system prompt

CNAME

@@ -1 +0,0 @@
-mixlabnodes.com

README.md

@@ -1,39 +1,15 @@
 ![](https://img.shields.io/github/release/shadowcz007/comfyui-mixlab-nodes)
 
-> 适配了最新版 comfyui 的 py3.11 ，torch 2.3.1+cu121
+> 适配了最新版 comfyui 的 py3.11 ，torch 2.1.2+cu121
 > [Mixlab nodes discord](https://discord.gg/cXs9vZSqeK)
 
-商务合作请联系 389570357@qq.com
-For business cooperation, please contact email 389570357@qq.com
 
 ##### `最新`：
 
-- [增加 Her 的DEMO页面，和数字人对话](https://github.com/shadowcz007/ComfyUI-Backend-MixlabNodes/blob/main/workflow/her_demo_workflow.json)
+ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。模型下载后，放置到 `models/llamafile/`
 
-- 右键菜单支持 text-to-text，方便对 prompt 词补全，支持云LLM或者是本地LLM。
+- 右键菜单支持 text-to-text，方便对 prompt 词补全
 
-- 增加 MiniCPM-V 2.6 int4
-
-This is the int4 quantized version of MiniCPM-V 2.6.
-Running with int4 version would use lower GPU memory (about 7GB).
-
-- 移动端适配、修改 app 模式的 Mask 编辑器
-
-- 增加 p5.js 作为输入节点
-  [workflow](./workflow/p5workflow.json)
-  [workflow2](./workflow/p5-video-workflow.json)
-
-- App 模式增加 batch prompt，批量提示词，可以把动态提示词批量组成后运行
-
-![alt text](./assets/1722517810720.png)
-
-- 增加 API Key Input 节点，用于管理 LLM 的 Key,同时优化 LLM 相关节点，为后续 agent 模式做准备
-
-- 增加 SiliconflowLLM，可以使用由 Siliconflow 提供的免费 LLM
-
-<!-- - ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。模型下载后，放置到 `models/llamafile/` -->
-
-<!--
 强烈推荐：
 [Phi-3-mini-4k-instruct-function-calling-GGUF](https://huggingface.co/nold/Phi-3-mini-4k-instruct-function-calling-GGUF)
 
@@ -42,15 +18,12 @@ Running with int4 version would use lower GPU memory (about 7GB).
 - 右键菜单支持 image-to-text，使用多模态模型，多模态使用 [llava-phi-3-mini-gguf](https://huggingface.co/xtuner/llava-phi-3-mini-gguf/tree/main)，注意需要把llava-phi-3-mini-mmproj-f16.gguf也下载
 
 ![](./assets/prompt_ai_setup.png)
-![](./assets/prompt-ai.png) -->
+![](./assets/prompt-ai.png)
+
 
 #### `相关插件推荐`
 
-[comfyui-liveportrait](https://github.com/shadowcz007/comfyui-liveportrait)
-
-[Comfyui-ChatTTS](https://github.com/shadowcz007/Comfyui-ChatTTS)
-
-[comfyui-sound-lab](https://github.com/shadowcz007/comfyui-sound-lab)
+<!-- [comfyui-sd-prompt-mixlab](https://github.com/shadowcz007/comfyui-sd-prompt-mixlab) -->
 
 [comfyui-Image-reward](https://github.com/shadowcz007/comfyui-Image-reward)
 
@@ -67,8 +40,6 @@ Running with int4 version would use lower GPU memory (about 7GB).
 - 发布为 app 的 workflow，可以在右键里再次编辑了
 - web app 可以设置分类，在 comfyui 右键菜单可以编辑更新 web app
 - 支持动态提示
-- 支持把输出显示到 comfyui 背景（TouchDesigner 风格）
-- 如果转为 web app 打开是空白的，注意检查下插件目录的名字需要是：comfyui-mixlab-nodes(如果是 zip 包下载会多了个-main 的后缀，需要去掉)
 
 ![](./assets/微信图片_20240421205440.png)
 
@@ -127,20 +98,15 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 [Voice + Real-time Face Swap Workflow](./workflow/语音+实时换脸workflow.json)
 
-- Preview Audio
-
-[text-to-audio](./workflow/text-to-audio-base-workflow.json)
-
 ### GPT
 
-> Support for calling multiple GPTs.Local LLM 、 ChatGPT、ChatGLM3 、ChatGLM4 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 . Azure OpenAI:https://xxxx.openai.azure.com
+> Support for calling multiple GPTs.Local LLM（llama.cpp）、 ChatGPT、ChatGLM3 、ChatGLM4 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 . Azure OpenAI:https://xxxx.openai.azure.com
 
-[LLM_base_workflow](./workflow/LLM_base_workflow.json)
+![gpt-workflow.svg](./assets/gpt-workflow.svg)
 
-- SiliconflowLLM
-- ChatGPTOpenAI
+[workflow-5](./workflow/5-gpt-workflow.json)
 
-<!-- 最新：ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。
+最新：ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。
 
 Model download,move to :`models/llamafile/`
 
@@ -168,7 +134,7 @@ pip install 'llama-cpp-python[server]'
 ```
 pip install llama-cpp-python \
   --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
-``` -->
+```
 
 ## Prompt
 
@@ -195,8 +161,6 @@ pip install llama-cpp-python \
 
 > A new layer class node has been added, allowing you to separate the image into layers. After merging the images, you can input the controlnet for further processing.
 
-> The composite images node overlays a foreground image onto a background image at specified positions and scales, with optional blending modes and masking capabilities. position : 'overall',"center_center","left_bottom","center_bottom","right_bottom","left_top","center_top","right_top"
-
 ![layers](./assets/layers-workflow.svg)
 
 ![poster](./assets/poster-workflow.svg)
@@ -228,19 +192,6 @@ pip install llama-cpp-python \
 
 > Conveniently load images from a fixed address on the internet to ensure that default images in the workflow can be executed.
 
-#### TextImage
-
-> [下载字体](https://drxie.github.io/OSFCC/)放到 `custom_nodes/comfyui-mixlab-nodes/assets/fonts`
-
-#### MiniCPM-VQA Simple
-
-This is the int4 quantized version of MiniCPM-V 2.6.
-Running with int4 version would use lower GPU memory (about 7GB).
-
-[模型](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4)
-
-![alt text](assets/1724308322276.png)
-
 ### Style
 
 > Apply VisualStyle Prompting , Modified from [ComfyUI_VisualStylePrompting](https://github.com/ExponentialML/ComfyUI_VisualStylePrompting)
@@ -263,8 +214,6 @@ Running with int4 version would use lower GPU memory (about 7GB).
 
 ### Other Nodes
 
-- 增加 Edit Mask，方便在生成的时候手动绘制 mask [workflow](./workflow/edit-mask-workflow.json)
-
 ![main](./assets/all-workflow.svg)
 ![main2](./assets/detect-face-all.png)
 
@@ -280,45 +229,27 @@ Add edges to an image.
 
 ![FeatheredMask](./assets/FlVou_Y6kaGWYoEj1Tn0aTd4AjMI.jpg)
 
-> LaMaInpainting（需要手动安装）
-
-- simple-lama-inpainting 里的 pillow 造成冲突，暂时从依赖里移除，如果有安装 simple-lama-inpainting ，节点会自动添加，没有，则不会自动添加。
+> LaMaInpainting
 
 from [simple-lama-inpainting](https://github.com/enesmsahin/simple-lama-inpainting)
 
-- [问题汇总](https://github.com/shadowcz007/comfyui-mixlab-nodes/issues/294)
-
 > rembgNode
 
 "briarmbg","u2net","u2netp","u2net_human_seg","u2net_cloth_seg","silueta","isnet-general-use","isnet-anime"
 
 **_ briarmbg _** model was developed by BRlA Al and can be used as an open-source model for non-commercial purposes
 
-### Enhancement
+### Improvement
 
-- Direct "Help" option accessible through node context menu.
+- Add "help" option to the context menu for each node.
+- Add "Nodes Map" option to the global context menu.
 
-- "Nodes Map" feature added to global context menu.
-
-- An improvement has been made to directly redirect to GitHub to search for missing nodes when loading the graph.
-
-*** If not needed, you can comment out ```app.showMissingNodesError``` in the ```ui_mixlab.js``` file.
+An improvement has been made to directly redirect to GitHub to search for missing nodes when loading the graph.
 
 ![help](./assets/help.png)
 
 ![node-not-found](./assets/node-not-found.png)
 
-
-- Right-click shortcut
-
-右键菜单支持 text-to-text，方便对 prompt 词补全，支持云LLM或者是本地LLM。
-The right-click menu supports text-to-text conversion, facilitating prompt word completion, and supports cloud LLMs or local LLMs.
-
-Local LLM API example:```http://localhost:1234/v1```
-
-![alt text](./assets/1724380841822.png)
-
-
 ### Models
 
 - [Download TripoSR](https://huggingface.co/stabilityai/TripoSR/blob/main/model.ckpt) and place it in `models/triposr`

__init__.py

@@ -1,38 +1,37 @@
-#
 import os
 import subprocess
 import importlib.util
 import sys,json
-import execution
-import uuid
+import urllib
 import hashlib
 import datetime
 import folder_paths
 import logging
 import base64,io,re
-import random
 from PIL import Image
 from comfy.cli_args import args
 python = sys.executable
 
+# print("sys.path", sys.path)
+
 #修复 sys.stdout.isatty()  object has no attribute 'isatty'
 try:
     sys.stdout.isatty()
 except:
-    print('#fix sys.stdout.isatty')
+    # print('#fix sys.stdout.isatty')
     sys.stdout.isatty = lambda: False
 
-_URL_=None
+llama_port=None
+llama_model=""
+llama_chat_format=""
 
+try:
+    from .nodes.ChatGPT import get_llama_models,get_llama_model_path,llama_cpp_client
+    llama_cpp_client("")
 
-# try:
-#     from .nodes.ChatGPT import get_llama_models,get_llama_model_path,llama_cpp_client
-#     llama_cpp_client("")
+except:
+    print("##nodes.ChatGPT ImportError")
 
-# except:
-#     print("##nodes.ChatGPT ImportError")
-
-from .nodes.ChatGPT import openai_client
 
 from .nodes.RembgNode import get_rembg_models,U2NET_HOME,run_briarmbg,run_rembg
 
@@ -47,35 +46,26 @@ except ImportError:
     print("or")
     print("pip install -r requirements.txt")
     sys.exit()
-
-
-def is_installed(package, package_overwrite=None,auto_install=True):
-    is_has=False
+   
+def is_installed(package, package_overwrite=None):
     try:
         spec = importlib.util.find_spec(package)
-        is_has=spec is not None
     except ModuleNotFoundError:
         pass
 
     package = package_overwrite or package
 
     if spec is None:
-        if auto_install==True:
-            print(f"Installing {package}...")
-            # 清华源 -i https://pypi.tuna.tsinghua.edu.cn/simple
-            command = f'"{python}" -m pip install {package}'
-    
-            result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ)
+        print(f"Installing {package}...")
+        # 清华源 -i https://pypi.tuna.tsinghua.edu.cn/simple
+        command = f'"{python}" -m pip install {package}'
+  
+        result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ)
 
-            is_has=True
-
-            if result.returncode != 0:
-                print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
-                is_has=False
+        if result.returncode != 0:
+            print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
     else:
         print(package+'## OK')
-
-    return is_has
      
 try:
     import OpenSSL
@@ -98,6 +88,7 @@ except ImportError:
     sys.exit()
 
 
+
 def install_openai():
     # Helper function to install the OpenAI module if not already installed
     try:
@@ -181,14 +172,15 @@ def create_for_https():
         os.mkdir(https_key_path)
     if not os.path.exists(crt):
         create_key(key,crt)
-    # print('https_key OK: ', crt,key)
+
+    print('https_key OK: ', crt,key)
     return (crt,key)
 
 
 
 # workflow  目录下的所有json
 def read_workflow_json_files_all(folder_path):
-    # print('#read_workflow_json_files_all',folder_path)
+    print('#read_workflow_json_files_all',folder_path)
     json_files = []
     for root, dirs, files in os.walk(folder_path):
         for file in files:
@@ -318,32 +310,31 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
         print('app_workflow_path: ',app_workflow_path)
         try:
             with open(app_workflow_path) as json_file:
-                json_data=json.load(json_file)
                 apps = [{
                     'filename':filename,
-                    'data':json_data
+                    'data':json.load(json_file)
                 }]
         except Exception as e:
             print("发生异常：", str(e))
         
         # 这个代码不需要
         # if len(apps)==1 and category!='' and category!=None:
-        data=read_workflow_json_files(category_path)
+            data=read_workflow_json_files(category_path)
             
-        for item in data:
-            x=item["data"]
-            # print(apps[0]['filename'] ,item["filename"])
-            if apps[0]['filename']!=item["filename"]:
-                category=''
-                input=None
-                output=None
-                if 'category' in x['app']:
-                    category=x['app']['category']
-                if 'input' in x['app']:
-                    input=x['app']['input']
-                if 'output' in x['app']:
-                    output=x['app']['output']
-                apps.append({
+            for item in data:
+                x=item["data"]
+                # print(apps[0]['filename'] ,item["filename"])
+                if apps[0]['filename']!=item["filename"]:
+                    category=''
+                    input=None
+                    output=None
+                    if 'category' in x['app']:
+                        category=x['app']['category']
+                    if 'input' in x['app']:
+                        input=x['app']['input']
+                    if 'output' in x['app']:
+                        output=x['app']['output']
+                    apps.append({
                         "filename":item["filename"],
                         # "category":category,
                         "data":{
@@ -463,7 +454,6 @@ async def check_port_available(address, port):
 
 # https
 async def new_start(self, address, port, verbose=True, call_on_start=None):
-    global _URL_
     try:
         runner = web.AppRunner(self.app, access_log=None)
         await runner.setup()
@@ -532,19 +522,10 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
             logging.info("\n")
             logging.info("\n\nStarting server")
 
-            import socket
-
-            hostname = socket.gethostname()
-            ip_address = socket.gethostbyname(hostname)
-
-            # print(f"本机的IP地址是: {ip_address}")
-
-
             # print("\033[93mStarting server\n")
-            logging.info("\033[93mTo see the GUI go to: http://{}:{} or http://{}:{}".format(ip_address, http_port,address,http_port))
-            logging.info("\033[93mTo see the GUI go to: https://{}:{} or https://{}:{}\033[0m".format(ip_address, https_port,address,https_port))
-
-            _URL_="http://{}:{}".format(address,http_port)
+            logging.info("\033[93mTo see the GUI go to: http://{}:{}".format(address, http_port))
+            logging.info("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
+ 
             # print("\033[93mTo see the GUI go to: http://{}:{}".format(address, http_port))
             # print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
 
@@ -585,78 +566,10 @@ async def mixlab_hander(request):
             print(e)
     return web.json_response(data)
 
-# llm的api key，使用硅基流动
-@routes.post('/mixlab/llm_api_key')  
-async def mixlab_llm_api_key_handler(request):  
-    data = await request.json()  
-    api_key = data.get('key')
-
-    app_folder = os.path.join(current_path, "app")
-    key_file_path = os.path.join(app_folder, "llm_api_key.txt")
-
-    if api_key:
-        if not os.path.exists(app_folder):
-            os.makedirs(app_folder)
-        try:
-            with open(key_file_path, 'w') as f:
-                f.write(api_key)
-            return web.json_response({'message': 'API key saved successfully'})
-        except Exception as e:
-            return web.json_response({'error': str(e)}, status=500)
-    else:
-        if os.path.exists(key_file_path):
-            try:
-                with open(key_file_path, 'r') as f:
-                    saved_api_key = f.read().strip()
-                return web.json_response({'key': saved_api_key})
-            except Exception as e:
-                return web.json_response({'error': str(e)}, status=500)
-        else:
-            return web.json_response({'error': 'No API key provided and no key found in local storage'}, status=400)
-
-
-@routes.post('/chat/completions')
-async def chat_completions(request):
-    data = await request.json()
-    messages = data.get('messages')
-    key=data.get('key')
-    api_url=data.get("api_url")
-    model_name=data.get("model_name")
-
-    if not api_url:
-        api_url="https://api.siliconflow.cn/v1"
-
-    if not model_name:
-        model_name="01-ai/Yi-1.5-9B-Chat-16K"
-
-    if not messages:
-        return web.json_response({"error": "No messages provided"}, status=400)
-
-    async def generate():
-        try:
-            headers = {
-                'Authorization': f'Bearer {key}',
-                'Content-Type': 'application/json'
-            }
-            payload = {
-                'model': model_name,
-                'messages': messages,
-                'stream': True
-            }
-            async with aiohttp.ClientSession() as session:
-                async with session.post(f'{api_url}/chat/completions', json=payload, headers=headers) as resp:
-                    async for line in resp.content:
-                        yield line
-
-        except Exception as e:
-            yield f"Error: {str(e)}".encode('utf-8') + b"\r\n"
-
-    return web.Response(body=generate(), content_type='text/event-stream')
-
 
 @routes.get('/mixlab/app')
 async def mixlab_app_handler(request):
-    html_file = os.path.join(current_path, "webApp/index.html")
+    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()
@@ -664,31 +577,6 @@ async def mixlab_app_handler(request):
     else:
         return web.Response(text="HTML file not found", status=404)
 
-# web app模式独立
-@routes.get('/mixlab/app/{filename:.*}')
-async def static_file_handler(request):
-    filename = request.match_info['filename']
-    file_path = os.path.join(current_path, "webApp", filename)
-    print(file_path)
-    
-    if os.path.exists(file_path) and os.path.isfile(file_path):
-        if filename.endswith('.js'):
-            content_type = 'application/javascript'
-        elif filename.endswith('.css'):
-            content_type = 'text/css'
-        elif filename.endswith('.html'):
-            content_type = 'text/html'
-        elif filename.endswith('.svg'):
-            content_type = 'image/svg+xml'
-        else:
-            content_type = 'application/octet-stream'
-        
-        with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
-            file_data = f.read()
-            return web.Response(text=file_data, content_type=content_type)
-    else:
-        return web.Response(text="File not found", status=404)
-
 
 @routes.post('/mixlab/workflow')
 async def mixlab_workflow_hander(request):
@@ -721,34 +609,13 @@ async def mixlab_workflow_hander(request):
                     category=data['category']
                 if 'admin' in data:
                     admin=data['admin']
-
-                ds=get_my_workflow_for_app(filename,category,admin)
-                data=[]           
-                for json_data in ds:
-                    # 不传给前端
-                    if 'output' in json_data['data']:
-                        del json_data['data']['output']
-                    if 'workflow' in json_data['data']:
-                        del json_data['data']['workflow']
-                    data.append(json_data)
-
                 result={
-                    'data':data,
+                    'data':get_my_workflow_for_app(filename,category,admin),
                     'status':'success',
                 }
             elif data['task']=='list':
-                ds=get_workflows()
-                data=[]
-                for json_data in ds:
-                    # 不传给前端
-                    if 'output' in json_data['data']:
-                        del json_data['data']['output']
-                    if 'workflow' in json_data['data']:
-                        del json_data['data']['workflow']
-                    data.append(json_data)
-
                 result={
-                    'data':data,
+                    'data':get_workflows(),
                     'status':'success',
                 }
     except Exception as e:
@@ -782,11 +649,11 @@ async def get_checkpoints(request):
     except Exception as e:
         print('/mixlab/folder_paths',False,e)
 
-    # try:
-    #     if data['type']=='llamafile':
-    #         names=get_llama_models()
-    # except:
-    #     print("llamafile none")
+    try:
+        if data['type']=='llamafile':
+            names=get_llama_models()
+    except:
+        print("llamafile none")
 
     try:
         if data['type']=='rembg':
@@ -833,145 +700,205 @@ async def rembg_hander(request):
 
     return web.json_response(result)
 
-# 保存运行结果？暂时去掉
-# @routes.post("/mixlab/prompt_result")
-# async def post_prompt_result(request):
-#     data = await request.json()
-#     res=None
-#     # print(data)
-#     try:
-#         action=data['action']
-#         if action=='save':
-#             result=data['data']
-#             res=save_prompt_result(result['prompt_id'],result)
-#         elif action=='all':
-#             res=get_prompt_result()
-#     except Exception as e:
-#         print('/mixlab/prompt_result',False,e)
-    
-#     return web.json_response({"result":res})
-
-# 种子设置
-def random_seed(seed, data):
-    max_seed = 4294967295
-
-    for id, value in data.items():
-        # print(seed,id)
-        if id in seed:
-            if 'seed' in value['inputs'] and not isinstance(value['inputs']['seed'], list) and seed[id] in ['increment', 'decrement', 'randomize']:
-                value['inputs']['seed'] = round(random.random() * max_seed)
-            
-            if 'noise_seed' in value['inputs'] and not isinstance(value['inputs']['noise_seed'], list) and seed[id] in ['increment', 'decrement', 'randomize']:
-                value['inputs']['noise_seed'] = round(random.random() * max_seed)
-            
-            if value.get('class_type') == "Seed_" and seed[id] in ['increment', 'decrement', 'randomize']:
-                value['inputs']['seed'] = round(random.random() * max_seed)
-            
-        # print('new Seed', value)
-    
-    return data
-
-
-# 运行工作流，代替官方的prompt接口
-@routes.post("/mixlab/prompt")
-async def mixlab_post_prompt(request):
-    p_intance=PromptServer.instance
-    logging.info("/mixlab/prompt")
-    resp_code = 200
-    out_string = ""
-    json_data =  await request.json()
-    # json_data = p_intance.trigger_on_prompt(json_data)
-    # filename,category, client_id ,input
-    # workflow 的 filename,category
-
-    # 输入的参数
-    input_data=json_data['input'] if "input" in json_data else []
-    # 种子
-    seed=json_data['seed'] if "seed" in json_data else {}
 
+@routes.post("/mixlab/prompt_result")
+async def post_prompt_result(request):
+    data = await request.json()
+    res=None
+    # print(data)
     try:
-        apps=json_data['apps']
+        action=data['action']
+        if action=='save':
+            result=data['data']
+            res=save_prompt_result(result['prompt_id'],result)
+        elif action=='all':
+            res=get_prompt_result()
+    except Exception as e:
+        print('/mixlab/prompt_result',False,e)
+    
+    return web.json_response({"result":res})
+
+
+
+def start_local_live_thread(data):
+    import asyncio
+    from VoiceStreamAI.server import Server
+    from VoiceStreamAI.asr.asr_factory import ASRFactory
+    from VoiceStreamAI.vad.vad_factory import VADFactory
+
+    model="large-v3"
+    if "model" in data:
+        model=data['model']
+
+    vad_pipeline = VADFactory.create_vad_pipeline("pyannote")
+    #device 
+    asr_pipeline = ASRFactory.create_asr_pipeline("faster_whisper", **{"model_size":model})
+
+    port=8765
+    if 'port' in data:
+        port=data['port']
+
+    llm_port=9000
+    if 'llm_port' in data:
+        llm_port=data['llm_port']
+
+    server = Server(vad_pipeline, 
+                    asr_pipeline, 
+                    host="127.0.0.1", 
+                    port=port, 
+                    sampling_rate=16000, 
+                    samples_width=2,
+                    llm_port=llm_port
+                    )
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    loop.run_until_complete(server.start())
+    loop.run_forever()
+
+
+async def start_local_llm(data):
+    global llama_port,llama_model,llama_chat_format
+    if llama_port and llama_model and llama_chat_format:
+        return {"port":llama_port,"model":llama_model,"chat_format":llama_chat_format}
+
+    import threading
+    import uvicorn
+    from llama_cpp.server.app import create_app
+    from llama_cpp.server.settings import (
+                Settings,
+                ServerSettings,
+                ModelSettings,
+                ConfigFileSettings,
+            )
+    
+    if not "model" in data and "model_path" in data:
+        data['model']= os.path.basename(data["model_path"])
+        model=data["model_path"]
+
+    elif "model" in data:
+        model=get_llama_model_path(data['model'])
+
+    n_gpu_layers=-1
+
+    if "n_gpu_layers" in data:
+        n_gpu_layers=data['n_gpu_layers']
+
+
+    chat_format="chatml"
+    if "model" in data and "function-calling" in data['model']:
+        chat_format="functionary-v2"
+
+    model_alias=os.path.basename(model)
+
+    # 多模态
+    clip_model_path=None
+    
+    prefix = "llava-phi-3-mini"
+    file_name = prefix+"-mmproj-"
+    if model_alias.startswith(prefix):
+        for file in os.listdir(os.path.dirname(model)):
+            if file.startswith(file_name):
+                clip_model_path=os.path.join(os.path.dirname(model),file)
+                chat_format='llava-1-5'
+    print('#clip_model_path',chat_format,clip_model_path)
+
+
+    address="127.0.0.1"
+    port=9090
+    success = False
+    for i in range(11):  # 尝试最多11次
+        if await check_port_available(address, port + i):
+            port = port + i
+            success = True
+            break
+
+    if success == False:
+        return {"port":None,"model":""}
+    
+    
+    server_settings=ServerSettings(host=address,port=port)
+
+    name, ext = os.path.splitext(os.path.basename(model))
+    print('#model',name)
+    app = create_app(
+                server_settings=server_settings,
+                model_settings=[
+                    ModelSettings(
+                    model=model,
+                    model_alias=name,
+                    n_gpu_layers=n_gpu_layers,
+                    n_ctx=4098,
+                    chat_format=chat_format,
+                    embedding=False,
+                    clip_model_path=clip_model_path
+                    )])
+
+    def run_uvicorn():
+        uvicorn.run(
+                app,
+                host=os.getenv("HOST", server_settings.host),
+                port=int(os.getenv("PORT", server_settings.port)),
+                ssl_keyfile=server_settings.ssl_keyfile,
+                ssl_certfile=server_settings.ssl_certfile,
+            )
+
+    # 创建一个子线程
+    thread = threading.Thread(target=run_uvicorn)
+
+    # 启动子线程
+    thread.start()
+
+    llama_port=port
+    llama_model=data['model']
+    llama_chat_format=chat_format
+
+    return  {"port":llama_port,"model":llama_model,"chat_format":llama_chat_format}
+
+# llam服务的开启
+@routes.post('/mixlab/start_llama')
+async def my_hander_method(request):
+    data =await request.json()
+    # print(data)
+    if llama_port and llama_model and llama_chat_format:
+        return web.json_response({"port":llama_port,"model":llama_model,"chat_format":llama_chat_format} )
+    try:
+        result=await start_local_llm(data)
     except:
-        apps=get_my_workflow_for_app(json_data['filename'],json_data['category'],False)
-    
-    prompt=json_data['prompt'] if 'prompt' in json_data else None
-    
-    if len(apps)>0:
-        # 取到prompt
-        prompt=apps[0]['data']['output']
-        # logging.info(prompt)
-        # 更新input_data到prompt里
-        '''
-          {
-                "inputs": {
-                    "number": 512,
-                    "min_value": 512,
-                    "max_value": 2048,
-                    "step": 1
-                },
-                "class_type": "IntNumber",
-                "id": "22"
-            },
-        '''
+        result= {"port":None,"model":"","llama_cpp_error":True}
+        print('start_local_llm error')
 
-        for inp in input_data:
-            id=inp['id']
-            if prompt[id]['class_type']==inp['class_type']:
-                prompt[id]['inputs'].update(inp['inputs']) 
+    return web.json_response(result)
 
 
-    if prompt==None:
-        return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
-    else:
-        # 种子更新 
-        '''
-            "seed": {
-                    "45": "randomize",
-                    "46": "randomize"
-                }
-            '''
-        json_data["prompt"]=random_seed(seed,prompt)
+@routes.post('/mixlab/start_live')
+async def mixlab_live_start_handler(request):
+    import threading
+    llm=await start_local_llm({
+        "model":"Phi-3-mini-4k-instruct-Q5_K_S.gguf",
+        "n_gpu_layers":2
+    })
 
-    # print("#json_data",prompt)
-    # 需要把apps处理成 prompt
-    # 注意seed的处理
-    
-    if "number" in json_data:
-        number = float(json_data['number'])
-    else:
-        number = p_intance.number
-        if "front" in json_data:
-            if json_data['front']:
-                number = -number
+    # {"port":llama_port,"model":llama_model,"chat_format":llama_chat_format}
 
-        p_intance.number += 1
+    os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' #hf_hub_download 里的下载地址修改
+    os.environ['PYANNOTE_AUTH_TOKEN'] = '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'
 
-    if "prompt" in json_data:
-        prompt = json_data["prompt"]
-        valid = execution.validate_prompt(prompt)
-        extra_data = {}
-        if "extra_data" in json_data:
-            extra_data = json_data["extra_data"]
+    # Create and start the thread
+    data = {
+        "llm_port":llm['port'],
+        "port":8725,
+        "model":"large-v3"
+    }  # Replace with your actual data if needed
+    thread = threading.Thread(target=start_local_live_thread, args=(data,))
+    thread.start()
 
-        if "client_id" in json_data:
-            extra_data["client_id"] = json_data["client_id"]
-        if valid[0]:
-            prompt_id = str(uuid.uuid4())
-            outputs_to_execute = valid[2]
-            p_intance.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
-            response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
-            return web.json_response(response)
-        else:
-            logging.warning("invalid prompt: {}".format(valid[1]))
-            return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
-    else:
-        return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
+    return web.json_response(data)
 
 
-# AR页面
-# @routes.get('/mixlab/AR')
-async def handle_ar_page(request):
-    html_file = os.path.join(current_path, "web/ar.html")
+@routes.get('/mixlab/live')
+async def mixlab_live_handler(request):
+    html_file = os.path.join(current_path, "web/live.html")
     if os.path.exists(html_file):
         with open(html_file, 'r', encoding='utf-8', errors='ignore') as f:
             html_data = f.read()
@@ -979,36 +906,33 @@ async def handle_ar_page(request):
     else:
         return web.Response(text="HTML file not found", status=404)
 
-
 # 重启服务
 @routes.post('/mixlab/re_start')
 def re_start(request):
-    p_intance=PromptServer.instance
     try:
-        p_intance.prompt_queue.set_flag("free_memory", True)
         sys.stdout.close_log()
     except Exception as e:
         pass
     return os.execv(sys.executable, [sys.executable] + sys.argv)
 
-# 状态
-@routes.get('/mixlab/status')
-def mix_status(request):
-    return web.Response(text="running#"+_URL_)
+
 
 # 导入节点
 from .nodes.PromptNode import GLIGENTextBoxApply_Advanced,EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
-from .nodes.ImageNode import DepthViewer_,ImageBatchToList_,ImageListToBatch_,ComparingTwoFrames,LoadImages_,CompositeImages,GridDisplayAndSave,GridInput,ImagesPrompt,SaveImageAndMetadata,SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.ImageNode import ComparingTwoFrames,LoadImages_,CompositeImages,GridDisplayAndSave,GridInput,ImagesPrompt,SaveImageAndMetadata,SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 # from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
 
-from .nodes.Audio import AudioPlayNode,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import KeyInput,IncrementingListNode,ListSplit,CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
+from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
+from .nodes.Utils import IncrementingListNode,ListSplit,CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
 from .nodes.Mask import PreviewMask_,MaskListReplace,MaskListMerge,OutlineMask,FeatheredMask
 
 from .nodes.Style import ApplyVisualStylePrompting,StyleAlignedReferenceSampler,StyleAlignedBatchAlign,StyleAlignedSampleReferenceLatents
 
-from .nodes.P5 import P5Input
+from .nodes.Video import VideoCombine_Adv,LoadVideoAndSegment,ImageListReplace,VAEEncodeForInpaint_Frames
+
+from .nodes.TripoSR import LoadTripoSRModel,TripoSRSampler,SaveTripoSRMesh
 
 
 # 要导出的所有节点及其名称的字典
@@ -1039,10 +963,7 @@ NODE_CLASS_MAPPINGS = {
     "ImageColorTransfer":ImageColorTransfer,
     "ShowLayer":ShowLayer,
     "NewLayer":NewLayer,
-    "ImageListToBatch_":ImageListToBatch_,
-    "ImageBatchToList_":ImageBatchToList_,
     "CompositeImages_":CompositeImages,
-    "DepthViewer": DepthViewer_,
     "SplitImage":SplitImage,
     "CenterImage":CenterImage,
     "GridOutput":GridOutput,
@@ -1063,10 +984,12 @@ NODE_CLASS_MAPPINGS = {
     # "VAEDecodeConsistencyDecoder":VAEDecode,
     "ScreenShare":ScreenShareNode,
     "FloatingVideo":FloatingVideo,
-   
+    "ChatGPTOpenAI":ChatGPTNode,
+    "ShowTextForGPT":ShowTextForGPT,
+    "CharacterInText":CharacterInText,
+    "TextSplitByDelimiter":TextSplitByDelimiter,
     "SpeechRecognition":SpeechRecognition,
     "SpeechSynthesis":SpeechSynthesis,
-    "KeyInput":KeyInput,
     "Color":ColorInput,
     "FloatSlider":FloatSlider,
     "IntNumber":IntNumber,
@@ -1088,26 +1011,26 @@ NODE_CLASS_MAPPINGS = {
     "ApplyVisualStylePrompting_":ApplyVisualStylePrompting,
     "StyleAlignedReferenceSampler_": StyleAlignedReferenceSampler,
     "StyleAlignedSampleReferenceLatents_": StyleAlignedSampleReferenceLatents,
-    "StyleAlignedBatchAlign_": StyleAlignedBatchAlign, 
+    "StyleAlignedBatchAlign_": StyleAlignedBatchAlign,
+    "LoadVideoAndSegment_":LoadVideoAndSegment,
+    "VideoCombine_Adv":VideoCombine_Adv,
     "ListSplit_":ListSplit,
-    "MaskListReplace_":MaskListReplace, 
+    "MaskListReplace_":MaskListReplace,
+    "ImageListReplace_":ImageListReplace,
+    "VAEEncodeForInpaint_Frames":VAEEncodeForInpaint_Frames,
     "IncrementingListNode_":IncrementingListNode,
     "PreviewMask_":PreviewMask_,
-    "AudioPlay":AudioPlayNode,
-
-    "P5Input":P5Input
+     "LoadTripoSRModel_": LoadTripoSRModel,
+    "TripoSRSampler_": TripoSRSampler,
+    "SaveTripoSRMesh": SaveTripoSRMesh
+    # "GamePal":GamePal
 }
 
 # 一个包含节点友好/可读的标题的字典
 NODE_DISPLAY_NAME_MAPPINGS = {
     "AppInfo":"App Info ♾️MixlabApp",
-    "ScreenShare":"Screen Share ♾️Mixlab",
-    "FloatingVideo":"Floating Video ♾️Mixlab",
-    "TextImage":"Text Image ♾️Mixlab",
-    
     "Color":"Color Input ♾️MixlabApp",
     "TextInput_":"Text Input ♾️MixlabApp",
-    "KeyInput":"API Key Input ♾️MixlabApp",
     "FloatSlider":"Float Slider Input ♾️MixlabApp",
     "IntNumber":"Int Input ♾️MixlabApp",
     "ImagesPrompt_":"Images Input ♾️MixlabApp",
@@ -1119,14 +1042,14 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "SplitLongMask":"Splitting a long image into sections",
     "VAELoaderConsistencyDecoder":"Consistency Decoder Loader",
     "VAEDecodeConsistencyDecoder":"Consistency Decoder Decode",
-    
-    
+    "ScreenShare":"Screen Share ♾️Mixlab",
+    "FloatingVideo":"FloatingVideo ♾️Mixlab",
+    "ChatGPTOpenAI":"ChatGPT & Local LLM ♾️Mixlab",
+    "ShowTextForGPT":"Show Text ♾️MixlabApp",
     "MergeLayers":"Merge Layers ♾️Mixlab",
     "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
     "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
     "3DImage":"3DImage ♾️Mixlab",
-    "ImageListToBatch_":"Image List To Batch",
-    "ImageBatchToList_":"Image Batch To List",
     "CompositeImages_":"Composite Images ♾️Mixlab",
     "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
     "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
@@ -1152,69 +1075,28 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "GridInput":"Grid Input ♾️Mixlab",
     "GridOutput":"Grid Output ♾️Mixlab",
     "GetImageSize_":"Get Image Size ♾️Mixlab",
+    "VAEEncodeForInpaint_Frames":"VAE Encode For Inpaint Frames ♾️Mixlab",
     "IncrementingListNode_":"Create Incrementing Number List ♾️Mixlab",
     "LoadImagesToBatch":"Load Images(base64) ♾️Mixlab",
     "PreviewMask_":"Preview Mask",
-    "AudioPlay":"Preview Audio ♾️Mixlab",
-
-     "MultiplicationNode":"Math Operation ♾️Mixlab",
-
-     "P5Input":"P5 Input ♾️Mixlab for test"
+    "LoadTripoSRModel_": "Load TripoSR Model",
+    "TripoSRSampler_": "TripoSR Sampler",
+    "SaveTripoSRMesh": "Save TripoSR Mesh"
 }
 
 # web ui的节点功能
 WEB_DIRECTORY = "./web"
 
+
 logging.info('--------------')
 logging.info('\033[91m ### Mixlab Nodes: \033[93mLoaded')
 # print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
 
 try:
-    from .nodes.ChatGPT import JsonRepair,ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter,SiliconflowFreeNode
-    logging.info('ChatGPT.available True')
-
-    NODE_CLASS_MAPPINGS_V = { 
-       "ChatGPTOpenAI":ChatGPTNode,
-       "SiliconflowLLM":SiliconflowFreeNode,
-        "ShowTextForGPT":ShowTextForGPT,
-        "CharacterInText":CharacterInText,
-        "TextSplitByDelimiter":TextSplitByDelimiter,
-        "JsonRepair":JsonRepair
-    }
-
-    # 一个包含节点友好/可读的标题的字典
-    NODE_DISPLAY_NAME_MAPPINGS_V = { 
-        "ChatGPTOpenAI":"ChatGPT & Local LLM ♾️Mixlab",
-        "SiliconflowLLM":"LLM Siliconflow ♾️Mixlab",
-        "ShowTextForGPT":"Show Text ♾️MixlabApp",
-        "CharacterInText":"Character In Text",
-        "TextSplitByDelimiter":"Text Split By Delimiter",
-        "JsonRepair":"Json Repair"
-    }
-
-
-    NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_V)
-    NODE_DISPLAY_NAME_MAPPINGS.update(NODE_DISPLAY_NAME_MAPPINGS_V)
-
-except Exception as e:
-    logging.info('ChatGPT.available False')
-
-
-try:
-    from .nodes.edit_mask import EditMask
-    logging.info('edit_mask.available True')
-    NODE_CLASS_MAPPINGS['EditMask']=EditMask
-    NODE_DISPLAY_NAME_MAPPINGS['EditMask']="Edit Mask ♾️Mixlab"
-except Exception as e:
-    logging.info('edit_mask.available False')
-
-try:
-    is_has=is_installed('simple_lama_inpainting',None,False)
-    if is_has:
-        from .nodes.Lama import LaMaInpainting
-        logging.info('LaMaInpainting.available {}'.format(LaMaInpainting.available))
-        if LaMaInpainting.available:
-            NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
+    from .nodes.Lama import LaMaInpainting
+    logging.info('LaMaInpainting.available {}'.format(LaMaInpainting.available))
+    if LaMaInpainting.available:
+        NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
 except Exception as e:
     logging.info('LaMaInpainting.available False')
 
@@ -1245,68 +1127,4 @@ try:
 except Exception as e:
     logging.info('RembgNode_.available False' )
 
-
-try:
-    from .nodes.Video import GenerateFramesByCount,scenesNode_,CombineAudioVideo,VideoCombine_Adv,LoadVideoAndSegment,ImageListReplace,VAEEncodeForInpaint_Frames,LoadAndCombinedAudio_
-    
-    NODE_CLASS_MAPPINGS_V = {
-        "VAEEncodeForInpaint_Frames":VAEEncodeForInpaint_Frames,
-        "ImageListReplace_":ImageListReplace,
-        "LoadVideoAndSegment_":LoadVideoAndSegment,
-        "VideoCombine_Adv":VideoCombine_Adv,
-        "LoadAndCombinedAudio_":LoadAndCombinedAudio_,
-        "CombineAudioVideo":CombineAudioVideo,
-        "ScenesNode_":scenesNode_,
-        "GenerateFramesByCount":GenerateFramesByCount 
-    }
-
-    # 一个包含节点友好/可读的标题的字典
-    NODE_DISPLAY_NAME_MAPPINGS_V = {
-        "VAEEncodeForInpaint_Frames":"VAE Encode For Inpaint Frames ♾️Mixlab",
-        "ImageListReplace_":"Image List Replace",
-        "LoadVideoAndSegment_":"Load Video And Segment",
-        "VideoCombine_Adv":"Video Combine",
-        "LoadAndCombinedAudio_":"Load And Combined Audio",
-        "CombineAudioVideo":"Combine Audio Video",
-        "ScenesNode_":"Select Scene", 
-        "GenerateFramesByCount":"Generate Frames By Count"
-    }
-
-
-    NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_V)
-    NODE_DISPLAY_NAME_MAPPINGS.update(NODE_DISPLAY_NAME_MAPPINGS_V)
-
-except:
-    logging.info('Video.available False')
-
-
-try:
-    from .nodes.TripoSR import LoadTripoSRModel,TripoSRSampler,SaveTripoSRMesh
-    logging.info('TripoSR.available')
-    # logging.info( folder_paths.get_temp_directory())
-    NODE_CLASS_MAPPINGS['LoadTripoSRModel_']=LoadTripoSRModel
-    NODE_DISPLAY_NAME_MAPPINGS["LoadTripoSRModel_"]= "Load TripoSR Model"
-    
-    NODE_CLASS_MAPPINGS['TripoSRSampler_']=TripoSRSampler
-    NODE_DISPLAY_NAME_MAPPINGS["TripoSRSampler_"]= "TripoSR Sampler"
-
-    NODE_CLASS_MAPPINGS['SaveTripoSRMesh']=SaveTripoSRMesh
-    NODE_DISPLAY_NAME_MAPPINGS["SaveTripoSRMesh"]= "Save TripoSR Mesh"
-    
-
-except Exception as e:
-    logging.info('TripoSR.available False' )
-
-from .nodes.MiniCPMNode import MiniCPM_VQA_Simple
-try:
-    
-    logging.info('MiniCPMNode.available')
-    # logging.info( folder_paths.get_temp_directory())
-    NODE_CLASS_MAPPINGS['MiniCPM_VQA_Simple']=MiniCPM_VQA_Simple
-    NODE_DISPLAY_NAME_MAPPINGS["MiniCPM_VQA_Simple"]= "MiniCPM VQA Simple"
-    
-except Exception as e:
-    logging.info('MiniCPMNode.available False' )
-
-
 logging.info('\033[93m -------------- \033[0m')

install.bat

@@ -11,9 +11,9 @@ if exist "%python_exec%" (
         %python_exec% -s -m pip install "%%i" -i https://pypi.tuna.tsinghua.edu.cn/simple
     )
 
-    @REM %python_exec% -s -m pip install --upgrade --force llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
+    %python_exec% -s -m pip install --upgrade --force llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
 
-    @REM %python_exec% -s -m pip install --upgrade --force llama-cpp-python[server]
+    %python_exec% -s -m pip install --upgrade --force llama-cpp-python[server]
     
 
 ) else (

nodes/Audio.py

@@ -1,7 +1,6 @@
 
-import os
-import folder_paths
-import torchaudio
+
+
 
 class SpeechRecognition:
     @classmethod
@@ -56,65 +55,46 @@ class SpeechSynthesis:
         return {"ui": {"text": text}, "result": (text,)}
     
 
-
-class AudioPlayNode:
-    def __init__(self):
-        self.output_dir = folder_paths.get_temp_directory()
-        self.type = "temp"
-        self.prefix_append = ""
-        self.compress_level = 4
+#
+class GamePal:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {
-            "audio": ("AUDIO",),
-              }, 
-                }
-    
-    RETURN_TYPES = ()
-  
+        return {
+            "required": {
+                "input_text": ("STRING",{"multiline": True,"default": ""}),
+            },
+            "optional": {
+                 
+                "input_num": ("INT",{
+                                "default":100, 
+                                "min": -1, #Minimum value
+                                "max": 0xffffffffffffffff, #Maximum value
+                                "step": 1, #Slider's step
+                                "display": "slider" # Cosmetic only: display as "number" or "slider"
+                                }), 
+                "python_code": ("STRING",{"multiline": True,"default": "result= 1 if 'Mixlab' in input_text else 0"}),
+            }
+        }
+
+    INPUT_IS_LIST = False
+    RETURN_TYPES = ("INT",)
     FUNCTION = "run"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (False,)
 
     CATEGORY = "♾️Mixlab/Audio"
 
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = ()
+    def run(self, input_text,input_num,python_code):
+        exec(python_code)
+        res=None
+        try:
+            # 可能会引发异常的代码
+            res=result
+        except:
+            # 处理异常的代码
+            print('')
 
-    OUTPUT_NODE = True
-  
-    def run(self,audio):
+        print(res)
 
-        # 判断是否是 Tensor 类型
-        is_tensor = not isinstance(audio, dict)
-        # print('#判断是否是 Tensor 类型',is_tensor,audio)
-        if not is_tensor and 'waveform' in audio and 'sample_rate' in audio:
-            # {'waveform': tensor([], size=(1, 1, 0)), 'sample_rate': 44100}
-            is_tensor=True
-
-        if is_tensor and (not 'audio_path' in audio):
-            filename_prefix=""
-            # 保存
-            filename_prefix += self.prefix_append
-            full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
-            results = list()
-            
-            filename_with_batch_num = filename.replace("%batch_num%", str(1))
-            file = f"{filename_with_batch_num}_{counter:05}_.wav"
-            
-            torchaudio.save(os.path.join(full_output_folder, file), audio['waveform'].squeeze(0), audio["sample_rate"])
-            results.append({
-                    "filename": file,
-                    "subfolder": subfolder,
-                    "type": self.type
-                })
-            
-        else:
-            results=[{
-                    "filename": audio['filename'],
-                    "subfolder":audio['subfolder'],
-                    "type": audio['type'],
-                    "audio_path":audio['audio_path']
-                }]
-                
-
-        # print(audio)
-        return {"ui": {"audio":results}}
+        # print(session_history)
+        return {"ui": {"text": [input_text],"num":[input_num]}, "result": (res,)}

nodes/ChatGPT.py

@@ -6,69 +6,14 @@ import folder_paths
 import hashlib
 import codecs,sys
 import importlib.util
-import subprocess
-
-python = sys.executable
-
-# 从文本中提取json
-def extract_json_strings(text):
-    json_strings = []
-    brace_level = 0
-    json_str = ''
-    in_json = False
-    
-    for char in text:
-        if char == '{':
-            brace_level += 1
-            in_json = True
-        if in_json:
-            json_str += char
-        if char == '}':
-            brace_level -= 1
-        if in_json and brace_level == 0:
-            json_strings.append(json_str)
-            json_str = ''
-            in_json = False
-
-    return json_strings[0] if len(json_strings)>0 else "{}"
 
 
-def is_installed(package, package_overwrite=None,auto_install=True):
-    is_has=False
+def is_installed(package):
     try:
         spec = importlib.util.find_spec(package)
-        is_has=spec is not None
     except ModuleNotFoundError:
-        pass
-
-    package = package_overwrite or package
-
-    if spec is None:
-        if auto_install==True:
-            print(f"Installing {package}...")
-            # 清华源 -i https://pypi.tuna.tsinghua.edu.cn/simple
-            command = f'"{python}" -m pip install {package}'
-    
-            result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ)
-
-            is_has=True
-
-            if result.returncode != 0:
-                print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
-                is_has=False
-    else:
-        print(package+'## OK')
-
-    return is_has
-  
-
-
-# def is_installed(package):
-#     try:
-#         spec = importlib.util.find_spec(package)
-#     except ModuleNotFoundError:
-#         return False
-#     return spec is not None
+        return False
+    return spec is not None
 
 
 def get_unique_hash(string):
@@ -108,14 +53,30 @@ def azure_client(key,url):
 
 def openai_client(key,url):
     client = openai.OpenAI(
-        api_key=key,
-        base_url=url
+    api_key=key,
+    base_url=url
     )
     return client
 
 def ZhipuAI_client(key):
+
     try:
-        if is_installed('zhipuai')==True:
+        if is_installed('zhipuai')==False:
+            import subprocess
+
+            # 安装
+            print('#pip install zhipuai')
+
+            result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'zhipuai'], capture_output=True, text=True)
+
+            #检查命令执行结果
+            if result.returncode == 0:
+                print("#install success")
+                from zhipuai import ZhipuAI
+            else:
+                print("#install error")
+            
+        else:
             from zhipuai import ZhipuAI
     except:
         print("#install zhipuai error")
@@ -136,76 +97,73 @@ def get_llama_path():
     except:
         return os.path.join(folder_paths.models_dir, "llamafile")
 
-# def get_llama_models():
-#     res=[]
+def get_llama_models():
+    res=[]
 
-#     model_path=get_llama_path()
-#     if os.path.exists(model_path):
-#         files = os.listdir(model_path)
-#         for file in files:
-#             if os.path.isfile(os.path.join(model_path, file)):
-#                 res.append(file)
-#         res=phi_sort(res)
-#     return res
+    model_path=get_llama_path()
+    if os.path.exists(model_path):
+        files = os.listdir(model_path)
+        for file in files:
+            if os.path.isfile(os.path.join(model_path, file)):
+                res.append(file)
+        res=phi_sort(res)
+    return res
 
-# llama_modes_list=get_llama_models()
-# llama_modes_list=[]
+llama_modes_list=get_llama_models()
 
-# def get_llama_model_path(file_name):
-#     model_path=get_llama_path()
-#     mp=os.path.join(model_path,file_name)
-#     return mp
+def get_llama_model_path(file_name):
+    model_path=get_llama_path()
+    mp=os.path.join(model_path,file_name)
+    return mp
 
-# def llama_cpp_client(file_name):
-#     try:
-#         if is_installed('llama_cpp')==False:
-#             import subprocess
+def llama_cpp_client(file_name):
+    try:
+        if is_installed('llama_cpp')==False:
+            import subprocess
 
-#             # 安装
-#             print('#pip install llama-cpp-python')
+            # 安装
+            print('#pip install llama-cpp-python')
  
-#             result = subprocess.run([sys.executable, '-s', '-m', 'pip', 
-#                                      'install', 
-#                                      'llama-cpp-python',
-#                                      '--extra-index-url',
-#                                      'https://abetlen.github.io/llama-cpp-python/whl/cu121'
-#                                      ], capture_output=True, text=True)
+            result = subprocess.run([sys.executable, '-s', '-m', 'pip', 
+                                     'install', 
+                                     'llama-cpp-python',
+                                     '--extra-index-url',
+                                     'https://abetlen.github.io/llama-cpp-python/whl/cu121'
+                                     ], capture_output=True, text=True)
 
-#             #检查命令执行结果
-#             if result.returncode == 0:
-#                 print("#install success")
-#                 from llama_cpp import Llama
+            #检查命令执行结果
+            if result.returncode == 0:
+                print("#install success")
+                from llama_cpp import Llama
 
-#                 subprocess.run([sys.executable, '-s', '-m', 'pip', 
-#                                      'install', 
-#                                      'llama-cpp-python[server]'
-#                                      ], capture_output=True, text=True)
+                subprocess.run([sys.executable, '-s', '-m', 'pip', 
+                                     'install', 
+                                     'llama-cpp-python[server]'
+                                     ], capture_output=True, text=True)
 
-#             else:
-#                 print("#install error")
+            else:
+                print("#install error")
             
-#         else:
-#             from llama_cpp import Llama
-#     except:
-#         print("#install llama-cpp-python error")
+        else:
+            from llama_cpp import Llama
+    except:
+        print("#install llama-cpp-python error")
 
-#     if file_name:
-#         mp=get_llama_model_path(file_name)
-#         # file_name=get_llama_models()[0]
-#         # model_path=os.path.join(folder_paths.models_dir, "llamafile")
-#         # mp=os.path.join(model_path,file_name)
+    if file_name:
+        mp=get_llama_model_path(file_name)
+        # file_name=get_llama_models()[0]
+        # model_path=os.path.join(folder_paths.models_dir, "llamafile")
+        # mp=os.path.join(model_path,file_name)
 
-#         llm = Llama(model_path=mp, chat_format="chatml",n_gpu_layers=-1,n_ctx=512)
+        llm = Llama(model_path=mp, chat_format="chatml",n_gpu_layers=-1,n_ctx=512)
 
-#         return llm
+        return llm
 
-
-if is_installed('json_repair'):
-    from json_repair import repair_json
+    
 
 
 def chat(client, model_name,messages ):
-        print('#chat',model_name,messages)
+
         try_count = 0
         while True:
             try_count += 1
@@ -248,36 +206,6 @@ def chat(client, model_name,messages ):
         return content
 
 
-llm_apis=[
-            {
-                "value": "https://api.openai.com/v1",
-                "label": "openai"
-            },
-            {
-                "value": "https://openai.api2d.net/v1",
-                "label": "api2d"
-            },
-            # {
-            #     "value": "https://docs-test-001.openai.azure.com",
-            #     "label": "https://docs-test-001.openai.azure.com"
-            # },
-             
-            {
-                "value": "https://api.moonshot.cn/v1",
-                "label": "Kimi"
-            },
-            {
-                "value": "https://api.deepseek.com/v1",
-                "label": "DeepSeek-V2"
-            },
-            {
-                "value": "https://api.siliconflow.cn/v1",
-                "label": "SiliconCloud"
-            }]
-        
-llm_apis_dict = {api["label"]: api["value"] for api in llm_apis}
-
-
 class ChatGPTNode:
     def __init__(self):
         # self.__client = OpenAI()
@@ -287,60 +215,35 @@ class ChatGPTNode:
 
     @classmethod
     def INPUT_TYPES(cls):
-
-        model_list=[ 
-            "gpt-3.5-turbo",
-            "gpt-3.5-turbo-16k",
-            "gpt-4o",
-            "gpt-4o-2024-05-13",
-            "gpt-4",
-            "gpt-4-0314",
-            "gpt-4-0613",
-            "gpt-3.5-turbo-0301",
-            "gpt-3.5-turbo-0613",
-            "gpt-3.5-turbo-16k-0613",
-            "qwen-turbo",
-            "qwen-plus",
-            "qwen-long",
-            "qwen-max",
-            "qwen-max-longcontext",
-            "glm-4",
-            "glm-3-turbo",
-            "moonshot-v1-8k",
-            "moonshot-v1-32k",
-            "moonshot-v1-128k",
-            "deepseek-chat",
-            "Qwen/Qwen2-7B-Instruct",
-            "THUDM/glm-4-9b-chat",
-            "01-ai/Yi-1.5-9B-Chat-16K",
-            "meta-llama/Meta-Llama-3.1-8B-Instruct"
+        model_list=llama_modes_list+[
+                    "gpt-3.5-turbo",
+                    "gpt-3.5-turbo-0125",
+                    "gpt-35-turbo",
+                    "gpt-3.5-turbo-16k", 
+                    "gpt-3.5-turbo-16k-0613", 
+                    "gpt-4-0613",
+                    "gpt-4-1106-preview",
+                    "glm-4"
                     ]
-        
         return {
             "required": {
-                # "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
-                # "api_key":("STRING", {"forceInput": True,}),
-               
+                "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
+                "api_url":("URL", {"default": "", "multiline": True,"dynamicPrompts": False}),
                 "prompt": ("STRING", {"multiline": True,"dynamicPrompts": False}),
                 "system_content": ("STRING", 
                                    {
                                        "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
                                        "multiline": True,"dynamicPrompts": False
                                        }),
-                 
                 "model": ( model_list, 
                     {"default": model_list[0]}),
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
                 "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
-                "api_url":(list(llm_apis_dict.keys()), 
-                    {"default": list(llm_apis_dict.keys())[0]}),
             },
-             "optional":{
-                    "api_key":("STRING", {"forceInput": True,}),
-                    "custom_model_name":("STRING", {"forceInput": True,}), #适合自定义model
-                     "custom_api_url":("STRING", {"forceInput": True,}), #适合自定义model
-                },
-             
+             "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
         }
 
     RETURN_TYPES = ("STRING","STRING","STRING",)
@@ -352,29 +255,12 @@ class ChatGPTNode:
 
     
     def generate_contextual_text(self,
-                                #  api_key,
+                                 api_key,
+                                 api_url, 
                                  prompt, 
                                  system_content,
-                                 model, 
-                                seed,
-                                context_size,
-                                api_url,
-                                api_key=None,
-                                custom_model_name=None,
-                                custom_api_url=None,
-                                ):
-        
-        if custom_model_name!=None:
-            model=custom_model_name
-
-        api_url=llm_apis_dict[api_url] if api_url in llm_apis_dict else ""
-
-        if custom_api_url!=None:
-            api_url=custom_api_url
-
-        if api_key==None:
-            api_key="lm_studio"
-
+                                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()
@@ -387,7 +273,7 @@ class ChatGPTNode:
             self.system_content=system_content
             # self.session_history=[]
             # self.session_history.append({"role": "system", "content": system_content})
-        print("api_key,api_url",api_key,api_url)
+        
         # 
         if is_azure_url(api_url):
             client=azure_client(api_key,api_url)
@@ -396,12 +282,12 @@ class ChatGPTNode:
             if model == "glm-4" :
                 client = ZhipuAI_client(api_key)  # 使用 Zhipuai 的接口
                 print('using Zhipuai interface')
-            # elif model in llama_modes_list:
-            #     #
-            #     client=llama_cpp_client(model)
+            elif model in llama_modes_list:
+                #
+                client=llama_cpp_client(model)
             else :
                 client = openai_client(api_key,api_url)  # 使用 ChatGPT  的接口
-                # print('using ChatGPT interface',api_key,api_url)
+                print('using ChatGPT interface')
 
         # 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
@@ -417,7 +303,6 @@ class ChatGPTNode:
         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}]
@@ -438,93 +323,6 @@ class ChatGPTNode:
         return (response_content,json.dumps(messages, indent=4),json.dumps(self.session_history, indent=4),)
 
 
-class SiliconflowFreeNode:
-    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):
-        model_list= [ 
-            "Qwen/Qwen2-7B-Instruct",
-            "THUDM/glm-4-9b-chat",
-            "01-ai/Yi-1.5-9B-Chat-16K",
-            "meta-llama/Meta-Llama-3.1-8B-Instruct"
-            ]
-        return {
-            "required": {
-                "api_key":("STRING", {"forceInput": 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,"dynamicPrompts": False
-                                       }),
-                "model": ( model_list, 
-                    {"default": model_list[0]}),
-                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
-                "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
-            },
-               "optional":{
-                    "custom_model_name":("STRING", {"forceInput": True,}), #适合自定义model
-                }, 
-        }
-
-    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,
-                                 prompt, 
-                                 system_content,
-                                model, 
-                                seed,context_size,custom_model_name=None):
-
-        if custom_model_name!=None:
-            model=custom_model_name
-
-        api_url="https://api.siliconflow.cn/v1"
-        
-        # 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})
-        
-        # 
-        client = openai_client(api_key,api_url)  # 使用 ChatGPT  的接口
-        # print('using ChatGPT interface',api_key,api_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}]
-
-        return (response_content,json.dumps(messages, indent=4),json.dumps(self.session_history, indent=4),)
-
-
-
 
 class ShowTextForGPT:
     @classmethod
@@ -686,41 +484,3 @@ class TextSplitByDelimiter:
         arr= arr[start_index:start_index + max_count * (skip_every+1):(skip_every+1)]
 
         return (arr,)
-
-
-class JsonRepair:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "json_string":("STRING", {"forceInput": True,}), 
-                 "key":("STRING", {"multiline": False,"dynamicPrompts": False,"default": ""}),
-            }
-        }
-
-    INPUT_IS_LIST = False
-    RETURN_TYPES = ("STRING","STRING",)
-    RETURN_NAMES = ("json_string","value",)
-    FUNCTION = "run"
-    # OUTPUT_NODE = True
-    OUTPUT_IS_LIST = (False,False,)
-
-    CATEGORY = "♾️Mixlab/GPT"
-
-    def run(self, json_string,key=""):
-
-        json_string=extract_json_strings(json_string)
-        # print(json_string)
-        good_json_string = repair_json(json_string)
-
-        # 将 JSON 字符串解析为 Python 对象
-        data = json.loads(good_json_string)
-
-        v=""
-        if key!="" and (key in data):
-            v=data[key]
-
-        # 将 Python 对象转换回 JSON 字符串，确保中文字符不被转义
-        json_str_with_chinese = json.dumps(data, ensure_ascii=False)
-
-        return (json_str_with_chinese,v,)

nodes/ClipInterrogator.py

@@ -79,7 +79,7 @@ def get_clip_interrogator_path():
 
 cache_path=get_clip_interrogator_path()
 
-caption_model_path=os.path.join(cache_path, "Salesforce","blip-image-captioning-base")
+caption_model_path=os.path.join(cache_path, "Salesforce/blip-image-captioning-base")
 if not os.path.exists(caption_model_path):
     print(f"## clip_interrogator_model not found: {caption_model_path}, pls download from https://huggingface.co/Salesforce/blip-image-captioning-base")
     caption_model_path='Salesforce/blip-image-captioning-base'

nodes/ImageNode.py

@@ -1,14 +1,12 @@
 import numpy as np
 import requests
 import torch
-import torchvision.transforms.v2 as T
 # from PIL import Image, ImageDraw
 from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
 from PIL.PngImagePlugin import PngInfo
 import base64,os,random
 from io import BytesIO
 import folder_paths
-import node_helpers
 import json,io
 import comfy.utils
 from comfy.cli_args import args
@@ -16,8 +14,9 @@ import cv2
 import string
 import math,glob
 from .Watcher import FolderWatcher
+import hashlib
+
 
-from itertools import product
 
 # 将PIL图片转换为OpenCV格式
 def pil_to_opencv(image):
@@ -29,93 +28,140 @@ def opencv_to_pil(image):
     pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
     return pil_image
 
-# 列出目录下面的所有文件
-def get_files_with_extension(directory, extensions):
-    file_list = []
-    # 确保extensions参数是一个list，即使只有一个元素
-    if not isinstance(extensions, (tuple, list)):
-        extensions = [extensions]
-    for root, dirs, files in os.walk(directory):
-        # print(f"Files at {root}: {files}")  # 确认files是一个字符串列表
-        for file in files:
-            # 检查文件是否以任何一个提供的扩展名结尾
-            if any(file.endswith(ext) for ext in extensions):
-                # 直接将文件名添加到列表中
-                file_list.append(file)
-    return file_list
-def composite_images(foreground, background, mask, is_multiply_blend=False, position="overall", scale=0.25):
-    width, height = foreground.size
-    bg_image = background
-    bwidth, bheight = bg_image.size
 
-    scale = max(scale, 1 / bwidth)
-    scale = max(scale, 1 / bheight)
+def composite_images(foreground, background, mask,is_multiply_blend=False,position="overall"):
+    width,height=foreground.size
+ 
+    bg_image=background
 
-    def determine_scale_option(width, height):
-        return 'height' if height > width else 'width'
+    bwidth,bheight=bg_image.size
 
-    if position == "overall":
+    # 按z-index排序
+    if position=="overall":
         layer = {
-            "x": 0,
-            "y": 0,
-            "width": bwidth,
-            "height": bheight,
-            "z_index": 88,
-            "scale_option": 'overall',
-            "image": foreground,
-            "mask": mask
+                "x":0,
+                "y":0,
+                "width":bwidth,
+                "height":bheight,
+                "z_index":88,
+                "scale_option":'overall',
+                "image":foreground,
+                "mask":mask
         }
-    else:
-        scale_option = determine_scale_option(width, height)
-        if scale_option == 'height':
-            scale = bheight * scale / height
-        else:
-            scale = bwidth * scale / width
 
-        new_width = int(width * scale)
+    elif position=='center_bottom':
+        
+        scale = int(bwidth*0.25) / width
         new_height = int(height * scale)
 
-        if position == 'center_bottom':
-            x_position = int((bwidth - new_width) * 0.5)
-            y_position = bheight - new_height - 24
-        elif position == 'right_bottom':
-            x_position = bwidth - new_width - 24
-            y_position = bheight - new_height - 24
-        elif position == 'center_top':
-            x_position = int((bwidth - new_width) * 0.5)
-            y_position = 24
-        elif position == 'right_top':
-            x_position = bwidth - new_width - 24
-            y_position = 24
-        elif position == 'left_top':
-            x_position = 24
-            y_position = 24
-        elif position == 'left_bottom':
-            x_position = 24
-            y_position = bheight - new_height - 24
-        elif position == 'center_center':
-            x_position = int((bwidth - new_width) * 0.5)
-            y_position = int((bheight - new_height) * 0.5)
-
         layer = {
-            "x": x_position,
-            "y": y_position,
-            "width": new_width,
-            "height": new_height,
-            "z_index": 88,
-            "scale_option": scale_option,
-            "image": foreground,
-            "mask": mask
+                "x":int(bwidth*0.75*0.5),
+                "y":bheight-new_height-24,
+                "width":int(bwidth*0.25),
+                "height":int(bheight*0.25),
+                "z_index":88,
+                "scale_option":'width',
+                "image":foreground,
+                "mask":mask
         }
 
-    # Resize the foreground image with antialiasing
-    layer_image = layer['image'].resize((layer['width'], layer['height']), Image.ANTIALIAS)
-    layer_mask = layer['mask'].resize((layer['width'], layer['height']), Image.ANTIALIAS)
+    elif position=='right_bottom':
+        
+        scale = int(bwidth*0.25) / width
+        new_height = int(height * scale)
 
-    bg_image.paste(layer_image, (layer['x'], layer['y']), layer_mask)
+        layer = {
+                "x":bwidth-int(bwidth*0.25)-24,
+                "y":bheight-new_height-24,
+                "width":int(bwidth*0.25),
+                "height":int(bheight*0.25),
+                "z_index":88,
+                "scale_option":'width',
+                "image":foreground,
+                "mask":mask
+        }
 
-    return bg_image.convert('RGB')
 
+    elif position=='center_top':
+        
+        scale = int(bwidth*0.25) / width
+        new_height = int(height * scale)
+
+        layer = {
+                "x":int( bwidth*0.75*0.5),
+                "y":24,
+                "width":int(bwidth*0.25),
+                "height":int(bheight*0.25),
+                "z_index":88,
+                "scale_option":'width',
+                "image":foreground,
+                "mask":mask
+        }
+
+    elif position=='right_top':
+        
+        scale = int(bwidth*0.25) / width
+        new_height = int(height * scale)
+
+        layer = {
+                "x":bwidth-int(bwidth*0.25)-24,
+                "y":24,
+                "width":int(bwidth*0.25),
+                "height":int(bheight*0.25),
+                "z_index":88,
+                "scale_option":'width',
+                "image":foreground,
+                "mask":mask
+        }
+    elif position=='left_top':
+        
+        scale = int(bwidth*0.25) / width
+        new_height = int(height * scale)
+
+        layer = {
+                "x":24,
+                "y":24,
+                "width":int(bwidth*0.25),
+                "height":int(bheight*0.25),
+                "z_index":88,
+                "scale_option":'width',
+                "image":foreground,
+                "mask":mask
+        }
+    elif position=='left_bottom':
+        
+        scale = int(bwidth*0.25) / width
+        new_height = int(height * scale)
+
+        layer = {
+                "x":24,
+                "y":bheight-new_height-24,
+                "width":int(bwidth*0.25),
+                "height":int(bheight*0.25),
+                "z_index":88,
+                "scale_option":'width',
+                "image":foreground,
+                "mask":mask
+        }
+                
+    # width, height = bg_image.size
+
+    layer_image=layer['image']
+    layer_mask=layer['mask']
+    
+    bg_image=merge_images(bg_image,
+                        layer_image,
+                        layer_mask,
+                        layer['x'],
+                        layer['y'],
+                        layer['width'],
+                        layer['height'],
+                        layer['scale_option'],
+                        is_multiply_blend )
+                   
+    bg_image=bg_image.convert('RGB')
+    
+    return bg_image
 
 
 def count_files_in_directory(directory):
@@ -154,8 +200,7 @@ class AnyType(str):
 any_type = AnyType("*")
 
 
-FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),"..","assets","fonts"))
-
+FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
 
 MAX_RESOLUTION=8192
 
@@ -481,53 +526,6 @@ def load_image(fp,white_bg=False):
         
     return images
 
-
-# 读取图片数据，转成tensor
-def load_image_to_tensor( image):
-    image_path = folder_paths.get_annotated_filepath(image)
-        
-    img = node_helpers.pillow(Image.open, image_path)
-        
-    output_images = []
-    output_masks = []
-    w, h = None, None
-
-    excluded_formats = ['MPO']
-        
-    for i in ImageSequence.Iterator(img):
-        i = node_helpers.pillow(ImageOps.exif_transpose, i)
-
-        if i.mode == 'I':
-            i = i.point(lambda i: i * (1 / 255))
-        image = i.convert("RGB")
-
-        if len(output_images) == 0:
-            w = image.size[0]
-            h = image.size[1]
-            
-        if image.size[0] != w or image.size[1] != h:
-            continue
-            
-        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)
-        else:
-            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-        output_images.append(image)
-        output_masks.append(mask.unsqueeze(0))
-
-    if len(output_images) > 1 and img.format not in excluded_formats:
-        output_image = torch.cat(output_images, dim=0)
-        output_mask = torch.cat(output_masks, dim=0)
-    else:
-        output_image = output_images[0]
-        output_mask = output_masks[0]
-
-    return (output_image, output_mask)
-
-
 def load_image_and_mask_from_url(url, timeout=10):
     # Load the image from the URL
     response = requests.get(url, timeout=timeout)
@@ -804,78 +802,85 @@ def multiply_blend(image1, image2):
 
 # cv2.imwrite('result.jpg', result)
 
-# 使用gpt4o优化代码
-# 为了消除图像合并时出现的灰色描边，可以使用以下方法：
-# 调整透明度：确保透明像素不会引入不需要的颜色。
-# 预处理图像：在缩放图像之前，可以先将图像的边缘进行预处理，例如扩展边缘颜色，减少抗锯齿带来的过渡效果。
 
-def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option, is_multiply_blend=False):
+def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option,is_multiply_blend=False):
     # 打开底图
     bg_image = bg_image.convert("RGBA")
 
     # 打开图层
     layer_image = layer_image.convert("RGBA")
-   
+    # layer_image = layer_image.resize((width, height))
+
     # 根据缩放选项调整图像大小
     if scale_option == "height":
         # 按照高度比例缩放
         original_width, original_height = layer_image.size
         scale = height / original_height
         new_width = int(original_width * scale)
-        layer_image = layer_image.resize((new_width, height), Image.NEAREST)
+        layer_image = layer_image.resize((new_width, height))
     elif scale_option == "width":
         # 按照宽度比例缩放
         original_width, original_height = layer_image.size
         scale = width / original_width
         new_height = int(original_height * scale)
-        layer_image = layer_image.resize((width, new_height), Image.NEAREST)
+        layer_image = layer_image.resize((width, new_height))
     elif scale_option == "overall":
         # 整体缩放
-        layer_image = layer_image.resize((width, height), Image.NEAREST)
+        layer_image = layer_image.resize((width, height))
+
     elif scale_option == "longest":
         original_width, original_height = layer_image.size
         if original_width > original_height:
-            new_width = width
+            new_width=width
             scale = width / original_width
             new_height = int(original_height * scale)
-            x = 0
-            y = int((height - new_height) * 0.5)
+            x=0
+            y=int((height-new_height)*0.5)
         else:
-            new_height = height
+            new_height=height
             scale = height / original_height
             new_width = int(original_height * scale)
-            x = int((width - new_width) * 0.5)
-            y = 0
+            x=int((width-new_width)*0.5)
+            y=0
+    # elif side == "shortest":
+    #         if width < height:
+    #              
+    #         else:
+    #              
+    
 
     # 调整mask的大小
     nw, nh = layer_image.size
-    mask = mask.resize((nw, nh), Image.NEAREST)
+    mask = mask.resize((nw, nh))
 
-    # 预处理图像边缘以减少灰色描边
-    layer_image = layer_image.filter(ImageFilter.SMOOTH)
+    # # 分离出a通道
+    # r, g, b, alpha = layer_image.split()
+    # alpha = ImageOps.invert(alpha)
+    # # 创建一个新的RGB图像
+    # new_rgb_image = Image.new("RGB", layer_image.size)
+    # # 将透明通道粘贴到新的RGB图像上
+    # new_rgb_image.paste(layer_image, (0, 0), mask=alpha)
+   
+    # new_rgb_image.paste(layer_image, (x, y), mask=mask)
+    # mask=new_rgb_image.convert('L')
+    # mask = ImageOps.invert(mask)
 
     if is_multiply_blend:
-        bg_image_white = Image.new("RGB", bg_image.size, (255, 255, 255))
+        bg_image_white=Image.new("RGB", bg_image.size,(255, 255, 255))
 
         bg_image_white.paste(layer_image, (x, y), mask=mask)
-        bg_image = multiply_blend(bg_image_white, bg_image)
-        bg_image = bg_image.convert("RGBA")
+        bg_image=multiply_blend(bg_image_white,bg_image)
+        bg_image=bg_image.convert("RGBA")
     else:
-        transparent_img = Image.new("RGBA", layer_image.size, (255, 255, 255, 0))
-        # 调整透明度处理
-        for i in range(transparent_img.size[0]):
-            for j in range(transparent_img.size[1]):
-                r, g, b, a = transparent_img.getpixel((i, j))
-                if a > 0:
-                    transparent_img.putpixel((i, j), (r, g, b, 255))
-
-        transparent_img.paste(layer_image, (0, 0), mask)
+        transparent_img = Image.new("RGBA",layer_image.size, (255, 255, 255, 0))
+        transparent_img.paste(layer_image,(0, 0), mask)
+        # transparent_img.save('test.png')
         bg_image.paste(transparent_img, (x, y), transparent_img)
 
+
     # 输出合成后的图片
     return bg_image
 
-#MixCopilot
 
 def resize_2(img):
     # 检查图像的高度是否是2的倍数，如果不是，则调整高度
@@ -949,13 +954,53 @@ def resize_image(layer_image, scale_option, width, height,color="white"):
     return layer_image
 
 
-def generate_text_image(text, font_path, font_size, text_color, vertical=True, stroke=False, stroke_color=(0, 0, 0), stroke_width=1, spacing=0, line_spacing=0,padding=4):
+
+
+
+
+# def generate_text_image(text_list, font_path, font_size, text_color, vertical=True, spacing=0):
+#     # Load Chinese font
+#     font = ImageFont.truetype(font_path, font_size)
+
+#     # Calculate image size based on the number of characters and orientation
+#     if vertical:
+#         width = font_size + 100
+#         height = font_size * len(text_list) + (len(text_list) - 1) * spacing + 100
+#     else:
+#         width = font_size * len(text_list) + (len(text_list) - 1) * spacing + 100
+#         height = font_size + 100
+
+#     # Create a blank image
+#     image = Image.new('RGBA', (width, height), (255, 255, 255,0))
+#     draw = ImageDraw.Draw(image)
+
+#     # Draw text
+#     if vertical:
+#         for i, char in enumerate(text_list):
+#             char_position = (50, 50 + i * font_size)
+#             draw.text(char_position, char, font=font, fill=text_color)
+#     else:
+#         for i, char in enumerate(text_list):
+#             char_position = (50 + i * (font_size + spacing), 50)
+#             draw.text(char_position, char, font=font, fill=text_color)
+
+#     # Save the image
+#     # image.save(output_image_path)
+
+#     # 分离alpha通道
+#     alpha_channel = image.split()[3]
+
+#     # 创建一个只有alpha通道的新图像
+#     alpha_image = Image.new('L', image.size)
+#     alpha_image.putdata(alpha_channel.getdata())
+
+#     image=image.convert('RGB')
+
+#     return (image,alpha_image)
+def generate_text_image(text, font_path, font_size, text_color, vertical=True, stroke=False, stroke_color=(0, 0, 0), stroke_width=1, spacing=0):
     # Split text into lines based on line breaks
     lines = text.split("\n")
 
-    # Load font
-    font = ImageFont.truetype(font_path, font_size)
-
     # 1. Determine layout direction
     if vertical:
         layout = "vertical"
@@ -964,54 +1009,49 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s
 
     # 2. Calculate absolute coordinates for each character
     char_coordinates = []
-    x, y = padding, padding
-    max_width, max_height = 0, 0
-
     if layout == "vertical":
-        for line in lines:
-            max_char_width = max(font.getsize(char)[0] for char in line)
+        x = 0
+        y = 0
+        for i in range(len(lines)):
+            line = lines[i]
             for char in line:
-                char_width, char_height = font.getsize(char)
                 char_coordinates.append((x, y))
-                y += char_height + spacing
-                max_height = max(max_height, y + padding)
-            x += max_char_width + line_spacing
-            y = padding
-        max_width = x
-        total_line_width = sum(font.getsize(line)[1] for line in lines)
-        total_spacing = line_spacing * (len(lines) - 1)
-        # 确保左边和右边的padding都被计入max_width
-        max_width = total_line_width + total_spacing + padding * 2
+                y += font_size + spacing
+            x += font_size + spacing
+            y = 0
     else:
+        x = 0
+        y = 0
         for line in lines:
-            line_width, line_height = font.getsize(line)
             for char in line:
-                char_width, char_height = font.getsize(char)
                 char_coordinates.append((x, y))
-                x += char_width + spacing
-                max_width = max(max_width, x + padding)
-            y += line_height + line_spacing
-            x = padding
-        # max_height = y
-        total_line_heights = sum(font.getsize(line)[1] for line in lines)
-        total_spacing = line_spacing * (len(lines) - 1)
-        # 确保顶部和底部的padding都被计入max_height
-        max_height = total_line_heights + total_spacing + padding * 2
+                x += font_size + spacing
+            y += font_size + spacing
+            x = 0
 
-    # 3. Create image with calculated width and height
-    image = Image.new('RGBA', (max_width, max_height), (255, 255, 255, 0))
-    draw = ImageDraw.Draw(image)
+    # 3. Calculate image width and height
+    if layout == "vertical":
+        width = (len(lines) * (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) - 1) * (font_size + spacing)) + font_size
 
     # 4. Draw each character on the image
+    image = Image.new('RGBA', (width, height), (255, 255, 255, 0))
+    draw = ImageDraw.Draw(image)
+    font = ImageFont.truetype(font_path, font_size)
+
     index = 0
-    for line in lines:
-        for char in line:
+    for i, line in enumerate(lines):
+        for j, char in enumerate(line):
             x, y = char_coordinates[index]
+            
             if stroke:
-                draw.text((x-stroke_width, y), char, font=font, fill=text_color)
-                draw.text((x+stroke_width, y), char, font=font, fill=text_color)
-                draw.text((x, y-stroke_width), char, font=font, fill=text_color)
-                draw.text((x, y+stroke_width), char, font=font, fill=text_color)
+                draw.text((x-stroke_width, y), char, font=font, fill=stroke_color)
+                draw.text((x+stroke_width, y), char, font=font, fill=stroke_color)
+                draw.text((x, y-stroke_width), char, font=font, fill=stroke_color)
+                draw.text((x, y+stroke_width), char, font=font, fill=stroke_color)
             
             draw.text((x, y), char, font=font, fill=text_color)
             index += 1
@@ -1336,9 +1376,6 @@ class LoadImages_:
             image=pil2tensor(image)
             ims.append(image)
 
-        if len(ims)==0:
-            image1 = Image.new('RGB', (512, 512), color='black')
-            return (pil2tensor(image1),)
         image1 = ims[0]
         for image2 in ims[1:]:
             if image1.shape[1:] != image2.shape[1:]:
@@ -1379,8 +1416,8 @@ class LoadImagesFromPath:
                     }),
                     "watcher":(["disable","enable"],),
                     "result": ("WATCHER",),#为了激活本节点运行
-                    "prompt": ("PROMPT",),
-                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                     "prompt": ("PROMPT",),
+                    # "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                 }
             }
     
@@ -1398,7 +1435,7 @@ class LoadImagesFromPath:
     watcher_folder=None
 
     # 运行的函数
-    def run(self,file_path,white_bg,newest_files,index_variable,watcher,result,prompt,seed=1):
+    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)
 
@@ -1446,8 +1483,7 @@ class LoadImagesFromPath:
             print("发生了一个未知的错误：", str(e))
 
         # print('#prompt::::',prompt)
-        # return  {"ui": {"seed": [1]}, "result":(imgs,masks,prompt,file_names,)}
-        return  (imgs,masks,prompt,file_names,)
+        return  {"ui": {"seed": [1]}, "result":(imgs,masks,prompt,file_names,)}
 
 
 # TODO 扩大选区的功能,重新输出mask
@@ -1542,7 +1578,7 @@ class ImageCropByAlpha:
 
 
 
-# get_files_with_extension(FONT_PATH,'.ttf')
+
 
 class TextImage:
     @classmethod
@@ -1550,32 +1586,18 @@ class TextImage:
         return {"required": { 
             
                     "text": ("STRING",{"multiline": True,"default": "龍馬精神迎新歲","dynamicPrompts": False}),
-                    "font": (get_files_with_extension(FONT_PATH,['.ttf','.otf']),),#后缀为 ttf
+                    "font_path": ("STRING",{"multiline": False,"default": FONT_PATH,"dynamicPrompts": False}),
                     "font_size": ("INT",{
                                 "default":100, 
-                                "min": 1, #Minimum value
-                                "max": 10000000, #Maximum value
+                                "min": 100, #Minimum value
+                                "max": 1000, #Maximum value
                                 "step": 1, #Slider's step
                                 "display": "number" # Cosmetic only: display as "number" or "slider"
                                 }), 
                     "spacing": ("INT",{
                                 "default":12, 
-                                "min": -2000000000, #Minimum value
-                                "max": 2000000000, #Maximum value
-                                "step": 1, #Slider's step
-                                "display": "number" # Cosmetic only: display as "number" or "slider"
-                                }), 
-                    "line_spacing": ("INT",{
-                                "default":12, 
-                                "min": -2000000000, #Minimum value
-                                "max": 2000000000, #Maximum value
-                                "step": 1, #Slider's step
-                                "display": "number" # Cosmetic only: display as "number" or "slider"
-                                }), 
-                    "padding": ("INT",{
-                                "default":8, 
-                                "min": 0, #Minimum value
-                                "max": 2000000000, #Maximum value
+                                "min": -200, #Minimum value
+                                "max": 200, #Maximum value
                                 "step": 1, #Slider's step
                                 "display": "number" # Cosmetic only: display as "number" or "slider"
                                 }), 
@@ -1586,7 +1608,7 @@ class TextImage:
                 }
     
     RETURN_TYPES = ("IMAGE","MASK",)
-    RETURN_NAMES = ("image","mask",)
+    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
 
     FUNCTION = "run"
 
@@ -1595,14 +1617,11 @@ class TextImage:
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,)
 
-    def run(self,text,font,font_size,spacing,line_spacing,padding,text_color,vertical,stroke):
+    def run(self,text,font_path,font_size,spacing,text_color,vertical,stroke):
         
-        font_path=os.path.join(FONT_PATH,font)
-
-        if text=="":
-            text=" "
+        # text_list=list(text)
         # stroke=False, stroke_color=(0, 0, 0), stroke_width=1, spacing=0
-        img,mask=generate_text_image(text,font_path,font_size,text_color,vertical,stroke,(0, 0, 0),1,spacing,line_spacing,padding)
+        img,mask=generate_text_image(text,font_path,font_size,text_color,vertical,stroke,(0, 0, 0),1,spacing)
         
         img=pil2tensor(img)
         mask=pil2tensor(mask)
@@ -1636,7 +1655,7 @@ class LoadImagesFromURL:
 
     def run(self,url,seed=0):
         global urls_image
-        # print(urls_image)
+        print(urls_image)
         def filter_http_urls(urls):
             filtered_urls = []
             for url in urls.split('\n'):
@@ -1725,51 +1744,28 @@ class Image3D:
     def run(self,upload,material=None):
         # print('material',material)
         # print(upload )
+        image = base64_to_image(upload['image'])
 
-        # 截取的系列角度截图
-        images=upload['images'] if "images" in upload else []
-
-        ims=[]
-        for im in images:
-            if 'type' in im and (not f"[{im['type']}]" in im['name']):
-                im['name']=im['name']+" "+f"[{im['type']}]"
-            output_image, output_mask = load_image_to_tensor(im['name'])
-            ims.append(output_image)
-        
-        
-        mask=None
-        bg_image=None
         mat=None
+        if 'material' in upload and upload['material']:
+            mat=base64_to_image(upload['material'])
+            mat=mat.convert('RGB')
+            mat=pil2tensor(mat)
 
-        # 如果没有系列截图
-        if len(ims)==0:
-            # 这个是3d模型当前截图
-            image = base64_to_image(upload['image'])
+        mask = image.split()[3]
+        image=image.convert('RGB')
+        
+        mask=mask.convert('L')
 
-            
-            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')
-            
-            mask=mask.convert('L')
-
-            
-            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)
+        bg_image=None
+        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)
 
 
-            mask=pil2tensor(mask)
-            image=pil2tensor(image)
-        else:
-            
-            image = torch.cat(ims, dim=0)
-
+        mask=pil2tensor(mask)
+        image=pil2tensor(image)
         
         m=[]
         if not material is None:
@@ -1858,16 +1854,10 @@ class CompositeImages:
                     "mask":("MASK",),
                     "background": ("IMAGE",),
                     },
-             "optional":{ 
+             "optional":{
+                           
                   "is_multiply_blend":  ("BOOLEAN", {"default": False}),
-                  "position":  (['overall',"center_center","left_bottom","center_bottom","right_bottom","left_top","center_top","right_top"],),
-                   "scale": ("FLOAT",{
-                                "default":0.35, 
-                                "min": 0.01, #Minimum value
-                                "max": 1, #Maximum value
-                                "step": 0.01, #Slider's step
-                                "display": "number" # Cosmetic only: display as "number" or "slider"
-                            }), 
+                  "position":  (['overall',"center_bottom","center_top","right_bottom","left_bottom","right_top","left_top"],),    
                     }
                 }
     
@@ -1880,30 +1870,15 @@ class CompositeImages:
 
     # OUTPUT_IS_LIST = (True,)
 
-    # def run(self, foreground,mask,background,is_multiply_blend,position,scale):
-    #     foreground= tensor2pil(foreground)
-    #     mask= tensor2pil(mask)
-    #     background= tensor2pil(background)
-    #     res=composite_images(foreground,background,mask,is_multiply_blend,position,scale)
+    def run(self, foreground,mask,background,is_multiply_blend,position):
+        foreground= tensor2pil(foreground)
+        mask= tensor2pil(mask)
+        background= tensor2pil(background)
+        res=composite_images(foreground,background,mask,is_multiply_blend,position)
         
-    #     return (pil2tensor(res),)
+        return (pil2tensor(res),)
 
-    def run(self, foreground,mask,background, is_multiply_blend, position, scale):
-        results = []
-        f1=[]
-        for fg, mask in zip(foreground, mask ):
-            f1.append([fg,mask])
-        for f, bg in product(f1, background):
-            [fg,mask]=f
-            fg_pil = tensor2pil(fg)
-            mask_pil = tensor2pil(mask)
-            bg_pil = tensor2pil(bg)
-            res = composite_images(fg_pil, bg_pil, mask_pil, is_multiply_blend, position, scale)
-            results.append(pil2tensor(res))
-        
-        output_image = torch.cat(results, dim=0)
 
-        return (output_image,)
 
 
 class EmptyLayer:
@@ -3232,148 +3207,3 @@ class SaveImageToLocal:
             counter += 1
 
         return ()
-
-
-class ImageBatchToList_:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"image_batch": ("IMAGE",), }}
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image_list",)
-    OUTPUT_IS_LIST = (True,)
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Image"
-
-    def run(self, image_batch):
-        images = [image_batch[i:i + 1, ...] for i in range(image_batch.shape[0])]
-        return (images, )
-
-class ImageListToBatch_:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "images": ("IMAGE",),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "run"
-    INPUT_IS_LIST = True
-    CATEGORY = "♾️Mixlab/Image"
-
-    def run(self, images):
-        shape = images[0].shape[1:3]
-        out = []
-
-        for i in range(len(images)):
-            img = images[i].permute([0,3,1,2])
-            if images[i].shape[1:3] != shape:
-                transforms = T.Compose([
-                    T.CenterCrop(min(img.shape[2], img.shape[3])),
-                    T.Resize((shape[0], shape[1]), interpolation=T.InterpolationMode.BICUBIC),
-                ])
-                img = transforms(img)
-            out.append(img.permute([0,2,3,1]))
-
-        out = torch.cat(out, dim=0)
-
-        return (out,)
-
-
-# https://github.com/gokayfem/ComfyUI-Depth-Visualization?tab=readme-ov-file
-class DepthViewer_:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "depth_map": ("IMAGE",),
-            },
-             "optional":{
-                  "frames":("IMAGEBASE64",), 
-                },
-        }
-
-    def __init__(self):
-        self.saved_reference = []
-        self.saved_depth = []
-
-        self.full_output_folder,self.filename,self.counter, self.subfolder, self.filename_prefix = folder_paths.get_save_image_path(
-            "imagesave", 
-            folder_paths.get_output_directory())
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("frames",)
-    
-    OUTPUT_NODE = True
-
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
-
-    FUNCTION = "run"
-    CATEGORY = "♾️Mixlab/3D"
-    def run(self, image, depth_map,frames=None):
-        self.saved_reference.clear()
-        self.saved_depth.clear()
-        image = image[0].detach().cpu().numpy()
-        depth = depth_map[0].detach().cpu().numpy()
-
-        image = Image.fromarray(np.clip(255. * image, 0, 255).astype(np.uint8)).convert('RGB')
-        depth = Image.fromarray(np.clip(255. * depth, 0, 255).astype(np.uint8))
-
-        return self.display([image], [depth],frames)
-
-    def display(self, reference_image, depth_map,frames):
-        for (batch_number, (single_image, single_depth)) in enumerate(zip(reference_image, depth_map)):
-            filename_with_batch_num = self.filename.replace("%batch_num%", str(batch_number))
-
-            image_file = f"{filename_with_batch_num}_{self.counter:05}_reference.png"
-            single_image.save(os.path.join(self.full_output_folder, image_file))
-
-            depth_file = f"{filename_with_batch_num}_{self.counter:05}_depth.png"
-            single_depth.save(os.path.join(self.full_output_folder, depth_file))
-
-            self.saved_reference.append({
-                "filename": image_file,
-                "subfolder": self.subfolder,
-                "type": "output"
-            })
-
-            self.saved_depth.append({
-                "filename": depth_file,
-                "subfolder": self.subfolder,
-                "type": "output"
-            })
-            self.counter += 1
-
-
-        ims=[]
-        image1 = Image.new('RGB', (512, 512), color='black')
-        image1=pil2tensor(image1)
-
-        # print('frames',frames)
-        if frames!=None and "images" in frames:
-            
-            for im in frames['images']:
-                # print(im)
-                if 'type' in im and (not f"[{im['type']}]" in im['name']):
-                    im['name']=im['name']+" "+f"[{im['type']}]"
-                
-                try:
-                    output_image, output_mask = load_image_to_tensor(im['name'])
-                    ims.append(output_image)
-                except:
-                    print("no")
-                
-
-            if len(ims)>0:
-                image1 = ims[0]
-                for image2 in ims[1:]:
-                    if image1.shape[1:] != image2.shape[1:]:
-                        image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
-                    image1 = torch.cat((image1, image2), dim=0)
-
-        return {"ui": {"reference_image": self.saved_reference, "depth_map": self.saved_depth}, "result": (image1,)}

nodes/Lama.py

@@ -85,6 +85,8 @@ class LaMaInpainting:
             "image": ("IMAGE",),
             "mask": ("MASK",),
                              },
+
+            
                 }
     
     RETURN_TYPES = ("IMAGE",)

nodes/MiniCPMNode.py

@@ -1,127 +0,0 @@
-# Referenced some code：https://github.com/IuvenisSapiens/ComfyUI_MiniCPM-V-2_6-int4
-
-import os
-import torch
-import folder_paths
-from transformers import AutoTokenizer, AutoModel
-from torchvision.transforms.v2 import ToPILImage
-# from decord import VideoReader, cpu  # pip install decord
-from PIL import Image
-
-def get_model_path(n=""):
-    try:
-        return folder_paths.get_folder_paths(n)[0]
-    except:
-        return os.path.join(folder_paths.models_dir, n)
- 
-
-class MiniCPM_VQA_Simple:
-    def __init__(self):
-        self.model_checkpoint = None
-        self.tokenizer = None
-        self.model = None
-        self.device = (
-            torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-        )
-        self.bf16_support = (
-            torch.cuda.is_available()
-            and torch.cuda.get_device_capability(self.device)[0] >= 8
-        )
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "images": ("IMAGE",),
-                "text": ("STRING", {"default": "", "multiline": True}),   
-                "seed": ("INT", {"default": -1}),  # add seed parameter, default is -1
-                "temperature": (
-                    "FLOAT",
-                    {
-                        "default": 0.7,
-                    },
-                ),
-                "keep_model_loaded": ("BOOLEAN", {"default": False}),
-            },
-            
-        }
-
-    RETURN_TYPES = ("STRING",)
-    FUNCTION = "inference"
-    CATEGORY = "♾️Mixlab/Image"
-
-    def inference(
-        self,
-        images,
-        text, 
-        seed,  # add seed parameter, default is -1
-        temperature,
-        keep_model_loaded,
-    ):
-        if seed != -1:
-            torch.manual_seed(seed)
-        model_id = "openbmb/MiniCPM-V-2_6-int4"
-
-        self.model_checkpoint = os.path.join( get_model_path("prompt_generator"), os.path.basename(model_id))
-
-        if not os.path.exists(self.model_checkpoint):
-            from huggingface_hub import snapshot_download
-
-            snapshot_download(
-                repo_id=model_id,
-                local_dir=self.model_checkpoint,
-                local_dir_use_symlinks=False,
-                endpoint='https://hf-mirror.com'
-            )
-
-        if self.tokenizer is None:
-            self.tokenizer = AutoTokenizer.from_pretrained(
-                self.model_checkpoint,
-                trust_remote_code=True,
-                low_cpu_mem_usage=True,
-            )
-
-        if self.model is None:
-            self.model = AutoModel.from_pretrained(
-                self.model_checkpoint,
-                trust_remote_code=True,
-                low_cpu_mem_usage=True,
-                attn_implementation="sdpa",
-                torch_dtype=torch.bfloat16 if self.bf16_support else torch.float16,
-            )
-
-        with torch.no_grad():
-            images = images.permute([0, 3, 1, 2])
-            images = [ToPILImage()(img).convert("RGB") for img in images]
-            msgs = [{"role": "user", "content": images + [text]}]
-
-            params = {"use_image_id": False, }
-
-            # offload model to CPU
-            # self.model = self.model.to(torch.device("cpu"))
-            # self.model.eval()
-
-            result = self.model.chat(
-                image=None,
-                msgs=msgs,
-                tokenizer=self.tokenizer,
-                sampling=True,
-                # top_k=top_k,
-                # top_p=top_p,
-                temperature=temperature,
-                # repetition_penalty=repetition_penalty,
-                # max_new_tokens=max_new_tokens,
-                **params,
-            )
-            # offload model to GPU
-            # self.model = self.model.to(torch.device("cpu"))
-            # self.model.eval()
-            if not keep_model_loaded:
-                del self.tokenizer  # release tokenizer memory
-                del self.model  # release model memory
-                self.tokenizer = None  # set tokenizer to None
-                self.model = None  # set model to None
-                torch.cuda.empty_cache()  # release GPU memory
-                torch.cuda.ipc_collect()
-
-            return (result,)

nodes/P5.py

@@ -1,104 +0,0 @@
-import torch
-import numpy as np
-from PIL import Image,ImageSequence,ImageOps
-import base64
-import io
-import comfy.utils
-import folder_paths
-import node_helpers
-
-
-# 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 load_image_to_tensor( image):
-    image_path = folder_paths.get_annotated_filepath(image)
-        
-    img = node_helpers.pillow(Image.open, image_path)
-        
-    output_images = []
-    output_masks = []
-    w, h = None, None
-
-    excluded_formats = ['MPO']
-        
-    for i in ImageSequence.Iterator(img):
-        i = node_helpers.pillow(ImageOps.exif_transpose, i)
-
-        if i.mode == 'I':
-            i = i.point(lambda i: i * (1 / 255))
-        image = i.convert("RGB")
-
-        if len(output_images) == 0:
-            w = image.size[0]
-            h = image.size[1]
-            
-        if image.size[0] != w or image.size[1] != h:
-            continue
-            
-        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)
-        else:
-            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-        output_images.append(image)
-        output_masks.append(mask.unsqueeze(0))
-
-    if len(output_images) > 1 and img.format not in excluded_formats:
-        output_image = torch.cat(output_images, dim=0)
-        output_mask = torch.cat(output_masks, dim=0)
-    else:
-        output_image = output_images[0]
-        output_mask = output_masks[0]
-
-    return (output_image, output_mask)
-
-          
-
-class P5Input:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "frames":("IMAGEBASE64",), 
-                             },
-                }
-    
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("frames",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Input"
-
-    OUTPUT_NODE = True
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
-
-    def run(self, frames):
-        ims=[]
-        for im in frames['images']:
-            # print(im)
-            if 'type' in im and (not f"[{im['type']}]" in im['name']):
-                im['name']=im['name']+" "+f"[{im['type']}]"
-                
-            output_image, output_mask = load_image_to_tensor(im['name'])
-            ims.append(output_image)
-
-        if len(ims)==0:
-            image1 = Image.new('RGB', (512, 512), color='black')
-            return (pil2tensor(image1),)
-        image1 = ims[0]
-        for image2 in ims[1:]:
-            if image1.shape[1:] != image2.shape[1:]:
-                image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
-            image1 = torch.cat((image1, image2), dim=0)
-
-        # 用于节点提示:p5节点提示有多少帧
-        return {"ui": {"_info": [len(frames['images'])]}, "result": (image1,)}

nodes/PromptNode.py

@@ -18,13 +18,7 @@ import json
 #     req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
 #     request.urlopen(req)    
 
-def get_model_path(n=""):
-    try:
-        return folder_paths.get_folder_paths(n)[0]
-    except:
-        return os.path.join(folder_paths.models_dir, n)
-    
-embeddings_path=get_model_path("embeddings")
+embeddings_path=os.path.join(folder_paths.models_dir, "embeddings")
 
 def get_files_with_extension(directory, extension):
     

nodes/ScreenShareNode.py

@@ -90,7 +90,7 @@ class ScreenShareNode:
                 } }
     
     RETURN_TYPES = ('IMAGE','STRING','FLOAT',"INT")
-    RETURN_NAMES = ("current frame (image)","prompt","denoise (float)","seed (int)")
+    RETURN_NAMES = ("IMAGE","PROMPT","FLOAT","INT")
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/Screen"
@@ -109,7 +109,7 @@ class FloatingVideo:
     @classmethod
     def INPUT_TYPES(s):
         return { "required":{
-            "image": ("IMAGE",)
+            "images": ("IMAGE",)
         }, }
     
     # RETURN_TYPES = ('IMAGE','MASK')
@@ -124,16 +124,16 @@ class FloatingVideo:
     # OUTPUT_IS_LIST = (False,False,)
   
     # 运行的函数
-    def run(self,image):
+    def run(self,images):
        
         results = list()
     
-        for im in image:
-            im=tensor2pil(im)
+        for image in images:
+            image=tensor2pil(image)
             # image_base64 = base64.b64encode(image.tobytes())
 
             buffered = BytesIO()
-            im.save(buffered, format="JPEG")
+            image.save(buffered, format="JPEG")
             image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
 
             results.append(image_base64)

nodes/TextGenerateNode.py

@@ -280,7 +280,7 @@ class ChinesePrompt:
                              },
                
                 "optional":{
-                    "seed":("INT", {"default": 100, "min": 100, "max":  0xffffffffffffffff}), 
+                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
                     
                 },
 
@@ -331,15 +331,13 @@ class ChinesePrompt:
 
         for t in texts:
             if t:
+                # translated_text =  translated_word = translate(zh_en_tokenizer,zh_en_model,str(t))
                 parser = Lark(grammar, start="start", parser="lalr", transformer=ChinesePromptTranslate())
-                try:
-                    result = parser.parse(t).children
-                    en_texts.append(result[0])
-                except:
-                    print(f"Error parsing '{t}'")
-                    t = translate(str(t))
-                    en_texts.append(t)
-                
+                # print('t',t)
+                result = parser.parse(t).children
+                # print('en_result',result)        
+                # en_text=translate(zh_en_tokenizer,zh_en_model,text_without_syntax)
+                en_texts.append(result[0])
 
         zh_en_model.to('cpu')
         print("test en_text",en_texts)
@@ -386,7 +384,7 @@ class PromptGenerate:
 
                 "optional":{
                     "multiple": (["off","on"],), 
-                    "seed":("INT", {"default": 100, "min": 100, "max":  0xffffffffffffffff}), 
+                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
                 },
 
                 }

nodes/Utils.py

@@ -82,13 +82,13 @@ def tensor2pil(image):
     return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
 
 
-def create_temp_file(image,counter=1):
+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)
@@ -133,7 +133,7 @@ def get_font_files(directory):
 
     return font_files
 
-r_directory = os.path.join(os.path.dirname(__file__), '..','assets','/')
+r_directory = os.path.join(os.path.dirname(__file__), '../assets/')
 
 font_files = get_font_files(r_directory)
 # print(font_files)
@@ -181,28 +181,6 @@ class ColorInput:
         return (h,r,g,b,a,)
 
 
-class KeyInput:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "key":("KEY",), 
-                             },
-                }
-    
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("key",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab/Input"
-
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
-
-    def run(self,key):
-        return (key,)
-
-
 
 class FontInput:
     @classmethod
@@ -588,7 +566,7 @@ class AppInfo:
                              },
 
                 "optional":{
-                    "image": ("IMAGE",),
+                    "IMAGE": ("IMAGE",),
                     "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
                     "version":("INT", {
                         "default": 1, 
@@ -601,7 +579,6 @@ class AppInfo:
                     "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
                     "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
                     "auto_save": (["enable","disable"],),
-                    "idle_animation": ("BOOLEAN", {"default": False},),
                 }
 
                 }
@@ -617,19 +594,14 @@ class AppInfo:
     INPUT_IS_LIST = True
     # OUTPUT_IS_LIST = (True,)
 
-    def run(self,name,input_ids,output_ids,image,description,version,share_prefix,link,category,auto_save,idle_animation):
+    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
         name=name[0]
-
-        idle_animation=idle_animation[0]
         
-        im=[]
-        if image:
-            # img=image[0][0]
-            print('AppInfo_image',len(image))
-            # batch 的方式需要处理
-            for i in range(len(image)):
-                img=image[i]
-                im.append(create_temp_file(img,i+1)[0])
+        im=None
+        if IMAGE:
+            im=IMAGE[0][0]
+            #TODO batch 的方式需要处理
+            im=create_temp_file(im)
         # image [img,] img[batch,w,h,a] 列表里面是batch，
 
         input_ids=input_ids[0]
@@ -642,7 +614,7 @@ class AppInfo:
         
         # id=get_json_hash([name,im,input_ids,output_ids,description,version])
 
-        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category,idle_animation]}, "result": ()}
+        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": ()}
     
 
     

nodes/Video.py

@@ -17,128 +17,9 @@ import folder_paths
 from comfy.k_diffusion.utils import FolderOfImages
 from comfy.utils import common_upscale
 
-import torchaudio
-import base64
-
-import mimetypes
 
 
 
-def get_frames(frame_count, frames, revert=False):
-    if not revert:
-        if frame_count <= len(frames):
-            return frames[:frame_count]
-        else:
-            return [frames[i % len(frames)] for i in range(frame_count)]
-    else:
-        extended_frames = frames + frames[-2:0:-1]  # 正向加反向中间部分
-        if frame_count <= len(extended_frames):
-            return extended_frames[:frame_count]
-        else:
-            return [extended_frames[i % len(extended_frames)] for i in range(frame_count)]
-
-# # 示例用法
-# frames = ["frame1", "frame2", "frame3"]
-# frame_count = 2
-
-# result = get_frames(frame_count, frames, revert=False)
-# print(result)  # 输出: ['frame1', 'frame2', 'frame3', 'frame1', 'frame2', 'frame3', 'frame1']
-
-# result = get_frames(frame_count, frames, revert=True)
-# print(result)  # 输出: ['frame1', 'frame2', 'frame3', 'frame2', 'frame1', 'frame2', 'frame3']
-
-
-
-
-def get_mime_type(file_path):
-    # 获取文件的 MIME 类型
-    mime_type, _ = mimetypes.guess_type(file_path)
-    
-    # 如果无法猜测类型，返回默认类型
-    if mime_type is None:
-        return 'application/octet-stream'
-    
-    return mime_type
-# import subprocess
-# from imageio_ffmpeg import get_ffmpeg_exe
-
-
-def save_audio_base64s_to_file(base64_audios, output_folder, file_name):
-    # Ensure the output folder exists
-    if not os.path.exists(output_folder):
-        os.makedirs(output_folder)
-
-    decoded_audios=[]
-    for a in base64_audios:
-    
-        # If the base64 string contains a header, remove it
-        if ',' in a:
-            a = a.split(',')[1]
-        
-        # 解码 base64 数据
-        a=base64.b64decode(a)
-        decoded_audios.append(a)
-
-    # 拼接音频数据
-    combined_audio = b''.join(decoded_audios)
-    
-    # Create the full file path
-    file_path = os.path.join(output_folder, file_name)
-    
-    # Write the decoded audio to the file
-    with open(file_path, 'wb') as audio_file:
-        audio_file.write(combined_audio)
-    
-    return file_path
-
-# Example usage
-# base64_audio = "data:audio/wav;base64,UklGRiQAAABXQVZFZm10IBAAAAABAAEAIlYAAESsAAACABAAZGF0YQAAAAA="
-# output_folder = "audio_files"
-# file_name = "output.wav"
-
-# file_path = save_audio_base64_to_file(base64_audio, output_folder, file_name)
-# print(f"Audio saved to: {file_path}")
-
-# 写一个python文件，用来 判断文件夹内命名为 所有chat_tts开头的文件数量（chat_tts_00001），并输出新的编号
-def get_new_counter(full_output_folder, filename_prefix):
-    # 获取目录中的所有文件
-    files = os.listdir(full_output_folder)
-    
-    # 过滤出以 filename_prefix 开头并且后续部分为数字的文件
-    filtered_files = []
-    for f in files:
-        if f.startswith(filename_prefix):
-            # 去掉文件名中的前缀和后缀，只保留中间的数字部分
-            base_name = f[len(filename_prefix)+1:]
-            number_part = base_name.split('.')[0]  # 假设文件名中只有一个点，即扩展名
-            if number_part.isdigit():
-                filtered_files.append(int(number_part))
-
-    if not filtered_files:
-        return 1
-
-    # 获取最大的编号
-    max_number = max(filtered_files)
-    
-    # 新的编号
-    return max_number + 1
-
-def crop_audio(input_file, start_time, duration):
-    # Load the audio file
-    audio_tensor, sample_rate = torchaudio.load(input_file)
-    
-    # Convert start_time and duration from seconds to sample indices
-    start_sample = int(start_time * sample_rate)
-    end_sample = start_sample + int(duration * sample_rate)
-    
-    # Perform the slicing
-    cropped_audio_tensor = audio_tensor[:, start_sample:end_sample]
-    
-    # Save the cropped audio to a new file
-    torchaudio.save(input_file, cropped_audio_tensor, sample_rate)
-    
-    return input_file
-
 def generate_folder_name(directory,video_path):
     # Get the directory and filename from the video path
     _, filename = os.path.split(video_path)
@@ -179,9 +60,6 @@ def split_video(video_path, video_segment_frames, transition_frames, output_dir)
         
         # 打印当前片段的起始帧和结束帧
         print(f"Segment {i+1}: Start Frame {start_frame}, End Frame {end_frame}")
-
-        if end_frame<start_frame:
-            break
         
         # 保存当前片段为一个视频文件
         segment_video_path = f"{output_dir}/segment_{i+1}.avi"
@@ -190,7 +68,6 @@ def split_video(video_path, video_segment_frames, transition_frames, output_dir)
         segment_video = cv2.VideoWriter(segment_video_path, fourcc, fps, (int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
                                                               int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))))
         
-
         for frame_num in range(start_frame, end_frame):
             ret, frame = video_capture.read()
             if ret:
@@ -224,25 +101,6 @@ if ffmpeg_path is None:
     except:
         print("ffmpeg could not be found. Outputs that require it have been disabled")
 
-
-def combine_audio_video(audio_path, video_path, output_path):
-   
-    command = [
-        ffmpeg_path,
-        '-i', video_path,
-        '-i', audio_path,
-        '-c:v', 'copy',
-        '-c:a', 'aac',
-        '-shortest',
-        output_path
-    ]
-    
-    subprocess.run(command, check=True)
-    return output_path
-
-
-
-
 # Tensor to PIL
 def tensor2pil(image):
     return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
@@ -404,7 +262,7 @@ class LoadVideoAndSegment:
                     files.append(f)
         return {"required": {
                     "video": (sorted(files), {"video_upload": True}),
-                     "video_segment_frames": ("INT", {"default": 10, "min": -1, "step": 1}),
+                     "video_segment_frames": ("INT", {"default": 10, "min": 1, "step": 1}),
                      "transition_frames": ("INT", {"default": 0, "min": 0, "step": 1}), 
                      },}
 
@@ -474,6 +332,63 @@ class LoadVideoAndSegment:
         
         video_path = folder_paths.get_annotated_filepath(video)
         
+        # check if video is a gif - will need to use cv fallback to read frames
+        # use cv fallback if ffmpeg not installed or gif
+        # if ffmpeg_path is None:
+        #     return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
+        # otherwise, continue with ffmpeg
+        
+        # args_dummy = [ffmpeg_path, "-i", video_path, "-f", "null", "-"]
+        # try:
+        #     with subprocess.Popen(args_dummy, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE) as proc:
+        #         for line in proc.stderr.readlines():
+        #             match = re.search(", ([1-9]|\\d{2,})x(\\d+)",line.decode('utf-8'))
+        #             if match is not None:
+        #                 size = [int(match.group(1)), int(match.group(2))]
+        #                 break
+        # except Exception as e:
+        #     print(f"Retrying with opencv due to ffmpeg error: {e}")
+        #     return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
+        # args_all_frames = [ffmpeg_path, "-i", video_path, "-v", "error",
+        #                      "-pix_fmt", "rgb24"]
+
+        # vfilters = []
+       
+        # if skip_first_frames > 0:
+        #     vfilters.append(f"select=gt(n\\,{skip_first_frames-1})")
+        # if frame_load_cap > 0:
+        #     vfilters.append(f"select=gt({frame_load_cap}\\,n)")
+        # #manually calculate aspect ratio to ensure reads remain aligned
+        
+        # if len(vfilters) > 0:
+        #     args_all_frames += ["-vf", ",".join(vfilters)]
+
+        # args_all_frames += ["-f", "rawvideo", "-"]
+        # images = []
+        # try:
+        #     with subprocess.Popen(args_all_frames, stdout=subprocess.PIPE) as proc:
+        #         #Manually buffer enough bytes for an image
+        #         bpi = size[0]*size[1]*3
+        #         current_bytes = bytearray(bpi)
+        #         current_offset=0
+        #         while True:
+        #             bytes_read = proc.stdout.read(bpi - current_offset)
+        #             if bytes_read is None:#sleep to wait for more data
+        #                 time.sleep(.2)
+        #                 continue
+        #             if len(bytes_read) == 0:#EOF
+        #                 break
+        #             current_bytes[current_offset:len(bytes_read)] = bytes_read
+        #             current_offset+=len(bytes_read)
+        #             if current_offset == bpi:
+        #                 images.append(np.array(current_bytes, dtype=np.float32).reshape(size[1], size[0], 3) / 255.0)
+        #                 current_offset = 0
+        # except Exception as e:
+        #     print(f"Retrying with opencv due to ffmpeg error: {e}")
+        #     return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
+
+        # imgs=split_list(images,video_segment_frames,transition_frames)
+
         # temp path 
         tp=folder_paths.get_temp_directory()
         basename = os.path.basename(video_path)  # 获取文件名
@@ -481,22 +396,15 @@ class LoadVideoAndSegment:
 
         folder_path = create_folder(tp,name_without_extension)
         
-        if video_segment_frames==-1:
-            # 不切割视频
-            scenes_video=[video_path]
-            # 读取视频文件
-            video_capture = cv2.VideoCapture(video_path)
-            
-            # 获取视频的总帧数和帧率
-            total_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
-            fps = video_capture.get(cv2.CAP_PROP_FPS)
-
-        else:
-            # 导出的数据
-            scenes_video,total_frames,fps=split_video(video_path,video_segment_frames,
-                                    transition_frames,folder_path)
+ 
+        # 导出的数据
+        scenes_video,total_frames,fps=split_video(video_path,video_segment_frames,
+                                 transition_frames,folder_path)
       
 
+        # imgs=[torch.from_numpy(np.stack(im)) for im in imgs]
+
+        # images = torch.from_numpy(np.stack(images))
 
         return (scenes_video,len(scenes_video), total_frames,fps,)
 
@@ -514,113 +422,7 @@ class LoadVideoAndSegment:
             return "Invalid image file: {}".format(video)
 
         return True
-
-
-
-class LoadAndCombinedAudio_:
-    @classmethod
-    def INPUT_TYPES(s):
-       
-        return {"required": {
-                    "audios": ("AUDIOBASE64",),
-                    "start_time": ("FLOAT" , {"default": 0, "min": 0, "max": 10000000, "step": 0.01}),
-                    "duration": ("FLOAT" , {"default": 10, "min": -1, "max": 10000000, "step": 0.01}),
-                     },
-                }
-
-    CATEGORY = "♾️Mixlab/Audio"
-
-    RETURN_TYPES = ("STRING","AUDIO",)
-    RETURN_NAMES = ("audio_file_path","audio",)
-    FUNCTION = "run"
-
-    def run(self,audios, start_time, duration):
-        output_dir = folder_paths.get_output_directory()
-        counter=get_new_counter(output_dir,'audio_')
-
-        audio_file_name = f"audio_{counter:05}.wav"
-
-        audio_file=save_audio_base64s_to_file(audios['base64'],output_dir,audio_file_name)
-        # duration == -1 则不裁切
-        if duration > -1:
-            crop_audio(audio_file, start_time, duration)
-
-        waveform, sample_rate = torchaudio.load(audio_file)
-        audio = {
-            "filename": audio_file_name,
-            "subfolder": "",
-            "type": "output",
-            "audio_path":audio_file,
-            "waveform": waveform.unsqueeze(0), 
-            "sample_rate": sample_rate}
-
-        return (audio_file,audio ,)
-
-class CombineAudioVideo:
-    @classmethod
-    def INPUT_TYPES(s):
-       
-        return {"required": {
-                     "video": ("SCENE_VIDEO",),
-                     "audio": ("AUDIO", ), 
-                     },
-                }
-
-    CATEGORY = "♾️Mixlab/Video"
-
-    OUTPUT_NODE = True
-    FUNCTION = "run" 
-    RETURN_TYPES = ("SCENE_VIDEO",)
-    RETURN_NAMES = ("SCENE_VIDEO",)
-
-    def run(self,video, audio):
-
-        output_dir = folder_paths.get_output_directory()
-
-        # 判断是否是 Tensor 类型
-        is_tensor = not isinstance(audio, dict)
-        # print('#判断是否是 Tensor 类型',is_tensor,audio)
-        if not is_tensor and 'waveform' in audio and 'sample_rate' in audio:
-            # {'waveform': tensor([], size=(1, 1, 0)), 'sample_rate': 44100}
-            is_tensor=True
-
-        if "audio_path" in audio:
-            is_tensor=False
-            audio_file_path=audio["audio_path"]
-
-        if is_tensor:
-            filename_prefix="audio_tmp"
-            full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
-                filename_prefix, 
-                folder_paths.get_temp_directory())
-            
-            filename_with_batch_num = filename.replace("%batch_num%", str(1))
-            file = f"{filename_with_batch_num}_{counter:05}_.wav"
-            
-            audio_file_path=os.path.join(full_output_folder, file)
-
-            torchaudio.save(audio_file_path, audio['waveform'].squeeze(0), audio["sample_rate"])
-            
-        # 获取文件名和扩展名
-        base, ext = os.path.splitext(video)
-        counter=get_new_counter(output_dir,'video_final_')
-
-        v_file = f"video_final_{counter:05}{ext}"
-        
-        v_file_path=os.path.join(output_dir, v_file)
-
-        combine_audio_video(audio_file_path,video,v_file_path)
-
-        previews = [
-            {
-                "filename": v_file,
-                "subfolder": "",
-                "type": "output",
-                "format": get_mime_type(v_file),
-            }
-        ]
-
-        return {"ui": {"gifs": previews},"result":(v_file_path,)}
+    
 
 # The code is based on ComfyUI-VideoHelperSuite modification.
 class VideoCombine_Adv:
@@ -652,8 +454,7 @@ class VideoCombine_Adv:
             },
         }
 
-    RETURN_TYPES = ("SCENE_VIDEO",)
-    RETURN_NAMES = ("scenes_video",)
+    RETURN_TYPES = ()
     OUTPUT_NODE = True
     CATEGORY = "♾️Mixlab/Video"
     FUNCTION = "run"
@@ -822,7 +623,7 @@ class VideoCombine_Adv:
                 "format": format,
             }
         ]
-        return {"ui": {"gifs": previews},"result":(file_path,)}
+        return {"ui": {"gifs": previews}}
 
 
 class VAEEncodeForInpaint_Frames:
@@ -889,113 +690,4 @@ class VAEEncodeForInpaint_Frames:
             result.append({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())})
 
 
-        return (result, )
-    
-
-
-class GenerateFramesByCount:
-    @classmethod
-    def INPUT_TYPES(cls):
-        
-        return {"required": {
-                    "frames": ('IMAGE',),
-                    "frame_count": ("INT", {"default": 72, "min": 1, "step": 1}), 
-                    "revert" :("BOOLEAN", {"default": True},),
-                     },}
-
-    RETURN_TYPES = ('IMAGE',)
-    RETURN_NAMES = ("frames",)
-
-    FUNCTION = "r"
-    CATEGORY = "♾️Mixlab/Video"
-    # INPUT_IS_LIST = True
-
-    def r(self, frames, frame_count, revert):
-        
-        image_list = [frames[i:i + 1, ...] for i in range(frames.shape[0])]
-        print('#image_list',len(image_list),frame_count)
-        image_list=get_frames(frame_count,image_list,revert)
-
-        images = torch.cat(image_list, dim=0)
-
-        return (images,)
-
-
-class scenesNode_:
-    @classmethod
-    def INPUT_TYPES(cls):
-        
-        return {"required": {
-                    "scenes_video": ('SCENE_VIDEO',),
-                    "index": ("INT", {"default": 0, "min": 0, "step": 1}),
-                     
-                     },}
-
-    RETURN_TYPES = ('IMAGE','INT',)
-    RETURN_NAMES = ("video frames (batch)","count",)
-    # OUTPUT_IS_LIST = (False,)
-
-    FUNCTION = "run"
-    CATEGORY = "♾️Mixlab/Video"
-    INPUT_IS_LIST = True
-
-    def load_video_cv_fallback(self, video, frame_load_cap, skip_first_frames):
-        # print('#video',video)
-        try:
-            video_cap = cv2.VideoCapture(video)
-            if not video_cap.isOpened():
-                raise ValueError(f"{video} could not be loaded with cv fallback.")
-            # set video_cap to look at start_index frame
-            images = []
-            total_frame_count = 0
-            frames_added = 0
-            base_frame_time = 1/video_cap.get(cv2.CAP_PROP_FPS)
-            
-            target_frame_time = base_frame_time
-
-            time_offset=0.0
-            while video_cap.isOpened():
-                if time_offset < target_frame_time:
-                    is_returned, frame = video_cap.read()
-                    # if didn't return frame, video has ended
-                    if not is_returned:
-                        break
-                    time_offset += base_frame_time
-                if time_offset < target_frame_time:
-                    continue
-                time_offset -= target_frame_time
-                # if not at start_index, skip doing anything with frame
-                total_frame_count += 1
-                if total_frame_count <= skip_first_frames:
-                    continue
-                # TODO: do whatever operations need to happen, like force_size, etc
-
-                # opencv loads images in BGR format (yuck), so need to convert to RGB for ComfyUI use
-                # follow up: can videos ever have an alpha channel?
-                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-                # convert frame to comfyui's expected format (taken from comfy's load image code)
-                image = Image.fromarray(frame)
-                image = ImageOps.exif_transpose(image)
-                image = np.array(image, dtype=np.float32) / 255.0
-                image = torch.from_numpy(image)[None,]
-                images.append(image)
-                frames_added += 1
-                # if cap exists and we've reached it, stop processing frames
-                if frame_load_cap > 0 and frames_added >= frame_load_cap:
-                    break
-        finally:
-            video_cap.release()
-        
-        images = torch.cat(images, dim=0)
-       
-        return (images, frames_added,)
-
-    def run(self, scenes_video,index):
-        print('#scenes_video',index,scenes_video)
-        index=index[0]
-        if len(scenes_video) > index:
-            vp=scenes_video[index]
-        else:
-            vp=scenes_video[-1]
-        
-        return self.load_video_cv_fallback(vp,0,0)
+        return (result, )

nodes/VoiceStreamAI/asr/asr_factory.py

@@ -0,0 +1,12 @@
+from VoiceStreamAI.asr.whisper_asr import WhisperASR
+from VoiceStreamAI.asr.faster_whisper_asr import FasterWhisperASR
+
+class ASRFactory:
+    @staticmethod
+    def create_asr_pipeline(type, **kwargs):
+        if type == "whisper":
+            return WhisperASR(**kwargs)
+        if type == "faster_whisper":
+            return FasterWhisperASR(**kwargs)
+        else:
+            raise ValueError(f"Unknown ASR pipeline type: {type}")

nodes/VoiceStreamAI/asr/asr_interface.py

@@ -0,0 +1,9 @@
+class ASRInterface:
+    async def transcribe(self, client):
+        """
+        Transcribe the given audio data.
+
+        :param client: The client object with all the member variables including the buffer
+        :return: The transcription structure, see for example the faster_whisper_asr.py file.
+        """
+        raise NotImplementedError("This method should be implemented by subclasses.")

nodes/VoiceStreamAI/asr/faster_whisper_asr.py

@@ -0,0 +1,142 @@
+import os
+from faster_whisper import WhisperModel
+
+from VoiceStreamAI.asr.asr_interface import ASRInterface
+from VoiceStreamAI.audio_utils import save_audio_to_file
+
+import folder_paths
+
+language_codes = {
+    "afrikaans": "af",
+    "amharic": "am",
+    "arabic": "ar",
+    "assamese": "as",
+    "azerbaijani": "az",
+    "bashkir": "ba",
+    "belarusian": "be",
+    "bulgarian": "bg",
+    "bengali": "bn",
+    "tibetan": "bo",
+    "breton": "br",
+    "bosnian": "bs",
+    "catalan": "ca",
+    "czech": "cs",
+    "welsh": "cy",
+    "danish": "da",
+    "german": "de",
+    "greek": "el",
+    "english": "en",
+    "spanish": "es",
+    "estonian": "et",
+    "basque": "eu",
+    "persian": "fa",
+    "finnish": "fi",
+    "faroese": "fo",
+    "french": "fr",
+    "galician": "gl",
+    "gujarati": "gu",
+    "hausa": "ha",
+    "hawaiian": "haw",
+    "hebrew": "he",
+    "hindi": "hi",
+    "croatian": "hr",
+    "haitian": "ht",
+    "hungarian": "hu",
+    "armenian": "hy",
+    "indonesian": "id",
+    "icelandic": "is",
+    "italian": "it",
+    "japanese": "ja",
+    "javanese": "jw",
+    "georgian": "ka",
+    "kazakh": "kk",
+    "khmer": "km",
+    "kannada": "kn",
+    "korean": "ko",
+    "latin": "la",
+    "luxembourgish": "lb",
+    "lingala": "ln",
+    "lao": "lo",
+    "lithuanian": "lt",
+    "latvian": "lv",
+    "malagasy": "mg",
+    "maori": "mi",
+    "macedonian": "mk",
+    "malayalam": "ml",
+    "mongolian": "mn",
+    "marathi": "mr",
+    "malay": "ms",
+    "maltese": "mt",
+    "burmese": "my",
+    "nepali": "ne",
+    "dutch": "nl",
+    "norwegian nynorsk": "nn",
+    "norwegian": "no",
+    "occitan": "oc",
+    "punjabi": "pa",
+    "polish": "pl",
+    "pashto": "ps",
+    "portuguese": "pt",
+    "romanian": "ro",
+    "russian": "ru",
+    "sanskrit": "sa",
+    "sindhi": "sd",
+    "sinhalese": "si",
+    "slovak": "sk",
+    "slovenian": "sl",
+    "shona": "sn",
+    "somali": "so",
+    "albanian": "sq",
+    "serbian": "sr",
+    "sundanese": "su",
+    "swedish": "sv",
+    "swahili": "sw",
+    "tamil": "ta",
+    "telugu": "te",
+    "tajik": "tg",
+    "thai": "th",
+    "turkmen": "tk",
+    "tagalog": "tl",
+    "turkish": "tr",
+    "tatar": "tt",
+    "ukrainian": "uk",
+    "urdu": "ur",
+    "uzbek": "uz",
+    "vietnamese": "vi",
+    "yiddish": "yi",
+    "yoruba": "yo",
+    "chinese": "zh",
+    "cantonese": "yue",
+}
+
+
+class FasterWhisperASR(ASRInterface):
+    def __init__(self, **kwargs):
+        model_size = kwargs.get('model_size', "large-v3")
+        device = kwargs.get('device', "cuda")
+        model_root = os.path.join(folder_paths.models_dir, "whisper") 
+        # Run on GPU with FP16
+        self.asr_pipeline = WhisperModel(model_size, device=device, compute_type="float16",download_root=model_root)
+
+    async def transcribe(self, client):
+        file_path = await save_audio_to_file(client.scratch_buffer, client.get_file_name())
+
+        language = None if client.config['language'] is None else language_codes.get(client.config['language'].lower())
+        segments, info = self.asr_pipeline.transcribe(file_path, word_timestamps=True, language=language)
+
+        segments = list(segments)  # The transcription will actually run here.
+        os.remove(file_path)
+
+        flattened_words = [word for segment in segments for word in segment.words]
+
+        to_return = {
+        "language": info.language,
+        "language_probability": info.language_probability,
+        "text": ' '.join([s.text.strip() for s in segments]),
+        "words":
+            [
+                {"word": w.word, "start": w.start, "end": w.end, "probability":w.probability} for w in flattened_words
+            ]
+        }
+        return to_return
+

nodes/VoiceStreamAI/asr/whisper_asr.py

@@ -0,0 +1,27 @@
+from transformers import pipeline
+from VoiceStreamAI.asr.asr_interface import ASRInterface
+from VoiceStreamAI.audio_utils import save_audio_to_file
+import os
+
+class WhisperASR(ASRInterface):
+    def __init__(self, **kwargs):
+        model_name = kwargs.get('model_name', "openai/whisper-large-v3")
+        self.asr_pipeline = pipeline("automatic-speech-recognition", model=model_name)
+
+    async def transcribe(self, client):
+        file_path = await save_audio_to_file(client.scratch_buffer, client.get_file_name())
+        
+        if client.config['language'] is not None:
+            to_return = self.asr_pipeline(file_path, generate_kwargs={"language": client.config['language']})['text']
+        else:
+            to_return = self.asr_pipeline(file_path)['text']
+
+        os.remove(file_path)
+
+        to_return = {
+            "language": "UNSUPPORTED_BY_HUGGINGFACE_WHISPER",
+            "language_probability": None,
+            "text": to_return.strip(),
+            "words": "UNSUPPORTED_BY_HUGGINGFACE_WHISPER"
+        }
+        return to_return

nodes/VoiceStreamAI/audio_utils.py

@@ -0,0 +1,26 @@
+import wave
+import os
+
+async def save_audio_to_file(audio_data, file_name, audio_dir="audio_files", audio_format="wav"):
+    """
+    Saves the audio data to a file.
+
+    :param client_id: Unique identifier for the client.
+    :param audio_data: The audio data to save.
+    :param file_counters: Dictionary to keep track of file counts for each client.
+    :param audio_dir: Directory where audio files will be saved.
+    :param audio_format: Format of the audio file.
+    :return: Path to the saved audio file.
+    """
+
+    os.makedirs(audio_dir, exist_ok=True)
+    
+    file_path = os.path.join(audio_dir, file_name)
+
+    with wave.open(file_path, 'wb') as wav_file:
+        wav_file.setnchannels(1)  # Assuming mono audio
+        wav_file.setsampwidth(2)
+        wav_file.setframerate(16000)
+        wav_file.writeframes(audio_data)
+
+    return file_path

nodes/VoiceStreamAI/buffering_strategy/buffering_strategies.py

@@ -0,0 +1,142 @@
+import os
+import asyncio
+import json
+import time
+
+from VoiceStreamAI.buffering_strategy.buffering_strategy_interface import BufferingStrategyInterface
+from openai import OpenAI
+
+class SilenceAtEndOfChunk(BufferingStrategyInterface):
+    """
+    A buffering strategy that processes audio at the end of each chunk with silence detection.
+
+    This class is responsible for handling audio chunks, detecting silence at the end of each chunk,
+    and initiating the transcription process for the chunk.
+
+    Attributes:
+        client (Client): The client instance associated with this buffering strategy.
+        chunk_length_seconds (float): Length of each audio chunk in seconds.
+        chunk_offset_seconds (float): Offset time in seconds to be considered for processing audio chunks.
+    """
+
+    def __init__(self, client, **kwargs):
+        """
+        Initialize the SilenceAtEndOfChunk buffering strategy.
+
+        Args:
+            client (Client): The client instance associated with this buffering strategy.
+            **kwargs: Additional keyword arguments, including 'chunk_length_seconds' and 'chunk_offset_seconds'.
+        """
+        self.client = client
+
+        self.chunk_length_seconds = os.environ.get('BUFFERING_CHUNK_LENGTH_SECONDS')
+        if not self.chunk_length_seconds:
+            self.chunk_length_seconds = kwargs.get('chunk_length_seconds')
+        self.chunk_length_seconds = float(self.chunk_length_seconds)
+
+        self.chunk_offset_seconds = os.environ.get('BUFFERING_CHUNK_OFFSET_SECONDS')
+        if not self.chunk_offset_seconds:
+            self.chunk_offset_seconds = kwargs.get('chunk_offset_seconds')
+        self.chunk_offset_seconds = float(self.chunk_offset_seconds)
+
+        self.error_if_not_realtime = os.environ.get('ERROR_IF_NOT_REALTIME')
+        if not self.error_if_not_realtime:
+            self.error_if_not_realtime = kwargs.get('error_if_not_realtime', False)
+        
+        self.processing_flag = False
+
+        self.messages=[]
+
+    def process_audio(self, websocket, vad_pipeline, asr_pipeline,llm_port):
+        """
+        Process audio chunks by checking their length and scheduling asynchronous processing.
+
+        This method checks if the length of the audio buffer exceeds the chunk length and, if so,
+        it schedules asynchronous processing of the audio.
+
+        Args:
+            websocket (Websocket): The WebSocket connection for sending transcriptions.
+            vad_pipeline: The voice activity detection pipeline.
+            asr_pipeline: The automatic speech recognition pipeline.
+        """
+        chunk_length_in_bytes = self.chunk_length_seconds * self.client.sampling_rate * self.client.samples_width
+        if len(self.client.buffer) > chunk_length_in_bytes:
+            if self.processing_flag:
+                exit("Error in realtime processing: tried processing a new chunk while the previous one was still being processed")
+
+            self.client.scratch_buffer += self.client.buffer
+            self.client.buffer.clear()
+            self.processing_flag = True
+            # Schedule the processing in a separate task
+            asyncio.create_task(self.process_audio_async(websocket, vad_pipeline, asr_pipeline,llm_port))
+    
+    async def process_audio_async(self, websocket, vad_pipeline, asr_pipeline,llm_port):
+        """
+        Asynchronously process audio for activity detection and transcription.
+
+        This method performs heavy processing, including voice activity detection and transcription of
+        the audio data. It sends the transcription results through the WebSocket connection.
+
+        Args:
+            websocket (Websocket): The WebSocket connection for sending transcriptions.
+            vad_pipeline: The voice activity detection pipeline.
+            asr_pipeline: The automatic speech recognition pipeline.
+        """   
+        start = time.time()
+        vad_results = await vad_pipeline.detect_activity(self.client)
+
+        if len(vad_results) == 0:
+            self.client.scratch_buffer.clear()
+            self.client.buffer.clear()
+            self.processing_flag = False
+            return
+
+        last_segment_should_end_before = ((len(self.client.scratch_buffer) / (self.client.sampling_rate * self.client.samples_width)) - self.chunk_offset_seconds)
+        if vad_results[-1]['end'] < last_segment_should_end_before:
+            transcription = await asr_pipeline.transcribe(self.client)
+            if transcription['text'] != '':
+                end = time.time()
+                transcription['processing_time'] = end - start
+
+                transcription['status']="chat_start"
+
+                json_transcription = json.dumps(transcription)
+
+                await websocket.send(json_transcription)
+
+                # Point to the local server
+                client = OpenAI(base_url=f"http://localhost:{llm_port}/v1", api_key="lm-studio")
+
+               
+                messages=[
+                    {"role": "system", "content": "You are a friendly and engaging AI designed to interact with users in a conversational manner. Your personality is that of a sophisticated and polite young professional who is both a designer and a programmer. You are well-mannered, articulate, and possess a good sense of humor. Your goal is to provide helpful and insightful responses while maintaining a pleasant and enjoyable conversation. Be sure to use your knowledge in design and programming to enrich the dialogue and offer relevant advice or information when appropriate. Always be respectful and considerate of the user's feelings and perspectives. Additionally, you are fluent in both English and Chinese, and can seamlessly switch between the two languages to best assist users."}, 
+                ]+self.messages[-10:0]+[{"role": "user", "content":transcription['text']}]
+                # print('#messages',messages)
+                
+                completion = client.chat.completions.create(
+                    model="model-identifier",
+                    messages=messages,
+                    temperature=0.7,
+                    )
+
+                transcription['asistant'] = completion.choices[0].message.content
+
+                transcription['status']="chat_end"
+
+                json_transcription = json.dumps(transcription) 
+
+                self.messages.append({
+                    "role": "user", 
+                    "content":transcription['text']})
+                
+                self.messages.append({
+                    "role": "asistant", 
+                    "content": transcription['asistant']
+                                      })
+                # print('#messages',completion.choices[0].message.content)
+
+                await websocket.send(json_transcription)
+            self.client.scratch_buffer.clear()
+            self.client.increment_file_counter()
+        
+        self.processing_flag = False

nodes/VoiceStreamAI/buffering_strategy/buffering_strategy_factory.py

@@ -0,0 +1,41 @@
+from VoiceStreamAI.buffering_strategy.buffering_strategies import SilenceAtEndOfChunk
+
+class BufferingStrategyFactory:
+    """
+    A factory class for creating instances of different buffering strategies.
+
+    This factory provides a centralized way to instantiate various buffering strategies
+    based on the type specified. It abstracts the creation logic, making it easier to
+    manage and extend with new buffering strategy types.
+
+    Methods:
+        create_buffering_strategy: Creates and returns an instance of a specified buffering strategy.
+    """
+
+    @staticmethod
+    def create_buffering_strategy(type, client, **kwargs):
+        """
+        Creates an instance of a buffering strategy based on the specified type.
+
+        This method acts as a factory for creating buffering strategy objects. It returns
+        an instance of the strategy corresponding to the given type. If the type is not
+        recognized, it raises a ValueError.
+
+        Args:
+            type (str): The type of buffering strategy to create. Currently supports 'silence_at_end_of_chunk'.
+            client (Client): The client instance to be associated with the buffering strategy.
+            **kwargs: Additional keyword arguments specific to the buffering strategy being created.
+
+        Returns:
+            An instance of the specified buffering strategy.
+
+        Raises:
+            ValueError: If the specified type is not recognized or supported.
+
+        Example:
+            strategy = BufferingStrategyFactory.create_buffering_strategy("silence_at_end_of_chunk", client)
+        """
+        if type == "silence_at_end_of_chunk":
+            return SilenceAtEndOfChunk(client, **kwargs)
+        else:
+            raise ValueError(f"Unknown buffering strategy type: {type}")

nodes/VoiceStreamAI/buffering_strategy/buffering_strategy_interface.py

@@ -0,0 +1,31 @@
+class BufferingStrategyInterface:
+    """
+    An interface class for buffering strategies in audio processing systems.
+
+    This class defines the structure for buffering strategies used in handling
+    and processing audio data. It serves as a template for creating custom buffering
+    strategies that fit specific requirements of an audio processing pipeline.
+
+    Subclasses should implement the methods defined in this interface to ensure
+    consistency and compatibility with the system's audio processing framework.
+
+    Methods:
+        process_audio: Process audio data. This method should be implemented by subclasses.
+    """
+
+    def process_audio(self, websocket, vad_pipeline, asr_pipeline):
+        """
+        Process audio data using the given WebSocket connection, VAD pipeline, and ASR pipeline.
+
+        This method is intended to be overridden in subclasses to provide specific logic
+        for handling and processing audio data in different buffering strategies.
+
+        Args:
+            websocket (Websocket): The WebSocket connection for communication with clients.
+            vad_pipeline: The Voice Activity Detection (VAD) pipeline used for detecting speech in the audio.
+            asr_pipeline: The Automatic Speech Recognition (ASR) pipeline used for transcribing speech in the audio.
+
+        Raises:
+            NotImplementedError: If the method is not implemented in the subclass.
+        """
+        raise NotImplementedError("This method should be implemented by subclasses.")

nodes/VoiceStreamAI/client.py

@@ -0,0 +1,54 @@
+from VoiceStreamAI.buffering_strategy.buffering_strategy_factory import BufferingStrategyFactory
+
+class Client:
+    """
+    Represents a client connected to the VoiceStreamAI server.
+
+    This class maintains the state for each connected client, including their
+    unique identifier, audio buffer, configuration, and a counter for processed audio files.
+
+    Attributes:
+        client_id (str): A unique identifier for the client.
+        buffer (bytearray): A buffer to store incoming audio data.
+        config (dict): Configuration settings for the client, like chunk length and offset.
+        file_counter (int): Counter for the number of audio files processed.
+        total_samples (int): Total number of audio samples received from this client.
+        sampling_rate (int): The sampling rate of the audio data in Hz.
+        samples_width (int): The width of each audio sample in bits.
+    """
+    def __init__(self, client_id, sampling_rate, samples_width):
+        self.client_id = client_id
+        self.buffer = bytearray()
+        self.scratch_buffer = bytearray()
+        self.config = {"language": None,
+                       "processing_strategy": "silence_at_end_of_chunk", 
+                       "processing_args": {
+                           "chunk_length_seconds": 5, 
+                           "chunk_offset_seconds": 0.1
+                           }
+                       }
+        self.file_counter = 0
+        self.total_samples = 0
+        self.sampling_rate = sampling_rate
+        self.samples_width = samples_width
+        self.buffering_strategy = BufferingStrategyFactory.create_buffering_strategy(self.config['processing_strategy'], self, **self.config['processing_args'])
+
+    def update_config(self, config_data):
+        self.config.update(config_data)
+        self.buffering_strategy = BufferingStrategyFactory.create_buffering_strategy(self.config['processing_strategy'], self, **self.config['processing_args'])
+
+    def append_audio_data(self, audio_data):
+        self.buffer.extend(audio_data)
+        self.total_samples += len(audio_data) / self.samples_width
+
+    def clear_buffer(self):
+        self.buffer.clear()
+
+    def increment_file_counter(self):
+        self.file_counter += 1
+
+    def get_file_name(self):
+        return f"{self.client_id}_{self.file_counter}.wav"
+    
+    def process_audio(self, websocket, vad_pipeline, asr_pipeline,llm_port):
+        self.buffering_strategy.process_audio(websocket, vad_pipeline, asr_pipeline,llm_port)

nodes/VoiceStreamAI/main.py

@@ -0,0 +1,54 @@
+import argparse
+import asyncio
+import json
+
+import os
+import sys
+from pathlib import Path
+
+
+# 获取当前文件的绝对路径
+current_file_path = os.path.abspath(__file__)
+
+# 获取当前文件的目录
+current_directory = os.path.dirname(current_file_path)
+sys.path.append(str(Path(current_directory).parent))
+# print("sys.path", current_directory)
+
+
+from VoiceStreamAI.server import Server
+from VoiceStreamAI.asr.asr_factory import ASRFactory
+from VoiceStreamAI.vad.vad_factory import VADFactory
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="VoiceStreamAI Server: Real-time audio transcription using self-hosted Whisper and WebSocket")
+    parser.add_argument("--vad-type", type=str, default="pyannote", help="Type of VAD pipeline to use (e.g., 'pyannote')")
+    parser.add_argument("--vad-args", type=str, default='{"auth_token": "huggingface_token"}', help="JSON string of additional arguments for VAD pipeline")
+    parser.add_argument("--asr-type", type=str, default="faster_whisper", help="Type of ASR pipeline to use (e.g., 'whisper')")
+    parser.add_argument("--asr-args", type=str, default='{"model_size": "large-v3"}', help="JSON string of additional arguments for ASR pipeline")
+    parser.add_argument("--host", type=str, default="127.0.0.1", help="Host for the WebSocket server")
+    parser.add_argument("--port", type=int, default=8765, help="Port for the WebSocket server")
+    parser.add_argument("--certfile", type=str, default=None, help="The path to the SSL certificate (cert file) if using secure websockets")
+    parser.add_argument("--keyfile", type=str, default=None, help="The path to the SSL key file if using secure websockets")
+    return parser.parse_args()
+
+def main():
+    args = parse_args()
+
+    try:
+        vad_args = json.loads(args.vad_args)
+        asr_args = json.loads(args.asr_args)
+    except json.JSONDecodeError as e:
+        print(f"Error parsing JSON arguments: {e}")
+        return
+
+    vad_pipeline = VADFactory.create_vad_pipeline(args.vad_type, **vad_args)
+    asr_pipeline = ASRFactory.create_asr_pipeline(args.asr_type, **asr_args)
+
+    server = Server(vad_pipeline, asr_pipeline, host=args.host, port=args.port, sampling_rate=16000, samples_width=2, certfile=args.certfile, keyfile=args.keyfile)
+
+    asyncio.get_event_loop().run_until_complete(server.start())
+    asyncio.get_event_loop().run_forever()
+
+if __name__ == "__main__":
+    main()

nodes/VoiceStreamAI/requirements.txt

@@ -0,0 +1,7 @@
+websockets
+speechbrain
+pyannote-audio
+asyncio
+sentence-transformers
+transformers
+faster-whisper

nodes/VoiceStreamAI/server.py

@@ -0,0 +1,88 @@
+import websockets
+import uuid
+import json
+import asyncio
+import ssl
+
+from VoiceStreamAI.audio_utils import save_audio_to_file
+from VoiceStreamAI.client import Client
+
+class Server:
+    """
+    Represents the WebSocket server for handling real-time audio transcription.
+
+    This class manages WebSocket connections, processes incoming audio data,
+    and interacts with VAD and ASR pipelines for voice activity detection and
+    speech recognition.
+
+    Attributes:
+        vad_pipeline: An instance of a voice activity detection pipeline.
+        asr_pipeline: An instance of an automatic speech recognition pipeline.
+        host (str): Host address of the server.
+        port (int): Port on which the server listens.
+        sampling_rate (int): The sampling rate of audio data in Hz.
+        samples_width (int): The width of each audio sample in bits.
+        connected_clients (dict): A dictionary mapping client IDs to Client objects.
+    """
+    def __init__(self, vad_pipeline, asr_pipeline, host='localhost', port=8765, sampling_rate=16000, samples_width=2, certfile = None, keyfile = None,llm_port=9000):
+        self.vad_pipeline = vad_pipeline
+        self.asr_pipeline = asr_pipeline
+        self.host = host
+        self.port = port
+        self.sampling_rate = sampling_rate
+        self.samples_width = samples_width
+        self.certfile = certfile
+        self.keyfile = keyfile
+        self.connected_clients = {}
+
+        self.llm_port=llm_port
+
+    async def handle_audio(self, client, websocket):
+        while True:
+            message = await websocket.recv()
+
+            if isinstance(message, bytes):
+                client.append_audio_data(message)
+            elif isinstance(message, str):
+                config = json.loads(message)
+                if config.get('type') == 'config':
+                    client.update_config(config['data'])
+                    continue
+            else:
+                print(f"Unexpected message type from {client.client_id}")
+
+            # this is synchronous, any async operation is in BufferingStrategy
+            client.process_audio(websocket, self.vad_pipeline, self.asr_pipeline,self.llm_port)
+
+
+    async def handle_websocket(self, websocket, path):
+        client_id = str(uuid.uuid4())
+        client = Client(client_id, self.sampling_rate, self.samples_width)
+        self.connected_clients[client_id] = client
+
+        print(f"Client {client_id} connected")
+
+        try:
+            await self.handle_audio(client, websocket)
+        except websockets.ConnectionClosed as e:
+            print(f"Connection with {client_id} closed: {e}")
+        finally:
+            del self.connected_clients[client_id]
+
+    def start(self):
+        if self.certfile:
+            # Create an SSL context to enforce encrypted connections
+            ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
+            
+            # Load your server's certificate and private key
+            # Replace 'your_cert_path.pem' and 'your_key_path.pem' with the actual paths to your files
+            ssl_context.load_cert_chain(certfile=self.certfile, keyfile=self.keyfile)
+
+            print(f"WebSocket server ready to accept secure connections on {self.host}:{self.port}")
+            
+            # Pass the SSL context to the serve function along with the host and port
+            # Ensure the secure flag is set to True if using a secure WebSocket protocol (wss://)
+            return websockets.serve(self.handle_websocket, self.host, self.port, ssl=ssl_context)
+        else:
+            print(f"WebSocket server ready to accept secure connections on {self.host}:{self.port}")
+            return websockets.serve(self.handle_websocket, self.host, self.port)

nodes/VoiceStreamAI/vad/pyannote_vad.py

@@ -0,0 +1,50 @@
+from os import remove
+import os
+
+from pyannote.core import Segment
+from pyannote.audio import Model
+from pyannote.audio.pipelines import VoiceActivityDetection
+
+from VoiceStreamAI.vad.vad_interface import VADInterface
+from VoiceStreamAI.audio_utils import save_audio_to_file
+
+
+class PyannoteVAD(VADInterface):
+    """
+    Pyannote-based implementation of the VADInterface.
+    """
+
+    def __init__(self, **kwargs):
+        """
+        Initializes Pyannote's VAD pipeline.
+
+        Args:
+            model_name (str): The model name for Pyannote.
+            auth_token (str, optional): Authentication token for Hugging Face.
+        """
+        
+        model_name = kwargs.get('model_name', "pyannote/segmentation")
+
+        auth_token = os.environ.get('PYANNOTE_AUTH_TOKEN')
+        if not auth_token:
+            auth_token = kwargs.get('auth_token')
+        
+        if auth_token is None:
+            raise ValueError("Missing required env var in PYANNOTE_AUTH_TOKEN or argument in --vad-args: 'auth_token'")
+        
+        pyannote_args = kwargs.get('pyannote_args', {"onset": 0.5, "offset": 0.5, "min_duration_on": 0.3, "min_duration_off": 0.3})
+        self.model = Model.from_pretrained(model_name, use_auth_token=auth_token)
+        self.vad_pipeline = VoiceActivityDetection(segmentation=self.model)
+        self.vad_pipeline.instantiate(pyannote_args)
+
+    async def detect_activity(self, client):
+        audio_file_path = await save_audio_to_file(client.scratch_buffer, client.get_file_name())
+        vad_results = self.vad_pipeline(audio_file_path)
+        remove(audio_file_path)
+        vad_segments = []
+        if len(vad_results) > 0:
+            vad_segments = [
+                {"start": segment.start, "end": segment.end, "confidence": 1.0}
+                for segment in vad_results.itersegments()
+            ]
+        return vad_segments

nodes/VoiceStreamAI/vad/vad_factory.py

@@ -0,0 +1,23 @@
+from VoiceStreamAI.vad.pyannote_vad import PyannoteVAD
+
+class VADFactory:
+    """
+    Factory for creating instances of VAD systems.
+    """
+
+    @staticmethod
+    def create_vad_pipeline(type, **kwargs):
+        """
+        Creates a VAD pipeline based on the specified type.
+
+        Args:
+            type (str): The type of VAD pipeline to create (e.g., 'pyannote').
+            kwargs: Additional arguments for the VAD pipeline creation.
+
+        Returns:
+            VADInterface: An instance of a class that implements VADInterface.
+        """
+        if type == "pyannote":
+            return PyannoteVAD(**kwargs)
+        else:
+            raise ValueError(f"Unknown VAD pipeline type: {type}")

nodes/VoiceStreamAI/vad/vad_interface.py

@@ -0,0 +1,16 @@
+class VADInterface:
+    """
+    Interface for voice activity detection (VAD) systems.
+    """
+
+    async def detect_activity(self, client):
+        """
+        Detects voice activity in the given audio data.
+
+        Args:
+            client (src.Client): The client to detect on
+
+        Returns:
+            List: VAD result, a list of objects containing "start", "end", "confidence"
+        """
+        raise NotImplementedError("This method should be implemented by subclasses.")

nodes/edit_mask.py

@@ -1,172 +0,0 @@
-import torch
-from PIL import Image, ImageOps, ImageSequence, ImageFile
-from PIL.PngImagePlugin import PngInfo
-
-import numpy as np
-import os
-import folder_paths 
-import node_helpers
-import hashlib
-
-# Tensor to PIL
-def tensor2pil(image):
-    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-
-# tensor 取hash值
-def tensor_to_hash(tensor):
-    # 将 Tensor 转换为 NumPy 数组
-    np_array = tensor.cpu().numpy()
-    
-    # 将 NumPy 数组转换为字节数据
-    byte_data = np_array.tobytes()
-    
-    # 计算哈希值
-    hash_value = hashlib.md5(byte_data).hexdigest()
-    
-    return hash_value
-
-
-def create_temp_file(image):
-    output_dir = folder_paths.get_temp_directory()
-
-    (
-            full_output_folder,
-            filename,
-            counter,
-            subfolder,
-            _,
-        ) = folder_paths.get_save_image_path('material', 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 (image_path,[{
-    "filename": image_file,
-    "subfolder": subfolder,
-    "type": "temp"
-    }])
-
-
-# image - tensor - 文件路径
-# loadImage的方法（ 文件路径 - image-mask ）
-class EditMask:
-
-    def __init__(self):
-        self.image_id = None
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {"image": ("IMAGE",), # 表示一个张量
-                     
-                     },
-
-                    "optional":{
-                            "image_update": ("IMAGE_FILE",)
-                        },
-                   
-                }
-
-    CATEGORY = "♾️Mixlab/Mask"
-
-    RETURN_TYPES = ("IMAGE", "MASK")
-    RETURN_NAMES = ("image", "mask")
-
-    FUNCTION = "edit"
-
-    OUTPUT_NODE = True
-
-    def edit(self, image,image_update=None):
-
-        # 根据image输入来判断是否是新的图片
-        if self.image_id==None:
-            self.image_id=tensor_to_hash(image)
-            image_update=None
-        else:
-            image_id=tensor_to_hash(image)
-            if image_id!=self.image_id:
-                image_update=None
-                self.image_id=image_id
-
-
-        image_path=None
-        # print('#image_update',self.image_id,image_update)
-        if image_update==None:
-            print('--')
-        else:
-            if 'images' in image_update:
-                images=image_update['images']
-                filename=images[0]['filename']
-                subfolder=images[0]['subfolder']
-                type=images[0]['type']
-                name, base_dir=folder_paths.annotated_filepath(filename)
-                if type.endswith("output"):
-                    base_dir = folder_paths.get_output_directory() 
-                elif type.endswith("input"):
-                    base_dir = folder_paths.get_input_directory() 
-                elif type.endswith("temp"):
-                    base_dir = folder_paths.get_temp_directory() 
-                #base_dir = folder_paths.get_input_directory()  
-                # print(base_dir,subfolder, name)
-                image_path = os.path.join(base_dir,subfolder, name)
-        
-        if image_path==None:
-            image_path,images=create_temp_file(image)
-
-        print('#image_path',os.path.exists(image_path),image_path)
-        # image_path = folder_paths.get_annotated_filepath(image) #文件名
-        
-        if not os.path.exists(image_path):
-            image_path,images=create_temp_file(image)
-
-
-        img = node_helpers.pillow(Image.open, image_path)
-        
-        output_images = []
-        output_masks = []
-        w, h = None, None
-
-        excluded_formats = ['MPO']
-        
-        for i in ImageSequence.Iterator(img):
-            i = node_helpers.pillow(ImageOps.exif_transpose, i)
-
-            if i.mode == 'I':
-                i = i.point(lambda i: i * (1 / 255))
-            image = i.convert("RGB")
-
-            if len(output_images) == 0:
-                w = image.size[0]
-                h = image.size[1]
-            
-            if image.size[0] != w or image.size[1] != h:
-                continue
-            
-            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)
-            else:
-                # 尺寸不对，需要按照image来
-                mask = torch.zeros((h, w), dtype=torch.float32, device="cpu")
-
-            output_images.append(image)
-            output_masks.append(mask.unsqueeze(0))
-
-        if len(output_images) > 1 and img.format not in excluded_formats:
-            output_image = torch.cat(output_images, dim=0)
-            output_mask = torch.cat(output_masks, dim=0)
-        else:
-            output_image = output_images[0]
-            output_mask = output_masks[0]
-
-        return {"ui":{"images": images},"result": (output_image, output_mask)}
-
-        # return (output_image, output_mask)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-mixlab-nodes"
 description = "3D, ScreenShareNode & FloatingVideoNode, SpeechRecognition & SpeechSynthesis, GPT, LoadImagesFromLocal, Layers, Other Nodes, ..."
-version = "0.41.0"
+version = "0.28.3"
 license = "MIT"
 dependencies = ["numpy", "pyOpenSSL", "watchdog", "opencv-python-headless", "matplotlib", "openai", "simple-lama-inpainting", "clip-interrogator==0.6.0", "transformers>=4.36.0", "lark-parser", "imageio-ffmpeg", "rembg[gpu]", "omegaconf==2.3.0", "Pillow>=9.5.0", "einops==0.7.0", "trimesh>=4.0.5", "huggingface-hub", "scikit-image"]
 

requirements.txt

@@ -4,21 +4,15 @@ watchdog
 opencv-python-headless
 matplotlib
 openai
-torchaudio
-# simple-lama-inpainting
+simple-lama-inpainting
 clip-interrogator==0.6.0
 transformers>=4.36.0
 lark-parser
 imageio-ffmpeg
 rembg[gpu]
-omegaconf>=2.3.0
+omegaconf==2.3.0
 Pillow>=9.5.0
-einops>=0.7.0
+einops==0.7.0
 trimesh>=4.0.5
 huggingface-hub
-scikit-image
-soundfile>=0.12.1
-json-repair
-
-bitsandbytes
-accelerate
+scikit-image

web/javascript/3d_mixlab.js

@@ -2,62 +2,6 @@ import { app } from '../../../scripts/app.js'
 import { api } from '../../../scripts/api.js'
 import { $el } from '../../../scripts/ui.js'
 
-import { loadExternalScript, get_position_style } from './common.js'
-
-function setCameraOrbit (modelview, distant, angles, screenNumber) {
-  //2.1 20
-  // const angles = {
-  //   1: -20.0,
-  //   2: -17.9,
-  //   3: -15.8,
-  //   4: -13.7,
-  //   5: -11.6,
-  //   6: -9.5,
-  //   7: -7.4,
-  //   8: -5.3,
-  //   9: -3.2,
-  //   10: -1.1,
-  //   11: 1.1,
-  //   12: 3.2,
-  //   13: 5.3,
-  //   14: 7.4,
-  //   15: 9.5,
-  //   16: 11.6,
-  //   17: 13.7,
-  //   18: 15.8,
-  //   19: 17.9,
-  //   20: 20.0
-  // };
-
-  // 12 3.6
-  // const angles = {
-  //   1: -20.0,
-  //   2: -16.4,
-  //   3: -12.7,
-  //   4: -9.1,
-  //   5: -5.5,
-  //   6: -1.8,
-  //   7: 1.8,
-  //   8: 5.5,
-  //   9: 9.1,
-  //   10: 12.7,
-  //   11: 16.4,
-  //   12: 20.0
-  // }
-
-  const angle = angles[screenNumber]
-
-  let co=modelview.cameraOrbit.split(" ")
-
-  if (angle !== undefined) {
-    
-    modelview.cameraOrbit = `${angle}deg ${co[1]} ${distant}m`
-    console.log(screenNumber, angle)
-  } else {
-    console.error('Invalid screen number')
-  }
-}
-
 const getLocalData = key => {
   let data = {}
   try {
@@ -82,8 +26,7 @@ const setLocalDataOfWin = (key, value) => {
   localStorage.setItem(key, JSON.stringify(value))
   // window[key] = value
 }
-
-async function uploadImage_ (blob, fileType = '.svg', filename) {
+async function uploadImage (blob, fileType = '.svg', filename) {
   // const blob = await (await fetch(src)).blob();
   const body = new FormData()
   body.append(
@@ -98,17 +41,13 @@ async function uploadImage_ (blob, fileType = '.svg', filename) {
 
   // console.log(resp)
   let data = await resp.json()
-  return data
-}
-
-async function uploadImage (blob, fileType = '.svg', filename) {
-  let data = await uploadImage_(blob, fileType, filename)
   let { name, subfolder } = data
   let src = api.apiURL(
     `/view?filename=${encodeURIComponent(
       name
     )}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
   )
+
   return src
 }
 
@@ -139,6 +78,38 @@ const parseImage = url => {
   })
 }
 
+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'
+  }
+}
+
 async function extractMaterial (
   modelViewerVariants,
   selectMaterial,
@@ -200,42 +171,6 @@ async function changeMaterial (
   targetMaterial.pbrMetallicRoughness.baseColorTexture.setTexture(targetTexture)
 }
 
-function inputFileClick (isFileURL = false, isGlb = false) {
-  return new Promise((res, rej) => {
-    // 创建一个input元素
-    var input = document.createElement('input')
-    input.type = 'file'
-    input.accept = isGlb ? '.glb' : 'image/*'
-
-    // 监听input的change事件
-    input.addEventListener('change', function () {
-      // 获取上传的文件
-      var file = input.files[0]
-
-      if (isFileURL) {
-        res(URL.createObjectURL(file))
-        return
-      }
-
-      // 创建一个FileReader对象来读取文件
-      var reader = new FileReader()
-
-      // 监听FileReader的load事件
-      reader.addEventListener('load', async () => {
-        let base64 = reader.result
-        input.remove()
-        res(base64)
-      })
-
-      // 读取文件
-      reader.readAsDataURL(file)
-    })
-
-    // 触发input的点击事件
-    input.click()
-  })
-}
-
 app.registerExtension({
   name: 'Mixlab.3D.3DImage',
   async getCustomWidgets (app) {
@@ -254,7 +189,7 @@ app.registerExtension({
             let d = getLocalData('_mixlab_3d_image')
             // console.log('serializeValue', node)
             if (d && d[node.id]) {
-              let { url, bg, material, images } = d[node.id]
+              let { url, bg, material } = d[node.id]
               let data = {}
               if (url) {
                 data.image = await parseImage(url)
@@ -270,10 +205,6 @@ app.registerExtension({
                 data.material = await parseImage(material)
               }
 
-              if (images) {
-                data.images = images
-              }
-
               return JSON.parse(JSON.stringify(data))
             } else {
               return {}
@@ -286,62 +217,66 @@ app.registerExtension({
     }
   },
 
-  async init () {
-    await loadExternalScript('/mixlab/app/lib/model-viewer.min.js', 'module')
-  },
-
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeType.comfyClass == '3DImage') {
-      console.log('nodeType.comfyClass', nodeType.comfyClass)
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = async function () {
         orig_nodeCreated?.apply(this, arguments)
+
         const uploadWidget = this.widgets.filter(w => w.name == 'upload')[0]
+
         const widget = {
           type: 'div',
           name: 'upload-preview',
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(ctx, widget_width - 122, 88, node.size[1], 44)
+              get_position_style(ctx, widget_width, 88, node.size[1])
             )
           }
         }
 
         widget.div = $el('div', {})
         widget.div.style.width = `120px`
+
         document.body.appendChild(widget.div)
 
         const inputDiv = (key, placeholder, preview) => {
           let div = document.createElement('div')
-          const ip = document.createElement('button')
+          const ip = document.createElement('input')
+          ip.type = 'file'
           ip.className = `${'comfy-multiline-input'} ${placeholder}`
           div.style = `display: flex;
             align-items: center; 
             margin: 6px 8px;
             margin-top: 0;`
+          ip.placeholder = placeholder
+          // ip.value = value
 
           ip.style = `outline: none;
             border: none;
             padding: 4px;
-            width: 100px;cursor: pointer;
+            width: 60%;cursor: pointer;
             height: 32px;`
-          ip.innerText = placeholder
+          const label = document.createElement('label')
+          label.style = 'font-size: 10px;min-width:32px'
+          label.innerText = placeholder
+          div.appendChild(label)
           div.appendChild(ip)
 
-          let that = this
+          let that = this,
+            filename = new Date().getTime()
 
-          ip.addEventListener('click', async event => {
-            let fileURL = await inputFileClick(true, true)
-
-            // console.log('文件URL: ', fileURL)
-            let html = `<model-viewer  src="${fileURL}" 
-                oncontextmenu="return false;"
-                style="outline:1px solid white"
-                min-field-of-view="0deg" 
-                max-field-of-view="180deg"
-                min-camera-orbit="auto auto 0m"
-                max-camera-orbit="auto auto 1000m"
+          ip.addEventListener('change', async event => {
+            const file = event.target.files[0]
+            const reader = new FileReader()
+            filename = new Date().getTime()
+            // 读取文件内容
+            reader.onload = async e => {
+              const fileURL = URL.createObjectURL(file)
+              // console.log('文件URL: ', fileURL)
+              let html = `<model-viewer  src="${fileURL}" 
+                min-field-of-view="0deg" max-field-of-view="180deg"
                  shadow-intensity="1" 
                  camera-controls 
                  touch-action="pan-y">
@@ -350,314 +285,230 @@ app.registerExtension({
                   <div>Variant: <select class="variant"></select></div>
                   <div>Material: <select class="material"></select></div>
                   <div>Material: <div class="material_img"> </div></div>
-                  <div>
-                    <button class="bg">BG</button>
-                   
-                  </div>
-                  <div>
-                   <input class="ddcap_distant" type="number" min="1" step="1" value="55">
-                   <input class="total_images" type="number" min="1" max="180" step="1" value="20">
-                   <input class="ddcap_range" type="number" min="0" max="20" step="0.1" value="2.1"> 
-                  <button class="ddcap">Capture Rotational Screenshots</button></div>
-                  
+                  <div><button class="bg">BG</button></div>
                   <div><button class="export">Export GLB</button></div>
                   
                 </div></model-viewer>`
 
-            preview.innerHTML = html
-            if (that.size[1] < 400) {
-              that.setSize([that.size[0], that.size[1] + 300])
-              app.canvas.draw(true, true)
-            }
-
-            const modelViewerVariants = preview.querySelector('model-viewer')
-            const select = preview.querySelector('.variant')
-            const selectMaterial = preview.querySelector('.material')
-            const material_img = preview.querySelector('.material_img')
-            const bg = preview.querySelector('.bg')
-
-            const exportGLB = preview.querySelector('.export')
-
-            const ddcap_distant = preview.querySelector('.ddcap_distant')
-            const total_images = preview.querySelector('.total_images')
-            const ddcap_range = preview.querySelector('.ddcap_range')
-            const ddCap = preview.querySelector('.ddcap')
-            const sleep = (t = 1000) => {
-              return new Promise((res, rej) => {
-                return setTimeout(() => {
-                  res(t)
-                }, t)
-              })
-            }
-
-            async function captureImage (isUrl = true) {
-              let base64Data = modelViewerVariants.toDataURL()
-
-              const contentType = getContentTypeFromBase64(base64Data)
-
-              const blob = await base64ToBlobFromURL(base64Data, contentType)
-
-              if (isUrl) return await uploadImage(blob, '.png')
-              return await uploadImage_(blob, '.png')
-            }
-
-            async function captureImages (
-              ddcap_range = 1,
-              total_images = 12,
-              distant = 0.23
-            ) {
-              //  初始 角度
-              var center = modelViewerVariants.getBoundingBoxCenter().toString()
-              modelViewerVariants.cameraTarget = center
-
-              const startAngle = -((total_images - 1) / 2) * ddcap_range
-              const angles = {}
-
-              for (let i = 0; i < total_images; i++) {
-                angles[i + 1] = startAngle + i * ddcap_range
-              }
-              console.log(angles)
-
-              let frames = []
-
-              modelViewerVariants.removeAttribute('camera-controls')
-
-              for (let i = 0; i < total_images; i++) {
-                setCameraOrbit(modelViewerVariants, distant, angles, i + 1)
-
-                // modelViewerVariants.cameraOrbit = `${currentAngle}deg ${initialCameraOrbit[1]} ${initialCameraOrbit[2]}`
-                await sleep(1000)
-                // console.log(`Capturing image at angle: ${currentAngle}deg`)
-                let file = await captureImage(false)
-                frames.push(file)
-                // currentAngle += angleIncrement
-              }
-              await sleep(1000)
-              // 恢复到初始旋转角度
-              // modelViewerVariants.cameraOrbit = initialCameraOrbit.join(' ')
-              modelViewerVariants.setAttribute('camera-controls', '')
-              return frames
-            }
-
-            ddCap.addEventListener('click', async e => {
-              const distant = Number(ddcap_distant.value), //  23m
-                totalImages = Number(total_images.value),
-                angleIncrement = Number(ddcap_range.value)
-              console.log(angleIncrement, totalImages)
-              let images = await captureImages(
-                angleIncrement,
-                totalImages,
-                distant
-              )
-
-              let dd = getLocalData(key)
-              dd[that.id].images = images
-              setLocalDataOfWin(key, dd)
-            })
-
-            ddcap_distant.addEventListener('input', async e => {
-              // console.log(ddcap_distant.value)
-              const center = modelViewerVariants.getBoundingBoxCenter().toString()
-              modelViewerVariants.cameraTarget = center;
-              const initialCameraOrbit =
-                modelViewerVariants.cameraOrbit.split(' ')
-              modelViewerVariants.cameraOrbit = `${initialCameraOrbit[2]} ${initialCameraOrbit[1]} ${ddcap_distant.value}m`
-              modelViewerVariants.setAttribute('camera-controls', '')
-            })
-
-            // ddcap_range_top.addEventListener('input', async e => {
-            //   // console.log(ddcap_range.value)
-            //   const initialCameraOrbit =
-            //     modelViewerVariants.cameraOrbit.split(' ')
-            //   modelViewerVariants.cameraOrbit = `${initialCameraOrbit[0]} ${ddcap_range_top.value}deg ${initialCameraOrbit[2]}`
-            //   modelViewerVariants.setAttribute('camera-controls', '')
-            // })
-
-            if (modelViewerVariants) {
-              modelViewerVariants.style.width = `${that.size[0] - 48}px`
-              modelViewerVariants.style.height = `${that.size[1] - 48}px`
-            }
-
-            modelViewerVariants.addEventListener('load', async () => {
-              const names = modelViewerVariants.availableVariants
-
-              // 变量
-              for (const name of names) {
-                const option = document.createElement('option')
-                option.value = name
-                option.textContent = name
-                select.appendChild(option)
-              }
-              // Adds a default option.
-              if (names.length === 0) {
-                const option = document.createElement('option')
-                option.value = 'default'
-                option.textContent = 'Default'
-                select.appendChild(option)
+              preview.innerHTML = html
+              if (that.size[1] < 400) {
+                that.setSize([that.size[0], that.size[1] + 300])
+                app.canvas.draw(true, true)
               }
 
-              // 材质
-              extractMaterial(modelViewerVariants, selectMaterial, material_img)
-            })
-
-            let timer = null
-            const delay = 500 // 延迟时间，单位为毫秒
-
-            async function checkCameraChange () {
-              let dd = getLocalData(key)
-
-              //  const fileBlob = new Blob([e.target.result], { type: file.type });
-              let url = await captureImage()
-
-              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 }
-              } 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)
-            }
-
-            function startTimer () {
-              if (timer) clearTimeout(timer)
-              timer = setTimeout(checkCameraChange, delay)
-            }
-
-            modelViewerVariants.addEventListener('camera-change', startTimer)
-
-            select.addEventListener('input', async event => {
-              modelViewerVariants.variantName =
-                event.target.value === 'default' ? null : event.target.value
-              // 材质
-              await extractMaterial(
-                modelViewerVariants,
-                selectMaterial,
-                material_img
-              )
-              checkCameraChange()
-            })
-
-            selectMaterial.addEventListener('input', event => {
-              // console.log(selectMaterial.value)
-              material_img.setAttribute('src', selectMaterial.value)
-
-              if (selectMaterial.getAttribute('data-new-material')) {
-                let index =
-                  ~~selectMaterial.selectedOptions[0].getAttribute('data-index')
-                changeMaterial(
-                  modelViewerVariants,
-                  modelViewerVariants.model.materials[index],
-                  selectMaterial.getAttribute('data-new-material')
-                )
-              }
-
-              checkCameraChange()
-            })
-
-            //更新bg
-            const updateBgData = (id, key, url, w, h) => {
-              let dd = getLocalData(key)
-              // console.log(dd[that.id],url)
-              if (!dd[id]) dd[id] = { url: '', bg: url }
-              dd[id] = {
-                ...dd[id],
-                bg: url,
-                bg_w: w,
-                bg_h: h
-              }
-              setLocalDataOfWin(key, dd)
-            }
-
-            bg.addEventListener('click', async () => {
-              //更新bg
-              updateBgData(that.id, key, '', 0, 0)
-              preview.style.backgroundImage = 'none'
-
-              let base64 = await inputFileClick(false, false)
-              // 将读取的文件内容设置为div的背景
-              preview.style.backgroundImage = 'url(' + base64 + ')'
-
-              const contentType = getContentTypeFromBase64(base64)
-
-              const blob = await base64ToBlobFromURL(base64, contentType)
-
-              //  const fileBlob = new Blob([e.target.result], { type: file.type });
-              let bg_url = await uploadImage(blob, '.png')
-              let bg_img = await createImage(base64)
-
-              //更新bg
-              updateBgData(
-                that.id,
-                key,
-                bg_url,
-                bg_img.naturalWidth,
-                bg_img.naturalHeight
-              )
-
-              // 更新尺寸
-              let w = that.size[0] - 128,
-                h = (w * bg_img.naturalHeight) / bg_img.naturalWidth
+              const modelViewerVariants = preview.querySelector('model-viewer')
+              const select = preview.querySelector('.variant')
+              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 = `${w}px`
-                modelViewerVariants.style.height = `${h}px`
+                modelViewerVariants.style.width = `${that.size[0] - 24}px`
+                modelViewerVariants.style.height = `${that.size[1] - 48}px`
               }
-              preview.style.width = `${w}px`
-            })
 
-            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()
-            })
+              modelViewerVariants.addEventListener('load', async () => {
+                const names = modelViewerVariants.availableVariants
 
-            uploadWidget.value = await uploadWidget.serializeValue()
-
-            // 更新尺寸
-            let dd = getLocalData(key)
-            // console.log(dd[that.id],bg_url)
-            if (dd[that.id]) {
-              const { bg_w, bg_h } = dd[that.id]
-              if (bg_h && bg_w) {
-                let w = that.size[0] - 48,
-                  h = (w * bg_h) / bg_w
-
-                if (modelViewerVariants) {
-                  modelViewerVariants.style.width = `${w}px`
-                  modelViewerVariants.style.height = `${h}px`
+                // 变量
+                for (const name of names) {
+                  const option = document.createElement('option')
+                  option.value = name
+                  option.textContent = name
+                  select.appendChild(option)
+                }
+                // Adds a default option.
+                if (names.length === 0) {
+                  const option = document.createElement('option')
+                  option.value = 'default'
+                  option.textContent = 'Default'
+                  select.appendChild(option)
+                }
+
+                // 材质
+                extractMaterial(
+                  modelViewerVariants,
+                  selectMaterial,
+                  material_img
+                )
+              })
+
+              let timer = null
+              const delay = 500 // 延迟时间，单位为毫秒
+
+              async function checkCameraChange () {
+                let dd = getLocalData(key)
+                let base64Data = modelViewerVariants.toDataURL()
+
+                const contentType = getContentTypeFromBase64(base64Data)
+
+                const blob = await base64ToBlobFromURL(base64Data, contentType)
+
+                //  const fileBlob = new Blob([e.target.result], { type: file.type });
+                let url = await uploadImage(blob, '.png')
+                // console.log(url)
+
+                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 }
+                } 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)
+              }
+
+              function startTimer () {
+                if (timer) clearTimeout(timer)
+                timer = setTimeout(checkCameraChange, delay)
+              }
+
+              modelViewerVariants.addEventListener('camera-change', startTimer)
+
+              select.addEventListener('input', async event => {
+                modelViewerVariants.variantName =
+                  event.target.value === 'default' ? null : event.target.value
+                // 材质
+                await extractMaterial(
+                  modelViewerVariants,
+                  selectMaterial,
+                  material_img
+                )
+                checkCameraChange()
+              })
+
+              selectMaterial.addEventListener('input', event => {
+                // console.log(selectMaterial.value)
+                material_img.setAttribute('src', selectMaterial.value)
+
+                if (selectMaterial.getAttribute('data-new-material')) {
+                  let index =
+                    ~~selectMaterial.selectedOptions[0].getAttribute(
+                      'data-index'
+                    )
+                  changeMaterial(
+                    modelViewerVariants,
+                    modelViewerVariants.model.materials[index],
+                    selectMaterial.getAttribute('data-new-material')
+                  )
+                }
+
+                checkCameraChange()
+              })
+
+              bg.addEventListener('click', () => {
+                // 创建一个input元素
+                var input = document.createElement('input')
+                input.type = 'file'
+
+                // 监听input的change事件
+                input.addEventListener('change', function () {
+                  // 获取上传的文件
+                  var file = input.files[0]
+
+                  // 创建一个FileReader对象来读取文件
+                  var reader = new FileReader()
+
+                  // 监听FileReader的load事件
+                  reader.addEventListener('load', async () => {
+                    let base64 = reader.result
+                    // 将读取的文件内容设置为div的背景
+                    preview.style.backgroundImage = 'url(' + base64 + ')'
+
+                    const contentType = getContentTypeFromBase64(base64)
+
+                    const blob = await base64ToBlobFromURL(base64, contentType)
+
+                    //  const fileBlob = new Blob([e.target.result], { type: file.type });
+                    let bg_url = await uploadImage(blob, '.png')
+                    let bg_img = await createImage(base64)
+
+                    let dd = getLocalData(key)
+                    // console.log(dd[that.id],bg_url)
+                    if (!dd[that.id]) dd[that.id] = { url: '', bg: bg_url }
+                    dd[that.id] = {
+                      ...dd[that.id],
+                      bg: bg_url,
+                      bg_w: bg_img.naturalWidth,
+                      bg_h: bg_img.naturalHeight
+                    }
+
+                    setLocalDataOfWin(key, dd)
+
+                    // 更新尺寸
+                    let w = that.size[0] - 24,
+                      h = (w * bg_img.naturalHeight) / bg_img.naturalWidth
+
+                    if (modelViewerVariants) {
+                      modelViewerVariants.style.width = `${w}px`
+                      modelViewerVariants.style.height = `${h}px`
+                    }
+                    preview.style.width = `${w}px`
+                  })
+
+                  // 读取文件
+                  reader.readAsDataURL(file)
+                })
+
+                // 触发input的点击事件
+                input.click()
+              })
+
+              exportGLB.addEventListener('click', async () => {
+                const glTF = await modelViewerVariants.exportScene()
+                const file = new File([glTF], 'export.glb')
+                const link = document.createElement('a')
+                link.download = file.name
+                link.href = URL.createObjectURL(file)
+                link.click()
+              })
+
+              uploadWidget.value = await uploadWidget.serializeValue()
+
+              // 更新尺寸
+              let dd = getLocalData(key)
+              // console.log(dd[that.id],bg_url)
+              if (dd[that.id]) {
+                const { bg_w, bg_h } = dd[that.id]
+                if (bg_h && bg_w) {
+                  let w = that.size[0] - 24,
+                    h = (w * bg_h) / bg_w
+
+                  if (modelViewerVariants) {
+                    modelViewerVariants.style.width = `${w}px`
+                    modelViewerVariants.style.height = `${h}px`
+                  }
+                  preview.style.width = `${w}px`
                 }
-                preview.style.width = `${w}px`
               }
             }
+
+            // 以文本形式读取文件
+            reader.readAsDataURL(file)
           })
           return div
         }
 
         let preview = document.createElement('div')
         preview.className = 'preview'
-        preview.style = `margin-top: 12px;
-          display: flex;
+        preview.style = `margin-top: 12px;display: flex;
           justify-content: center;
-          align-items: center;background-repeat: no-repeat;
-          background-size: contain;`
+          align-items: center;background-repeat: no-repeat;background-size: contain;`
 
         let upload = inputDiv('_mixlab_3d_image', '3D Model', preview)
 
@@ -672,25 +523,18 @@ app.registerExtension({
 
           // 更新尺寸
           let dd = getLocalData('_mixlab_3d_image')
-
+          // console.log(dd[that.id],bg_url)
           if (dd[that.id]) {
             const { bg_w, bg_h } = dd[that.id]
-            let w = that.size[0] - 128
-            preview.style.width = `${w}px`
-            console.log('更新尺寸', w)
-
-            if (modelViewerVariants) {
-              modelViewerVariants.style.width = `${w}px`
-              modelViewerVariants.style.height = `${Math.round(
-                that.size[1] * 0.8
-              )}px`
-            }
-
             if (bg_h && bg_w) {
-              let h = (w * bg_h) / bg_w
+              let w = that.size[0] - 24,
+                h = (w * bg_h) / bg_w
+
               if (modelViewerVariants) {
+                modelViewerVariants.style.width = `${w}px`
                 modelViewerVariants.style.height = `${h}px`
               }
+              preview.style.width = `${w}px`
             }
           }
 
@@ -717,7 +561,7 @@ app.registerExtension({
         const r = onExecuted?.apply?.(this, arguments)
 
         let div = this.widgets.filter(d => d.div)[0]?.div
-        // console.log('Test', this.widgets)
+        console.log('Test', this.widgets)
 
         let material = message.material[0]
         if (material) {
@@ -773,7 +617,7 @@ app.registerExtension({
       // let base64 = await parseImage(url)
 
       let pre = widget.div.querySelector('.preview')
-      pre.style.width = `${node.size[0] - 24}px`
+      pre.style.width = `${node.size[0]}px`
       pre.innerHTML = `
         ${url ? `<img src="${url}" style="width:100%"/>` : ''}
         `

web/javascript/app_mixlab.js

@@ -2,18 +2,27 @@ import { app } from '../../../scripts/app.js'
 import { $el } from '../../../scripts/ui.js'
 import { api } from '../../../scripts/api.js'
 
-import { td_bg } from './td_background.js'
-// console.log('td_bg', td_bg)
-import {
-  getUrl,
-  base64Df,
-  get_position_style,
-  getObjectInfo
-} from './common.js'
-
 //本机安装的插件节点全集
 window._nodesAll = null
 
+//获取当前系统的插件，节点清单
+function getObjectInfo () {
+  return new Promise(async (resolve, reject) => {
+    let url = getUrl()
+
+    try {
+      const response = await fetch(`${url}/object_info`)
+      const data = await response.json()
+      resolve(data)
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
+
+const base64Df =
+  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
+
 const parseImageToBase64 = url => {
   return new Promise((res, rej) => {
     fetch(url)
@@ -33,6 +42,39 @@ const parseImageToBase64 = url => {
   })
 }
 
+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: 'column',
+    // alignItems: 'center',
+    justifyContent: 'flex-start',
+    zIndex: 9999999
+  }
+}
+
 async function drawImageToCanvas (imageUrl, sFactor = 320) {
   var canvas = document.createElement('canvas')
   var ctx = canvas.getContext('2d')
@@ -143,25 +185,6 @@ async function extractInputAndOutputData (
         if (node.type == 'Color') {
         }
 
-        // 语音输入的支持
-        if (node.type == 'LoadAndCombinedAudio_') {
-          // if (
-          //   data[id].widgets_values &&
-          //   data[id].widgets_values[0] &&
-          //   data[id].widgets_values[0].base64 &&
-          //   data[id].widgets_values[0].base64.length > 0
-          // ) {
-          //   options.defaultBase64 = data[id].widgets_values[0].base64
-          // }
-
-          input[inputIds.indexOf(id)] = {
-            ...data[id],
-            title: node.title,
-            id,
-            options
-          }
-        }
-
         if (node.type === 'LoadImage') {
           // loadImage的mask支持
           let output = node.outputs.filter(ot => ot.type == 'MASK')[0]
@@ -212,9 +235,7 @@ async function extractInputAndOutputData (
         node.type === 'KSampler' ||
         node.type == 'SamplerCustom' ||
         node.type === 'ChinesePrompt_Mix' ||
-        node.type === 'Seed_' ||
-        node.type === 'SiliconflowLLM' ||
-        node.type === 'ChatGPTOpenAI'
+        node.type === 'Seed_'
       ) {
         // seed 的类型收集
         try {
@@ -234,6 +255,23 @@ async function extractInputAndOutputData (
   return { input, output, seed, seedTitle }
 }
 
+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
+}
+
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+
 async function save_app (json) {
   let url = getUrl()
 
@@ -268,20 +306,18 @@ function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
 async function save (json, download = false, showInfo = true) {
   let nodesAll = window._nodesAll || (await getObjectInfo())
 
-  console.log('####SAVE', nodesAll, json)
+  console.log('####SAVE', nodesAll, json[0])
 
   const name = json[0],
     version = json[5],
     share_prefix = json[6], //用于分享的功能扩展
     link = json[7], //用于创建界面上的跳转链接
     category = json[8] || '', //用于分类
-    idle_animation = json[9], //用于动画，比如数字人her
     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=${encodeURIComponent(
@@ -334,27 +370,6 @@ async function save (json, download = false, showInfo = true) {
     try {
       data.app.icon = await drawImageToCanvas(iconUrl)
     } catch (error) {}
-
-    let images = []
-    if (json[1].length > 1 && idle_animation) {
-      images = Array.from(json[1], j => {
-        let { filename, subfolder, type } = j
-        return api.apiURL(
-          `/view?filename=${encodeURIComponent(
-            filename
-          )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
-        )
-      })
-    }
-
-    try {
-      for (let index = 0; index < images.length; index++) {
-        const imgurl = images[index]
-        images[index] = await drawImageToCanvas(imgurl)
-      }
-      data.app.idle_animation = images
-    } catch (error) {}
-
     // console.log(data.app)
     // let http_workflow = app.graph.serialize()
     await save_app(data)
@@ -364,17 +379,13 @@ async function save (json, download = false, showInfo = true) {
 
     if (showInfo) {
       let open = window.confirm(
-        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app${
-          data.app.idle_animation ? '/her.html' : ''
-        }?filename=${encodeURIComponent(
+        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app?filename=${encodeURIComponent(
           data.app.filename
         )}&category=${encodeURIComponent(data.app.category)}`
       )
       if (open)
         window.open(
-          `${getUrl()}/mixlab/app${
-            data.app.idle_animation ? '/her.html' : ''
-          }?filename=${encodeURIComponent(
+          `${getUrl()}/mixlab/app?filename=${encodeURIComponent(
             data.app.filename
           )}&category=${encodeURIComponent(data.app.category)}`
         )
@@ -386,11 +397,11 @@ async function save (json, download = false, showInfo = true) {
 
 function getInputsAndOutputs () {
   const inputs =
-      `LoadImage LoadImagesToBatch ImagesPrompt_ LoadAndCombinedAudio_ LoadVideoAndSegment_ VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
+      `LoadImage LoadImagesToBatch ImagesPrompt_ VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
         ' '
       ),
     outputs =
-      `SaveTripoSRMesh,PreviewImage,SaveImage,TransparentImage,ShowTextForGPT,CombineAudioVideo,VHS_VideoCombine,VideoCombine_Adv,Image Save,SaveImageAndMetadata_,ClipInterrogator`.split(
+      `SaveTripoSRMesh,PreviewImage,SaveImage,TransparentImage,ShowTextForGPT,VHS_VideoCombine,VideoCombine_Adv,Image Save,SaveImageAndMetadata_,ClipInterrogator`.split(
         ','
       )
 
@@ -434,19 +445,20 @@ app.registerExtension({
         const { input, output } = getInputsAndOutputs()
         input_ids.value = input.join('\n')
         output_ids.value = output.join('\n')
+
         const widget = {
           type: 'div',
           name: 'AppInfoRun',
           draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(this.div.style, {
-              ...get_position_style(
+            Object.assign(
+              this.div.style,
+              get_position_style(
                 ctx,
                 widget_width,
                 node.size[1] - widget_height,
                 node.size[1]
-              ),
-              zIndex: 1
-            })
+              )
+            )
           }
         }
 
@@ -491,21 +503,6 @@ app.registerExtension({
           }
         })
 
-        //td bg
-        const tdBG = document.createElement('button')
-        tdBG.innerText = 'Canvas Mode'
-        tdBG.style = style
-        tdBG.style.marginLeft = '12px'
-
-        tdBG.addEventListener('click', () => {
-          td_bg.toggle()
-          if (td_bg.running) {
-            tdBG.style.background = 'yellow'
-          } else {
-            tdBG.style.background = 'transparent'
-          }
-        })
-
         // author
         let author = document.createElement('div')
         // author.style=`display: flex`
@@ -662,7 +659,6 @@ app.registerExtension({
 
         btns.appendChild(btn)
         btns.appendChild(download)
-        btns.appendChild(tdBG)
 
         document.body.appendChild(widget.div)
         this.addCustomWidget(widget)
@@ -691,8 +687,9 @@ app.registerExtension({
           }
 
           const div = this.widgets.filter(w => w.div)[0].div
-          Array.from(div.querySelectorAll('button'), b =>
-            b.innerText != 'Canvas Mode' ? (b.style.background = 'yellow') : ''
+          Array.from(
+            div.querySelectorAll('button'),
+            b => (b.style.background = 'yellow')
           )
         } catch (error) {}
       }

web/javascript/audio_mixlab.js

@@ -19,10 +19,7 @@ function get_position_style (ctx, widget_width, y, node_height) {
   return {
     transformOrigin: '0 0',
     transform: transform,
-    left:
-    document.querySelector('.comfy-menu').style.display === 'none'
-      ? `60px`
-      : `0`,
+    left: `0`,
     top: `0`,
     cursor: 'pointer',
     position: 'absolute',
@@ -399,218 +396,3 @@ app.registerExtension({
     }
   }
 })
-
-// 上传音频转为base64
-async function uploadAndConvertAudio (file) {
-  if (!file) {
-    alert('Please select a WAV file.')
-    return
-  }
-
-  if (file.type !== 'audio/wav') {
-    alert('Only WAV files are supported.')
-    return
-  }
-
-  try {
-    const base64Audio = await readFileAsDataURL(file)
-    return base64Audio
-  } catch (error) {
-    console.error('Error reading file:', error)
-    alert('Error reading file.')
-  }
-}
-
-function readFileAsDataURL (file) {
-  return new Promise((resolve, reject) => {
-    const reader = new FileReader()
-
-    reader.onload = function (event) {
-      resolve(event.target.result)
-    }
-
-    reader.onerror = function (error) {
-      reject(error)
-    }
-
-    reader.readAsDataURL(file)
-  })
-}
-
-const createInputAudioForBatch = (base64, widget) => {
-  // Create an audio element
-  let audio = document.createElement('audio')
-  audio.src = base64
-  audio.controls = true
-  audio.style = 'width: 120px; display: block'
-
-  // Create a delete button
-  let deleteButton = document.createElement('button')
-  deleteButton.textContent = 'Delete'
-
-  deleteButton.style = `cursor: pointer;
-        font-weight: 300;
-        margin: 2px; 
-        margin-left: 10px;
-        color: var(--descrip-text);
-        background-color: var(--comfy-input-bg);
-        border-radius: 8px;
-        border-color: var(--border-color);
-        border-style: solid;height: 30px;min-width: 122px;
-       `
-
-  // Create a container for the audio and delete button
-  let container = document.createElement('div')
-  container.appendChild(audio)
-  container.appendChild(deleteButton)
-  container.style = `display: flex;margin-top: 12px;`
-
-  // Add event listener for the delete button
-  deleteButton.addEventListener('click', e => {
-    let newValue = []
-    let items = widget.value?.base64 || []
-    for (const v of items) {
-      if (v != base64) newValue.push(v)
-    }
-    widget.value.base64 = newValue
-    container.remove()
-  })
-
-  return container
-}
-
-app.registerExtension({
-  name: 'Mixlab.Comfy.LoadAndCombinedAudio_',
-  async getCustomWidgets (app) {
-    return {
-      AUDIOBASE64 (node, inputName, inputData, app) {
-        // console.log('##node', node)
-        const widget = {
-          value: {
-            base64: []
-          }, // 不能[x,x,x]
-          type: inputData[0], // the type
-          name: inputName, // the name, slice
-          size: [128, 32], // a default size
-          draw (ctx, node, width, y) {},
-          computeSize (...args) {
-            return [128, 122] // a method to compute the current size of the widget
-          }
-          // serializeValue (nodeId, widgetIndex) {
-          //   return widget.value
-          // },
-        }
-        //  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 == 'LoadAndCombinedAudio_') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-
-      nodeType.prototype.onNodeCreated = function () {
-        orig_nodeCreated?.apply(this, arguments)
-
-        let audiosWidget = this.widgets.filter(w => w.name == 'audios')[0]
-
-        const widget = {
-          type: 'div',
-          name: 'audio_base64',
-          draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(
-              this.div.style,
-              get_position_style(ctx, widget_width, 44, node.size[1])
-            )
-          },
-          serialize: false
-        }
-
-        widget.div = $el('div', {})
-
-        document.body.appendChild(widget.div)
-
-        let audioPreview = document.createElement('div')
-        let audiosDiv = document.createElement('div') //显示图片
-        audiosDiv.className = 'audios_preview'
-        audiosDiv.style = `width: calc(100% - 14px);
-        display: flex;
-        flex-wrap: wrap;
-        padding: 7px;    justify-content: space-between;
-        align-items: center;`
-
-        const btn = document.createElement('button')
-        btn.innerText = 'Upload Audio'
-
-        btn.style = `cursor: pointer;
-        font-weight: 300;
-        margin: 2px; 
-        color: var(--descrip-text);
-        background-color: var(--comfy-input-bg);
-        border-radius: 8px;
-        border-color: var(--border-color);
-        border-style: solid;height: 30px;min-width: 122px;
-       `
-
-        btn.addEventListener('click', e => {
-          e.preventDefault()
-          let inputAudio = document.createElement('input')
-          inputAudio.type = 'file'
-          inputAudio.accept = "audio/*"
-          inputAudio.style.display = 'none'
-          inputAudio.addEventListener('change', async e => {
-            e.preventDefault()
-            const file = e.target.files[0]
-            let base64 = await uploadAndConvertAudio(file)
-            if (!audiosWidget.value) audiosWidget.value = { base64: [] }
-            audiosWidget.value.base64.push(base64)
-
-            let a = createInputAudioForBatch(base64, audiosWidget)
-            audiosDiv.appendChild(a)
-          })
-
-          inputAudio.click()
-          inputAudio.remove()
-        })
-
-        widget.div.appendChild(audioPreview)
-        audioPreview.appendChild(audiosDiv)
-        audioPreview.appendChild(btn)
-        // audioPreview.appendChild(inputAudio)
-
-        this.addCustomWidget(widget)
-
-        // document.addEventListener('wheel', handleMouseWheel)
-
-        const onRemoved = this.onRemoved
-        this.onRemoved = () => {
-          widget.div.remove()
-          try {
-            // document.removeEventListener('wheel', handleMouseWheel)
-          } catch (error) {
-            console.log(error)
-          }
-
-          return onRemoved?.()
-        }
-
-        this.serialize_widgets = true //需要保存参数
-      }
-    }
-  },
-  async loadedGraphNode (node, app) {
-    if (node.type === 'LoadAndCombinedAudio_') {
-      // await sleep(0)
-      let audiosWidget = node.widgets.filter(w => w.name === 'audios')[0]
-      let audioPreview = node.widgets.filter(w => w.name == 'audio_base64')[0]
-
-      let pre = audioPreview.div.querySelector('.audios_preview')
-      for (const d of audiosWidget.value?.base64 || []) {
-        let im = createInputAudioForBatch(d, audiosWidget)
-        pre.appendChild(im)
-      }
-    }
-  }
-})

web/javascript/chat.js

@@ -1,5 +1,3 @@
-import { getUrl } from './common.js'
-
 async function* completion (url, messages, controller) {
   let data = {
     model: 'gpt-3.5-turbo-16k',
@@ -93,116 +91,16 @@ async function* completion (url, messages, controller) {
   return content
   // return (await response.json()).content
 }
-export async function completion_ (
-  apiKey,
-  url,
-  model_name,
-  messages,
-  controller,
-  callback
-) {
-  let request = await chatCompletion(
-    apiKey,
-    url,
-    model_name,
-    messages,
-    controller
-  )
+
+export async function completion_ (url, messages, controller, callback) {
+  let request = await completion(url, messages, controller)
   for await (const chunk of request) {
-    if (callback) callback(chunk)
-  }
-}
-
-export async function* chatCompletion (
-  apiKey,
-  api_url,
-  model_name,
-  messages,
-  controller
-) {
-  const mixlabAPI = `${getUrl()}/chat/completions`
-
-  const requestBody = {
-    messages: messages,
-    stream: true,
-    key: apiKey,
-    model_name: model_name,
-    api_url
-  }
-
-  let response = await fetch(mixlabAPI, {
-    method: 'POST',
-    headers: {
-      'Content-Type': 'application/json'
-      // Authorization: `Bearer ${apiKey}`
-    },
-    body: JSON.stringify(requestBody),
-    mode: 'cors', // This is to ensure the request is made with CORS
-    signal: controller.signal
-  })
-
-  const reader = response.body.getReader()
-  const decoder = new TextDecoder()
-
-  let content = ''
-  let leftover = '' // Buffer for partially read lines
-
-  try {
-    let cont = true
-    while (cont) {
-      let result = await reader.read()
-      if (result.done) {
-        break
-      }
-      const text = leftover + decoder.decode(result.value)
-      // Check if the last character is a line break
-      const endsWithLineBreak = text.endsWith('\n')
-
-      // Split the text into lines
-      let lines = text.split('\n')
-
-      // If the text doesn't end with a line break, then the last line is incomplete
-      // Store it in leftover to be added to the next chunk of data
-      if (!endsWithLineBreak) {
-        leftover = lines.pop()
-      } else {
-        leftover = '' // Reset leftover if we have a line break at the end
-      }
-
-      // Parse all sse events and add them to result
-      const regex = /^(\S+):\s(.*)$/gm
-      for (const line of lines) {
-        const match = regex.exec(line)
-        if (match) {
-          result[match[1]] = match[2]
-          // since we know this is llama.cpp, let's just decode the json in data
-          if (result.data) {
-            result.data = JSON.parse(result.data)
-            
-
-            content += result.data.choices[0].delta?.content || ''
-            // console.log('#result.content',content)
-            // yield
-            yield result
-
-            // if we got a stop token from server, we will break here
-            if (result.data.choices[0].finish_reason == 'stop') {
-              if (result.data.generation_settings) {
-                // generation_settings = result.data.generation_settings;
-              }
-              cont = false
-              break
-            }
-          }
-        }
-      }
+    let content = chunk.data.choices[0].delta.content || ''
+    if (chunk.data.choices[0].role == 'assistant') {
+      //开始
+      content = ''
     }
-  } catch (e) {
-    console.error('chat error: ', e)
-    throw e
-  } finally {
-    controller.abort()
-  }
 
-  return content
+    if (callback) callback(content)
+  }
 }

web/javascript/checkVersion_mixlab.js

@@ -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 = 'v0.41.0'
+const version = 'v0.28.3'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/common.js

@@ -1,227 +0,0 @@
-export const base64Df =
-  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
-
-export function getUrl () {
-  let api_host = `${window.location.hostname}:${window.location.port}`
-  let api_base = ''
-  let url = `${window.location.protocol}//${api_host}${api_base}`
-  return url
-}
-
-// 获得插件/节点的索引数据
-export async function get_nodes_map () {
-  let url = getUrl()
-
-  const res = await fetch(`${url}/mixlab/nodes_map`, {
-    method: 'POST',
-    body: JSON.stringify({
-      data: 'json'
-    })
-  })
-  return await res.json()
-}
-
-// 更新或者获取key
-export const updateLLMAPIKey = async key => {
-  try {
-    const res = await fetch(`${getUrl()}/mixlab/llm_api_key`, {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json'
-      },
-      body: JSON.stringify({
-        key: key || null
-      })
-    })
-
-    const data = await res.json()
-
-    if (!res.ok) {
-      console.error('Error:', data.error)
-      return
-    }
-
-    if (key) {
-      console.log('API key saved successfully:', data.message)
-      return key
-    } else {
-      console.log('Retrieved API key:', data.key)
-      return data.key
-    }
-  } catch (error) {
-    console.error('Request failed:', error)
-  }
-}
-
-//获取当前系统的插件，节点清单
-export function getObjectInfo () {
-  return new Promise(async (resolve, reject) => {
-    let url = getUrl()
-
-    try {
-      const response = await fetch(`${url}/object_info`)
-      const data = await response.json()
-      resolve(data)
-    } catch (error) {
-      reject(error)
-    }
-  })
-}
-
-export function get_position_style (
-  ctx,
-  widget_width,
-  y,
-  node_height,
-  left = 44
-) {
-  const MARGIN = 0 // the margin around the html element
-
-  /* Create a transform that deals with all the scrolling and zooming */
-  const elRect = ctx.canvas.getBoundingClientRect()
-
-  const scaleX = elRect.width / ctx.canvas.width
-  const scaleY = elRect.height / ctx.canvas.height
-
-  const transform = new DOMMatrix()
-    .scaleSelf(scaleX, scaleY)
-    .multiplySelf(ctx.getTransform())
-    .translateSelf(MARGIN, MARGIN + y)
-
-  return {
-    transformOrigin: '0 0',
-    transform: transform,
-    left:
-      document.querySelector('.comfy-menu').style.display === 'none'
-        ? `${left}px`
-        : `0`,
-    top: `0`,
-    cursor: 'pointer',
-    position: 'absolute',
-    maxWidth: `${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: `${node_height * 0.3 - MARGIN * 2}px`,
-    // background: '#EEEEEE',
-    display: 'flex',
-    flexDirection: 'column',
-    // alignItems: 'center',
-    justifyContent: 'flex-start',
-    zIndex: 99
-  }
-}
-
-export function loadCSS (url) {
-  var link = document.createElement('link')
-  link.rel = 'stylesheet'
-  link.type = 'text/css'
-  link.href = url
-  document.getElementsByTagName('head')[0].appendChild(link)
-}
-
-
-export function injectCSS (css) {
-  // 检查页面中是否已经存在具有相同内容的style标签
-  const existingStyle = document.querySelector('style')
-  if (existingStyle && existingStyle.textContent === css) {
-    return // 如果已经存在相同的样式，则不进行注入
-  }
-
-  // 创建一个新的style标签，并将CSS内容注入其中
-  const style = document.createElement('style')
-  style.textContent = css
-
-  // 将style标签插入到页面的head元素中
-  const head = document.querySelector('head')
-  head.appendChild(style)
-}
-
-
-export function loadExternalScript (url, type) {
-  return new Promise((resolve, reject) => {
-    const existingScript = document.querySelector(`script[src="${url}"]`)
-    if (existingScript) {
-      existingScript.onload = () => {
-        resolve()
-      }
-      existingScript.onerror = reject
-      return
-    }
-
-    const script = document.createElement('script')
-    script.src = url
-    if (type) script.type = type // Add this line to load the script as an ES module
-    script.onload = () => {
-      resolve()
-    }
-    script.onerror = reject
-    document.head.appendChild(script)
-  })
-}
-
-export async function getQueue () {
-  try {
-    const res = await fetch(`${getUrl()}/queue`)
-    const data = await res.json()
-    // console.log(data.queue_running,data.queue_pending)
-    return {
-      // Running action uses a different endpoint for cancelling
-      Running: data.queue_running.length,
-      Pending: data.queue_pending.length
-    }
-  } catch (error) {
-    console.error(error)
-    return { Running: 0, Pending: 0 }
-  }
-}
-
-export async function interrupt () {
-  const resp = await fetch(`${getUrl()}/interrupt`, {
-    method: 'POST'
-  })
-}
-
-export async function sleep (t = 200) {
-  return new Promise((res, rej) => {
-    setTimeout(() => {
-      res(true)
-    }, t)
-  })
-}
-
-export function createImage (url) {
-  let im = new Image()
-  return new Promise((res, rej) => {
-    im.onload = () => res(im)
-    im.src = url
-  })
-}
-
-export function convertImageUrlToBase64 (imageUrl) {
-  return fetch(imageUrl)
-    .then(response => response.blob())
-    .then(blob => {
-      return new Promise((resolve, reject) => {
-        const reader = new FileReader()
-        reader.onloadend = () => resolve(reader.result)
-        reader.onerror = reject
-        reader.readAsDataURL(blob)
-      })
-    })
-}
-
-export const getLocalData = key => {
-  let data = {}
-  try {
-    data = JSON.parse(localStorage.getItem(key)) || {}
-  } catch (error) {
-    return {}
-  }
-  return data
-}
-
-export const saveLocalData = (key, id, val) => {
-  let data = getLocalData(key)
-  data[id] = val
-  localStorage.setItem(key, JSON.stringify(data))
-}

web/javascript/gpt_mixlab.js

@@ -1,5 +1,205 @@
 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('##inputData', inputData)
+        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',
@@ -9,16 +209,13 @@ app.registerExtension({
         text = text.filter(t => t && t?.trim())
 
         if (this.widgets) {
-          // console.log('#ShowTextForGPT',this.widgets)
           // const pos = this.widgets.findIndex(w => w.name === 'text')
           for (let i = 0; i < this.widgets.length; i++) {
-            if (this.widgets[i].name == 'show_text')
-              this.widgets[i].onRemove?.()
-       
+            if (this.widgets[i].name == 'show_text') this.widgets[i].onRemove?.()
           }
-          this.widgets.length = 2
+          this.widgets.length = 1
         }
-
+        // console.log('ShowTextForGPT',text)
         for (let list of text) {
           if (list) {
             // console.log('#####', list)
@@ -31,8 +228,6 @@ app.registerExtension({
             w.inputEl.readOnly = true
             w.inputEl.style.opacity = 0.6
 
-            // w.inputEl.style.display='none'
-
             try {
               if (typeof list != 'string') {
                 let data = JSON.parse(list)

web/javascript/image_mixlab.js

@@ -4,8 +4,6 @@ import { api } from '../../../scripts/api.js'
 import { $el } from '../../../scripts/ui.js'
 import { applyTextReplacements } from '../../../scripts/utils.js'
 
-import { loadExternalScript, get_position_style } from './common.js'
-
 function loadImageToCanvas (base64Image) {
   var img = new Image()
   var canvas = document.createElement('canvas')
@@ -90,40 +88,37 @@ function getContentTypeFromBase64 (base64Data) {
 // const blob = base64ToBlob(base64Data, contentType);
 // console.log(blob);
 
-// function get_position_style (ctx, widget_width, y, node_height) {
-//   const MARGIN = 4 // the margin around the html element
+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)
+  /* 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:
-//       document.querySelector('.comfy-menu').style.display === 'none'
-//         ? `60px`
-//         : `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'
-//   }
-// }
+  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 = {}
@@ -349,7 +344,7 @@ app.registerExtension({
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(ctx, widget_width, 44, node.size[1], 60)
+              get_position_style(ctx, widget_width, 44, node.size[1])
             )
           }
         }
@@ -535,7 +530,7 @@ app.registerExtension({
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(ctx, widget_width, y, node.size[1], 36)
+              get_position_style(ctx, widget_width, y, node.size[1])
             )
           }
         }
@@ -680,18 +675,6 @@ const createInputImageForBatch = (base64, widget) => {
   return im
 }
 
-// 添加新图片
-const addBase64ToWidgetForLoadImagesToBatch = (
-  base64,
-  imagesWidget,
-  imagesDiv
-) => {
-  if (!imagesWidget.value.base64) imagesWidget.value.base64 = []
-  imagesWidget.value.base64.push(base64)
-  let im = createInputImageForBatch(base64, imagesWidget)
-  imagesDiv.appendChild(im)
-}
-
 app.registerExtension({
   name: 'Mixlab.Comfy.LoadImagesToBatch',
   async getCustomWidgets (app) {
@@ -722,6 +705,7 @@ app.registerExtension({
 
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeType.comfyClass == 'LoadImagesToBatch') {
+
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
 
       nodeType.prototype.onNodeCreated = function () {
@@ -733,10 +717,10 @@ app.registerExtension({
           type: 'div',
           name: 'image_base64',
           draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(this.div.style, {
-              ...get_position_style(ctx, widget_width, y, node.size[1], 72),
-              top: `${widget_height}px`
-            })
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 44, node.size[1])
+            )
           },
           serialize: false
         }
@@ -767,18 +751,13 @@ app.registerExtension({
             base64 = await loadImageToCanvas(base64)
             // console.log(base64)
             if (!imagesWidget.value) imagesWidget.value = { base64: [] }
-            addBase64ToWidgetForLoadImagesToBatch(
-              base64,
-              imagesWidget,
-              imagesDiv
-            )
+            imagesWidget.value.base64.push(base64)
+            let im = createInputImageForBatch(base64, imagesWidget)
+            imagesDiv.appendChild(im)
           }
           reader.readAsDataURL(file)
         })
 
-        // 如果是复制的，有数据 , 这个不生效，取不到数据， 需要在nodeCreated里获取
-        // console.log('#LoadImagesToBatch', imagesWidget.value?.base64)
-
         const btn = document.createElement('button')
         btn.innerText = 'Upload Image'
 
@@ -850,36 +829,18 @@ app.registerExtension({
       }
     }
   },
-
   async loadedGraphNode (node, app) {
     if (node.type === 'LoadImagesToBatch') {
+      // await sleep(0)
       let imagesWidget = node.widgets.filter(w => w.name === 'images')[0]
       let imagePreview = node.widgets.filter(w => w.name == 'image_base64')[0]
-      // console.log('#LoadImagesToBatch', imagesWidget.value?.base64)
-      let imagesDiv = imagePreview.div.querySelector('.images_preview')
-      imagesDiv.innerHTML = ''
+
+      let pre = imagePreview.div.querySelector('.images_preview')
       for (const d of imagesWidget.value?.base64 || []) {
         let im = createInputImageForBatch(d, imagesWidget)
-        imagesDiv.appendChild(im)
+        pre.appendChild(im)
       }
     }
-  },
-  nodeCreated (node, app) {
-    //数据延迟？？
-    setTimeout(() => {
-      // console.log('#LoadImagesToBatch', node.type)
-      if (node.type === 'LoadImagesToBatch') {
-        let imagesWidget = node.widgets.filter(w => w.name === 'images')[0]
-        let imagePreview = node.widgets.filter(w => w.name == 'image_base64')[0]
-
-        let imagesDiv = imagePreview?.div?.querySelector('.images_preview')
-        imagesDiv.innerHTML = ''
-        for (const d of imagesWidget.value?.base64 || []) {
-          let im = createInputImageForBatch(d, imagesWidget)
-          imagesDiv.appendChild(im)
-        }
-      }
-    }, 1000)
   }
 })
 
@@ -887,11 +848,9 @@ app.registerExtension({
 app.registerExtension({
   name: 'Mixlab.output.ComparingTwoFrames_',
   init () {
-    loadExternalScript('/mixlab/app/lib/juxtapose.min.js')
-
     $el('link', {
       rel: 'stylesheet',
-      href: '/mixlab/app/lib/juxtapose.css',
+      href: '/extensions/comfyui-mixlab-nodes/lib/juxtapose.css',
       parent: document.head
     })
 
@@ -909,8 +868,8 @@ app.registerExtension({
       const onNodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = function () {
         const r = onNodeCreated
-          ? onNodeCreated.apply(this, arguments)
-          : undefined
+        ? onNodeCreated.apply(this, arguments)
+        : undefined
 
         this.size = [400, this.size[1]]
         console.log('##onNodeCreated', this)
@@ -918,10 +877,10 @@ app.registerExtension({
           type: 'div',
           name: 'preview',
           draw (ctx, node, widget_width, y, widget_height) {
-            let s = get_position_style(ctx, widget_width, 44, node.size[1], 36)
-            delete s.height
-
-            Object.assign(this.div.style, s)
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, 400, 44, node.size[1])
+            )
           },
           serialize: false
         }
@@ -932,15 +891,20 @@ app.registerExtension({
         this.addCustomWidget(widget)
         this.serialize_widgets = true //需要保存参数
 
+
         const onRemoved = this.onRemoved
         this.onRemoved = () => {
           widget.div.remove()
           return onRemoved?.()
         }
 
-        return r
+
+       return r
+
       }
 
+     
+
       const onExecuted = nodeType.prototype.onExecuted
       nodeType.prototype.onExecuted = function (message) {
         onExecuted?.apply(this, arguments)
@@ -1000,7 +964,7 @@ app.registerExtension({
             label: 'After'
           }
         ]
-        this.size = [this.size[0], 300]
+        this.size=[this.size[0],300]
       }
     }
   },
@@ -1010,6 +974,7 @@ app.registerExtension({
       // node.widgets[0].div.id = 'mix_comparingtowframes_' + node.id
       // if (node.widgets_values && node.widgets_values[0]) {
       //   node.widgets[0].div.innerHTML = ''
+
       //   let slider = new juxtapose.JXSlider(
       //     '#mix_comparingtowframes_' + node.id,
       //     node.widgets_values,

web/javascript/layer_mixlab.js

@@ -81,10 +81,7 @@ function get_position_style (ctx, widget_width, y, node_height) {
   return {
     transformOrigin: '0 0',
     transform: transform,
-    left:
-    document.querySelector('.comfy-menu').style.display === 'none'
-      ? `60px`
-      : `0`,
+    left: `0`,
     top: `0`,
     cursor: 'pointer',
     position: 'absolute',

web/javascript/main_mixlab.js

@@ -3,17 +3,31 @@ import { app } from '../../../scripts/app.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
 
-import {
-  getQueue,
-  interrupt,
-  get_position_style,
-  base64Df,
-  getUrl,
-  createImage,
-  sleep
-} from './common.js'
+let api_host = `${window.location.hostname}:${window.location.port}`
+let api_base = ''
+let url = `${window.location.protocol}//${api_host}${api_base}`
 
-// let url = getUrl()
+async function getQueue () {
+  try {
+    const res = await fetch(`${url}/queue`)
+    const data = await res.json()
+    // console.log(data.queue_running,data.queue_pending)
+    return {
+      // Running action uses a different endpoint for cancelling
+      Running: data.queue_running.length,
+      Pending: data.queue_pending.length
+    }
+  } catch (error) {
+    console.error(error)
+    return { Running: 0, Pending: 0 }
+  }
+}
+
+async function interrupt () {
+  const resp = await fetch(`${url}/interrupt`, {
+    method: 'POST'
+  })
+}
 
 async function clipboardWriteImage (win, url) {
   const canvas = document.createElement('canvas')
@@ -194,6 +208,22 @@ async function shareScreen (
   }
 }
 
+async function sleep (t = 200) {
+  return new Promise((res, rej) => {
+    setTimeout(() => {
+      res(true)
+    }, t)
+  })
+}
+
+function createImage (url) {
+  let im = new Image()
+  return new Promise((res, rej) => {
+    im.onload = () => res(im)
+    im.src = url
+  })
+}
+
 async function compareImages (threshold, previousImage, currentImage) {
   // 将 base64 转换为 Image 对象
   var previousImg = await createImage(previousImage)
@@ -428,6 +458,44 @@ async function requestCamera () {
   return false
 }
 
+/* 
+A method that returns the required style for the html 
+*/
+function get_position_style (ctx, widget_width, y, node_height, top) {
+  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: `${top}px`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+const base64Df =
+  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
+
 app.registerExtension({
   name: 'Mixlab.image.ScreenShareNode',
   async getCustomWidgets (app) {
@@ -525,12 +593,17 @@ app.registerExtension({
           type: 'HTML', // whatever
           name: 'sreen_share', // whatever
           draw (ctx, node, widget_width, y, widget_height) {
-            // console.log('ScreenSHare', node)
+            // console.log('ScreenSHare', y, widget_height)
             Object.assign(
               this.card.style,
-              get_position_style(ctx, widget_width, y, node.size[1], 40)
+              get_position_style(
+                ctx,
+                widget_width,
+                widget_height * 5,
+                node.size[1],
+                40
+              )
             )
- 
           }
         }
 
@@ -970,13 +1043,12 @@ async function setArea (src) {
   div.innerHTML = `
     <div id='ml_overlay' style='position: absolute;top:0;background: #251f1fc4;
     height: 100vh;
-    z-index:99999999999999;
+    z-index:999999;
     width: 100%;'>
       <img id='ml_video' style='position: absolute; 
       height: ${displayHeight}px;user-select: none; 
       -webkit-user-drag: none;
       outline: 2px solid #eaeaea;
-      left: 0;
       box-shadow: 8px 9px 17px #575757;' />
       <div id='ml_selection' style='position: absolute; 
       border: 2px dashed red; 
@@ -1147,10 +1219,10 @@ app.registerExtension({
           type: 'video',
           name: 'FloatingVideo',
           draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(this.card.style, {
-              ...get_position_style(ctx, widget_width, y, node.size[1], 40),
-              top: `${widget_height}px`
-            })
+            Object.assign(
+              this.card.style,
+              get_position_style(ctx, widget_width, y, node.size[1], 0)
+            )
           }
         }
 
@@ -1195,7 +1267,7 @@ app.registerExtension({
         })
 
         widget.PictureInPicture = $el('button', {
-          innerText: 'Picture In Picture',
+          innerText: 'PictureInPicture',
           style: {
             display: 'pictureInPictureEnabled' in document ? 'block' : 'none',
             cursor: 'pointer',

web/javascript/p5.js

@@ -1,195 +0,0 @@
-import { app } from '../../../scripts/app.js'
-import { api } from '../../../scripts/api.js'
-import { $el } from '../../../scripts/ui.js'
-
-import { get_position_style } from './common.js'
-
-function base64ToBlobFromURL (base64URL, contentType) {
-  return fetch(base64URL).then(response => response.blob())
-}
-
-async function uploadImage (blob, fileType = '.svg', filename) {
-  // const blob = await (await fetch(src)).blob();
-  const body = new FormData()
-  body.append(
-    'image',
-    new File([blob], (filename || new Date().getTime()) + fileType)
-  )
-
-  const resp = await api.fetchApi('/upload/image', {
-    method: 'POST',
-    body
-  })
-
-  // console.log(resp)
-  let data = await resp.json()
-  let { name, subfolder } = data
-  // let src = api.apiURL(
-  //   `/view?filename=${encodeURIComponent(
-  //     name
-  //   )}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
-  // )
-
-  return data
-}
-// 上传得到url
-async function uploadBase64ToFile (base64) {
-  let bg_blob = await base64ToBlobFromURL(base64)
-  let url = await uploadImage(bg_blob, '.png')
-  return url
-}
-
-const p5InputNode = {
-  name: 'Mixlab.Comfy.P5Input',
-  async getCustomWidgets (app) {
-    return {
-      IMAGEBASE64 (node, inputName, inputData, app) {
-        const widget = {
-          value: {
-            images: []
-          }, // 不能[x,x,x]
-          type: inputData[0], // the type
-          name: inputName, // the name, slice
-          size: [320, 120], // a default size
-          draw (ctx, node, width, y) {},
-          computeSize (...args) {
-            return [128, 32] // a method to compute the current size of the widget
-          }
-        }
-        node.addCustomWidget(widget)
-        return widget
-      }
-    }
-  },
-
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeType.comfyClass == 'P5Input') {
-      // console.log('P5Input')
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-
-      nodeType.prototype.onNodeCreated = function () {
-        orig_nodeCreated?.apply(this, arguments)
-
-        const widget = {
-          type: 'div',
-          name: 'image_base64',
-          draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(
-              this.div.style,
-              get_position_style(
-                ctx,
-                widget_width - 24,
-                44,
-                node.size[1] * 2.8,
-                44
-              )
-            )
-          },
-          serialize: false
-        }
-
-        widget.div = $el('div', {})
-
-        widget.div.style = `margin:12px;width:400px;height:480px;background:white`
-
-        document.body.appendChild(widget.div)
-
-        this.addCustomWidget(widget)
-
-        // document.addEventListener('wheel', handleMouseWheel)
-
-        const onRemoved = this.onRemoved
-        this.onRemoved = () => {
-          widget.div.remove()
-          // window.removeEventListener('message', ms)
-          return onRemoved?.()
-        }
-
-        // 节点的大小控制
-        this.setSize([480, 560])
-        app.canvas.draw(true, true)
-
-        const onResize = this.onResize
-        this.onResize = () => {
-          // 设置最小尺寸
-          if (
-            Math.max(this.size[0], 480) != this.size[0] &&
-            Math.max(this.size[1], 560) != this.size[1]
-          ) {
-            this.setSize([
-              Math.max(this.size[0], 480),
-              Math.max(this.size[1], 560)
-            ])
-          }
-
-          return onResize?.apply(this, arguments)
-        }
-
-        this.serialize_widgets = true //需要保存参数
-      }
-
-      const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = function (message) {
-        onExecuted?.apply(this, arguments)
-        // console.log('##onExecuted', this, message._info)
-        //   app.graph.getNodeById(8).widgets[1].div.querySelector('iframe').contentWindow.postMessage('Hello from parent', '*');
-      }
-    }
-  },
-
-  async loadedGraphNode (node, app) {
-    if (node.type === 'P5Input') {
-    }
-  },
-  nodeCreated (node, app) {
-    //数据延迟？？
-    setTimeout(() => {
-      let widget = node.widgets?.filter(w => w.name == 'image_base64')[0]
-      let framesWidget = node.widgets?.filter(w => w.name == 'frames')[0]
-      if (node.type === 'P5Input' && widget) {
-        console.log('#nodeCreated P5Input')
-        if (framesWidget && !framesWidget.value)
-          framesWidget.value = { images: [] }
-
-        framesWidget.value._seed = Math.random()
-
-        let nodeId = node.id
-        //延迟才能获得this.id
-        widget.div.innerHTML = `<iframe src="mixlab/app/p5_export/p5.html?id=${nodeId}" 
-          style="border:0;width:100%;height:100%;"
-         ></iframe>`
-
-        // 监听来自iframe的消息
-        const ms = async event => {
-          const data = event.data
-          console.log('#P5 Input #', data)
-          if (
-            data.from === 'p5.widget' &&
-            data.status === 'save' &&
-            data.frames &&
-            data.frames.length >= 0 &&
-            data.nodeId == nodeId &&
-            data.id != framesWidget.value.id
-          ) {
-            const frames = data.frames
-
-            //workflow会存储到local，会卡死
-            framesWidget.value.images = []
-            for (const f of frames) {
-              let file = await uploadBase64ToFile(f)
-              framesWidget.value.images.push(file)
-            }
-            // framesWidget.value.base64 = frames
-            // framesWidget.value._seed = Math.random()
-            node.title = 'P5 Input #' + frames.length
-            framesWidget.value.id = data.id
-          }
-        }
-
-        window.addEventListener('message', ms)
-      }
-    }, 1000)
-  }
-}
-
-app.registerExtension(p5InputNode)

web/javascript/prompt_mixlab.js

@@ -3,37 +3,51 @@ import { api } from '../../../scripts/api.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
 
-import { loadCSS, injectCSS } from './common.js'
+import PhotoSwipeLightbox from '/extensions/comfyui-mixlab-nodes/lib/photoswipe-lightbox.esm.min.js'
+function loadCSS (url) {
+  var link = document.createElement('link')
+  link.rel = 'stylesheet'
+  link.type = 'text/css'
+  link.href = url
+  document.getElementsByTagName('head')[0].appendChild(link)
 
-import PhotoSwipeLightbox from '/mixlab/app/lib/photoswipe-lightbox.esm.min.js'
+  // Create a style element
+  const style = document.createElement('style')
+  // Define the CSS rule for scrollbar width
+  const cssRule = `.pswp__custom-caption {
+    background: rgb(20 27 70);
+    font-size: 16px;
+    color: #fff;
+    width: calc(100% - 32px);
+    max-width: 980px;
+    padding: 2px 8px;
+    border-radius: 4px;
+    position: absolute;
+    left: 50%;
+    bottom: 16px;
+    transform: translateX(-50%);
+  }
+  .pswp__custom-caption a {
+    color: #fff;
+    text-decoration: underline;
+  }
+  .hidden-caption-content {
+    display: none;
+  }`
+  // Add the CSS rule to the style element
+  style.appendChild(document.createTextNode(cssRule))
 
-loadCSS('/mixlab/app/lib/photoswipe.min.css')
-injectCSS(`.pswp__custom-caption {
-      background: rgb(20 27 70);
-      font-size: 16px;
-      color: #fff;
-      width: calc(100% - 32px);
-      max-width: 980px;
-      padding: 2px 8px;
-      border-radius: 4px;
-      position: absolute;
-      left: 50%;
-      bottom: 16px;
-      transform: translateX(-50%);
-    }
-    .pswp__custom-caption a {
-      color: #fff;
-      text-decoration: underline;
-    }
-    .hidden-caption-content {
-      display: none;
-    }`)
+  // Append the style element to the document head
+  document.head.appendChild(style)
+}
+loadCSS('/extensions/comfyui-mixlab-nodes/lib/photoswipe.min.css')
 
 function initLightBox () {
   const lightbox = new PhotoSwipeLightbox({
     gallery: '.prompt_image_output',
     children: 'a',
-    pswpModule: () => import('/mixlab/app/lib/photoswipe.esm.min.js')
+    pswpModule: () =>
+      import('/extensions/comfyui-mixlab-nodes/lib/photoswipe.esm.min.js')
   })
 
   lightbox.on('uiRegister', function () {
@@ -86,10 +100,7 @@ function get_position_style (ctx, widget_width, y, node_height) {
   return {
     transformOrigin: '0 0',
     transform: transform,
-    left:
-      document.querySelector('.comfy-menu').style.display === 'none'
-        ? `60px`
-        : `0`,
+    left: `0`,
     top: `0`,
     cursor: 'pointer',
     position: 'absolute',
@@ -167,7 +178,7 @@ app.registerExtension({
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = async function () {
         orig_nodeCreated?.apply(this, arguments)
-
+         
         const mutable_prompt = this.widgets.filter(
           w => w.name == 'mutable_prompt'
         )[0]
@@ -179,12 +190,7 @@ app.registerExtension({
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(
-                ctx,
-                widget_width,
-                y + widget_height + 24,
-                node.size[1]
-              )
+              get_position_style(ctx, widget_width, y, node.size[1])
             )
           }
         }
@@ -201,7 +207,7 @@ app.registerExtension({
         background-color: var(--comfy-input-bg);
         border-radius: 8px;
         border-color: var(--border-color);
-        border-style: solid;height: 30px;min-width: 122px;
+        border-style: solid;    height: 30px;min-width: 122px;
        `
 
         // const btn=document.createElement('button');
@@ -260,6 +266,7 @@ app.registerExtension({
   },
   async loadedGraphNode (node, app) {
     if (node.type === 'RandomPrompt') {
+     
     }
   }
 })
@@ -401,7 +408,7 @@ const _createResult = async (node, widget, message) => {
   const width = node.size[0] * 0.5 - 12
 
   let height_add = 0
-
+  
   for (let index = 0; index < message._images.length; index++) {
     const imgs = message._images[index]
 
@@ -552,7 +559,7 @@ app.registerExtension({
 
       let cards = widget.div.querySelectorAll('.card')
       if (cards.length == 0) node.size = [280, 120]
-      if (widget.value) _createResult(node, widget, widget.value)
+      if(widget.value) _createResult(node, widget, widget.value)
     }
   }
 })

web/javascript/saveTripoSrMesh.js

@@ -19,10 +19,7 @@ function get_position_style (ctx, widget_width, y, node_height) {
   return {
     transformOrigin: '0 0',
     transform: transform,
-    left:
-    document.querySelector('.comfy-menu').style.display === 'none'
-      ? `60px`
-      : `0`,
+    left: `0`,
     top: `0`,
     cursor: 'pointer',
     position: 'absolute',

web/javascript/sound-lab.js

@@ -1,298 +0,0 @@
-import { app } from '../../../scripts/app.js'
-import { api } from '../../../scripts/api.js'
-import { ComfyWidgets } from '../../../scripts/widgets.js'
-import { $el } from '../../../scripts/ui.js'
-
-import WaveSurfer from 'https://cdn.jsdelivr.net/npm/wavesurfer.js@7/dist/wavesurfer.esm.js'
-
-function get_position_style (ctx, widget_width, y, node_height) {
-  const MARGIN = 4 // the margin around the html element
-
-  /* Create a transform that deals with all the scrolling and zooming */
-  const elRect = ctx.canvas.getBoundingClientRect()
-  const transform = new DOMMatrix()
-    .scaleSelf(
-      elRect.width / ctx.canvas.width,
-      elRect.height / ctx.canvas.height
-    )
-    .multiplySelf(ctx.getTransform())
-    .translateSelf(MARGIN, MARGIN + y)
-
-  return {
-    transformOrigin: '0 0',
-    transform: transform,
-    left:
-    document.querySelector('.comfy-menu').style.display === 'none'
-      ? `60px`
-      : `0`,
-    top: '0',
-    cursor: 'pointer',
-    position: 'absolute',
-    maxWidth: `${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: `${node_height * 0.3 - MARGIN * 2}px`,
-    // background: '#EEEEEE',
-    display: 'flex',
-    flexDirection: 'column',
-    // alignItems: 'center',
-    justifyContent: 'space-around'
-  }
-}
-
-//把文件转为url访问
-const parseUrl = data => {
-  let { filename, subfolder, type, prompt } = data
-  return {
-    url: api.apiURL(
-      `/view?filename=${encodeURIComponent(
-        filename
-      )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
-    ),
-    prompt
-  }
-}
-
-const createWaveSurfer = (wavesurfer, id,url) => {
-  // Create an instance of WaveSurfer
-  if (wavesurfer) {
-    wavesurfer.destroy()
-  }
-  wavesurfer = WaveSurfer.create({
-    container: '#' + id,
-    waveColor: 'rgb(200, 0, 200)',
-    progressColor: 'rgb(100, 0, 100)',
-    // Set a bar width
-    barWidth: 10,
-    // Optionally, specify the spacing between bars
-    barGap: 2,
-    // And the bar radius
-    barRadius: 6,
-    url
-  })
-
-  wavesurfer._auto = true
-
-  // 监听播放结束事件，重新开始播放以实现循环播放
-  wavesurfer.on('finish', function () {
-    // console.log(wavesurfer)
-    if (wavesurfer._auto) wavesurfer.play()
-  })
-
-  wavesurfer.on('interaction', () => {
-    wavesurfer._auto = false
-    if (!wavesurfer.isPlaying()) wavesurfer.play()
-  })
-
-  // 获取当前播放时间的峰值
-  wavesurfer.on('audioprocess', () => {
-    if (wavesurfer.isPlaying()&&wavesurfer.getDecodedData()) {
-      const channelData = wavesurfer.getDecodedData().getChannelData(0); 
-      const currentTime = wavesurfer.getCurrentTime()
-      // console.log(wavesurfer)
-      const sampleRate = wavesurfer.getDecodedData().sampleRate
-
-      // 定义要分析的时间窗口（例如1秒）
-      const windowSize = 1
-      const startSample = Math.floor(currentTime * sampleRate)
-      const endSample = Math.min(
-        startSample + windowSize * sampleRate,
-        channelData.length
-      )
-
-      let peak = 0
-      for (let i = startSample; i < endSample; i++) {
-        const value = Math.abs(channelData[i])
-        if (value > peak) {
-          peak = value
-        }
-      }
-      // console.log('Current Peak:', peak)
-    }
-  })
-
-  return wavesurfer
-}
-
-//更新gui
-function updateWaveWidgetValue (widgets, id, url, prompt, wavesurfer) {
-  let widget = widgets.filter(w => w.name == 'AudioPlay')[0]
-  // 手动更新widget值
-  widget.value = [url, prompt]
-
-  if (widget.div) {
-    widget.div.querySelector('.wave').id = `AudioPlay_${id}`
-  }
-
-  wavesurfer = createWaveSurfer(wavesurfer, `AudioPlay_${id}`,url)
-
-  wavesurfer.on('ready', duration => {
-    console.log('Audio duration: ' + duration + ' seconds')
-    if (widget.div) {
-      widget.div.setAttribute('data-url', url)
-      widget.div.querySelector('.link').setAttribute('href', url)
-      widget.div.querySelector(
-        '.info'
-      ).innerHTML = `<span style="font-size: 12px;
-      margin: 8px;">${duration.toFixed(
-        2
-      )} seconds</span> <br><span style="font-size: 14px;">${prompt||''}</span> <br>`
-    }
-  })
-  
-  
-  wavesurfer.load(url)
-  // console.log('updateWaveWidgetValue' ,url,wavesurfer)
-  return wavesurfer
-}
-
-app.registerExtension({
-  name: 'SoundLab.AudioPlay',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeType.comfyClass == 'AudioPlay') {
-      let that = this
-      // console.log('that', that)
-
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = function () {
-        orig_nodeCreated?.apply(this, arguments)
-
-        const widget = {
-          type: 'div',
-          name: 'AudioPlay',
-          draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(
-              this.div.style,
-              get_position_style(ctx, widget_width, y, node.size[1])
-            )
-          }
-        }
-
-        // console.log('AudioPlay nodeData', this)
-        widget.div = $el('div', {})
-
-        document.body.appendChild(widget.div)
-
-        // wave
-        const waveDiv = document.createElement('div')
-        waveDiv.className = 'wave'
-        waveDiv.style.minHeight = '172px'
-        widget.div.appendChild(waveDiv)
-
-        //prompt 相关信息展示
-        const infoDiv = document.createElement('div')
-        infoDiv.className = 'info'
-        infoDiv.style.marginBottom = '20px'
-        widget.div.appendChild(infoDiv)
-
-        // 按钮的区域
-        let btns = document.createElement('div')
-        btns.className = 'btns'
-        btns.style = `display: flex;
-        width: 100%;
-        justify-content: space-between;`
-        widget.div.appendChild(btns)
-
-        //play button
-        const playBtn = document.createElement('a')
-        playBtn.innerText = 'Play/Pause'
-
-        playBtn.style = `
-        display: flex;
-        padding: 4px 15px;
-        background-color: var(--comfy-input-bg);
-        border-radius: 8px;
-        border-color: var(--border-color);
-        border-style: solid;
-        color: var(--descrip-text);
-        text-decoration: none;
-        border-radius: 5px;
-        transition: background-color 0.3s ease 0s;
-          `
-
-        playBtn.addEventListener('click', e => {
-          e.preventDefault()
-          if (that[`wavesurfer_${this.id}`]) {
-            that[`wavesurfer_${this.id}`]?.playPause()
-            that[`wavesurfer_${this.id}`]._auto = true
-          }
-        })
-        btns.appendChild(playBtn)
-
-        const urlLink = document.createElement('a')
-        urlLink.className = 'link'
-        urlLink.innerText = 'URL'
-        urlLink.setAttribute('target', '_blank')
-        urlLink.style = `display: flex;
-        padding: 4px 15px;
-        background-color: var(--comfy-input-bg);
-        border-radius: 8px;
-        border-color: var(--border-color);
-        border-style: solid;
-        color: var(--descrip-text);
-        text-decoration: none;
-        border-radius: 5px;
-        transition: background-color 0.3s ease 0s;`
-        // urlLink.style.minHeight = '200px'
-        btns.appendChild(urlLink)
-
-
-        //todo 导出视频 that[`wavesurfer_${this.id}`].renderer.exportImage('image/png',1,'dataURL')
-        // https://github.com/diffusion-studio/ffmpeg-js
-
-
-        this.addCustomWidget(widget)
-
-        const onRemoved = this.onRemoved
-        this.onRemoved = () => {
-          widget.div.remove()
-          return onRemoved?.()
-        }
-
-        this.size = [this.size[0], 280]
-        this.serialize_widgets = true //需保存widget的值
-      }
-
-      const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = function (message) {
-        onExecuted?.apply(this, arguments)
-        const audio = message.audio
-        console.log('#onExecuted', `AudioPlay_${this.id}`, message,audio)
-        try {
-          let { url, prompt } = parseUrl(audio[0])
-
-          that[`wavesurfer_${this.id}`] = updateWaveWidgetValue(
-            this.widgets,
-            this.id,
-            url,
-            prompt,
-            that[`wavesurfer_${this.id}`]
-          )
-
-          that[`wavesurfer_${this.id}`]?.playPause()
-        } catch (error) {
-          console.log(error)
-        }
-      }
-    }
-  },
-  async loadedGraphNode (node, app) {
-    if (node.type === 'AudioPlay') {
-      let widget = node.widgets.filter(w => w.name == 'AudioPlay')[0]
-
-      if (widget.value) {
-        let [url, prompt] = widget.value
-
-        this[`wavesurfer_${node.id}`] = updateWaveWidgetValue(
-          node.widgets,
-          node.id,
-          url,
-          prompt,
-          this[`wavesurfer_${node.id}`]
-        )
-      }
-
-      console.log('#loadedGraphNode', node)
-    }
-  }
-})

web/javascript/td_background.js

@@ -1,323 +0,0 @@
-// touchdesigner的背景效果，把appinfo的输出，选择一张图片作为背景
-
-window._bg_img = null
-
-/**
- * draws the back canvas (the one containing the background and the connections)
- * @method drawBackCanvas
- **/
-LGraphCanvas.prototype.drawBackCanvas = function () {
-  var canvas = this.bgcanvas
-  if (
-    canvas.width != this.canvas.width ||
-    canvas.height != this.canvas.height
-  ) {
-    canvas.width = this.canvas.width
-    canvas.height = this.canvas.height
-  }
-
-  if (!this.bgctx) {
-    this.bgctx = this.bgcanvas.getContext('2d')
-  }
-  var ctx = this.bgctx
-  if (ctx.start) {
-    ctx.start()
-  }
-
-  var viewport = this.viewport || [0, 0, ctx.canvas.width, ctx.canvas.height]
-
-  //clear
-  if (this.clear_background) {
-    ctx.clearRect(viewport[0], viewport[1], viewport[2], viewport[3])
-  }
-
-  //show subgraph stack header
-  if (this._graph_stack && this._graph_stack.length) {
-    ctx.save()
-    var parent_graph = this._graph_stack[this._graph_stack.length - 1]
-    var subgraph_node = this.graph._subgraph_node
-    ctx.strokeStyle = subgraph_node.bgcolor
-    ctx.lineWidth = 10
-    ctx.strokeRect(1, 1, canvas.width - 2, canvas.height - 2)
-    ctx.lineWidth = 1
-    ctx.font = '40px Arial'
-    ctx.textAlign = 'center'
-    ctx.fillStyle = subgraph_node.bgcolor || '#AAA'
-    var title = ''
-    for (var i = 1; i < this._graph_stack.length; ++i) {
-      title += this._graph_stack[i]._subgraph_node.getTitle() + ' >> '
-    }
-    ctx.fillText(title + subgraph_node.getTitle(), canvas.width * 0.5, 40)
-    ctx.restore()
-  }
-
-  var bg_already_painted = false
-  if (this.onRenderBackground) {
-    bg_already_painted = this.onRenderBackground(canvas, ctx)
-  }
-
-  //reset in case of error
-  if (!this.viewport) {
-    ctx.restore()
-    ctx.setTransform(1, 0, 0, 1, 0, 0)
-  }
-  this.visible_links.length = 0
-
-  if (this.graph) {
-    //apply transformations
-    ctx.save()
-    this.ds.toCanvasContext(ctx)
-
-    //render BG
-    if (
-      this.ds.scale < 1 &&
-      !bg_already_painted &&
-      this.clear_background_color
-    ) {
-      ctx.fillStyle = this.clear_background_color
-      ctx.fillRect(
-        this.visible_area[0],
-        this.visible_area[1],
-        this.visible_area[2],
-        this.visible_area[3]
-      )
-    }
-
-    // 主要修改
-    if (this.background_image && this.ds.scale > 0.5 && !bg_already_painted) {
-      if (this.zoom_modify_alpha) {
-        //使得 alpha 越接近0时变化越缓慢。
-        let alpha = (1.0 - 0.5 / this.ds.scale) * this.editor_alpha
-        ctx.globalAlpha = Math.min(Math.max(0, Math.sqrt(alpha)), 1)
-        // console.log((1.0 - 0.5 / this.ds.scale) * this.editor_alpha)
-      } else {
-        ctx.globalAlpha = this.editor_alpha
-      }
-      ctx.imageSmoothingEnabled = ctx.imageSmoothingEnabled = false // ctx.mozImageSmoothingEnabled =
-      if (!this._bg_img || this._bg_img.name != this.background_image) {
-        this._bg_img = new Image()
-        this._bg_img.name = this.background_image
-        this._bg_img.src = this.background_image
-        var that = this
-        this._bg_img.onload = function () {
-          that.draw(true, true)
-        }
-      }
-
-      var pattern = null
-      if (this._pattern == null && this._bg_img.width > 0) {
-        pattern = ctx.createPattern(this._bg_img, 'repeat')
-        this._pattern_img = this._bg_img
-        this._pattern = pattern
-      } else {
-        pattern = this._pattern
-      }
-
-      if (pattern) {
-        ctx.fillStyle = pattern
-        ctx.fillRect(
-          this.visible_area[0],
-          this.visible_area[1],
-          this.visible_area[2],
-          this.visible_area[3]
-        )
-        ctx.fillStyle = 'transparent'
-      }
-
-      ctx.globalAlpha = 1.0
-      ctx.imageSmoothingEnabled = ctx.imageSmoothingEnabled = true //= ctx.mozImageSmoothingEnabled
-    }
-
-    //groups
-    if (this.graph._groups.length && !this.live_mode) {
-      this.drawGroups(canvas, ctx)
-    }
-
-    if (this.onDrawBackground) {
-      this.onDrawBackground(ctx, this.visible_area)
-    }
-    if (this.onBackgroundRender) {
-      //LEGACY
-      console.error(
-        'WARNING! onBackgroundRender deprecated, now is named onDrawBackground '
-      )
-      this.onBackgroundRender = null
-    }
-
-    //DEBUG: show clipping area
-    //ctx.fillStyle = "red";
-    //ctx.fillRect( this.visible_area[0] + 10, this.visible_area[1] + 10, this.visible_area[2] - 20, this.visible_area[3] - 20);
-
-    //bg
-    if (this.render_canvas_border) {
-      ctx.strokeStyle = '#235'
-      ctx.strokeRect(0, 0, canvas.width, canvas.height)
-    }
-
-    if (this.render_connections_shadows) {
-      ctx.shadowColor = '#000'
-      ctx.shadowOffsetX = 0
-      ctx.shadowOffsetY = 0
-      ctx.shadowBlur = 6
-    } else {
-      ctx.shadowColor = 'rgba(0,0,0,0)'
-    }
-
-    //draw connections
-    if (!this.live_mode) {
-      this.drawConnections(ctx)
-    }
-
-    ctx.shadowColor = 'rgba(0,0,0,0)'
-
-    //restore state
-    ctx.restore()
-  }
-
-  if (ctx.finish) {
-    ctx.finish()
-  }
-
-  this.dirty_bgcanvas = false
-  this.dirty_canvas = true //to force to repaint the front canvas with the bgcanvas
-}
-
-function imgToCanvasBase64 (img) {
-  const canvas = document.createElement('canvas')
-  const ctx = canvas.getContext('2d')
-  canvas.width = img.width
-  canvas.height = img.height
-  ctx.drawImage(img, 0, 0)
-  const base64 = canvas.toDataURL('image/png')
-
-  return base64
-}
-
-// 使用示例
-function convertImageToBase64 (img) {
-  //   const img = new Image()
-  //   img.src = 'path/to/your/image.jpg' // 替换为你的图片路径
-  // console.log('convertImageToBase64',img)
-  try {
-    const base64 = imgToCanvasBase64(img)
-    return base64
-  } catch (error) {
-    console.error(error)
-  }
-}
-
-function getInputsAndOutputs () {
-  const outputs =
-    `PreviewImage,SaveImage,TransparentImage,VHS_VideoCombine,VideoCombine_Adv,Image Save,SaveImageAndMetadata_`.split(
-      ','
-    )
-
-  let outputsId = []
-
-  for (let node of app.graph._nodes) {
-    if (outputs.includes(node.type)) {
-      outputsId.push(node.id)
-    }
-  }
-
-  return outputsId
-}
-
-function getRandomElement (arr) {
-  const randomIndex = Math.floor(Math.random() * arr.length)
-  return arr[randomIndex]
-}
-
-async function getBG () {
-  var outputs = []
-
-  for (let id of app.graph
-    .getNodeById(50)
-    .widgets.filter(w => w.name === 'output_ids')[0]
-    .value.split('\n')) {
-    if (getInputsAndOutputs().map(Number).includes(Number(id))) {
-      if (app.graph.getNodeById(id).imgs && app.graph.getNodeById(id).imgs[0]) {
-        let b = convertImageToBase64(app.graph.getNodeById(id).imgs[0])
-        // console.log(b)
-        outputs.push(b)
-      }
-    }
-  }
-
-  var BACKGROUND_IMAGE = getRandomElement(outputs),
-    CLEAR_BACKGROUND_COLOR = 'rgba(0,0,0,0.9)'
-
-  if (!window._bg_img) {
-    window._bg_img = app.canvas._bg_img.src
-  }
-  // let img=new Image();
-  // img.src=BACKGROUND_IMAGE;
-
-  //去掉透明度过度
-  // app.canvas.zoom_modify_alpha=false;
-  //整体透明度
-  app.canvas.editor_alpha = 1.1
-  //   app.canvas._pattern=ctx.createPattern(img, "no-repeat");
-  app.canvas.updateBackground(BACKGROUND_IMAGE, CLEAR_BACKGROUND_COLOR)
-  app.canvas.draw(true, true)
-}
-
-class BgRunner {
-  constructor () {
-    this.intervalId = null
-    this.running = false
-  }
-
-  // 要运行的方法
-  bg () {
-    console.log('方法bg正在运行')
-    getBG()
-  }
-
-  // 启动bg方法每秒运行一次
-  start () {
-    if (!this.running) {
-      this.intervalId = setInterval(() => this.bg(), 1500)
-      this.running = true
-    }
-  }
-
-  // 停止bg方法的运行
-  stop () {
-    if (this.running) {
-      clearInterval(this.intervalId)
-      this.intervalId = null
-      this.running = false
-
-      if (window._bg_img) {
-        var BACKGROUND_IMAGE = window._bg_img,
-          CLEAR_BACKGROUND_COLOR = 'rgba(0,0,0,1)'
-        app.canvas.editor_alpha = 1
-
-        app.canvas.updateBackground(BACKGROUND_IMAGE, CLEAR_BACKGROUND_COLOR)
-        app.canvas.draw(true, true)
-      }
-    }
-  }
-
-  // 切换start和stop
-  toggle () {
-    if (this.running) {
-      this.stop()
-    } else {
-      this.start()
-    }
-  }
-
-  // 获取运行状态
-  isRunning () {
-    return this.running
-  }
-}
-
-// 示例用法
-// const runner = new BgRunner();
-// runner.start();
-// setTimeout(() => runner.stop(), 5000);
-
-export const td_bg = new BgRunner()

web/javascript/utils_mixlab.js

@@ -1,13 +1,46 @@
 import { app } from '../../../scripts/app.js'
-import { $el } from '../../../scripts/ui.js'
-import {
-  loadExternalScript,
-  updateLLMAPIKey,
-  get_position_style,
-  getLocalData
-} from './common.js'
+import { $el } from '../../../scripts/ui.js' 
 
-loadExternalScript('/mixlab/app/lib/pickr.min.js')
+const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+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'
+  }
+}
 
 function hexToRGBA (hexColor) {
   var hex = hexColor.replace('#', '')
@@ -29,7 +62,7 @@ app.registerExtension({
   init () {
     $el('link', {
       rel: 'stylesheet',
-      href: '/mixlab/app/lib/classic.min.css',
+      href: '/extensions/comfyui-mixlab-nodes/lib/classic.min.css',
       parent: document.head
     })
 
@@ -89,7 +122,7 @@ app.registerExtension({
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
 
-        // console.log('Color nodeData', this.div)
+        // console.log('Color nodeData', this.widgets)
 
         const widget = {
           type: 'div',
@@ -240,19 +273,19 @@ app.registerExtension({
 })
 
 const min_max = node => {
-  if (node.widgets) {
+  if(node.widgets){
     const min_value = node.widgets.filter(w => w.name === 'min_value')[0]
     const max_value = node.widgets.filter(w => w.name === 'max_value')[0]
-
+  
     const number = node.widgets.filter(w => w.name === 'number')[0]
     if (number) {
       number.options.min = min_value.value
       number.options.max = max_value.value
-
+  
       number.value = Math.min(number.options.max, number.value)
       number.value = Math.max(number.options.min, number.value)
     }
-
+  
     if (min_value)
       min_value.callback = e => {
         number.options.min = e
@@ -264,18 +297,22 @@ const min_max = node => {
         number.value = e
       }
   }
+  
 }
 
 app.registerExtension({
   name: 'Mixlab.utils.FloatSlider',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
+
     if (nodeType.comfyClass == 'FloatSlider') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated;
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
         min_max(this)
       }
     }
+
+   
   },
   async loadedGraphNode (node, app) {
     if (node.type === 'FloatSlider') {
@@ -286,6 +323,7 @@ app.registerExtension({
 app.registerExtension({
   name: 'Mixlab.utils.IntNumber',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    
     if (nodeType.comfyClass == 'IntNumber') {
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = function () {
@@ -293,6 +331,7 @@ app.registerExtension({
         min_max(this)
       }
     }
+   
   },
   async loadedGraphNode (node, app) {
     if (node.type === 'IntNumber') {
@@ -301,157 +340,22 @@ app.registerExtension({
   }
 })
 
+
 app.registerExtension({
   name: 'Mixlab.utils.TESTNODE_',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    
     if (nodeType.comfyClass == 'TESTNODE_') {
-      const onExecuted = nodeType.prototype.onExecuted
-      nodeType.prototype.onExecuted = function (message) {
-        onExecuted?.apply(this, arguments)
-        console.log('##', message)
-      }
+    
+      const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = function (message) {
+				onExecuted?.apply(this, arguments);
+        console.log('##',message)
+				 
+			};
+
+
     }
-  }
-})
-
-app.registerExtension({
-  name: 'Mixlab.utils.KeyInput',
-  init () {},
-  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, 24], // 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_llm_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.
-      }
-    }
-  },
-
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeType.comfyClass == 'KeyInput') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = function () {
-        orig_nodeCreated?.apply(this, arguments)
-
-        const rowHeight = this.rowHeight
-        const widget = {
-          type: 'div',
-          name: 'input_key',
-          draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(
-              this.div.style,
-              get_position_style(ctx, widget_width, 24, node.size[1])
-            )
-          }
-        }
-
-        widget.div = $el('div', {})
-
-        document.body.appendChild(widget.div)
-
-        const inputDiv = (key, placeholder) => {
-          let div = document.createElement('div')
-          div.style = `
-          display: flex;
-          align-items: center; 
-          margin: 6px 8px;
-          margin-top:0px;
-          height:44px;
-          width:220px; 
-          `
-
-          const ip = document.createElement('input')
-          ip.type = 'password'
-          ip.className = `${'comfy-multiline-input'} ${placeholder}`
-
-          ip.placeholder = placeholder
-          // ip.value = placeholder
-
-          ip.style = `margin-left:8px;
-            outline: none;
-            border: none;
-            padding:12px;
-            width: 100%;
-            `
-
-          div.appendChild(ip)
-
-          ip.addEventListener('change', () => {
-            let data = getLocalData(key)
-            data[this.id] = ip.value.trim()
-            localStorage.setItem(key, JSON.stringify(data))
-            updateLLMAPIKey(data[this.id])
-          })
-
-          return div
-        }
-
-        let inputKey = inputDiv('_mixlab_llm_api_key', 'Key')
-
-        widget.div.appendChild(inputKey)
-
-        this.addCustomWidget(widget)
-
-        const onRemoved = this.onRemoved
-        this.onRemoved = () => {
-          inputKey.remove()
-          widget.div.remove()
-          return onRemoved?.()
-        }
-
-        // const processMouseWheel=app.canvas.processMouseWheel
-        // app.canvas.processMouseWheel=()=>{
-        //   console.log(app.canvas.ds.scale)
-        //   return processMouseWheel?.()
-        // }
-
-        this.serialize_widgets = true //需要保存参数
-      }
-    }
-  },
-  async loadedGraphNode (node, app) {
-    if (node.type === 'KeyInput') {
-      let widget = node.widgets.filter(w => w.div)[0]
-
-      let apiKey = getLocalData('_mixlab_llm_api_key')
-
-      let id = node.id
-      if (widget.div.querySelector('.Key'))
-        widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
-
-      if (apiKey[id]) updateLLMAPIKey(apiKey[id])
-    }
-  },
-  nodeCreated (node, app) {
-    //数据延迟？？
-    setTimeout(() => {
-      // console.log('#LoadImagesToBatch', node.type)
-      if (node.type === 'KeyInput') {
-        let widget = node.widgets.filter(w => w.div)[0]
-
-        let apiKey = getLocalData('_mixlab_llm_api_key')
-
-        let id = node.id
-
-        if (widget.div.querySelector('.Key'))
-          widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
-
-        if (apiKey[id]) updateLLMAPIKey(apiKey[id])
-      }
-    }, 1000)
-  }
+   
+  }, 
 })

web/javascript/video_mixlab.js

@@ -3,15 +3,64 @@ import { api } from '../../../scripts/api.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 
 import { $el } from '../../../scripts/ui.js'
-import { injectCSS } from './common.js'
 
-import { get_position_style } from './common.js'
+// The code is based on ComfyUI-VideoHelperSuite modification.
+
+
+
+function injectCSS (css) {
+  // 检查页面中是否已经存在具有相同内容的style标签
+  const existingStyle = document.querySelector('style')
+  if (existingStyle && existingStyle.textContent === css) {
+    return // 如果已经存在相同的样式，则不进行注入
+  }
+
+  // 创建一个新的style标签，并将CSS内容注入其中
+  const style = document.createElement('style')
+  style.textContent = css
+
+  // 将style标签插入到页面的head元素中
+  const head = document.querySelector('head')
+  head.appendChild(style)
+}
 
 injectCSS(`
 .hidden{
   display:none !important
 }`)
 
+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'
+  }
+}
+
 function videoUpload (node, inputName, inputData, app) {
   const imageWidget = node.widgets.find(w => w.name === 'video')
   let uploadWidget
@@ -20,15 +69,13 @@ function videoUpload (node, inputName, inputData, app) {
     type: 'div',
     name: 'upload-preview',
     draw (ctx, node, widget_width, y, widget_height) {
-      let d = {
-        ...get_position_style(ctx, widget_width - 20, 220, node.size[1], 72),
-        outline: '1px solid',
-        top: `${widget_height + 24}px`
-      }
-
-      delete d.height
-
-      Object.assign(this.div.style, d)
+      Object.assign(
+        this.div.style,
+        get_position_style(ctx, widget_width, 220, node.size[1]),
+        {
+          outline: '1px solid'
+        }
+      )
     }
   }
 
@@ -193,7 +240,15 @@ app.registerExtension({
   }
 })
 
-function offsetDOMWidget (widget, ctx, node, widgetWidth, widgetY, height) {
+
+function offsetDOMWidget(
+  widget,
+  ctx,
+  node,
+  widgetWidth,
+  widgetY,
+  height
+) {
   const margin = 10
   const elRect = ctx.canvas.getBoundingClientRect()
   const transform = new DOMMatrix()
@@ -202,32 +257,31 @@ function offsetDOMWidget (widget, ctx, node, widgetWidth, widgetY, height) {
       elRect.height / ctx.canvas.height
     )
     .multiplySelf(ctx.getTransform())
-    .translateSelf(margin, widgetY + margin)
+    .translateSelf(0, widgetY + margin)
 
   const scale = new DOMMatrix().scaleSelf(transform.a, transform.d)
-
   Object.assign(widget.inputEl.style, {
     transformOrigin: '0 0',
     transform: scale,
-    left: `${transform.a + transform.e + 56}px`,
-    top: `${transform.d + transform.f + 44}px`,
-    width: `${widgetWidth - 32}px`,
-    height: `${(height || widget.parent?.inputHeight || 32) - margin * 2}px`,
+    left: `${transform.e}px`,
+    top: `${transform.d + transform.f}px`,
+    width: `${widgetWidth}px`,
+    height: `${(height || widget.parent?.inputHeight || 32) - margin}px`,
     position: 'absolute',
     background: !node.color ? '' : node.color,
     color: !node.color ? '' : 'white',
-    zIndex: 5 //app.graph._nodes.indexOf(node),
+    zIndex: 5, //app.graph._nodes.indexOf(node),
   })
 }
 
-export const hasWidgets = node => {
+export const hasWidgets = (node) => {
   if (!node.widgets || !node.widgets?.[Symbol.iterator]) {
     return false
   }
   return true
 }
 
-export const cleanupNode = node => {
+export const cleanupNode = (node) => {
   if (!hasWidgets(node)) {
     return
   }
@@ -244,56 +298,43 @@ export const cleanupNode = node => {
   }
 }
 
-const createPreviewElement = (name, val, format) => {
-  const [type] = format.split('/')
+const CreatePreviewElement = (name, val, format) => {
+  const [type] = format.split('/')  
   const w = {
-    name,
-    type,
-    value: val,
-    draw: function (ctx, node, widgetWidth, widgetY, height) {
-      
-      Object.assign(
-        this.inputEl.style,
-        get_position_style(ctx, widgetWidth - 12, 220, node.size[1], 44),
-        {
-          outline: '1px solid',
-          display: 'flex',
-          flexWrap: 'wrap',
-          flexDirection: 'row',
-          justifyContent: 'flex-start',
-          top: `${height + 24}px`
+      name,
+      type,
+      value: val,
+      draw: function (ctx, node, widgetWidth, widgetY, height) {
+        const [cw, ch] = this.computeSize(widgetWidth)
+        offsetDOMWidget(this, ctx, node, widgetWidth, widgetY, ch)
+      },
+      computeSize: function (_) {
+        const ratio = this.inputRatio || 1
+        const width = Math.max(220, this.parent.size[0])
+        return [width, (width / ratio + 10)]
+      },
+      onRemoved: function () {
+        if (this.inputEl) {
+          this.inputEl.remove()
         }
-      )
-
-
-    },
-    computeSize: function (_) {
-      const ratio = this.inputRatio || 1
-      const width = Math.max(220, this.parent.size[0])
-      return [width, width / ratio + 10]
-    },
-    onRemoved: function () {
-      if (this.inputEl) {
-        this.inputEl.remove()
-      }
+      },
     }
+
+    w.inputEl = document.createElement(type === 'video' ? 'video' : 'img')
+    w.inputEl.src = w.value
+    if (type === 'video') {
+      w.inputEl.setAttribute('type', 'video/webm');
+      w.inputEl.autoplay = true
+      w.inputEl.loop = true
+      w.inputEl.controls = false;
+    }
+    w.inputEl.onload = function () {
+      w.inputRatio = w.inputEl.naturalWidth / w.inputEl.naturalHeight
+    }
+    document.body.appendChild(w.inputEl)
+    return w
   }
 
-  w.inputEl = document.createElement(type === 'video' ? 'video' : 'img')
-  w.inputEl.src = w.value
-
-  if (type === 'video' || format.match('.mp4')) {
-    w.inputEl.setAttribute('type', 'video/webm')
-    w.inputEl.autoplay = true
-    w.inputEl.loop = true
-    w.inputEl.controls = true
-  }
-  w.inputEl.onload = function () {
-    w.inputRatio = w.inputEl.naturalWidth / w.inputEl.naturalHeight
-  }
-  document.body.appendChild(w.inputEl)
-  return w
-}
 
 app.registerExtension({
   name: 'Mixlab.Video.ImageListReplace',
@@ -308,14 +349,13 @@ app.registerExtension({
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(ctx, widget_width - 12, 220, node.size[1], 44),
+              get_position_style(ctx, widget_width, 188, node.size[1]),
               {
                 outline: '1px solid',
                 display: 'flex',
                 flexWrap: 'wrap',
                 flexDirection: 'row',
-                justifyContent: 'flex-start',
-                top: `${widget_height + 24}px`
+                justifyContent: 'flex-start'
               }
             )
           }
@@ -429,17 +469,12 @@ app.registerExtension({
       }
     }
 
-    if (
-      nodeData?.name == 'VideoCombine_Adv' ||
-      nodeData?.name == 'CombineAudioVideo'
-    ) {
+    if (nodeData?.name == 'VideoCombine_Adv') {
       const onExecuted = nodeType.prototype.onExecuted
       nodeType.prototype.onExecuted = function (message) {
         const prefix = 'vhs_gif_preview_'
         const r = onExecuted ? onExecuted.apply(this, message) : undefined
 
-        if (!this.widgets) this.widgets = []
-
         if (this.widgets) {
           const pos = this.widgets.findIndex(w => w.name === `${prefix}_0`)
           if (pos !== -1) {
@@ -454,13 +489,12 @@ app.registerExtension({
                 '/view?' + new URLSearchParams(params).toString()
               )
               const w = this.addCustomWidget(
-                createPreviewElement(
+                CreatePreviewElement(
                   `${prefix}_${i}`,
                   previewUrl,
                   params.format || 'image/gif'
                 )
               )
-              console.log(w)
               w.parent = this
             })
           }

web/javascript/visualization.js

@@ -1,238 +0,0 @@
-import { app } from '../../../scripts/app.js'
-import { api } from '../../../scripts/api.js'
-
-function base64ToBlobFromURL (base64URL, contentType) {
-  return fetch(base64URL).then(response => response.blob())
-}
-
-async function uploadImage (blob, fileType = '.svg', filename) {
-  // const blob = await (await fetch(src)).blob();
-  const body = new FormData()
-  body.append(
-    'image',
-    new File([blob], (filename || new Date().getTime()) + fileType)
-  )
-
-  const resp = await api.fetchApi('/upload/image', {
-    method: 'POST',
-    body
-  })
-
-  // console.log(resp)
-  let data = await resp.json()
-
-  return data
-}
-
-// 上传得到url
-async function uploadBase64ToFile (base64) {
-  let bg_blob = await base64ToBlobFromURL(base64)
-  let url = await uploadImage(bg_blob, '.png')
-  return url
-}
-
-class Visualizer {
-  constructor (node, container, visualSrc) {
-    this.node = node
-
-    this.iframe = document.createElement('iframe')
-    Object.assign(this.iframe, {
-      scrolling: 'no',
-      overflow: 'hidden'
-    })
-    this.iframe.src = '/mixlab/app/' + visualSrc + '.html'
-    console.log('#Visualizer', container, this.iframe)
-    container.appendChild(this.iframe)
-  }
-
-  updateVisual (params) {
-    console.log('#updateVisual', params, this.iframe)
-    // const iframeDocument = this.iframe.contentWindow.document
-    // const previewScript = iframeDocument.getElementById('visualizer')
-    // previewScript.setAttribute(
-    //   'reference_image',
-    //   JSON.stringify(params.reference_image)
-    // )
-    // previewScript.setAttribute('depth_map', JSON.stringify(params.depth_map))
-    // Update the reference image and depth map
-    this.iframe.contentWindow.postMessage(params, '*')
-  }
-
-  remove () {
-    this.container.remove()
-  }
-}
-
-function createVisualizer (node, inputName, typeName, inputData, app) {
-  node.name = inputName
-
-  const widget = {
-    type: typeName,
-    name: 'preview3d',
-    callback: () => {},
-    draw: function (ctx, node, widgetWidth, widgetY, widgetHeight) {
-      const margin = 10
-      const top_offset = 5
-      const visible = app.canvas.ds.scale > 0.5 && this.type === typeName
-      const w = widgetWidth - margin * 4
-      const clientRectBound = ctx.canvas.getBoundingClientRect()
-      const transform = new DOMMatrix()
-        .scaleSelf(
-          clientRectBound.width / ctx.canvas.width,
-          clientRectBound.height / ctx.canvas.height
-        )
-        .multiplySelf(ctx.getTransform())
-        .translateSelf(margin, margin + widgetY)
-
-      Object.assign(this.visualizer.style, {
-        left: `${transform.a * margin + transform.e + 40}px`,
-        top: `${transform.d + transform.f + top_offset}px`,
-        width: `${w * transform.a}px`,
-        height: `${
-          w * transform.d - widgetHeight - margin * 15 * transform.d
-        }px`,
-        position: 'absolute',
-        overflow: 'hidden',
-        zIndex: app.graph._nodes.indexOf(node)
-      })
-
-      Object.assign(this.visualizer.children[0].style, {
-        transformOrigin: '50% 50%',
-        width: '100%',
-        height: '100%',
-        border: '0 none'
-      })
-
-      this.visualizer.hidden = !visible
-    }
-  }
-
-  const container = document.createElement('div')
-  container.id = `Comfy3D_${inputName}`
-
-  node.visualizer = new Visualizer(node, container, typeName)
-  widget.visualizer = container
-  widget.parent = node
-
-  document.body.appendChild(widget.visualizer)
-
-  node.addCustomWidget(widget)
-
-  node.updateParameters = params => {
-    // console.log('#updateParameters', params)
-    params.id = node.id
-    // node.visualizer = new Visualizer(node, container, typeName)
-    node.visualizer.updateVisual(params)
-  }
-
-  // Events for drawing backgound
-  node.onDrawBackground = function (ctx) {
-    if (!this.flags.collapsed) {
-      node.visualizer.iframe.hidden = false
-    } else {
-      node.visualizer.iframe.hidden = true
-    }
-  }
-
-  // Make sure visualization iframe is always inside the node when resize the node
-  node.onResize = function () {
-    let [w, h] = this.size
-    if (w <= 600) w = 600
-    if (h <= 500) h = 500
-
-    if (w > 600) {
-      h = w - 100
-    }
-
-    this.size = [w, h]
-  }
-
-  // Events for remove nodes
-  node.onRemoved = () => {
-    for (let w in node.widgets) {
-      if (node.widgets[w].visualizer) {
-        node.widgets[w].visualizer.remove()
-      }
-    }
-  }
-
-  return {
-    widget: widget
-  }
-}
-
-function registerVisualizer (nodeType, nodeData, nodeClassName, typeName) {
-  if (nodeData.name == nodeClassName) {
-    const onNodeCreated = nodeType.prototype.onNodeCreated
-
-    nodeType.prototype.onNodeCreated = async function () {
-      const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined
-
-      let Preview3DNode = app.graph._nodes.filter(
-        wi => wi.type == nodeClassName
-      )
-      let nodeName = `Preview3DNode_${Preview3DNode.length}`
-
-      const result = await createVisualizer.apply(this, [
-        this,
-        nodeName,
-        typeName,
-        {},
-        app
-      ])
-
-      this.setSize([600, 500])
-
-      return r
-    }
-
-    nodeType.prototype.onExecuted = async function (message) {
-      // Check if reference image and depth map are available
-      console.log("#message",message)
-      if (message.reference_image && message.depth_map) {
-        const params = {}
-        params.reference_image = message.reference_image[0]
-        params.depth_map = message.depth_map[0]
-        this.updateParameters(params)
-      }
-    }
-  }
-}
-
-app.registerExtension({
-  name: 'Mixlab.nodes.depthviewer',
-
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    registerVisualizer(nodeType, nodeData, 'DepthViewer', 'threeVisualizer')
-  },
-  nodeCreated (node, app) {
-    //数据延迟？？
-    setTimeout(() => {
-      let widget = node.widgets?.filter(w => w.name == 'preview3d')[0]
-      let framesWidget = node.widgets?.filter(w => w.name == 'frames')[0]
-
-      if (node.type === 'DepthViewer' && widget) {
-        let nodeId = node.id
-        //延迟才能获得this.id
-        widget.visualizer.querySelector('iframe').src += '?id=' + nodeId
-        // console.log('DepthViewer',widget)
-        window.addEventListener('message', async event => {
-          // 检查消息的来源，确保消息来自可信的源
-          console.log(event)
-          const { id, imgs } = event.data
-          if (id == nodeId) {
-            framesWidget.value = { images: [] }
-
-            for (const f of imgs) {
-              let file = await uploadBase64ToFile(f)
-              framesWidget.value.images.push(file)
-            }
-            // framesWidget.value.base64 = frames
-            framesWidget.value._seed = Math.random()
-            node.title = 'Input #' + imgs.length
-          }
-        })
-      }
-    }, 1000)
-  }
-})

web/javascript/watcher_mixlab.js

@@ -34,10 +34,7 @@ function get_position_style (ctx, widget_width, y, node_height) {
   return {
     transformOrigin: '0 0',
     transform: transform,
-    left:
-      document.querySelector('.comfy-menu').style.display === 'none'
-        ? `60px`
-        : `0`,
+    left: `0`,
     top: `0`,
     cursor: 'pointer',
     position: 'absolute',
@@ -105,14 +102,13 @@ app.registerExtension({
         orig_nodeCreated?.apply(this, arguments)
 
         // 虚拟的widget，用于更新节点，让其每次都运行
-        // 改成了seed输入
-        // const widget = {
-        //   type: 'div',
-        //   name: 'seed',
-        //   draw (ctx, node, widget_width, y, widget_height) {}
-        // }
+        const widget = {
+          type: 'div',
+          name: 'seed',
+          draw (ctx, node, widget_width, y, widget_height) {}
+        }
 
-        // this.addCustomWidget(widget)
+        this.addCustomWidget(widget)
 
         const watcher = this.widgets.filter(w => w.name == 'watcher')[0]
 
@@ -129,7 +125,7 @@ app.registerExtension({
                   window._mixlab_file_path_watcher = json.event_type
                   // widget.card.innerText = window._mixlab_file_path_watcher || ''
                   //运行
-                  if (app) app.queuePrompt()
+                  // document.querySelector('#queue-button').click()
                 }
               })
             }, 1000)
@@ -157,19 +153,18 @@ app.registerExtension({
         this.serialize_widgets = true
       }
 
-      // 改成seed的输入
-      // const onExecuted = nodeType.prototype.onExecuted
-      // nodeType.prototype.onExecuted = function (message) {
-      //   onExecuted?.apply(this, arguments)
-      //   console.log(message)
-      //   try {
-      //     let seed = this.widgets.filter(w => w.name === 'seed')[0]
-      //     if (seed) {
-      //       if (!seed.value) seed.value = 0
-      //       seed.value += 1
-      //     }
-      //   } catch (error) {}
-      // }
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log(message)
+        try {
+          let seed = this.widgets.filter(w => w.name === 'seed')[0]
+          if (seed) {
+            if (!seed.value) seed.value = 0
+            seed.value += 1
+          }
+        } catch (error) {}
+      }
     }
   },
   async loadedGraphNode (node, app) {
@@ -198,13 +193,13 @@ app.registerExtension({
         }
       }
 
-      // try {
-      //   let seed = node.widgets.filter(w => w.name === 'seed')[0]
-      //   if (seed) {
-      //     if (!seed.value) seed.value = 0
-      //     seed.value += 1
-      //   }
-      // } catch (error) {}
+      try {
+        let seed = node.widgets.filter(w => w.name === 'seed')[0]
+        if (seed) {
+          if (!seed.value) seed.value = 0
+          seed.value += 1
+        }
+      } catch (error) {}
     }
   }
 })

web/lib/miniPaint-4.14.2/dist/bundle.js.LICENSE.txt

@@ -0,0 +1,47 @@
+/*
+object-assign
+(c) Sindre Sorhus
+@license MIT
+*/
+
+/*!
+
+pica
+https://github.com/nodeca/pica
+
+*/
+
+/*!
+ * Block below copied from Protovis: http://mbostock.github.com/protovis/
+ * Copyright 2010 Stanford Visualization Group
+ * Licensed under the BSD License: http://www.opensource.org/licenses/bsd-license.php
+ * @license
+ */
+
+/*!
+ * jQuery JavaScript Library v3.7.1
+ * https://jquery.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2023-08-28T13:37Z
+ */
+
+/*!
+ * quantize.js Copyright 2008 Nick Rabinowitz.
+ * Licensed under the MIT license: http://www.opensource.org/licenses/mit-license.php
+ * @license
+ */
+
+/*! alertifyjs - v1.13.1 - Mohammad Younes <Mohammad@alertifyjs.com> (http://alertifyjs.com) */
+
+/*! regenerator-runtime -- Copyright (c) 2014-present, Facebook, Inc. -- license (MIT): https://github.com/facebook/regenerator/blob/main/LICENSE */
+
+/**
+ * hermite-resize - Canvas image resize/resample using Hermite filter with JavaScript.
+ * @version v2.2.10
+ * @link https://github.com/viliusle/miniPaint
+ * @license MIT
+ */

web/lib/miniPaint-4.14.2/dist/bundle.js.map

@@ -0,0 +1 @@
+{"version":3,"file":"bundle.js","sources":["webpack://miniPaint/bundle.js"],"mappings":";AAAA","sourceRoot":""}

web/lib/miniPaint-4.14.2/images/favicon.svg

@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 18.1.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 298.73 298.73" style="enable-background:new 0 0 298.73 298.73;" xml:space="preserve">
+<g>
+	<path style="fill:#010002;" d="M264.959,9.35H33.787C15.153,9.35,0,24.498,0,43.154v212.461c0,18.634,15.153,33.766,33.787,33.766
+		h231.171c18.634,0,33.771-15.132,33.771-33.766V43.154C298.73,24.498,283.593,9.35,264.959,9.35z M193.174,59.623
+		c18.02,0,32.634,14.615,32.634,32.634s-14.615,32.634-32.634,32.634c-18.025,0-32.634-14.615-32.634-32.634
+		S175.149,59.623,193.174,59.623z M254.363,258.149H149.362H49.039c-9.013,0-13.027-6.521-8.964-14.566l56.006-110.93
+		c4.058-8.044,11.792-8.762,17.269-1.605l56.316,73.596c5.477,7.158,15.05,7.767,21.386,1.354l13.777-13.951
+		c6.331-6.413,15.659-5.619,20.826,1.762l35.675,50.959C266.487,252.16,263.376,258.149,254.363,258.149z"/>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/animation.svg

@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 18.1.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 30.051 30.051" style="enable-background:new 0 0 30.051 30.051;" xml:space="preserve">
+<g>
+	<path d="M19.982,14.438l-6.24-4.536c-0.229-0.166-0.533-0.191-0.784-0.062c-0.253,0.128-0.411,0.388-0.411,0.669v9.069
+		c0,0.284,0.158,0.543,0.411,0.671c0.107,0.054,0.224,0.081,0.342,0.081c0.154,0,0.31-0.049,0.442-0.146l6.24-4.532
+		c0.197-0.145,0.312-0.369,0.312-0.607C20.295,14.803,20.177,14.58,19.982,14.438z"/>
+	<path d="M15.026,0.002C6.726,0.002,0,6.728,0,15.028c0,8.297,6.726,15.021,15.026,15.021c8.298,0,15.025-6.725,15.025-15.021
+		C30.052,6.728,23.324,0.002,15.026,0.002z M15.026,27.542c-6.912,0-12.516-5.601-12.516-12.514c0-6.91,5.604-12.518,12.516-12.518
+		c6.911,0,12.514,5.607,12.514,12.518C27.541,21.941,21.937,27.542,15.026,27.542z"/>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/arrow-down.svg

@@ -0,0 +1,5 @@
+<svg width="124" height="150" viewBox="0 0 124 150" xmlns="http://www.w3.org/2000/svg">
+<rect x="55" y="14" width="14" height="63"/>
+<rect x="116" width="14" height="61" transform="rotate(90 116 0)"/>
+<path d="M62 150L8.30643 75L115.694 75L62 150Z"/>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/blur.svg

@@ -0,0 +1,28 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 512 512" style="enable-background:new 0 0 512 512;" xml:space="preserve">
+<g>
+	<g>
+		<path d="M264.574,4.675C262.697,1.761,259.467,0,256,0c-3.467,0-6.697,1.761-8.574,4.675
+			c-6.532,10.14-159.966,249.362-159.966,338.784C87.459,436.393,163.066,512,256,512s168.541-75.607,168.541-168.541
+			C424.541,254.037,271.106,14.815,264.574,4.675z M256,491.602c-81.686,0-148.142-66.456-148.142-148.143
+			c0-34.037,26.926-101.269,77.865-194.427C213.83,97.626,242.219,51.324,256,29.29c13.77,22.016,42.123,68.259,70.223,119.64
+			c50.976,93.212,77.92,160.478,77.92,194.529C404.142,425.146,337.686,491.602,256,491.602z"/>
+	</g>
+</g>
+<g>
+	<g>
+		<path d="M375.907,332.939c-5.633,0-10.199,4.566-10.199,10.199c0,43.197-25.482,82.521-64.919,100.181
+			c-5.141,2.301-7.442,8.335-5.14,13.476c1.695,3.788,5.416,6.034,9.314,6.034c1.393,0,2.809-0.287,4.163-0.893
+			c46.764-20.941,76.981-67.572,76.981-118.797C386.106,337.505,381.54,332.939,375.907,332.939z"/>
+	</g>
+</g>
+<g>
+	<g>
+		<path d="M281.818,460.702c-0.729-5.586-5.85-9.519-11.435-8.791c-4.736,0.619-9.574,0.933-14.383,0.933
+			c-5.633,0-10.199,4.566-10.199,10.199c0,5.633,4.566,10.199,10.199,10.199c5.69,0,11.419-0.372,17.028-1.106
+			C278.613,471.407,282.548,466.287,281.818,460.702z"/>
+	</g>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/bold.svg

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<svg width="1em" height="1em" viewBox="0 0 16 16" fill="currentColor" xmlns="http://www.w3.org/2000/svg">
+	<path d="M8.21 13c2.106 0 3.412-1.087 3.412-2.823 0-1.306-.984-2.283-2.324-2.386v-.055a2.176 2.176 0 0 0 1.852-2.14c0-1.51-1.162-2.46-3.014-2.46H3.843V13H8.21zM5.908 4.674h1.696c.963 0 1.517.451 1.517 1.244 0 .834-.629 1.32-1.73 1.32H5.908V4.673zm0 6.788V8.598h1.73c1.217 0 1.88.492 1.88 1.415 0 .943-.643 1.449-1.832 1.449H5.907z"/>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/brush.svg

@@ -0,0 +1 @@
+<svg height="443pt" viewBox="0 0 443.06138 443" width="443pt" xmlns="http://www.w3.org/2000/svg"><path d="m431.328125 25.894531-14.136719-14.136719c-8.070312-8.078124-19.210937-12.320312-30.613281-11.6601558-11.402344.6601558-21.976563 6.1640628-29.058594 15.1249998l-117.023437 164.839844c-4.089844 5.761719-10.511719 9.425781-17.554688 10.019531-7.039062.59375-13.984375-1.945312-18.980468-6.941406l-34.234376-34.207031c-9.480468-9.109375-24.46875-9.109375-33.949218 0l-11.296875 11.304687 158.398437 158.398438 11.304688-11.304688c4.527344-4.488281 7.070312-10.597656 7.070312-16.96875 0-6.375-2.542968-12.484375-7.070312-16.972656l-34.222656-34.234375c-4.996094-4.996094-7.535157-11.941406-6.941407-18.980469.59375-7.042969 4.257813-13.464843 10.019531-17.554687l165-117.183594c8.890626-7.109375 14.332032-17.671875 14.960938-29.035156.628906-11.367188-3.617188-22.464844-11.671875-30.507813zm-24 43.808594c-9.375 9.371094-24.570313 9.371094-33.945313 0-9.371093-9.375-9.371093-24.574219 0-33.945313 9.496094-9.0625 24.441407-9.0625 33.9375 0 9.375 9.371094 9.378907 24.570313.007813 33.945313zm0 0"/><path d="m390.351562 44.734375c-3.234374 0-6.152343 1.949219-7.390624 4.9375-1.234376 2.988281-.550782 6.429687 1.734374 8.71875 3.160157 3.03125 8.148438 3.03125 11.304688 0 2.289062-2.289063 2.972656-5.730469 1.734375-8.71875s-4.15625-4.9375-7.390625-4.9375zm0 0"/><path d="m135.792969 420.429688 22.65625 22.65625 113.085937-113.167969-158.398437-158.402344-113.136719 113.121094 22.65625 22.65625 84.847656-84.855469c2.007813-2.078125 4.984375-2.914062 7.78125-2.183594 2.796875.734375 4.980469 2.917969 5.710938 5.714844.734375 2.796875-.101563 5.773438-2.179688 7.78125l-84.847656 84.855469 22.632812 22.625 50.902344-50.90625c3.140625-3.03125 8.128906-2.988281 11.214844.097656s3.128906 8.078125.097656 11.214844l-50.90625 50.921875 22.617188 22.621094 62.214844-62.222657c3.125-3.125 8.191406-3.128906 11.316406-.003906 3.128906 3.125 3.128906 8.191406.003906 11.316406l-62.222656 62.222657 22.625 22.625 73.535156-73.535157c3.140625-3.03125 8.128906-2.988281 11.214844.097657 3.085937 3.085937 3.128906 8.074218.097656 11.214843zm0 0"/></svg>

web/lib/miniPaint-4.14.2/images/icons/bulge_pinch.svg

@@ -0,0 +1,17 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 402.56 402.56" style="enable-background:new 0 0 402.56 402.56;" xml:space="preserve">
+<g>
+	<g>
+		<polygon points="38.613,234.88 38.4,274.56 96.64,274.56 116.693,265.173 9.6,372.48 39.68,402.56 147.2,295.253 137.813,316.587 
+			137.813,373.76 177.493,374.187 177.493,234.88 		"/>
+	</g>
+</g>
+<g>
+	<g>
+		<polygon points="306.987,128.213 285.653,137.6 392.96,30.08 362.88,0 255.573,107.307 264.96,87.04 264.96,28.8 225.28,29.013 
+			225.28,167.893 364.587,167.893 364.16,128.213 		"/>
+	</g>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/clone.svg

@@ -0,0 +1,21 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.1.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 540.721 540.721" style="enable-background:new 0 0 540.721 540.721;" xml:space="preserve">
+<g>
+	<g>
+		<path d="M521.858,465.271H18.862c-7.545,0-12.575,5.03-12.575,12.575v50.3c0,7.545,5.03,12.575,12.575,12.575h502.996
+			c7.545,0,12.575-5.03,12.575-12.575v-50.3C534.433,470.301,529.403,465.271,521.858,465.271z"/>
+		<path d="M227.606,98.084c5.03-5.03,20.12-12.575,30.18-12.575c7.545,0,12.575-5.03,12.575-12.575c0-7.545-5.03-10.06-12.575-10.06
+			l0,0c-17.605,0-37.725,7.545-47.785,17.605c-12.575,10.06-22.635,27.665-22.635,47.785c0,7.545,5.03,10.06,12.575,10.06l0,0
+			c7.545,0,12.575-2.515,12.575-10.06C212.516,118.204,220.061,103.114,227.606,98.084z"/>
+		<path d="M18.862,440.121h502.996c5.03,0,7.545-2.515,10.06-5.03c2.515-2.515,2.515-7.545,0-12.575l-47.785-100.599
+			c0-5.03-5.03-7.545-10.06-7.545H333.235v-77.964c25.15-20.12,60.359-60.36,60.359-110.659C393.594,55.33,338.265,0,270.36,0
+			S147.126,57.845,147.126,125.749c-2.515,47.785,35.21,88.024,60.36,110.659v77.964H66.647c-5.03,0-10.06,2.515-10.06,7.545
+			L8.802,422.517c-2.515,5.03-2.515,7.545,0,12.575C11.317,440.121,13.832,440.121,18.862,440.121z M169.761,125.749
+			c0-55.33,45.27-100.599,98.084-100.599c52.815,0,98.084,45.27,98.084,100.599c0,37.725-27.665,75.449-55.33,93.054
+			c-2.515,2.515-5.03,5.03-5.03,10.06v123.234c0,2.515-12.575,10.06-22.635,10.06h-37.725c-7.545,0-15.09-7.545-15.09-10.06V228.863
+			c0-5.03-2.515-7.545-5.03-10.06C207.486,206.228,169.761,168.504,169.761,125.749z"/>
+	</g>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/crop.svg

@@ -0,0 +1,17 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 16.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 width="956.815px" height="956.815px" viewBox="0 0 956.815 956.815" style="enable-background:new 0 0 956.815 956.815;"
+	 xml:space="preserve">
+<g>
+	<path d="M137.621,162.622H20c-11.046,0-20,8.954-20,20v72.919c0,11.046,8.954,20,20,20h117.622L137.621,162.622L137.621,162.622z"
+		/>
+	<path d="M774.193,956.815c11.046,0,20-8.954,20-20V819.193H681.274v117.621c0,11.046,8.954,20,20,20L774.193,956.815
+		L774.193,956.815z"/>
+	<path d="M794.193,656.275V182.622c0-11.046-8.954-20-20-20H300.54v112.919h380.734v380.734H794.193z"/>
+	<path d="M936.814,681.275H794.193H681.274H275.54V275.541V162.622V20c0-11.046-8.954-20-20-20h-72.918c-11.046,0-20,8.954-20,20
+		v142.622v112.919v498.653c0,11.046,8.954,20,20,20h498.653h112.918h142.622c11.045,0,20-8.954,20-20v-72.918
+		C956.814,690.229,947.86,681.275,936.814,681.275z"/>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/delete.svg

@@ -0,0 +1,6 @@
+<svg height="365pt" viewBox="0 0 365.71733 365" width="365pt" xmlns="http://www.w3.org/2000/svg">
+	<g fill="#f44336">
+		<path d="m356.339844 296.347656-286.613282-286.613281c-12.5-12.5-32.765624-12.5-45.246093 0l-15.105469 15.082031c-12.5 12.503906-12.5 32.769532 0 45.25l286.613281 286.613282c12.503907 12.5 32.769531 12.5 45.25 0l15.082031-15.082032c12.523438-12.480468 12.523438-32.75.019532-45.25zm0 0"/>
+		<path d="m295.988281 9.734375-286.613281 286.613281c-12.5 12.5-12.5 32.769532 0 45.25l15.082031 15.082032c12.503907 12.5 32.769531 12.5 45.25 0l286.632813-286.59375c12.503906-12.5 12.503906-32.765626 0-45.246094l-15.082032-15.082032c-12.5-12.523437-32.765624-12.523437-45.269531-.023437zm0 0"/>
+	</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/desaturate.svg

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Svg Vector Icons : http://www.onlinewebfonts.com/icon -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 1000 1000" enable-background="new 0 0 1000 1000" xml:space="preserve">
+<metadata> Svg Vector Icons : http://www.onlinewebfonts.com/icon </metadata>
+<g><g transform="translate(0.000000,511.000000) scale(0.100000,-0.100000)"><path d="M2646.5,3789.8c-1040.2-119.8-2106-953.5-2445-1917.3l-91.8-260.1L102,355.5c-5.1-1081-2.5-1279.9,33.1-1415c155.5-609.4,578.8-986.7,1282.4-1139.7c300.9-66.3,670.5-96.9,1534.8-127.5c400.3-12.7,831.2-43.3,961.2-63.7c464-76.5,836.3-229.5,1448.1-599.2c698.6-420.7,1063.2-550.7,1682.7-599.2c234.6-17.9,645,20.4,866.9,81.6c494.6,132.6,1007.1,476.8,1341.1,902.6c318.7,405.4,484.4,749.6,589,1223.8c53.5,255,56.1,313.6,58.6,1527.2c0,1155-5.1,1274.8-45.9,1415c-186.1,596.6-609.4,917.9-1374.2,1045.3c-135.1,22.9-550.7,51-963.7,63.7c-1560.3,53.5-1828,112.2-2957.5,662.9c-685.8,334-928,415.6-1328.3,458.9C2944.8,3820.4,2919.3,3820.4,2646.5,3789.8z M3378.2,3165.2c255-51,461.5-135.1,1014.7-413c543.1-270.3,821-382.4,1213.6-487c311-84.2,798-155.5,1042.8-155.5c142.8,0,158.1-5.1,234.6-91.8c112.2-124.9,214.2-163.2,420.7-163.2c193.8,0,359.5,61.2,423.2,153c40.8,56.1,48.5,58.6,328.9,43.3c894.9-51,1249.3-328.9,1246.7-971.4c0-117.3-7.6-247.3-17.8-288.1c-30.6-147.9-94.3-331.4-168.3-494.6l-76.5-168.3l-158.1-12.7c-379.9-30.6-563.4-270.3-372.2-487l73.9-86.7l-81.6-76.5c-211.6-198.9-527.8-359.5-843.9-428.3c-160.6-35.7-272.8-43.3-560.9-30.6c-555.8,20.4-777.6,99.4-1494,525.2C5111.9-174.8,4857-52.4,4482.2,70c-410.5,137.7-685.8,175.9-1402.3,206.5c-1287.5,51-1596,86.7-1891.8,219.3c-191.2,84.1-379.9,288.1-443.6,471.7c-66.3,193.8-58.6,471.7,17.9,696l61.2,175.9l155.5,5.1c226.9,5.1,402.8,119.8,433.4,280.5c20.4,107.1,0,168.3-84.1,249.9l-66.3,66.3l61.2,66.3c249.9,267.7,808.2,571.1,1200.8,657.8C2745.9,3213.6,3143.6,3213.6,3378.2,3165.2z M3391-391.5c221.8-112.2,397.7-351.8,362-492.1c-51-211.6-418.1-183.6-657.8,51C2972.8-712.8,2921.8-610.8,2932-496c5.1,79.1,22.9,102,96.9,137.7C3130.9-307.4,3240.5-317.6,3391-391.5z M1328.3-414.5c140.2-86.7,221.8-209.1,221.8-323.8c0-216.7-392.6-153-573.7,91.8C792.9-394.1,1040.2-230.9,1328.3-414.5z M5550.4-1273.7c135.1-84.1,244.8-257.5,221.8-349.3c-23-89.2-145.3-132.6-267.7-96.9c-137.7,43.4-293.2,163.2-341.7,267.7c-56.1,117.3-53.5,160.6,10.2,224.4C5244.5-1156.4,5387.3-1174.2,5550.4-1273.7z M2682.2-1339.9c293.2-165.7,341.6-476.8,73.9-476.8c-196.3,0-466.6,232-466.6,397.8C2289.5-1278.7,2503.7-1238,2682.2-1339.9z M6659.5-2446.5c209-107.1,372.2-308.5,372.2-461.5c0-68.8-79-150.4-163.2-173.4c-168.3-40.8-517.6,158.1-617,351.8C6111.3-2459.2,6353.6-2290.9,6659.5-2446.5z"/><path d="M3013.6,2639.9c-135.1-45.9-221.8-130-221.8-219.3c0-160.6,181-270.3,453.8-270.3c418.1-2.5,599.2,288.1,280.5,453.8C3408.8,2665.4,3138.5,2683.3,3013.6,2639.9z"/><path d="M2213,1490.1c-168.3-48.5-295.8-132.6-334-226.9c-28-68.8-25.5-89.2,17.9-168.3c188.7-351.9,1106.5-288.1,1106.5,79c0,48.5-22.9,114.7-51,150.4C2837.7,1472.2,2450.2,1556.4,2213,1490.1z"/><path d="M5693.2,1492.6c-119.8-40.8-216.7-145.3-216.7-232c0-267.7,594-372.2,846.5-147.9c201.4,181-17.9,410.5-387.5,407.9C5843.6,1520.7,5736.6,1507.9,5693.2,1492.6z"/><path d="M8051.6,1278.5c-124.9-53.5-178.5-109.6-178.5-188.7c0-137.7,165.7-229.5,415.6-229.5c175.9,0,341.7,73.9,377.3,168.3C8737.4,1227.5,8321.8,1395.8,8051.6,1278.5z"/><path d="M4321.5,1013.3c-147.9-28-234.6-102-234.6-204c0-124.9,96.9-186.1,313.6-198.9c204-12.7,346.7,35.7,392.6,132.6C4869.7,916.4,4609.6,1064.3,4321.5,1013.3z"/><path d="M6662.1,225.5c-196.3-48.4-344.2-181-344.2-311c0-323.8,775.1-453.8,1070.8-175.9C7671.7,3.7,7161.8,345.3,6662.1,225.5z"/></g></g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/erase.svg

@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 16.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 width="548.157px" height="548.157px" viewBox="0 0 548.157 548.157" style="enable-background:new 0 0 548.157 548.157;"
+	 xml:space="preserve">
+<g>
+	<path d="M545.027,112.765c-3.046-6.471-7.57-11.657-13.565-15.555c-5.996-3.9-12.614-5.852-19.846-5.852H292.351
+		c-11.04,0-20.175,4.184-27.408,12.56L9.13,396.279c-4.758,5.328-7.661,11.56-8.708,18.698c-1.049,7.139-0.144,13.941,2.712,20.417
+		c3.044,6.468,7.564,11.652,13.561,15.553c5.997,3.898,12.612,5.853,19.845,5.853h219.268c11.042,0,20.177-4.179,27.41-12.56
+		l255.813-292.363c4.75-5.33,7.655-11.561,8.699-18.699C548.788,126.039,547.877,119.238,545.027,112.765z M255.811,420.254H36.54
+		l95.93-109.632h219.27L255.811,420.254z"/>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/fill.svg

@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 512 512" style="enable-background:new 0 0 512 512;" xml:space="preserve">
+<g>
+	<path d="M494.412,245.154L371.59,122.507l93.792-93.934L437.07,0.304L343.28,94.237l-56.613-56.532L37.714,286.659
+		l207.754,207.459c11.699,11.699,27.069,17.549,42.436,17.549c15.367,0,30.735-5.849,42.436-17.549l164.082-164.082
+		c11.389-11.388,17.632-26.53,17.578-42.635C511.946,271.446,505.704,256.446,494.412,245.154z M466.131,301.745L302.048,465.828
+		c-7.799,7.8-20.489,7.8-28.3-0.01L94.314,286.638L286.687,94.266l28.324,28.283L211.272,226.445l28.312,28.269L343.322,150.82
+		l122.811,122.636c3.761,3.761,5.842,8.761,5.859,14.079C472.009,292.902,469.928,297.948,466.131,301.745z"/>
+</g>
+<g>
+	<path d="M95.137,398.966c-10.179-15.198-20.245-27.482-20.669-27.997l-15.455-18.808l-15.455,18.808
+		c-0.424,0.516-10.49,12.799-20.669,27.997C2.372,429.596,0,444.293,0,452.684c0,32.54,26.474,59.012,59.012,59.012
+		c32.539,0,59.012-26.473,59.012-59.012C118.025,444.293,115.652,429.596,95.137,398.966z M59.012,471.688
+		c-10.479,0-19.004-8.525-19.005-18.956c0.004-0.08,0.526-8.271,16.291-31.757c0.907-1.35,1.813-2.678,2.714-3.972
+		c0.899,1.294,1.806,2.622,2.714,3.972c15.491,23.079,16.265,31.388,16.29,31.745C77.996,463.182,69.48,471.688,59.012,471.688z"/>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/italic.svg

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<svg width="1em" height="1em" viewBox="0 0 16 16" class="bi bi-type-italic" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M7.991 11.674L9.53 4.455c.123-.595.246-.71 1.347-.807l.11-.52H7.211l-.11.52c1.06.096 1.128.212 1.005.807L6.57 11.674c-.123.595-.246.71-1.346.806l-.11.52h3.774l.11-.52c-1.06-.095-1.129-.211-1.006-.806z"/></svg>

web/lib/miniPaint-4.14.2/images/icons/magic_erase.svg

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 512 512" style="enable-background:new 0 0 512 512;" xml:space="preserve">
+<g id="XMLID_1_">
+	<path id="XMLID_12_" d="M128.4,96.2L64.2,32H32.6v31.6l64.2,64.2L128.4,96.2z M160,0.4h31.6v64.2H160V0.4z M287.5,160.4h64.2V192
+		h-64.2V160.4z M320,64.6V33h-31.6l-64.2,64.2l31.6,31.6L320,64.6z M0,160.4h64.2V192H0V160.4z M160,287.9h31.6v64.2H160V287.9z
+		 M31.6,287.9v31.6h31.6l64.2-64.2l-31.6-31.6L31.6,287.9z M504.2,441.4L187,123.2c-9.3-9.3-24.2-9.3-33.5,0L123.7,153
+		c-9.3,9.3-9.3,24.2,0,33.5l318.2,318.2c9.3,9.3,24.2,9.3,33.5,0l29.8-29.8C514.4,465.5,514.4,450.7,504.2,441.4z M240,272
+		l-95.8-95.8l31.6-31.6l95.8,95.8L240,272z"/>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/media.svg

@@ -0,0 +1,24 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 16.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 width="548.176px" height="548.176px" viewBox="0 0 548.176 548.176" style="enable-background:new 0 0 548.176 548.176;"
+	 xml:space="preserve">
+<g>
+	<g>
+		<path d="M534.75,68.238c-8.945-8.945-19.694-13.417-32.261-13.417H45.681c-12.562,0-23.313,4.471-32.264,13.417
+			C4.471,77.185,0,87.936,0,100.499v347.173c0,12.566,4.471,23.318,13.417,32.264c8.951,8.946,19.702,13.419,32.264,13.419h456.815
+			c12.56,0,23.312-4.473,32.258-13.419c8.945-8.945,13.422-19.697,13.422-32.264V100.499
+			C548.176,87.936,543.699,77.185,534.75,68.238z M511.623,447.672c0,2.478-0.899,4.613-2.707,6.427
+			c-1.81,1.8-3.952,2.703-6.427,2.703H45.681c-2.473,0-4.615-0.903-6.423-2.703c-1.807-1.813-2.712-3.949-2.712-6.427V100.495
+			c0-2.474,0.902-4.611,2.712-6.423c1.809-1.803,3.951-2.708,6.423-2.708h456.815c2.471,0,4.613,0.905,6.42,2.708
+			c1.801,1.812,2.707,3.949,2.707,6.423V447.672L511.623,447.672z"/>
+		<path d="M127.91,237.541c15.229,0,28.171-5.327,38.831-15.987c10.657-10.66,15.987-23.601,15.987-38.826
+			c0-15.23-5.333-28.171-15.987-38.832c-10.66-10.656-23.603-15.986-38.831-15.986c-15.227,0-28.168,5.33-38.828,15.986
+			c-10.656,10.66-15.986,23.601-15.986,38.832c0,15.225,5.327,28.169,15.986,38.826C99.742,232.211,112.683,237.541,127.91,237.541z
+			"/>
+		<polygon points="210.134,319.765 164.452,274.088 73.092,365.447 73.092,420.267 475.085,420.267 475.085,292.36 356.315,173.587 
+					"/>
+	</g>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/menu.svg

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 16.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 width="124px" height="124px" viewBox="0 0 124 124" style="enable-background:new 0 0 124 124;" xml:space="preserve">
+<g>
+	<path d="M112,6H12C5.4,6,0,11.4,0,18s5.4,12,12,12h100c6.6,0,12-5.4,12-12S118.6,6,112,6z"/>
+	<path d="M112,50H12C5.4,50,0,55.4,0,62c0,6.6,5.4,12,12,12h100c6.6,0,12-5.4,12-12C124,55.4,118.6,50,112,50z"/>
+	<path d="M112,94H12c-6.6,0-12,5.4-12,12s5.4,12,12,12h100c6.6,0,12-5.4,12-12S118.6,94,112,94z"/>
+</g>
+</svg>

web/lib/miniPaint-4.14.2/images/icons/pencil.svg

@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 512 512" style="enable-background:new 0 0 512 512;" xml:space="preserve">
+<g>
+	<g>
+		<polygon points="51.2,353.28 0,512 158.72,460.8 		"/>
+	</g>
+</g>
+<g>
+	<g>
+		
+			<rect x="89.73" y="169.097" transform="matrix(0.7071 -0.7071 0.7071 0.7071 -95.8575 260.3719)" width="353.277" height="153.599"/>
+	</g>
+</g>
+<g>
+	<g>
+		<path d="M504.32,79.36L432.64,7.68c-10.24-10.24-25.6-10.24-35.84,0l-23.04,23.04l107.52,107.52l23.04-23.04
+			C514.56,104.96,514.56,89.6,504.32,79.36z"/>
+	</g>
+</g>
+</svg>