ing

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
28 changed files with 3854 additions and 888 deletions
@@ -1,5 +1,15 @@
 > 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
@@ -29,12 +39,14 @@ APP-JSON:
 - [image-to-image](./example/Image-to-Image_2.json)
 - text-to-text

-> 暂时支持8种节点作为界面上的输入节点：Load Image、CLIPTextEncode、PromptSlide、TextInput_、Color、FloatSlider、IntNumber、CheckpointLoaderSimple、LoraLoader
+> 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、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
 > ScreenShareNode & FloatingVideoNode. Now comfyui supports capturing screen pixel streams from any software and can be used for LCM-Lora integration. Let's get started with implementation and design! 💻🌐
@@ -67,7 +79,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 > PromptSlide
 ![](./assets/prompt_weight.png)

-![](./workflow/promptslide-appinfo-workflow.svg)
+<!-- ![](./workflow/promptslide-appinfo-workflow.svg) -->

 > randomPrompt

@@ -115,6 +127,11 @@ 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

@@ -130,15 +147,6 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 ![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.
@@ -151,6 +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.
@@ -164,13 +178,14 @@ An improvement has been made to directly redirect to GitHub to search for missin


 ### Models
-[Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : 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

 [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:text_generator/text2image-prompt-generator
+[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

@@ -203,18 +218,26 @@ 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)


-
 <picture>
  <source
    media="(prefers-color-scheme: dark)"
@@ -192,7 +192,7 @@ 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)
@@ -222,7 +222,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
            # 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'],
@@ -231,8 +232,14 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                    })
                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,
@@ -244,6 +251,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                                "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"]
@@ -271,8 +280,14 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                # 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,
@@ -284,6 +299,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                                "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"]
@@ -414,7 +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):
@@ -500,27 +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
-        try:
-            self.user_manager.add_routes(self.routes)
-        except:
-            print('pls update')
-        
-        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
@@ -534,15 +530,15 @@ PromptServer.add_routes=new_add_routes


 # 导入节点
-from .nodes.PromptNode import RandomPrompt,PromptSlide,PromptSimplification,PromptImage
-from .nodes.ImageNode import GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
-from .nodes.Vae import VAELoader,VAEDecode
+from .nodes.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.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode

+from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
+from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
+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
@@ -550,7 +546,10 @@ from .nodes.Utils import TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,Color
 NODE_CLASS_MAPPINGS = {
    "AppInfo":AppInfo,
    "TESTNODE_":TESTNODE_,
+    "TESTNODE_TOKEN":TESTNODE_TOKEN,
    "RandomPrompt":RandomPrompt,
+    # "LoraPrompt":LoraPrompt,
+    "EmbeddingPrompt":EmbeddingPrompt,
    "PromptSlide":PromptSlide,
    "PromptSimplification":PromptSimplification,
    "PromptImage":PromptImage,
@@ -568,6 +567,9 @@ NODE_CLASS_MAPPINGS = {
    "ImageColorTransfer":ImageColorTransfer,
    "ShowLayer":ShowLayer,
    "NewLayer":NewLayer,
+    "SplitImage":SplitImage,
+    "CenterImage":CenterImage,
+    "GridOutput":GridOutput,
    "MergeLayers":MergeLayers,
    "SplitLongMask":SplitLongMask,
    "FeatheredMask":FeatheredMask,
@@ -575,15 +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,
@@ -597,6 +599,12 @@ NODE_CLASS_MAPPINGS = {
    "GetImageSize_":GetImageSize_,
    "SwitchByIndex":SwitchByIndex,
    "LimitNumber":LimitNumber, 
+    "OutlineMask":OutlineMask,
+    "JoinWithDelimiter":JoinWithDelimiter,
+    "Seed_":CreateSeedNode,
+    "CkptNames_":CreateCkptNames,
+    "SamplerNames_":CreateSampler_names,
+    "LoraNames_":CreateLoraNames
    # "LaMaInpainting":LaMaInpainting
    # "GamePal":GamePal
 }
@@ -622,7 +630,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "PromptSlide":"PromptSlide ♾️Mixlab",
    "PromptGenerate_Mix":"PromptGenerate ♾️Mixlab",
    "ChinesePrompt_Mix":"ChinesePrompt ♾️Mixlab",
-    "GamePal":"GamePal ♾️Mixlab"
+    "GamePal":"GamePal ♾️Mixlab",
+    "RembgNode_Mix":"Removebg",
+    "LoraNames_":"LoraName_TriggerWords.safetensors"
 }

 # web ui的节点功能
@@ -636,16 +646,16 @@ try:
    print('LaMaInpainting.available',LaMaInpainting.available)
    if LaMaInpainting.available:
        NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
-except:
-    print('LaMaInpainting.available',False)
+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:
-    print('ClipInterrogator.available',False)
+except Exception as e:
+    print('ClipInterrogator.available',False,e)

 try:
    from .nodes.TextGenerateNode import PromptGenerate,ChinesePrompt
@@ -655,8 +665,15 @@ try:
    print('ChinesePrompt.available',ChinesePrompt.available)
    if ChinesePrompt.available:
        NODE_CLASS_MAPPINGS['ChinesePrompt_Mix']=ChinesePrompt
-except:
-    print('TextGenerateNode.available',False)
- 
+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')
@@ -4761,7 +4761,10 @@
    ],
    "https://github.com/shadowcz007/comfyui-mixlab-nodes": [
        [
+            "GridOutput",
+            "SplitImage",
            "PromptGenerate_Mix",
+            "JoinWithDelimiter",
            "ChinesePrompt_Mix",
            "3DImage",
            "AppInfo",
@@ -4770,12 +4773,15 @@
            "ResizeImage",
            "NoiseImage",
            "PromptImage",
+            "SaveImageToLocal",
            "AreaToMask",
-            "CLIPSeg_",
            "CharacterInText",
            "ChatGPTOpenAI",
            "Color",
-            "CombineMasks_",
+            "Seed_",
+            "CkptNames_",
+            "SamplerNames_",
+            "LoraNames_",
            "EnhanceImage",
            "GradientImage",
            "FaceToMask",
@@ -4787,6 +4793,7 @@
            "LoadImagesFromURL",
            "MergeLayers",
            "NewLayer",
+            "CenterImage",
            "RandomPrompt",
            "PromptSlide",
            "PromptSimplification",
@@ -4802,12 +4809,11 @@
            "TextImage",
            "ResizeImageMixlab",
            "TransparentImage",
-            "VAEDecodeConsistencyDecoder",
-            "VAELoaderConsistencyDecoder",
            "TextToNumber",
            "TextInput_",
            "DynamicDelayProcessor",
-            "LaMaInpainting"
+            "LaMaInpainting",
+            "Moondream"
        ],
        {
            "title_aux": "comfyui-mixlab-nodes"
@@ -0,0 +1 @@
+{}
@@ -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
@@ -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
@@ -179,7 +201,60 @@ class ShowTextForGPT:

    CATEGORY = "♾️Mixlab/GPT"

-    def run(self, text):
+    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,)}
        
@@ -212,7 +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,)
+
+
@@ -24,6 +24,7 @@ def is_installed(package):
        return False
    return spec is not None

+
 try:
    if is_installed('clip_interrogator')==False:
        import subprocess
@@ -36,20 +37,25 @@ try:
        #检查命令执行结果
        if result.returncode == 0:
            print("#install success")
-            from transformers import AutoProcessor, BlipForConditionalGeneration
            from clip_interrogator import Config, Interrogator
            _available=True
        else:
            print("#install error")
        
    else:
-        from transformers import AutoProcessor, BlipForConditionalGeneration
        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
@@ -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": 3}),
-                        "threshold": ("FLOAT", {"min": 0, "max": 1, "step": 0.05, "default": 0.3}),
-                        "dilation_factor": ("INT", {"min": 0, "max": 10, "step": 1, "default": 4}),
-                    }
-                }
-
-    CATEGORY = "♾️Mixlab/Mask"
-    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,
-# }
@@ -9,10 +9,23 @@ from io import BytesIO
 import folder_paths
 import json,io
 from comfy.cli_args import args
-import cv2 
-import math
+import cv2
+import string
+import math,glob
 from .Watcher import FolderWatcher

+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("*")
+

 FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))

@@ -70,6 +83,48 @@ def color_transfer(source,target):



+# 组合
+def create_big_image(image_folder, image_count):
+    # 计算行数和列数
+    rows = math.ceil(math.sqrt(image_count))
+    cols = math.ceil(image_count / rows)
+
+    # 获取每个小图的尺寸
+    small_width = 100
+    small_height = 100
+
+    # 计算大图的尺寸
+    big_width = small_width * cols
+    big_height = small_height * rows
+
+    # 创建一个新的大图
+    big_image = Image.new('RGB', (big_width, big_height))
+
+    # 获取所有图片文件的路径
+    image_files = [f for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))]
+
+    # 遍历所有图片文件
+    for i, image_file in enumerate(image_files):
+        # 打开图片并调整大小
+        image = Image.open(os.path.join(image_folder, image_file))
+        image = image.resize((small_width, small_height))
+
+        # 计算当前小图的位置
+        row = i // cols
+        col = i % cols
+        x = col * small_width
+        y = row * small_height
+
+        # 将小图粘贴到大图上
+        big_image.paste(image, (x, y))
+
+    return big_image
+
+# # 调用方法并保存大图
+# image_folder = 'path/to/folder/containing/images'
+# image_count = 100
+# big_image = create_big_image(image_folder, image_count)
+# big_image.save('path/to/save/big_image.jpg')



@@ -85,10 +140,10 @@ def naive_cutout(img, mask,invert=True):
    image using the mask.
    """

-    img=img.convert("RGBA")
+    # img=img.convert("RGBA")
    mask=mask.convert("RGBA")

-    empty = Image.new("RGBA", (img.size), 0)
+    empty = Image.new("RGBA", (mask.size), 0)

    red, green, blue, alpha = mask.split()

@@ -249,6 +304,11 @@ def generate_gradient_image(width, height, start_color_hex, end_color_hex):
 # gradient_image = generate_gradient_image(width, height, start_color_hex, end_color_hex)
 # gradient_image.save('gradient_image.png')

+def rgb_to_hex(rgb):
+    r, g, b = rgb
+    hex_color = "#{:02x}{:02x}{:02x}".format(r, g, b)
+    return hex_color
+

 # 读取不了分层
 def load_psd(image):
@@ -320,6 +380,7 @@ def get_images_filepath(f,white_bg=False):
        for root, dirs, files in os.walk(f):
            for file in files:
                file_path = os.path.join(root, file)
+                file_name=os.path.basename(file_path)
                try:
                    imgs=load_image(file_path,white_bg)
                    for img in imgs:
@@ -327,6 +388,7 @@ def get_images_filepath(f,white_bg=False):
                            "image":img['image'],
                            "mask":img['mask'],
                            "file_path":file_path,
+                            "file_name":file_name,
                            "psd":len(imgs)>1
                        })
                except:
@@ -334,12 +396,15 @@ def get_images_filepath(f,white_bg=False):
 
    elif os.path.isfile(f):
        try:
+            file_path = os.path.join(root, f)
+            file_name=os.path.basename(file_path)
            imgs=load_image(f,white_bg)
            for img in imgs:
                images.append({
                    "image":img['image'],
                    "mask":img['mask'],
                    "file_path":file_path,
+                    "file_name":file_name,
                    "psd":len(imgs)>1
                })
        except:
@@ -390,7 +455,8 @@ def get_average_color_image(image):

    im = Image.new("RGB", (image.width, image.height), (average_red, average_green, average_blue))

-    return im
+    hex=rgb_to_hex((average_red, average_green, average_blue))
+    return (im,hex)



@@ -797,79 +863,6 @@ class SmoothMask:



-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 = (False,)
-  
-    # 运行的函数
-    def run(self,mask,start_offset, feathering_weight):
-        # print(mask.shape,mask.size())
-        
-        image=tensor2pil(mask)
-
-        # 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)
-        
-        mask=pil2tensor(result_image)
-        # print(mask.shape,mask.size())
-        return mask
-
-


 class SplitLongMask:
@@ -939,7 +932,7 @@ class TransparentImage:

    # 运行的函数
    def run(self,images,masks,invert,save,filename_prefix,prompt=None, extra_pnginfo=None):
-        print('TransparentImage',images.shape,images.size())
+        # print('TransparentImage',images.shape,images.size(),masks.shape,masks.size())
        # print(masks.shape,masks.size())

        ui_images=[]
@@ -949,11 +942,16 @@ class TransparentImage:
        masks_new=[]
        nh=masks.shape[0]//count

-        #INPUT_IS_LIST = False, 一个batch传进来
-        if nh*count==masks.shape[0]:
-            masks_new=split_mask_by_new_height(masks,nh)
+        masks_new=masks
+
+        if images.shape[0]==masks.shape[0] and  images.shape[1]==masks.shape[1] and  images.shape[2]==masks.shape[2]:
+            print('TransparentImage',images.shape,images.size(),masks.shape,masks.size())
        else:
-            masks_new=split_mask_by_new_height(masks,masks.shape[0])
+            #INPUT_IS_LIST = False, 一个batch传进来
+            if nh*count==masks.shape[0]:
+                masks_new=split_mask_by_new_height(masks,nh)
+            else:
+                masks_new=split_mask_by_new_height(masks,masks.shape[0])


        is_save=True if save=='yes' else False
@@ -979,6 +977,7 @@ class TransparentImage:
        # ui.images 节点里显示图片，和 传参，image_path自定义的数据，需要写节点的自定义ui
        # result 里输出给下个节点的数据 
        # print('TransparentImage',len(images_rgb))
+        
        return {"ui":{"images": ui_images,"image_paths":image_paths},"result": (image_paths,images_rgb,images_rgba)}


@@ -1065,14 +1064,15 @@ class LoadImagesFromPath:
                }
            }
    
-    RETURN_TYPES = ('IMAGE','MASK','STRING',)
+    RETURN_TYPES = ('IMAGE','MASK','STRING','STRING',)
+    RETURN_NAMES = ("IMAGE","MASK","prompt_for_FloatingVideo","filepaths",)

    FUNCTION = "run"

    CATEGORY = "♾️Mixlab/Image"

    # INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,True,False,)
+    OUTPUT_IS_LIST = (True,True,False,True,)
  
    global watcher_folder
    watcher_folder=None
@@ -1108,10 +1108,12 @@ class LoadImagesFromPath:

        imgs=[]
        masks=[]
+        file_names=[]

        for im in sorted_files:
            imgs.append(im['image'])
            masks.append(im['mask'])
+            file_names.append(im['file_name'])
        
        # print('index_variable',index_variable)
        
@@ -1119,11 +1121,12 @@ class LoadImagesFromPath:
            if index_variable!=-1:
                imgs=[imgs[index_variable]] if index_variable < len(imgs) else None
                masks=[masks[index_variable]] if index_variable < len(masks) else None
+                file_names=[file_names[index_variable]] if index_variable < len(file_names) else None
        except Exception as e:
            print("发生了一个未知的错误：", str(e))

        # print('#prompt::::',prompt)
-        return  {"ui": {"seed": [1]}, "result":(imgs,masks,prompt,)}
+        return  {"ui": {"seed": [1]}, "result":(imgs,masks,prompt,file_names,)}


 # TODO 扩大选区的功能,重新输出mask
@@ -1154,7 +1157,12 @@ class ImageCropByAlpha:

        # print(RGBA)
        im=tensor2pil(RGBA)
-        im=naive_cutout(im,im)
+
+        # 要把im的alpha通道转为mask
+        im=im.convert('RGBA')
+        red, green, blue, alpha = im.split()
+
+        im=naive_cutout(bf_im,alpha)
        x, y, w, h=get_not_transparent_area(im)
        # print('#ForImageCrop:',w, h,x, y,)

@@ -1169,11 +1177,12 @@ class ImageCropByAlpha:
        height_1=h

        img = image[:,y:to_y, x:to_x, :]
-
+        # tensor2pil(img).save('test2.png')

        # 原图的mask
        ori=RGBA[:,y:to_y, x:to_x, :]
        ori=tensor2pil(ori)
+        # ori.save('test.png')

        # 创建一个新的图像对象，大小和模式与原始图像相同
        new_image = Image.new("RGBA", ori.size)
@@ -1194,7 +1203,7 @@ class ImageCropByAlpha:
                if a != 0:
                    new_pixel_data[x, y] = (255, 255, 255, 255)
                else:
-                    new_pixel_data[x, y] = (r, g, b, a)
+                    new_pixel_data[x, y] = (0,0,0,0)

        # 保存修改后的图像
        # new_image.save("output.png")
@@ -1268,6 +1277,9 @@ class LoadImagesFromURL:
        return {"required": { 
                    "url": ("STRING",{"multiline": True,"default": "https://","dynamicPrompts": False}),
                             },
+                "optional":{
+                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                }
                }
    
    RETURN_TYPES = ("IMAGE","MASK",)
@@ -1284,7 +1296,7 @@ class LoadImagesFromURL:
    global urls_image
    urls_image={}

-    def run(self,url):
+    def run(self,url,seed=0):
        global urls_image
        print(urls_image)
        def filter_http_urls(urls):
@@ -1616,6 +1628,201 @@ class NewLayer:

        return (layer_n,)

+
+
+def createMask(image,x,y,w,h):
+    mask = Image.new("L", image.size)
+    pixels = mask.load()
+    # 遍历指定区域的像素，将其设置为黑色（0 表示黑色）
+    for i in range(int(x), int(x + w)):
+        for j in range(int(y), int(y + h)):
+            pixels[i, j] = 255
+    # mask.save("mask.png")
+    return mask
+
+
+def splitImage(image, num):
+    width, height = image.size
+
+    num_rows = int(num ** 0.5)
+    num_cols = int(num / num_rows)
+    
+    grid_width = width // num_cols
+    grid_height = height // num_rows
+
+    grid_coordinates = []
+    for i in range(num_rows):
+        for j in range(num_cols):
+            x = j * grid_width
+            y = i * grid_height
+            grid_coordinates.append((x, y, grid_width, grid_height))
+
+    return grid_coordinates
+
+
+def centerImage(margin,canvas):
+    w,h=canvas.size
+
+    l,t,r,b=margin
+
+    x=l
+    y=t
+    width=w-r-l
+    height=h-t-b
+
+    return (x,y,width,height)
+
+# # 读取图片
+# image = Image.open("path_to_your_image.jpg")
+
+# # 定义要切割的区域数量
+# num = 9
+
+# # 切割图片
+# grid_coordinates = splitImage(image, num)
+
+# # 输出切割区域坐标
+# for i, coordinates in enumerate(grid_coordinates):
+#     print(f"Region {i + 1}: x={coordinates[0]}, y={coordinates[1]}, width={coordinates[2]}, height={coordinates[3]}")
+
+
+class SplitImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return { 
+            "required": { 
+                "image": ("IMAGE",), 
+                "num": ("INT",{
+                    "default": 4, 
+                    "min": 1, #Minimum value
+                    "max": 500, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                "seed": ("INT",{
+                    "default": 4, 
+                    "min": 1, #Minimum value
+                    "max": 500, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+            }
+                }
+    
+    RETURN_TYPES = ("_GRID","_GRID","MASK",)
+    RETURN_NAMES = ("grids","grid","mask",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    INPUT_IS_LIST = False
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,image,num,seed):
+        image=tensor2pil(image)
+        
+        grids=splitImage(image,num)
+
+        if seed>num:
+            num=int(seed / 500 * num)-1
+        else:
+            num=seed-1
+
+        num=max(0,num) 
+
+        g=grids[num]
+
+        x,y,w,h=g
+        mask=createMask(image, x,y,w,h)
+        mask=pil2tensor(mask)
+
+        return (grids,g,mask,)
+
+
+
+class CenterImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return { 
+            "required": { 
+                "canvas": ("IMAGE",), 
+                "left": ("INT",{
+                    "default":24, 
+                    "min": 0, #Minimum value
+                    "max": 5000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+               "top": ("INT",{
+                    "default":24, 
+                    "min": 0, #Minimum value
+                    "max": 5000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                "right": ("INT",{
+                    "default": 24, 
+                    "min": 0, #Minimum value
+                    "max": 5000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                 "bottom": ("INT",{
+                    "default": 24, 
+                    "min": 0, #Minimum value
+                    "max": 5000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+            }
+                }
+    
+    RETURN_TYPES = ("_GRID","MASK",)
+    RETURN_NAMES = ("grid","mask",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    INPUT_IS_LIST = False
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,canvas,left,top,right,bottom):
+        canvas=tensor2pil(canvas)
+
+        grid=centerImage((left,top,right,bottom),canvas)
+
+        mask=createMask(canvas,left,top,canvas.width-left-right,canvas.height-top-bottom)
+
+        return (grid,pil2tensor(mask),)
+
+
+
+
+class GridOutput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return { 
+            "required": {
+                "grid": ("_GRID",)
+            }
+                }
+    
+    RETURN_TYPES = ("INT","INT","INT","INT",)
+    RETURN_NAMES = ("x","y","width","height",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    INPUT_IS_LIST = False
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,grid):
+        x,y,w,h=grid
+        return (x,y,w,h,)
+
 class ShowLayer:
    @classmethod
    def INPUT_TYPES(s):
@@ -1952,14 +2159,14 @@ class ResizeImage:
                    "default": 512, 
                    "min": 1, #Minimum value
                    "max": 8192, #Maximum value
-                    "step": 1, #Slider's step
+                    "step": 8, #Slider's step
                    "display": "number" # Cosmetic only: display as "number" or "slider"
                }),
                "height": ("INT",{
                    "default": 512, 
                    "min": 1, #Minimum value
                    "max": 8192, #Maximum value
-                    "step": 1, #Slider's step
+                    "step": 8, #Slider's step
                    "display": "number" # Cosmetic only: display as "number" or "slider"
                }),
                "scale_option": (["width","height",'overall','center'],),
@@ -1970,20 +2177,21 @@ class ResizeImage:
                    "image": ("IMAGE",),
                    "average_color": (["on",'off'],),
                    "fill_color":("STRING",{"multiline": False,"default": "#FFFFFF","dynamicPrompts": False}),
+                    "mask": ("MASK",),
            }
                }
    
-    RETURN_TYPES = ("IMAGE","IMAGE")
-    RETURN_NAMES = ("image","average_image",)
+    RETURN_TYPES = ("IMAGE","IMAGE","STRING","MASK",)
+    RETURN_NAMES = ("image","average_image","average_hex","mask",)

    FUNCTION = "run"

    CATEGORY = "♾️Mixlab/Image"

    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,True,)
+    OUTPUT_IS_LIST = (True,True,True,True,)

-    def run(self,width,height,scale_option,image=None,average_color=['on'],fill_color=["#FFFFFF"]):
+    def run(self,width,height,scale_option,image=None,average_color=['on'],fill_color=["#FFFFFF"],mask=None):
        
        w=width[0]
        h=height[0]
@@ -1992,17 +2200,20 @@ class ResizeImage:
        fill_color=fill_color[0]

        imgs=[]
+        masks=[]
        average_images=[]
+        hexs=[]

        if image==None:
            im=create_noisy_image(w,h,"RGB")
-            a_im=get_average_color_image(im)
+            a_im,hex=get_average_color_image(im)
            
            im=pil2tensor(im)
            imgs.append(im)

            a_im=pil2tensor(a_im)
            average_images.append(a_im)
+            hexs.append(hex)
        else:
            for ims in image:
                for im in ims:
@@ -2010,15 +2221,28 @@ class ResizeImage:
                    im=resize_image(im,scale_option,w,h,fill_color)
                    im=im.convert('RGB')

-                    a_im=get_average_color_image(im)
+                    a_im,hex=get_average_color_image(im)

                    im=pil2tensor(im)
                    imgs.append(im)

                    a_im=pil2tensor(a_im)
                    average_images.append(a_im)
+                    hexs.append(hex)
+
+            try:
+                for mas in mask:
+                    for ma in mas:
+                        ma=tensor2pil(ma)
+                        ma=ma.convert('RGB')
+                        ma=resize_image(ma,scale_option,w,h,fill_color)
+                        ma=ma.convert('L')
+                        ma=pil2tensor(ma)
+                        masks.append(ma)
+            except:
+                print('')
        
-        return (imgs,average_images,)
+        return (imgs,average_images,hexs,masks,)


 class MirroredImage:
@@ -2062,19 +2286,41 @@ class GetImageSize_:
        return {
            "required": {
                "image": ("IMAGE",),
-            }
+            },
+             "optional":{
+                    "min_width":("INT", {
+                        "default": 512, 
+                        "min":1, #Minimum value
+                        "max": 2048, #Maximum value
+                        "step": 8, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    })
+                },
        }

-    RETURN_TYPES = ("INT", "INT")
-    RETURN_NAMES = ("width", "height")
+    RETURN_TYPES = ("INT", "INT","INT", "INT",)
+    RETURN_NAMES = ("width", "height","min_width", "min_height",)

    FUNCTION = "get_size"

    CATEGORY = "♾️Mixlab/Image"

-    def get_size(self, image):
+    def get_size(self, image,min_width):
        _, height, width, _ = image.shape
-        return (width, height)
+        
+        # 如果比min_widht,还小，则输出 min width
+        if min_width>width:
+            im=tensor2pil(image)
+            im=resize_image(im,'width',min_width,min_width,"white")
+            im=im.convert('RGB')
+
+            min_width,min_height=im.size
+
+        else:
+            min_width=width
+            min_height=height
+
+        return (width, height,min_width,min_height,)



@@ -2120,3 +2366,105 @@ class ImageColorTransfer:
        return (res,)


+
+class SaveImageToLocal:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": 
+                    {"images": ("IMAGE", ),
+                     "file_path": ("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
+                     },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "♾️Mixlab/Image"
+
+    def save_images(self, images,file_path , prompt=None, extra_pnginfo=None):
+        filename_prefix = os.path.basename(file_path)
+        if file_path=='':
+            filename_prefix="ComfyUI"
+        
+        filename_prefix, _ = os.path.splitext(filename_prefix)
+
+        _, extension = os.path.splitext(file_path)
+
+        if extension:
+            # 是文件名，需要处理
+            file_path=os.path.dirname(file_path)
+            # filename_prefix=
+
+            
+        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])
+        
+    
+        if not os.path.exists(file_path) and not extension:
+            # 使用os.makedirs函数创建新目录
+            os.makedirs(file_path)
+            print("目录已创建")
+        else:
+            print("目录已存在")
+
+        # 使用glob模块获取当前目录下的所有文件
+        if file_path=="":
+            files = glob.glob(full_output_folder + '/*')
+        else:
+            files = glob.glob(file_path + '/*')
+        # 统计文件数量
+        file_count = len(files)
+        counter+=file_count
+        print('统计文件数量',file_count,counter)
+
+        results = list()
+        for image in images:
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            metadata = None
+            if not args.disable_metadata:
+                metadata = PngInfo()
+                if prompt is not None:
+                    metadata.add_text("prompt", json.dumps(prompt))
+                if extra_pnginfo is not None:
+                    for x in extra_pnginfo:
+                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+            file = f"{filename}_{counter:05}_.png"
+            
+            if file_path=="":
+                fp=os.path.join(full_output_folder, file)
+                if os.path.exists(fp):
+                    file = f"{filename}_{counter:05}_{generate_random_string(8)}.png"
+                    fp=os.path.join(full_output_folder, file)
+                img.save(fp, pnginfo=metadata, compress_level=self.compress_level)
+                results.append({
+                    "filename": file,
+                    "subfolder": subfolder,
+                    "type": self.type
+                })
+            
+            else:
+
+                fp=os.path.join(file_path, file)
+                if os.path.exists(fp):
+                    file = f"{filename}_{counter:05}_{generate_random_string(8)}.png"
+                    fp=os.path.join(file_path, file)
+
+                img.save(os.path.join(file_path, file), pnginfo=metadata, compress_level=self.compress_level)
+                results.append({
+                    "filename": file,
+                    "subfolder": file_path,
+                    "type": self.type
+                })
+            counter += 1
+
+        return ()
@@ -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,)
@@ -6,12 +6,86 @@ 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):
 #     p = {"prompt": prompt_workflow}
 #     data = json.dumps(p).encode('utf-8')
 #     req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
 #     request.urlopen(req)    

+embeddings_path=os.path.join(folder_paths.models_dir, "embeddings")
+
+def get_files_with_extension(directory, extension):
+    file_list = []
+    for root, dirs, files in os.walk(directory):
+        for file in files:
+            if file.endswith(extension):
+                file_name = os.path.splitext(file)[0]
+                file_list.append(file_name)
+    return file_list
+
+def join_with_(text_list,delimiter):
+    joined_text = delimiter.join(text_list)
+    return joined_text
+
+
+
+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
@@ -54,7 +128,7 @@ 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
@@ -297,6 +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}),
                }
            }
    
@@ -313,7 +391,7 @@ class RandomPrompt:


    # 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
@@ -334,7 +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)
@@ -354,65 +435,142 @@ class RandomPrompt:
        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,)
@@ -6,7 +6,7 @@ import os,sys
 import folder_paths

 # from PIL import Image
-# import importlib.util
+import importlib.util

 import comfy.utils
 # import numpy as np
@@ -29,18 +29,50 @@ if not os.path.exists(zh_en_model_path):



-def translate(zh_en_tokenizer,zh_en_model,texts):
+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(texts, return_tensors="pt")
+        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)
+        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:
@@ -66,6 +98,55 @@ def text_generate(text_pipe,input,seed=None):

 # 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:
@@ -76,12 +157,13 @@ class ChinesePrompt:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
-            "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
-            
+               "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
+               "generation": (["on","off"],{"default": "off"}), 
                             },
-
+               
                "optional":{
                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
+                    
                },

                }
@@ -102,17 +184,20 @@ class ChinesePrompt:
    zh_en_model=None
    zh_en_tokenizer=None 

-    def run(self,text,seed):
+    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)
+            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")
@@ -124,24 +209,31 @@ class ChinesePrompt:
        prompt_result=[]

        # print('zh_en_model device',zh_en_model.device,text_pipe.model.device,torch.cuda.current_device() )
-        en_text=translate(zh_en_tokenizer,zh_en_model,text)
+        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_text:
-            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)
+        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
@@ -5,6 +5,8 @@ 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):
@@ -131,7 +133,12 @@ def flatten_list(nested_list):
        if isinstance(item, list):
            flat_list.extend(flatten_list(item))
        else:
-            flat_list.append(item)
+            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


@@ -193,14 +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",)
@@ -213,7 +223,7 @@ class TextToNumber:
    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
@@ -222,7 +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,)}
    

@@ -336,11 +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"
                    })
                             },
@@ -355,16 +372,16 @@ class MultiplicationNode:
    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": ""})
                             },
                }
    
@@ -413,7 +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"})
@@ -485,7 +502,7 @@ class AppInfo:
                             },

                "optional":{
-                    "LOGO": ("IMAGE",),
+                    "IMAGE": ("IMAGE",),
                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
                    "version":("INT", {
                        "default": 1, 
@@ -513,12 +530,12 @@ class AppInfo:
    INPUT_IS_LIST = True
    # OUTPUT_IS_LIST = (True,)

-    def run(self,name,input_ids,output_ids,LOGO,description,version,share_prefix,link,category,auto_save):
+    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
        name=name[0]
        
        im=None
-        if LOGO:
-            im=LOGO[0][0]
+        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，
@@ -573,7 +590,8 @@ class SwitchByIndex:

        C=[]
        index=index[0]
-        for a in A:
+
+        for a in A:    
            C.append(a)
        for b in B:
            C.append(b)
@@ -677,7 +695,9 @@ class ListStatistics:
 class TESTNODE_:
    @classmethod
    def INPUT_TYPES(s):
-        return {"required": { "ANY":(any_type,), },
+        return {"required": { 
+            "ANY":(any_type,),                
+                              },
                }
    
    RETURN_TYPES = (any_type,)
@@ -697,5 +717,151 @@ class TESTNODE_:

        # 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, )
@@ -4,5 +4,6 @@ watchdog
 opencv-python-headless
 matplotlib
 openai
-# simple-lama-inpainting
-# clip-interrogator==0.6.0
+simple-lama-inpainting
+clip-interrogator==0.6.0
+transformers>=4.36.0
@@ -11,6 +11,12 @@
            padding: 0;
        }

+        .header {
+            display: flex;
+            align-items: center;
+            justify-content: space-around;
+        }
+
        .app {
            display: flex;
            width: 90%;
@@ -142,6 +148,10 @@
            word-wrap: break-word;
        }

+        .description img {
+            min-height: unset !important;
+        }
+
        .panel {
            display: flex;
            flex-direction: column;
@@ -172,6 +182,7 @@
            width: fit-content;
            max-width: 100%;
            margin-left: 12px;
+            min-height: 200px;
        }

        .input_card {
@@ -378,9 +389,7 @@

 <body>
    <div id="editor_container"></div>
-    <div style="display: flex;
-    align-items: center;
-    justify-content: space-around;">
+    <div class="header">
        <div style="margin: 0 24px;
            margin-bottom: 24px;
            padding: 8px;
@@ -391,14 +400,14 @@
            <a class="link" href="https://www.mixcomfy.com" target="_blank">ComfyUI中文爱好者社区推荐</a>
        </div>

-        <a target="_blank" href="https://github.com/shadowcz007/comfyui-mixlab-nodes" style="text-decoration: none;
+        <a target="_blank" href="https://www.mixcomfy.com/blog/" style="text-decoration: none;
        color: black;font-size:12px">
            <svg height="32" aria-hidden="true" viewBox="0 0 16 16" version="1.1" width="32" data-view-component="true"
                class="octicon octicon-mark-github v-align-middle color-fg-default">
                <path
                    d="M8 0c4.42 0 8 3.58 8 8a8.013 8.013 0 0 1-5.45 7.59c-.4.08-.55-.17-.55-.38 0-.27.01-1.13.01-2.2 0-.75-.25-1.23-.54-1.48 1.78-.2 3.65-.88 3.65-3.95 0-.88-.31-1.59-.82-2.15.08-.2.36-1.02-.08-2.12 0 0-.67-.22-2.2.82-.64-.18-1.32-.27-2-.27-.68 0-1.36.09-2 .27-1.53-1.03-2.2-.82-2.2-.82-.44 1.1-.16 1.92-.08 2.12-.51.56-.82 1.28-.82 2.15 0 3.06 1.86 3.75 3.64 3.95-.23.2-.44.55-.51 1.07-.46.21-1.61.55-2.33-.66-.15-.24-.6-.83-1.23-.82-.67.01-.27.38.01.53.34.19.73.9.82 1.13.16.45.68 1.31 2.69.94 0 .67.01 1.3.01 1.49 0 .21-.15.45-.55.38A7.995 7.995 0 0 1 0 8c0-4.42 3.58-8 8-8Z">
                </path>
-            </svg> Code
+            </svg> Community

        </a>
    </div>
@@ -435,8 +444,9 @@
                body
            })

-            // console.log(resp)
+
            let data = await resp.json()
+            // console.log(data)
            let { name, subfolder } = data
            let src = `${url}/view?filename=${encodeURIComponent(
                name
@@ -643,7 +653,7 @@
            return true
        }

-
+        // 种子的处理
        function randomSeed(seed, data) {
            for (const id in data) {
                if (data[id].inputs.seed != undefined
@@ -761,19 +771,44 @@
            } catch (error) {
                isURL = false;
            }
+
+            let isAElement = undefined;
+            try {
+                let div = document.createElement('div');
+                div.innerHTML = link;
+                let a = div.querySelector('a');
+                if (a.href) {
+                    new URL(a.href);
+                    isAElement = div.innerHTML;
+                }
+            } catch (error) {
+
+            }
            // new URL(link)
-            if (isURL) {
+            if (isURL || isAElement) {
                const linkBtn = document.createElement('button');
-                // linkBtn.href = link;
-                linkBtn.innerText = 'go to'
+
+                if (isURL) {
+                    // linkBtn.href = link;
+                    linkBtn.innerText = 'go to'
+                } else if (isAElement) {
+                    linkBtn.innerHTML = isAElement
+                }
+
                action.appendChild(linkBtn)
                linkBtn.style.marginLeft = '18px';
                linkBtn.addEventListener('click', e => {
-                    e.preventDefault();
-                    window.open(link);
+
+                    if (isURL) {
+                        e.preventDefault();
+                        window.open(link);
+                    }
+                    //   if(isURL)  window.open(link);
                })
+
            }

+
            const output_card = document.createElement("div");
            output_card.className = 'output_card'
            container.appendChild(output_card)
@@ -863,6 +898,49 @@
        }


+        function generateRainbowVideo() {
+
+            // 创建一个canvas元素
+            const canvas = document.createElement('canvas');
+            canvas.width = 640; // 设置canvas宽度
+            canvas.height = 480; // 设置canvas高度
+            const context = canvas.getContext('2d');
+
+            // 绘制第一帧彩虹
+            context.fillStyle = 'red';
+            context.fillRect(0, 0, canvas.width / 2, canvas.height);
+            context.fillStyle = 'orange';
+            context.fillRect(canvas.width / 2, 0, canvas.width / 2, canvas.height);
+
+            // 绘制第二帧彩虹
+            context.fillStyle = 'yellow';
+            context.fillRect(0, 0, canvas.width / 2, canvas.height);
+            context.fillStyle = 'green';
+            context.fillRect(canvas.width / 2, 0, canvas.width / 2, canvas.height);
+
+            const stream = canvas.captureStream();
+
+            return new Promise((res, rej) => {
+                // 导出视频
+                const mediaRecorder = new MediaRecorder(stream);
+                const chunks = [];
+                mediaRecorder.ondataavailable = function (event) {
+                    chunks.push(event.data);
+                };
+                mediaRecorder.onstop = function () {
+                    const blob = new Blob(chunks, { type: 'video/mp4' });
+                    const url = URL.createObjectURL(blob);
+                    res(url)
+                };
+                mediaRecorder.start();
+                setTimeout(function () {
+                    mediaRecorder.stop();
+                }, 1000); // 设置录制时长，这里设置为1秒
+            })
+
+        }
+
+
        async function calculateImageHash(blob) {
            const buffer = await blob.arrayBuffer();
            const hashBuffer = await crypto.subtle.digest('SHA-256', buffer);
@@ -1056,20 +1134,25 @@
            inputData = inputData.filter(inp => inp);
            // console.log('inputData',inputData)
            inputData.forEach(data => {
-                // console.log(data)
-                // Check if the class_type is "LoadImage"
-                if (data.class_type === "LoadImage") {
+                console.log(data)
+
+                // 图片 or 视频输入
+                if (data.class_type === "LoadImage" || data.class_type === "VHS_LoadVideo") {
+
+                    let isVideoUpload = data.class_type === "VHS_LoadVideo";
+
                    // Create a container for the upload control
                    const uploadContainer = document.createElement("div");
                    uploadContainer.className = 'card';

                    // Create a label for the upload control
                    const nameLabel = document.createElement("label");
-                    nameLabel.textContent = data.title || "LoadImage: ";
+                    nameLabel.textContent = data.title || (isVideoUpload ? "LoadVideo: " : "LoadImage: ");
                    nameLabel.style.marginBottom = '12px'
                    uploadContainer.appendChild(nameLabel);

                    let actionDiv = document.createElement('div');
+                    actionDiv.style = `padding: 0 8px;`

                    // Create an input field for the image name
                    const uploadImageInput = document.createElement("button");
@@ -1084,25 +1167,52 @@
                    const btnFromClipboard = document.createElement("button");
                    btnFromClipboard.style = `width: 156px; margin-left: 18px;`
                    btnFromClipboard.innerText = 'paste from clipboard'
-                    actionDiv.appendChild(btnFromClipboard);
+                    if (!isVideoUpload) actionDiv.appendChild(btnFromClipboard);

                    const btnForImageEdit = document.createElement("button");
                    btnForImageEdit.style = ` width: 32px; background: none;margin-left: 18px;`
                    btnForImageEdit.innerHTML = '<?xml version="1.0" ?><svg version="1.1" style="width: 24px;" viewBox="0 0 50 50" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g id="Layer_1_1_"><path d="M18.293,31.707h6.414l24-24l-6.414-6.414l-24,24V31.707z M45.879,7.707l-3.586,3.586l-3.586-3.586l3.586-3.586   L45.879,7.707z M20.293,26.121l17-17l3.586,3.586l-17,17h-3.586V26.121z"/><polygon points="43.293,19.707 41.293,19.707 41.293,46.707 3.293,46.707 3.293,8.707 31.293,8.707 31.293,6.707 1.293,6.707    1.293,48.707 43.293,48.707  "/></g></svg>'
-                    actionDiv.appendChild(btnForImageEdit);
+                    if (!isVideoUpload) actionDiv.appendChild(btnForImageEdit);


                    uploadContainer.appendChild(actionDiv)

                    // Create an image element to display the uploaded image
-                    const imageElement = document.createElement("img");
-                    imageElement.src = base64Df
+                    let imageElement = document.createElement("img");
+                    if (isVideoUpload) {
+                        // 视频
+                        imageElement = document.createElement('video');
+                        imageElement.setAttribute('controls', true)
+                        let [subfolder, name] = data.inputs.video.split('/');
+                        //    console.log(subfolder,name)
+                        if (!name) {
+                            subfolder = "";
+                            name = data.inputs.video;
+                        }
+                        let url = `${get_url()}/view?filename=${encodeURIComponent(name)}&type=input&subfolder=${subfolder}&rand=${Math.random()}`
+                        imageElement.src = url;
+
+                        // imageElement.innerHTML=`<img src="${base64Df}"/>`
+
+                    } else {
+                        // 图片
+                        let [subfolder, name] = data.inputs.image.split('/');
+                        if (!name) {
+                            subfolder = "";
+                            name = data.inputs.image;
+                        }
+                        // imageElement.src = base64Df
+                        let url = `${get_url()}/view?filename=${encodeURIComponent(name)}&type=input&subfolder=${subfolder}&rand=${Math.random()}`
+                        imageElement.src = url;
+                    }
+
                    imageElement.style.maxWidth = '200px';


-                    btnFromClipboard.addEventListener('click', (event) => handleClipboardImage(imageElement, data));

-                    btnForImageEdit.addEventListener('click', e => editImage(imageElement, data))
+                    if (!isVideoUpload) btnFromClipboard.addEventListener('click', (event) => handleClipboardImage(imageElement, data));
+
+                    if (!isVideoUpload) btnForImageEdit.addEventListener('click', e => editImage(imageElement, data))


                    uploadImageInput.addEventListener('click', (event) => {
@@ -1120,15 +1230,24 @@
                        reader.onloadend = async function () {
                            // 获取读取的文件内容，即 Blob 对象
                            const fileBlob = new Blob([reader.result], { type: file.type });
-
+                            // console.log( file.type.split('/')[1])
                            let hashId = await calculateImageHash(fileBlob)

                            if (hashId == window._appData.data[data.id].hashId) return

-                            let { url, name } = await uploadImage(fileBlob)
+                            let { url, name } = await uploadImage(fileBlob, '.' + file.type.split('/')[1])
+                            if (isVideoUpload) {
+                                imageElement.srcObject = null;
+                            }
                            // 在这里可以对 Blob 对象进行进一步处理
                            imageElement.src = url;
-                            window._appData.data[data.id].inputs.image = name;
+
+                            if (isVideoUpload) {
+                                window._appData.data[data.id].inputs.video = name;
+                            } else {
+                                window._appData.data[data.id].inputs.image = name;
+                            }
+
                            window._appData.data[data.id].hashId = hashId;

                            console.log("上传的文件：", url, data.id, name);
@@ -1147,6 +1266,7 @@
                    container.appendChild(uploadContainer);
                }

+                // 滑块输入
                if (["PromptSlide"].includes(data.class_type)) {
                    // 滑块输入
                    let options = data.options || {
@@ -1176,6 +1296,7 @@
                }


+                // 数字输入支持
                if (['FloatSlider', 'IntNumber'].includes(data.class_type)) {
                    // console.log('data.options',data.options)
                    // 滑块输入
@@ -1198,7 +1319,7 @@
                    container.appendChild(silde);
                }

-                // Check if the class_type is "CLIPTextEncode"
+                // 文本输入支持
                if (["TextInput_", "CLIPTextEncode", "PromptSimplification"].includes(data.class_type)) {
                    // Create a container for the upload control
                    const uploadContainer = document.createElement("div");
@@ -1264,7 +1385,7 @@
                    container.appendChild(uploadContainer);
                }

-
+                // lora的输入支持
                if (["CheckpointLoaderSimple", "LoraLoader"].includes(data.class_type)) {
                    let value = data.inputs.ckpt_name || data.inputs.lora_name;

@@ -1291,6 +1412,7 @@
                        }
                    }), value);

+                    // 选择事件绑定
                    selectDom.addEventListener('change', e => {
                        e.preventDefault();
                        // console.log(selectDom.value)
@@ -1307,6 +1429,7 @@
                    container.appendChild(div);
                }

+                // 色彩选择器
                if (["Color"].includes(data.class_type)) {
                    let value = data.inputs.color.hex || '#000000';
                    let d = document.createElement('div');
@@ -1525,6 +1648,7 @@

        }

+        // 创建下拉选择
        function createSelect(options, defaultValue) {
            var selectElement = document.createElement("select");
            selectElement.className = "select"
@@ -1533,7 +1657,7 @@
            for (var i = 0; i < options.length; i++) {
                var option = document.createElement("option");
                option.value = options[i].value;
-                option.text = options[i].text;
+                option.innerText = options[i].text;
                selectElement.appendChild(option);
                // if(options[i].selected) 
            }
@@ -1544,6 +1668,7 @@
            return selectElement
        }

+        // 创建下拉选择 - 带说明
        function createSelectWithOptions(title, options, defaultValue) {

            const div = document.createElement("div");
@@ -1826,7 +1951,7 @@
                                    p.className = 'prompt_image'
                                    p.innerText = prompt;
                                    a.appendChild(p)
-                                    img.alt=prompt
+                                    img.alt = prompt
                                }

                                // imgDiv.parentElement.appendChild(a);
@@ -1987,9 +2112,10 @@
            };

            api.addEventListener("status", ({ detail }) => {
-                console.log("status", detail, detail.exec_info?.queue_remaining);
+                console.log("status", detail, detail?.exec_info?.queue_remaining);
                try {
                    ui.status.update(`queue#${detail.exec_info?.queue_remaining}`);
+                    window.parent.postMessage({ cmd: 'status', data: `queue#${detail.exec_info?.queue_remaining}` }, '*');
                    if (detail.exec_info?.queue_remaining === 0) {
                        // 运行按钮重设
                        ui.submitButton.reset()
@@ -1997,6 +2123,7 @@
                    }
                } catch (error) {
                    console.log(error)
+                    window.parent.postMessage({ cmd: 'status' }, '*');
                }

            });
@@ -2096,6 +2223,7 @@
            api.addEventListener("execution_error", ({ detail }) => {

                console.log("execution_error", detail)
+                window.parent.postMessage({ cmd: 'status', data: `execution_error:${JSON.stringify(detail)}` }, '*');
                // show(URL.createObjectURL(detail));
            });

@@ -2125,7 +2253,6 @@
            api.init();

            // 外挂的UI
-
            createAllColorInput();


@@ -2176,7 +2303,7 @@
        }

        // 创建app的选择菜单
-        function createAppList(apps = []) {
+        function createAppList(apps = [], innerApp = false) {
            let details = document.createElement('details');
            details.className = 'apps';

@@ -2222,27 +2349,72 @@
                // console.log(div)
            };

-            let uploadApp = createUploadJson(details);
-            div.appendChild(uploadApp);
+            if (!innerApp) {
+                let uploadApp = createUploadJson(details);
+                div.appendChild(uploadApp);
+            }

            document.body.appendChild(details);
        }
-
        async function init_app() {

-            const { category, filename } = getFilenameAndCategoryFromUrl(location.href);
-            window._apps = await get_my_app(category, filename);
+            const innerApp = checkIsInnerApp();

-            window._appData = window._apps[0];
+            if (!innerApp) {
+                const { category, filename } = getFilenameAndCategoryFromUrl(location.href);
+                window._apps = await get_my_app(category, filename);

-            createAppList(window._apps);
+                window._appData = window._apps[0];

-            createApp(window._appData);
+                createAppList(window._apps);
+
+                createApp(window._appData);
+            }

        };

        init_app();

+        // 支持内嵌app
+        function checkIsInnerApp() {
+            const url = new URL(window.location.href);
+            const params = new URLSearchParams(url.search);
+            const innerApp = params.get("innerApp");
+            // console.log(window.location.href, innerApp == 1, document.body);
+            if (innerApp == 1) {
+                document.body.querySelector('.header').style.display = 'none';
+                window.parent.postMessage({ innerApp, cmd: 'init' }, '*');
+
+                // 在iframe中监听来自父窗口的消息
+                window.addEventListener("message", function (event) {
+                    console.log("Received message from parent:", event.data);
+                    const { init, url } = event.data;
+
+                    window._hostUrl = url;
+
+                    window._apps = init;
+
+                    window._appData = window._apps[0];
+
+                    if (window._appData) {
+                        createAppList(window._apps, innerApp);
+
+                        createApp(window._appData);
+                    } else {
+                        // todo welcome页面
+                        document.body.innerHTML = `<h3>Welcome to Mixlab Nodes App!</h3>`
+                    }
+
+
+
+                });
+
+
+            }
+
+            return innerApp == 1
+
+        }


    </script>
@@ -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') {
@@ -113,6 +117,15 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
        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)] = {
          ...data[id],
          title: node.title,
@@ -138,6 +151,10 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
    }
  }

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

@@ -211,8 +228,8 @@ async function save (json, download = false, showInfo = true) {
  try {
    let data = await app.graphToPrompt()

-    const { input, output, seed } = extractInputAndOutputData(
-      data.output,
+    let { input, output, seed } = extractInputAndOutputData(
+      data,
      inputIds,
      outputIds
    )
@@ -260,7 +277,7 @@ async function save (json, download = false, showInfo = true) {

 function getInputsAndOutputs () {
  const inputs =
-      `LoadImage CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
+      `LoadImage VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
        ' '
      ),
    outputs = `PreviewImage SaveImage ShowTextForGPT VHS_VideoCombine`.split(
@@ -399,7 +416,7 @@ app.registerExtension({
    }
  },
  async loadedGraphNode (node, app) {
-    console.log('#loadedGraphNode1111')
+    // 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]
@@ -408,6 +425,9 @@ app.registerExtension({
          auto_save.value = 'enable'
        }
      }
+
+      // app.canvas.centerOnNode(node)
+      // app.canvas.setZoom(0.45)
    }
  }
 })
@@ -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 = 'v0.12.0'
+const version = 'v0.17.0'

 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
  .then(response => response.json())
@@ -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')
@@ -203,76 +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);
-        console.log('##',message.text)
-				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,
@@ -2137,8 +2143,8 @@ const node = {
  loadedGraphNode (node, app) {
    if (node.type === 'RandomPrompt') {
      try {
-        let max_count = node.widgets.filter(w => w.name === "max_count")[0];
-        max_count.value= node.widgets_values[0]
+        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)
@@ -19,7 +19,7 @@ function loadCSS (url) {
    font-size: 16px;
    color: #fff;
    width: calc(100% - 32px);
-    max-width: 400px;
+    max-width: 980px;
    padding: 2px 8px;
    border-radius: 4px;
    position: absolute;
@@ -178,8 +178,7 @@ app.registerExtension({
      const orig_nodeCreated = nodeType.prototype.onNodeCreated
      nodeType.prototype.onNodeCreated = async function () {
        orig_nodeCreated?.apply(this, arguments)
-        name
-
+         
        const mutable_prompt = this.widgets.filter(
          w => w.name == 'mutable_prompt'
        )[0]
@@ -267,16 +266,7 @@ app.registerExtension({
  },
  async loadedGraphNode (node, app) {
    if (node.type === 'RandomPrompt') {
-      // try {
-      //   let mutable_prompt = node.widgets.filter(w => w.name === 'mutable_prompt')[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)
-      //   if (keywords && keywords[0]) {
-      //     mutable_prompt.value=keywords.join('\n')
-      //   }
-      // } catch (error) {}
+     
    }
  }
 })
@@ -418,7 +408,7 @@ const _createResult = async (node, widget, message) => {
  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]

@@ -569,8 +559,7 @@ app.registerExtension({

      let cards = widget.div.querySelectorAll('.card')
      if (cards.length == 0) node.size = [280, 120]
-
-      _createResult(node, widget, widget.value)
+      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
+}
@@ -1,14 +1,239 @@
 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 { closeIcon } from './svg_icons.js'
+import { api } from '../../../scripts/api.js'

 import {
  GroupNodeConfig,
  GroupNodeHandler
 } from '../../../extensions/core/groupNode.js'

+import { smart_init, addSmartMenu } from './smart_connect.js'
+
+let isScriptLoaded = {}
+
+function loadExternalScript (url) {
+  return new Promise((resolve, reject) => {
+    if (isScriptLoaded[url]) {
+      resolve()
+      return
+    }
+
+    const script = document.createElement('script')
+    script.src = url
+    script.onload = () => {
+      isScriptLoaded[url] = true
+      resolve()
+    }
+    script.onerror = reject
+    document.head.appendChild(script)
+  })
+}
+
+//
+
+function createChart (chartDom, nodes) {
+  var myChart = echarts.init(chartDom)
+  var option
+
+  console.log(nodes)
+  option = {
+    series: [
+      {
+        type: 'treemap',
+        data: [
+          {
+            name: 'nodeA',
+            value: 10,
+            children: Array.from(nodes, n => {
+              return {
+                name: n.type,
+                value: n.count
+              }
+            })
+          }
+        ]
+      }
+    ]
+  }
+
+  option && myChart.setOption(option)
+}
+
+async function createNodesCharts () {
+  await loadExternalScript(
+    '/extensions/comfyui-mixlab-nodes/lib/echarts.min.js'
+  )
+  const templates = await loadTemplate()
+  var nodes = {}
+  Array.from(templates, t => {
+    let j = JSON.parse(t.data)
+    for (let node of j.nodes) {
+      if (!nodes[node.type]) nodes[node.type] = { type: node.type, count: 0 }
+      nodes[node.type].count++
+    }
+  })
+  nodes = Object.values(nodes).sort((a, b) => b.count - a.count)
+
+  const menu = document.querySelector('.comfy-menu')
+  const separator = document.createElement('div')
+  separator.style = `margin: 20px 0px;
+  width: 100%;
+  height: 1px;
+  background: var(--border-color);
+  `
+  menu.append(separator)
+
+  const appsButton = document.createElement('button')
+  appsButton.textContent = 'Nodes'
+
+  appsButton.onclick = () => {
+    let div = document.querySelector('#mixlab_apps')
+    if (!div) {
+      div = document.createElement('div')
+      div.id = 'mixlab_apps'
+      document.body.appendChild(div)
+
+      let btn = document.createElement('div')
+      btn.style = `display: flex;
+     width: calc(100% - 24px);
+     justify-content: space-between;
+     align-items: center;
+     padding: 0 12px;
+     height: 44px;`
+      let btnB = document.createElement('button')
+      let textB = document.createElement('p')
+      btn.appendChild(textB)
+      btn.appendChild(btnB)
+      textB.style.fontSize = '12px'
+      textB.innerText = `Nodes`
+
+      btnB.style = `float: right; border: none; color: var(--input-text);
+     background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
+      btnB.addEventListener('click', () => {
+        div.style.display = 'none'
+      })
+      btnB.innerText = 'X'
+
+      // 悬浮框拖动事件
+      div.addEventListener('mousedown', function (e) {
+        var startX = e.clientX
+        var startY = e.clientY
+        var offsetX = div.offsetLeft
+        var offsetY = div.offsetTop
+
+        function moveBox (e) {
+          var newX = e.clientX
+          var newY = e.clientY
+          var deltaX = newX - startX
+          var deltaY = newY - startY
+          div.style.left = offsetX + deltaX + 'px'
+          div.style.top = offsetY + deltaY + 'px'
+          localStorage.setItem(
+            'mixlab_app_pannel',
+            JSON.stringify({ x: div.style.left, y: div.style.top })
+          )
+        }
+
+        function stopMoving () {
+          document.removeEventListener('mousemove', moveBox)
+          document.removeEventListener('mouseup', stopMoving)
+        }
+
+        document.addEventListener('mousemove', moveBox)
+        document.addEventListener('mouseup', stopMoving)
+      })
+
+      div.appendChild(btn)
+
+      let chartDom = document.createElement('div')
+      chartDom.style = `height:80vh;width:450px`
+      chartDom.className = 'chart'
+      div.appendChild(chartDom)
+    }
+    if (div.style.display == 'flex') {
+      div.style.display = 'none'
+    } else {
+      let pos = JSON.parse(
+        localStorage.getItem('mixlab_app_pannel') ||
+          JSON.stringify({ x: 0, y: 0 })
+      )
+
+      div.style = `
+      flex-direction: column;
+      align-items: end;
+      display:flex;
+      position: absolute; 
+      top: ${pos.y}; left: ${pos.x}; width: 450px; 
+      color: var(--descrip-text);
+      background-color: var(--comfy-menu-bg);
+      padding: 10px; 
+      border: 1px solid black;z-index: 999999999;padding-top: 0;`
+    }
+
+    createChart(div.querySelector('.chart'), nodes)
+  }
+  menu.append(appsButton)
+}
+
+function copyNodeValues (src, dest) {
+  // title
+  dest.title = src.title
+
+  // copy input connections
+
+  for (let i in src.inputs) {
+    let input = src.inputs[i]
+    if (input.link) {
+      let link = app.graph.links[input.link]
+      let src_node = app.graph.getNodeById(link.origin_id)
+      if (dest.inputs.filter(inp => inp.name === input.name).length === 0) {
+        // 没有，name换了
+        let dInp = dest.inputs.filter(inp => inp.type === input.type)
+        if (dInp.length === 1) {
+          src_node.connect(link.origin_slot, dest.id, dInp[0].name)
+        }
+      } else {
+        src_node.connect(link.origin_slot, dest.id, input.name)
+      }
+    }
+  }
+
+  // copy output connections
+  let output_links = {}
+  for (let i in src.outputs) {
+    let output = src.outputs[i]
+    if (output.links) {
+      let links = []
+      for (let j in output.links) {
+        links.push(app.graph.links[output.links[j]])
+      }
+      output_links[output.name] = links
+    }
+  }
+
+  for (let i in dest.outputs) {
+    let links = output_links[dest.outputs[i].name]
+    if (links) {
+      for (let j in links) {
+        let link = links[j]
+        let target_node = app.graph.getNodeById(link.target_id)
+        dest.connect(parseInt(i), target_node, link.target_slot)
+      }
+    }
+  }
+
+  // copy  widgets
+  for (const w of src.widgets) {
+    for (const d of dest.widgets) {
+      if (w.name === d.name) {
+        d.value = w.value
+      }
+    }
+  }
+
+  app.graph.afterChange()
+}
+
 function deepEqual (obj1, obj2) {
  if (typeof obj1 !== typeof obj2) {
    return false
@@ -133,7 +358,14 @@ injectCSS(`::-webkit-scrollbar {
  animation-name: loading_mixlab;
  animation-duration: 2s;
  animation-iteration-count: infinite;
-}`)
+}
+
+.dynamic_prompt{
+  border-left: 2px solid var(--input-text); 
+}
+ 
+
+`)

 async function getCustomnodeMappings (mode = 'url') {
  // mode = "local";
@@ -558,6 +790,106 @@ function createModal (url, markdown, title) {
  div.appendChild(bgElement)
 }

+const loadTemplate = async () => {
+  const id = 'Comfy.NodeTemplates'
+  const file = 'comfy.templates.json'
+
+  let templates = []
+  if (app.storageLocation === 'server') {
+    if (app.isNewUserSession) {
+      // New user so migrate existing templates
+      const json = localStorage.getItem(id)
+      if (json) {
+        templates = JSON.parse(json)
+      }
+      await api.storeUserData(file, json, { stringify: false })
+    } else {
+      const res = await api.getUserData(file)
+      if (res.status === 200) {
+        try {
+          templates = await res.json()
+        } catch (error) {}
+      } else if (res.status !== 404) {
+        console.error(res.status + ' ' + res.statusText)
+      }
+    }
+  } else {
+    const json = localStorage.getItem(id)
+    if (json) {
+      templates = JSON.parse(json)
+    }
+  }
+
+  return templates ?? []
+}
+
+function drawBadge (node, orig, restArgs) {
+  let ctx = restArgs[0]
+  const r = orig?.apply?.(node, restArgs)
+
+  if (
+    !node.flags.collapsed &&
+    node.constructor.title_mode != LiteGraph.NO_TITLE
+  ) {
+    let text = `#${node.id} `
+
+    let nick = node.getNickname()
+    if (nick) {
+      if (nick == 'ComfyUI') {
+        nick = '🦊'
+      }
+
+      if (nick.length > 25) {
+        text += nick.substring(0, 23) + '..'
+      } else {
+        text += nick
+      }
+    }
+
+    if (text != '') {
+      let fgColor = 'white'
+      let bgColor = '#0F1F0F'
+      let visible = true
+
+      ctx.save()
+      ctx.font = '12px sans-serif'
+      const sz = ctx.measureText(text)
+      ctx.fillStyle = bgColor
+      ctx.beginPath()
+      ctx.roundRect(
+        node.size[0] - sz.width - 12,
+        -LiteGraph.NODE_TITLE_HEIGHT - 20,
+        sz.width + 12,
+        20,
+        5
+      )
+      ctx.fill()
+
+      ctx.fillStyle = fgColor
+      ctx.fillText(
+        text,
+        node.size[0] - sz.width - 6,
+        -LiteGraph.NODE_TITLE_HEIGHT - 6
+      )
+      ctx.restore()
+
+      if (node.has_errors) {
+        ctx.save()
+        ctx.font = 'bold 14px sans-serif'
+        const sz2 = ctx.measureText(node.type)
+        ctx.fillStyle = 'white'
+        ctx.fillText(
+          node.type,
+          node.size[0] / 2 - sz2.width / 2,
+          node.size[1] / 2
+        )
+        ctx.restore()
+      }
+    }
+  }
+  return r
+}
+
 app.registerExtension({
  name: 'Comfy.Mixlab.ui',
  init () {
@@ -575,22 +907,71 @@ app.registerExtension({
      }
    }

+    LGraphCanvas.prototype.fixTheNode = function (node) {
+      let new_node = LiteGraph.createNode(node.comfyClass)
+      new_node.pos = [node.pos[0], node.pos[1]]
+      app.canvas.graph.add(new_node, false)
+      copyNodeValues(node, new_node)
+      app.canvas.graph.remove(node)
+    }
+
+    smart_init()
+
    const getNodeMenuOptions = LGraphCanvas.prototype.getNodeMenuOptions // store the existing method
    LGraphCanvas.prototype.getNodeMenuOptions = function (node) {
      // replace it
      const options = getNodeMenuOptions.apply(this, arguments) // start by calling the stored one
      node.setDirtyCanvas(true, true) // force a redraw of (foreground, background)
+      console.log('getNodeMenuOptions', node.type == 'CLIPTextEncode')

-      return [
+      let opts = [
        {
          content: 'Help ♾️Mixlab', // with a name
          callback: () => {
            LGraphCanvas.prototype.helpAboutNode(node)
          } // and the callback
        },
-        null,
-        ...options
-      ] // and return the options
+        {
+          content: 'Fix node v2', // with a name
+          callback: () => {
+            LGraphCanvas.prototype.fixTheNode(node)
+          }
+        }
+      ]
+
+      opts = addSmartMenu(opts, node)
+
+      // if (node.type == 'CLIPTextEncode') {
+      //   // 则出现 randomPrompt
+      //   // CLIPTextEncode 的widget ，name== 'text'
+      //   let node_widget_name = 'text'
+      //   const widget = node.widgets.filter(w => w.name === node_widget_name)[0]
+
+      //  let mixlab_nodes_smart_connect= [{node_type:'CLIPTextEncode',
+      //   node_widget_name:'text',
+      //   inputNodeName:'RandomPrompt',
+      //   inputNode_output_type:'STRING'}]
+
+      //   if (widget) {
+      //     opts = [
+      //       {
+      //         content: 'RandomPrompt',
+      //         callback: () => {
+      //           LGraphCanvas.prototype._createNodeForInput(
+      //             node, //当前node
+      //             widget,//当前node里需要自动连线的widget
+      //             'RandomPrompt',//作为input的node type
+      //             'STRING'// 作为input的node的outputs的type. the input slot type of the target node
+      //           )
+      //         }
+      //       },
+      //       null,
+      //       ...opts
+      //     ]
+      //   }
+      // }
+
+      return [...opts, null, ...options] // and return the options
    }

    const getGroupMenuOptions = LGraphCanvas.prototype.getGroupMenuOptions // store the existing method
@@ -599,16 +980,6 @@ app.registerExtension({
      const options = getGroupMenuOptions.apply(this, arguments) // start by calling the stored one
      node.setDirtyCanvas(true, true) // force a redraw of (foreground, background)

-      // templete
-      const key = 'Comfy.NodeTemplates'
-      let templates = localStorage.getItem(key)
-      if (templates) {
-        templates = JSON.parse(templates)
-      } else {
-        templates = []
-      }
-      const store = () => localStorage.setItem(key, JSON.stringify(templates))
-
      return [
        {
          content: 'Clone Group ♾️Mixlab', // with a name
@@ -666,7 +1037,7 @@ app.registerExtension({
              localStorage.setItem('litegrapheditor_clipboard', old)
            }

-            clipboardAction(() => {
+            clipboardAction(async () => {
              let name = group.title + ' ♾️Mixlab'
              let nodes = group._nodes

@@ -679,6 +1050,8 @@ app.registerExtension({
                const nodeData = node.serialize()

                let groupData = GroupNodeHandler.getGroupData(node)
+
+                // console.log('groupData',GroupNodeHandler.isGroupNode(node),groupData)
                if (groupData) {
                  groupData = groupData.nodeData
                  if (!data.groupNodes) {
@@ -689,14 +1062,46 @@ app.registerExtension({
                }
              }

-              templates.push({
+              // templete
+              const store = async nt => {
+                const id = 'Comfy.NodeTemplates'
+                const file = 'comfy.templates.json'
+                let templates = await loadTemplate()
+                templates.push(nt)
+                if (app.storageLocation === 'server') {
+                  const ts = JSON.stringify(templates, undefined, 4)
+                  localStorage.setItem(id, ts) // Backwards compatibility
+                  try {
+                    await api.storeUserData(file, ts, {
+                      stringify: false
+                    })
+                  } catch (error) {
+                    console.error(error)
+                    alert(error.message)
+                  }
+                } else {
+                  localStorage.setItem(id, JSON.stringify(templates))
+                }
+              }
+              console.log('data', data)
+              store({
                name,
                data: JSON.stringify(data)
              })
-              store()
            })
          } // and the callback
        },
+        {
+          content: `Remove Group&Nodes ♾️Mixlab`, // with a name
+          callback: async (value, opts, e, menu, group) => {
+            // console.log(group)
+            let nodes = group._nodes
+            for (const node of nodes) {
+              app.graph.remove(node)
+            }
+            app.graph.remove(group)
+          } // and the callback
+        },
        null,
        ...options
      ] // and return the options
@@ -722,7 +1127,7 @@ app.registerExtension({

      const apps = await get_my_app()

-      let apps_map = { '0': [] }
+      let apps_map = { 0: [] }

      for (const app of apps) {
        if (app.category) {
@@ -735,12 +1140,13 @@ app.registerExtension({

      let apps_opts = []
      for (const category in apps_map) {
-        console.log('category',typeof(category))
+        console.log('category', typeof category)
        if (category === '0') {
          apps_opts.push(
            ...Array.from(apps_map[category], a => {
+              // console.log('#1级',a)
              return {
-                content: a.name,
+                content: `${a.name}_${a.version}`,
                has_submenu: false,
                callback: async () => {
                  try {
@@ -763,13 +1169,14 @@ app.registerExtension({
        } else {
          // 二级
          apps_opts.push({
-            content:  '🚀 '+category,
+            content: '🚀 ' + category,
            has_submenu: true,
            disabled: false,
            submenu: {
              options: Array.from(apps_map[category], a => {
+                // console.log('#二级',a)
                return {
-                  content: a.name,
+                  content: `${a.name}_${a.version}`,
                  callback: async () => {
                    try {
                      let item = (await get_my_app(a.filename, a.category))[0]
@@ -1045,7 +1452,7 @@ app.registerExtension({
            has_submenu: true,
            disabled: false,
            submenu: {
-              options:apps_opts
+              options: apps_opts
            }
          }
        )
@@ -1053,5 +1460,66 @@ app.registerExtension({
        return options
      }
    }, 1000)
+
+    // createNodesCharts()
+  },
+  nodeCreated (node) {
+    if (node.widgets) {
+      // Locate dynamic prompt text widgets
+      // Include any widgets with dynamicPrompts set to true, and customtext
+
+      for (let index = 0; index < node.widgets.length; index++) {
+        const widget = node.widgets[index]
+        if (
+          (widget.type === 'customtext' && widget.dynamicPrompts !== false) ||
+          widget.dynamicPrompts
+        ) {
+          widget.element.classList.add('dynamic_prompt')
+
+          widget.element.addEventListener('mouseover', e => {
+            // console.log(node.widgets_values[index])
+            if (node.widgets_values&&node.widgets_values[index])
+              widget.element.setAttribute('title', node.widgets_values[index])
+          })
+        }
+      }
+    }
+
+    fetch('manager/badge_mode').then(r => {
+      if (r.status === 404) {
+        // 右上角的badge是否已经绘制
+        if (!node.badge_enabled) {
+          if (!node.getNickname) {
+            node.getNickname = function () {
+              if (node.nickname) {
+                return node.nickname
+              }
+              return
+              // return getNickname(node, node.comfyClass.trim())
+            }
+          }
+
+          const orig = node.__proto__.onDrawForeground
+          node.onDrawForeground = function (ctx) {
+            drawBadge(node, orig, arguments)
+          }
+          node.badge_enabled = true
+        }
+      }
+    })
+  },
+  async loadedGraphNode (node, app) {
+    // console.log(
+    //   '#ui init',
+    //   app.graph._nodes[app.graph._nodes.length - 1].id,
+    //   node.id
+    // )
+    try {
+      // 用来居中显示节点
+      if ((app.graph._nodes[app.graph._nodes.length - 1].id, node.id)) {
+        app.canvas.centerOnNode(node)
+        app.canvas.setZoom(0.45)
+      }
+    } catch (error) {}
  }
 })
@@ -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 = {}
@@ -0,0 +1,558 @@
+{
+  "last_node_id": 69,
+  "last_link_id": 72,
+  "nodes": [
+    {
+      "id": 37,
+      "type": "CLIPTextEncode",
+      "pos": [
+        6705,
+        -216
+      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 33
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            34
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "CLIPTextEncode",
+      "pos": [
+        6693,
+        61
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 6
+        },
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 25,
+          "widget": {
+            "name": "text"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            3
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "text, watermark"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "EmptyLatentImage",
+      "pos": [
+        6689,
+        306
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            4
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 27,
+      "type": "EmbeddingPrompt",
+      "pos": [
+        6104,
+        23
+      ],
+      "size": {
+        "0": 399.6408996582031,
+        "1": 82
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            25
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmbeddingPrompt"
+      },
+      "widgets_values": [
+        "negative-embed-verybadimagenegative_v1.3",
+        1
+      ]
+    },
+    {
+      "id": 67,
+      "type": "VAEDecode",
+      "pos": [
+        7551,
+        -184
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 70
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 69
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            71
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 1,
+      "type": "KSampler",
+      "pos": [
+        7187,
+        -145
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 1
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 34
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 3
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 4
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            70
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        408562451564429,
+        "fixed",
+        20,
+        8,
+        "euler",
+        "normal",
+        1
+      ]
+    },
+    {
+      "id": 61,
+      "type": "PromptImage",
+      "pos": [
+        7853,
+        -238
+      ],
+      "size": [
+        465.6378949342379,
+        760.4568424013569
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 71
+        },
+        {
+          "name": "prompts",
+          "type": "STRING",
+          "link": 72,
+          "widget": {
+            "name": "prompts"
+          }
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PromptImage"
+      },
+      "widgets_values": [
+        "",
+        "disable",
+        {
+          "_images": [
+            [
+              {
+                "filename": "mixlab_PromptImage_0_00027_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ],
+            [
+              {
+                "filename": "mixlab_PromptImage_1_00028_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ],
+            [
+              {
+                "filename": "mixlab_PromptImage_2_00029_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ],
+            [
+              {
+                "filename": "mixlab_PromptImage_3_00030_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ],
+            [
+              {
+                "filename": "mixlab_PromptImage_4_00031_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ],
+            [
+              {
+                "filename": "mixlab_PromptImage_5_00032_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ]
+          ],
+          "prompts": [
+            "512-inpainting-ema.safetensors",
+            "SSD-1B.safetensors",
+            "awportrait_v12.safetensors",
+            "cardosAnime_v20.safetensors",
+            "deliberate_v2.safetensors",
+            "gameIconInstitute_v40.safetensors"
+          ]
+        }
+      ]
+    },
+    {
+      "id": 2,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        6105,
+        -139
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "ckpt_name",
+          "type": [
+            "512-inpainting-ema.safetensors",
+            "SSD-1B.safetensors",
+            "awportrait_v12.safetensors",
+            "cardosAnime_v20.safetensors",
+            "deliberate_v2.safetensors",
+            "gameIconInstitute_v40.safetensors",
+            "illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors",
+            "sd_xl_turbo_1.0_fp16.safetensors",
+            "svd.safetensors"
+          ],
+          "link": 64,
+          "widget": {
+            "name": "ckpt_name"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            1
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            6,
+            33
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            69
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "deliberate_v2.safetensors"
+      ]
+    },
+    {
+      "id": 56,
+      "type": "CkptNames_",
+      "pos": [
+        7860,
+        -494
+      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "ckpt_names",
+          "type": "*",
+          "links": [
+            64,
+            72
+          ],
+          "shape": 6,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CkptNames_"
+      },
+      "widgets_values": [
+        "512-inpainting-ema.safetensors\nSSD-1B.safetensors\nawportrait_v12.safetensors\ncardosAnime_v20.safetensors\ndeliberate_v2.safetensors\ngameIconInstitute_v40.safetensors"
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      1,
+      0,
+      "MODEL"
+    ],
+    [
+      3,
+      5,
+      0,
+      1,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      4,
+      3,
+      0,
+      1,
+      3,
+      "LATENT"
+    ],
+    [
+      6,
+      2,
+      1,
+      5,
+      0,
+      "CLIP"
+    ],
+    [
+      25,
+      27,
+      0,
+      5,
+      1,
+      "STRING"
+    ],
+    [
+      33,
+      2,
+      1,
+      37,
+      0,
+      "CLIP"
+    ],
+    [
+      34,
+      37,
+      0,
+      1,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      64,
+      56,
+      0,
+      2,
+      0,
+      [
+        "512-inpainting-ema.safetensors",
+        "SSD-1B.safetensors",
+        "awportrait_v12.safetensors",
+        "cardosAnime_v20.safetensors",
+        "deliberate_v2.safetensors",
+        "gameIconInstitute_v40.safetensors",
+        "illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors",
+        "sd_xl_turbo_1.0_fp16.safetensors",
+        "svd.safetensors"
+      ]
+    ],
+    [
+      69,
+      2,
+      2,
+      67,
+      1,
+      "VAE"
+    ],
+    [
+      70,
+      1,
+      0,
+      67,
+      0,
+      "LATENT"
+    ],
+    [
+      71,
+      67,
+      0,
+      61,
+      0,
+      "IMAGE"
+    ],
+    [
+      72,
+      56,
+      0,
+      61,
+      1,
+      "STRING"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}
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