v0.2.7

新增GPT节点

.gitignore

@@ -1,3 +1,4 @@
 __pycache__/
 https/
-nodes/config.json
+nodes/config.json
+workflow/my_workflow.json

README.md

@@ -1,12 +1,20 @@
 ## 
-In progress.
-！！
+
+v0.2.6 🚀🚗🚚🏃‍
+
+- [Add getting camera video stream](./workflow/7-camera-workflow.json)
+- Add a slider to the floating window, which can be used as input for denoise
+- OSupport for calling multiple GPTs
+
+
 ![screenshare](./assets/screenshare.png)
 
 
 ### 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! 💻🌐
 
+> 
+
 https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43e-410a-ab3a-1952b7b4e7da
 
 
@@ -15,6 +23,24 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 !! Please use the address with HTTPS (https://127.0.0.1).
 
+
+### 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)
+
+
+### 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)
+
+
 ## Installation
 
 manually install, simply clone the repo into the custom_nodes directory with this command:
@@ -48,7 +74,7 @@ pip3 install -r requirements.txt
 
 ## Nodes
 
-![main](./assets/all.png)
+![main](./assets/all-workflow.svg)
 ![main2](./assets/detect-face-all.png)
 
 [workflow-1](./workflow/1-workflow.json)
@@ -63,11 +89,7 @@ pip3 install -r requirements.txt
 
 
 
->LoadImagesFromLocal
 
-![watch](./assets/load-watch.png)
-
-[workflow-4](./workflow/4-loadfromlocal-watcher-workflow.json)
 
 > Consistency Decoder
 
@@ -85,6 +107,13 @@ Add edges to an image.
 ![FeatheredMask](./assets/FlVou_Y6kaGWYoEj1Tn0aTd4AjMI.jpg)
 
 
+
+### Improvement 
+An improvement has been made to directly redirect to GitHub to search for missing nodes when loading the graph.
+
+![node-not-found](./assets/node-not-found.png)
+
+
 ### Models
 [Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : model/clipseg
 
@@ -94,6 +123,8 @@ Add edges to an image.
 #### Thanks:
 [ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)
 
+#### discussions:
+[discussions](https://github.com/shadowcz007/comfyui-mixlab-nodes/discussions)
 
 ### TODO:
 - vector https://github.com/GeorgLegato/stable-diffusion-webui-vectorstudio

__init__.py

@@ -5,6 +5,9 @@ import importlib.util
 import sys,json
 import urllib
 
+import datetime
+
+
 python = sys.executable
 
 
@@ -61,6 +64,18 @@ except ImportError:
     sys.exit()
 
 
+
+def install_openai():
+    # Helper function to install the OpenAI module if not already installed
+    try:
+        importlib.import_module('openai')
+    except ImportError:
+        import pip
+        pip.main(['install', 'openai'])
+
+install_openai()
+
+
 current_path = os.path.abspath(os.path.dirname(__file__))
 
 
@@ -95,23 +110,64 @@ def create_key(key_p,crt_p):
     return
 
 
-
 def create_for_https():
     # print("#####path::", current_path)
-
     https_key_path=os.path.join(current_path, "https")
     crt=os.path.join(https_key_path, "certificate.crt")
     key=os.path.join(https_key_path, "private.key")
     # print("##https_key_path", crt,key)
-    print('\033[91mhttps_key: ', crt,key)
     if not os.path.exists(https_key_path):
         # 使用mkdir()方法创建新目录
         os.mkdir(https_key_path)
     if not os.path.exists(crt):
         create_key(key,crt)
+
+    print('https_key OK: ', crt,key)
     return (crt,key)
 
 
+# workflow  
+def read_workflow_json_files(folder_path):
+    json_files = []
+    for filename in os.listdir(folder_path):
+        if filename.endswith('.json'):
+            json_files.append(filename)
+
+    data = []
+    for file in json_files:
+        file_path = os.path.join(folder_path, file)
+        try:
+            with open(file_path) as json_file:
+                json_data = json.load(json_file)
+                creation_time=datetime.datetime.fromtimestamp(os.path.getctime(file_path))
+                numeric_timestamp = creation_time.timestamp()
+                file_info = {
+                    'filename': file,
+                    'data': json_data,
+                    'date': numeric_timestamp
+                }
+                data.append(file_info)
+        except Exception as e:
+            print(e)
+    sorted_data = sorted(data, key=lambda x: x['date'], reverse=True)
+    return sorted_data
+
+def get_workflows():
+    # print("#####path::", current_path)
+    workflow_path=os.path.join(current_path, "workflow")
+    print('workflow_path: ',workflow_path)
+    if not os.path.exists(workflow_path):
+        # 使用mkdir()方法创建新目录
+        os.mkdir(workflow_path)
+    workflows=read_workflow_json_files(workflow_path)
+    return workflows
+
+def 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
+
 # https
 async def new_start(self, address, port, verbose=True, call_on_start=None):
         runner = web.AppRunner(self.app, access_log=None)
@@ -152,13 +208,37 @@ routes = web.RouteTableDef()
 async def mixlab_hander(request):
     config=os.path.join(current_path, "nodes/config.json")
     data={}
-    # print(config)
-    if os.path.exists(config):
-        with open(config, 'r') as f:
-            data = json.load(f)
-            # print(data)
+    try:
+        if os.path.exists(config):
+            with open(config, 'r') as f:
+                data = json.load(f)
+                # print(data)
+    except Exception as e:
+            print(e)
     return web.json_response(data)
 
+@routes.post('/mixlab/workflow')
+async def mixlab_workflow_hander(request):
+    data = await request.json()
+    result={}
+    try:
+        if 'task' in data:
+            if data['task']=='save':
+                file_path=save_workflow_json(data['data'])
+                result={
+                    'status':'success',
+                    'file_path':file_path
+                }
+            elif data['task']=='list':
+                result={
+                    'data':get_workflows(),
+                    'status':'success',
+                }
+    except Exception as e:
+            print(e)
+
+    return web.json_response(result)
+
 def new_add_routes(self):
         import nodes
         self.app.add_routes(routes)
@@ -192,6 +272,7 @@ from .nodes.ImageNode import TransparentImage,LoadImagesFromPath,AreaToMask,Smoo
 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
 
 # 要导出的所有节点及其名称的字典
 # 注意：名称应全局唯一
@@ -210,18 +291,23 @@ NODE_CLASS_MAPPINGS = {
     "VAEDecodeConsistencyDecoder":VAEDecode,
     "ScreenShare":ScreenShareNode,
     "FloatingVideo":FloatingVideo,
-    "CLIPSeg":CLIPSeg,
-    "CombineMasks":CombineMasks
+    "CLIPSeg_":CLIPSeg,
+    "CombineMasks_":CombineMasks,
+    "ChatGPTOpenAI":ChatGPTNode,
+    "ShowTextForGPT":ShowTextForGPT,
+    "CharacterInText":CharacterInText
 }
 
 # 一个包含节点友好/可读的标题的字典
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "RandomPrompt": "Random Prompt #Example Node",
+    "RandomPrompt": "Random Prompt ♾️Mixlab",
     "SplitLongMask":"Splitting a long image into sections",
     "VAELoaderConsistencyDecoder":"Consistency Decoder Loader",
     "VAEDecodeConsistencyDecoder":"Consistency Decoder Decode",
-    "ScreenShare":"ScreenShare #Mixlab",
-    "FloatingVideo":"FloatingVideo #Mixlab"
+    "ScreenShare":"ScreenShare ♾️Mixlab",
+    "FloatingVideo":"FloatingVideo ♾️Mixlab",
+    "ChatGPTOpenAI":"ChatGPT ♾️Mixlab",
+    "ShowTextForGPT":"ShowTextForGPT ♾️Mixlab"
 }
 
 # web ui的节点功能

nodes/ChatGPT.py

@@ -0,0 +1,217 @@
+import openai 
+import time
+import urllib.error
+import re,json
+
+#  判断是否是azure服务
+def is_azure_url(url):
+    pattern = r'.*\.azure\.com$'
+    if re.match(pattern, url):
+        return True
+    else:
+        return False
+
+def azure_client(key,url):
+    client = openai.AzureOpenAI(
+        api_key=key,
+    # https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning
+    api_version="2023-07-01-preview",
+    # https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource
+    azure_endpoint=url
+    )
+    return client
+
+def openai_client(key,url):
+    client = openai.OpenAI(
+    api_key=key,
+    base_url=url
+    )
+    return client
+
+
+
+def chat(client, model_name,messages ):
+
+        try_count = 0
+        while True:
+            try_count += 1
+            try:
+                response = client.chat.completions.create(
+                    model=model_name,
+                    messages=messages
+                )
+                break
+            except openai.AuthenticationError as ex:
+                raise ex
+            except (urllib.error.HTTPError, openai.OpenAIError) as ex:
+                if try_count >= 3:
+                    raise ex
+                time.sleep(5)
+                continue
+
+        finish_reason = response.choices[0].finish_reason
+        if finish_reason != "stop":
+            raise RuntimeError("API finished with unexpected reason: " + finish_reason)
+
+        content=""
+        try:
+            content=response.choices[0].message.content
+        except:
+            content=response.choices[0].delta['content']
+
+        return content
+
+
+class ChatGPTNode:
+    def __init__(self):
+        # self.__client = OpenAI()
+        self.session_history = []  # 用于存储会话历史的列表
+        # self.seed=0
+        self.system_content="You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible."
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_key":("KEY", {"default": "", "multiline": True}),
+                "api_url":("URL", {"default": "", "multiline": True}),
+                "prompt": ("STRING", {"multiline": True}),
+                "system_content": ("STRING", 
+                                   {
+                                       "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
+                                       "multiline": True
+                                       }),
+                "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}),
+                "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
+            },
+             "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ("STRING","STRING","STRING",)
+    RETURN_NAMES = ("text","messages","session_history",)
+    FUNCTION = "generate_contextual_text"
+    CATEGORY = "♾️Mixlab/GPT"
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,False,False,)
+
+    
+    def generate_contextual_text(self,
+                                 api_key,
+                                 api_url, 
+                                 prompt, 
+                                 system_content,
+                                   model, 
+                                   seed,context_size,unique_id = None, extra_pnginfo=None):
+        # print(api_key!='',api_url,prompt,system_content,model,seed)
+        # 可以选择保留会话历史以维持上下文记忆
+        # 或者在此处清除会话历史 self.session_history.clear()
+        # if seed!=self.seed:
+        #     self.seed=seed
+        #     self.session_history=[]
+        
+        # 把系统信息和初始信息添加到会话历史中
+        if system_content:
+            self.system_content=system_content
+            # self.session_history=[]
+            # self.session_history.append({"role": "system", "content": system_content})
+        
+        # 
+        if is_azure_url(api_url):
+            client=azure_client(api_key,api_url)
+        else:
+            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
+
+        def crop_list_tail(lst, size):
+            if size >= len(lst):
+                return lst
+            elif size==0:
+                return []
+            else:
+                return lst[-size:]
+            
+        session_history=crop_list_tail(self.session_history,context_size)
+
+        messages=[{"role": "system", "content": self.system_content}]+session_history+[{"role": "user", "content": prompt}]
+        response_content = chat(client,model,messages)
+        
+        self.session_history=self.session_history+[{"role": "user", "content": prompt}]+[{'role':'assistant',"content":response_content}]
+
+
+        # if unique_id and extra_pnginfo and "workflow" in extra_pnginfo[0]:
+        #     workflow = extra_pnginfo[0]["workflow"]
+        #     node = next((x for x in workflow["nodes"] if str(x["id"]) == unique_id[0]), None)
+        #     if node:
+        #         node["widgets_values"] = ["",
+        #                          api_url, 
+        #                          prompt, 
+        #                          system_content,
+        #                            model,
+        #                            seed,
+        #                            context_size]
+        
+        return (response_content,json.dumps(messages, indent=4),json.dumps(self.session_history, indent=4),)
+
+
+
+class ShowTextForGPT:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "text": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "♾️Mixlab/GPT"
+
+    def run(self, text):
+        # print(session_history)
+        return {"ui": {"text": text}, "result": (text,)}
+
+
+class CharacterInText:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "text": ("STRING", {"multiline": True}),
+                 "character": ("STRING", {"multiline": True}),
+                 "start_index": ("INT", {
+                    "default": 1,
+                    "min": 0, #Minimum value
+                    "max": 1024, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+            }
+        }
+
+    INPUT_IS_LIST = False
+    RETURN_TYPES = ("INT",)
+    FUNCTION = "run"
+    # OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (False,)
+
+    CATEGORY = "♾️Mixlab/GPT"
+
+    def run(self, text,character,start_index):
+        # print(text,character,start_index)
+        b=1 if character in text else 0
+        
+        return (b+start_index,)
+

nodes/Clipseg.py

@@ -99,7 +99,7 @@ class CLIPSeg:
                     }
                 }
 
-    CATEGORY = "Mixlab/mask"
+    CATEGORY = "♾️Mixlab/mask"
     RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
     RETURN_NAMES = ("Mask","Heatmap Mask", "BW Mask")
 
@@ -204,7 +204,7 @@ class CombineMasks:
                     },
                 }
         
-    CATEGORY = "Mixlab/mask"
+    CATEGORY = "♾️Mixlab/mask"
     RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
     RETURN_NAMES = ("Combined Mask","Heatmap Mask", "BW Mask")
 

nodes/ImageNode.py

@@ -1,6 +1,6 @@
 import numpy as np
 import torch
-from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw
+from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence
 from PIL.PngImagePlugin import PngInfo
 import base64,os
 from io import BytesIO
@@ -153,25 +153,48 @@ def get_not_transparent_area(image):
     return (x, y, w, h)
 
 
-
+# 读取不了分层
+def load_psd(image):
+    layers=[]
+    print('load_psd',image.format)
+    if image.format=='PSD':
+        layers = [frame.copy() for frame in ImageSequence.Iterator(image)]
+        print('#PSD',len(layers))
+    else:
+        image = ImageOps.exif_transpose(image) #校对方向
+    layers.append(image)
+    return layers
 
 
 def load_image(fp,white_bg=False):
-    i = Image.open(fp)
-    i = ImageOps.exif_transpose(i)
-    image = i.convert("RGB")
-    image = np.array(image).astype(np.float32) / 255.0
-    image = torch.from_numpy(image)[None,]
-    if 'A' in i.getbands():
-        mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
-        mask = 1. - torch.from_numpy(mask)
-        if white_bg==True:
-            nw = mask.unsqueeze(0).unsqueeze(-1).repeat(1, 1, 1, 3)
-            # 将mask的黑色部分对image进行白色处理
-            image[nw == 1] = 1.0
-    else:
-        mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-    return (image,mask)
+    im = Image.open(fp)
+
+    # ims=load_psd(im)
+    im = ImageOps.exif_transpose(im) #校对方向
+    ims=[im]
+
+    images=[]
+ 
+    for i in ims:
+        image = i.convert("RGB")
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        if 'A' in i.getbands():
+            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+            mask = 1. - torch.from_numpy(mask)
+            if white_bg==True:
+                nw = mask.unsqueeze(0).unsqueeze(-1).repeat(1, 1, 1, 3)
+                # 将mask的黑色部分对image进行白色处理
+                image[nw == 1] = 1.0
+        else:
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        
+        images.append({
+            "image":image,
+            "mask":mask
+        })
+        
+    return images
 
 
 # 获取图片s
@@ -183,23 +206,27 @@ def get_images_filepath(f,white_bg=False):
             for file in files:
                 file_path = os.path.join(root, file)
                 try:
-                    (im,mask)=load_image(file_path,white_bg)
-                    images.append({
-                        "image":im,
-                        "mask":mask,
-                        "file_path":file_path
-                    })
+                    imgs=load_image(file_path,white_bg)
+                    for img in imgs:
+                        images.append({
+                            "image":img['image'],
+                            "mask":img['mask'],
+                            "file_path":file_path,
+                            "psd":len(imgs)>1
+                        })
                 except:
                     print('非图片',file_path)
  
     elif os.path.isfile(f):
         try:
-            (im,mask)=load_image(f,white_bg)
-            images.append({
-                        "image":im,
-                        "mask":mask,
-                        "file_path":f
-                    })
+            imgs=load_image(f,white_bg)
+            for img in imgs:
+                images.append({
+                    "image":img['image'],
+                    "mask":img['mask'],
+                    "file_path":file_path,
+                    "psd":len(imgs)>1
+                })
         except:
             print('非图片',f)
     else:
@@ -314,7 +341,7 @@ class SmoothMask:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/mask"
+    CATEGORY = "♾️Mixlab/mask"
 
     INPUT_IS_LIST = False
 
@@ -362,7 +389,7 @@ class FeatheredMask:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/mask"
+    CATEGORY = "♾️Mixlab/mask"
 
     OUTPUT_IS_LIST = (False,)
   
@@ -429,7 +456,7 @@ class SplitLongMask:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/mask"
+    CATEGORY = "♾️Mixlab/mask"
 
     OUTPUT_IS_LIST = (True,)
   
@@ -472,7 +499,7 @@ class TransparentImage:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/image"
+    CATEGORY = "♾️Mixlab/image"
 
     # INPUT_IS_LIST = True， 一个batch传进来
     OUTPUT_IS_LIST = (True,True,True,)
@@ -541,7 +568,7 @@ class EnhanceImage:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/image"
+    CATEGORY = "♾️Mixlab/image"
 
     INPUT_IS_LIST = False
 
@@ -583,43 +610,45 @@ class LoadImagesFromPath:
                     "white_bg": (["disable","enable"],),
                     "newest_files": (["enable", "disable"],),
                     "index_variable":("INT", {
-                        "default": -1, 
+                        "default": 0, 
                         "min": -1, #Minimum value
                         "max": 2048, #Maximum value
                         "step": 1, #Slider's step
                         "display": "number" # Cosmetic only: display as "number" or "slider"
                     }),
                     "watcher":(["disable","enable"],),
-                    "result": ("WATCHER",),
+                    "result": ("WATCHER",),#为了激活本节点运行
+                     "prompt": ("PROMPT",),
                     # "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                 }
             }
     
-    RETURN_TYPES = ('IMAGE','MASK',)
+    RETURN_TYPES = ('IMAGE','MASK','STRING')
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/image"
+    CATEGORY = "♾️Mixlab/image"
 
     # INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,True,)
+    OUTPUT_IS_LIST = (True,True,False,)
   
     global watcher_folder
     watcher_folder=None
 
     # 运行的函数
-    def run(self,file_path,white_bg,newest_files,index_variable,watcher,result):
+    def run(self,file_path,white_bg,newest_files,index_variable,watcher,result,prompt):
         global watcher_folder
         # print('###监听:',watcher_folder,watcher,file_path,result)
 
-        if watcher=='enable':
-            if watcher_folder==None:
-                watcher_folder = FolderWatcher(file_path)
-            
+        if watcher_folder==None:
+            watcher_folder = FolderWatcher(file_path)
+
+        watcher_folder.set_folder_path(file_path)
+        
+        if watcher=='enable': 
             # 在这里可以进行其他操作，监听会在后台持续
             watcher_folder.set_folder_path(file_path)
             watcher_folder.start()
-
         else:
             if watcher_folder!=None:
                 watcher_folder.stop()
@@ -642,8 +671,8 @@ class LoadImagesFromPath:
             imgs=[imgs[index_variable]] if index_variable < len(imgs) else None
             masks=[masks[index_variable]] if index_variable < len(masks) else None
 
-        
-        return (imgs,masks,)
+        print('#prompt::::',prompt)
+        return (imgs,masks,prompt,)
 
 
 # TODO 扩大选区的功能,重新输出mask
@@ -659,7 +688,7 @@ class ImageCropByAlpha:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/image"
+    CATEGORY = "♾️Mixlab/image"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -690,7 +719,7 @@ class AreaToMask:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/mask"
+    CATEGORY = "♾️Mixlab/mask"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -725,7 +754,7 @@ class FaceToMask:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/mask"
+    CATEGORY = "♾️Mixlab/mask"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)

nodes/PromptNode.py

@@ -79,7 +79,7 @@ class RandomPrompt:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/prompt"
+    CATEGORY = "♾️Mixlab/prompt"
 
     OUTPUT_IS_LIST = (True,)
     OUTPUT_NODE = True
@@ -158,7 +158,7 @@ class RunWorkflow:
 
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/workflow"
+    CATEGORY = "♾️Mixlab/workflow"
 
     OUTPUT_IS_LIST = (True,)
     OUTPUT_NODE = True

nodes/ScreenShareNode.py

@@ -24,9 +24,42 @@ def base64_save(base64_data):
     return (image,mask)
 
 
+# # 把白色部分处理成黑色
+# def convert_to_bw(image):
+#     # 读取图片
+#     # image = Image.open(image_path)
+    
+#     # 获取图片的宽度和高度
+#     width, height = image.size
+    
+#     # 遍历图片的每个像素点
+#     for x in range(width):
+#         for y in range(height):
+#             # 获取当前像素点的RGB值
+#             r, g, b = image.getpixel((x, y))
+            
+#             # 判断当前像素点是否为白色
+#             if r == 255 and g == 255 and b == 255:
+#                 # 将白色部分处理成黑色
+#                 image.putpixel((x, y), (0, 0, 0))
+#             else:
+#                 # 将非白色部分处理成白色
+#                 image.putpixel((x, y), (255, 255, 255))
+    
+#     # 转换为黑白图
+#     mask = image.convert("L")
+    
+#     # # 保存处理后的图片
+#     # image.save("black_white_image.jpg")
+    
+#     # print("图片处理完成！")
+#     return mask
+
+
 def load_image(i,white_bg=False):
     # i = Image.open(fp)
     image = i.convert("RGB")
+
     image = np.array(image).astype(np.float32) / 255.0
     image = torch.from_numpy(image)[None,]
     if 'A' in i.getbands():
@@ -49,23 +82,25 @@ class ScreenShareNode:
         },
           "optional":{
               "prompt": ("PROMPT",),
+              "slide": ("SLIDE",),
+              "seed": ("SEED",),
             #   "seed": ("INT", {"default": 1, "min": 0, "max": 0xffffffffffffffff}),
                 } }
     
-    RETURN_TYPES = ('IMAGE','MASK','STRING')
-
+    RETURN_TYPES = ('IMAGE','STRING','FLOAT',"INT")
+    RETURN_NAMES = ("IMAGE","PROMPT","FLOAT","INT")
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/image"
+    CATEGORY = "♾️Mixlab/image"
 
     # INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (False,False,False)
+    OUTPUT_IS_LIST = (False,False,False,False)
   
     # 运行的函数
-    def run(self,image_base64,prompt):
+    def run(self,image_base64,prompt,slide,seed):
         im,mask=base64_save(image_base64)
         # print('##########prompt',prompt)
-        return (im,mask,prompt)
+        return (im,prompt,slide,seed)
     
 
 class FloatingVideo:
@@ -81,7 +116,7 @@ class FloatingVideo:
     OUTPUT_NODE = True
     FUNCTION = "run"
 
-    CATEGORY = "Mixlab/image"
+    CATEGORY = "♾️Mixlab/image"
 
     # INPUT_IS_LIST = True
     # OUTPUT_IS_LIST = (False,False,)
@@ -104,3 +139,15 @@ class FloatingVideo:
 
         return { "ui": { "images_": results } }
 
+
+
+# class SildeNode:
+#     CATEGORY = "quicknodes"
+#     @classmethod    
+#     def INPUT_TYPES(s):
+#         return { "required":{} }
+#     RETURN_TYPES = ()
+#     RETURN_NAMES = ()
+#     FUNCTION = "func"
+#     def func(self):
+#         return ()

nodes/Vae.py

@@ -145,7 +145,7 @@ class VAELoader:
     RETURN_TYPES = ("VAE",)
     FUNCTION = "load_vae"
 
-    CATEGORY = "Mixlab/ConsistencyDecoder"
+    CATEGORY = "♾️Mixlab/ConsistencyDecoder"
 
     #TODO: scale factor?
     def load_vae(self, vae_name):
@@ -165,7 +165,7 @@ class VAEDecode:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
 
-    CATEGORY = "Mixlab/ConsistencyDecoder"
+    CATEGORY = "♾️Mixlab/ConsistencyDecoder"
 
     def decode(self, vae, samples):
         image = vae.decode(samples["samples"].to("cuda:0"))

nodes/Watcher.py

@@ -8,25 +8,34 @@ import os
 # print('Watcher:',current_directory)
 
 def save_to_json(file_path, data):
-    with open(file_path, 'w') as f:
-        json.dump(data, f)
+    try:
+        with open(file_path, 'w') as f:
+            json.dump(data, f)
+    except Exception as e:
+            print(e)
 
 def read_from_json(file_path):
-    with open(file_path, 'r') as f:
-        data = json.load(f)
+    data={}
+    try:
+        with open(file_path, 'r') as f:
+            data = json.load(f)
+    except Exception as e:
+            print(e)
     return data
 
 
 # read_from_json()
 current_path = os.path.abspath(os.path.dirname(__file__))
 config_json=os.path.join(current_path,'config.json')
-print('Watcher:',config_json)
+# print('Watcher:',config_json)
 
 def read_config():
     config={}
-    if os.path.exists(config_json):
-        # print('exists')
-        config=read_from_json(config_json)
+    try:
+        if os.path.exists(config_json):
+            config=read_from_json(config_json)
+    except Exception as e:
+            print(e)  
     return config
 
 
@@ -39,10 +48,10 @@ class FolderWatcher:
             config['folder_path']=folder_path
             save_to_json(config_json,config)
 
-        # self.observer = Observer()
+        self.observer = None
         self.event_handler = self._create_event_handler()
         self.status = "Not started"
-        self.event_type=''
+        self.event_type='-'
         
     def _create_event_handler(self):
 
@@ -77,22 +86,23 @@ class FolderWatcher:
             config=read_config()
             config['folder_path']=new_folder_path
             save_to_json(config_json,config)
-            self.event_type=''
+            self.event_type='-'
 
     def start(self):
         self.observer = Observer()
         self.observer.schedule(self.event_handler, self.folder_path, recursive=True)
         self.observer.start()
         self.status = "Listening"
-        self.event_type=''
+        self.event_type='-'
         print('Listening')
 
     def stop(self):
-        self.observer.stop()
-        self.observer.join()
-        self.observer=None
-        self.status = "Stopped"
-        self.event_type=''
+        if self.observer!=None:
+            self.observer.stop()
+            self.observer.join()
+            self.observer=None
+            self.status = "Stopped"
+            self.event_type='-'
         print('Stopped')
     
   

requirements.txt

@@ -1,3 +1,6 @@
 numpy
 pyOpenSSL
-watchdog
+watchdog
+opencv-python-headless
+matplotlib
+openai

web/javascript/checkVersion.js

@@ -1,23 +0,0 @@
-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='v0.1'
-
-fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
-  .then(response => response.json())
-  .then(data => {
-    const latestVersion = data.tag_name
-    console.log('Latest release version:', latestVersion)
-    if(latestVersion!=version){
-        window.alert(
-            `Please proceed to the official repository to download the latest version.https://github.com/shadowcz007/comfyui-mixlab-nodes/releases/`
-          )
-          window.open(
-            'https://github.com/shadowcz007/comfyui-mixlab-nodes/releases/'
-          )
-    }
-  })
-  .catch(error => {
-    console.error('Error fetching release information:', error)
-  })
-//  #MixCopilot

web/javascript/checkVersion_mixlab.js

@@ -0,0 +1,42 @@
+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 = 'v0.2.7'
+
+fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
+  .then(response => response.json())
+  .then(data => {
+    const latestVersion = data.tag_name
+    console.log('Latest release version:', latestVersion)
+    if (
+      latestVersion &&
+      latestVersion === localStorage.getItem('_mixlab_nodes_vesion')
+    )
+      return
+    if (latestVersion && latestVersion != version) {
+      localStorage.setItem('_mixlab_nodes_vesion', latestVersion)
+      app.ui.dialog.show(`<h4 style="font-size: 18px;">${repoName} <br>
+      Latest release version: ${latestVersion}</h4>
+      <p>Please proceed to the official repository to download the latest version.</p>
+      <a style="color: #2196F3;
+      font-size: 18px;
+      font-weight: 800;
+      letter-spacing: 2px;
+  }"
+  href="https://github.com/shadowcz007/comfyui-mixlab-nodes/releases/">https://github.com/shadowcz007/comfyui-mixlab-nodes/releases</a>
+      `)
+
+      // window.alert(
+      //     `Please proceed to the official repository to download the latest version.https://github.com/shadowcz007/comfyui-mixlab-nodes/releases/`
+      //   )
+      //   window.open(
+      //     'https://github.com/shadowcz007/comfyui-mixlab-nodes/releases/'
+      //   )
+    }
+  })
+  .catch(error => {
+    console.error('Error fetching release information:', error)
+  })
+//  #MixCopilot

web/javascript/gpt_mixlab.js

@@ -0,0 +1,262 @@
+import { app } from '../../../scripts/app.js'
+// import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+async function getConfig () {
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+
+  const res = await fetch(`${url}/mixlab`, {
+    method: 'POST'
+  })
+  return await res.json()
+}
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+
+app.registerExtension({
+  name: 'Mixlab.GPT.ChatGPTOpenAI',
+  async getCustomWidgets (app) {
+    return {
+      KEY (node, inputName, inputData, app) {
+        // console.log('##node', node)
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          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
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let data = getLocalData('_mixlab_api_key')
+            return data[node.id] || 'by Mixlab'
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      },
+      URL (node, inputName, inputData, app) {
+        // console.log('node', inputName, inputData[0])
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          size: [128, 32], // a default size
+          draw (ctx, node, width, y) {
+            // a method to draw the widget (ctx is a CanvasRenderingContext2D)
+          },
+          computeSize (...args) {
+            return [128, 32] // a method to compute the current size of the widget
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let data = getLocalData('_mixlab_api_url')
+            return data[node.id] || 'https://api.openai.com/v1'
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'ChatGPTOpenAI') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const api_key = this.widgets.filter(w => w.name == 'api_key')[0]
+        const api_url = this.widgets.filter(w => w.name == 'api_url')[0]
+
+        console.log('ChatGPTOpenAI nodeData', this.widgets)
+
+        const widget = {
+          type: 'div',
+          name: 'chatgptdiv',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, api_key.y, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        const inputDiv = (key, placeholder) => {
+          let div = document.createElement('div')
+          const ip = document.createElement('input')
+          ip.type = placeholder === 'Key' ? 'password' : 'text'
+          ip.className = `${'comfy-multiline-input'} ${placeholder}`
+          div.style = `display: flex;
+          align-items: center; 
+          margin: 6px 8px;
+          margin-top: 0;`
+          ip.placeholder = placeholder
+          ip.value = placeholder
+
+          ip.style = `margin-left: 24px;
+          outline: none;
+          border: none;
+          padding: 4px;width: 100%;`
+          const label = document.createElement('label')
+          label.style = 'font-size: 10px;min-width:32px'
+          label.innerText = placeholder
+          div.appendChild(label)
+          div.appendChild(ip)
+
+          ip.addEventListener('change', () => {
+            let data = getLocalData(key)
+            data[this.id] = ip.value.trim()
+            localStorage.setItem(key, JSON.stringify(data))
+            console.log(this.id, key)
+          })
+          return div
+        }
+
+        let inputKey = inputDiv('_mixlab_api_key', 'Key')
+        let inputUrl = inputDiv('_mixlab_api_url', 'URL')
+
+        widget.div.appendChild(inputKey)
+        widget.div.appendChild(inputUrl)
+
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          inputUrl.remove()
+          inputKey.remove()
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    // Fires every time a node is constructed
+    // You can modify widgets/add handlers/etc here
+
+    if (node.type === 'ChatGPTOpenAI') {
+      let widget = node.widgets.filter(w => w.div)[0]
+
+      let apiKey = getLocalData('_mixlab_api_key'),
+        url = getLocalData('_mixlab_api_url')
+
+      let id = node.id
+
+      console.log('ChatGPTOpenAI serialize_widgets', this)
+
+      widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
+      widget.div.querySelector('.URL').value =
+        url[id] || 'https://api.openai.com/v1'
+    }
+  }
+})
+
+app.registerExtension({
+  name: 'Mixlab.GPT.ShowTextForGPT',
+  async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === "ShowTextForGPT") {
+			function populate(text) {
+				if (this.widgets) {
+          
+					const pos = this.widgets.findIndex((w) => w.name === "text");
+					if (pos !== -1) {
+						for (let i = pos; i < this.widgets.length; i++) {
+							this.widgets[i].onRemove?.();
+						}
+						this.widgets.length = pos;
+					}
+				}
+        // console.log('ShowTextForGPT',this.widgets.length)
+				for (const list of text) {
+					const w = ComfyWidgets["STRING"](this, "text", ["STRING", { multiline: true }], app).widget;
+					w.inputEl.readOnly = true;
+					w.inputEl.style.opacity = 0.6;
+					w.value = list;
+				}
+        // console.log('ShowTextForGPT',this.widgets.length)
+				requestAnimationFrame(() => {
+					const sz = this.computeSize();
+					if (sz[0] < this.size[0]) {
+						sz[0] = this.size[0];
+					}
+					if (sz[1] < this.size[1]) {
+						sz[1] = this.size[1];
+					}
+					this.onResize?.(sz);
+					app.graph.setDirtyCanvas(true, false);
+				});
+			}
+
+			// When the node is executed we will be sent the input text, display this in the widget
+			const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = function (message) {
+				onExecuted?.apply(this, arguments);
+				populate.call(this, message.text);
+			};
+
+			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
+
+		}
+
+
+	},
+})

web/javascript/ui_mixlab.js

@@ -0,0 +1,88 @@
+import { app } from '../../../scripts/app.js'
+
+async function getCustomnodeMappings (mode = 'url') {
+  // mode = "local";
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+
+  const response = await fetch(`${url}/customnode/getmappings?mode=${mode}`)
+  const data = await response.json()
+
+  let nodes = {}
+  try {
+    for (let url in data) {
+      let n = data[url]
+      for (let node of n[0]) {
+        nodes[node] = { url, title: n[1].title_aux }
+      }
+    }
+  } catch (error) {}
+
+  return nodes
+}
+
+const missingNodeGithub = (missingNodeTypes, nodesMap) => {
+  let ts = {}
+
+  Array.from(new Set(missingNodeTypes), n => {
+    if (nodesMap[n]) {
+      let title = nodesMap[n].title
+      if (!ts[title]) {
+        ts[title] = {
+          title,
+          nodes: {},
+          url: nodesMap[n].url
+        }
+      }
+      ts[title].nodes[n] = 1
+    } else {
+      ts[n] = {
+        title: n,
+        nodes: {},
+        url: `https://github.com/search?q=${n}&type=code`
+      }
+      ts[n].nodes[n] = 1
+    }
+  })
+
+  return Array.from(Object.values(ts), n => {
+    const url = n.url
+    return `<li style="color: white;
+   background: black;
+   padding: 8px;
+   font-size: 12px;">${n.title}<a href="${url}" target="_blank"> 🔗</a></li>`
+  })
+}
+
+app.showMissingNodesError = async function (
+  missingNodeTypes,
+  hasAddedNodes = true
+) {
+  const nodesMap = await getCustomnodeMappings()
+  console.log('#nodesMap', nodesMap)
+  //   console.log('###MIXLAB', missingNodeTypes, hasAddedNodes)
+  this.ui.dialog.show(
+    `When loading the graph, the following node types were not found: <ul>${missingNodeGithub(
+      missingNodeTypes,
+      nodesMap
+    ).join('')}</ul>${
+      hasAddedNodes
+        ? 'Nodes that have failed to load will show as red on the graph.'
+        : ''
+    }`
+  )
+  this.logging.addEntry('Comfy.App', 'warn', {
+    MissingNodes: missingNodeTypes
+  })
+}
+
+// app.ui.dialog.show = function (html) {
+//   console.log('###MIXLAB', html)
+//   if (typeof html === 'string') {
+//     this.textElement.innerHTML = html
+//   } else {
+//     this.textElement.replaceChildren(html)
+//   }
+//   this.element.style.display = 'flex'
+// }

web/javascript/watcher_mixlab.js

@@ -14,6 +14,10 @@ async function getConfig () {
   return await res.json()
 }
 
+if (!window._mixlab_screen_prompt)
+  window._mixlab_screen_prompt =
+    'beautiful scenery nature glass bottle landscape,under water'
+
 function get_position_style (ctx, widget_width, y, node_height) {
   const MARGIN = 4 // the margin around the html element
 
@@ -57,7 +61,28 @@ app.registerExtension({
           name: inputName, // the name, slice
           size: [128, 24], // a default size
           draw (ctx, node, width, y) {
-            // a method to draw the widget (ctx is a CanvasRenderingContext2D)
+            // // 绘制文件图标的函数
+            // function drawFileIcon () {
+            //   // 清空画布
+            //   // ctx.clearRect(0, 0, canvas.width, canvas.height)
+
+            //   // 绘制文件外框
+            //   ctx.fillStyle = '#000'
+            //   ctx.fillRect(5, 5, 40, 40)
+
+            //   // 绘制文件夹图标
+            //   ctx.fillStyle = '#f00'
+            //   ctx.fillRect(10, 15, 30, 20)
+
+            //   // 绘制监听符号
+            //   ctx.beginPath()
+            //   ctx.arc(30, 35, 5, 0, 2 * Math.PI)
+            //   ctx.fillStyle = '#00f'
+            //   ctx.fill()
+            // }
+
+            // // 调用绘制函数
+            // drawFileIcon()
           },
           computeSize (...args) {
             return [128, 24] // a method to compute the current size of the widget
@@ -69,6 +94,26 @@ app.registerExtension({
         //  widget.something = something;          // maybe adds stuff to it
         node.addCustomWidget(widget) // adds it to the node
         return widget // and returns it.
+      },
+      PROMPT (node, inputName, inputData, app) {
+        // console.log('node', inputName, inputData[0])
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          size: [128, 24], // a default size
+          draw (ctx, node, width, y) {
+            // a method to draw the widget (ctx is a CanvasRenderingContext2D)
+          },
+          computeSize (...args) {
+            return [128, 24] // a method to compute the current size of the widget
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            return window._mixlab_screen_prompt || ''
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
       }
     }
   },
@@ -79,11 +124,7 @@ app.registerExtension({
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
 
-         
-        // console.log(
-        //   'watch widtget',
-        //   this.widgets.filter(w => w.name == 'watcher')[0]
-        // )
+        console.log('watch widtget', this.widgets)
 
         const watcher = this.widgets.filter(w => w.name == 'watcher')[0]
 
@@ -111,7 +152,6 @@ app.registerExtension({
           }
         }
 
-      
         // 上次路径填充
         getConfig().then(json => {
           let w = this.widgets.filter(w => w.name == 'file_path')[0]
@@ -120,7 +160,6 @@ app.registerExtension({
           }
           // console.log(json.event_type)
           window._mixlab_file_path_watcher = json.event_type
-          
         })
 
         /*
@@ -130,7 +169,7 @@ app.registerExtension({
         this.onRemoved = function () {
           // widget.card.remove()
         }
-        this.serialize_widgets = false
+        this.serialize_widgets = true
       }
     }
   }

workflow/3-FloatVideo-workflow.json

@@ -1,6 +1,6 @@
 {
-  "last_node_id": 21,
-  "last_link_id": 45,
+  "last_node_id": 22,
+  "last_link_id": 48,
   "nodes": [
     {
       "id": 7,
@@ -14,7 +14,7 @@
         "1": 200
       },
       "flags": {},
-      "order": 7,
+      "order": 4,
       "mode": 0,
       "inputs": [
         {
@@ -89,12 +89,12 @@
         504,
         33
       ],
-      "size": [
-        400,
-        200
-      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
       "flags": {},
-      "order": 6,
+      "order": 8,
       "mode": 0,
       "inputs": [
         {
@@ -105,7 +105,7 @@
         {
           "name": "text",
           "type": "STRING",
-          "link": 43,
+          "link": 48,
           "widget": {
             "name": "text"
           }
@@ -295,7 +295,7 @@
         "Node name for S&R": "KSampler"
       },
       "widgets_values": [
-        613900833686415,
+        1115769645491668,
         "randomize",
         4,
         1.6,
@@ -311,94 +311,24 @@
         338,
         786
       ],
-      "size": [
-        210,
-        246
-      ],
+      "size": {
+        "0": 210,
+        "1": 246
+      },
       "flags": {},
-      "order": 4,
+      "order": 6,
       "mode": 0,
       "inputs": [
         {
           "name": "images",
           "type": "IMAGE",
-          "link": 37
+          "link": 46
         }
       ],
       "properties": {
         "Node name for S&R": "PreviewImage"
       }
     },
-    {
-      "id": 19,
-      "type": "ScreenShare",
-      "pos": [
-        -55,
-        512
-      ],
-      "size": {
-        "0": 325.3117370605469,
-        "1": 459.7692565917969
-      },
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            37,
-            42
-          ],
-          "shape": 3,
-          "slot_index": 0
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": null,
-          "shape": 3
-        },
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": [
-            43
-          ],
-          "shape": 3,
-          "slot_index": 2
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ScreenShare"
-      }
-    },
-    {
-      "id": 20,
-      "type": "FloatingVideo",
-      "pos": [
-        2041,
-        277
-      ],
-      "size": [
-        315,
-        106
-      ],
-      "flags": {},
-      "order": 12,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 41
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "FloatingVideo"
-      }
-    },
     {
       "id": 6,
       "type": "LoraLoader",
@@ -411,7 +341,7 @@
         "1": 126
       },
       "flags": {},
-      "order": 8,
+      "order": 5,
       "mode": 0,
       "inputs": [
         {
@@ -463,7 +393,7 @@
         "1": 98
       },
       "flags": {},
-      "order": 3,
+      "order": 2,
       "mode": 0,
       "outputs": [
         {
@@ -515,13 +445,13 @@
         "1": 58
       },
       "flags": {},
-      "order": 5,
+      "order": 7,
       "mode": 0,
       "inputs": [
         {
           "name": "image",
           "type": "IMAGE",
-          "link": 42
+          "link": 47
         }
       ],
       "outputs": [
@@ -541,6 +471,70 @@
       "widgets_values": [
         512
       ]
+    },
+    {
+      "id": 22,
+      "type": "ScreenShare",
+      "pos": [
+        -63,
+        483
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            46,
+            47
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            48
+          ],
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ScreenShare"
+      }
+    },
+    {
+      "id": 20,
+      "type": "FloatingVideo",
+      "pos": [
+        1928,
+        295
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 41
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "FloatingVideo"
+      }
     }
   ],
   "links": [
@@ -744,14 +738,6 @@
       0,
       "LATENT"
     ],
-    [
-      37,
-      19,
-      0,
-      2,
-      0,
-      "IMAGE"
-    ],
     [
       38,
       16,
@@ -784,22 +770,6 @@
       0,
       "IMAGE"
     ],
-    [
-      42,
-      19,
-      0,
-      18,
-      0,
-      "IMAGE"
-    ],
-    [
-      43,
-      19,
-      2,
-      8,
-      1,
-      "STRING"
-    ],
     [
       44,
       6,
@@ -815,6 +785,30 @@
       6,
       1,
       "CLIP"
+    ],
+    [
+      46,
+      22,
+      0,
+      2,
+      0,
+      "IMAGE"
+    ],
+    [
+      47,
+      22,
+      0,
+      18,
+      0,
+      "IMAGE"
+    ],
+    [
+      48,
+      22,
+      1,
+      8,
+      1,
+      "STRING"
     ]
   ],
   "groups": [],

workflow/4-loadfromlocal-watcher-workflow.json

@@ -1,6 +1,6 @@
 {
   "last_node_id": 23,
-  "last_link_id": 47,
+  "last_link_id": 48,
   "nodes": [
     {
       "id": 11,
@@ -94,7 +94,7 @@
         "Node name for S&R": "KSampler"
       },
       "widgets_values": [
-        882790958612696,
+        1088992701378297,
         "randomize",
         4,
         1.6,
@@ -110,10 +110,10 @@
         -470,
         840
       ],
-      "size": [
-        430.924072265625,
-        253.48239135742188
-      ],
+      "size": {
+        "0": 430.924072265625,
+        "1": 253.48239135742188
+      },
       "flags": {},
       "order": 0,
       "mode": 0,
@@ -212,7 +212,7 @@
         "1": 58
       },
       "flags": {},
-      "order": 9,
+      "order": 8,
       "mode": 0,
       "inputs": [
         {
@@ -271,45 +271,6 @@
         "control_scribble-fp16.safetensors"
       ]
     },
-    {
-      "id": 8,
-      "type": "CLIPTextEncode",
-      "pos": [
-        678,
-        -77
-      ],
-      "size": {
-        "0": 400,
-        "1": 200
-      },
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "clip",
-          "type": "CLIP",
-          "link": 32
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            39
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "CLIPTextEncode"
-      },
-      "widgets_values": [
-        "superman,"
-      ]
-    },
     {
       "id": 7,
       "type": "CLIPTextEncode",
@@ -322,7 +283,7 @@
         "1": 200
       },
       "flags": {},
-      "order": 6,
+      "order": 5,
       "mode": 0,
       "inputs": [
         {
@@ -361,7 +322,7 @@
         "1": 126
       },
       "flags": {},
-      "order": 7,
+      "order": 6,
       "mode": 0,
       "inputs": [
         {
@@ -465,7 +426,7 @@
         "1": 246
       },
       "flags": {},
-      "order": 8,
+      "order": 7,
       "mode": 0,
       "inputs": [
         {
@@ -512,6 +473,53 @@
         1
       ]
     },
+    {
+      "id": 8,
+      "type": "CLIPTextEncode",
+      "pos": [
+        678,
+        -77
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 32
+        },
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 48,
+          "widget": {
+            "name": "text"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            39
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "superman,"
+      ]
+    },
     {
       "id": 22,
       "type": "LoadImagesFromPath",
@@ -521,7 +529,7 @@
       ],
       "size": {
         "0": 315,
-        "1": 202
+        "1": 238
       },
       "flags": {},
       "order": 4,
@@ -542,6 +550,15 @@
           "type": "MASK",
           "links": null,
           "shape": 6
+        },
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            48
+          ],
+          "shape": 3,
+          "slot_index": 2
         }
       ],
       "properties": {
@@ -813,6 +830,14 @@
       18,
       0,
       "IMAGE"
+    ],
+    [
+      48,
+      22,
+      2,
+      8,
+      1,
+      "STRING"
     ]
   ],
   "groups": [

workflow/5-gpt-workflow.json

@@ -0,0 +1,631 @@
+{
+  "last_node_id": 47,
+  "last_link_id": 46,
+  "nodes": [
+    {
+      "id": 27,
+      "type": "CLIPTextEncode",
+      "pos": [
+        1961.178268896482,
+        527.6060791015625
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 24
+        },
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 46,
+          "widget": {
+            "name": "text"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            21
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"
+      ]
+    },
+    {
+      "id": 28,
+      "type": "CLIPTextEncode",
+      "pos": [
+        1958.7452854980445,
+        266
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 25
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            22
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "text, watermark"
+      ]
+    },
+    {
+      "id": 24,
+      "type": "KSampler",
+      "pos": [
+        2434.0233006347635,
+        80
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 20
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 21
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 22
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 23
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            26
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        971428736321335,
+        "randomize",
+        15,
+        8,
+        "euler",
+        "karras",
+        1
+      ]
+    },
+    {
+      "id": 29,
+      "type": "VAEDecode",
+      "pos": [
+        2799.0233006347635,
+        80
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {
+        "collapsed": false
+      },
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 26
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 27
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            31
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 26,
+      "type": "EmptyLatentImage",
+      "pos": [
+        2069.0233006347635,
+        80
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            23
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 25,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        1593.7452854980445,
+        80
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            20
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            24,
+            25
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            27
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "deliberate_v2.safetensors"
+      ]
+    },
+    {
+      "id": 31,
+      "type": "PreviewImage",
+      "pos": [
+        2439,
+        408
+      ],
+      "size": {
+        "0": 563.4671630859375,
+        "1": 720.8866577148438
+      },
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 31
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 44,
+      "type": "ShowTextForGPT",
+      "pos": [
+        1171,
+        425
+      ],
+      "size": {
+        "0": 432.46002197265625,
+        "1": 264.40771484375
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 44,
+          "widget": {
+            "name": "text"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": null,
+          "shape": 6
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ShowTextForGPT"
+      },
+      "widgets_values": [
+        "[\n    {\n        \"role\": \"system\",\n        \"content\": \"You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.\"\n    },\n    {\n        \"role\": \"user\",\n        \"content\": \"\"\n    }\n]"
+      ]
+    },
+    {
+      "id": 43,
+      "type": "ShowTextForGPT",
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workflow/6-gpt-all-workflow.json

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