Update App.py

test appnode
2024-01-29 10:49:04 +08:00 · 2024-01-28 23:25:46 +08:00
31 changed files with 1287 additions and 2913 deletions
@@ -3,5 +3,4 @@ https/
 nodes/config.json
 workflow/my_workflow.json
 workflow/my_workflow_app.json
-workflow/prompt_result.json
-app/*
+app/*
@@ -1,14 +1,6 @@
 > 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)
-
+> [discord](https://discord.gg/cXs9vZSqeK)

 ## 🚀🚗🚚🏃 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。
@@ -39,19 +31,12 @@ APP-JSON:
 - [image-to-image](./example/Image-to-Image_2.json)
 - text-to-text

-> 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
+> 暂时支持8种节点作为界面上的输入节点：Load Image、CLIPTextEncode、PromptSlide、TextInput_、Color、FloatSlider、IntNumber、CheckpointLoaderSimple、LoraLoader

 > 输出节点：PreviewImage 、SaveImage、ShowTextForGPT、VHS_VideoCombine、PromptImage

 > seed统一输入控件，支持：SamplerCustom、KSampler

-> 配套[ps插件](https://github.com/shadowcz007/comfyui-ps-plugin)
-
-> 如果遇到上传图片不成功，请检查下：局域网或者是云服务，请使用https，端口8189这个服务（ 感谢 @Damien 反馈问题）
-
-> If you encounter difficulties in uploading images, please check the following: for local network or cloud services, please use HTTPS and the service on port 8189. (Thanks to @Damien for reporting the issue.)
-
-

 ## 🏃🚗🚚🚀  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! 💻🌐
@@ -73,7 +58,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 [Voice + Real-time Face Swap Workflow](./workflow/语音+实时换脸workflow.json)

 ### GPT
-> Support for calling multiple GPTs.ChatGPT、ChatGLM3 、ChatGLM4 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 .  Azure OpenAI:https://xxxx.openai.azure.com 
+> Support for calling multiple GPTs.ChatGPT、ChatGLM3 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 .  Azure OpenAI:https://xxxx.openai.azure.com 

 ![gpt-workflow.svg](./assets/gpt-workflow.svg)

@@ -152,6 +137,15 @@ 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.
@@ -164,13 +158,6 @@ 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"
-
-*** briarmbg ***  model was developed by BRlA Al and can be used as an open-source model for non-commercial purposes
-
-
 ### Improvement 

 - Add "help" option to the context menu for each node.
@@ -187,6 +174,8 @@ An improvement has been made to directly redirect to GitHub to search for missin

 [Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:models/rembg

+[Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : models/clipseg
+
 [Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : models/lama

 [Download Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base
@@ -224,26 +213,18 @@ 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


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

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


+
 <picture>
  <source
    media="(prefers-color-scheme: dark)"
@@ -6,7 +6,7 @@ import sys,json
 import urllib
 import hashlib
 import datetime
-import folder_paths
+

 python = sys.executable

@@ -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)
@@ -222,8 +222,7 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
            # print(item)
            try:
                x=item["data"]
-                # 管理员模式，读取全部数据
-                if i==0 or is_all:
+                if i==0:
                    apps.append({
                        "filename":item["filename"],
                        # "category":item['category'],
@@ -252,8 +251,7 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                                "name":x['app']['name'],
                                "version":x['app']['version'],
                                "input":input,
-                                "output":output,
-                                "id":x['app']['id']
+                                "output":output
                            }
                        },
                        "date":item["date"]
@@ -301,8 +299,7 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                                "name":x['app']['name'],
                                "version":x['app']['version'],
                                "input":input,
-                                "output":output,
-                                "id":x['app']['id']
+                                "output":output
                            }
                        },
                        "date":item["date"]
@@ -310,31 +307,6 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F

    return apps

-# 历史记录
-def save_prompt_result(id,data):
-    prompt_result_path=os.path.join(current_path, "workflow/prompt_result.json")
-    prompt_result={}
-    if os.path.exists(prompt_result_path):
-        with open(prompt_result_path) as json_file:
-            prompt_result = json.load(json_file)
-    
-    prompt_result[id]=data
-    
-    with open(prompt_result_path, 'w') as file:
-        json.dump(prompt_result, file)
-    return prompt_result_path
-
-def get_prompt_result():
-    prompt_result_path=os.path.join(current_path, "workflow/prompt_result.json")
-    prompt_result={}
-    if os.path.exists(prompt_result_path):
-        with open(prompt_result_path) as json_file:
-            prompt_result = json.load(json_file)
-    res=list(prompt_result.values())
-    # print(res)
-    return res
-
-
 def save_workflow_json(data):
    workflow_path=os.path.join(current_path, "workflow/my_workflow.json")
    with open(workflow_path, 'w') as file:
@@ -419,6 +391,9 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
        site = web.TCPSite(runner, address, port)
        await site.start()

+        PromptServer.instance.port=port
+
+
        import ssl
        crt, key = create_for_https()
        ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
@@ -521,6 +496,7 @@ async def mixlab_workflow_hander(request):
                    'status':'success',
                }
            elif data['task']=='list':
+                # 暂时没有用到
                result={
                    'data':get_workflows(),
                    'status':'success',
@@ -545,40 +521,6 @@ async def nodes_map_hander(request):
    return web.json_response(result)


-@routes.post("/mixlab/folder_paths")
-async def get_checkpoints(request):
-    data = await request.json()
-    t="checkpoints"
-    try:
-        t=data['type']
-    except Exception as e:
-        print('/mixlab/folder_paths',False,e)
-    
-    names = folder_paths.get_filename_list(t)
-
-    return web.json_response({"names":names,"types":list(folder_paths.folder_names_and_paths.keys())})
-
-
-@routes.post("/mixlab/prompt_result")
-async def post_prompt_result(request):
-    data = await request.json()
-    res=None
-    # print(data)
-    try:
-        action=data['action']
-        if action=='save':
-            result=data['data']
-            res=save_prompt_result(result['prompt_id'],result)
-        elif action=='all':
-            res=get_prompt_result()
-    except Exception as e:
-        print('/mixlab/prompt_result',False,e)
-    
-    return web.json_response({"result":res})
-
-
-
-
 # 扩展api接口
 # from server import PromptServer
 # from aiohttp import web
@@ -593,23 +535,22 @@ async def post_prompt_result(request):
 # 导入节点
 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.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
-
-from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
+from .nodes.Clipseg import CLIPSeg,CombineMasks
+from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
 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.Utils import CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
 from .nodes.Mask import OutlineMask,FeatheredMask
-
+from .nodes.App import AppInfo,AppNode

 # 要导出的所有节点及其名称的字典
 # 注意：名称应全局唯一
 NODE_CLASS_MAPPINGS = {
    "AppInfo":AppInfo,
+    "AppNode":AppNode,
    "TESTNODE_":TESTNODE_,
-    "TESTNODE_TOKEN":TESTNODE_TOKEN,
    "RandomPrompt":RandomPrompt,
-    # "LoraPrompt":LoraPrompt,
    "EmbeddingPrompt":EmbeddingPrompt,
    "PromptSlide":PromptSlide,
    "PromptSimplification":PromptSimplification,
@@ -638,15 +579,16 @@ NODE_CLASS_MAPPINGS = {
    "FaceToMask":FaceToMask,
    "AreaToMask":AreaToMask,
    "ImageCropByAlpha":ImageCropByAlpha,
-    # "VAELoaderConsistencyDecoder":VAELoader,
+    "VAELoaderConsistencyDecoder":VAELoader,
    "SaveImageToLocal":SaveImageToLocal,
-    # "VAEDecodeConsistencyDecoder":VAEDecode,
+    "VAEDecodeConsistencyDecoder":VAEDecode,
    "ScreenShare":ScreenShareNode,
    "FloatingVideo":FloatingVideo,
+    "CLIPSeg_":CLIPSeg,
+    "CombineMasks_":CombineMasks,
    "ChatGPTOpenAI":ChatGPTNode,
    "ShowTextForGPT":ShowTextForGPT,
    "CharacterInText":CharacterInText,
-    "TextSplitByDelimiter":TextSplitByDelimiter,
    "SpeechRecognition":SpeechRecognition,
    "SpeechSynthesis":SpeechSynthesis,
    "Color":ColorInput,
@@ -4768,6 +4768,7 @@
            "ChinesePrompt_Mix",
            "3DImage",
            "AppInfo",
+            "AppNode",
            "IntNumber",
            "FloatSlider",
            "ResizeImage",
@@ -4775,6 +4776,7 @@
            "PromptImage",
            "SaveImageToLocal",
            "AreaToMask",
+            "CLIPSeg_",
            "CharacterInText",
            "ChatGPTOpenAI",
            "Color",
@@ -4782,6 +4784,7 @@
            "CkptNames_",
            "SamplerNames_",
            "LoraNames_",
+            "CombineMasks_",
            "EnhanceImage",
            "GradientImage",
            "FaceToMask",
@@ -4809,6 +4812,8 @@
            "TextImage",
            "ResizeImageMixlab",
            "TransparentImage",
+            "VAEDecodeConsistencyDecoder",
+            "VAELoaderConsistencyDecoder",
            "TextToNumber",
            "TextInput_",
            "DynamicDelayProcessor",
@@ -1 +0,0 @@
-{}
@@ -0,0 +1,397 @@
+import os,sys
+# import re,random,json
+from PIL import Image
+import numpy as np
+# FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
+import folder_paths
+#
+import os
+import json
+import datetime
+
+import folder_paths
+from server import PromptServer
+import importlib.util
+
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+
+class AnyType(str):
+  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+
+  def __ne__(self, __value: object) -> bool:
+    return False
+
+any_type = AnyType("*")
+
+app_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'../app'))
+
+# workflow  目录下的所有json
+def read_workflow_json_files_all(folder_path):
+    print('#read_workflow_json_files_all',folder_path)
+    json_files = []
+    for root, dirs, files in os.walk(folder_path):
+        for file in files:
+            if file.endswith('.json'):
+                json_files.append(os.path.join(root, file))
+
+    data = []
+    for file_path in json_files:
+        try:
+            with open(file_path) as json_file:
+                json_data = json.load(json_file)
+                creation_time = datetime.datetime.fromtimestamp(os.path.getctime(file_path))
+                numeric_timestamp = creation_time.timestamp()
+
+                option=os.path.basename(os.path.dirname(file_path))+'/'+os.path.basename(file_path)
+                if os.path.dirname(file_path) == folder_path:
+                    option=os.path.basename(file_path)
+
+                file_info = {
+                    'filename': os.path.basename(file_path),
+                    'category': os.path.dirname(file_path),
+                    'data': json_data,
+                    'date': numeric_timestamp,
+                    "option":option
+                }
+                data.append(file_info)
+        except Exception as e:
+            print(e)
+    
+    sorted_data = sorted(data, key=lambda x: x['date'], reverse=True)
+    return sorted_data
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+
+def create_temp_file(image):
+    output_dir = folder_paths.get_temp_directory()
+
+    (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('tmp', output_dir)
+
+    
+    im=tensor2pil(image)
+ 
+    image_file = f"{filename}_{counter:05}.png"
+     
+    image_path=os.path.join(full_output_folder, image_file)
+
+    im.save(image_path,compress_level=4)
+
+    return [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type": "temp"
+                }]
+
+
+
+try:
+    if is_installed('websocket')==False:
+        import subprocess
+
+        # 安装
+        print('#pip install websocket-client')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'websocket-client'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0:
+            print("#install success")
+            import websocket 
+        else:
+            print("#install error")
+        
+    else:
+        import websocket 
+        # NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+
+except:
+    print("#websocket-client error")
+#This is an example that uses the websockets api to know when a prompt execution is done
+#Once the prompt execution is done it downloads the images using the /history endpoint
+
+import uuid
+import json
+import urllib.request
+import urllib.parse
+
+server_address = "127.0.0.1:8188"
+client_id = str(uuid.uuid4())
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt, "client_id": client_id}
+    data = json.dumps(p).encode('utf-8')
+    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
+    return json.loads(urllib.request.urlopen(req).read())
+
+def get_image(filename, subfolder, folder_type):
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
+        return response.read()
+
+def get_history(prompt_id):
+    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
+        return json.loads(response.read())
+
+def get_images(ws, prompt):
+    prompt_id = queue_prompt(prompt)['prompt_id']
+    output_images = {}
+    while True:
+        out = ws.recv()
+        if isinstance(out, str):
+            message = json.loads(out)
+            if message['type'] == 'executing':
+                data = message['data']
+                if data['node'] is None and data['prompt_id'] == prompt_id:
+                    break #Execution is done
+        else:
+            continue #previews are binary data
+
+    history = get_history(prompt_id)[prompt_id]
+    for o in history['outputs']:
+        for node_id in history['outputs']:
+            node_output = history['outputs'][node_id]
+            if 'images' in node_output:
+                images_output = []
+                for image in node_output['images']:
+                    image_data = get_image(image['filename'], image['subfolder'], image['type'])
+                    images_output.append(image_data)
+            output_images[node_id] = images_output
+
+    return output_images
+
+prompt_text = """
+{
+    "3": {
+        "class_type": "KSampler",
+        "inputs": {
+            "cfg": 8,
+            "denoise": 1,
+            "latent_image": [
+                "5",
+                0
+            ],
+            "model": [
+                "4",
+                0
+            ],
+            "negative": [
+                "7",
+                0
+            ],
+            "positive": [
+                "6",
+                0
+            ],
+            "sampler_name": "euler",
+            "scheduler": "normal",
+            "seed": 8566257,
+            "steps": 20
+        }
+    },
+    "4": {
+        "class_type": "CheckpointLoaderSimple",
+        "inputs": {
+            "ckpt_name": "v1-5-pruned-emaonly.ckpt"
+        }
+    },
+    "5": {
+        "class_type": "EmptyLatentImage",
+        "inputs": {
+            "batch_size": 1,
+            "height": 512,
+            "width": 512
+        }
+    },
+    "6": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "masterpiece best quality girl"
+        }
+    },
+    "7": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "bad hands"
+        }
+    },
+    "8": {
+        "class_type": "VAEDecode",
+        "inputs": {
+            "samples": [
+                "3",
+                0
+            ],
+            "vae": [
+                "4",
+                2
+            ]
+        }
+    },
+    "9": {
+        "class_type": "SaveImage",
+        "inputs": {
+            "filename_prefix": "ComfyUI",
+            "images": [
+                "8",
+                0
+            ]
+        }
+    }
+}
+"""
+
+# prompt = json.loads(prompt_text)
+# #set the text prompt for our positive CLIPTextEncode
+# prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
+
+# #set the seed for our KSampler node
+# prompt["3"]["inputs"]["seed"] = 5
+
+# ws = websocket.WebSocket()
+# ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+# images = get_images(ws, prompt)
+
+# #Commented out code to display the output images:
+
+# # for node_id in images:
+# #     for image_data in images[node_id]:
+# #         from PIL import Image
+# #         import io
+# #         image = Image.open(io.BytesIO(image_data))
+# #         image.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# app 配置节点
+class AppInfo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "name": ("STRING",{"multiline": False,"default": "Mixlab-App","dynamicPrompts": False}),
+                    "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"]),"dynamicPrompts": False}),
+                    "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"]),"dynamicPrompts": False}),
+                             },
+
+                "optional":{
+                    "IMAGE": ("IMAGE",),
+                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
+                    "version":("INT", {
+                        "default": 1, 
+                        "min": 1, 
+                        "max": 10000, 
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                    "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                    "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
+                    "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                    "auto_save": (["enable","disable"],),
+                }
+
+                }
+    
+    RETURN_TYPES = ()
+    # RETURN_NAMES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
+        name=name[0]
+        
+        im=None
+        if IMAGE:
+            im=IMAGE[0][0]
+            #TODO batch 的方式需要处理
+            im=create_temp_file(im)
+        # image [img,] img[batch,w,h,a] 列表里面是batch，
+
+        input_ids=input_ids[0]
+        output_ids=output_ids[0]
+        description=description[0]
+        version=version[0]
+        share_prefix=share_prefix[0]
+        link=link[0]
+        category=category[0]
+        
+        # id=get_json_hash([name,im,input_ids,output_ids,description,version])
+
+        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": ()}
+    
+
+
+#  app可以当成节点运行
+class AppNode:
+   
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "name": ([x['option'] for x in read_workflow_json_files_all(app_path)],),
+                    "image":("IMAGE",),
+                             }
+                }
+    
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("output",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+    
+
+    def run(self,name,input):
+        print('#app_path',input)
+        print(PromptServer.instance.port)
+
+        return (name,)
+    
+
@@ -3,24 +3,13 @@ import time
 import urllib.error
 import re,json,os,string,random
 import folder_paths
-import hashlib
-from zhipuai import ZhipuAI
-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):
@@ -46,11 +35,6 @@ def openai_client(key,url):
    base_url=url
    )
    return client
-def ZhipuAI_client(key):
-    client = ZhipuAI(
-        api_key=key, # 填写您的 APIKey
-    ) 
-    return client



@@ -105,7 +89,7 @@ class ChatGPTNode:
                                       "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
                                       "multiline": True,"dynamicPrompts": False
                                       }),
-                "model": (["gpt-3.5-turbo","gpt-35-turbo","gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-4-0613","gpt-4-1106-preview","glm-4"], 
+                "model": (["gpt-3.5-turbo","gpt-35-turbo","gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-4-0613","gpt-4-1106-preview"], 
                          {"default": "gpt-3.5-turbo"}),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
                "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
@@ -148,13 +132,8 @@ class ChatGPTNode:
        if is_azure_url(api_url):
            client=azure_client(api_key,api_url)
        else:
-            # 根据用户选择的模型，设置相应的接口和模型名称
-            if model == "glm-4" :
-                client = ZhipuAI_client(api_key)  # 使用 Zhipuai 的接口
-                print('using Zhipuai interface')
-            else :
-                client = openai_client(api_key,api_url)  # 使用 ChatGPT  的接口
-                print('using ChatGPT interface')
+            client=openai_client(api_key,api_url)
+            print('openai url')

        # 把用户的提示添加到会话历史中
        # 调用API时传递整个会话历史
@@ -199,7 +178,7 @@ class ShowTextForGPT:
                "text": ("STRING", {"forceInput": True,"dynamicPrompts": False}),
            },
            "optional":{ 
-                    "output_dir": ("STRING",{"forceInput": True,"default": "","multiline": True,"dynamicPrompts": False}), 
+                    "output_dir": ("STRING",{"default": "","multiline": True,"dynamicPrompts": False}), 
                }
        }

@@ -211,60 +190,16 @@ class ShowTextForGPT:

    CATEGORY = "♾️Mixlab/GPT"

-    def run(self, text,output_dir=[""]):
+    def run(self, text,output_dir):
+        output_dir=output_dir[0]
+        filename=generate_random_string(4)+'.txt'
+
+        if output_dir=='':
+            output_dir = folder_paths.get_temp_directory()
        
-        # 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)
-
+        save_to_dirpath=os.path.join(output_dir,filename)
+        with open(save_to_dirpath, 'w') as file:
+            file.write("\n".join(text))
        # print(text)
        return {"ui": {"text": text}, "result": (text,)}
        
@@ -297,56 +232,7 @@ class CharacterInText:

    def run(self, text,character,start_index):
        # print(text,character,start_index)
-        b=1 if character.lower() in text.lower() else 0
+        b=1 if character in text 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,)
@@ -77,7 +77,8 @@ if not os.path.exists(caption_model_path):
    caption_model_path='Salesforce/blip-image-captioning-base'

 cache_path=os.path.join(folder_paths.models_dir, "clip_interrogator")
-
+if not os.path.exists(cache_path):
+    os.mkdir(cache_path)

 # Tensor to PIL
 def tensor2pil(image):
@@ -0,0 +1,272 @@
+#### 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,
+# }
@@ -380,7 +380,6 @@ 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:
@@ -388,7 +387,6 @@ 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:
@@ -396,15 +394,12 @@ 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:
@@ -1064,15 +1059,14 @@ class LoadImagesFromPath:
                }
            }
    
-    RETURN_TYPES = ('IMAGE','MASK','STRING','STRING',)
-    RETURN_NAMES = ("IMAGE","MASK","prompt_for_FloatingVideo","filepaths",)
+    RETURN_TYPES = ('IMAGE','MASK','STRING',)

    FUNCTION = "run"

    CATEGORY = "♾️Mixlab/Image"

    # INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,True,False,True,)
+    OUTPUT_IS_LIST = (True,True,False,)
  
    global watcher_folder
    watcher_folder=None
@@ -1108,12 +1102,10 @@ 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)
        
@@ -1121,12 +1113,11 @@ 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,file_names,)}
+        return  {"ui": {"seed": [1]}, "result":(imgs,masks,prompt,)}


 # TODO 扩大选区的功能,重新输出mask
@@ -1629,18 +1620,6 @@ 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

@@ -1709,8 +1688,8 @@ class SplitImage:
            }
                }
    
-    RETURN_TYPES = ("_GRID","_GRID","MASK",)
-    RETURN_NAMES = ("grids","grid","mask",)
+    RETURN_TYPES = ("_GRID","_GRID",)
+    RETURN_NAMES = ("grids","grid")

    FUNCTION = "run"

@@ -1721,10 +1700,10 @@ class SplitImage:

    def run(self,image,num,seed):
        image=tensor2pil(image)
-        
+
        grids=splitImage(image,num)

-        if seed>num:
+        if seed>=num:
            num=int(seed / 500 * num)-1
        else:
            num=seed-1
@@ -1733,11 +1712,7 @@ class SplitImage:

        g=grids[num]

-        x,y,w,h=g
-        mask=createMask(image, x,y,w,h)
-        mask=pil2tensor(mask)
-
-        return (grids,g,mask,)
+        return (grids,g,)



@@ -1778,8 +1753,8 @@ class CenterImage:
            }
                }
    
-    RETURN_TYPES = ("_GRID","MASK",)
-    RETURN_NAMES = ("grid","mask",)
+    RETURN_TYPES = ("_GRID",)
+    RETURN_NAMES = ("grid",)

    FUNCTION = "run"

@@ -1793,9 +1768,7 @@ class CenterImage:

        grid=centerImage((left,top,right,bottom),canvas)

-        mask=createMask(canvas,left,top,canvas.width-left-right,canvas.height-top-bottom)
-
-        return (grid,pil2tensor(mask),)
+        return (grid,)



@@ -2286,41 +2259,19 @@ 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","INT", "INT",)
-    RETURN_NAMES = ("width", "height","min_width", "min_height",)
+    RETURN_TYPES = ("INT", "INT")
+    RETURN_NAMES = ("width", "height")

    FUNCTION = "get_size"

    CATEGORY = "♾️Mixlab/Image"

-    def get_size(self, image,min_width):
+    def get_size(self, image):
        _, height, width, _ = image.shape
-        
-        # 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,)
+        return (width, height)



@@ -2394,20 +2345,9 @@ class SaveImageToLocal:
        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):
            # 使用os.makedirs函数创建新目录
            os.makedirs(file_path)
@@ -2453,7 +2393,6 @@ class SaveImageToLocal:
                })
            
            else:
-
                fp=os.path.join(file_path, file)
                if os.path.exists(fp):
                    file = f"{filename}_{counter:05}_{generate_random_string(8)}.png"
@@ -42,6 +42,9 @@ else:
    _available=True


+lama_path=os.path.join(folder_paths.models_dir, "lama")
+if not os.path.exists(lama_path):
+    os.mkdir(lama_path)
 
 llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
 if not os.path.exists(llma_model_path):
@@ -6,12 +6,6 @@ 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')
@@ -21,7 +15,6 @@ import json
 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:
@@ -35,50 +28,6 @@ def join_with_(text_list,delimiter):
    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"""

@@ -436,92 +385,16 @@ class RandomPrompt:
        return {"ui": {"prompts": prompts}, "result": (prompts,)}


-# class LoraPrompt:
-#     @classmethod
-#     def INPUT_TYPES(s):
-#         return {
-#             "required": {
-#                 "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 = ("STRING","STRING",any_type)
-#     RETURN_NAMES = ("lora_name","prompt","tags",)
-
-#     FUNCTION = "run"
-
-#     CATEGORY = "♾️Mixlab/Prompt"
-
-#     OUTPUT_IS_LIST = (False,False,True,)
-#     # OUTPUT_NODE = True
-
-#     # 运行的函数
-#     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')
-
-#         if not os.path.exists(json_tags_path):
-#             save_json({},json_tags_path)
-
-#         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 = ""


-#         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(','),)
-
-
-import folder_paths
 class EmbeddingPrompt:
    @classmethod
    def INPUT_TYPES(s):
-
        return {
            "required": {
-                "embedding":(folder_paths.get_filename_list("embeddings"),),
+                "embedding":(get_files_with_extension(embeddings_path,'.pt'),),
                "weight": ("FLOAT", {"default": 1, "min": -2, "max": 2,"step":0.01 ,"display": "slider"}),
                },
                
@@ -553,7 +426,7 @@ class JoinWithDelimiter:
    def INPUT_TYPES(s):
        return {"required": {
                    "text_list": (any_type,),
-                    "delimiter":(["newline","comma","backslash","space"],),
+                    "delimiter":(["newline","comma"],),
                             },
                }
    
@@ -572,10 +445,6 @@ class JoinWithDelimiter:
            delimiter='\n'
        elif delimiter=='comma':
            delimiter=','
-        elif delimiter=='backslash':
-            delimiter='\\'
-        elif delimiter=='space':
-            delimiter=' '
        t=''
        if isinstance(text_list, list):
            t=join_with_(text_list,delimiter)
@@ -8,467 +8,6 @@ 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
@@ -509,70 +48,6 @@ 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)
@@ -643,16 +118,14 @@ class RembgNode_:
    def INPUT_TYPES(s):
        return {"required": {
            "image": ("IMAGE",),
-            "model_name": ([
-                            "briarmbg",
-                            "u2net",
+            "model_name": (["u2net",
                            "u2netp",
                            "u2net_human_seg",
                            "u2net_cloth_seg",
                            "silueta",
                            "isnet-general-use",
                            "isnet-anime",
-                            
+                            # "sam"
                            ],),
            
                             },
@@ -680,10 +153,7 @@ class RembgNode_:
                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,rgba_images,rgb_images=run_bg(model_name,images)

        masks=[pil2tensor(m) for m in masks]

@@ -62,14 +62,7 @@ except:



-def translate(text):
-    global text_pipe,zh_en_model,zh_en_tokenizer
-    
-    if zh_en_model==None:
-        zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval()
-        zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path,padding=True, truncation=True)
-    
-    zh_en_model.to("cuda" if torch.cuda.is_available() else "cpu")
+def translate(zh_en_tokenizer,zh_en_model,text):
    with torch.no_grad():
        encoded = zh_en_tokenizer([text], return_tensors="pt")
        encoded.to(zh_en_model.device)
@@ -111,22 +104,16 @@ import re

 def correct_prompt_syntax(prompt):

-    # print("input prompt",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()
-    prompt = prompt.replace("< ","<").replace(" >",">").replace("( ","(").replace(" )",")").replace("[ ","[").replace(' ]',']')

    # 分词
    prompt_elements = prompt.split(',')

-    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)
-
    for element in prompt_elements:
        element = element.strip()

@@ -146,118 +133,21 @@ def correct_prompt_syntax(prompt):
        corrected_elements.append(corrected_element)

    # 重组修正后的prompt
-    return  ','.join(corrected_elements)
+    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)

-def detect_language(input_str):
-    # 统计中文和英文字符的数量
-    count_cn = count_en = 0
-    for char in input_str:
-        if '\u4e00' <= char <= '\u9fff':
-            count_cn += 1
-        elif char.isalpha():
-            count_en += 1
-
-    # 根据统计的字符数量判断主要语言
-    if count_cn > count_en:
-        return "cn"
-    elif count_en > count_cn:
-        return "en"
-    else:
-        return "unknow"
-
-
-
-
-#定义Prompt文法
-grammar = """
-start: sentence
-sentence: phrase ("," phrase)*
-phrase: emphasis | weight | word | lora | embedding | schedule
-emphasis: "(" sentence ")" -> emphasis
-        | "[" sentence "]" -> weak_emphasis
-weight: "(" word ":" NUMBER ")"
-schedule: "[" word ":" word ":" NUMBER "]"
-lora: "<" WORD ":" WORD (":" NUMBER)? (":" NUMBER)? ">"
-embedding: "embedding" ":" WORD (":" NUMBER)? (":" NUMBER)?
-word: WORD
-
-NUMBER: /\s*-?\d+(\.\d+)?\s*/
-WORD: /[^,:\(\)\[\]<>]+/
-"""
-
-from lark import Lark, Transformer, v_args
-
-@v_args(inline=True)  # Decorator to flatten the tree directly into the function arguments
-class ChinesePromptTranslate(Transformer):
- 
-    def sentence(self, *args):
-        return ", ".join(args)
-
-    def phrase(self, *args):
-        return "".join(args)
-
-    def emphasis(self, *args):
-        # Reconstruct the emphasis with translated content
-        return "(" + "".join(args) + ")"
-
-    def weak_emphasis(self, *args):
-        print('weak_emphasis:',args)
-        return "[" + "".join(args) + "]"
-    
-    def embedding(self,*args):
-        print('prompt embedding',args[0])
-        if len(args) == 1:
-            # print('prompt embedding',str(args[0]))
-            # 只传递了一个参数，意味着只有embedding名称没有数字
-            embedding_name = str(args[0])
-            return f"embedding:{embedding_name}"
-        elif len(args) > 1:
-            embedding_name,*numbers = args
-           
-            if len(numbers)==2:
-                return f"embedding:{embedding_name}:{numbers[0]}:{numbers[1]}"
-            elif len(numbers)==1:
-                return f"embedding:{embedding_name}:{numbers[0]}"
-            else:
-                return f"embedding:{embedding_name}"
-
-    def lora(self,*args):
-        print('lora prompt',*args)
-        if len(args) == 1:
-            return f"<lora:{loar_name}>"
-        elif len(args) > 1: 
-            # print('lora', args)
-            _,loar_name,*numbers = args
-            loar_name = str(loar_name).strip()
-            if len(numbers)==2:
-                return f"<lora:{loar_name}:{numbers[0]}:{numbers[1]}>"
-            elif len(numbers)==1:
-                return f"<lora:{loar_name}:{numbers[0]}>"
-            else:
-                return f"<lora:{loar_name}>"
-    
-    def weight(self, word,number):
-        translated_word = translate(str(word)).rstrip('.')
-        return f"({translated_word}:{str(number).strip()})"
-    
-    def schedule(self,*args):
-        print('prompt schedule',args)
-        data = [str(arg).strip() for arg in args] 
        
-        return f"[{':'.join(data)}]"
-
-    def word(self, word):
-        # Translate each word using the dictionary
-        if detect_language(str(word)) == "cn":
-            return  translate(str(word)).rstrip('.')
-        else:
-            return str(word).rstrip('.')
        
 class ChinesePrompt:

@@ -295,17 +185,16 @@ class ChinesePrompt:
    zh_en_tokenizer=None 

    def run(self,text,seed,generation):
-        
+        global text_pipe,zh_en_model,zh_en_tokenizer

        seed=seed[0]
        generation=generation[0]

        # 进度条
        pbar = comfy.utils.ProgressBar(len(text)+1)
+        
        texts = [correct_prompt_syntax(t) for t in text]
-
-
-        global text_pipe,zh_en_model,zh_en_tokenizer
+        print('correct_prompt_syntax::',texts)
        if zh_en_model==None:
            zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval()
            zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path,padding=True, truncation=True)
@@ -321,15 +210,9 @@ class ChinesePrompt:

        # print('zh_en_model device',zh_en_model.device,text_pipe.model.device,torch.cuda.current_device() )
        en_texts=[]
-
        for t in texts:
-            # translated_text =  translated_word = translate(zh_en_tokenizer,zh_en_model,str(t))
-            parser = Lark(grammar, start="start", parser="lalr", transformer=ChinesePromptTranslate())
-            # print('t',t)
-            result = parser.parse(t).children
-            # print('en_result',result)        
-            # en_text=translate(zh_en_tokenizer,zh_en_model,text_without_syntax)
-            en_texts.append(result[0])
+            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)
@@ -349,9 +232,7 @@ class ChinesePrompt:
            pbar.update(1)

        text_pipe.model.to('cpu')
-
-        print('prompt_result',prompt_result,)
-        # prompt_result = [','.join(correct_prompt_syntax(p)) for p in prompt_result]
+        prompt_result = [correct_prompt_syntax(p) for p in prompt_result]
        
        return {
            "ui":{
@@ -360,8 +241,6 @@ class ChinesePrompt:
            "result": (prompt_result,)}


-
-
 class PromptGenerate:

    global _available
@@ -1,12 +1,11 @@
 import os,platform
 import re,random,json
-from PIL import Image
-import numpy as np
+
 # FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
 import folder_paths
-import matplotlib.font_manager as fm
+# import matplotlib.font_manager as fm
 import torch
-import importlib.util
+


 def recursive_search(directory, excluded_dir_names=None):
@@ -65,37 +64,7 @@ def get_system_font_path():
 #     return hash_value
    

-
-def tensor2pil(image):
-    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-
-
-def create_temp_file(image):
-    output_dir = folder_paths.get_temp_directory()
-
-    (
-            full_output_folder,
-            filename,
-            counter,
-            subfolder,
-            _,
-        ) = folder_paths.get_save_image_path('tmp', output_dir)
-
-    
-    im=tensor2pil(image)
 
-    image_file = f"{filename}_{counter:05}.png"
-     
-    image_path=os.path.join(full_output_folder, image_file)
-
-    im.save(image_path,compress_level=4)
-
-    return [{
-                "filename": image_file,
-                "subfolder": subfolder,
-                "type": "temp"
-                }]
-
 def get_font_files(directory):
    font_files = {}

@@ -347,7 +316,7 @@ class MultiplicationNode:
        return {"required": {
                    "numberA":(any_type,),
                    "multiply_by":("FLOAT", {
-                        "default": 1, 
+                        "default": 0, 
                        "min": -2, #Minimum value
                        "max": 0xffffffffffffffff,
                        "step": 0.01, #Slider's step
@@ -381,7 +350,7 @@ class TextInput:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
-                    "text": ("STRING",{"multiline": True,"default": ""})
+                    "text": ("STRING",{"multiline": True,"default": ""}),
                             },
                }
    
@@ -413,6 +382,7 @@ class AnyType(str):
    return False

 any_type = AnyType("*")
+
 import time

 class DynamicDelayProcessor:
@@ -490,69 +460,6 @@ class DynamicDelayProcessor:
        


-
-# app 配置节点
-class AppInfo:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "name": ("STRING",{"multiline": False,"default": "Mixlab-App","dynamicPrompts": False}),
-                    "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"]),"dynamicPrompts": False}),
-                    "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"]),"dynamicPrompts": False}),
-                             },
-
-                "optional":{
-                    "IMAGE": ("IMAGE",),
-                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
-                    "version":("INT", {
-                        "default": 1, 
-                        "min": 1, 
-                        "max": 10000, 
-                        "step": 1, 
-                        "display": "number"  
-                    }),
-                    "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
-                    "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
-                    "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
-                    "auto_save": (["enable","disable"],),
-                }
-
-                }
-    
-    RETURN_TYPES = ()
-    # RETURN_NAMES = ("IMAGE",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab"
-
-    OUTPUT_NODE = True
-    INPUT_IS_LIST = True
-    # OUTPUT_IS_LIST = (True,)
-
-    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
-        name=name[0]
-        
-        im=None
-        if IMAGE:
-            im=IMAGE[0][0]
-            #TODO batch 的方式需要处理
-            im=create_temp_file(im)
-        # image [img,] img[batch,w,h,a] 列表里面是batch，
-
-        input_ids=input_ids[0]
-        output_ids=output_ids[0]
-        description=description[0]
-        version=version[0]
-        share_prefix=share_prefix[0]
-        link=link[0]
-        category=category[0]
-        
-        # id=get_json_hash([name,im,input_ids,output_ids,description,version])
-
-        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": ()}
-    
-
    


@@ -560,11 +467,9 @@ class SwitchByIndex:
    @classmethod
    def INPUT_TYPES(cls):
        return {
-             "optional":{
-                  "A":(any_type,),
-                  "B":(any_type,),
-            },
-            "required": { 
+            "required": {
+                "A":(any_type,),
+                "B":(any_type,),
                "index":("INT", {
                        "default": -1, 
                        "min": -1, 
@@ -576,17 +481,17 @@ class SwitchByIndex:
            }
        }

-    RETURN_TYPES = (any_type,"INT",)
-    RETURN_NAMES = ("C","count",)
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("C",)

    FUNCTION = "run"

    CATEGORY = "♾️Mixlab/Utils"

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

-    def run(self, A=[],B=[],index=-1,flat='on'):
+    def run(self, A,B,index,flat):

        flat=flat[0]

@@ -606,8 +511,8 @@ class SwitchByIndex:
                C=[C[index]]
            except Exception as e:
                C=[]
-
-        return (C,len(C),)
+        
+        return (C,)



@@ -697,9 +602,7 @@ class ListStatistics:
 class TESTNODE_:
    @classmethod
    def INPUT_TYPES(s):
-        return {"required": { 
-            "ANY":(any_type,),                
-                              },
+        return {"required": { "ANY":(any_type,), },
                }
    
    RETURN_TYPES = (any_type,)
@@ -713,62 +616,16 @@ class TESTNODE_:
    OUTPUT_IS_LIST = (True,)

    def run(self,ANY):
-        # print(ANY)
+        print(ANY)
        # data=ANY
        list_stats = ListStatistics()

        # 调用count_types方法进行统计
        result = list_stats.count_types(ANY)
- 
-
-        # 假设我们有一个模块文件名为 my_module.py，它位于 'importables' 目录下
-        module_path = os.path.join(os.path.dirname(__file__),'test.py')
-
-        # 使用 spec_from_file_location 获取模块的元数据（名称、定义等）
-        spec = importlib.util.spec_from_file_location('test', module_path)
-
-        module = importlib.util.module_from_spec(spec)
-        spec.loader.exec_module(module)
-
-        functions = getattr(module, 'run')  # 获取函数
-        
-        functions(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):
@@ -0,0 +1,179 @@
+# 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, )
@@ -1,8 +0,0 @@
-import folder_paths
-
-# 外挂一个文件，用来编写新的节点
-def run(v):
-
-    output_dir = folder_paths.get_temp_directory()
-
-    print('1323',v,output_dir)
@@ -5,7 +5,4 @@ opencv-python-headless
 matplotlib
 openai
 simple-lama-inpainting
-clip-interrogator==0.6.0
-transformers>=4.36.0
-zhipuai
-lark-parser
+clip-interrogator==0.6.0
@@ -2,9 +2,6 @@ import { app } from '../../../scripts/app.js'
 import { $el } from '../../../scripts/ui.js'
 import { api } from '../../../scripts/api.js'

-const base64Df =
-  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
-
 function get_position_style (ctx, widget_width, y, node_height) {
  const MARGIN = 12 // the margin around the html element

@@ -31,7 +28,7 @@ function get_position_style (ctx, widget_width, y, node_height) {
    // height: `${node_height * 0.3 - MARGIN * 2}px`,
    // background: '#EEEEEE',
    display: 'flex',
-    flexDirection: 'column',
+    flexDirection: 'row',
    // alignItems: 'center',
    justifyContent: 'flex-start'
  }
@@ -70,13 +67,9 @@ async function drawImageToCanvas (imageUrl) {
 }

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

  for (const id in data) {
@@ -84,7 +77,7 @@ 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') {
@@ -120,15 +113,6 @@ 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,
@@ -154,10 +138,6 @@ 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 }
 }

@@ -231,20 +211,12 @@ async function save (json, download = false, showInfo = true) {
  try {
    let data = await app.graphToPrompt()

-    let { input, output, seed } = extractInputAndOutputData(
-      data,
+    const { input, output, seed } = extractInputAndOutputData(
+      data.output,
      inputIds,
      outputIds
    )

-    let authorAvatar =
-        localStorage.getItem('_mixlab_author_avatar') || base64Df,
-      authorName =
-        localStorage.getItem('_mixlab_author_name') ||
-        localStorage.getItem('Comfy.userName'),
-        authorLink =
-        localStorage.getItem('_mixlab_author_link') || ''
-
    data.app = {
      name,
      description,
@@ -255,12 +227,7 @@ async function save (json, download = false, showInfo = true) {
      share_prefix,
      link,
      category,
-      filename: `${name}_${version}.json`,
-      author: {
-        avatar: authorAvatar,
-        name: authorName,
-        link:authorLink
-      }
+      filename: `${name}_${version}.json`
    }

    try {
@@ -293,7 +260,7 @@ async function save (json, download = false, showInfo = true) {

 function getInputsAndOutputs () {
  const inputs =
-      `LoadImage VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
+      `LoadImage CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
        ' '
      ),
    outputs = `PreviewImage SaveImage ShowTextForGPT VHS_VideoCombine`.split(
@@ -320,7 +287,7 @@ function getInputsAndOutputs () {
 }

 app.registerExtension({
-  name: 'Mixlab.utils.AppInfo',
+  name: 'Mixlab.app.AppInfo',
  async beforeRegisterNodeDef (nodeType, nodeData, app) {
    if (nodeType.comfyClass == 'AppInfo') {
      const orig_nodeCreated = nodeType.prototype.onNodeCreated
@@ -393,167 +360,10 @@ app.registerExtension({
          }
        })

-        // author
-        let author = document.createElement('div')
-        // author.style=`display: flex`
-
-        let authorAvatar = document.createElement('img')
-        authorAvatar.className = `${'comfy-multiline-input'}`
-        authorAvatar.style = `outline: none;
-          border: none;
-          padding: 4px;
-          width: 32px;
-          cursor: pointer;
-          height: 32px;`
-
-        if (localStorage.getItem('_mixlab_author_avatar')) {
-          authorAvatar.src =
-            localStorage.getItem('_mixlab_author_avatar') || base64Df
-        }
-
-        let authorAvatarUpload = document.createElement('input')
-        authorAvatarUpload.type = 'file'
-        authorAvatarUpload.style = `display:none`
-
-        let authorAvatarInput = document.createElement('div')
-        authorAvatarInput.style = `display: flex;justify-content: flex-start;
-        align-items: center;`
-        let authorAvatarInputLabel = document.createElement('p')
-        authorAvatarInputLabel.innerText = 'Author Avatar'
-        authorAvatarInputLabel.className = `${'comfy-multiline-input'}`
-        authorAvatarInputLabel.style = `font-size:12px`
-
-        authorAvatar.addEventListener('click', e => {
-          authorAvatarUpload.click()
-        })
-
-        authorAvatarInputLabel.addEventListener('click', e => {
-          authorAvatarUpload.click()
-        })
-
-        authorAvatarUpload.addEventListener('change', event => {
-          const file = event.target.files[0]
-          const reader = new FileReader()
-
-          reader.onload = async e => {
-            let im = new Image()
-            im.src = e.target.result
-            authorAvatar.src = e.target.result
-            im.onload = () => {
-              let c = document.createElement('canvas')
-              let ctx = c.getContext('2d')
-              c.width = 72
-              c.height = 72
-              ctx.drawImage(
-                im,
-                0,
-                0,
-                im.naturalWidth,
-                im.naturalHeight,
-                0,
-                0,
-                c.width,
-                c.height
-              )
-              window._mixlab_author_avatar = c.toDataURL()
-              localStorage.setItem(
-                '_mixlab_author_avatar',
-                window._mixlab_author_avatar
-              )
-            }
-          }
-
-          // 以文本形式读取文件
-          reader.readAsDataURL(file)
-        })
-
-        author.appendChild(authorAvatarInput)
-        authorAvatarInput.appendChild(authorAvatarInputLabel)
-        authorAvatarInput.appendChild(authorAvatar)
-        authorAvatarInput.appendChild(authorAvatarUpload)
-
-        let authorName = document.createElement('input')
-        authorName.type = 'text'
-        authorName.value =
-          localStorage.getItem('_mixlab_author_name') ||
-          localStorage.getItem('Comfy.userName')
-        authorName.placeholder = 'author name'
-        authorName.className = `${'comfy-multiline-input'}`
-        authorName.style = `
-          outline: none;
-          border: none;
-          padding: 4px;
-          width: 100%;
-          cursor: pointer;
-          height: 32px;`
-
-        let authorNameInput = document.createElement('div')
-        authorNameInput.style = `display: flex;justify-content: flex-start;
-          align-items: center;`
-        let authorNameInputLabel = document.createElement('p')
-        authorNameInputLabel.innerText = 'Author Name'
-        authorNameInputLabel.className = `${'comfy-multiline-input'}`
-        authorNameInputLabel.style = `font-size:12px;width: 110px`
-
-        authorName.addEventListener('change', e => {
-          window._mixlab_author_name = authorName.value.trim()
-          localStorage.setItem(
-            '_mixlab_author_name',
-            window._mixlab_author_name
-          )
-        })
-
-        author.appendChild(authorNameInput)
-        authorNameInput.appendChild(authorNameInputLabel)
-        authorNameInput.appendChild(authorName)
-
-
-        // 社交链接
-        let authorLink = document.createElement('input')
-        authorLink.type = 'text'
-        authorLink.value =
-          localStorage.getItem('_mixlab_author_link') ||''
-          authorLink.placeholder = 'author link'
-          authorLink.className = `${'comfy-multiline-input'}`
-          authorLink.style = `
-          outline: none;
-          border: none;
-          padding: 4px;
-          width: 100%;
-          cursor: pointer;
-          height: 32px;`
-
-        let authorLinkInput = document.createElement('div')
-        authorLinkInput.style = `display: flex;justify-content: flex-start;
-          align-items: center;`
-        let authorLinkInputLabel = document.createElement('p')
-        authorLinkInputLabel.innerText = 'Author Link'
-        authorLinkInputLabel.className = `${'comfy-multiline-input'}`
-        authorLinkInputLabel.style = `font-size:12px;width: 110px`
-
-        authorLink.addEventListener('change', e => {
-          window._mixlab_author_link = authorLink.value.trim()
-          localStorage.setItem(
-            '_mixlab_author_link',
-            window._mixlab_author_link
-          )
-        })
-
-        author.appendChild(authorLinkInput)
-        authorLinkInput.appendChild(authorLinkInputLabel)
-        authorLinkInput.appendChild(authorLink)
-
-
-        widget.div.appendChild(author)
-
-        let btns = document.createElement('div')
-
-        widget.div.appendChild(btns)
-
-        btns.appendChild(btn)
-        btns.appendChild(download)
-
        document.body.appendChild(widget.div)
+        widget.div.appendChild(btn)
+        widget.div.appendChild(download)
+
        this.addCustomWidget(widget)

        const onRemoved = this.onRemoved
@@ -626,3 +436,67 @@ api.addEventListener('executed', async ({ detail }) => {
    }
  }
 })
+
+app.registerExtension({
+  name: 'Mixlab.app.AppNode',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'AppNode') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        console.log('#orig_nodeCreated', this)
+        let node=this;
+
+        const name = this.widgets.filter(w => w.name === 'name')[0]
+
+        name.callback = async e => {
+          let es = e.split('/')
+          let filename = '',
+            category = ''
+          if (es.length == 2) {
+            category = es[0]
+            filename = es[1]
+          } else if (es.length === 1) {
+            filename = e
+          }
+          const res = await api.fetchApi('/mixlab/workflow', {
+            method: 'POST',
+            headers: {
+              'Content-Type': 'application/json'
+            },
+            body: JSON.stringify({
+              task: 'my_app',
+              filename,
+              category
+            })
+          })
+
+          if (res.status !== 200) {
+            throw {
+              response: await res.json()
+            }
+          }
+          let result=await res.json()
+          const {app,output:workflow,}=result.data[0].data;
+          let input=app.input
+          console.log(input)
+
+          if(input.length==1){
+            let widget = node.inputs.filter(w => w.name === 'input')[0];
+            let sc=input[0];
+            LGraphCanvas.prototype._createNodeForInput(
+              node, //当前node
+              widget, //当前node里需要自动连线的widget
+              sc.class_type, //作为input的node type
+              'IMAGE' // 作为input的node的outputs的name. the input slot type of the target node
+            )
+          }
+
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {}
+})
@@ -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 = 'v0.17.1'
+const version = 'v0.15.0'

 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')
@@ -203,82 +203,76 @@ app.registerExtension({

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

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

-      // When the node is executed we will be sent the input text, display this in the widget
-      const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = function (message) {
-        onExecuted?.apply(this, arguments)
-        // console.log('##onExecuted', this, message)
-        if (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('##',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
-    }
-  }
+
+		}
+
+
+	},
 })
@@ -408,7 +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]

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

      let cards = widget.div.querySelectorAll('.card')
      if (cards.length == 0) node.size = [280, 120]
-      if(widget.value) _createResult(node, widget, widget.value)
+
+      _createResult(node, widget, widget.value)
    }
  }
 })
@@ -1,6 +1,5 @@
 import { app } from '../../../scripts/app.js'
 import { closeIcon } from './svg_icons.js'
-import { api } from '../../../scripts/api.js'

 import {
  GroupNodeConfig,
@@ -9,172 +8,6 @@ import {

 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
@@ -282,22 +115,18 @@ function get_url () {

 async function get_my_app (filename = null, category = '') {
  let url = get_url()
-  let data = null
-
-  try {
-    const res = await fetch(`${url}/mixlab/workflow`, {
-      method: 'POST',
-      body: JSON.stringify({
-        task: 'my_app',
-        filename,
-        category,
-        admin: true
-      })
+  const res = await fetch(`${url}/mixlab/workflow`, {
+    method: 'POST',
+    body: JSON.stringify({
+      task: 'my_app',
+      filename,
+      category,
+      admin: true
    })
-    let result = await res.json()
-
-    data = []
-
+  })
+  let result = await res.json()
+  let data = []
+  try {
    for (const res of result.data) {
      let { app, workflow } = res.data
      if (app.filename)
@@ -362,14 +191,7 @@ 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";
@@ -794,106 +616,6 @@ 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 () {
@@ -943,7 +665,7 @@ app.registerExtension({
        }
      ]

-      opts = addSmartMenu(opts, node)
+      opts = addSmartMenu(opts,node)

      // if (node.type == 'CLIPTextEncode') {
      //   // 则出现 randomPrompt
@@ -984,6 +706,16 @@ 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
@@ -1041,7 +773,7 @@ app.registerExtension({
              localStorage.setItem('litegrapheditor_clipboard', old)
            }

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

@@ -1054,8 +786,6 @@ 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) {
@@ -1066,32 +796,11 @@ app.registerExtension({
                }
              }

-              // 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({
+              templates.push({
                name,
                data: JSON.stringify(data)
              })
+              store()
            })
          } // and the callback
        },
@@ -1130,7 +839,6 @@ app.registerExtension({
      const orig = LGraphCanvas.prototype.getCanvasMenuOptions

      const apps = await get_my_app()
-      if (!apps) return

      let apps_map = { 0: [] }

@@ -1155,30 +863,8 @@ app.registerExtension({
                has_submenu: false,
                callback: async () => {
                  try {
-                    let ddd = await get_my_app(a.filename)
-                    if (!ddd) return
-                    let item = ddd[0]
+                    let item = (await get_my_app(a.filename))[0]
                    if (item) {
-                      if (item.author) {
-                        // 有作者信息
-                        if (item.author.avatar)
-                          localStorage.setItem(
-                            '_mixlab_author_avatar',
-                            item.author.avatar
-                          )
-                        if (item.author.name)
-                          localStorage.setItem(
-                            '_mixlab_author_name',
-                            item.author.name
-                          )
-
-                        if (item.author.link)
-                          localStorage.setItem(
-                            '_mixlab_author_link',
-                            item.author.link
-                          )
-                      }
-
                      // console.log(item.data)
                      app.loadGraphData(item.data)
                      setTimeout(() => {
@@ -1206,31 +892,8 @@ app.registerExtension({
                  content: `${a.name}_${a.version}`,
                  callback: async () => {
                    try {
-                      let ddd = await get_my_app(a.filename, a.category)
-
-                      if (!ddd) return
-                      let item = ddd[0]
+                      let item = (await get_my_app(a.filename, a.category))[0]
                      if (item) {
-                        console.log(item)
-                        if (item.author) {
-                          // 有作者信息
-                          if (item.author.avatar)
-                            localStorage.setItem(
-                              '_mixlab_author_avatar',
-                              item.author.avatar
-                            )
-                          if (item.author.name)
-                            localStorage.setItem(
-                              '_mixlab_author_name',
-                              item.author.name
-                            )
-                          if (item.author.link)
-                            localStorage.setItem(
-                              '_mixlab_author_link',
-                              item.author.link
-                            )
-                        }
-
                        // console.log(item.data)
                        app.loadGraphData(item.data)
                        setTimeout(() => {
@@ -1510,53 +1173,6 @@ 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(
@@ -1,5 +1,8 @@
 import { app } from '../../../scripts/app.js'
-import { $el } from '../../../scripts/ui.js' 
+import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+import { addValueControlWidget } from '../../../scripts/widgets.js'

 const getLocalData = key => {
  let data = {}
@@ -1,277 +0,0 @@
-* {
-    transition: all 0.6s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-#app-login {
-    width: 480px;
-    height: 90vh;
-    padding: 6vh;
-    background: white;
-    box-shadow: 0 0 2rem rgba(0, 0, 0, 0.1);
-    z-index: 999;
-    position: fixed;
-    top: 5vh;
-    left: calc(50vw - 240px);
-}
-
-.login-app-view {
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    z-index: 999;
-}
-
-.login-background {
-    background-color: #202020e6;
-    position: fixed;
-    width: 100%;
-    height: 100vh;
-    left: 0;
-    top: 0;
-    z-index: 998;
-}
-
-.app-header {
-    padding: 6vh;
-}
-
-.app-header,
-.app-header>* {
-    font-size: 1.2em;
-    margin: 0;
-    font-weight: 300;
-}
-
-.app-header>h1 {
-    font-size: 4.8vh;
-    font-weight: 400;
-    margin-bottom: 4.8vh;
-}
-
-.app-header>h2 {
-    font-size: 3vh;
-}
-
-.app-subheading {
-    color: rgba(0, 0, 0, 0.45);
-}
-
-.app-register {
-    position: absolute;
-    bottom: 0;
-    height: 10vh;
-    line-height: 10vh;
-    padding: 0 6vh;
-    color: rgba(0, 0, 0, 0.45);
-}
-
-.app-register>a {
-    font-weight: 400;
-}
-
-
-#app-login input {
-    font-size: 2.5vh;
-    width: calc(100% - 13vh);
-    height: 7.5vh;
-    margin-bottom: 2vh;
-    background: transparent;
-    position: absolute;
-    top: 0;
-    left: 6.5vh;
-    z-index: 2;
-    border: none;
-    box-shadow: inset 0 -0.5vh rgba(0, 0, 0, 0.1);
-}
-
-#app-login input:focus {
-    outline: none;
-    box-shadow: inset 0 -0.5vh transparent;
-}
-
-#app-login input[type=email] {
-    top: 58%;
-}
-
-#app-login input[type=password] {
-    top: calc(58% + 7.5vh);
-}
-
-#app-login input[type=email]:valid~* .st1 {
-    transition-timing-function: ease-in-out;
-    stroke-dasharray: 50, 153;
-    stroke-dashoffset: 25;
-}
-
-#app-login input[type=password]:focus~* .st0,
-#app-login input[type=password]:valid~* .st0,
-#login_run:focus~* .st0 {
-    stroke-dasharray: 210, 900;
-    stroke-dashoffset: -305;
-}
-
-#app-login input[type=email]:focus~* .st0 {
-    stroke-dasharray: 210, 900;
-    stroke-dashoffset: 0;
-}
-
-#app-login input:not(:valid)~#login_run {
-    /* pointer-events: none; */
-    opacity: 0.6;
-}
-
-#login_run {
-    text-decoration: none;
-    color: #0f9ede;
-    font-size: 1.5em;
-    padding: 0 6vh;
-    position: absolute;
-    bottom: 10vh;
-    font-weight: 400;
-    z-index: 998;
-    cursor: pointer;
-}
-
-#login_run:focus {
-    outline: none;
-}
-
-.login-app-view:nth-child(2) {
-    display: flex;
-    flex-direction: column;
-    pointer-events: none;
-}
-
-.login-app-view:nth-child(2)>.app-header {
-    font-size: 1rem;
-    flex-basis: 25%;
-    display: flex;
-    flex-direction: column;
-    justify-content: space-between;
-    padding: 4vh;
-    padding-bottom: 1rem;
-}
-
-.login-app-view:nth-child(2)>.app-header>h2 {
-    transform: translateY(1rem);
-}
-
-.login-app-view:nth-child(2)>.app-header>h2>em {
-    color: #0f9ede;
-    font-style: normal;
-}
-
-.login-app-view:nth-child(2)>.app-header>h2,
-.login-app-view:nth-child(2) .app-item>*:not(.app-graphic) {
-    transition-duration: 0.9s;
-    opacity: 0;
-}
-
-
-.st0,
-.st1,
-.svg-loader-segment {
-    fill: none;
-    stroke: #0f9ede;
-    stroke-width: 0.5vh;
-    stroke-alignment: inside;
-    opacity: 1;
-    transition: all 0.6s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-.svg-loader {
-    opacity: 0;
-}
-
-.st0 {
-    stroke-dasharray: 0, 900;
-    stroke-dashoffset: 0;
-}
-
-.st1 {
-    transition-delay: 0.3s;
-    stroke-dasharray: 50, 153;
-    stroke-dashoffset: -153;
-}
-
-.svg-loader-segment {
-    transition: transform 1.2s cubic-bezier(0.77, 0, 0.175, 1), opacity 0.85s cubic-bezier(0.77, 0, 0.175, 1), stroke 0.85s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-#svg-lines {
-    position: absolute;
-    top: 45%;
-    left: 0;
-    width: 100%;
-    z-index: 0;
-    overflow: visible;
-    transform-origin: center 4vh;
-}
-
-.svg-data {
-    fill: none;
-    stroke-width: 0.5vh;
-}
-
-.svg-data.-temp {
-    stroke: #f4814b;
-    stroke-dasharray: 20, 118;
-}
-
-.svg-data.-cal {
-    stroke: #08b5cf;
-    stroke-dasharray: 20, 113;
-}
-
-.svg-data.-steps-bg {
-    stroke: #e0e1e0;
-    stroke-dasharray: 40, 100;
-    stroke-dashoffset: -60;
-}
-
-.svg-data.-steps {
-    stroke: #0f9ede;
-    stroke-dasharray: 20, 73;
-    stroke-dashoffset: -53;
-}
-
-.svg-data.-heart {
-    stroke: #9965aa;
-    stroke-dasharray: 50, 200;
-    stroke-dashoffset: -150;
-}
-
-.svg-activity-fill {
-    fill: #c4e4f8;
-}
-
-.svg-activity-line {
-    fill: none;
-    stroke: #65bcea;
-    stroke-miterlimit: 10;
-    stroke-width: 0.25vh;
-}
-
-.svg-activity-avg,
-.svg-activity-indicator {
-    fill: none;
-    stroke: #d0dff0;
-    stroke-width: 0.25vh;
-    mix-blend-mode: multiply;
-}
-
-.svg-activity-fill,
-.svg-activity-line {
-    transform: translateY(10vh);
-    opacity: 0;
-}
-
-
-*,
-*:before,
-*:after {
-    box-sizing: border-box;
-    position: relative;
-}
@@ -1,67 +0,0 @@
-;(() => {
-  let div = document.createElement('div')
-
-  div.innerHTML = ` 
-    <div id="app-login">
-        <div class="login-app-view">
-            <header class="app-header">
-                <h1>Hi</h1>
-                Welcome back,<br />
-                <span class="app-subheading">
-                    sign in to continue<br />
-    
-                </span>
-            </header>
-            <input class="email" type="email" required pattern=".*\.\w{2,}" placeholder="Email Address" />
-            <input class="password" type="password" required placeholder="Password" />
-            <a class="app-button" id="login_run">登录</a>
-            <!-- <div class="app-register">
-                Don't have an account? <a>Sign Up</a>
-            </div> -->
-            <svg id="svg-lines" version="1.1" xmlns="http://www.w3.org/2000/svg"
-                xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 284.2 152.7"
-                xml:space="preserve">
-                <path class="st0"
-                    d="M37.7,107.3h222.6c12,0,21.8,9.7,21.8,21.7s-9.7,21.8-21.8,21.8c0,0-203.6,0-222.6,0S2.2,138.6,2.2,103.3   c0-52,113.5-101.5,141-101.5c13.5,0,21.8,9.7,21.8,21.8s-9.7,21.7-21.8,21.7s-21.8-9.7-21.8-21.7s9.7-21.8,21.8-21.8" />
-                <path class="st1"
-                    d="M260.2,76.3L250,87.8l-9-9c-6.2-6.2,2-24.7,17.2-24.7c15.2,0,23.9,17.7,23.9,29.7s-11.7,23.5-23.9,23.5h-10.2">
-                </path>
-                <g class="svg-loader" xmlns="http://www.w3.org/2000/svg">
-                    <path class="svg-loader-segment -cal" d="M164.7,23.5c0-12-9.7-21.8-21.8-21.8" />
-                    <path class="svg-loader-segment -heart" d="M143,45.2c12,0,21.8-9.7,21.8-21.7" />
-                    <path class="svg-loader-segment -steps" d="M121.2,23.5c0,12,9.7,21.7,21.8,21.7" />
-                    <path class="svg-loader-segment -temp" d="M143,1.7c-12,0-21.8,9.7-21.8,21.8" />
-                </g>
-            </svg>
-        </div>
-    </div>
-    <div class="login-background"></div>
-     `
-
-  document.body.appendChild(div)
-  let bg = div.querySelector('.login-background')
-  bg.addEventListener('click', e => {
-    div.style.display = 'none'
-  })
-  let login_btn = document.body.querySelector('#login_btn')
-  // login_btn.href="";
-  if (login_btn) {
-    login_btn.innerHTML =
-      '<svg stroke="currentColor" fill="none" stroke-width="0" viewBox="0 0 24 24" height="40px" width="40px" xmlns="http://www.w3.org/2000/svg"><path d="M12 17C14.2091 17 16 15.2091 16 13H8C8 15.2091 9.79086 17 12 17Z" fill="currentColor"></path><path d="M10 10C10 10.5523 9.55228 11 9 11C8.44772 11 8 10.5523 8 10C8 9.44772 8.44772 9 9 9C9.55228 9 10 9.44772 10 10Z" fill="currentColor"></path><path d="M15 11C15.5523 11 16 10.5523 16 10C16 9.44772 15.5523 9 15 9C14.4477 9 14 9.44772 14 10C14 10.5523 14.4477 11 15 11Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12ZM20 12C20 16.4183 16.4183 20 12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12Z" fill="currentColor"></path></svg>LOGIN'
-    login_btn.addEventListener('click', e => {
-      e.preventDefault()
-      div.style.display = 'block'
-    })
-  }
-
-  let login_run = div.querySelector('#login_run')
-  if (login_run) {
-    login_run.addEventListener('click', e => {
-      e.preventDefault()
-      let ps = div.querySelector('.password')
-      let email = div.querySelector('.email')
-      div.style.display = 'none'
-      console.log(ps.value, email.value)
-    })
-  }
-})()
@@ -0,0 +1,24 @@
+::-webkit-scrollbar {
+  width: 2px;
+}
+
+@keyframes loading_mixlab {
+  0% {
+    background-color: green;
+  }
+
+  50% {
+    background-color: lightgreen;
+  }
+
+  100% {
+    background-color: green;
+  }
+}
+
+.loading_mixlab {
+  background-color: green;
+  animation-name: loading_mixlab;
+  animation-duration: 2s;
+  animation-iteration-count: infinite;
+}
@@ -1,6 +1,6 @@
 {
-  "last_node_id": 24,
-  "last_link_id": 26,
+  "last_node_id": 23,
+  "last_link_id": 25,
  "nodes": [
    {
      "id": 9,
@@ -203,7 +203,6 @@
        "Node name for S&R": "ShowTextForGPT"
      },
      "widgets_values": [
-        "a girl face,super,(Pop Art:1.26),(Black and White:1.26)",
        "a girl face,super,(Pop Art:1.26),(Black and White:1.26)"
      ]
    },
@@ -538,7 +537,7 @@
          "type": "IMAGE",
          "links": [
            24,
-            26
+            25
          ],
          "slot_index": 0
        }
@@ -548,7 +547,7 @@
      }
    },
    {
-      "id": 24,
+      "id": 23,
      "type": "AppInfo",
      "pos": [
        3363.0014990624995,
@@ -563,9 +562,9 @@
      "mode": 0,
      "inputs": [
        {
-          "name": "IMAGE",
+          "name": "LOGO",
          "type": "IMAGE",
-          "link": 26
+          "link": 25
        }
      ],
      "properties": {
@@ -581,7 +580,7 @@
        "https://",
        "",
        "enable",
-        2
+        1
      ]
    }
  ],
@@ -707,10 +706,10 @@
      "IMAGE"
    ],
    [
-      26,
+      25,
      10,
      0,
-      24,
+      23,
      0,
      "IMAGE"
    ]
Author	SHA1	Message	Date
shadowcz007	d55cbdfde5	Update App.py	2024-01-29 10:49:04 +08:00
shadowcz007	00cef5f37f	test appnode	2024-01-28 23:25:46 +08:00