed .pswp__button--zoom{display:block}.pswp--zoomed-in .pswp__zoom-icn-bar-v{display:none}.pswp__preloader{position:relative;overflow:hidden;width:50px;height:60px;margin-right:auto}.pswp__preloader .pswp__icn{opacity:0;transition:opacity .2s linear;animation:pswp-clockwise .6s linear infinite}.pswp__preloader--active .pswp__icn{opacity:.85}@keyframes pswp-clockwise{0%{transform:rotate(0)}100%{transform:rotate(360deg)}}.pswp__counter{height:30px;margin-top:15px;margin-inline-start:20px;font-size:14px;line-height:30px;color:var(--pswp-icon-color);text-shadow:1px 1px 3px var(--pswp-icon-color-secondary);opacity:.85}.pswp--one-slide .pswp__counter{display:none}

workflow/Prompt-weight-workflow.json

@@ -0,0 +1,723 @@
+{
+  "last_node_id": 22,
+  "last_link_id": 23,
+  "nodes": [
+    {
+      "id": 9,
+      "type": "CLIPTextEncode",
+      "pos": [
+        2070,
+        830
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            4
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workflow/Text-to-Image-app.json

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+}

workflow/workflow.md

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