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Update index.html
2024-03-04 20:31:45 +08:00 · 2024-03-03 11:58:47 +08:00 · 2024-03-03 11:11:39 +08:00 · 2024-03-02 23:55:46 +08:00 · 2024-03-02 23:16:08 +08:00 · 2024-02-27 15:18:58 +08:00
63 changed files with 16571 additions and 2702 deletions
@@ -1,13 +1,26 @@
 > 适配了最新版comfyui的py3.11 ，torch 2.1.2+cu121

+> [Mixlab nodes discord](https://discord.gg/cXs9vZSqeK)
+
+#### 
+[comfyui-ultralytics-yolo](https://github.com/shadowcz007/comfyui-ultralytics-yolo)
+
+[comfyui-moondream](https://github.com/shadowcz007/comfyui-moondream)
+
+[comfyui-CLIPSeg](https://github.com/shadowcz007/comfyui-CLIPSeg)
+
+
 ## 🚀🚗🚚🏃 Workflow-to-APP 
 - 新增AppInfo节点，可以通过简单的配置，把workflow转变为一个Web APP。
 - 支持多个web app 切换
 - 发布为app的workflow，可以在右键里再次编辑了
+- web app可以设置分类，在comfyui右键菜单可以编辑更新web app
+

 - Support multiple web app switching.
 - Add the AppInfo node, which allows you to transform the workflow into a web app by simple configuration.
 - The workflow, which is now released as an app, can also be edited again by right-clicking.
+- The web app can be configured with categories, and the web app can be edited and updated in the right-click menu of ComfyUI.


 ![](./assets/0-m-app.png)
@@ -26,9 +39,13 @@ APP-JSON:
 - [image-to-image](./example/Image-to-Image_2.json)
 - text-to-text

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

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


 ## 🏃🚗🚚🚀  Real-time Design
@@ -57,9 +74,26 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43

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

-### 3D
-![](./assets/3dimage.png)
-[workflow](./workflow/3D-workflow.json)
+
+## Prompt
+> PromptSlide
+![](./assets/prompt_weight.png)
+
+<!-- ![](./workflow/promptslide-appinfo-workflow.svg) -->
+
+> randomPrompt
+
+![randomPrompt](./assets/randomPrompt.png)
+
+> ClipInterrogator
+
+[add clip-interrogator](https://github.com/pharmapsychotic/clip-interrogator)
+
+> PromptImage & PromptSimplification,Assist in simplifying prompt words, comparing images and prompt word nodes.
+
+> ChinesePrompt && PromptGenerate，中文prompt节点，直接用中文书写你的prompt
+
+![](./assets/ChinesePrompt_workflow.svg)


 ### Layers
@@ -70,6 +104,11 @@ 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. 

@@ -88,6 +127,12 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43

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

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

 ![main](./assets/all-workflow.svg)
@@ -95,24 +140,13 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43

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

-> randomPrompt

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

 > TransparentImage

 ![TransparentImage](./assets/TransparentImage.png)


-> Consistency Decoder
-
-[openai Consistency Decoder]( https://github.com/openai/consistencydecoder)
-
-![Consistency](./assets/consistency.png)
-After downloading the OpenAI VAE model, place it in the "model/vae" directory for use.
-https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt
-
-
 > FeatheredMask、SmoothMask

 Add edges to an image.
@@ -125,6 +159,12 @@ Add edges to an image.
 from [simple-lama-inpainting](https://github.com/enesmsahin/simple-lama-inpainting)


+> rembgNode
+
+"briarmbg","u2net","u2netp","u2net_human_seg","u2net_cloth_seg","silueta","isnet-general-use","isnet-anime"
+
+
+
 ### Improvement 

 - Add "help" option to the context menu for each node.
@@ -137,25 +177,17 @@ 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 : models/clipseg
+
+[Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:models/rembg

 [Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : models/lama

-<!-- ### Workflow
-[Workflow](./workflow.md) -->
+[Download Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base

+[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:prompt_generator/text2image-prompt-generator
+
+[Download Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main),move to:prompt_generator/opus-mt-zh-en

 ## Installation

@@ -186,20 +218,25 @@ If you are using a venv, make sure you have it activated before installation and
 pip3 install -r requirements.txt
 ```

+
+
+
+
 #### Chinese community
 访问 [www.mixcomfy.com](https://www.mixcomfy.com)，获得更多内测功能，关注微信公众号：Mixlab无界社区


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

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

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

 <picture>
  <source
@@ -133,8 +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'):
@@ -162,41 +192,67 @@ def read_workflow_json_files(folder_path):
 def get_workflows():
    # print("#####path::", current_path)
    workflow_path=os.path.join(current_path, "workflow")
-    print('workflow_path: ',workflow_path)
+    # print('workflow_path: ',workflow_path)
    if not os.path.exists(workflow_path):
        # 使用mkdir()方法创建新目录
        os.mkdir(workflow_path)
    workflows=read_workflow_json_files(workflow_path)
    return workflows

-def get_my_workflow_for_app(filename="my_workflow_app.json"):
+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:
-        data=read_workflow_json_files(app_path)
+
+        #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:
+                # 管理员模式，读取全部数据
+                if i==0 or is_all:
                    apps.append({
                        "filename":item["filename"],
+                        # "category":item['category'],
                        "data":x,
-                        "date":item["date"]
+                        "date":item["date"],
                    })
                else:
+                    category=''
+                    input=None
+                    output=None
+                    if 'category' in x['app']:
+                        category=x['app']['category']
+                    if 'input' in x['app']:
+                        input=x['app']['input']
+                    if 'output' in x['app']:
+                        output=x['app']['output']
                    apps.append({
                        "filename":item["filename"],
+                        "category":category,
                        "data":{
                            "app":{
+                                "category":category,
                                "description":x['app']['description'],
                                "filename":(x['app']['filename'] if 'filename' in x['app'] else "") ,
                                "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
                                "name":x['app']['name'],
                                "version":x['app']['version'],
+                                "input":input,
+                                "output":output
                            }
                        },
                        "date":item["date"]
@@ -205,7 +261,7 @@ def get_my_workflow_for_app(filename="my_workflow_app.json"):
            except Exception as e:
                print("发生异常：", str(e))
    else:
-        app_workflow_path=os.path.join(app_path, filename)
+        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:
@@ -216,22 +272,35 @@ def get_my_workflow_for_app(filename="my_workflow_app.json"):
        except Exception as e:
            print("发生异常：", str(e))
        
-        if len(apps)==1:
-            data=read_workflow_json_files(app_path)
+        if len(apps)==1 and category!='' and category!=None:
+            data=read_workflow_json_files(category_path)
            
            for item in data:
                x=item["data"]
                # print(apps[0]['filename'] ,item["filename"])
                if apps[0]['filename']!=item["filename"]:
+                    category=''
+                    input=None
+                    output=None
+                    if 'category' in x['app']:
+                        category=x['app']['category']
+                    if 'input' in x['app']:
+                        input=x['app']['input']
+                    if 'output' in x['app']:
+                        output=x['app']['output']
                    apps.append({
                        "filename":item["filename"],
+                        # "category":category,
                        "data":{
                            "app":{
+                                "category":category,
                                "description":x['app']['description'],
                                "filename":(x['app']['filename'] if 'filename' in x['app'] else "") ,
                                "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
                                "name":x['app']['name'],
                                "version":x['app']['version'],
+                                "input":input,
+                                "output":output
                            }
                        },
                        "date":item["date"]
@@ -245,11 +314,16 @@ def save_workflow_json(data):
        json.dump(data, file)
    return workflow_path

-def save_workflow_for_app(data,filename="my_workflow_app.json"):
+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)
-    app_workflow_path=os.path.join(app_path, filename)
+
+    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'])
@@ -294,33 +368,59 @@ async def new_request(self, method, url, *args, **kwargs):

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

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

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

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

+    except Exception as e:
+        print(f"Error starting the server: {e}")
+
        # import webbrowser
        # if os.name == 'nt' and address == '0.0.0.0':
        #     address = '127.0.0.1'
@@ -331,7 +431,7 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
 PromptServer.start=new_start

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

@routes.post('/mixlab')
 async def mixlab_hander(request):
@@ -371,17 +471,26 @@ async def mixlab_workflow_hander(request):
                    'file_path':file_path
                }
            elif data['task']=='save_app':
-                file_path=save_workflow_for_app(data['data'],data['filename'])
+                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),
+                    'data':get_my_workflow_for_app(filename,category,admin),
                    'status':'success',
                }
            elif data['task']=='list':
@@ -408,22 +517,6 @@ async def nodes_map_hander(request):

    return web.json_response(result)

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

 # 扩展api接口
 # from server import PromptServer
@@ -437,23 +530,32 @@ PromptServer.add_routes=new_add_routes


 # 导入节点
-from .nodes.PromptNode import RandomPrompt,PromptSlide
-from .nodes.ImageNode import NoiseImage,TransparentImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
-from .nodes.Vae import VAELoader,VAEDecode
+from .nodes.PromptNode import EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
+from .nodes.ImageNode import SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+# from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
-from .nodes.Clipseg import CLIPSeg,CombineMasks
-from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
+
+from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,GetImageSize_,MultiplicationNode
-from .nodes.Lama import LaMaInpainting
+from .nodes.Utils import CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+from .nodes.Mask import OutlineMask,FeatheredMask
+

 # 要导出的所有节点及其名称的字典
 # 注意：名称应全局唯一
 NODE_CLASS_MAPPINGS = {
    "AppInfo":AppInfo,
+    "TESTNODE_":TESTNODE_,
+    "TESTNODE_TOKEN":TESTNODE_TOKEN,
    "RandomPrompt":RandomPrompt,
+    # "LoraPrompt":LoraPrompt,
+    "EmbeddingPrompt":EmbeddingPrompt,
    "PromptSlide":PromptSlide,
+    "PromptSimplification":PromptSimplification,
+    "PromptImage":PromptImage,
+    "MirroredImage":MirroredImage,
    "NoiseImage":NoiseImage,
+    "GradientImage":GradientImage,
    "TransparentImage":TransparentImage,
    "ResizeImageMixlab":ResizeImage,
    "LoadImagesFromPath":LoadImagesFromPath,
@@ -462,8 +564,12 @@ NODE_CLASS_MAPPINGS = {
    "EnhanceImage":EnhanceImage,
    "SvgImage":SvgImage,
    "3DImage":Image3D, 
+    "ImageColorTransfer":ImageColorTransfer,
    "ShowLayer":ShowLayer,
    "NewLayer":NewLayer,
+    "SplitImage":SplitImage,
+    "CenterImage":CenterImage,
+    "GridOutput":GridOutput,
    "MergeLayers":MergeLayers,
    "SplitLongMask":SplitLongMask,
    "FeatheredMask":FeatheredMask,
@@ -471,15 +577,15 @@ NODE_CLASS_MAPPINGS = {
    "FaceToMask":FaceToMask,
    "AreaToMask":AreaToMask,
    "ImageCropByAlpha":ImageCropByAlpha,
-    "VAELoaderConsistencyDecoder":VAELoader,
-    "VAEDecodeConsistencyDecoder":VAEDecode,
+    # "VAELoaderConsistencyDecoder":VAELoader,
+    "SaveImageToLocal":SaveImageToLocal,
+    # "VAEDecodeConsistencyDecoder":VAEDecode,
    "ScreenShare":ScreenShareNode,
    "FloatingVideo":FloatingVideo,
-    "CLIPSeg_":CLIPSeg,
-    "CombineMasks_":CombineMasks,
    "ChatGPTOpenAI":ChatGPTNode,
    "ShowTextForGPT":ShowTextForGPT,
    "CharacterInText":CharacterInText,
+    "TextSplitByDelimiter":TextSplitByDelimiter,
    "SpeechRecognition":SpeechRecognition,
    "SpeechSynthesis":SpeechSynthesis,
    "Color":ColorInput,
@@ -493,7 +599,13 @@ NODE_CLASS_MAPPINGS = {
    "GetImageSize_":GetImageSize_,
    "SwitchByIndex":SwitchByIndex,
    "LimitNumber":LimitNumber, 
-    "LaMaInpainting":LaMaInpainting
+    "OutlineMask":OutlineMask,
+    "JoinWithDelimiter":JoinWithDelimiter,
+    "Seed_":CreateSeedNode,
+    "CkptNames_":CreateCkptNames,
+    "SamplerNames_":CreateSampler_names,
+    "LoraNames_":CreateLoraNames
+    # "LaMaInpainting":LaMaInpainting
    # "GamePal":GamePal
 }

@@ -515,14 +627,53 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "3DImage":"3DImage ♾️Mixlab",
    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
    "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
-    "PromptSlide":"PromptSlide ♾️Mixlab"
-
-    # "GamePal":"GamePal ♾️Mixlab"
+    "PromptSlide":"PromptSlide ♾️Mixlab",
+    "PromptGenerate_Mix":"PromptGenerate ♾️Mixlab",
+    "ChinesePrompt_Mix":"ChinesePrompt ♾️Mixlab",
+    "GamePal":"GamePal ♾️Mixlab",
+    "RembgNode_Mix":"Removebg",
+    "LoraNames_":"LoraName_TriggerWords.safetensors"
 }

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

 print('--------------')
-print('\033[91m ### Mixlab Nodes: \033[93mLoaded\033[0m')
-print('--------------')
+print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
+
+try:
+    from .nodes.Lama import LaMaInpainting
+    print('LaMaInpainting.available',LaMaInpainting.available)
+    if LaMaInpainting.available:
+        NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
+except Exception as e:
+    print('LaMaInpainting.available',False,e)
+
+try:
+    from .nodes.ClipInterrogator import ClipInterrogator
+    print('ClipInterrogator.available',ClipInterrogator.available)
+    if ClipInterrogator.available:
+        NODE_CLASS_MAPPINGS['ClipInterrogator']=ClipInterrogator
+except Exception as e:
+    print('ClipInterrogator.available',False,e)
+
+try:
+    from .nodes.TextGenerateNode import PromptGenerate,ChinesePrompt
+    print('PromptGenerate.available',PromptGenerate.available)
+    if PromptGenerate.available:
+        NODE_CLASS_MAPPINGS['PromptGenerate_Mix']=PromptGenerate
+    print('ChinesePrompt.available',ChinesePrompt.available)
+    if ChinesePrompt.available:
+        NODE_CLASS_MAPPINGS['ChinesePrompt_Mix']=ChinesePrompt
+except Exception as e:
+    print('TextGenerateNode.available',False,e)
+
+try:
+    from .nodes.RembgNode import RembgNode_
+    print('RembgNode_.available',RembgNode_.available)
+    if RembgNode_.available:
+        NODE_CLASS_MAPPINGS['RembgNode_Mix']=RembgNode_
+except Exception as e:
+    print('RembgNode_.available',False,e)
+
+print('\033[93m -------------- \033[0m')
@@ -0,0 +1,30 @@
+Jony Ive
+Dieter Rams
+Philippe Starck
+Karim Rashid
+Yves Béhar
+Marc Newson
+Naoto Fukasawa
+Jonathan Adler
+Patricia Urquiola
+Ross Lovegrove
+Tom Dixon
+Jasper Morrison
+Charles Eames
+Ray Eames
+Achille Castiglioni
+Ron Arad
+Konstantin Grcic
+Marcel Wanders
+Maarten Baas
+Stefan Sagmeister
+Ingo Maurer
+Hella Jongerius
+Sam Hecht
+Kim Colin
+Jaime Hayon
+Michael Anastassiades
+Nendo
+Oki Sato
+Matali Crasset
+Tokujin Yoshioka
@@ -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
@@ -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
@@ -0,0 +1,30 @@
+Elegant evening gown
+Casual jeans and t-shirt
+Formal black suit
+Stylish leather jacket
+Flowy bohemian dress
+Sporty tracksuit
+Chic little black dress
+Trendy ripped jeans
+Classic white button-down shirt
+Cozy oversized sweater
+Sophisticated tailored blazer
+Quirky patterned leggings
+Striped sailor top
+Polished knee-length skirt
+Vintage-inspired floral dress
+Edgy motorcycle jacket
+Preppy polo shirt
+Boho maxi skirt
+Professional pinstripe suit
+Relaxed denim shorts
+Glamorous sequined dress
+Athletic running shoes
+Formal bow tie
+Casual baseball cap
+Stylish fedora hat
+Warm woolen scarf
+Comfortable cotton socks
+Trendy ankle boots
+Cute summer sandals
+Cozy pajama set
@@ -0,0 +1,30 @@
+Happy
+Sad
+Angry
+Surprised
+Excited
+Worried
+Confused
+Disgusted
+Amused
+Bored
+Curious
+Embarrassed
+Frustrated
+Nervous
+Pleased
+Relieved
+Shy
+Tired
+Serious
+Silly
+Proud
+Grumpy
+Smug
+Sarcastic
+Flirty
+Skeptical
+Shocked
+Blissful
+Envious
+Mischievous
@@ -4761,19 +4761,29 @@
    ],
    "https://github.com/shadowcz007/comfyui-mixlab-nodes": [
        [
+            "GridOutput",
+            "SplitImage",
+            "PromptGenerate_Mix",
+            "JoinWithDelimiter",
+            "ChinesePrompt_Mix",
            "3DImage",
            "AppInfo",
            "IntNumber",
            "FloatSlider",
            "ResizeImage",
            "NoiseImage",
+            "PromptImage",
+            "SaveImageToLocal",
            "AreaToMask",
-            "CLIPSeg_",
            "CharacterInText",
            "ChatGPTOpenAI",
            "Color",
-            "CombineMasks_",
+            "Seed_",
+            "CkptNames_",
+            "SamplerNames_",
+            "LoraNames_",
            "EnhanceImage",
+            "GradientImage",
            "FaceToMask",
            "FeatheredMask",
            "FloatingVideo",
@@ -4783,8 +4793,11 @@
            "LoadImagesFromURL",
            "MergeLayers",
            "NewLayer",
+            "CenterImage",
            "RandomPrompt",
            "PromptSlide",
+            "PromptSimplification",
+            "ClipInterrogator",
            "ScreenShare",
            "ShowLayer",
            "ShowTextForGPT",
@@ -4796,12 +4809,11 @@
            "TextImage",
            "ResizeImageMixlab",
            "TransparentImage",
-            "VAEDecodeConsistencyDecoder",
-            "VAELoaderConsistencyDecoder",
            "TextToNumber",
            "TextInput_",
            "DynamicDelayProcessor",
-            "LaMaInpainting"
+            "LaMaInpainting",
+            "Moondream"
        ],
        {
            "title_aux": "comfyui-mixlab-nodes"
@@ -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
@@ -0,0 +1 @@
+{}
@@ -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
@@ -0,0 +1,101 @@
+Doctor
+Teacher
+Engineer
+Lawyer
+Accountant
+Nurse
+Architect
+Chef
+Pilot
+Scientist
+Artist
+Writer
+Musician
+Actor
+Photographer
+Police officer
+Firefighter
+Dentist
+Pharmacist
+Veterinarian
+Electrician
+Plumber
+Carpenter
+Mechanic
+Farmer
+Astronaut
+Athlete
+Journalist
+Politician
+Economist
+Psychologist
+Social worker
+Librarian
+Translator
+Salesperson
+Entrepreneur
+Financial advisor
+Graphic designer
+Web developer
+Marketing manager
+Human resources manager
+Project manager
+Event planner
+Fashion designer
+Interior decorator
+Real estate agent
+Archaeologist
+Biologist
+Chemist
+Geologist
+Physicist
+Mathematician
+Historian
+Geographer
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+                     #MixCopilot
@@ -0,0 +1,58 @@
+Residential space
+Apartment building
+Villa
+Bungalow
+Condominium
+Commercial space
+Shopping mall
+Supermarket
+Restaurant
+Store
+Market
+Office space
+Office building
+Office
+Meeting room
+Co-working space
+Educational space
+School
+University
+Training institution
+Library
+Laboratory
+Medical space
+Hospital
+Clinic
+Pharmacy
+Nursing home
+Rehabilitation center
+Cultural space
+Museum
+Library
+Theater
+Concert hall
+Gallery
+Sports space
+Sports stadium
+Gym
+Swimming pool
+Basketball court
+Football field
+Transportation space
+Airport
+Train station
+Subway station
+Bus stop
+Parking lot
+Public space
+Park
+Square
+Street
+Pedestrian street
+Community center
+Industrial space
+Factory
+Warehouse
+Production workshop
+Mine
+Power plant
@@ -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
@@ -24,7 +24,7 @@ class SpeechRecognition:

    FUNCTION = "run"

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

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

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

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

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

    def run(self, input_text,input_num,python_code):
        exec(python_code)
@@ -1,7 +1,26 @@
 import openai 
 import time
 import urllib.error
-import re,json
+import re,json,os,string,random
+import folder_paths
+import hashlib
+
+def get_unique_hash(string):
+    hash_object = hashlib.sha1(string.encode())
+    unique_hash = hash_object.hexdigest()
+    return unique_hash
+
+def generate_random_string(length):
+    letters = string.ascii_letters + string.digits
+    return ''.join(random.choice(letters) for _ in range(length))
+
+class AnyType(str):
+  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+
+  def __ne__(self, __value: object) -> bool:
+    return False
+
+any_type = AnyType("*")

 #  判断是否是azure服务
 def is_azure_url(url):
@@ -73,8 +92,8 @@ class ChatGPTNode:
    def INPUT_TYPES(cls):
        return {
            "required": {
-                "api_key":("KEY", {"default": "", "multiline": True}),
-                "api_url":("URL", {"default": "", "multiline": True}),
+                "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
+                "api_url":("URL", {"default": "", "multiline": True,"dynamicPrompts": False}),
                "prompt": ("STRING", {"multiline": True,"dynamicPrompts": False}),
                "system_content": ("STRING", 
                                   {
@@ -168,7 +187,10 @@ class ShowTextForGPT:
        return {
            "required": {
                "text": ("STRING", {"forceInput": True,"dynamicPrompts": False}),
-            }
+            },
+            "optional":{ 
+                    "output_dir": ("STRING",{"forceInput": True,"default": "","multiline": True,"dynamicPrompts": False}), 
+                }
        }

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

    CATEGORY = "♾️Mixlab/GPT"

-    def run(self, text):
-        # print(session_history)
+    def run(self, text,output_dir=[""]):
+        
+        # 类型纠正
+        texts=[]
+        for t in text:
+            if not isinstance(t, str):
+                t = str(t)
+            texts.append(t)
+
+        text=texts
+
+        if len(output_dir)==1 and (output_dir[0]=='' or os.path.dirname(output_dir[0])==''):
+            t='\n'.join(text)
+            output_dir=[
+                os.path.join(folder_paths.get_temp_directory(),
+                             get_unique_hash(t)+'.txt'
+                             )
+            ]
+        elif len(output_dir)==1:
+            base=os.path.basename(output_dir[0])
+            t='\n'.join(text)
+            if base=='' or os.path.splitext(base)[1]=='':
+                base=get_unique_hash(t)+'.txt'
+            output_dir=[
+                os.path.join(output_dir[0],
+                             base
+                             )
+            ]
+        # elif len(output_dir)>1:
+
+        
+
+        if len(output_dir)==1 and len(text)>1:
+            output_dir=[output_dir[0] for _ in range(len(text))]
+        
+        for i in range(len(text)):
+
+            o_fp=output_dir[i]
+            dirp=os.path.dirname(o_fp)
+            if dirp=='':
+                dirp=folder_paths.get_temp_directory()
+                o_fp=os.path.join(folder_paths.get_temp_directory(),o_fp
+                             )
+
+            if not os.path.exists(dirp):
+                os.mkdir(dirp)
+
+            if not os.path.splitext(o_fp)[1].lower()=='.txt':
+                o_fp=o_fp+'.txt'
+
+            t=text[i]
+            with open(o_fp, 'w') as file:
+                file.write(t)
+
+        # print(text)
        return {"ui": {"text": text}, "result": (text,)}
+        


 class CharacterInText:
@@ -211,7 +287,58 @@ class CharacterInText:

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

+class TextSplitByDelimiter:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                "delimiter":(["newline","comma"],),
+                "start_index": ("INT", {
+                    "default": 0,
+                    "min": 0, #Minimum value
+                    "max": 1000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                 "skip_every": ("INT", {
+                    "default": 0,
+                    "min": 0, #Minimum value
+                    "max": 10, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                "max_count": ("INT", {
+                    "default": 10,
+                    "min": 1, #Minimum value
+                    "max": 1000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+            }
+        }
+
+    INPUT_IS_LIST = False
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "run"
+    # OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "♾️Mixlab/GPT"
+
+    def run(self, text,delimiter,start_index,skip_every,max_count):
+        arr=[]
+        if delimiter=='newline':
+            arr = [line for line in text.split('\n') if line.strip()]
+        elif delimiter=='comma':
+            arr = [line for line in text.split(',') if line.strip()]
+        
+        arr= arr[start_index:start_index + max_count * (skip_every+1):(skip_every+1)]
+
+        return (arr,)
+
+
@@ -0,0 +1,277 @@
+import os,sys
+import folder_paths
+
+from PIL import Image
+import importlib.util
+
+import comfy.utils
+import numpy as np
+import json
+import torch
+import random
+
+
+# from clip_interrogator import Config, Interrogator
+
+
+global _available
+_available=False
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+
+try:
+    if is_installed('clip_interrogator')==False:
+        import subprocess
+
+        # 安装
+        print('#pip install clip-interrogator==0.6.0')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'clip-interrogator==0.6.0'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0:
+            print("#install success")
+            from clip_interrogator import Config, Interrogator
+            _available=True
+        else:
+            print("#install error")
+        
+    else:
+        from clip_interrogator import Config, Interrogator
+        _available=True
+
+except:
+    _available=False
+
+try:
+    from transformers import AutoProcessor, BlipForConditionalGeneration
+except:
+    _available=False
+    print('pls check  transformers.__version__>=4.36.0:: AutoProcessor, BlipForConditionalGeneration')
+
+
+
+def load_caption_model(model_path,config,t='blip-base'):
+    dtype=torch.float16 if config.device == 'cuda' else torch.float32
+    caption_model = BlipForConditionalGeneration.from_pretrained(model_path, torch_dtype=dtype)
+    
+    caption_processor = AutoProcessor.from_pretrained(model_path)
+
+    caption_model.eval()
+    if not config.caption_offload:
+        caption_model = caption_model.to(config.device)
+    
+    return (caption_model,caption_processor)
+    
+
+
+caption_model_path=os.path.join(folder_paths.models_dir, "clip_interrogator/Salesforce/blip-image-captioning-base")
+if not os.path.exists(caption_model_path):
+    print(f"## clip_interrogator_model not found: {caption_model_path}, pls download from https://huggingface.co/Salesforce/blip-image-captioning-base")
+    caption_model_path='Salesforce/blip-image-captioning-base'
+
+cache_path=os.path.join(folder_paths.models_dir, "clip_interrogator")
+
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+def image_analysis_fn(ci,image):
+    image = image.convert('RGB')
+    image_features = ci.image_to_features(image)
+
+    top_mediums = ci.mediums.rank(image_features, 5)
+    top_artists = ci.artists.rank(image_features, 5)
+    top_movements = ci.movements.rank(image_features, 5)
+    top_trendings = ci.trendings.rank(image_features, 5)
+    top_flavors = ci.flavors.rank(image_features, 5)
+
+    medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))}
+    artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))}
+    movement_ranks = {movement: sim for movement, sim in zip(top_movements, ci.similarities(image_features, top_movements))}
+    trending_ranks = {trending: sim for trending, sim in zip(top_trendings, ci.similarities(image_features, top_trendings))}
+    flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))}
+    
+    return {
+        "medium_ranks":medium_ranks, 
+        "artist_ranks":artist_ranks, 
+        "movement_ranks":movement_ranks, 
+        "trending_ranks":trending_ranks, 
+        "flavor_ranks":flavor_ranks
+        }
+
+
+def generate_sentences(data):
+    sentences = []
+
+    # Get the length of data
+    data_length = len(data)
+
+    # Use a recursive function to handle variable-length data
+    def generate_recursive(index, current_sentence, current_score):
+        # Check if recursion is complete
+        if index == data_length:
+            sentences.append({"sentence": current_sentence, "score": current_score})
+            return
+
+        # Get the current level data
+        current_data = data[index]
+
+        # Iterate through the current level data
+        for phrase in current_data:
+            sentence = current_sentence + ("," if current_sentence.strip() else "") + phrase
+            score = current_score + current_data[phrase]
+            generate_recursive(index + 1, sentence, score)
+
+    # Start recursive generation of sentences
+    generate_recursive(0, "", 0)
+
+    # Sort the generated sentences by score in descending order
+    sentences.sort(key=lambda x: x["score"], reverse=True)
+
+    def get_random_elements(elements, num):
+        return random.sample(elements, num)
+
+    ps = get_random_elements(sentences, 5)
+    ps = [s["sentence"] for s in sorted(ps, key=lambda x: x["score"], reverse=True)]
+
+    return ps
+
+
+
+
+def image_to_prompt(ci,image, mode):
+    ci.config.chunk_size = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+    ci.config.flavor_intermediate_count = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+    image = image.convert('RGB')
+    if mode == 'best':
+        return ci.interrogate(image)
+    elif mode == 'classic':
+        return ci.interrogate_classic(image)
+    elif mode == 'fast':
+        return ci.interrogate_fast(image)
+    elif mode == 'negative':
+        return ci.interrogate_negative(image)
+
+# image = Image.open(image_path).convert('RGB')
+# ci = Interrogator(Config(clip_model_name="ViT-L-14/openai"))
+# print(ci.interrogate(image))
+
+
+class ClipInterrogator:
+
+    global _available
+    available=_available
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "prompt_mode": (['fast','classic','best','negative'],),
+            "image_analysis": (["off","on"],), 
+                             },
+
+                # "optional":{
+                #     "output":("CLIPINTERROGATOR", {"multiline": True,"default": "", "dynamicPrompts": False})
+                # },
+
+                }
+    
+    RETURN_TYPES = ("STRING","STRING",)
+    RETURN_NAMES = ("prompt","random_samples",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,True,)
+    global ci
+    ci = None
+    def run(self,image,prompt_mode,image_analysis):
+        global ci
+
+        prompt_mode=prompt_mode[0]
+        analysis=image_analysis[0]
+
+        prompt_result=[]
+        analysis_result=[]
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(image)*(2 if analysis=='on' else 1))
+        
+        if ci==None:
+            config=Config(
+                clip_model_name="ViT-L-14/openai",
+                device="cuda" if torch.cuda.is_available() else "cpu",
+                download_cache=True,
+                clip_model_path=cache_path,
+                cache_path=cache_path
+                )
+            config.apply_low_vram_defaults()
+
+            caption_model,caption_processor=load_caption_model(caption_model_path,config)
+
+            config.caption_model= caption_model
+            config.caption_processor= caption_processor
+
+            ci = Interrogator(config)
+        # else:
+        #     simple_lama.model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        for i in range(len(image)):
+            im=image[i]
+
+            im=tensor2pil(im)
+            im=im.convert('RGB')
+
+            if analysis=='on':
+                analysis_res=image_analysis_fn(ci,im)
+                analysis_result.append( analysis_res )
+                pbar.update(1)
+
+            prompt=image_to_prompt(ci,im,prompt_mode)
+            pbar.update(1)
+            prompt_result.append(prompt)
+
+
+        # result.save("inpainted.png")
+        if ci.config.clip_offload and not ci.clip_offloaded:
+            ci.clip_model = ci.clip_model.to('cpu')
+            ci.clip_offloaded = True
+
+        if ci.config.caption_offload and not ci.caption_offloaded:
+            ci.caption_model = ci.caption_model.to('cpu')
+            ci.caption_offloaded = True
+
+        # analysis_result=[]
+        # items = app.graph.getNodeById(31).widgets[2].value["items"]
+        
+        random_samples=[]
+
+        for r in analysis_result:
+            random_sample = generate_sentences([r['medium_ranks'], r['artist_ranks'],r['movement_ranks'],r['trending_ranks'],r['flavor_ranks']])
+            for s in random_sample:
+                random_samples.append(s)
+        # print(len(random_samples))
+        # print('-----')
+        # print( random_samples)
+        return {
+            "ui":{
+                    "prompt": prompt_result,
+                    "analysis":analysis_result,
+                    "random_samples":random_samples
+                },
+            "result": (prompt_result,random_samples,)}
@@ -1,272 +0,0 @@
-#### Thanks:
-# [ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)
-
-from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
-
-from PIL import Image
-import torch
-import torchvision.transforms as T
-import numpy as np
-
-from torchvision.transforms.functional import to_pil_image
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-
-
-import cv2
-
-from scipy.ndimage import gaussian_filter
-
-from typing import Optional, Tuple
-
-import warnings,os
-warnings.filterwarnings("ignore", category=UserWarning, module="torch")
-warnings.filterwarnings("ignore", category=UserWarning, module="safetensors")
-
-import folder_paths
-import logging
-
-logger = logging.getLogger('CLIPSeg nodes')
-
-clipseg_model_dir = os.path.join(folder_paths.models_dir, "clipseg")
-
-if not os.path.exists(clipseg_model_dir):
-    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()
-    return (array * 255).astype(np.uint8)
-
-def numpy_to_tensor(array: np.ndarray) -> torch.Tensor:
-    """Convert a numpy array to a tensor and scale its values from 0-255 to 0-1."""
-    array = array.astype(np.float32) / 255.0
-    return torch.from_numpy(array)[None,]
-
-def apply_colormap(mask: torch.Tensor, colormap) -> np.ndarray:
-    """Apply a colormap to a tensor and convert it to a numpy array."""
-    colored_mask = colormap(mask.numpy())[:, :, :3]
-    return (colored_mask * 255).astype(np.uint8)
-
-def resize_image(image: np.ndarray, dimensions: Tuple[int, int]) -> np.ndarray:
-    """Resize an image to the given dimensions using linear interpolation."""
-    return cv2.resize(image, dimensions, interpolation=cv2.INTER_LINEAR)
-
-def overlay_image(background: np.ndarray, foreground: np.ndarray, alpha: float) -> np.ndarray:
-    """Overlay the foreground image onto the background with a given opacity (alpha)."""
-    return cv2.addWeighted(background, 1 - alpha, foreground, alpha, 0)
-
-def dilate_mask(mask: torch.Tensor, dilation_factor: float) -> torch.Tensor:
-    """Dilate a mask using a square kernel with a given dilation factor."""
-    kernel_size = int(dilation_factor * 2) + 1
-    kernel = np.ones((kernel_size, kernel_size), np.uint8)
-    mask_dilated = cv2.dilate(mask.numpy(), kernel, iterations=1)
-    return torch.from_numpy(mask_dilated)
-
-
-
-class CLIPSeg:
-
-    def __init__(self):
-        pass
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        """
-            Return a dictionary which contains config for all input fields.
-            Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
-            Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
-            The type can be a list for selection.
-
-            Returns: `dict`:
-                - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
-                - Value input_fields (`dict`): Contains input fields config:
-                    * Key field_name (`string`): Name of a entry-point method's argument
-                    * Value field_config (`tuple`):
-                        + First value is a string indicate the type of field or a list for selection.
-                        + Secound value is a config for type "INT", "STRING" or "FLOAT".
-        """
-        return {"required":
-                    {
-                        "image": ("IMAGE",),
-                        "text": ("STRING", {"multiline": False,"dynamicPrompts": False}),
-                        
-                     },
-                "optional":
-                    {
-                        "blur": ("FLOAT", {"min": 0, "max": 15, "step": 0.1, "default": 7}),
-                        "threshold": ("FLOAT", {"min": 0, "max": 1, "step": 0.05, "default": 0.4}),
-                        "dilation_factor": ("INT", {"min": 0, "max": 10, "step": 1, "default": 4}),
-                    }
-                }
-
-    CATEGORY = "♾️Mixlab/mask"
-    RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
-    RETURN_NAMES = ("Mask","Heatmap Mask", "BW Mask")
-
-    # INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (False,False,False,)
-
-    FUNCTION = "segment_image"
-    def segment_image(self, image: torch.Tensor, text: str, blur: float, threshold: float, dilation_factor: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """Create a segmentation mask from an image and a text prompt using CLIPSeg.
-
-        Args:
-            image (torch.Tensor): The image to segment.
-            text (str): The text prompt to use for segmentation.
-            blur (float): How much to blur the segmentation mask.
-            threshold (float): The threshold to use for binarizing the segmentation mask.
-            dilation_factor (int): How much to dilate the segmentation mask.
-
-        Returns:
-            Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: The segmentation mask, the heatmap mask, and the binarized mask.
-        """
-            
-        # Convert the Tensor to a PIL image
-        image_np = image.numpy().squeeze()  # Remove the first dimension (batch size of 1)
-        # Convert the numpy array back to the original range (0-255) and data type (uint8)
-        image_np = (image_np * 255).astype(np.uint8)
-        # Create a PIL image from the numpy array
-        i = Image.fromarray(image_np, mode="RGB")
-
-        processor = CLIPSegProcessor.from_pretrained(clipseg_model_dir)
-        model = CLIPSegForImageSegmentation.from_pretrained(clipseg_model_dir)
-        
-        prompt = text
-        
-        input_prc = processor(text=prompt, images=i, padding="max_length", return_tensors="pt")
-        
-        # Predict the segemntation mask
-        with torch.no_grad():
-            outputs = model(**input_prc)
-        
-        tensor = torch.sigmoid(outputs[0]) # get the mask
-        
-        # Apply a threshold to the original tensor to cut off low values
-        thresh = threshold
-        tensor_thresholded = torch.where(tensor > thresh, tensor, torch.tensor(0, dtype=torch.float))
-
-        # Apply Gaussian blur to the thresholded tensor
-        sigma = blur
-        tensor_smoothed = gaussian_filter(tensor_thresholded.numpy(), sigma=sigma)
-        tensor_smoothed = torch.from_numpy(tensor_smoothed)
-
-        # Normalize the smoothed tensor to [0, 1]
-        mask_normalized = (tensor_smoothed - tensor_smoothed.min()) / (tensor_smoothed.max() - tensor_smoothed.min())
-
-        # Dilate the normalized mask
-        mask_dilated = dilate_mask(mask_normalized, dilation_factor)
-
-        # Convert the mask to a heatmap and a binary mask
-        heatmap = apply_colormap(mask_dilated, cm.viridis)
-        binary_mask = apply_colormap(mask_dilated, cm.Greys_r)
-
-        # Overlay the heatmap and binary mask on the original image
-        dimensions = (image_np.shape[1], image_np.shape[0])
-        heatmap_resized = resize_image(heatmap, dimensions)
-        binary_mask_resized = resize_image(binary_mask, dimensions)
-
-        alpha_heatmap, alpha_binary = 0.5, 1
-        overlay_heatmap = overlay_image(image_np, heatmap_resized, alpha_heatmap)
-        overlay_binary = overlay_image(image_np, binary_mask_resized, alpha_binary)
-
-        # Convert the numpy arrays to tensors
-        image_out_heatmap = numpy_to_tensor(overlay_heatmap)
-        image_out_binary = numpy_to_tensor(overlay_binary)
-
-        # Save or display the resulting binary mask
-        binary_mask_image = Image.fromarray(binary_mask_resized[..., 0])
-
-        # convert PIL image to numpy array
-        tensor_bw = binary_mask_image.convert("L")
-        tensor_bw=pil2tensor(tensor_bw)
-        # tensor_bw = np.array(tensor_bw).astype(np.float32) / 255.0
-        # tensor_bw = torch.from_numpy(tensor_bw)[None,]
-        # tensor_bw = tensor_bw.squeeze(0)[..., 0]
-
-        return (tensor_bw, image_out_heatmap, image_out_binary,)
-
-    #OUTPUT_NODE = False
-
-class CombineMasks:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {
-                        "input_image": ("IMAGE", ),
-                        "mask_1": ("MASK", ), 
-                        "mask_2": ("MASK", ),
-                    },
-                "optional": 
-                    {
-                        "mask_3": ("MASK",), 
-                    },
-                }
-        
-    CATEGORY = "♾️Mixlab/mask"
-    RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
-    RETURN_NAMES = ("Combined Mask","Heatmap Mask", "BW Mask")
-
-    FUNCTION = "combine_masks"
-            
-    def combine_masks(self, input_image: torch.Tensor, mask_1: torch.Tensor, mask_2: torch.Tensor, mask_3: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """A method that combines two or three masks into one mask. Takes in tensors and returns the mask as a tensor, as well as the heatmap and binary mask as tensors."""
-        
-        # Combine masks
-        if mask_1 is not None:
-            mask_1 = mask_1.squeeze()
-        if mask_2 is not None:
-            mask_2 = mask_2.squeeze()
-        if mask_3 is not None:
-            mask_3 = mask_3.squeeze()
-        
-        print(mask_1.shape,mask_2.shape , mask_3.shape)
-        combined_mask = mask_1 + mask_2 + mask_3 if mask_3 is not None else mask_1 + mask_2
-        # print(combined_mask)
-
-        # Convert image and masks to numpy arrays
-        image_np = tensor_to_numpy(input_image)
-        heatmap = apply_colormap(combined_mask, cm.viridis)
-        binary_mask = apply_colormap(combined_mask, cm.Greys_r)
-        
-        # Resize heatmap and binary mask to match the original image dimensions
-        dimensions = (image_np.shape[1], image_np.shape[0])
-        # print('heatmap',heatmap)
-        if dimensions is None or dimensions[0] == 0 or dimensions[1] == 0:
-            raise ValueError("Invalid dimensions")
-        
-        heatmap_resized = resize_image(heatmap, dimensions)
-        binary_mask_resized = resize_image(binary_mask, dimensions)
-
-        # Overlay the heatmap and binary mask onto the original image
-        alpha_heatmap, alpha_binary = 0.5, 1
-        overlay_heatmap = overlay_image(image_np, heatmap_resized, alpha_heatmap)
-        overlay_binary = overlay_image(image_np, binary_mask_resized, alpha_binary)
-
-        # Convert overlays to tensors
-        image_out_heatmap = numpy_to_tensor(overlay_heatmap)
-        image_out_binary = numpy_to_tensor(overlay_binary)
-
-        return combined_mask, image_out_heatmap, image_out_binary
-
-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
-# NODE_CLASS_MAPPINGS = {
-#     "CLIPSeg": CLIPSeg,
-#     "CombineSegMasks": CombineMasks,
-# }
@@ -1,12 +1,46 @@
-import os
+import os,sys
 import folder_paths
-from simple_lama_inpainting import SimpleLama
-from PIL import Image

+from PIL import Image
+import importlib.util
 import numpy as np

 import torch

+global _available
+_available=False
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+if is_installed('simple_lama_inpainting')==False:
+    import subprocess
+    from packaging import version
+    
+    if version.parse(torch.__version__)>=version.parse('2.1'):
+        # 安装
+        print('#pip install simple_lama_inpainting')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'simple_lama_inpainting'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0:
+            print("#install success")
+            from simple_lama_inpainting import SimpleLama
+            _available=True
+        else:
+            print("#install error")
+    else:
+        print('#pls check your torch version >= 2.1')
+
+else:
+    from simple_lama_inpainting import SimpleLama
+    _available=True
+

 
 llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
@@ -38,6 +72,8 @@ def pil2tensor(image):


 class LaMaInpainting:
+    global _available
+    available=_available
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
@@ -52,7 +88,7 @@ class LaMaInpainting:

    FUNCTION = "run"

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

    INPUT_IS_LIST = True
    OUTPUT_IS_LIST = (True,)
@@ -0,0 +1,171 @@
+ 
+import scipy.ndimage
+import torch
+
+from nodes import MAX_RESOLUTION
+
+import numpy as np 
+# from PIL import Image, ImageDraw
+from PIL import Image, ImageOps 
+
+from comfy.cli_args import args
+import cv2
+
+
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+def grow(mask, expand, tapered_corners):
+    c = 0 if tapered_corners else 1
+    kernel = np.array([[c, 1, c],
+                            [1, 1, 1],
+                            [c, 1, c]])
+    mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
+    out = []
+    for m in mask:
+        output = m.numpy()
+        for _ in range(abs(expand)):
+            if expand < 0:
+                output = scipy.ndimage.grey_erosion(output, footprint=kernel)
+            else:
+                output = scipy.ndimage.grey_dilation(output, footprint=kernel)
+        output = torch.from_numpy(output)
+        out.append(output)
+    return torch.stack(out, dim=0)
+
+def combine(destination, source, x, y):
+    output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
+    source = source.reshape((-1, source.shape[-2], source.shape[-1]))
+
+    left, top = (x, y,)
+    right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
+    visible_width, visible_height = (right - left, bottom - top,)
+
+    source_portion = source[:, :visible_height, :visible_width]
+    destination_portion = destination[:, top:bottom, left:right]
+ 
+    #operation == "subtract":
+    output[:, top:bottom, left:right] = destination_portion - source_portion
+        
+    output = torch.clamp(output, 0.0, 1.0)
+
+    return output
+
+class OutlineMask:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                                "mask": ("MASK",),
+                                "outline_width":("INT", {"default": 10,"min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                                "tapered_corners": ("BOOLEAN", {"default": True}),
+                            }
+            }
+    
+    RETURN_TYPES = ('MASK',)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Mask"
+
+    # 运行的函数
+    def run(self, mask, outline_width, tapered_corners):
+
+        m1=grow(mask,outline_width,tapered_corners)
+        m2=grow(mask,-outline_width,tapered_corners)
+
+        m3=combine(m1,m2,0,0)
+
+        return (m3,)
+    
+
+
+
+class FeatheredMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                                "mask": ("MASK",),
+                                "start_offset":("INT", {"default": 1, 
+                                                        "min": -150, 
+                                                        "max": 150, 
+                                                        "step": 1,
+                                                        "display": "slider"}),
+                                "feathering_weight":("FLOAT", {"default": 0.1,
+                                                                "min": 0.0,
+                                                                "max": 1,
+                                                                "step": 0.1,
+                                                                "display": "slider"})
+                            }
+            }
+    
+    RETURN_TYPES = ('MASK',)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Mask"
+
+    OUTPUT_IS_LIST = (True,)
+  
+    # 运行的函数
+    def run(self,mask,start_offset, feathering_weight):
+        # print(mask.shape,mask.size())
+        
+        num,_,_=mask.size()
+
+        masks=[]
+
+        for i in range(num):
+            mm=mask[i]
+            image=tensor2pil(mm)
+
+            # Open the image using PIL
+            image = image.convert("L")
+            if start_offset>0:
+                image=ImageOps.invert(image)
+
+            # Convert the image to a numpy array
+            image_np = np.array(image)
+
+            # Use Canny edge detection to get black contours
+            edges = cv2.Canny(image_np, 30, 150)
+
+            for i in range(0,abs(start_offset)):
+                # int(100*feathering_weight)
+                a=int(abs(start_offset)*0.1*i)
+                # Dilate the black contours to make them wider
+                kernel = np.ones((a, a), np.uint8)
+
+                dilated_edges = cv2.dilate(edges, kernel, iterations=1)
+                # dilated_edges = cv2.erode(edges, kernel, iterations=1)
+                # Smooth the dilated edges using Gaussian blur
+                smoothed_edges = cv2.GaussianBlur(dilated_edges, (5, 5), 0)
+
+                # Adjust the feathering weight
+                feathering_weight = max(0, min(feathering_weight, 1))
+
+                # Blend the smoothed edges with the original image to achieve feathering effect
+                image_np = cv2.addWeighted(image_np, 1, smoothed_edges, feathering_weight, feathering_weight)
+
+            # Convert the result back to PIL image
+            result_image = Image.fromarray(np.uint8(image_np))
+            result_image=result_image.convert("L")
+
+            if start_offset>0:
+                result_image=ImageOps.invert(result_image)
+            
+            result_image=result_image.convert("L")
+            mt=pil2tensor(result_image)
+            masks.append(mt)
+         
+            # print( mt.size())
+        return (masks,)
@@ -1,7 +1,15 @@
 import random
 import comfy.utils
-import json
+import os
+import numpy as np
 from urllib import request, parse
+import folder_paths
+from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
+from PIL.PngImagePlugin import PngInfo
+
+import hashlib
+import requests
+import json


 # def queue_prompt(prompt_workflow):
@@ -10,6 +18,74 @@ from urllib import request, parse
 #     req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
 #     request.urlopen(req)    

+embeddings_path=os.path.join(folder_paths.models_dir, "embeddings")
+
+def get_files_with_extension(directory, extension):
+    file_list = []
+    for root, dirs, files in os.walk(directory):
+        for file in files:
+            if file.endswith(extension):
+                file_name = os.path.splitext(file)[0]
+                file_list.append(file_name)
+    return file_list
+
+def join_with_(text_list,delimiter):
+    joined_text = delimiter.join(text_list)
+    return joined_text
+
+
+
+def load_json(file_path):
+    try:
+        with open(file_path, 'r') as json_file:
+            data = json.load(json_file)
+            return data
+    except FileNotFoundError:
+        print(f"File not found: {file_path}")
+        return None
+    except json.JSONDecodeError:
+        print(f"Error decoding JSON in file: {file_path}")
+        return None
+
+def save_json(data_dict, file_path):
+    try:
+        with open(file_path, 'w') as json_file:
+            json.dump(data_dict, json_file, indent=4)
+            print(f"Data saved to {file_path}")
+    except Exception as e:
+        print(f"Error saving JSON to file: {e}")
+
+# pysss的lora加载器
+# def get_model_version_info(hash_value):
+#     # http://127.0.0.1:1082
+#     proxies = {'http': 'http://127.0.0.1:1082', 'https': 'https://127.0.0.1:1082'}
+#     api_url = f"https://civitai.com/api/v1/model-versions/by-hash/{hash_value}"
+#     print(api_url)
+#     response = requests.get(api_url,proxies=proxies, verify=False)
+    
+#     if response.status_code == 200:
+#         return response.json()
+#     else:
+#         return None
+    
+# def calculate_sha256(file_path):
+#     sha256_hash = hashlib.sha256()
+#     with open(file_path, "rb") as f:
+#         for chunk in iter(lambda: f.read(4096), b""):
+#             sha256_hash.update(chunk)
+#     return sha256_hash.hexdigest()
+
+
+
+
+class AnyType(str):
+  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+
+  def __ne__(self, __value: object) -> bool:
+    return False
+
+any_type = AnyType("*")
+

 default_prompt1='''Swing
                                                Slide
@@ -45,11 +121,170 @@ default_prompt1='''Swing
 default_prompt1="\n".join([p.strip() for p in default_prompt1.split('\n') if p.strip()!=''])


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



@@ -62,7 +297,8 @@ class PromptSlide:
                "prompt_keyword": ("STRING", 
                         {
                            "multiline": False, 
-                            "default": ''
+                            "default": '',
+                            "dynamicPrompts": False
                          }),

                "weight":("FLOAT", {"default": 1, "min": -3,"max": 3,
@@ -92,7 +328,7 @@ class PromptSlide:

    FUNCTION = "run"

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

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -135,6 +371,10 @@ class RandomPrompt:
                            "default": 'sticker, Cartoon, ``'
                          }),
                "random_sample": (["enable", "disable"],),
+                # "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
+                },
+            "optional":{
+                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                }
            }
    
@@ -144,14 +384,14 @@ class RandomPrompt:

    FUNCTION = "run"

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

    OUTPUT_IS_LIST = (True,)
    OUTPUT_NODE = True


    # 运行的函数
-    def run(self,max_count,mutable_prompt,immutable_prompt,random_sample):
+    def run(self,max_count,mutable_prompt,immutable_prompt,random_sample,seed=0):
        # print('#运行的函数',mutable_prompt,immutable_prompt,max_count,random_sample)
        
        # Split the text into an array of words
@@ -172,7 +412,10 @@ class RandomPrompt:
            for w2 in words2:
                w2=w2.strip()
                if '``' not in w2:
-                    w2=w2+',``'
+                    if w2=="":
+                        w2='``'
+                    else:
+                        w2=w2+',``'
                if w1!='' and w2!='':
                    prompts.append(w2.replace('``', w1))
                pbar.update(1)
@@ -186,69 +429,148 @@ class RandomPrompt:
        else:
            prompts = prompts[:min(max_count,len(prompts))]

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


-
-
-
-# class RunWorkflow:
+# class LoraPrompt:
 #     @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": ''
-#                           }),
+#                 "lora_name":(sorted(folder_paths.get_filename_list("loras"), key=str.lower),),
+#                 "weight": ("FLOAT", {"default": 1, "min": -2, "max": 2,"step":0.01 ,"display": "slider"}),
+#                 "force_update": ("BOOLEAN", {"default": False}),
 #                 },
                
 #             }
    
-    
-
-#     RETURN_TYPES = ("IMAGE","STRING",)
+#     RETURN_TYPES = ("STRING","STRING",any_type)
+#     RETURN_NAMES = ("lora_name","prompt","tags",)

 #     FUNCTION = "run"

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

+#     OUTPUT_IS_LIST = (False,False,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
+#     def run(self,lora_name,weight,force_update=False):
+       
+#         # print('##LoraPrompt',__file__)
+#         # 从本地数据库读取
+#         json_tags_path = os.path.join(os.path.dirname(os.path.dirname(__file__)),r'data/loras_tags.json')

-#         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
+#         if not os.path.exists(json_tags_path):
+#             save_json({},json_tags_path)

-#         queue_prompt(workflow_new)
-#         print('#运行的函数',workflow_new[input_node[0]])
+#         lora_tags = load_json(json_tags_path)
+#         output_tags = lora_tags.get(lora_name, None) if lora_tags is not None else None
+#         if output_tags is not None:
+#             output_tags = ",".join(output_tags)
+#             print("trainedWords:",output_tags)
+#         else:
+#             output_tags = ""

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

+#         lora_path = folder_paths.get_full_path("loras", lora_name)
+#         if output_tags == "" or force_update:
+#             print("calculating lora hash")
+#             LORAsha256 = calculate_sha256(lora_path)
+#             print("requesting infos")
+#             model_info = get_model_version_info(LORAsha256)
+#             if model_info is not None:
+#                 if "trainedWords" in model_info:
+#                     print("tags found!")
+#                     if lora_tags is None:
+#                         lora_tags = {}
+#                     lora_tags[lora_name] = model_info["trainedWords"]
+#                     save_json(lora_tags,json_tags_path)
+#                     output_tags = ",".join(model_info["trainedWords"])
+#                     print("trainedWords:",output_tags)
+#             else:
+#                 print("No informations found.")
+#                 if lora_tags is None:
+#                     lora_tags = {}
+#                 lora_tags[lora_name] = []
+#                 save_json(lora_tags,json_tags_path)
+
+
+#         weight = round(weight, 3)
+#         prompt=[]
+#         for p in output_tags.split(','):
+            
+#             if weight!=1:
+#                 prompt.append('('+p+':'+str(weight)+')')
+#             else:
+#                 prompt.append(p)
+
+#         prompt=",".join(prompt)
+
+#         return (lora_name,prompt,output_tags.split(','),)
+
+
+
+class EmbeddingPrompt:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "embedding":(get_files_with_extension(embeddings_path,'.pt'),),
+                "weight": ("FLOAT", {"default": 1, "min": -2, "max": 2,"step":0.01 ,"display": "slider"}),
+                },
+                
+            }
+    
+    RETURN_TYPES = ("STRING",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+
+    OUTPUT_IS_LIST = (False,)
+    # OUTPUT_NODE = True
+
+    # 运行的函数
+    def run(self,embedding,weight):
+        weight = round(weight, 3)
+        prompt='embedding:'+embedding
+        if weight!=1:
+            prompt='('+prompt+':'+str(weight)+')'
+        prompt=" "+prompt+' ' 
+        # return (new_prompt)
+        return (prompt,)
+
+RETURN_TYPES = (any_type,)
+
+class JoinWithDelimiter:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "text_list": (any_type,),
+                    "delimiter":(["newline","comma"],),
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+
+    INPUT_IS_LIST = True # 当true的时候，输入时list，当false的时候，如果输入是list，则会自动包一层for循环调用
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,text_list,delimiter):
+        delimiter=delimiter[0]
+        if delimiter =='newline':
+            delimiter='\n'
+        elif delimiter=='comma':
+            delimiter=','
+        t=''
+        if isinstance(text_list, list):
+            t=join_with_(text_list,delimiter)
+        return (t,)
@@ -0,0 +1,694 @@
+import os,sys
+import folder_paths
+
+from PIL import Image
+import importlib.util
+
+import comfy.utils
+import numpy as np
+import torch
+
+from huggingface_hub import hf_hub_download
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+# BRIA-RMBG-1.4 / briarmbg.py
+class REBNCONV(nn.Module):
+    def __init__(self,in_ch=3,out_ch=3,dirate=1,stride=1):
+        super(REBNCONV,self).__init__()
+
+        self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate,stride=stride)
+        self.bn_s1 = nn.BatchNorm2d(out_ch)
+        self.relu_s1 = nn.ReLU(inplace=True)
+
+    def forward(self,x):
+
+        hx = x
+        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
+
+        return xout
+
+## upsample tensor 'src' to have the same spatial size with tensor 'tar'
+def _upsample_like(src,tar):
+
+    src = F.interpolate(src,size=tar.shape[2:],mode='bilinear')
+
+    return src
+
+
+### RSU-7 ###
+class RSU7(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
+        super(RSU7,self).__init__()
+
+        self.in_ch = in_ch
+        self.mid_ch = mid_ch
+        self.out_ch = out_ch
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) ## 1 -> 1/2
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+        b, c, h, w = x.shape
+
+        hx = x
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+        hx = self.pool5(hx5)
+
+        hx6 = self.rebnconv6(hx)
+
+        hx7 = self.rebnconv7(hx6)
+
+        hx6d =  self.rebnconv6d(torch.cat((hx7,hx6),1))
+        hx6dup = _upsample_like(hx6d,hx5)
+
+        hx5d =  self.rebnconv5d(torch.cat((hx6dup,hx5),1))
+        hx5dup = _upsample_like(hx5d,hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+
+### RSU-6 ###
+class RSU6(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU6,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+
+        hx6 = self.rebnconv6(hx5)
+
+
+        hx5d =  self.rebnconv5d(torch.cat((hx6,hx5),1))
+        hx5dup = _upsample_like(hx5d,hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-5 ###
+class RSU5(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU5,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+
+        hx5 = self.rebnconv5(hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-4 ###
+class RSU4(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-4F ###
+class RSU4F(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4F,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
+
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx2 = self.rebnconv2(hx1)
+        hx3 = self.rebnconv3(hx2)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
+        hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
+        hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
+
+        return hx1d + hxin
+
+
+class myrebnconv(nn.Module):
+    def __init__(self, in_ch=3,
+                       out_ch=1,
+                       kernel_size=3,
+                       stride=1,
+                       padding=1,
+                       dilation=1,
+                       groups=1):
+        super(myrebnconv,self).__init__()
+
+        self.conv = nn.Conv2d(in_ch,
+                              out_ch,
+                              kernel_size=kernel_size,
+                              stride=stride,
+                              padding=padding,
+                              dilation=dilation,
+                              groups=groups)
+        self.bn = nn.BatchNorm2d(out_ch)
+        self.rl = nn.ReLU(inplace=True)
+
+    def forward(self,x):
+        return self.rl(self.bn(self.conv(x)))
+
+
+class BriaRMBG(nn.Module):
+
+    def __init__(self,in_ch=3,out_ch=1):
+        super(BriaRMBG,self).__init__()
+
+        self.conv_in = nn.Conv2d(in_ch,64,3,stride=2,padding=1)
+        self.pool_in = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage1 = RSU7(64,32,64)
+        self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage2 = RSU6(64,32,128)
+        self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage3 = RSU5(128,64,256)
+        self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage4 = RSU4(256,128,512)
+        self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage5 = RSU4F(512,256,512)
+        self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage6 = RSU4F(512,256,512)
+
+        # decoder
+        self.stage5d = RSU4F(1024,256,512)
+        self.stage4d = RSU4(1024,128,256)
+        self.stage3d = RSU5(512,64,128)
+        self.stage2d = RSU6(256,32,64)
+        self.stage1d = RSU7(128,16,64)
+
+        self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
+        self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
+        self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
+        self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
+        self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
+        self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
+
+        # self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.conv_in(hx)
+        #hx = self.pool_in(hxin)
+
+        #stage 1
+        hx1 = self.stage1(hxin)
+        hx = self.pool12(hx1)
+
+        #stage 2
+        hx2 = self.stage2(hx)
+        hx = self.pool23(hx2)
+
+        #stage 3
+        hx3 = self.stage3(hx)
+        hx = self.pool34(hx3)
+
+        #stage 4
+        hx4 = self.stage4(hx)
+        hx = self.pool45(hx4)
+
+        #stage 5
+        hx5 = self.stage5(hx)
+        hx = self.pool56(hx5)
+
+        #stage 6
+        hx6 = self.stage6(hx)
+        hx6up = _upsample_like(hx6,hx5)
+
+        #-------------------- decoder --------------------
+        hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
+        hx5dup = _upsample_like(hx5d,hx4)
+
+        hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
+
+
+        #side output
+        d1 = self.side1(hx1d)
+        d1 = _upsample_like(d1,x)
+
+        d2 = self.side2(hx2d)
+        d2 = _upsample_like(d2,x)
+
+        d3 = self.side3(hx3d)
+        d3 = _upsample_like(d3,x)
+
+        d4 = self.side4(hx4d)
+        d4 = _upsample_like(d4,x)
+
+        d5 = self.side5(hx5d)
+        d5 = _upsample_like(d5,x)
+
+        d6 = self.side6(hx6)
+        d6 = _upsample_like(d6,x)
+
+        return [F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)],[hx1d,hx2d,hx3d,hx4d,hx5d,hx6]
+    
+
+
+
+
+
+
+U2NET_HOME=os.path.join(folder_paths.models_dir, "rembg")
+os.environ["U2NET_HOME"] = U2NET_HOME
+
+global _available
+_available=False
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+
+try:
+    if is_installed('rembg')==False:
+        import subprocess
+
+        # 安装
+        print('#pip install rembg[gpu]')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'rembg[gpu]'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0:
+            print("#install success")
+            from rembg import new_session, remove
+            _available=True
+        else:
+            print("#install error")
+        
+    else:
+        from rembg import new_session, remove
+        _available=True
+
+except:
+    _available=False
+
+
+def briarmbg_run(images=[]):
+    mroot=os.path.join(folder_paths.models_dir, "rembg")
+    m=os.path.join(mroot,'briarmbg.pth')
+    if os.path.exists(m)==False:
+        # 下载
+        m1=hf_hub_download("briaai/RMBG-1.4",
+                       local_dir=mroot,
+                       filename='model.pth',
+                       local_dir_use_symlinks=False,
+                       endpoint='https://hf-mirror.com')
+        os.rename(m1, m)
+    
+    net=BriaRMBG()
+    if torch.cuda.is_available():
+        net.load_state_dict(torch.load(m))
+        net=net.cuda()
+    else:
+        net.load_state_dict(torch.load(m,map_location="cpu"))
+    net.eval() 
+
+    masks=[]
+    rgba_images=[]
+    rgb_images=[]
+    for orig_image in images:
+
+        w,h = orig_im_size = orig_image.size
+
+        image = orig_image.convert('RGB')
+        model_input_size = (1024, 1024)
+        image = image.resize(model_input_size, Image.BILINEAR)
+
+        im_np = np.array(image)
+        im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1)
+        im_tensor = torch.unsqueeze(im_tensor,0)
+        im_tensor = torch.divide(im_tensor,255.0)
+        im_tensor = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0])
+        if torch.cuda.is_available():
+            im_tensor=im_tensor.cuda()
+
+        result=net(im_tensor)
+        result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
+        ma = torch.max(result)
+        mi = torch.min(result)
+        result = (result-mi)/(ma-mi)
+        im_array = (result*255).cpu().data.numpy().astype(np.uint8)
+        mask = Image.fromarray(np.squeeze(im_array))
+        # mask.save('test.png')
+        # mask=tensor2pil(result)
+        mask=mask.convert('L') 
+
+        masks.append(mask)
+
+        # rgba图
+        image_rgba =orig_image.convert("RGBA")
+        image_rgba.putalpha(mask)
+        rgba_images.append(image_rgba)
+
+        #rgb
+        rgb_image = Image.new("RGB", image_rgba.size, (0, 0, 0))
+        rgb_image.paste(image_rgba, mask=image_rgba.split()[3])
+        rgb_images.append(rgb_image)
+    return (masks,rgba_images,rgb_images)   
+
+
+def run_bg(model_name= "unet",images=[]):
+    # model_name = "unet" # "isnet-general-use"
+    rembg_session = new_session(model_name)
+    masks=[]
+    rgba_images=[]
+    rgb_images=[]
+    # 进度条
+    pbar = comfy.utils.ProgressBar(len(images) )
+    for img in images:
+        # use the post_process_mask argument to post process the mask to get better results.
+        mask = remove(img, session=rembg_session,only_mask=True,post_process_mask=True)
+        # mask=mask.convert('L')
+        # masks.append(mask)
+        if model_name=="u2net_cloth_seg":
+            width, original_height = mask.size
+            num_slices = original_height // img.height
+            for i in range(num_slices):
+                top = i * img.height
+                bottom = (i + 1) * img.height
+                slice_image = mask.crop((0, top, width, bottom))
+                slice_mask=slice_image.convert('L')
+                masks.append(slice_mask)
+
+                # rgba图
+                image_rgba = img.convert("RGBA")
+                image_rgba.putalpha(slice_mask)
+                rgba_images.append(image_rgba)
+
+                #rgb
+                rgb_image = Image.new("RGB", image_rgba.size, (0, 0, 0))
+                rgb_image.paste(image_rgba, mask=image_rgba.split()[3])
+                rgb_images.append(rgb_image)
+
+        else:
+            mask=mask.convert('L')    
+            # mask.save(output_path)
+            masks.append(mask)
+
+            # rgba图
+            image_rgba = img.convert("RGBA")
+            image_rgba.putalpha(mask)
+            rgba_images.append(image_rgba)
+
+            #rgb
+            rgb_image = Image.new("RGB", image_rgba.size, (0, 0, 0))
+            rgb_image.paste(image_rgba, mask=image_rgba.split()[3])
+            rgb_images.append(rgb_image)
+            
+        pbar.update(1)
+    return (masks,rgba_images,rgb_images)
+
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+class RembgNode_:
+
+    global _available
+    available=_available
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "model_name": ([
+                            "briarmbg",
+                            "u2net",
+                            "u2netp",
+                            "u2net_human_seg",
+                            "u2net_cloth_seg",
+                            "silueta",
+                            "isnet-general-use",
+                            "isnet-anime",
+                            
+                            ],),
+            
+                             },
+                }
+    
+    RETURN_TYPES = ("MASK","IMAGE","RGBA",)
+    RETURN_NAMES = ("masks","images","RGBAs")
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Mask"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,True,True,)
+  
+    def run(self,image,model_name):
+        # 兼容list输入和batch输入
+
+        model_name=model_name[0]
+
+        images=[]
+        
+        for ims in image:
+            for im in ims:
+                im=tensor2pil(im)
+                images.append(im)
+
+        if model_name=='briarmbg':
+            masks,rgba_images,rgb_images=briarmbg_run(images)
+        else:
+            masks,rgba_images,rgb_images=run_bg(model_name,images)
+
+        masks=[pil2tensor(m) for m in masks]
+
+        rgba_images=[pil2tensor(m) for m in rgba_images]
+
+        rgb_images=[pil2tensor(m) for m in rgb_images]
+
+        return (masks,rgb_images,rgba_images,)
@@ -93,7 +93,7 @@ class ScreenShareNode:
    RETURN_NAMES = ("IMAGE","PROMPT","FLOAT","INT")
    FUNCTION = "run"

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

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

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

    # INPUT_IS_LIST = True
    # OUTPUT_IS_LIST = (False,False,)
@@ -0,0 +1,307 @@
+from transformers import pipeline, set_seed,AutoTokenizer, AutoModelForSeq2SeqLM
+import random
+import re
+
+import os,sys
+import folder_paths
+
+# from PIL import Image
+import importlib.util
+
+import comfy.utils
+# import numpy as np
+import torch
+import random
+
+global _available
+_available=True
+
+
+text_generator_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/text2image-prompt-generator")
+if not os.path.exists(text_generator_model_path):
+    print(f"## text_generator_model not found: {text_generator_model_path}, pls download from https://huggingface.co/succinctly/text2image-prompt-generator/tree/main")
+    text_generator_model_path='succinctly/text2image-prompt-generator'
+
+zh_en_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/opus-mt-zh-en")
+if not os.path.exists(zh_en_model_path):
+    print(f"## zh_en_model not found: {zh_en_model_path}, pls download from https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main")
+    zh_en_model_path='Helsinki-NLP/opus-mt-zh-en'
+
+
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+
+try:
+    if is_installed('sentencepiece')==False:
+        import subprocess
+
+        # 安装
+        print('#pip install sentencepiece')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'sentencepiece'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0 and is_installed('sentencepiece'):
+            print("#install success")
+            _available=True
+        else:
+            print("#install error")
+            _available=False
+        
+    else:
+        _available=True
+
+except:
+    _available=False
+
+
+
+def translate(zh_en_tokenizer,zh_en_model,text):
+    with torch.no_grad():
+        encoded = zh_en_tokenizer([text], return_tensors="pt")
+        encoded.to(zh_en_model.device)
+        sequences = zh_en_model.generate(**encoded)
+        return zh_en_tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
+
+# input = "青春不能回头，所以青春没有终点。 ——《火影忍者》"
+# print(input, translate(input))
+
+
+def text_generate(text_pipe,input,seed=None):
+    
+    if seed==None:
+        seed = random.randint(100, 1000000)
+    
+    set_seed(seed)
+
+    for count in range(6):    
+        sequences = text_pipe(input, max_length=random.randint(60, 90), num_return_sequences=8)
+        list = []
+        for sequence in sequences:
+            line = sequence['generated_text'].strip()
+            if line != input and len(line) > (len(input) + 4) and line.endswith((":", "-", "—")) is False:
+                list.append(line)
+
+        result = "\n".join(list)
+        result = re.sub('[^ ]+\.[^ ]+','', result)
+        result = result.replace("<", "").replace(">", "")
+        if result != "":
+            return result
+        if count == 5:
+            return result
+
+# input = "Youth can't turn back, so there's no end to youth."
+# print(input, text_generate(input))
+
+    
+import re
+
+def correct_prompt_syntax(prompt):
+
+    print("input prompt",prompt)
+    corrected_elements = []
+    # 处理成统一的英文标点
+    prompt = prompt.replace('（', '(').replace('）', ')').replace('，', ',').replace(';', ',').replace('。', '.').replace('：',':')
+    # 删除多余的空格
+    prompt = re.sub(r'\s+', ' ', prompt).strip()
+
+    # 分词
+    prompt_elements = prompt.split(',')
+
+    for element in prompt_elements:
+        element = element.strip()
+
+        # 处理空元素
+        if not element:
+            continue
+
+        # 检查并处理圆括号、方括号、尖括号
+        if element[0] in '([':
+            corrected_element = balance_brackets(element, '(', ')') if element[0] == '(' else balance_brackets(element, '[', ']')
+        elif element[0] == '<':
+            corrected_element = balance_brackets(element, '<', '>')
+        else:
+            # 删除开头的右括号或右方括号
+            corrected_element = element.lstrip(')]')
+
+        corrected_elements.append(corrected_element)
+
+    # 重组修正后的prompt
+    corrected_prompt = ', '.join(corrected_elements)
+    print("output prompt",corrected_prompt)
+    return corrected_prompt
+
+def balance_brackets(element, open_bracket, close_bracket):
+    open_brackets_count = element.count(open_bracket)
+    close_brackets_count = element.count(close_bracket)
+    return element + close_bracket * (open_brackets_count - close_brackets_count)
+
+# # 示例使用
+# test_prompt = "((middle-century castles)), [forsaken: 0.8], (mystery dragons: 1.3, mist forests, sunsets, quiet; (((dummy)), [fisting city: 0.5] background, radiant, soft and flavoured,] promising mountains, ((starry: 1.6), [[crowds], [middle-century castle: urban landscapes of the future: 0.5], [yellow: bright sun: 0.7], overlooking"
+# corrected_prompt = correct_prompt_syntax(test_prompt)
+# print(corrected_prompt)
+
+        
+        
+class ChinesePrompt:
+
+    global _available
+    available=_available
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+               "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
+               "generation": (["on","off"],{"default": "off"}), 
+                             },
+               
+                "optional":{
+                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
+                    
+                },
+
+                }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    global text_pipe,zh_en_model,zh_en_tokenizer
+
+    text_pipe= None
+    zh_en_model=None
+    zh_en_tokenizer=None 
+
+    def run(self,text,seed,generation):
+        global text_pipe,zh_en_model,zh_en_tokenizer
+
+        seed=seed[0]
+        generation=generation[0]
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(text)+1)
+        
+        texts = [correct_prompt_syntax(t) for t in text]
+        print('correct_prompt_syntax::',texts)
+        if zh_en_model==None:
+            zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval()
+            zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path,padding=True, truncation=True)
+        
+        zh_en_model.to("cuda" if torch.cuda.is_available() else "cpu")
+            # zh_en_tokenizer.to("cuda" if torch.cuda.is_available() else "cpu")
+        
+        text_pipe=pipeline('text-generation', model=text_generator_model_path,device="cuda" if torch.cuda.is_available() else "cpu")
+        
+        # text_pipe.model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        prompt_result=[]
+
+        # print('zh_en_model device',zh_en_model.device,text_pipe.model.device,torch.cuda.current_device() )
+        en_texts=[]
+        for t in texts:
+            en_text=translate(zh_en_tokenizer,zh_en_model,t)
+            en_texts.append(en_text)
+
+        zh_en_model.to('cpu')
+        print("test en_text",en_texts)
+        # en_text.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        pbar.update(1)
+        for t in en_texts:
+            if generation=='on':
+                prompt =text_generate(text_pipe,t,seed)
+                # 多条，还是单条
+                lines = prompt.split("\n")
+                longest_line = max(lines, key=len)
+                # print(longest_line)
+                prompt_result.append(longest_line)
+            else:
+                prompt_result.append(t)
+            pbar.update(1)
+
+        text_pipe.model.to('cpu')
+        prompt_result = [correct_prompt_syntax(p) for p in prompt_result]
+        
+        return {
+            "ui":{
+                    "prompt": prompt_result
+                },
+            "result": (prompt_result,)}
+
+
+class PromptGenerate:
+
+    global _available
+    available=_available
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
+                             },
+
+                "optional":{
+                    "multiple": (["off","on"],), 
+                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
+                },
+
+                }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    global text_pipe
+
+    text_pipe= None
+    # 
+
+    def run(self,text,multiple,seed):
+        global text_pipe
+
+        seed=seed[0]
+
+        multiple=multiple[0]
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(text))
+        
+        text_pipe=pipeline('text-generation', model=text_generator_model_path,device="cuda" if torch.cuda.is_available() else "cpu")
+        
+        prompt_result=[]
+
+        for t in text:
+            prompt =text_generate(text_pipe,t,seed)
+            prompt = prompt.split("\n")
+            if multiple=='off':
+                prompt = [max(prompt, key=len)]
+            
+            for p in prompt:
+                prompt_result.append(p)
+            pbar.update(1)
+
+        text_pipe.model.to('cpu')
+
+        return {
+            "ui":{
+                    "prompt": prompt_result
+                },
+            "result": (prompt_result,)}
@@ -1,10 +1,58 @@
-import os
-import re,random
+import os,platform
+import re,random,json
 from PIL import Image
 import numpy as np
 # FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
 import folder_paths
 import matplotlib.font_manager as fm
+import torch
+
+
+
+def recursive_search(directory, excluded_dir_names=None):
+    if not os.path.isdir(directory):
+        return [], {}
+
+    if excluded_dir_names is None:
+        excluded_dir_names = []
+
+    result = []
+    dirs = {directory: os.path.getmtime(directory)}
+    for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
+        subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
+        for file_name in filenames:
+            relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+            result.append(relative_path)
+        for d in subdirs:
+            path = os.path.join(dirpath, d)
+            dirs[path] = os.path.getmtime(path)
+    return result, dirs
+
+def filter_files_extensions(files, extensions):
+    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))
+
+
+def get_system_font_path():
+    ps=[]
+    system = platform.system()
+    if system == "Windows":
+        ps.append(os.path.join(os.environ["WINDIR"], "Fonts"))
+    elif system == "Darwin":
+        ps.append(os.path.join("/Library", "Fonts"))
+    elif system == "Linux":
+        ps.append(os.path.join("/usr", "share", "fonts"))
+        ps.append(os.path.join("/usr", "local", "share", "fonts"))
+    ps=[p for p in ps if os.path.exists(p)]
+    file_paths=[]
+    for f in ps:
+        result, dirs=recursive_search(f)
+        for r in result:
+            file_paths.append(r)
+    file_paths=filter_files_extensions(file_paths,[".otf", ".ttf"])
+
+    return file_paths
+
+

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

    
-    image=tensor2pil(image)
+    im=tensor2pil(image)
 
    image_file = f"{filename}_{counter:05}.png"
     
    image_path=os.path.join(full_output_folder, image_file)

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

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

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

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


+def flatten_list(nested_list):
+    flat_list = []
+    for item in nested_list:
+        if isinstance(item, list):
+            flat_list.extend(flatten_list(item))
+        else:
+            if torch.is_tensor(item):
+                print('item.shape',item.shape)
+                for i in range(item.shape[0]):
+                    flat_list.append(item[i:i + 1, ...])
+            else:
+                flat_list.append(item)
+    return flat_list
+
+
 class ColorInput:
    @classmethod
    def INPUT_TYPES(s):
@@ -93,7 +156,7 @@ class ColorInput:

    FUNCTION = "run"

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

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,False,False,False,False,)
@@ -122,7 +185,7 @@ class FontInput:

    FUNCTION = "run"

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

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -137,14 +200,17 @@ class TextToNumber:
        return {"required": { 
                    "text": ("STRING",{"multiline": False,"default": "1"}),
                    "random_number": (["enable", "disable"],),
-                    "number":("INT", {
-                        "default": 0, 
-                        "min": 0, #Minimum value
+                    "max_num":("INT", {
+                        "default": 10, 
+                        "min":2, #Minimum value
                        "max": 10000000000, #Maximum value
                        "step": 1, #Slider's step
                        "display": "number" # Cosmetic only: display as "number" or "slider"
                    }),
                             },
+                 "optional":{
+                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                }
                }
    
    RETURN_TYPES = ("INT",)
@@ -152,12 +218,12 @@ class TextToNumber:

    FUNCTION = "run"

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

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)

-    def run(self,text,random_number,number):
+    def run(self,text,random_number,max_num,seed=0):
        
        numbers = re.findall(r'\d+', text)
        result=0
@@ -166,7 +232,7 @@ class TextToNumber:
            # print(result)
        
        if random_number=='enable' and result>0:
-            result= random.randint(1, 10000000000)
+            result= random.randint(1, max_num)
        return {"ui": {"text": [text],"num":[result]}, "result": (result,)}
    

@@ -178,7 +244,7 @@ class FloatSlider:
                    "number":("FLOAT", {
                        "default": 0, 
                        "min": 0, #Minimum value
-                        "max": 1, #Maximum value
+                        "max": 0xffffffffffffffff, #Maximum value
                        "step": 0.001, #Slider's step
                        "display": "slider" # Cosmetic only: display as "number" or "slider"
                    }),
@@ -210,17 +276,18 @@ class FloatSlider:

    FUNCTION = "run"

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

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)

-    def run(self,number,min_value,max_value,step):
+    def run(self, number, min_value, max_value, step):
        if number < min_value:
-            number= min_value
+            number = min_value
        elif number > max_value:
-            number= max_value
-        return (number,)
+            number = max_value
+        scaled_number = (number - min_value) / (max_value - min_value)
+        return (scaled_number,)
    
  
 class IntNumber:
@@ -252,7 +319,7 @@ class IntNumber:
                            "default": 1, 
                            "min": -0xffffffffffffffff,
                            "max": 0xffffffffffffffff,
-                            "step":1, 
+                            "step":1,
                            "display": "number"  
                        }),
                             },
@@ -262,7 +329,7 @@ class IntNumber:

    FUNCTION = "run"

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

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -279,11 +346,18 @@ class MultiplicationNode:
    def INPUT_TYPES(s):
        return {"required": {
                    "numberA":(any_type,),
-                    "numberB":("FLOAT", {
+                    "multiply_by":("FLOAT", {
                        "default": 0, 
-                        "min": -1, #Minimum value
+                        "min": -2, #Minimum value
                        "max": 0xffffffffffffffff,
-                        "step": 0.1, #Slider's step
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                     "add_by":("FLOAT", {
+                        "default": 0, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
                        "display": "number" # Cosmetic only: display as "number" or "slider"
                    })
                             },
@@ -293,21 +367,21 @@ class MultiplicationNode:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/utils"
+    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)
+    def run(self,numberA,multiply_by,add_by):
+        b=int(numberA*multiply_by+add_by)
+        a=float(numberA*multiply_by+add_by)
        return (a,b,) 

 class TextInput:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
-                    "text": ("STRING",{"multiline": True,"default": ""}),
+                    "text": ("STRING",{"multiline": True,"default": ""})
                             },
                }
    
@@ -315,7 +389,7 @@ class TextInput:

    FUNCTION = "run"

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

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -356,7 +430,7 @@ class DynamicDelayProcessor:
            },
            "optional":{
                "any_input":(any_type,),
-                "delay_by_text":("STRING",{"multiline":True,}),
+                "delay_by_text":("STRING",{"multiline":True,"dynamicPrompts": False,}),
                "words_per_seconds":("FLOAT",{ "default":1.50,"min": 0.0,"max": 1000.00,"display":"Chars per second?"}),
                "replace_output": (["disable","enable"],),
                "replace_value":("INT",{ "default":-1,"min": 0,"max": 1000000,"display":"Replacement value"})
@@ -389,7 +463,7 @@ class DynamicDelayProcessor:
    RETURN_TYPES = (any_type,)
    RETURN_NAMES = ('output',)

-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
    def run(self,any_input,delay_seconds,delay_by_text,words_per_seconds,replace_output,replace_value):
        # print(f"Delay text:",delay_by_text )
        # 获取开始时间戳
@@ -423,12 +497,12 @@ class AppInfo:
    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":{
+                    "IMAGE": ("IMAGE",),
                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
                    "version":("INT", {
                        "default": 1, 
@@ -439,52 +513,48 @@ class AppInfo:
                    }),
                    "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
                    "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
+                    "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                    "auto_save": (["enable","disable"],),
                }

                }
    
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("IMAGE",)
+    RETURN_TYPES = ()
+    # RETURN_NAMES = ("IMAGE",)

    FUNCTION = "run"

    CATEGORY = "♾️Mixlab"

-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    # OUTPUT_IS_LIST = (True,)

-    def run(self,name,image,input_ids,output_ids,description,version,share_prefix,link):
+    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
+        name=name[0]
+        
+        im=None
+        if IMAGE:
+            im=IMAGE[0][0]
+            #TODO batch 的方式需要处理
+            im=create_temp_file(im)
+        # image [img,] img[batch,w,h,a] 列表里面是batch，

-        im=create_temp_file(image)
+        input_ids=input_ids[0]
+        output_ids=output_ids[0]
+        description=description[0]
+        version=version[0]
+        share_prefix=share_prefix[0]
+        link=link[0]
+        category=category[0]
        
        # id=get_json_hash([name,im,input_ids,output_ids,description,version])

-        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link]}, "result": (image,)}
+        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": ()}
    

    

-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
@@ -500,6 +570,7 @@ class SwitchByIndex:
                        "step": 1, 
                        "display": "number"  
                    }),
+                 "flat": (['off',"on"],),
            }
        }

@@ -508,23 +579,32 @@ class SwitchByIndex:

    FUNCTION = "run"

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

    INPUT_IS_LIST = True
    OUTPUT_IS_LIST = (True,)

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


@@ -557,7 +637,7 @@ class LimitNumber:

    FUNCTION = "run"

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

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -579,3 +659,209 @@ class LimitNumber:
        
        return (nn,)

+
+
+class ListStatistics:
+    @staticmethod
+    def count_types(lst):
+        type_count = {}
+
+        for item in lst:
+            item_type = type(item).__name__
+            if item_type not in type_count:
+                type_count[item_type] = []
+
+            if item_type in ['dict', 'str', 'int', 'float']:
+                type_count[item_type].append(item)
+
+        return type_count
+
+# # 示例列表
+# my_list = [1, 'hello', {'name': 'John'}, 3.14, {'age': 25}, 'world', 10]
+
+# # 创建ListStatistics对象
+# list_stats = ListStatistics()
+
+# # 调用count_types方法进行统计
+# result = list_stats.count_types(my_list)
+
+# # 输出结果
+# for item_type, values in result.items():
+#     print(item_type + ':')
+#     for value in values:
+#         print(value)
+#     print('---')
+
+class TESTNODE_:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "ANY":(any_type,),                
+                              },
+                }
+    
+    RETURN_TYPES = (any_type,)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/__TEST"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self,ANY):
+        print(ANY)
+        # data=ANY
+        list_stats = ListStatistics()
+
+        # 调用count_types方法进行统计
+        result = list_stats.count_types(ANY)
+
+
+        return {"ui": {"data": result,"type":[str(type(ANY[0]))]}, "result": (ANY,)}
+
+
+class TESTNODE_TOKEN:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "text":("STRING", {"forceInput": True,}),
+            "clip": ("CLIP", )                 
+                              },
+                }
+    
+    RETURN_TYPES = ("STRING",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/__TEST"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,text,clip=None):
+        # print(text)
+        
+        tokens = clip.tokenize(text)
+        
+        tokens=[v for v in tokens.values()][0][0]
+
+        tokens=json.dumps(tokens)
+        
+        return (tokens,)
+
+
+
+class CreateSeedNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            }
+        }
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("seed",)
+  
+    OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, seed):
+        return (seed,)
+    
+
+class CreateCkptNames:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "ckpt_names": ("STRING",{"multiline": True,"default": "\n".join(folder_paths.get_filename_list("checkpoints")),"dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("ckpt_names",)
+    
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,)
+
+    # OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, ckpt_names):
+        ckpt_names=ckpt_names.split('\n')
+        ckpt_names = [name for name in ckpt_names if name.strip()]
+        return (ckpt_names,)
+
+
+class CreateLoraNames:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "lora_names": ("STRING",{"multiline": True,"default": "\n".join(folder_paths.get_filename_list("loras")),"dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = (any_type,"STRING",)
+    RETURN_NAMES = ("lora_names","prompt",)
+    
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,True,)
+
+    # OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, lora_names):
+        lora_names=lora_names.split('\n')
+        lora_names = [name for name in lora_names if name.strip()]
+        prompts=[os.path.splitext(n)[0] for n in lora_names]
+        return (lora_names,prompts,)
+   
+
+
+class CreateSampler_names:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "sampler_names": ("STRING",{"multiline": True,"default": "\n".join(comfy.samplers.KSampler.SAMPLERS),"dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("sampler_names",)
+    
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,)
+
+    # OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, sampler_names):
+        sampler_names=sampler_names.split('\n')
+        sampler_names = [name for name in sampler_names if name.strip()]
+        return (sampler_names,)
@@ -1,179 +0,0 @@
-# https://github.com/openai/consistencydecoder/blob/main/consistencydecoder/__init__.py
-
-import folder_paths
-from comfy import model_management
-
-import math
-import torch
-import numpy as np
-from PIL import Image 
-
-
-class ConsistencyDecoderWrapper:
-    def __init__(self, decoder):
-        self.decoder = decoder
-    def decode(self, x):
-        return self.decoder(x)
-
-def _extract_into_tensor(arr, timesteps, broadcast_shape):
-    
-    # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895    """
-    res = arr[timesteps].float()
-    dims_to_append = len(broadcast_shape) - len(res.shape)
-    return res[(...,) + (None,) * dims_to_append]
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L45
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return torch.tensor(betas)
-
-class ConsistencyDecoder:
-    def __init__(self, device="cuda:0", download_target=""):
-        self.n_distilled_steps = 64
-        # download_target = _download("https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt", download_root)
-        self.ckpt = torch.jit.load(download_target).to(device)
-        self.device = device 
-        sigma_data = 0.5
-        betas = betas_for_alpha_bar(
-            1024, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-        ).to(device)
-        alphas = 1.0 - betas
-        alphas_cumprod = torch.cumprod(alphas, dim=0)
-        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
-        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
-        sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
-        sigmas = torch.sqrt(1.0 / alphas_cumprod - 1)
-        self.c_skip = (
-            sqrt_recip_alphas_cumprod
-            * sigma_data**2
-            / (sigmas**2 + sigma_data**2)
-        )
-        self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5
-        self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5
-
-    @staticmethod
-    def round_timesteps(
-        timesteps, total_timesteps, n_distilled_steps, truncate_start=True
-    ):
-        with torch.no_grad():
-            space = torch.div(total_timesteps, n_distilled_steps, rounding_mode="floor")
-            rounded_timesteps = (
-                torch.div(timesteps, space, rounding_mode="floor") + 1
-            ) * space
-            if truncate_start:
-                rounded_timesteps[rounded_timesteps == total_timesteps] -= space
-            else:
-                rounded_timesteps[rounded_timesteps == total_timesteps] -= space
-                rounded_timesteps[rounded_timesteps == 0] += space
-            return rounded_timesteps
-
-    @staticmethod
-    def ldm_transform_latent(z, extra_scale_factor=1):
-        channel_means = [0.38862467, 0.02253063, 0.07381133, -0.0171294]
-        channel_stds = [0.9654121, 1.0440036, 0.76147926, 0.77022034]
-
-        if len(z.shape) != 4:
-            raise ValueError()
-
-        z = z * 0.18215
-        channels = [z[:, i] for i in range(z.shape[1])]
-
-        channels = [
-            extra_scale_factor * (c - channel_means[i]) / channel_stds[i]
-            for i, c in enumerate(channels)
-        ]
-        return torch.stack(channels, dim=1)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        features: torch.Tensor,
-        schedule=[1.0, 0.5],
-    ):
-        features = self.ldm_transform_latent(features)
-  
-        ts = self.round_timesteps(
-            torch.arange(0, 1024),
-            1024,
-            self.n_distilled_steps,
-            truncate_start=False,
-        )
-        shape = (
-            features.size(0),
-            3,
-            8 * features.size(2),
-            8 * features.size(3),
-        )
-        
-        x_start = torch.zeros(shape, device=features.device, dtype=features.dtype)
-        schedule_timesteps = [int((1024 - 1) * s) for s in schedule]
-        for i in schedule_timesteps:
-            t = ts[i].item()
-            t_ = torch.tensor([t] * features.shape[0]).to(self.device)
-            noise = torch.randn_like(x_start)
-            
-            x_start = (
-                _extract_into_tensor(self.sqrt_alphas_cumprod, t_, x_start.shape)
-                * x_start
-                + _extract_into_tensor(
-                    self.sqrt_one_minus_alphas_cumprod, t_, x_start.shape
-                )
-                * noise
-            )
-            c_in = _extract_into_tensor(self.c_in, t_, x_start.shape)
-            model_output = self.ckpt(c_in * x_start, t_, features=features)
-            B, C = x_start.shape[:2]
-            model_output, _ = torch.split(model_output, C, dim=1)
-            pred_xstart = (
-                _extract_into_tensor(self.c_out, t_, x_start.shape) * model_output
-                + _extract_into_tensor(self.c_skip, t_, x_start.shape) * x_start
-            ).clamp(-1, 1)
-            x_start = pred_xstart
-        return x_start
-
-
-
-class VAELoader:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "vae_name": (folder_paths.get_filename_list("vae"), )}}
-    RETURN_TYPES = ("VAE",)
-    FUNCTION = "load_vae"
-
-    CATEGORY = "♾️Mixlab/_test"
-
-    #TODO: scale factor?
-    def load_vae(self, vae_name):
-        vae_path = folder_paths.get_full_path("vae", vae_name)
-        device = 'cuda:0'
-        # print('device',device)
-        consistencyDecoder = ConsistencyDecoder(device=device,
-                                 download_target=vae_path) # Model size: 2.49 GB
-        vae = ConsistencyDecoderWrapper(consistencyDecoder)
-        return (vae,)
-    
-
-class VAEDecode:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "decode"
-
-    CATEGORY = "♾️Mixlab/_test"
-
-    def decode(self, vae, samples):
-        image = vae.decode(samples["samples"].to("cuda:0"))
-        image = image[0].cpu().numpy()
-        image = (image + 1.0) * 127.5
-        image = image.clip(0, 255).astype(np.uint8)
-        image = Image.fromarray(image.transpose(1, 2, 0))
-        image = image.convert("RGB")
-        image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image)[None,]
-        return (image, )
@@ -4,4 +4,6 @@ watchdog
 opencv-python-headless
 matplotlib
 openai
-simple-lama-inpainting
+simple-lama-inpainting
+clip-interrogator==0.6.0
+transformers>=4.36.0
@@ -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) {
@@ -67,9 +67,13 @@ async function drawImageToCanvas (imageUrl) {
 }

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

  for (const id in data) {
@@ -77,7 +81,7 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
      let node = app.graph.getNodeById(id)
      if (inputIds.includes(id)) {
        // let node = app.graph.getNodeById(id)
-        let options = []
+        let options = {}
        // 模型
        try {
          if (node.type === 'CheckpointLoaderSimple') {
@@ -99,6 +103,27 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
        if (node.type == 'PromptSlide') {
          // min max step
          options = node.widgets.filter(w => w.type === 'slider')[0].options
+          // 备选的keywords清单
+          try {
+            let keywords = node.widgets.filter(w => w.name === 'upload')[0]
+              .value
+            keywords = JSON.parse(keywords)
+            options.keywords = keywords
+          } catch (error) {
+            console.log(error)
+          }
+        }
+
+        if (node.type == 'Color') {
+        }
+
+        // loadImage的mask支持
+        if (node.type === 'LoadImage') {
+          let output = node.outputs.filter(ot => ot.type == 'MASK')[0]
+          if (output.links) {
+            // 有输出
+            options.hasMask = true
+          }
        }

        input[inputIds.indexOf(id)] = {
@@ -115,17 +140,21 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
        output[outputIds.indexOf(id)] = { ...data[id], title: node.title, id }
      }

-      if (node.type === 'KSampler') {
+      if (node.type === 'KSampler' || node.type == 'SamplerCustom') {
        // seed 的类型收集
        try {
          seed[id] = node.widgets.filter(
-            w => w.name === 'seed'
+            w => w.name === 'seed' || w.name == 'noise_seed'
          )[0].linkedWidgets[0].value
        } catch (error) {}
      }
    }
  }

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

@@ -136,6 +165,16 @@ function getUrl () {
  return url
 }

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

@@ -144,7 +183,8 @@ async function save_app (json) {
    body: JSON.stringify({
      data: json,
      task: 'save_app',
-      filename: json.app.filename
+      filename: json.app.filename,
+      category: json.app.category
    })
  })
  return await res.json()
@@ -166,11 +206,13 @@ function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
  }, 0)
 }

-async function save (json, download = false) {
+async function save (json, download = false, showInfo = true) {
+  console.log('####SAVE', json[0])
  const name = json[0],
    version = json[5],
    share_prefix = json[6], //用于分享的功能扩展
-    link=json[7],//用于创建界面上的跳转链接
+    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)
@@ -186,8 +228,8 @@ async function save (json, download = false) {
  try {
    let data = await app.graphToPrompt()

-    const { input, output, seed } = extractInputAndOutputData(
-      data.output,
+    let { input, output, seed } = extractInputAndOutputData(
+      data,
      inputIds,
      outputIds
    )
@@ -201,6 +243,7 @@ async function save (json, download = false) {
      seed, //控制是fixed 还是random
      share_prefix,
      link,
+      category,
      filename: `${name}_${version}.json`
    }

@@ -209,31 +252,54 @@ async function save (json, download = false) {
    } catch (error) {}
    // console.log(data.app)
    // let http_workflow = app.graph.serialize()
-
+    await save_app(data)
    if (download) {
-      await save_app(data)
      await downloadJsonFile(data, data.app.filename)
+    }
+
+    if (showInfo) {
      let open = window.confirm(
        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app?filename=${encodeURIComponent(
          data.app.filename
-        )}`
+        )}&category=${encodeURIComponent(data.app.category)}`
      )
      if (open)
        window.open(
          `${getUrl()}/mixlab/app?filename=${encodeURIComponent(
            data.app.filename
-          )}`
+          )}&category=${encodeURIComponent(data.app.category)}`
        )
-    } else {
-      await save_app(data)
-
-      let open = window.confirm(
-        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app`
-      )
-      if (open) window.open(`${getUrl()}/mixlab/app`)
    }
  } catch (error) {
-    console.log('###SpeechRecognition', error)
+    console.log('###error', error)
+  }
+}
+
+function getInputsAndOutputs () {
+  const inputs =
+      `LoadImage VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
+        ' '
+      ),
+    outputs = `PreviewImage SaveImage ShowTextForGPT VHS_VideoCombine`.split(
+      ' '
+    )
+
+  let inputsId = [],
+    outputsId = []
+
+  for (let node of app.graph._nodes) {
+    if (inputs.includes(node.type)) {
+      inputsId.push(node.id)
+    }
+
+    if (outputs.includes(node.type)) {
+      outputsId.push(node.id)
+    }
+  }
+
+  return {
+    input: inputsId,
+    output: outputsId
  }
 }

@@ -244,7 +310,16 @@ app.registerExtension({
      const orig_nodeCreated = nodeType.prototype.onNodeCreated
      nodeType.prototype.onNodeCreated = function () {
        orig_nodeCreated?.apply(this, arguments)
-        console.log('#orig_nodeCreated', this)
+        // console.log('#orig_nodeCreated', this)
+
+        // 自动计算workflow里哪些节点支持
+        let input_ids = this.widgets.filter(w => w.name == 'input_ids')[0],
+          output_ids = this.widgets.filter(w => w.name == 'output_ids')[0]
+
+        const { input, output } = getInputsAndOutputs()
+        input_ids.value = input.join('\n')
+        output_ids.value = output.join('\n')
+
        const widget = {
          type: 'div',
          name: 'AppInfoRun',
@@ -315,14 +390,22 @@ app.registerExtension({
        }

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

      const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = async function (message) {
+      nodeType.prototype.onExecuted = function (message) {
        onExecuted?.apply(this, arguments)
        console.log(message.json)
        window._mixlab_app_json = message.json
        try {
+          let a = this.widgets.filter(w => w.name === 'AppInfoRun')[0]
+          if (a) {
+            if (!a.value) a.value = 0
+            a.value += 1
+          }
+
          const div = this.widgets.filter(w => w.div)[0].div
          Array.from(
            div.querySelectorAll('button'),
@@ -331,5 +414,42 @@ app.registerExtension({
        } catch (error) {}
      }
    }
+  },
+  async loadedGraphNode (node, app) {
+    // console.log('#loadedGraphNode1111')
+    window._mixlab_app_json = null //切换workflow需要清空
+    if (node.type === 'AppInfo') {
+      let auto_save = node.widgets.filter(w => w.name == 'auto_save')[0]
+      if (auto_save) {
+        if (!['enable', 'disable'].includes(auto_save.value)) {
+          auto_save.value = 'enable'
+        }
+      }
+
+      // app.canvas.centerOnNode(node)
+      // app.canvas.setZoom(0.45)
+    }
+  }
+})
+
+api.addEventListener('execution_start', async ({ detail }) => {
+  console.log('#execution_start', detail)
+  window._mixlab_app_json = null
+})
+
+api.addEventListener('executed', async ({ detail }) => {
+  console.log('#executed', detail)
+  // window._mixlab_app_json=null;
+  const { output } = getInputsAndOutputs()
+  if (output.includes(parseInt(detail.node))) {
+    let appinfo = app.graph.findNodesByType('AppInfo')[0]
+    if (appinfo) {
+      let auto_save = appinfo.widgets.filter(w => w.name == 'auto_save')[0]
+      if (auto_save?.value === 'enable') {
+        // 自动保存
+        console.log('auto_save')
+        if (window._mixlab_app_json) save(window._mixlab_app_json, false, false)
+      }
+    }
  }
 })
@@ -3,7 +3,7 @@ import { app } from '../../../scripts/app.js'
 const repoOwner = 'shadowcz007' // 替换为仓库的所有者
 const repoName = 'comfyui-mixlab-nodes' // 替换为仓库的名称

-const version = 'v0.8.0'
+const version = 'v0.17.0'

 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
  .then(response => response.json())
@@ -0,0 +1,119 @@
+import { app } from '../../../scripts/app.js'
+// import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+function getRandomElements (arr, num) {
+  var result = []
+  var len = arr.length
+
+  for (var i = 0; i < num; i++) {
+    var randomIndex = Math.floor(Math.random() * len)
+    result.push(arr[randomIndex])
+  }
+
+  return result
+}
+
+const createPrompt = (node, prompts, items, sample) => {
+  const w = ComfyWidgets['STRING'](
+    node,
+    'text',
+    ['STRING', { multiline: true }],
+    app
+  ).widget
+  w.inputEl.readOnly = true
+  w.inputEl.style.opacity = 0.6
+
+  w.value = typeof prompts === 'string' ? prompts : prompts.join('\n\n')
+
+  const w2 = ComfyWidgets['STRING'](
+    node,
+    'text',
+    ['STRING', { multiline: true }],
+    app
+  ).widget
+  w2.inputEl.readOnly = true
+  w2.inputEl.style.opacity = 0.6
+
+  w2.value = typeof items === 'string' ? items : JSON.stringify(items, null, 2)
+
+  const w3 = ComfyWidgets['STRING'](
+    node,
+    'text',
+    ['STRING', { multiline: true }],
+    app
+  ).widget
+  w3.inputEl.readOnly = true
+  w3.inputEl.style.opacity = 0.6
+  w3.value = typeof sample === 'string' ? sample : sample.join('\n\n')
+}
+
+app.registerExtension({
+  name: 'Mixlab.prompt.ClipInterrogator',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData.name === 'ClipInterrogator') {
+      function populate (prompts, items, random_samples) {
+        if (this.widgets) {
+          for (let i = 0; i < this.widgets.length; i++) {
+            if (this.widgets[i].type !== 'combo') this.widgets[i].onRemove?.()
+          }
+          this.widgets.length = 2
+        }
+
+        createPrompt(this, prompts, items, random_samples)
+
+        // console.log('ClipInterrogator', w, w2)
+        requestAnimationFrame(() => {
+          const sz = this.computeSize()
+          if (sz[0] < this.size[0]) {
+            sz[0] = this.size[0]
+          }
+          if (sz[1] < this.size[1]) {
+            sz[1] = this.size[1]
+          }
+          this.onResize?.(sz)
+          app.graph.setDirtyCanvas(true, false)
+        })
+      }
+
+      // When the node is executed we will be sent the input text, display this in the widget
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        // console.log('##', message)
+        populate.call(
+          this,
+          message.prompt,
+          message.analysis,
+          message.random_samples
+        )
+      }
+
+      this.serialize_widgets = true //需要保存参数
+    }
+  },
+  async loadedGraphNode (node, app) {
+    // Fires every time a node is constructed
+    // You can modify widgets/add handlers/etc here
+
+    if (node.type === 'ClipInterrogator') {
+      try {
+        
+        let widgets_values = node.widgets_values
+        console.log(widgets_values )
+        try {
+          if (widgets_values[2] && widgets_values[3] && widgets_values[4])
+            createPrompt(
+              node,
+              widgets_values[2],
+              widgets_values[3],
+              widgets_values[4]
+            )
+        } catch (error) {
+          console.log(error)
+        }
+      } catch (error) {}
+    }
+  }
+})
@@ -68,7 +68,7 @@ app.registerExtension({
          size: [128, 32], // a default size
          draw (ctx, node, width, y) {},
          computeSize (...args) {
-            return [128,32] // a method to compute the current size of the widget
+            return [128, 32] // a method to compute the current size of the widget
          },
          async serializeValue (nodeId, widgetIndex) {
            let data = getLocalData('_mixlab_api_key')
@@ -203,75 +203,82 @@ app.registerExtension({

 app.registerExtension({
  name: 'Mixlab.GPT.ShowTextForGPT',
-  async beforeRegisterNodeDef(nodeType, nodeData, app) {
-		if (nodeData.name === "ShowTextForGPT") {
-			function populate(text) {
-				if (this.widgets) {
-          
-					const pos = this.widgets.findIndex((w) => w.name === "text");
-					if (pos !== -1) {
-						for (let i = pos; i < this.widgets.length; i++) {
-							this.widgets[i].onRemove?.();
-						}
-						this.widgets.length = pos;
-					}
-				}
-        // console.log('ShowTextForGPT',text)
-				for (let list of text) {
-					const w = ComfyWidgets["STRING"](this, "text", ["STRING", { multiline: true }], app).widget;
-					w.inputEl.readOnly = true;
-					w.inputEl.style.opacity = 0.6;
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData.name === 'ShowTextForGPT') {
+      function populate (text) {
+        text = text.filter(t => t && t?.trim())

-          try {
-            let data=JSON.parse(list);
-            data=Array.from(data,d=>{
-              return {
-                ...d,
-                content:decodeURIComponent(d.content)
-              }
-            })
-            list=JSON.stringify(data,null,2)
-          } catch (error) {
-            // console.log(error)
+        if (this.widgets) {
+          // const pos = this.widgets.findIndex(w => w.name === 'text')
+          for (let i = 0; i < this.widgets.length; i++) {
+            if (this.widgets[i].name == 'show_text') this.widgets[i].onRemove?.()
          }
+          this.widgets.length = 1
+        }
+        // console.log('ShowTextForGPT',text)
+        for (let list of text) {
+          if (list) {
+            // console.log('#####', list)
+            const w = ComfyWidgets['STRING'](
+              this,
+              'show_text',
+              ['STRING', { multiline: true }],
+              app
+            ).widget
+            w.inputEl.readOnly = true
+            w.inputEl.style.opacity = 0.6

-					w.value =list;
-        
-				}
+            try {
+              if (typeof list != 'string') {
+                let data = JSON.parse(list)
+                data = Array.from(data, d => {
+                  return {
+                    ...d,
+                    content: decodeURIComponent(d.content)
+                  }
+                })
+                list = JSON.stringify(data, null, 2)
+              }
+            } catch (error) {
+              console.log(error)
+            }
+
+            w.value = list
+          }
+        }
        // console.log('ShowTextForGPT',this.widgets.length)
-				requestAnimationFrame(() => {
-					const sz = this.computeSize();
-					if (sz[0] < this.size[0]) {
-						sz[0] = this.size[0];
-					}
-					if (sz[1] < this.size[1]) {
-						sz[1] = this.size[1];
-					}
-					this.onResize?.(sz);
-					app.graph.setDirtyCanvas(true, false);
-				});
-			}
+        requestAnimationFrame(() => {
+          if (this) {
+            const sz = this.computeSize()
+            if (sz[0] < this.size[0]) {
+              sz[0] = this.size[0]
+            }
+            if (sz[1] < this.size[1]) {
+              sz[1] = this.size[1]
+            }
+            this.onResize?.(sz)
+            app.graph.setDirtyCanvas(true, false)
+          }
+        })
+      }

-			// When the node is executed we will be sent the input text, display this in the widget
-			const onExecuted = nodeType.prototype.onExecuted;
-			nodeType.prototype.onExecuted = function (message) {
-				onExecuted?.apply(this, arguments);
-				populate.call(this, message.text);
-			};
+      // When the node is executed we will be sent the input text, display this in the widget
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        // console.log('##onExecuted', this, message)
+        if (message.text) populate.call(this, message.text)
+      }

-			const onConfigure = nodeType.prototype.onConfigure;
-			nodeType.prototype.onConfigure = function () {
-				onConfigure?.apply(this, arguments);
-				if (this.widgets_values?.length) {
-          
-					populate.call(this, this.widgets_values);
-				}
-			};
+      const onConfigure = nodeType.prototype.onConfigure
+      nodeType.prototype.onConfigure = function () {
+        onConfigure?.apply(this, arguments)
+        if (this.widgets_values?.length) {
+          populate.call(this, this.widgets_values)
+        }
+      }

      this.serialize_widgets = true //需要保存参数
-
-		}
-
-
-	},
+    }
+  }
 })
@@ -156,6 +156,32 @@ const parseSvg = async svgContent => {
  return { data, image: base64, svgElement }
 }

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

              let linkId = this.inputs[3].link
              let nodeId = app.graph.links[linkId].origin_id
              // console.log(linkId,this.inputs)
-              let im = app.graph.getNodeById(nodeId).imgs[0]
+              let imgs2=findImages(nodeId)
+              let im = imgs2[0]
+              console.log(topIm,im)
              // let src = im.src
              setArea(
                im.naturalWidth,
@@ -589,7 +619,9 @@ app.registerExtension({
                data,
                updateValue
              )
-            } catch (error) {}
+            } catch (error) {
+              console.log(error)
+            }
          })
        }
      }
@@ -1331,7 +1331,13 @@ app.registerExtension({
          let w = 360,
            s = widget.preview.videoWidth / widget.preview.videoHeight,
            h = w / s || w
-          console.log(h)
+          // console.log(h)
+
+          if (!window.documentPictureInPicture) {
+            window.alert(
+              'This feature is available only in secure contexts (HTTPS), in some or all supporting browsers. https://developer.mozilla.org/en-US/docs/Web/API/Document_Picture-in-Picture_API'
+            )
+          }

          const pipWindow = await documentPictureInPicture.requestWindow({
            width: w,
@@ -1800,15 +1806,19 @@ 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 +2099,13 @@ const node = {
  name: 'RandomPrompt',
  async init (app) {
    // Any initial setup to run as soon as the page loads
-    console.log('[logging]', 'extension init')
+    // console.log('[logging]', 'extension init')

    if (window.location.href.match('/?')) {
      const { workflow } = getURLParameters(window.location.href)
      if (workflow)
        get_my_workflow().then(data => {
-          console.log('#get_my_workflow', data)
+          // console.log('#get_my_workflow', data)
          let my_workflow = data.filter(
            d => d.filename == 'my_workflow.json'
          )[0]
@@ -2131,10 +2141,15 @@ 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 +2242,7 @@ const node = {
        const r = onExecuted?.apply?.(this, arguments)

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

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

 const getLocalData = key => {
  let data = {}
@@ -57,11 +54,35 @@ function hexToRGBA (hexColor) {
  // 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]
+  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) {
@@ -75,9 +96,10 @@ app.registerExtension({
            return [128, 32] // a method to compute the current size of the widget
          },
          async serializeValue (nodeId, widgetIndex) {
-            let data = getLocalData('_mixlab_utils_color');
-            let hex=data[node.id] || '#000000'
-            let [r,g,b,a]=hexToRGBA(hex)
+            // 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,
@@ -100,7 +122,7 @@ 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',
@@ -110,6 +132,7 @@ app.registerExtension({
              this.div.style,
              get_position_style(ctx, widget_width, 44, node.size[1])
            )
+            // console.log('draw',y,node.widgets[0].last_y)
          }
        }

@@ -117,35 +140,9 @@ 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'} ${placeholder}`
-          div.style = `display: flex;
-            align-items: center; 
-            margin: 6px 8px;
-            margin-top: 0;`
-          ip.placeholder = placeholder
-          ip.value = value
-
-          ip.style = `outline: none;
-            border: none;
-            padding: 4px;
-            width: 70%;cursor: pointer;
-            height: 32px;`
-          const label = document.createElement('label')
-          label.style = 'font-size: 10px;min-width:32px'
-          label.innerText = placeholder
-          div.appendChild(label)
-          div.appendChild(ip)
-
-          ip.addEventListener('change', () => {
-            let data = getLocalData(key)
-            data[this.id] = ip.value.trim()
-            localStorage.setItem(key, JSON.stringify(data))
-            // console.log(this.id, ip.value.trim())
-          })
+          div.id = `color_picker_${this.id}`
          return div
        }

@@ -155,10 +152,85 @@ 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?.()
        }

@@ -171,20 +243,17 @@ 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) {}
    }
  }
 })

 app.registerExtension({
  name: 'Mixlab.utils.TextToNumber',
-
  async beforeRegisterNodeDef (nodeType, nodeData, app) {
    if (nodeType.comfyClass == 'TextToNumber') {
      const onExecuted = nodeType.prototype.onExecuted
@@ -197,9 +266,96 @@ app.registerExtension({
          const n = this.widgets.filter(w => w.name === 'number')[0]
          n.value = message.num[0]
        }
-
        console.log('TextToNumber', random_number.value)
      }
    }
  }
 })
+
+const min_max = node => {
+  if(node.widgets){
+    const min_value = node.widgets.filter(w => w.name === 'min_value')[0]
+    const max_value = node.widgets.filter(w => w.name === 'max_value')[0]
+  
+    const number = node.widgets.filter(w => w.name === 'number')[0]
+    if (number) {
+      number.options.min = min_value.value
+      number.options.max = max_value.value
+  
+      number.value = Math.min(number.options.max, number.value)
+      number.value = Math.max(number.options.min, number.value)
+    }
+  
+    if (min_value)
+      min_value.callback = e => {
+        number.options.min = e
+        number.value = e
+      }
+    if (max_value)
+      max_value.callback = e => {
+        number.options.max = e
+        number.value = e
+      }
+  }
+  
+}
+
+app.registerExtension({
+  name: 'Mixlab.utils.FloatSlider',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+
+    if (nodeType.comfyClass == 'FloatSlider') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated;
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        min_max(this)
+      }
+    }
+
+   
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'FloatSlider') {
+      min_max(node)
+    }
+  }
+})
+app.registerExtension({
+  name: 'Mixlab.utils.IntNumber',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    
+    if (nodeType.comfyClass == 'IntNumber') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        min_max(this)
+      }
+    }
+   
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'IntNumber') {
+      min_max(node)
+    }
+  }
+})
+
+
+app.registerExtension({
+  name: 'Mixlab.utils.TESTNODE_',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    
+    if (nodeType.comfyClass == 'TESTNODE_') {
+    
+      const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = function (message) {
+				onExecuted?.apply(this, arguments);
+        console.log('##',message)
+				 
+			};
+
+
+    }
+   
+  }, 
+})
@@ -60,30 +60,7 @@ app.registerExtension({
          type: inputData[0], // the type, CHEESE
          name: inputName, // the name, slice
          size: [128, 24], // a default size
-          draw (ctx, node, width, y) {
-            // // 绘制文件图标的函数
-            // function drawFileIcon () {
-            //   // 清空画布
-            //   // ctx.clearRect(0, 0, canvas.width, canvas.height)
-
-            //   // 绘制文件外框
-            //   ctx.fillStyle = '#000'
-            //   ctx.fillRect(5, 5, 40, 40)
-
-            //   // 绘制文件夹图标
-            //   ctx.fillStyle = '#f00'
-            //   ctx.fillRect(10, 15, 30, 20)
-
-            //   // 绘制监听符号
-            //   ctx.beginPath()
-            //   ctx.arc(30, 35, 5, 0, 2 * Math.PI)
-            //   ctx.fillStyle = '#00f'
-            //   ctx.fill()
-            // }
-
-            // // 调用绘制函数
-            // drawFileIcon()
-          },
+          draw (ctx, node, width, y) {},
          computeSize (...args) {
            return [128, 24] // a method to compute the current size of the widget
          },
@@ -124,11 +101,19 @@ app.registerExtension({
      nodeType.prototype.onNodeCreated = function () {
        orig_nodeCreated?.apply(this, arguments)

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

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

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

-        /*
-                Add the widget, make sure we clean up nicely, and we do not want to be serialized!
-                */
-        // this.addCustomWidget(widget)
        this.onRemoved = function () {
          // widget.card.remove()
        }
        this.serialize_widgets = true
      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log(message)
+        try {
+          let seed = this.widgets.filter(w => w.name === 'seed')[0]
+          if (seed) {
+            if (!seed.value) seed.value = 0
+            seed.value += 1
+          }
+        } catch (error) {}
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'LoadImagesFromPath') {
+      const watcher = node.widgets.filter(w => w.name == 'watcher')[0]
+      if (watcher) {
+        if (watcher.value === 'enable') {
+          if (window._mixlab_watcher_t) clearInterval(window._mixlab_watcher_t)
+          window._mixlab_watcher_t = setInterval(() => {
+            // 上次路径填充
+            getConfig().then(json => {
+              console.log(json.event_type)
+              if (json.event_type != window._mixlab_file_path_watcher) {
+                window._mixlab_file_path_watcher = json.event_type
+                // widget.card.innerText = window._mixlab_file_path_watcher || ''
+                //运行
+                document.querySelector('#queue-button').click()
+              }
+            })
+          }, 1000)
+        } else {
+          if (window._mixlab_watcher_t) {
+            clearInterval(window._mixlab_watcher_t)
+          }
+          window._mixlab_watcher_t = null
+        }
+      }
+
+      try {
+        let seed = node.widgets.filter(w => w.name === 'seed')[0]
+        if (seed) {
+          if (!seed.value) seed.value = 0
+          seed.value += 1
+        }
+      } catch (error) {}
    }
  }
 })
@@ -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}
@@ -0,0 +1,736 @@
+{
+  "last_node_id": 29,
+  "last_link_id": 32,
+  "nodes": [
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        108,
+        316
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 16
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            6
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "text, watermark"
+      ]
+    },
+    {
+      "id": 18,
+      "type": "ControlNetApply",
+      "pos": [
+        485,
+        792
+      ],
+      "size": {
+        "0": 317.4000244140625,
+        "1": 98
+      },
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "conditioning",
+          "type": "CONDITIONING",
+          "link": 22,
+          "slot_index": 0
+        },
+        {
+          "name": "control_net",
+          "type": "CONTROL_NET",
+          "link": 18,
+          "slot_index": 1
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 26
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            21
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ControlNetApply"
+      },
+      "widgets_values": [
+        1
+      ]
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        117,
+        95
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 15
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            22
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "a future city,buiding,future,magic,under water"
+      ]
+    },
+    {
+      "id": 15,
+      "type": "LoraLoader",
+      "pos": [
+        116,
+        -113
+      ],
+      "size": {
+        "0": 315,
+        "1": 126
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 13
+        },
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 14
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            12,
+            23
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            15,
+            16
+          ],
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoraLoader"
+      },
+      "widgets_values": [
+        "lcm-lora-sdv1-5.safetensors",
+        1,
+        1
+      ]
+    },
+    {
+      "id": 4,
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@@ -41,48 +41,6 @@
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Author	SHA1	Message	Date
shadowcz007	65d8b490ca	ing	2024-03-04 20:31:45 +08:00
shadowcz007	0fef12c3b1	Update index.html	2024-03-03 11:58:47 +08:00
shadowcz007	8516bff224	Update index.html	2024-03-03 11:11:39 +08:00
shadowcz007	1bcc501352	Update index.html	2024-03-02 23:55:46 +08:00
shadowcz007	f492b17fbe	Update index.html	2024-03-02 23:16:08 +08:00
shadowcz007	7b2d0e29c6	Update PromptNode.py	2024-02-27 15:18:58 +08:00
shadowcz007	a53a3e772a	Update README.md	2024-02-24 10:40:11 +08:00
shadowcz007	acc3f76654	ing	2024-02-18 20:45:58 +08:00
shadowcz007	8c977fb442	ing	2024-02-18 17:33:11 +08:00
shadowcz007	aa20a2de67	fixbug	2024-02-13 15:55:50 +08:00
shadowcz007	5fcb154d89	Update ui_mixlab.js	2024-02-13 15:48:22 +08:00
shadowcz007	0980129f4e	Update ui_mixlab.js	2024-02-13 15:45:56 +08:00
shadowcz007	1258746886	v0.17.0 - app模式支持VHS_LoadVideo节点作为输入 - 动态提示，鼠标悬浮可显示结果	2024-02-13 15:18:48 +08:00
shadowcz007	ed128b0ad6	app 支持VHS_LoadVideo 节点作为输入	2024-02-13 15:09:34 +08:00
shadowcz007	f0db08acd6	add TESTNODE_TOKEN 显示text-to-token的过程，方便对prompt进行精修	2024-02-12 21:32:37 +08:00
shadowcz007	7c655e3080	Update ui_mixlab.js	2024-02-12 19:40:09 +08:00
shadowcz007	1b9871c3df	Update ui_mixlab.js	2024-02-12 19:20:34 +08:00
shadowcz007	7568aaf243	Update ui_mixlab.js	2024-02-12 17:42:11 +08:00
shadowcz007	a76be8450d	mouseover show dynamic_prompt's result	2024-02-12 17:11:03 +08:00
shadowcz007	5564ee1246	rembgNode update "briarmbg","u2net","u2netp","u2net_human_seg","u2net_cloth_seg","silueta","isnet-general-use","isnet-anime"	2024-02-08 14:01:36 +08:00
shadowcz007	a6e9251521	add briarmbg to rembgNode	2024-02-08 13:57:01 +08:00
shadowcz007	0bee093916	Update README.md	2024-02-08 11:57:10 +08:00
shadowcz007	13a9878823	Update README.md	2024-02-08 11:56:41 +08:00
shadowcz007	acd35d50f8	Update ChatGPT.py	2024-02-08 11:53:03 +08:00
shadowcz007	5c0d99e72d	comfyui-CLIPSeg	2024-02-08 10:16:49 +08:00
shadowcz007	e37af93be3	0.15.1	2024-02-06 23:06:33 +08:00
shadowcz007	244c1700e1	Update ImageNode.py	2024-02-06 22:22:56 +08:00
shadowcz007	a3a15473ba	LoadImage 的mask保存到appinfo	2024-02-06 19:01:41 +08:00
shadowcz007	d734b5077c	GetImageSize_ 增加最小尺寸	2024-02-05 22:44:04 +08:00
shadowcz007	5430072b19	Update app_mixlab.js	2024-02-05 22:25:25 +08:00
shadowcz007	465aebaed4	Update app_mixlab.js	2024-02-05 22:24:34 +08:00
shadowcz007	ee0b16c2ea	修复appinfo配置的bug	2024-02-05 17:56:25 +08:00
shadowcz007	21d5eacb41	Update index.html	2024-02-04 23:46:25 +08:00
shadowcz007	c06688eb0b	comfyui-consistency-decoder	2024-02-02 09:47:51 +08:00
shadowcz007	b74bbcd279	fixbug :SaveImageToLocal	2024-02-01 23:52:23 +08:00
shadowcz007	64d8d9b05d	Delete echarts.min.js	2024-02-01 00:48:25 +08:00
shadowcz007	6ab60f281b	Update ImageNode.py	2024-01-31 00:24:23 +08:00
shadowcz007	e35be3b2fa	Update README.md	2024-01-30 22:57:13 +08:00
shadowcz007	0a0c27ac96	fixbug:Save Group as Template	2024-01-30 22:54:39 +08:00
shadowcz007	fb249e84eb	SplitImage增加mask输出	2024-01-30 18:07:21 +08:00
shadowcz007	76ad86fcae	Update __init__.py	2024-01-29 23:18:16 +08:00
shadowcz007	037614d227	showText 可以保存txt到本地目录	2024-01-29 14:37:51 +08:00
shadowcz007	4a50e445fd	从本地读取文件-输出文件名	2024-01-29 13:46:02 +08:00
shadowcz007	6f3c1c4393	update	2024-01-29 11:55:10 +08:00
shadowcz007	c6a9b4b592	Update gpt_mixlab.js	2024-01-29 11:45:53 +08:00
shadowcz007	e915ac4eca	Update ChatGPT.py	2024-01-29 11:37:49 +08:00
shadowcz007	a857793f63	fixbug	2024-01-29 11:34:06 +08:00
shadowcz007	31914f7510	Update ChatGPT.py	2024-01-29 11:12:02 +08:00
shadowcz007	0be859f0ee	fixbug	2024-01-28 21:38:13 +08:00
shadowcz007	14b9c3697b	Update ImageNode.py	2024-01-28 20:38:22 +08:00
shadowcz007	96b66a57bb	showText can save to local	2024-01-28 18:13:50 +08:00
shadowcz007	f13701c489	v0.15.0	2024-01-28 16:40:31 +08:00
shadow	31515b810e	Merge pull request #159 from wfjsw/debloat-init-1 publish routes without having to replicate add_routes	2024-01-28 16:01:17 +08:00
shadowcz007	29e84e08a4	add CenterImage	2024-01-28 15:59:54 +08:00
shadowcz007	0ac9ad9757	修复批量保存本地图片的bug	2024-01-27 22:44:47 +08:00
shadowcz007	9a432e0608	Update Utils.py	2024-01-27 00:58:08 +08:00
shadowcz007	c83ba5fe7f	Update PromptNode.py	2024-01-27 00:57:40 +08:00
shadowcz007	1b55c743ea	增加一些seed来控制节点	2024-01-27 00:33:49 +08:00
shadowcz007	a93579376c	不覆盖文件	2024-01-26 22:47:34 +08:00
shadowcz007	eba49f3c68	add SaveImageToLocal	2024-01-26 12:15:01 +08:00
shadowcz007	3a3da49c69	Update ImageNode.py	2024-01-25 20:10:29 +08:00
shadowcz007	3d68e48219	Update ImageNode.py	2024-01-25 20:07:31 +08:00
shadowcz007	4351fa6a0e	增加mask 的resize	2024-01-25 17:54:51 +08:00
shadowcz007	77222d2808	修复ImageCropByAlpha的bug	2024-01-25 15:40:00 +08:00
Jabasukuriputo Wang	36db7e5a9a	publish routes without having to replicate add_routes	2024-01-25 00:44:51 -06:00
shadowcz007	3572368f16	fixbug	2024-01-25 10:38:23 +08:00
shadowcz007	0755dc1462	CreateLoraNames	2024-01-24 22:59:49 +08:00
shadowcz007	94f81b7102	fixbug	2024-01-24 17:45:15 +08:00
shadowcz007	08f8fe3d7e	Update README.md	2024-01-23 23:44:47 +08:00
shadowcz007	a6cd383d67	add Sampler_names	2024-01-23 14:23:04 +08:00
shadowcz007	10face6ab0	CkptNames	2024-01-23 14:04:26 +08:00
shadowcz007	a6259ff600	add CkptNames	2024-01-23 13:58:59 +08:00
shadowcz007	d7d9e6cbfe	add smart_connect_v1	2024-01-23 12:18:38 +08:00
shadowcz007	8263609470	优化LoadImageURL，增加seed，保证图片加载失败后可以继续	2024-01-23 10:03:55 +08:00
shadowcz007	fc2367de76	centerOnNode & fix node (widgets)	2024-01-21 20:31:57 +08:00
shadowcz007	9a4f2ebc70	Update ui_mixlab.js	2024-01-20 22:39:56 +08:00
shadowcz007	812879610a	v0.14.0 发布新节点splitImage & gridoutput，用于分割图片和随机摆放元素修复若干bug	2024-01-20 21:43:18 +08:00
shadowcz007	c5e521ccc1	add splitImage&gridoutput	2024-01-20 17:39:53 +08:00
shadowcz007	bc1c8fa351	Update index.html	2024-01-19 09:45:03 +08:00
shadowcz007	7271fcf9c1	api	2024-01-18 22:49:13 +08:00
shadowcz007	ace3b7707b	Update README.md	2024-01-18 12:45:29 +08:00
shadowcz007	9eb65cc4ee	Update ClipInterrogator.py	2024-01-18 11:12:03 +08:00
shadowcz007	c5c2bc779c	add JoinWithDelimiter	2024-01-18 11:00:37 +08:00
shadowcz007	ae1751d9c0	Update Utils.py	2024-01-18 00:32:12 +08:00
shadowcz007	d17583ef7d	fixbug	2024-01-17 17:56:23 +08:00
shadowcz007	a363713ae0	v0.13.0 EmbeddingPrompt & 修复若干bug	2024-01-16 23:17:56 +08:00
shadowcz007	1566165bd4	Create space.txt	2024-01-16 17:50:51 +08:00
shadowcz007	fe065fa318	OutlineMask for inpaint	2024-01-16 10:54:26 +08:00
shadowcz007	202d5cf071	支持富文本定义跳转按钮	2024-01-15 14:21:39 +08:00
shadowcz007	b785a9dc5b	add EmbeddingPrompt	2024-01-15 13:02:26 +08:00
shadowcz007	73bc658b2f	修复 sentencepiece 未安装的bug	2024-01-15 08:40:07 +08:00
shadowcz007	0b94216138	Update ui_mixlab.js	2024-01-14 20:35:27 +08:00
shadowcz007	86eec2b4cc	Update ui_mixlab.js	2024-01-14 20:34:07 +08:00
shadowcz007	c99b531d28	Update ui_mixlab.js	2024-01-14 20:33:53 +08:00
shadowcz007	74a4338cb5	Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes	2024-01-14 13:14:42 +08:00
shadowcz007	d691c52e49	Update TextGenerateNode.py	2024-01-14 13:12:10 +08:00
gold3bear	bd3e9e4b3c	去掉调试数据	2024-01-14 12:31:05 +08:00
shadow	3c3ca5fb9c	Merge pull request #138 from shadowcz007/fix-chinese-prompt Fix chinese prompt	2024-01-14 11:58:08 +08:00
shadowcz007	e4ff4fce1c	update	2024-01-14 11:57:18 +08:00
gold3bear	2918d4b07d	correct Chinese text to prompt syntax	2024-01-14 11:50:14 +08:00
gold3bear	c40e49be46	fix chinese prompt	2024-01-14 11:34:58 +08:00
shadowcz007	4ccda20975	add rembg	2024-01-14 11:06:46 +08:00
shadowcz007	09957617d3	修复SwitchByIndex的bug	2024-01-13 20:59:29 +08:00
shadowcz007	c703aa7058	中文prompt增加选项，可控制是否添加更多	2024-01-13 20:59:07 +08:00
shadowcz007	64d366d323	Update prompt_mixlab.js	2024-01-13 17:12:03 +08:00
shadowcz007	333e0a2faa	Update ImageNode.py	2024-01-13 16:32:51 +08:00
shadowcz007	a677d95bc8	Update README.md	2024-01-13 16:02:24 +08:00
shadowcz007	aafd87e84b	v0.12.0 ChinesePrompt && PromptGenerate > ChinesePrompt && PromptGenerate，中文prompt节点，直接用中文书写你的prompt ![](./assets/ChinesePrompt_workflow.svg) > Web App增加图片编辑器	2024-01-13 15:59:52 +08:00
shadowcz007	efa3bae54b	Create profession.txt	2024-01-13 12:25:05 +08:00
shadowcz007	8e4362689d	Lama、ClipInterrogator安装移到节点内	2024-01-13 12:11:46 +08:00
shadowcz007	b86634284e	appinfo运行bug	2024-01-13 11:34:17 +08:00
shadowcz007	b3293ddccd	优化promptImage的预览	2024-01-12 17:16:47 +08:00
shadowcz007	4b40831b83	prompt keywords	2024-01-11 23:05:25 +08:00
shadowcz007	e426b0521c	添加图片编辑功能	2024-01-11 23:01:58 +08:00
shadowcz007	1777bf6e06	修复切换workflow，数据未清空的情况	2024-01-11 23:01:42 +08:00
shadowcz007	dafc892f0f	add image edit for web app	2024-01-11 15:41:05 +08:00
shadowcz007	fea0cfd5dd	更新workflow示例	2024-01-11 15:40:31 +08:00
shadowcz007	a7e158db6d	update workflow example	2024-01-11 15:32:20 +08:00
shadowcz007	455ac4abd3	Update promptslide-appinfo-workflow.svg	2024-01-11 15:27:11 +08:00
shadowcz007	778dfa2cf5	update workflow example	2024-01-11 15:24:31 +08:00
shadowcz007	329f2e6f81	系统字体的获取	2024-01-10 16:19:49 +08:00
shadowcz007	63ad6d97d7	Update ImageNode.py	2024-01-09 22:59:34 +08:00
shadowcz007	8db56db7cf	Update ImageNode.py	2024-01-09 22:34:13 +08:00
shadowcz007	bd542f1e0b	Update app_mixlab.js	2024-01-09 18:42:36 +08:00
shadowcz007	a162e53dea	Update ImageNode.py	2024-01-09 18:05:18 +08:00
shadowcz007	a8a4c848ed	Update __init__.py	2024-01-09 16:15:15 +08:00
shadowcz007	bddd38996a	Update ImageNode.py	2024-01-09 15:16:38 +08:00
shadowcz007	9f084eae94	修复LoadImagesFromPath的bug	2024-01-09 14:33:13 +08:00
shadowcz007	c54c635161	v0.11.4	2024-01-09 12:59:36 +08:00
shadowcz007	fa8b42e05e	增强ImageCrop功能	2024-01-09 12:48:20 +08:00
shadowcz007	9c3c323884	fixbug: user_manager	2024-01-09 12:41:29 +08:00
shadowcz007	c8a46439be	v0.11.3 - 修复appinfo的logo输入	2024-01-09 09:17:00 +08:00
shadowcz007	b7ec701259	Update Utils.py	2024-01-09 09:16:15 +08:00
shadowcz007	a2cd0e0a38	v0.11.2 - 优化appinfo - 自动保存 - 优化mergeLayer，可以输出合成的mask - 添加一个实验性的节点 ImageColorTransfer - random prompt ,增加上传关键词功能 - 修复LoadImageFromPath的bug	2024-01-08 23:03:37 +08:00
shadowcz007	7bccc0e236	优化appinfo-不需要输出，每次运行都会自动更新数据	2024-01-08 23:01:31 +08:00
shadowcz007	a3a649a79f	修复 LoadImagesFromPath 不更新的bug	2024-01-08 22:11:10 +08:00
shadowcz007	2a14d30552	添加一个实验性的节点 ImageColorTransfer	2024-01-08 16:40:00 +08:00
shadowcz007	313bef0609	ClipInterrogator优化	2024-01-07 22:55:24 +08:00
shadowcz007	960a80aeca	Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes	2024-01-07 19:59:24 +08:00
shadowcz007	20e6d50a98	修复bug	2024-01-07 19:59:22 +08:00
shadow	507c3417d6	Merge pull request #114 from shadowcz007/fix_ssl_prot_occupied fix:ssl prot occupied	2024-01-07 17:08:08 +08:00
gold3bear	d1adb8d4ed	fix:ssl prot occupied	2024-01-07 15:56:23 +08:00
shadowcz007	915ff12747	支持图片的batch	2024-01-06 23:20:43 +08:00
shadowcz007	fbade79137	Update utils_mixlab.js	2024-01-06 20:15:17 +08:00
shadowcz007	a240a677d0	Update utils_mixlab.js	2024-01-06 20:14:17 +08:00
shadowcz007	ab64cf31f6	FloatSlider 优化	2024-01-06 20:13:00 +08:00
shadowcz007	67f2e32dae	floatSlider 优化	2024-01-06 20:00:58 +08:00
shadowcz007	0dc40fe052	修复bug	2024-01-06 17:55:26 +08:00
shadowcz007	b7225be552	Update ImageNode.py	2024-01-06 16:25:35 +08:00
shadowcz007	629e00ec94	fixbug	2024-01-06 12:14:02 +08:00
shadowcz007	6d033c9314	random prompt ,增加上传关键词功能	2024-01-06 08:40:27 +08:00
shadowcz007	4f8926ed00	promptslide 上传txt后写入workflow保留列表数据	2024-01-06 08:12:59 +08:00
shadow	1daa1a4603	Merge pull request #111 from shadowcz007/v.11.0-PromptImage-node-图片和prompt匹配 V0.11.0 PromptImage & PromptSimplification	2024-01-06 00:17:26 +08:00
shadowcz007	062773d929	v0.11.0 > PromptImage & PromptSimplification,Assist in simplifying prompt words, comparing images and prompt word nodes.	2024-01-06 00:16:33 +08:00
shadowcz007	57decadaef	Update index.html	2024-01-06 00:14:04 +08:00
shadowcz007	ec804ab7c9	优化	2024-01-05 23:09:08 +08:00
shadowcz007	3ee7533098	Update prompt_mixlab.js	2024-01-05 16:40:30 +08:00
shadowcz007	0d383ccc1f	add PromptImage	2024-01-05 15:27:13 +08:00
shadowcz007	e2f2257c34	Update index.html	2024-01-05 12:22:23 +08:00
shadowcz007	d62b9fc4c6	ClipInterrogator可以作为输出	2024-01-05 11:52:04 +08:00
shadowcz007	be7ad0c7fb	Update index.html	2024-01-04 23:35:56 +08:00
shadowcz007	156864cc8b	PromptSimplification	2024-01-04 23:33:24 +08:00
shadowcz007	7240e496cc	Update PromptNode.py	2024-01-04 23:02:48 +08:00
shadowcz007	8acdf4018d	test PromptSimplification	2024-01-04 20:32:43 +08:00
shadowcz007	1ea7c3e203	修复floatSlide最大值问题	2024-01-04 19:43:33 +08:00
shadowcz007	e54aeb6125	Update index.html	2024-01-04 18:35:54 +08:00
shadowcz007	d59f51fbcf	Update README.md	2024-01-04 18:20:52 +08:00
shadowcz007	a667eb6982	修复seed 为fixed 的运行按钮bug & 支持sd-xl 的SamplerCustom	2024-01-04 18:20:00 +08:00
shadowcz007	8d72732247	Update index.html	2024-01-04 17:19:46 +08:00
shadowcz007	f0f3b30a62	Update index.html	2024-01-04 17:03:36 +08:00
shadowcz007	d99fe24542	fixbug	2024-01-04 16:05:27 +08:00
shadowcz007	ea4c7381bd	Update index.html	2024-01-04 14:07:38 +08:00
shadow	e900d20641	Merge pull request #107 from shadowcz007/v0.10-add-clip-interrogator Update index.html	2024-01-04 14:02:42 +08:00
shadowcz007	8a46647d8c	Update index.html	2024-01-04 14:02:19 +08:00
shadow	968178bf57	Merge pull request #106 from shadowcz007/v0.10-add-clip-interrogator V0.10 add clip interrogator	2024-01-04 13:37:31 +08:00
shadowcz007	406a255db0	v0.10.0 增加 ClipInterrogator、优化APP功能	2024-01-04 13:37:07 +08:00
shadowcz007	574557810e	Update index.html	2024-01-04 13:24:50 +08:00
shadowcz007	998a02c3a4	上一次输入记录	2024-01-04 13:12:02 +08:00
shadowcz007	c6f964c921	textarea输入，增加上一次输入记录	2024-01-04 12:57:55 +08:00
shadowcz007	efb0e147c5	Update index.html	2024-01-04 12:45:00 +08:00
shadowcz007	9cf7356f98	Update index.html	2024-01-04 12:33:59 +08:00
shadowcz007	af05c43174	支持image的batch输出	2024-01-04 12:31:45 +08:00
shadowcz007	380c68ff2b	EnhanceImage节点支持batch多张输入和输出	2024-01-04 12:03:27 +08:00
shadowcz007	068b00b99f	update	2024-01-04 11:24:00 +08:00
shadowcz007	cd6a42ab64	clip-interrogator	2024-01-04 11:03:25 +08:00
shadowcz007	f115abec92	add clip interrogator	2024-01-04 11:01:08 +08:00
shadowcz007	f0e23cf878	AIPC大赛模板	2024-01-03 22:28:38 +08:00
shadowcz007	a94f11d809	Update index.html	2024-01-03 20:22:24 +08:00
shadowcz007	38972bea5f	更新AIPC大赛模板-直接合成，免去ps	2024-01-03 18:03:33 +08:00
shadowcz007	a761ff552a	Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes	2024-01-03 17:54:25 +08:00
shadowcz007	dbeb84ea9a	resizeImage 缩放图像新增center模式，多余的背景可以设定填充颜色	2024-01-03 17:54:23 +08:00
shadow	0a4938f39a	Merge pull request #105 from shadowcz007/v0.9.2-中断生成修复3d image的bug，未上传bg图也可以运行了	2024-01-03 14:21:26 +08:00
shadowcz007	b2182c716d	修复3d image的bug，未上传bg图也可以运行了	2024-01-03 14:20:48 +08:00
shadow	8253be73f6	Merge pull request #104 from shadowcz007/v0.9.2-中断生成添加中断生成的功能	2024-01-03 09:35:12 +08:00
shadowcz007	20318e296e	添加中断生成的功能	2024-01-03 09:32:29 +08:00
shadow	cea1b69286	Merge pull request #103 from shadowcz007/v0.9.1-优化app模式 V0.9.1 优化app模式	2024-01-02 23:56:18 +08:00
shadowcz007	ab8aa69389	v0.9.1 web app可以设置分类，在comfyui右键菜单可以编辑更新web app The web app can be configured with categories, and the web app can be edited and updated in the right-click menu of ComfyUI. 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	2024-01-02 23:54:24 +08:00
shadowcz007	681491f1d0	v0.9.1	2024-01-02 23:51:19 +08:00
shadowcz007	c2fb815074	更新示例：TwinShot	2024-01-02 23:50:39 +08:00
shadowcz007	fffa14dc44	修复了randomprompt里的一个小bug	2024-01-02 17:48:41 +08:00
shadowcz007	df37166d42	Switch节点增加flat功能，可以把list里的某个元素取出来单独处理	2024-01-02 17:44:17 +08:00
shadowcz007	25fa3a8f6a	支持按照分类隔离应用	2024-01-02 16:19:01 +08:00
shadowcz007	b11507c5e6	样式	2024-01-02 15:11:29 +08:00
shadowcz007	f06d02489f	app支持color组件	2024-01-02 14:59:59 +08:00
shadowcz007	c203af2f71	优化	2024-01-02 13:42:39 +08:00
shadowcz007	c715155a70	渐变节点	2024-01-02 13:25:48 +08:00
shadowcz007	8163133294	优化颜色选择器	2024-01-02 12:17:41 +08:00
shadowcz007	765be5dab4	1	2024-01-02 11:03:22 +08:00
shadowcz007	7c1523389d	Update index.html	2024-01-02 09:53:25 +08:00
shadowcz007	7a2b1ba166	支持category	2024-01-02 09:32:46 +08:00
shadowcz007	45b4dcfcd0	Update index.html	2024-01-01 22:46:10 +08:00
shadowcz007	3b2e535566	add photoswipe	2024-01-01 22:34:58 +08:00
shadowcz007	db556d13a3	1	2024-01-01 21:33:54 +08:00
shadowcz007	a987063c68	nodes map - appinfo	2024-01-01 21:08:38 +08:00
shadowcz007	4ce30ef899	Update ui_mixlab.js	2024-01-01 20:33:00 +08:00
shadowcz007	d988282d98	增加种子生成模式切换	2024-01-01 20:24:12 +08:00
shadowcz007	695fdf7ceb	update	2024-01-01 20:08:39 +08:00
shadowcz007	0befe164cc	v0.9.0	2024-01-01 16:12:58 +08:00
shadowcz007	d506c68a80	promptslide-appinfo-workflow.svg	2024-01-01 16:10:46 +08:00
shadowcz007	c59c429b75	update	2024-01-01 16:05:19 +08:00
shadowcz007	c726b6e4a2	prompt weight 提供选项	2024-01-01 15:56:07 +08:00
shadowcz007	96075ad4e1	Update ImageNode.py	2024-01-01 14:17:58 +08:00
shadowcz007	7a8dc07a8a	Update ui_mixlab.js	2024-01-01 11:46:21 +08:00
shadowcz007	1fdac0bc09	Update index.html	2024-01-01 11:32:06 +08:00
shadowcz007	804b942a36	Update index.html	2024-01-01 11:16:56 +08:00
shadowcz007	6335d4378b	优化	2024-01-01 10:55:09 +08:00
shadowcz007	cfc2189616	v0.8.1	2023-12-31 23:58:16 +08:00
				`@@ -0,0 +1 @@`
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