Update checkVersion_mixlab.js

### Update 0.8.0
v0.8.0 🚀🚗🚚🏃‍ LaMaInpainting - 新增 LaMaInpainting - 优化color节点的输出 - 修复高清显示屏上定位节点不准的情况 - Add LaMaInpainting - Optimize the output of the color node - Fix the issue of inaccurate positioning node on high-definition display screens
2023-12-31 13:12:34 +08:00 · 2023-12-31 13:11:31 +08:00 · 2023-12-31 13:04:28 +08:00 · 2023-12-31 10:07:07 +08:00 · 2023-12-30 23:52:50 +08:00 · 2023-12-30 23:39:36 +08:00
30 changed files with 8258 additions and 912 deletions
@@ -1,4 +1,6 @@
 __pycache__/
 https/
 nodes/config.json
-workflow/my_workflow.json
+workflow/my_workflow.json
+workflow/my_workflow_app.json
+app/*
@@ -1,30 +1,33 @@
-## 
-v0.5.0 🚀🚗🚚🏃‍
- Added video composition support to the MergeLayers.
- Enhanced visual selection support for the NewLayer node.
- Introduced the NoiseImage node and ResizeImage node.
- Improved compatibility for TextImage with line breaks.
- Optimized the 3DImage node to export textures for modification.
- [Added DynamicDelayByText, enabling delayed execution based on input text length.](./workflow/audio-chatgpt-workflow.json)
+
+### Workflow-to-APP 🚀🚗🚚🏃
+- 新增AppInfo节点，可以通过简单的配置，把workflow转变为一个Web APP。
+- 支持多个web app 切换
+
+- Support multiple web app switching.
+- Add the AppInfo node, which allows you to transform the workflow into a web app by simple configuration.
+
+![](./assets/0-m-app.png)
+
+![](./assets/appinfo-readme.png)
+
+Example:
+- workflow
+![APP info](./workflow/appinfo-workflow.svg)
+[text-to-image](./workflow/Text-to-Image-app.json)
+
+APP-JSON:
+- [text-to-image](./example/text-to-image_1_Wed%20Dec%2027%202023.json)
+- [image-to-image](./example/image-to-image_1_Wed%20Dec%2027%202023.json)
+- text-to-text
+
+> 暂时支持6种节点作为界面上的输入节点：Load Image、CLIPTextEncode、TextInput_、FloatSlider、IntNumber、CheckpointLoaderSimple、LoraLoader
+
+> 输出节点：PreviewImage 、SaveImage、ShowTextForGPT、VHS_VideoCombine


- 为MergeLayers添加了视频合成功能。
- NewLayer节点增加了视觉选择支持。
- 添加了NoiseImage节点和ResizeImage节点。
- 支持带有换行的文本图像。
- 对3D节点进行了优化，支持导出纹理以进行修改。
- [添加了DynamicDelayByText功能，可以根据输入文本的长度进行延迟执行。](./workflow/audio-chatgpt-workflow.json)
+### 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! 💻🌐

-
-### 3D
-![](./assets/3dimage.png)
-[workflow](./workflow/3D-workflow.json)
-
-
-### ScreenShareNode & FloatingVideoNode
-> Now comfyui supports capturing screen pixel streams from any software and can be used for LCM-Lora integration. Let's get started with implementation and design! 💻🌐
-
-> 
 ![screenshare](./assets/screenshare.png)

 https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43e-410a-ab3a-1952b7b4e7da
@@ -35,6 +38,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43

 !! Please use the address with HTTPS (https://127.0.0.1).

+
 ### SpeechRecognition & SpeechSynthesis
 ![f](./assets/audio-workflow.svg)

@@ -43,17 +47,13 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 ### GPT
 > Support for calling multiple GPTs.ChatGPT、ChatGLM3 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 .  Azure OpenAI:https://xxxx.openai.azure.com 

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

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

-### LoadImagesFromLocal
-> Monitor changes to images in a local folder, and trigger real-time execution of workflows, supporting common image formats, especially PSD format, in conjunction with Photoshop. 
-
-![watch](./assets/4-loadfromlocal-watcher-workflow.svg)
-
-[workflow-4](./workflow/4-loadfromlocal-watcher-workflow.json)
+### 3D
+![](./assets/3dimage.png)
+[workflow](./workflow/3D-workflow.json)


 ### Layers
@@ -63,10 +63,24 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43

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

+
+### LoadImagesFromLocal
+> Monitor changes to images in a local folder, and trigger real-time execution of workflows, supporting common image formats, especially PSD format, in conjunction with Photoshop. 
+
+![watch](./assets/4-loadfromlocal-watcher-workflow.svg)
+
+[workflow-4](./workflow/4-loadfromlocal-watcher-workflow.json)
+
+### LoadImagesFromURL
+> Conveniently load images from a fixed address on the internet to ensure that default images in the workflow can be executed.
+
+
 ## Utils
 > The Color node provides a color picker for easy color selection, the Font node offers built-in font selection for use with TextImage to generate text images, and the DynamicDelayByText node allows delayed execution based on the length of the input text.

+- [添加了DynamicDelayByText功能，可以根据输入文本的长度进行延迟执行。](./workflow/audio-chatgpt-workflow.json)

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

 ## Other Nodes

@@ -100,11 +114,15 @@ Add edges to an image.
 ![FeatheredMask](./assets/FlVou_Y6kaGWYoEj1Tn0aTd4AjMI.jpg)


+> LaMaInpainting
+
+from [simple-lama-inpainting](https://github.com/enesmsahin/simple-lama-inpainting)
+

 ### Improvement 

 - Add "help" option to the context menu for each node.
- Add "find the node" option to the global context menu.
+- Add "Nodes Map" option to the global context menu.

 An improvement has been made to directly redirect to GitHub to search for missing nodes when loading the graph.

@@ -113,14 +131,26 @@ An improvement has been made to directly redirect to GitHub to search for missin
 ![node-not-found](./assets/node-not-found.png)


+### Update 
+v0.8.0 🚀🚗🚚🏃‍ LaMaInpainting
+- 新增 LaMaInpainting
+- 优化color节点的输出
+- 修复高清显示屏上定位节点不准的情况
+
+- Add LaMaInpainting
+- Optimize the output of the color node
+- Fix the issue of inaccurate positioning node on high-definition display screens
+
+
 ### Models
-[Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : model/clipseg
+[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

 <!-- ### Workflow
 [Workflow](./workflow.md) -->


-
 ## Installation

 manually install, simply clone the repo into the custom_nodes directory with this command:
@@ -154,7 +184,6 @@ pip3 install -r requirements.txt
 访问 [www.mixcomfy.com](https://www.mixcomfy.com)，获得更多内测功能，关注微信公众号：Mixlab无界社区


-
 #### Thanks:
 [ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)

@@ -4,7 +4,7 @@ import subprocess
 import importlib.util
 import sys,json
 import urllib
-
+import hashlib
 import datetime


@@ -79,6 +79,13 @@ install_openai()
 current_path = os.path.abspath(os.path.dirname(__file__))


+
+def calculate_md5(string):
+    encoded_string = string.encode()
+    md5_hash = hashlib.md5(encoded_string).hexdigest()
+    return md5_hash
+
+
 def create_key(key_p,crt_p):
    import OpenSSL
    # 生成自签名证书
@@ -162,12 +169,98 @@ def get_workflows():
    workflows=read_workflow_json_files(workflow_path)
    return workflows

+def get_my_workflow_for_app(filename="my_workflow_app.json"):
+    app_path=os.path.join(current_path, "app")
+    if not os.path.exists(app_path):
+        os.mkdir(app_path)
+
+    apps=[]
+    if filename==None:
+        data=read_workflow_json_files(app_path)
+        i=0
+        for item in data:
+            try:
+                x=item["data"]
+                if i==0:
+                    apps.append({
+                        "filename":item["filename"],
+                        "data":x,
+                        "date":item["date"]
+                    })
+                else:
+                    apps.append({
+                        "filename":item["filename"],
+                        "data":{
+                            "app":{
+                                "description":x['app']['description'],
+                                "filename":(x['app']['filename'] if 'filename' in x['app'] else "") ,
+                                "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
+                                "name":x['app']['name'],
+                                "version":x['app']['version'],
+                            }
+                        },
+                        "date":item["date"]
+                    })
+                i+=1
+            except Exception as e:
+                print("发生异常：", str(e))
+    else:
+        app_workflow_path=os.path.join(app_path, filename)
+        # print('app_workflow_path: ',app_workflow_path)
+        try:
+            with open(app_workflow_path) as json_file:
+                apps = [{
+                    'filename':filename,
+                    'data':json.load(json_file)
+                }]
+        except Exception as e:
+            print("发生异常：", str(e))
+        
+        if len(apps)==1:
+            data=read_workflow_json_files(app_path)
+            
+            for item in data:
+                x=item["data"]
+                print(apps[0]['filename'] ,item["filename"])
+                if apps[0]['filename']!=item["filename"]:
+                    apps.append({
+                        "filename":item["filename"],
+                        "data":{
+                            "app":{
+                                "description":x['app']['description'],
+                                "filename":(x['app']['filename'] if 'filename' in x['app'] else "") ,
+                                "icon":(x['app']['icon'] if 'icon' in x['app'] else None),
+                                "name":x['app']['name'],
+                                "version":x['app']['version'],
+                            }
+                        },
+                        "date":item["date"]
+                    })
+
+    return apps
+
 def save_workflow_json(data):
    workflow_path=os.path.join(current_path, "workflow/my_workflow.json")
    with open(workflow_path, 'w') as file:
        json.dump(data, file)
    return workflow_path

+def save_workflow_for_app(data,filename="my_workflow_app.json"):
+    app_path=os.path.join(current_path, "app")
+    if not os.path.exists(app_path):
+        os.mkdir(app_path)
+    app_workflow_path=os.path.join(app_path, filename)
+ 
+    try:
+        output_str = json.dumps(data['output'])
+        data['app']['id']=calculate_md5(output_str)
+        # id=data['app']['id']
+    except Exception as e:
+        print("发生异常：", str(e))
+    
+    with open(app_workflow_path, 'w') as file:
+        json.dump(data, file)
+    return filename

 def get_nodes_map():
    # print("#####path::", current_path)
@@ -253,6 +346,18 @@ async def mixlab_hander(request):
            print(e)
    return web.json_response(data)

+
+@routes.get('/mixlab/app')
+async def mixlab_app_handler(request):
+    html_file = os.path.join(current_path, "web/index.html")
+    if os.path.exists(html_file):
+        with open(html_file, 'r', encoding='utf-8', errors='ignore') as f:
+            html_data = f.read()
+            return web.Response(text=html_data, content_type='text/html')
+    else:
+        return web.Response(text="HTML file not found", status=404)
+    
+
@routes.post('/mixlab/workflow')
 async def mixlab_workflow_hander(request):
    data = await request.json()
@@ -265,6 +370,20 @@ async def mixlab_workflow_hander(request):
                    'status':'success',
                    'file_path':file_path
                }
+            elif data['task']=='save_app':
+                file_path=save_workflow_for_app(data['data'],data['filename'])
+                result={
+                    'status':'success',
+                    'file_path':file_path
+                }
+            elif data['task']=='my_app':
+                filename=None
+                if 'filename' in data:
+                    filename=data['filename']
+                result={
+                    'data':get_my_workflow_for_app(filename),
+                    'status':'success',
+                }
            elif data['task']=='list':
                result={
                    'data':get_workflows(),
@@ -289,6 +408,7 @@ async def nodes_map_hander(request):

    return web.json_response(result)

+# 把插件自定义的路由添加到comfyui server里
 def new_add_routes(self):
        import nodes
        self.app.add_routes(routes)
@@ -318,23 +438,25 @@ PromptServer.add_routes=new_add_routes

 # 导入节点
 from .nodes.PromptNode import RandomPrompt
-from .nodes.ImageNode import NoiseImage,TransparentImage,LoadImagesFromPath,ResizeImage,TextImage,SvgImage,Image3D,EmptyLayer,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.ImageNode import NoiseImage,TransparentImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
 from .nodes.Clipseg import CLIPSeg,CombineMasks
 from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import ColorInput,FontInput,TextToNumber,DynamicDelayProcessor
-
+from .nodes.Utils import AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,GetImageSize_,MultiplicationNode
+from .nodes.Lama import LaMaInpainting

 # 要导出的所有节点及其名称的字典
 # 注意：名称应全局唯一
 NODE_CLASS_MAPPINGS = {
+    "AppInfo":AppInfo,
    "RandomPrompt":RandomPrompt,
    "NoiseImage":NoiseImage,
    "TransparentImage":TransparentImage,
    "ResizeImageMixlab":ResizeImage,
    "LoadImagesFromPath":LoadImagesFromPath,
+    "LoadImagesFromURL":LoadImagesFromURL,
    "TextImage":TextImage,
    "EnhanceImage":EnhanceImage,
    "SvgImage":SvgImage,
@@ -360,15 +482,24 @@ NODE_CLASS_MAPPINGS = {
    "SpeechRecognition":SpeechRecognition,
    "SpeechSynthesis":SpeechSynthesis,
    "Color":ColorInput,
+    "FloatSlider":FloatSlider,
+    "IntNumber":IntNumber,
+    "TextInput_":TextInput,
    "Font":FontInput,
    "TextToNumber":TextToNumber,
-    "DynamicDelayProcessor":DynamicDelayProcessor
+    "DynamicDelayProcessor":DynamicDelayProcessor,
+    "MultiplicationNode":MultiplicationNode,
+    "GetImageSize_":GetImageSize_,
+    "SwitchByIndex":SwitchByIndex,
+    "LimitNumber":LimitNumber, 
+    "LaMaInpainting":LaMaInpainting
    # "GamePal":GamePal
 }

 # 一个包含节点友好/可读的标题的字典
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "ResizeImageMixlab":"ResizeImage",
+    "AppInfo":"AppInfo ♾️Mixlab",
+    "ResizeImageMixlab":"ResizeImage ♾️Mixlab",
    "RandomPrompt": "Random Prompt ♾️Mixlab",
    "SplitLongMask":"Splitting a long image into sections",
    "VAELoaderConsistencyDecoder":"Consistency Decoder Loader",
@@ -381,7 +512,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
    "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
    "3DImage":"3DImage ♾️Mixlab",
-    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab"
+    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
+    "LaMaInpainting":"LaMaInpainting ♾️Mixlab"

    # "GamePal":"GamePal ♾️Mixlab"
 }
@@ -390,5 +522,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
 WEB_DIRECTORY = "./web"

 print('--------------')
-print('\033[91mMixlab Nodes: \033[93mLoaded\033[0m')
+print('\033[91m ### Mixlab Nodes: \033[93mLoaded\033[0m')
 print('--------------')
@@ -4762,15 +4762,17 @@
    "https://github.com/shadowcz007/comfyui-mixlab-nodes": [
        [
            "3DImage",
+            "AppInfo",
+            "IntNumber",
+            "FloatSlider",
+            "ResizeImage",
+            "NoiseImage",
            "AreaToMask",
-            "CLIPSeg",
            "CLIPSeg_",
            "CharacterInText",
            "ChatGPTOpenAI",
            "Color",
            "CombineMasks_",
-            "CombineSegMasks",
-            "EmptyLayer",
            "EnhanceImage",
            "FaceToMask",
            "FeatheredMask",
@@ -4778,6 +4780,7 @@
            "Font",
            "ImageCropByAlpha",
            "LoadImagesFromPath",
+            "LoadImagesFromURL",
            "MergeLayers",
            "NewLayer",
            "RandomPrompt",
@@ -4790,12 +4793,17 @@
            "SplitLongMask",
            "SvgImage",
            "TextImage",
+            "ResizeImageMixlab",
            "TransparentImage",
            "VAEDecodeConsistencyDecoder",
-            "VAELoaderConsistencyDecoder"
+            "VAELoaderConsistencyDecoder",
+            "TextToNumber",
+            "TextInput_",
+            "DynamicDelayProcessor",
+            "LaMaInpainting"
        ],
        {
-            "title_aux": "comfyui-mixlab-nodes [WIP]"
+            "title_aux": "comfyui-mixlab-nodes"
        }
    ],
    "https://github.com/shiimizu/ComfyUI_smZNodes": [
@@ -75,15 +75,15 @@ class ChatGPTNode:
            "required": {
                "api_key":("KEY", {"default": "", "multiline": True}),
                "api_url":("URL", {"default": "", "multiline": True}),
-                "prompt": ("STRING", {"multiline": True}),
+                "prompt": ("STRING", {"multiline": True,"dynamicPrompts": False}),
                "system_content": ("STRING", 
                                   {
                                       "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
-                                       "multiline": True
+                                       "multiline": True,"dynamicPrompts": False
                                       }),
                "model": (["gpt-3.5-turbo","gpt-35-turbo","gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-4-0613","gpt-4-1106-preview"], 
                          {"default": "gpt-3.5-turbo"}),
-                "seed": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
                "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
            },
             "hidden": {
@@ -167,7 +167,7 @@ class ShowTextForGPT:
    def INPUT_TYPES(s):
        return {
            "required": {
-                "text": ("STRING", {"forceInput": True}),
+                "text": ("STRING", {"forceInput": True,"dynamicPrompts": False}),
            }
        }

@@ -189,8 +189,8 @@ class CharacterInText:
    def INPUT_TYPES(s):
        return {
            "required": {
-                 "text": ("STRING", {"multiline": True}),
-                 "character": ("STRING", {"multiline": True}),
+                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                 "character": ("STRING", {"multiline": True,"dynamicPrompts": False}),
                 "start_index": ("INT", {
                    "default": 1,
                    "min": 0, #Minimum value
@@ -35,6 +35,16 @@ if not os.path.exists(clipseg_model_dir):

 """Helper methods for CLIPSeg nodes"""

+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
 def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
    """Convert a tensor to a numpy array and scale its values to 0-255."""
    array = tensor.numpy().squeeze()
@@ -91,7 +101,7 @@ class CLIPSeg:
        return {"required":
                    {
                        "image": ("IMAGE",),
-                        "text": ("STRING", {"multiline": False}),
+                        "text": ("STRING", {"multiline": False,"dynamicPrompts": False}),
                        
                     },
                "optional":
@@ -107,7 +117,7 @@ class CLIPSeg:
    RETURN_NAMES = ("Mask","Heatmap Mask", "BW Mask")

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

    FUNCTION = "segment_image"
    def segment_image(self, image: torch.Tensor, text: str, blur: float, threshold: float, dilation_factor: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
@@ -180,12 +190,13 @@ class CLIPSeg:
        binary_mask_image = Image.fromarray(binary_mask_resized[..., 0])

        # convert PIL image to numpy array
-        tensor_bw = binary_mask_image.convert("RGB")
-        tensor_bw = np.array(tensor_bw).astype(np.float32) / 255.0
-        tensor_bw = torch.from_numpy(tensor_bw)[None,]
-        tensor_bw = tensor_bw.squeeze(0)[..., 0]
+        tensor_bw = binary_mask_image.convert("L")
+        tensor_bw=pil2tensor(tensor_bw)
+        # tensor_bw = np.array(tensor_bw).astype(np.float32) / 255.0
+        # tensor_bw = torch.from_numpy(tensor_bw)[None,]
+        # tensor_bw = tensor_bw.squeeze(0)[..., 0]

-        return tensor_bw, image_out_heatmap, image_out_binary
+        return (tensor_bw, image_out_heatmap, image_out_binary,)

    #OUTPUT_NODE = False

@@ -235,7 +246,7 @@ class CombineMasks:
        
        # Resize heatmap and binary mask to match the original image dimensions
        dimensions = (image_np.shape[1], image_np.shape[0])
-        print('heatmap',heatmap)
+        # print('heatmap',heatmap)
        if dimensions is None or dimensions[0] == 0 or dimensions[1] == 0:
            raise ValueError("Invalid dimensions")
        
@@ -1,4 +1,5 @@
 import numpy as np
+import requests
 import torch
 from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
 from PIL.PngImagePlugin import PngInfo
@@ -197,6 +198,24 @@ def load_image(fp,white_bg=False):
        
    return images

+def load_image_and_mask_from_url(url, timeout=10):
+    # Load the image from the URL
+    response = requests.get(url, timeout=timeout)
+
+    content_type = response.headers.get('Content-Type')
+    
+    image = Image.open(BytesIO(response.content))
+
+    # Create a mask from the image's alpha channel
+    mask = image.convert('RGBA').split()[-1]
+
+    # Convert the mask to a black and white image
+    mask = mask.convert('L')
+
+    image=image.convert('RGB')
+
+    return (image, mask)
+

 # 获取图片s
 def get_images_filepath(f,white_bg=False):
@@ -487,6 +506,7 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s
        y = 0
        for line in lines:
            for char in line:
+                #print('char',char)
                char_coordinates.append((x, y))
                x += font_size + spacing
            y += font_size + spacing
@@ -495,10 +515,10 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s
    # 3. Calculate image width and height
    if layout == "vertical":
        width = (len(lines) * (font_size + spacing)) - spacing
-        height = (len(max(lines, key=len)) * (font_size + spacing)) - spacing
+        height = ((len(max(lines, key=len))+1) * (font_size + spacing)) + spacing
    else:
        width = (len(max(lines, key=len)) * (font_size + spacing)) - spacing
-        height = (len(lines) * (font_size + spacing)) - spacing
+        height = ((len(lines)-1) * (font_size + spacing)) + font_size

    # 4. Draw each character on the image
    image = Image.new('RGBA', (width, height), (255, 255, 255,0))
@@ -869,7 +889,7 @@ class LoadImagesFromPath:
                }
            }
    
-    RETURN_TYPES = ('IMAGE','MASK','STRING')
+    RETURN_TYPES = ('IMAGE','MASK','STRING',)

    FUNCTION = "run"

@@ -902,6 +922,11 @@ class LoadImagesFromPath:

        images=get_images_filepath(file_path,white_bg=='enable')

+        # 当开启了监听，则取最新的，第一个文件
+        if watcher=='enable':
+            index_variable=0
+            newest_files='enable'
+
        # 排序
        sorted_files = sorted(images, key=lambda x: os.path.getmtime(x['file_path']), reverse=(newest_files=='enable'))

@@ -913,9 +938,13 @@ class LoadImagesFromPath:
            masks.append(im['mask'])
        
        # print('index_variable',index_variable)
-        if index_variable!=-1:
-            imgs=[imgs[index_variable]] if index_variable < len(imgs) else None
-            masks=[masks[index_variable]] if index_variable < len(masks) else None
+        
+        try:
+            if index_variable!=-1:
+                imgs=[imgs[index_variable]] if index_variable < len(imgs) else None
+                masks=[masks[index_variable]] if index_variable < len(masks) else None
+        except Exception as e:
+            print("发生了一个未知的错误：", str(e))

        # print('#prompt::::',prompt)
        return (imgs,masks,prompt,)
@@ -962,8 +991,8 @@ class TextImage:
    def INPUT_TYPES(s):
        return {"required": { 
            
-                    "text": ("STRING",{"multiline": True,"default": "龍馬精神迎新歲"}),
-                    "font_path": ("STRING",{"multiline": False,"default": FONT_PATH}),
+                    "text": ("STRING",{"multiline": True,"default": "龍馬精神迎新歲","dynamicPrompts": False}),
+                    "font_path": ("STRING",{"multiline": False,"default": FONT_PATH,"dynamicPrompts": False}),
                    "font_size": ("INT",{
                                "default":100, 
                                "min": 100, #Minimum value
@@ -973,17 +1002,17 @@ class TextImage:
                                }), 
                    "spacing": ("INT",{
                                "default":12, 
-                                "min": 1, #Minimum value
+                                "min": -200, #Minimum value
                                "max": 200, #Maximum value
                                "step": 1, #Slider's step
                                "display": "number" # Cosmetic only: display as "number" or "slider"
                                }), 
-                    "text_color":("STRING",{"multiline": False,"default": "#000000"}),
+                    "text_color":("STRING",{"multiline": False,"default": "#000000","dynamicPrompts": False}),
                    "vertical":("BOOLEAN", {"default": True},),
                             },
                }
    
-    RETURN_TYPES = ("IMAGE","MASK")
+    RETURN_TYPES = ("IMAGE","MASK",)
    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)

    FUNCTION = "run"
@@ -1004,6 +1033,62 @@ class TextImage:

        return (img,mask,)

+class LoadImagesFromURL:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "url": ("STRING",{"multiline": True,"default": "https://","dynamicPrompts": False}),
+                             },
+                }
+    
+    RETURN_TYPES = ("IMAGE","MASK",)
+    RETURN_NAMES = ("images","masks",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/image"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,True,)
+
+
+    global urls_image
+    urls_image={}
+
+    def run(self,url):
+        global urls_image
+        print(urls_image)
+        def filter_http_urls(urls):
+            filtered_urls = []
+            for url in urls.split('\n'):
+                if url.startswith('http'):
+                    filtered_urls.append(url)
+            return filtered_urls
+
+        filtered_urls = filter_http_urls(url)
+
+        images=[]
+        masks=[]
+
+        for img_url in filtered_urls:
+            try:
+                if img_url in urls_image:
+                    img,mask=urls_image[img_url]
+                else:
+                    img,mask=load_image_and_mask_from_url(img_url)
+                    urls_image[img_url]=(img,mask)
+
+                img1=pil2tensor(img)
+                mask1=pil2tensor(mask)
+
+                images.append(img1)
+                masks.append(mask1)
+            except Exception as e:
+                print("发生了一个未知的错误：", str(e))
+            
+        return (images,masks,)
+
+


 class SvgImage:
@@ -1060,16 +1145,22 @@ class Image3D:

    def run(self,upload,material=None):
        # print('material',material)
-        # print(upload['image'])
+        # print(upload )
        image = base64_to_image(upload['image'])
-        mat=base64_to_image(upload['material'])
+
+        mat=None
+        if 'material' in upload and upload['material']:
+            mat=base64_to_image(upload['material'])
+            mat=mat.convert('RGB')
+            mat=pil2tensor(mat)
+
        mask = image.split()[3]
        image=image.convert('RGB')
-        mat=mat.convert('RGB')
+        
        mask=mask.convert('L')

        bg_image=None
-        if upload['bg_image']:
+        if 'bg_image' in upload and upload['bg_image']:
            bg_image = base64_to_image(upload['bg_image'])
            bg_image=bg_image.convert('RGB')
            bg_image=pil2tensor(bg_image)
@@ -1077,8 +1168,7 @@ class Image3D:

        mask=pil2tensor(mask)
        image=pil2tensor(image)
-        mat=pil2tensor(mat)
-
+        
        m=[]
        if not material is None:
            m=create_temp_file(material[0])
@@ -1402,49 +1492,55 @@ class MergeLayers:
        bg_images=[]
        masks=[]
 
-        for bg_image in images[0]:
-            # bg_image=image[0]
-            bg_image=tensor2pil(bg_image)
-            # 按z-index排序
-            layers_new = sorted(layers, key=lambda x: x["z_index"])
-        
-            for layer in layers_new:
-                image=layer['image']
-                mask=layer['mask']
-                if 'type' in layer and layer['type']=='base64' and type(image) == str:
-                    im=base64_to_image(image)
-                    im=im.convert('RGB')
-                    image=pil2tensor(im)
+        # print(len(images),images[0].shape)
+        # 1  torch.Size([2, 512, 512, 3])
+        # 4  torch.Size([1, 1024, 768, 3])

-                    mask=base64_to_image(mask)
-                    mask=mask.convert('L')
-                    mask=pil2tensor(mask)
-                
-                
-                layer_image=tensor2pil(image)
-                layer_mask=tensor2pil(mask)
-                bg_image=merge_images(bg_image,
-                                    layer_image,
-                                    layer_mask,
-                                    layer['x'],
-                                    layer['y'],
-                                    layer['width'],
-                                    layer['height'],
-                                    layer['scale_option']
-                                    )
-                
-            mask=bg_image.convert('RGBA')
-            mask=pil2tensor(mask)
+        for img in images:
+
+            for bg_image in img:
+                # bg_image=image[0]
+                bg_image=tensor2pil(bg_image)
+                # 按z-index排序
+                layers_new = sorted(layers, key=lambda x: x["z_index"])
            
-            bg_image=bg_image.convert('RGB')
-            bg_image=pil2tensor(bg_image)
+                for layer in layers_new:
+                    image=layer['image']
+                    mask=layer['mask']
+                    if 'type' in layer and layer['type']=='base64' and type(image) == str:
+                        im=base64_to_image(image)
+                        im=im.convert('RGB')
+                        image=pil2tensor(im)

-            channels = ["red", "green", "blue", "alpha"]
-            # print(mask,mask.shape)
-            mask = mask[:, :, :, channels.index("green")]
+                        mask=base64_to_image(mask)
+                        mask=mask.convert('L')
+                        mask=pil2tensor(mask)
+                    
+                    
+                    layer_image=tensor2pil(image)
+                    layer_mask=tensor2pil(mask)
+                    bg_image=merge_images(bg_image,
+                                        layer_image,
+                                        layer_mask,
+                                        layer['x'],
+                                        layer['y'],
+                                        layer['width'],
+                                        layer['height'],
+                                        layer['scale_option']
+                                        )
+                    
+                mask=bg_image.convert('RGBA')
+                mask=pil2tensor(mask)
+                
+                bg_image=bg_image.convert('RGB')
+                bg_image=pil2tensor(bg_image)

-            bg_images.append(bg_image)
-            masks.append(mask)
+                channels = ["red", "green", "blue", "alpha"]
+                # print(mask,mask.shape)
+                mask = mask[:, :, :, channels.index("green")]
+
+                bg_images.append(bg_image)
+                masks.append(mask)
        
        bg_images=torch.cat(bg_images, dim=0)
        masks=torch.cat(masks, dim=0)
@@ -0,0 +1,85 @@
+import os
+import folder_paths
+from simple_lama_inpainting import SimpleLama
+from PIL import Image
+
+import numpy as np
+
+import torch
+
+
+os.environ['LAMA_MODEL'] =  os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
+if os.environ.get("LAMA_MODEL"):
+    model_path=os.environ.get("LAMA_MODEL")
+    if not os.path.exists(model_path):
+        os.environ['LAMA_MODEL']=''
+        raise FileNotFoundError(
+            f"lama torchscript model not found: {model_path}"
+                )
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+# simple_lama = SimpleLama()
+
+# img_path = "image.png"
+# mask_path = "mask.png"
+
+# image = Image.open(img_path)
+# mask = Image.open(mask_path).convert('L')
+
+# result = simple_lama(image, mask)
+# result.save("inpainted.png")
+
+
+class LaMaInpainting:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "mask": ("MASK",),
+                             },
+
+            
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/image"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+    global simple_lama
+    simple_lama = None
+    def run(self,image,mask):
+        global simple_lama
+
+        result=[]
+        if simple_lama==None:
+            simple_lama = SimpleLama()
+        else:
+            simple_lama.model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        for i in range(len(image)):
+            im=image[i]
+            ma=mask[i]
+            im=tensor2pil(im)
+            ma=tensor2pil(ma)
+            ma =ma.convert('L')
+            
+            res = simple_lama(im, ma)
+            res=pil2tensor(res)
+            result.append(res)
+        # result.save("inpainted.png")
+        if simple_lama.device=='cuda':
+            simple_lama.model.to('cpu')
+
+        return (result,)
@@ -1,9 +1,53 @@
 import os
 import re,random
+from PIL import Image
+import numpy as np
 # FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
-
+import folder_paths
 import matplotlib.font_manager as fm

+# import json
+# import hashlib
+
+
+# def get_json_hash(json_content):
+#     json_string = json.dumps(json_content, sort_keys=True)
+#     hash_object = hashlib.sha256(json_string.encode())
+#     hash_value = hash_object.hexdigest()
+#     return hash_value
+    
+
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+
+def create_temp_file(image):
+    output_dir = folder_paths.get_temp_directory()
+
+    (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('tmp', output_dir)
+
+    
+    image=tensor2pil(image)
+ 
+    image_file = f"{filename}_{counter:05}.png"
+     
+    image_path=os.path.join(full_output_folder, image_file)
+
+    image.save(image_path,compress_level=4)
+
+    return [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type": "temp"
+                }]
+
 def get_font_files(directory):
    font_files = {}

@@ -44,18 +88,23 @@ class ColorInput:
                             },
                }
    
-    RETURN_TYPES = ("STRING",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_TYPES = ("STRING","INT","INT","INT","FLOAT",)
+    RETURN_NAMES = ("hex","r","g","b","a",)

    FUNCTION = "run"

    CATEGORY = "♾️Mixlab/utils"

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

    def run(self,color):
-        return (color,)
+        h=color['hex']
+        r=color['r']
+        g=color['g']
+        b=color['b']
+        a=color['a']
+        return (h,r,g,b,a,)



@@ -120,6 +169,113 @@ class TextToNumber:
            result= random.randint(1, 10000000000)
        return {"ui": {"text": [text],"num":[result]}, "result": (result,)}
    
+
+   
+class FloatSlider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "number":("FLOAT", {
+                        "default": 0, 
+                        "min": 0, #Minimum value
+                        "max": 1, #Maximum value
+                        "step": 0.001, #Slider's step
+                        "display": "slider" # Cosmetic only: display as "number" or "slider"
+                    }),
+                             },
+                }
+    
+    RETURN_TYPES = ("FLOAT",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,number):
+       
+        return (number,)
+    
+  
+class IntNumber:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "number":("INT", {
+                        "default": 0, 
+                        "min": -1, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 1, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                             },
+                }
+    
+    RETURN_TYPES = ("INT",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,number):
+       
+        return (number,)
+
+class MultiplicationNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "numberA":(any_type,),
+                    "numberB":("FLOAT", {
+                        "default": 0, 
+                        "min": -1, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.1, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    })
+                             },
+                }
+    
+    RETURN_TYPES = ("FLOAT","INT",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,False,)
+
+    def run(self,numberA,numberB):
+        b=int(numberA*numberB)
+        a=float(numberA*numberB)
+        return (a,b,) 
+
+class TextInput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "text": ("STRING",{"multiline": True,"default": ""}),
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING",) 
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,text):
+       
+        return (text,)
+    
 # 接收一个值，然后根据字符串或数值长度计算延迟时间，用户可以自定义延迟"字/s"，延迟之后将转化

 import comfy.samplers
@@ -141,7 +297,7 @@ class DynamicDelayProcessor:

    @classmethod
    def INPUT_TYPES(cls):
-        print("print INPUT_TYPES",cls)
+        # print("print INPUT_TYPES",cls)
        return {
            "required":{
                    "delay_seconds":("INT",{
@@ -210,3 +366,167 @@ class DynamicDelayProcessor:
        # 根据 replace_output 决定输出值
        return (max(0, replace_value),) if replace_output == "enable" else (any_input,)
        
+
+
+
+# app 配置节点
+class AppInfo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "name": ("STRING",{"multiline": False,"default": "Mixlab-App","dynamicPrompts": False}),
+                    "image": ("IMAGE",),
+                    "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"]),"dynamicPrompts": False}),
+                    "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"]),"dynamicPrompts": False}),
+                             },
+
+                "optional":{
+                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
+                    "version":("INT", {
+                        "default": 1, 
+                        "min": 1, 
+                        "max": 10000, 
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                    "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                }
+
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,name,image,input_ids,output_ids,description,version,share_prefix):
+
+        im=create_temp_file(image)
+        
+        # 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]}, "result": (image,)}
+    
+
+    
+
+class GetImageSize_:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("INT", "INT")
+    RETURN_NAMES = ("width", "height")
+
+    FUNCTION = "get_size"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    def get_size(self, image):
+        _, height, width, _ = image.shape
+        return (width, height)
+
+
+
+class SwitchByIndex:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "A":(any_type,),
+                "B":(any_type,),
+                "index":("INT", {
+                        "default": -1, 
+                        "min": -1, 
+                        "max": 1000, 
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("C",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self, A,B,index):
+        C=[]
+        index=index[0]
+        for a in A:
+            C.append(a)
+        for b in B:
+            C.append(b)
+        if index>-1:
+            try:
+                C=[C[index]]
+            except Exception as e:
+                C=[]
+        return (C,)
+
+
+
+class LimitNumber:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "number":(any_type,),
+                "min_value":("INT", {
+                        "default": 0, 
+                        "min": 0, 
+                        "max": 0xffffffffffffffff,
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                "max_value":("INT", {
+                        "default": 1, 
+                        "min": 1, 
+                        "max": 0xffffffffffffffff,
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("number",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/utils"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self, number, min_value, max_value):
+        nn=number
+
+        if isinstance(number, int):
+            min_value=int(min_value)
+            max_value=int(max_value)
+        if isinstance(number, float):
+            min_value=float(min_value)
+            max_value=float(max_value)
+
+        if number < min_value:
+            nn= min_value
+        elif number > max_value:
+            nn= max_value
+        
+        return (nn,)
+
@@ -3,4 +3,5 @@ pyOpenSSL
 watchdog
 opencv-python-headless
 matplotlib
-openai
+openai
+simple-lama-inpainting
@@ -187,20 +187,22 @@ app.registerExtension({
          },
          async serializeValue (nodeId, widgetIndex) {
            let d = getLocalData('_mixlab_3d_image')
-            console.log('serializeValue', node)
+            // console.log('serializeValue', node)
            if (d && d[node.id]) {
              let { url, bg, material } = d[node.id]
-              let base64 = await parseImage(url)
-              let bg_base64 = await parseImage(bg)
-              let material_base64 = await parseImage(material)
+              let data = {}
+              if (url) {
+                data.image = await parseImage(url)
+              }
+              if (bg) {
+                data.bg_image = await parseImage(bg)
+              }

-              return JSON.parse(
-                JSON.stringify({
-                  image: base64,
-                  bg_image: bg_base64,
-                  material: material_base64
-                })
-              )
+              if (material) {
+                data.material = await parseImage(material)
+              }
+
+              return JSON.parse(JSON.stringify(data))
            } else {
              return {}
            }
@@ -281,6 +283,8 @@ app.registerExtension({
                  <div>Material: <select class="material"></select></div>
                  <div>Material: <div class="material_img"> </div></div>
                  <div><button class="bg">BG</button></div>
+                  <div><button class="export">Export GLB</button></div>
+                  
                </div></model-viewer>`

              preview.innerHTML = html
@@ -294,6 +298,7 @@ app.registerExtension({
              const selectMaterial = preview.querySelector('.material')
              const material_img = preview.querySelector('.material_img')
              const bg = preview.querySelector('.bg')
+              const exportGLB = preview.querySelector('.export')

              if (modelViewerVariants) {
                modelViewerVariants.style.width = `${that.size[0] - 24}px`
@@ -341,21 +346,26 @@ app.registerExtension({
                let url = await uploadImage(blob, '.png')
                // console.log(url)

-                //  材质贴图
-                let thumbUrl = material_img.getAttribute('src')
-                let tb = await base64ToBlobFromURL(thumbUrl)
-                let tUrl = await uploadImage(tb, '.png')
-
-                // console.log(tUrl)
-
                let bg_blob = await base64ToBlobFromURL(
                  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mN88uXrPQAFwwK/6xJ6CQAAAABJRU5ErkJggg=='
                )
                let url_bg = await uploadImage(bg_blob, '.png')
+                // console.log('url_bg',url_bg)

-                if (!dd[that.id])
-                  dd[that.id] = { url, bg: url_bg, material: tUrl }
-                dd[that.id] = { ...dd[that.id], url, material: tUrl }
+                if (!dd[that.id]) {
+                  dd[that.id] = { url, bg: url_bg }
+                } else {
+                  dd[that.id] = { ...dd[that.id], url }
+                }
+
+                //  材质贴图
+                let thumbUrl = material_img.getAttribute('src')
+                if (thumbUrl) {
+                  let tb = await base64ToBlobFromURL(thumbUrl)
+                  let tUrl = await uploadImage(tb, '.png')
+                  // console.log('材质贴图', tUrl, thumbUrl)
+                  dd[that.id].material = tUrl
+                }

                setLocalDataOfWin(key, dd)
              }
@@ -367,11 +377,11 @@ app.registerExtension({

              modelViewerVariants.addEventListener('camera-change', startTimer)

-              select.addEventListener('input', event => {
+              select.addEventListener('input', async event => {
                modelViewerVariants.variantName =
                  event.target.value === 'default' ? null : event.target.value
                // 材质
-                extractMaterial(
+                await extractMaterial(
                  modelViewerVariants,
                  selectMaterial,
                  material_img
@@ -456,6 +466,15 @@ app.registerExtension({
                input.click()
              })

+              exportGLB.addEventListener('click', async () => {
+                const glTF = await modelViewerVariants.exportScene()
+                const file = new File([glTF], 'export.glb')
+                const link = document.createElement('a')
+                link.download = file.name
+                link.href = URL.createObjectURL(file)
+                link.click()
+              })
+
              uploadWidget.value = await uploadWidget.serializeValue()

              // 更新尺寸
@@ -0,0 +1,306 @@
+import { app } from '../../../scripts/app.js'
+import { $el } from '../../../scripts/ui.js'
+import { api } from '../../../scripts/api.js'
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 12 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'row',
+    // alignItems: 'center',
+    justifyContent: 'flex-start'
+  }
+}
+
+async function drawImageToCanvas (imageUrl) {
+  var canvas = document.createElement('canvas')
+  var ctx = canvas.getContext('2d')
+  var img = new Image()
+
+  await new Promise((resolve, reject) => {
+    img.onload = function () {
+      var scaleFactor = 320 / img.width
+      var canvasWidth = img.width * scaleFactor
+      var canvasHeight = img.height * scaleFactor
+
+      canvas.width = canvasWidth
+      canvas.height = canvasHeight
+
+      ctx.drawImage(img, 0, 0, canvasWidth, canvasHeight)
+
+      resolve()
+    }
+
+    img.onerror = function () {
+      reject(new Error('Failed to load image'))
+    }
+
+    img.src = imageUrl
+  })
+
+  var base64 = canvas.toDataURL('image/jpeg')
+  // console.log(base64); // 输出Base64数据
+  return base64
+  // 可以在这里执行其他操作，比如将Base64数据保存到服务器或显示在页面上
+}
+
+function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
+  const data = jsonData
+  const input = []
+  const output = []
+
+  for (const id in data) {
+    if (data.hasOwnProperty(id)) {
+      if (inputIds.includes(id)) {
+        let node = app.graph.getNodeById(id)
+        let options = []
+        // 模型
+        try {
+          if (node.type === 'CheckpointLoaderSimple') {
+            options = node.widgets.filter(w => w.name === 'ckpt_name')[0]
+              .options.values
+          } else if (node.type === 'LoraLoader') {
+            options = node.widgets.filter(w => w.name === 'lora_name')[0]
+              .options.values
+          }
+        } catch (error) {}
+
+        input[inputIds.indexOf(id)] = {
+          ...data[id],
+          title: node.title,
+          id,
+          options
+        }
+        // input.push()
+      }
+      if (outputIds.includes(id)) {
+        let node = app.graph.getNodeById(id)
+        // output.push()
+        output[outputIds.indexOf(id)] = { ...data[id], title: node.title, id }
+      }
+    }
+  }
+
+  return { input, output }
+}
+
+function getUrl () {
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+  return url
+}
+
+async function save_app (json) {
+  let url = getUrl()
+
+  const res = await fetch(`${url}/mixlab/workflow`, {
+    method: 'POST',
+    body: JSON.stringify({
+      data: json,
+      task: 'save_app',
+      filename: json.app.filename
+    })
+  })
+  return await res.json()
+}
+
+function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
+  const dataString = JSON.stringify(jsonData)
+  const blob = new Blob([dataString], { type: 'application/json' })
+  const url = URL.createObjectURL(blob)
+
+  const link = document.createElement('a')
+  link.href = url
+  link.download = fileName
+  link.click()
+
+  // 释放URL对象
+  setTimeout(() => {
+    URL.revokeObjectURL(url)
+  }, 0)
+}
+
+async function save (json, download = false) {
+  const name = json[0],
+    version = json[5],
+    share_prefix = json[6], //用于分享的功能扩展
+    description = json[4],
+    inputIds = json[2].split('\n').filter(f => f),
+    outputIds = json[3].split('\n').filter(f => f)
+
+  const iconData = json[1][0]
+  let { filename, subfolder, type } = iconData
+  let iconUrl = api.apiURL(
+    `/view?filename=${encodeURIComponent(
+      filename
+    )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+  )
+
+  try {
+    let data = await app.graphToPrompt()
+
+    const { input, output } = extractInputAndOutputData(
+      data.output,
+      inputIds,
+      outputIds
+    )
+
+    data.app = {
+      name,
+      description,
+      version,
+      input,
+      output,
+      share_prefix,
+      filename: `${name}_${version}_${new Date().toDateString()}.json`
+    }
+
+    try {
+      data.app.icon = await drawImageToCanvas(iconUrl)
+    } catch (error) {}
+    // console.log(data.app)
+    // let http_workflow = app.graph.serialize()
+
+    if (download) {
+       await save_app(data)
+       await downloadJsonFile(data, data.app.filename)
+      let open = window.confirm(
+        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app?filename=${encodeURIComponent(data.app.filename)}`
+      )
+      if (open)
+        window.open(
+          `${getUrl()}/mixlab/app?filename=${encodeURIComponent(data.app.filename)}`
+        )
+    } else {
+      await save_app(data)
+
+      let open = window.confirm(
+        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app`
+      )
+      if (open) window.open(`${getUrl()}/mixlab/app`)
+    }
+  } catch (error) {
+    console.log('###SpeechRecognition', error)
+  }
+}
+
+app.registerExtension({
+  name: 'Mixlab.utils.AppInfo',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'AppInfo') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        console.log('#orig_nodeCreated', this)
+        const widget = {
+          type: 'div',
+          name: 'AppInfoRun',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(
+                ctx,
+                widget_width,
+                node.size[1] - widget_height,
+                node.size[1]
+              )
+            )
+          }
+        }
+
+        const style = `
+        flex-direction: row;
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;
+        color: var(--descrip-text);`
+
+        widget.div = $el('div', {})
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Save & Open'
+        btn.style = style
+
+        btn.addEventListener('click', () => {
+          //   console.log('hahhah')
+          if (window._mixlab_app_json) {
+            save(window._mixlab_app_json)
+          } else {
+            alert('Please run the workflow before saving')
+            // app.queuePrompt(0, 1)
+            this.widgets.filter(w => w.name === 'version')[0].value += 1
+          }
+        })
+
+        const download = document.createElement('button')
+        download.innerText = 'Download For App'
+        download.style = style
+        download.style.marginLeft = '12px'
+
+        download.addEventListener('click', () => {
+          //   console.log('hahhah')
+          if (window._mixlab_app_json) {
+            save(window._mixlab_app_json, true)
+          } else {
+            alert('Please run the workflow before saving')
+            // app.queuePrompt(0, 1)
+            this.widgets.filter(w => w.name === 'version')[0].value += 1
+          }
+        })
+
+        document.body.appendChild(widget.div)
+        widget.div.appendChild(btn)
+        widget.div.appendChild(download)
+
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = async function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log(message.json)
+
+        window._mixlab_app_json = message.json
+        try {
+          const div = this.widgets.filter(w => w.div)[0].div
+          Array.from(
+            div.querySelectorAll('button'),
+            b => (b.style.background = 'yellow')
+          )
+        } catch (error) {}
+      }
+    }
+  }
+})
@@ -3,7 +3,7 @@ import { app } from '../../../scripts/app.js'
 const repoOwner = 'shadowcz007' // 替换为仓库的所有者
 const repoName = 'comfyui-mixlab-nodes' // 替换为仓库的名称

-const version = 'v0.5.0'
+const version = 'v0.8.0'

 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
  .then(response => response.json())
@@ -406,7 +406,9 @@ app.registerExtension({

        this.serialize_widgets = true //需要保存参数
      }
-    }
+    };
+
+ 
  },
  async loadedGraphNode (node, app) {
    // Fires every time a node is constructed
@@ -156,8 +156,8 @@ const parseSvg = async svgContent => {
  return { data, image: base64, svgElement }
 }

-async function setArea (cw, ch, base64, data, fn) {
-  let displayHeight = Math.round(window.screen.availHeight * 0.6)
+async function setArea (cw, ch, topBase64, base64, data, fn) {
+  let displayHeight = Math.round(window.screen.availHeight * 0.8)
  let div = document.createElement('div')
  div.innerHTML = `
    <div id='ml_overlay' style='position: absolute;top:0;background: #251f1fc4;
@@ -170,8 +170,13 @@ async function setArea (cw, ch, base64, data, fn) {
      outline: 2px solid #eaeaea;
      box-shadow: 8px 9px 17px #575757;' />
      <div id='ml_selection' style='position: absolute; 
-      border: 2px dashed red; 
-      pointer-events: none;'></div>
+      border: 2px dashed red;
+      pointer-events: none;
+      background-image: url("${topBase64}");
+            background-repeat: no-repeat;
+            background-size: cover;
+            '></div>
+      <div class="mx_close"> X </div>
    </div>`
  // document.body.querySelector('#ml_overlay')
  document.body.appendChild(div)
@@ -181,13 +186,25 @@ async function setArea (cw, ch, base64, data, fn) {
  // canvas.height = ch

  let img = div.querySelector('#ml_video')
-  let overlay = div.querySelector('#ml_overlay')
+  // let overlay = div.querySelector('#ml_overlay')
  let selection = div.querySelector('#ml_selection')
+  let close = div.querySelector('.mx_close')
  let startX, startY, endX, endY
  let start = false
+  let setDone = false
  // Set video source
  img.src = base64
  // canvas.toDataURL();
+  close.style = `cursor: pointer;
+  position: fixed;
+  left: 12px;
+  top: 12px;
+  z-index: 99999999;
+  background: black;
+  width: 44px;
+  height: 44px;
+  text-align: center;
+  line-height: 44px;`

  // init area
  // const data = getSetAreaData()
@@ -216,14 +233,37 @@ async function setArea (cw, ch, base64, data, fn) {
  img.addEventListener('mousedown', startSelection)
  img.addEventListener('mousemove', updateSelection)
  img.addEventListener('mouseup', endSelection)
-  overlay.addEventListener('click', remove)

-  function remove () {
-    overlay.removeEventListener('click', remove)
+  const removeDiv = () => {
+    div.remove()
+    close.removeEventListener('click', removeDiv)
    img.removeEventListener('mousedown', startSelection)
    img.removeEventListener('mousemove', updateSelection)
    img.removeEventListener('mouseup', endSelection)
-    div.remove()
+    img.removeEventListener('mousedown', setDoneCheck)
+  }
+  close.addEventListener('click', removeDiv)
+
+  const setDoneCheck = event => {
+    console.log(setDone)
+    if (setDone) {
+      img.addEventListener('mousedown', startSelection)
+      img.addEventListener('mousemove', updateSelection)
+      img.addEventListener('mouseup', endSelection)
+      setDone = false
+      start = false
+      startX = event.clientX
+      startY = event.clientY
+    }
+  }
+  img.addEventListener('mousedown', setDoneCheck)
+
+  function remove () {
+    img.removeEventListener('mousedown', startSelection)
+    img.removeEventListener('mousemove', updateSelection)
+    img.removeEventListener('mouseup', endSelection)
+    setDone = true
+    // div.remove()
  }

  function startSelection (event) {
@@ -531,12 +571,24 @@ app.registerExtension({
              }
            }
            try {
+              console.log('this.inputs', this.inputs)
+              let topLinkId = this.inputs[0].link
+              let topNodeId = app.graph.links[topLinkId].origin_id
+              let topIm = app.graph.getNodeById(topNodeId).imgs[0]
+
              let linkId = this.inputs[3].link
              let nodeId = app.graph.links[linkId].origin_id
              // console.log(linkId,this.inputs)
              let im = app.graph.getNodeById(nodeId).imgs[0]
-              let src = im.src
-              setArea(im.naturalWidth, im.naturalHeight, src, data, updateValue)
+              // let src = im.src
+              setArea(
+                im.naturalWidth,
+                im.naturalHeight,
+                topIm.src,
+                im.src,
+                data,
+                updateValue
+              )
            } catch (error) {}
          })
        }
@@ -4,6 +4,7 @@ import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
 import { closeIcon } from './svg_icons.js'

+
 import {
  GroupNodeConfig,
  GroupNodeHandler
@@ -633,7 +634,7 @@ app.registerExtension({
            }

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

              app.canvas.copyToClipboard(nodes)
@@ -667,15 +668,28 @@ app.registerExtension({
        ...options
      ] // and return the options
    }
+    LGraphCanvas.prototype.centerOnNode = function(node) {
+      var dpr = window.devicePixelRatio || 1;  // 获取设备像素比
+      this.ds.offset[0] =
+          -node.pos[0] -
+          node.size[0] * 0.5 +
+          (this.canvas.width * 0.5) / (this.ds.scale * dpr);  // 考虑设备像素比
+      this.ds.offset[1] =
+          -node.pos[1] -
+          node.size[1] * 0.5 +
+          (this.canvas.height * 0.5) / (this.ds.scale * dpr);  // 考虑设备像素比
+      this.setDirty(true, true);
+    };
  },
  async setup () {
    // Add canvas menu options
    const orig = LGraphCanvas.prototype.getCanvasMenuOptions
+
    LGraphCanvas.prototype.getCanvasMenuOptions = function () {
      const options = orig.apply(this, arguments)

      options.push(null, {
-        content: `Find ♾️Mixlab`,
+        content: `Nodes Map ♾️Mixlab`,
        disabled: false, // or a function determining whether to disable
        callback: async () => {
          nodesMap =
@@ -709,12 +723,13 @@ app.registerExtension({
            justify-content: space-between;
            align-items: center;
            padding: 0 12px;
-            height: 32px;`
+            height: 44px;`
          let btnB = document.createElement('button')
          let textB = document.createElement('p')
          btn.appendChild(textB)
          btn.appendChild(btnB)
-          textB.innerText = `Find The Node`
+          textB.style.fontSize = '12px'
+          textB.innerText = `Locate and navigate nodes ♾️Mixlab`

          btnB.style = `float: right; border: none; color: var(--input-text);
            background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
@@ -753,32 +768,34 @@ app.registerExtension({
          const updateNodes = (ns, nd) => {
            for (let nodeId in ns) {
              let n = ns[nodeId].class_type
-              const { url, title } = nodesMap[n]
-              let d = document.createElement('button')
-              d.style = `text-align: left;margin:6px;color: var(--input-text);
-                background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
-              d.addEventListener('click', () => {
-                const node = app.graph.getNodeById(nodeId)
-                if (!node) return
-                app.canvas.centerOnNode(node)
-                app.canvas.setZoom(1)
-              })
-              d.addEventListener('mouseover', async () => {
-                // console.log('mouseover')
-                let n = (await app.graphToPrompt()).output
-                if (!deepEqual(n, ns)) {
-                  nd.innerHTML = ''
-                  updateNodes(n, nd)
-                }
-              })
+              if (nodesMap[n]) {
+                const { url, title } = nodesMap[n]
+                let d = document.createElement('button')
+                d.style = `text-align: left;margin:6px;color: var(--input-text);
+                  background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
+                d.addEventListener('click', () => {
+                  const node = app.graph.getNodeById(nodeId)
+                  if (!node) return
+                  app.canvas.centerOnNode(node)
+                  app.canvas.setZoom(1)
+                })
+                d.addEventListener('mouseover', async () => {
+                  // console.log('mouseover')
+                  let n = (await app.graphToPrompt()).output
+                  if (!deepEqual(n, ns)) {
+                    nd.innerHTML = ''
+                    updateNodes(n, nd)
+                  }
+                })

-              d.innerHTML = `
-                <span>${'#' + nodeId} ${n}</span>
-                <a href="${url}" target="_blank" style="text-decoration: none;">🔗</a>
-                `
-              d.title = title
+                d.innerHTML = `
+                  <span>${'#' + nodeId} ${n}</span>
+                  <a href="${url}" target="_blank" style="text-decoration: none;">🔗</a>
+                  `
+                d.title = title

-              nd.appendChild(d)
+                nd.appendChild(d)
+              }
            }
          }

@@ -786,32 +803,35 @@ app.registerExtension({

          for (let nodeId in nodes) {
            let n = nodes[nodeId].class_type
-            const { url, title } = nodesMap[n]
-            let d = document.createElement('button')
-            d.style = `text-align: left;margin:6px;color: var(--input-text);
-              background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
-            d.addEventListener('click', () => {
-              const node = app.graph.getNodeById(nodeId)
-              if (!node) return
-              app.canvas.centerOnNode(node)
-              app.canvas.setZoom(1)
-            })
-            d.addEventListener('mouseover', async () => {
-              console.log('mouseover')
-              let n = (await app.graphToPrompt()).output
-              if (!deepEqual(n, nodes)) {
-                nodesDivv.innerHTML = ''
-                updateNodes(n, nodesDivv)
-              }
-            })
+            if (nodesMap[n]) {
+              const { url, title: _title } = nodesMap[n]
+              let title = app.graph.getNodeById(nodeId).title || _title
+              let d = document.createElement('button')
+              d.style = `text-align: left;margin:6px;color: var(--input-text);
+                background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
+                d.addEventListener('click', () => {
+                const node = app.graph.getNodeById(nodeId)
+                if (!node) return
+                app.canvas.centerOnNode(node)
+                app.canvas.setZoom(1)
+              })
+              d.addEventListener('mouseover', async () => {
+                console.log('mouseover')
+                let n = (await app.graphToPrompt()).output
+                if (!deepEqual(n, nodes)) {
+                  nodesDivv.innerHTML = ''
+                  updateNodes(n, nodesDivv)
+                }
+              })

-            d.innerHTML = `
-              <span>${'#' + nodeId} ${n}</span>
-              <a href="${url}" target="_blank" style="text-decoration: none;">🔗</a>
-              `
-            d.title = title
+              d.innerHTML = `
+                <span>${'#' + nodeId} ${title}</span>
+                <a href="${url}" target="_blank" style="text-decoration: none;">🔗</a>
+                `
+              d.title = n

-            nodesDiv.appendChild(d)
+              nodesDiv.appendChild(d)
+            }
          }

          nodesDivv.appendChild(nodesDiv)
@@ -824,6 +844,14 @@ app.registerExtension({
            document.body.appendChild(div)
        }
      })
+
+      // options.push({
+      //   content: `Save For App ♾️Mixlab`,
+      //   disabled: false, // or a function determining whether to disable
+      //   callback: async () => {
+
+      //   }
+      // })
      return options
    }
  }
@@ -2,7 +2,7 @@ import { app } from '../../../scripts/app.js'
 import { api } from '../../../scripts/api.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
-import { addValueControlWidget } from "../../../scripts/widgets.js";
+import { addValueControlWidget } from '../../../scripts/widgets.js'

 const getLocalData = key => {
  let data = {}
@@ -45,6 +45,21 @@ function get_position_style (ctx, widget_width, y, node_height) {
  }
 }

+function hexToRGBA (hexColor) {
+  var hex = hexColor.replace('#', '')
+  var r = parseInt(hex.substring(0, 2), 16)
+  var g = parseInt(hex.substring(2, 4), 16)
+  var b = parseInt(hex.substring(4, 6), 16)
+
+  // 获取透明度的十六进制值
+  var alphaHex = hex.substring(6)
+
+  // 将透明度的十六进制值转换为十进制值
+  var alpha = parseInt(alphaHex, 16) / 255
+
+  return [r,g,b,alpha]
+}
+
 app.registerExtension({
  name: 'Mixlab.utils.Color',
  async getCustomWidgets (app) {
@@ -60,8 +75,16 @@ app.registerExtension({
            return [128, 32] // a method to compute the current size of the widget
          },
          async serializeValue (nodeId, widgetIndex) {
-            let data = getLocalData('_mixlab_utils_color')
-            return data[node.id] || '#000000'
+            let data = getLocalData('_mixlab_utils_color');
+            let hex=data[node.id] || '#000000'
+            let [r,g,b,a]=hexToRGBA(hex)
+            return {
+              hex,
+              r,
+              g,
+              b,
+              a
+            }
          }
        }
        //  widget.something = something;          // maybe adds stuff to it
@@ -109,7 +132,7 @@ app.registerExtension({
          ip.style = `outline: none;
            border: none;
            padding: 4px;
-            width: 100%;cursor: pointer;
+            width: 70%;cursor: pointer;
            height: 32px;`
          const label = document.createElement('label')
          label.style = 'font-size: 10px;min-width:32px'
@@ -164,16 +187,17 @@ app.registerExtension({

  async beforeRegisterNodeDef (nodeType, nodeData, app) {
    if (nodeType.comfyClass == 'TextToNumber') {
-
      const onExecuted = nodeType.prototype.onExecuted
      nodeType.prototype.onExecuted = function (message) {
        onExecuted?.apply(this, arguments)
-        const random_number=this.widgets.filter(w=>w.name==='random_number')[0]
-        if(random_number.value==='enable'){
-          const n=this.widgets.filter(w=>w.name==='number')[0]
-          n.value=message.num[0]
+        const random_number = this.widgets.filter(
+          w => w.name === 'random_number'
+        )[0]
+        if (random_number.value === 'enable') {
+          const n = this.widgets.filter(w => w.name === 'number')[0]
+          n.value = message.num[0]
        }
-        
+
        console.log('TextToNumber', random_number.value)
      }
    }
@@ -0,0 +1,717 @@
+{
+  "last_node_id": 25,
+  "last_link_id": 32,
+  "nodes": [
+    {
+      "id": 5,
+      "type": "CLIPTextEncode",
+      "pos": [
+        1029,
+        -2149
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 6
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            3
+          ],
+          "slot_index": 0
+        }
+      ],
+      "title": "负向prompt",
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "text, watermark"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "VAEDecode",
+      "pos": [
+        1867,
+        -2378
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {
+        "collapsed": false
+      },
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            9
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 15,
+      "type": "EnhanceImage",
+      "pos": [
+        2591,
+        -2531
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 25
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            18
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EnhanceImage"
+      },
+      "widgets_values": [
+        1.1
+      ]
+    },
+    {
+      "id": 21,
+      "type": "EmptyLatentImage",
+      "pos": [
+        998,
+        -2674
+      ],
+      "size": [
+        315,
+        106
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "width",
+          "type": "INT",
+          "link": 30,
+          "widget": {
+            "name": "width"
+          }
+        },
+        {
+          "name": "height",
+          "type": "INT",
+          "link": 32,
+          "widget": {
+            "name": "height"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            26
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 24,
+      "type": "LimitNumber",
+      "pos": [
+        665,
+        -2958
+      ],
+      "size": {
+        "0": 315,
+        "1": 82
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "number",
+          "type": "*",
+          "link": 29
+        }
+      ],
+      "outputs": [
+        {
+          "name": "number",
+          "type": "*",
+          "links": [
+            30
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LimitNumber"
+      },
+      "widgets_values": [
+        512,
+        4089
+      ]
+    },
+    {
+      "id": 25,
+      "type": "LimitNumber",
+      "pos": [
+        648,
+        -2659
+      ],
+      "size": {
+        "0": 315,
+        "1": 82
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "number",
+          "type": "*",
+          "link": 31
+        }
+      ],
+      "outputs": [
+        {
+          "name": "number",
+          "type": "*",
+          "links": [
+            32
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LimitNumber"
+      },
+      "widgets_values": [
+        512,
+        4089
+      ]
+    },
+    {
+      "id": 22,
+      "type": "IntNumber",
+      "pos": [
+        302,
+        -2758
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            29
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "title": "Width",
+      "properties": {
+        "Node name for S&R": "IntNumber"
+      },
+      "widgets_values": [
+        512
+      ]
+    },
+    {
+      "id": 23,
+      "type": "IntNumber",
+      "pos": [
+        299,
+        -2601
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            31
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "title": "Height",
+      "properties": {
+        "Node name for S&R": "IntNumber"
+      },
+      "widgets_values": [
+        512
+      ]
+    },
+    {
+      "id": 2,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        567,
+        -2422
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            1
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            5,
+            6
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            8
+          ],
+          "slot_index": 2
+        }
+      ],
+      "title": "Model",
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "deliberate_v2.safetensors"
+      ]
+    },
+    {
+      "id": 1,
+      "type": "KSampler",
+      "pos": [
+        1509,
+        -2394
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 1
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 2
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 3
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 26,
+          "slot_index": 3
+        },
+        {
+          "name": "denoise",
+          "type": "FLOAT",
+          "link": 16,
+          "widget": {
+            "name": "denoise"
+          },
+          "slot_index": 4
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            7
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        730250870715434,
+        "randomize",
+        15,
+        6.9,
+        "euler",
+        "karras",
+        0.59
+      ]
+    },
+    {
+      "id": 14,
+      "type": "FloatSlider",
+      "pos": [
+        1007,
+        -2819
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "FLOAT",
+          "type": "FLOAT",
+          "links": [
+            16
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "title": "denoise",
+      "properties": {
+        "Node name for S&R": "FloatSlider"
+      },
+      "widgets_values": [
+        1
+      ]
+    },
+    {
+      "id": 16,
+      "type": "PreviewImage",
+      "pos": [
+        2949,
+        -2615
+      ],
+      "size": {
+        "0": 210,
+        "1": 246
+      },
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 18
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 4,
+      "type": "CLIPTextEncode",
+      "pos": [
+        1022,
+        -2375
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            2
+          ],
+          "slot_index": 0
+        }
+      ],
+      "title": "prompt",
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "superman,fat cat"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "AppInfo",
+      "pos": [
+        2134,
+        -2528
+      ],
+      "size": {
+        "0": 408.4201965332031,
+        "1": 406.9195861816406
+      },
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 9
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            25
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AppInfo"
+      },
+      "widgets_values": [
+        "Text-to-Image",
+        "4\n22\n23\n2\n\n\n",
+        "16",
+        "演示基本的文生图流程",
+        1,
+        "#comfyui-mixlab-nodes# ",
+        null
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      1,
+      0,
+      "MODEL"
+    ],
+    [
+      2,
+      4,
+      0,
+      1,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      3,
+      5,
+      0,
+      1,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      5,
+      2,
+      1,
+      4,
+      0,
+      "CLIP"
+    ],
+    [
+      6,
+      2,
+      1,
+      5,
+      0,
+      "CLIP"
+    ],
+    [
+      7,
+      1,
+      0,
+      6,
+      0,
+      "LATENT"
+    ],
+    [
+      8,
+      2,
+      2,
+      6,
+      1,
+      "VAE"
+    ],
+    [
+      9,
+      6,
+      0,
+      7,
+      0,
+      "IMAGE"
+    ],
+    [
+      16,
+      14,
+      0,
+      1,
+      4,
+      "FLOAT"
+    ],
+    [
+      18,
+      15,
+      0,
+      16,
+      0,
+      "IMAGE"
+    ],
+    [
+      25,
+      7,
+      0,
+      15,
+      0,
+      "IMAGE"
+    ],
+    [
+      26,
+      21,
+      0,
+      1,
+      3,
+      "LATENT"
+    ],
+    [
+      29,
+      22,
+      0,
+      24,
+      0,
+      "*"
+    ],
+    [
+      30,
+      24,
+      0,
+      21,
+      0,
+      "INT"
+    ],
+    [
+      31,
+      23,
+      0,
+      25,
+      0,
+      "*"
+    ],
+    [
+      32,
+      25,
+      0,
+      21,
+      1,
+      "INT"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}
@@ -1,2 +0,0 @@
-![workflow](./assets/City_Snapshot_00005_.png)
-![workflow](./assets/挖掘机_00076_1.png)
Author	SHA1	Message	Date
shadowcz007	ea24f52b13	Update checkVersion_mixlab.js	2023-12-31 13:12:34 +08:00
shadowcz007	4abfc47346	### Update 0.8.0 v0.8.0 🚀🚗🚚🏃‍ LaMaInpainting - 新增 LaMaInpainting - 优化color节点的输出 - 修复高清显示屏上定位节点不准的情况 - Add LaMaInpainting - Optimize the output of the color node - Fix the issue of inaccurate positioning node on high-definition display screens	2023-12-31 13:11:31 +08:00
shadowcz007	3b95010d06	新增LaMaInpainting & 优化color节点的输出	2023-12-31 13:04:28 +08:00
shadow	b3c1b96088	Merge pull request #97 from shadowcz007/fix_hidpi_node_move_center Fix:node can't move to center on HiDPI device	2023-12-31 10:07:07 +08:00
shadowcz007	a9f1326873	update	2023-12-30 23:52:50 +08:00
shadowcz007	75a696fb64	update	2023-12-30 23:39:36 +08:00
shadowcz007	24aaacba6d	update	2023-12-30 23:38:52 +08:00
shadowcz007	e3cd7d5f91	更新示例	2023-12-30 21:05:07 +08:00
shadow	2e228e8db5	Merge pull request #95 from shadowcz007/v0.7-apps V0.7 apps	2023-12-30 20:53:19 +08:00
shadowcz007	5c9dd80370	fixbug	2023-12-30 20:51:31 +08:00
shadowcz007	727f5f2e48	upate	2023-12-30 20:37:09 +08:00
shadowcz007	1c238f7697	sharebutton	2023-12-30 20:16:51 +08:00
shadowcz007	119d7cce15	0.7.0	2023-12-30 18:19:18 +08:00
shadowcz007	4afc8f6083	Support multiple web app switching. 支持多个web app 切换	2023-12-30 18:16:06 +08:00
shadowcz007	a9ec3af066	改进input range	2023-12-30 18:07:43 +08:00
shadowcz007	9edae81fee	update	2023-12-30 17:48:59 +08:00
shadowcz007	b941b12f12	update	2023-12-30 17:40:28 +08:00
shadowcz007	3069de188a	1	2023-12-30 17:02:44 +08:00
shadowcz007	a968f08abd	update	2023-12-30 14:16:44 +08:00
shadowcz007	4153d3e5ff	1	2023-12-30 12:37:03 +08:00
shadowcz007	9d9c1a6c84	update	2023-12-30 12:29:01 +08:00
shadowcz007	16ef10a4d9	优化node map	2023-12-30 10:10:21 +08:00
shadowcz007	b3766e440a	VHS_VideoCombine	2023-12-30 09:56:08 +08:00
gold3bear	6359c3f70f	Fix:node can't move to center on HiDPI device	2023-12-30 02:09:45 +08:00
shadowcz007	efe73fb965	Update README.md	2023-12-29 10:39:01 +08:00
shadowcz007	c45a962fcc	workflow-to-app支持checkpoints和lora	2023-12-29 10:38:22 +08:00
shadowcz007	f98a03e2e9	Update README.md	2023-12-29 00:00:16 +08:00
shadowcz007	5b6257814d	优化	2023-12-28 23:56:48 +08:00
shadowcz007	69a445d4ed	新增切换节点	2023-12-28 23:18:25 +08:00
shadowcz007	e82c786b8a	增加了从剪切板获取图片的控件	2023-12-28 18:36:34 +08:00
shadowcz007	eec2225c89	支持视频	2023-12-28 16:02:25 +08:00
shadowcz007	f7355e0b71	update	2023-12-28 14:33:59 +08:00
shadowcz007	6c6a99cfe4	优化LoadImagefromlocal ，新增LoadImageFromURL	2023-12-28 13:24:05 +08:00
shadowcz007	b4634e2e0d	修复clipseg的bug	2023-12-28 12:09:36 +08:00
shadowcz007	3f4cba0612	fixbug：textimage的高宽不对	2023-12-27 21:45:35 +08:00
shadowcz007	38db99cc75	支持showtext作为输出。GPT聊天也可以实现workflow-to-app了	2023-12-27 20:39:21 +08:00
shadowcz007	4d5906394b	优化newlayer的可视化效果	2023-12-27 20:12:55 +08:00
shadowcz007	2fc212b156	update	2023-12-27 19:38:39 +08:00
shadowcz007	53fbb5b027	fixbug	2023-12-27 17:48:49 +08:00
shadowcz007	4f24721450	Update README.md	2023-12-27 16:48:29 +08:00
shadow	83043727b5	Merge pull request #83 from shadowcz007/v0.6---simple-app V0.6 simple app	2023-12-27 16:29:27 +08:00
shadowcz007	2d336afb85	v0.6.0	2023-12-27 16:29:00 +08:00
shadowcz007	4d309435c8	Update index.html	2023-12-26 17:15:33 +08:00
shadowcz007	099ce9cdfd	1	2023-12-26 16:32:01 +08:00
shadowcz007	8914e60cb8	初步打通	2023-12-26 16:23:43 +08:00
shadowcz007	dbd30a40e9	init	2023-12-26 12:06:55 +08:00
shadowcz007	c9a598fd59	更新下workflow示例	2023-12-26 10:56:14 +08:00
shadowcz007	e331e588cf	v0.5.2 The bug of missing texture mapping for 3D nodes has been fixed.	2023-12-25 22:28:53 +08:00
shadowcz007	2011557771	fixbug	2023-12-25 22:24:56 +08:00
shadowcz007	f0ba45d14e	GLB can export	2023-12-25 09:14:39 +08:00
shadowcz007	8352a521b7	v0.5.1	2023-12-24 23:07:01 +08:00
shadowcz007	aa3d4d79f8	fixbug	2023-12-24 23:04:16 +08:00
shadowcz007	4f650d760c	fixbug-mergeLayer的多图片支持	2023-12-24 22:57:43 +08:00