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

README.md

@@ -6,6 +6,10 @@
 
 ##### `最新`：
 
+- 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 SiliconflowLLM，可以使用由Siliconflow提供的免费LLM
 
 - 增加 Edit Mask，方便在生成的时候手动绘制 mask [workflow](./workflow/edit-mask-workflow.json)
@@ -115,7 +119,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 [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/`
 
@@ -143,7 +147,7 @@ pip install 'llama-cpp-python[server]'
 ```
 pip install llama-cpp-python \
   --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
-```
+``` -->
 
 ## Prompt
 

__init__.py

@@ -26,12 +26,12 @@ 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.RembgNode import get_rembg_models,U2NET_HOME,run_briarmbg,run_rembg
@@ -679,11 +679,11 @@ 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':
@@ -753,6 +753,7 @@ def random_seed(seed, data):
     max_seed = 4294967295
 
     for id, value in data.items():
+        # print(seed,id)
         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)
         
@@ -859,117 +860,128 @@ async def mixlab_post_prompt(request):
         return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
 
 
+# AR页面
+# @routes.get('/mixlab/AR')
+async def handle_ar_page(request):
+    html_file = os.path.join(current_path, "web/ar.html")
+    if os.path.exists(html_file):
+        with open(html_file, 'r', encoding='utf-8', errors='ignore') as f:
+            html_data = f.read()
+            return web.Response(text=html_data, content_type='text/html')
+    else:
+        return web.Response(text="HTML file not found", status=404)
 
-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,
-            )
+# 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"]
+#     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'])
+#     elif "model" in data:
+#         model=get_llama_model_path(data['model'])
 
-    n_gpu_layers=-1
+#     n_gpu_layers=-1
 
-    if "n_gpu_layers" in data:
-        n_gpu_layers=data['n_gpu_layers']
+#     if "n_gpu_layers" in data:
+#         n_gpu_layers=data['n_gpu_layers']
 
 
-    chat_format="chatml"
+#     chat_format="chatml"
 
-    model_alias=os.path.basename(model)
+#     model_alias=os.path.basename(model)
 
-    # 多模态
-    clip_model_path=None
+#     # 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,model)
+#     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,model)
 
-    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
+#     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":""}
+#     if success == False:
+#         return {"port":None,"model":""}
     
     
-    server_settings=ServerSettings(host=address,port=port)
+#     server_settings=ServerSettings(host=address,port=port)
 
-    name, ext = os.path.splitext(os.path.basename(model))
-    if name:
-        # 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
-                        )])
+#     name, ext = os.path.splitext(os.path.basename(model))
+#     if name:
+#         # 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,
-                )
+#         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)
+#         # 创建一个子线程
+#         thread = threading.Thread(target=run_uvicorn)
 
-        # 启动子线程
-        thread.start()
+#         # 启动子线程
+#         thread.start()
 
-        llama_port=port
-        llama_model=data['model']
-        llama_chat_format=chat_format
+#         llama_port=port
+#         llama_model=data['model']
+#         llama_chat_format=chat_format
 
-    return  {"port":llama_port,"model":llama_model,"chat_format":llama_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:
-        result= {"port":None,"model":"","llama_cpp_error":True}
-        print('start_local_llm error')
+# @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:
+#         result= {"port":None,"model":"","llama_cpp_error":True}
+#         print('start_local_llm error')
 
-    return web.json_response(result)
+#     return web.json_response(result)
 
 # 重启服务
 @routes.post('/mixlab/re_start')

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
     )
 
-    %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 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
 
-    %python_exec% -s -m pip install --upgrade --force llama-cpp-python[server]
+    @REM %python_exec% -s -m pip install --upgrade --force llama-cpp-python[server]
     
 
 ) else (

nodes/ChatGPT.py

@@ -97,67 +97,68 @@ 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=get_llama_models()
+# llama_modes_list=[]
 
-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
 
     
 
@@ -215,7 +216,7 @@ class ChatGPTNode:
 
     @classmethod
     def INPUT_TYPES(cls):
-        model_list=llama_modes_list+[ 
+        model_list=[ 
                     "gpt-3.5-turbo",
             "gpt-3.5-turbo-16k",
             "gpt-4o",
@@ -299,9 +300,9 @@ 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)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-mixlab-nodes"
 description = "3D, ScreenShareNode & FloatingVideoNode, SpeechRecognition & SpeechSynthesis, GPT, LoadImagesFromLocal, Layers, Other Nodes, ..."
-version = "0.32.0"
+version = "0.33.0"
 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"]
 

web/ar.html

@@ -0,0 +1,22 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Mixlab AR</title>
+</head>
+
+<body>
+
+    <script type="module">
+
+        import { api } from "../../../scripts/api.js";
+        import Command from '/extensions/comfyui-mixlab-nodes/javascript/command.js'
+
+
+    </script>
+
+</body>
+
+</html>

web/javascript/app_mixlab.js

@@ -256,7 +256,9 @@ async function extractInputAndOutputData (
         node.type === 'KSampler' ||
         node.type == 'SamplerCustom' ||
         node.type === 'ChinesePrompt_Mix' ||
-        node.type === 'Seed_'
+        node.type === 'Seed_'||
+        node.type==='SiliconflowLLM'||
+        node.type==='ChatGPTOpenAI'
       ) {
         // seed 的类型收集
         try {

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 = 'v0.32.0'
+const version = 'v0.33.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/command.js

@@ -0,0 +1,680 @@
+function get_url () {
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+  return url
+}
+
+function getFilenameAndCategoryFromUrl (url) {
+  const queryString = url.split('?')[1]
+  if (!queryString) {
+    return {}
+  }
+
+  const params = new URLSearchParams(queryString)
+
+  const filename = params.get('filename')
+    ? decodeURIComponent(params.get('filename'))
+    : null
+  const category = params.get('category')
+    ? decodeURIComponent(params.get('category') || '')
+    : ''
+
+  return { category, filename }
+}
+
+async function get_my_app (category = '', filename = null) {
+  let url = get_url()
+  const res = await fetch(`${url}/mixlab/workflow`, {
+    method: 'POST',
+    mode: 'cors', // 允许跨域请求
+    headers: {
+      'Content-Type': 'application/json'
+    },
+    body: JSON.stringify({
+      task: 'my_app',
+      filename,
+      category
+    })
+  })
+  let result = await res.json()
+  let data = []
+  try {
+    for (const res of result.data) {
+      let { output, app } = res.data
+      if (app.filename)
+        data.push({
+          ...app,
+          data: output,
+          date: res.date
+        })
+    }
+  } catch (error) {}
+
+  return data
+}
+
+async function getAppInit () {
+  const { category, filename } = getFilenameAndCategoryFromUrl(
+    window.location.href
+  )
+  return await get_my_app(category, filename)
+}
+
+function success (isSuccess, btn, text) {
+  isSuccess ? (btn.innerText = 'success') : text
+  setTimeout(() => {
+    btn.innerText = text
+  }, 5000)
+}
+
+async function interrupt () {
+  try {
+    await fetch(`${get_url()}/interrupt`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: undefined
+    })
+  } catch (error) {
+    console.error(error)
+  }
+  return true
+}
+
+async function getQueue (clientId) {
+  try {
+    const res = await fetch(`${get_url()}/queue`)
+    const data = await res.json()
+    return {
+      // Running action uses a different endpoint for cancelling
+      Running: Array.from(data.queue_running, prompt => {
+        if (prompt[3].client_id === clientId) {
+          let prompt_id = prompt[1]
+          return {
+            prompt_id,
+            remove: () => interrupt()
+          }
+        }
+      }),
+      Pending: data.queue_pending.map(prompt => ({ prompt }))
+    }
+  } catch (error) {
+    console.error(error)
+    return { Running: [], Pending: [] }
+  }
+}
+
+// 请求历史数据
+async function getPromptResult (category) {
+  let url = get_url()
+  try {
+    const response = await fetch(`${url}/mixlab/prompt_result`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        action: 'all'
+      })
+    })
+
+    if (response.ok) {
+      const data = await response.json()
+      console.log('#getPromptResult:', category, data)
+
+      return data.result.filter(r => r.appInfo.category == category)
+      // 处理返回的数据
+    } else {
+      console.log('Error:', response.status)
+      // 处理错误情况
+    }
+  } catch (error) {
+    console.log('Error:', error)
+    // 处理异常情况
+  }
+}
+
+// 新的运行工作流的接口
+function queuePromptNew (filename, category, seed, input, client_id) {
+  let url = get_url()
+  // var filename = "Text-to-Image_1.json", category = "";
+
+  // 随机seed
+  //  promptWorkflow = randomSeed(seed, promptWorkflow);
+
+  const data = JSON.stringify({ filename, category, seed, input, client_id })
+  return new Promise((res, rej) => {
+    fetch(`${url}/mixlab/prompt`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: data
+    })
+      .then(response => {
+        if (!response.ok) {
+          // Handle HTTP error responses
+          if (response.status === 400) {
+            return response.json().then(errorData => {
+              // Process the error data
+              console.error('Error 400:', errorData)
+              alert(JSON.stringify(errorData, null, 2))
+              res(null)
+            })
+          }
+          throw new Error('Network response was not ok')
+        }
+        return response.json() // Process the response data
+      })
+      .then(data => {
+        // Handle the response data
+        console.log('Success:', data)
+        res(true)
+      })
+      .catch(error => {
+        // Handle fetch errors
+        console.error('Fetch error:', error)
+        res(null)
+      })
+  })
+}
+
+// 保存历史数据
+async function savePromptResult (data) {
+  let url = get_url()
+  try {
+    const response = await fetch(`${url}/mixlab/prompt_result`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        action: 'save',
+        data
+      })
+    })
+
+    if (response.ok) {
+      const res = await response.json()
+      console.log('Response:', res)
+      return res
+      // 处理返回的数据
+    } else {
+      console.log('Error:', response.status)
+      // 处理错误情况
+    }
+  } catch (error) {
+    console.log('Error:', error)
+    // 处理异常情况
+  }
+}
+
+async function uploadImage (blob, fileType = '.png', filename) {
+  const body = new FormData()
+  body.append(
+    'image',
+    new File([blob], (filename || new Date().getTime()) + fileType)
+  )
+
+  const url = get_url()
+
+  const resp = await fetch(`${url}/upload/image`, {
+    method: 'POST',
+    body
+  })
+
+  let data = await resp.json()
+  // console.log(data)
+  let { name, subfolder } = data
+  let src = `${url}/view?filename=${encodeURIComponent(
+    name
+  )}&type=input&subfolder=${subfolder}&rand=${Math.random()}`
+
+  return { url: src, name }
+}
+
+async function uploadMask (arrayBuffer, imgurl) {
+  const body = new FormData()
+  const filename = 'clipspace-mask-' + performance.now() + '.png'
+
+  let original_url = new URL(imgurl)
+
+  const original_ref = { filename: original_url.searchParams.get('filename') }
+
+  let original_subfolder = original_url.searchParams.get('subfolder')
+  if (original_subfolder) original_ref.subfolder = original_subfolder
+
+  let original_type = original_url.searchParams.get('type')
+  if (original_type) original_ref.type = original_type
+
+  body.append('image', arrayBuffer, filename)
+  body.append('original_ref', JSON.stringify(original_ref))
+  body.append('type', 'input')
+  body.append('subfolder', 'clipspace')
+
+  const url = get_url()
+
+  const resp = await fetch(`${url}/upload/mask`, {
+    method: 'POST',
+    body
+  })
+
+  // console.log(resp)
+  let data = await resp.json()
+  let { name, subfolder, type } = data
+  let src = `${url}/view?filename=${encodeURIComponent(
+    name
+  )}&type=${type}&subfolder=${subfolder}&rand=${Math.random()}`
+
+  return { url: src, name: 'clipspace/' + name }
+}
+
+const parseImageToBase64 = url => {
+  return new Promise((res, rej) => {
+    fetch(url)
+      .then(response => response.blob())
+      .then(blob => {
+        const reader = new FileReader()
+        reader.onloadend = () => {
+          const base64data = reader.result
+          res(base64data)
+          // 在这里可以将base64数据用于进一步处理或显示图片
+        }
+        reader.readAsDataURL(blob)
+      })
+      .catch(error => {
+        console.log('发生错误:', error)
+      })
+  })
+}
+
+function createImage (url) {
+  let im = new Image()
+  return new Promise((res, rej) => {
+    im.onload = () => res(im)
+    im.src = url
+  })
+}
+
+function convertImageToBlackBasedOnAlpha (image) {
+  const canvas = document.createElement('canvas')
+  const ctx = canvas.getContext('2d')
+
+  // Draw the image onto the canvas
+  canvas.width = image.width
+  canvas.height = image.height
+  ctx.drawImage(image, 0, 0)
+
+  // Get the image data from the canvas
+  const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height)
+  const pixels = imageData.data
+
+  // Modify the RGB values based on the alpha channel
+  for (let i = 0; i < pixels.length; i += 4) {
+    const alpha = pixels[i + 3]
+    if (alpha !== 0) {
+      // Set non-transparent pixels to black
+      pixels[i] = 0 // Red
+      pixels[i + 1] = 0 // Green
+      pixels[i + 2] = 0 // Blue
+    }
+  }
+
+  // Put the modified image data back onto the canvas
+  ctx.putImageData(imageData, 0, 0)
+
+  // Convert the modified canvas to base64 data URL
+  const base64ImageData = canvas.toDataURL('image/png') // Replace 'png' with your desired image format
+
+  return base64ImageData
+}
+
+const blobToBase64 = blob => {
+  return new Promise((res, rej) => {
+    const reader = new FileReader()
+    reader.onloadend = () => {
+      const base64data = reader.result
+      res(base64data)
+      // 在这里可以将base64数据用于进一步处理或显示图片
+    }
+    reader.readAsDataURL(blob)
+  })
+}
+
+function base64ToBlob (base64) {
+  // 去除base64编码中的前缀
+  const base64WithoutPrefix = base64.replace(/^data:image\/\w+;base64,/, '')
+
+  // 将base64编码转换为字节数组
+  const byteCharacters = atob(base64WithoutPrefix)
+
+  // 创建一个存储字节数组的数组
+  const byteArrays = []
+
+  // 将字节数组放入数组中
+  for (let offset = 0; offset < byteCharacters.length; offset += 1024) {
+    const slice = byteCharacters.slice(offset, offset + 1024)
+
+    const byteNumbers = new Array(slice.length)
+    for (let i = 0; i < slice.length; i++) {
+      byteNumbers[i] = slice.charCodeAt(i)
+    }
+
+    const byteArray = new Uint8Array(byteNumbers)
+    byteArrays.push(byteArray)
+  }
+
+  // 创建blob对象
+  const blob = new Blob(byteArrays, { type: 'image/png' }) // 根据实际情况设置MIME类型
+
+  return blob
+}
+
+async function calculateImageHash (blob) {
+  const buffer = await blob.arrayBuffer()
+  const hashBuffer = await crypto.subtle.digest('SHA-256', buffer)
+  const hashArray = Array.from(new Uint8Array(hashBuffer))
+  const hashHex = hashArray
+    .map(byte => byte.toString(16).padStart(2, '0'))
+    .join('')
+  return hashHex
+}
+
+// 获取 rembg 模型
+async function get_rembg_models () {
+  try {
+    const response = await fetch(`${get_url()}/mixlab/folder_paths`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        type: 'rembg'
+      })
+    })
+
+    const data = await response.json()
+    // console.log(data)
+    return data.names
+  } catch (error) {
+    console.error(error)
+  }
+}
+
+//自动抠图
+async function run_rembg (model, base64) {
+  try {
+    const response = await fetch(`${get_url()}/mixlab/rembg`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        model,
+        base64
+      })
+    })
+
+    const data = await response.json()
+    // console.log(data)
+    return data.data
+  } catch (error) {
+    console.error(error)
+  }
+}
+
+function copyHtmlWithImagesToClipboard (data, cb) {
+  // 创建一个临时div元素
+  const tempDiv = document.createElement('div')
+
+  // 将HTML字符串赋值给div的innerHTML属性
+  tempDiv.innerHTML = data
+
+  // 获取div中的所有图像元素
+  const images = tempDiv.getElementsByTagName('img')
+
+  // 遍历图像元素，并将图像数据转换为Base64编码
+  for (let i = 0; i < images.length; i++) {
+    const image = images[i]
+    const canvas = document.createElement('canvas')
+    const context = canvas.getContext('2d')
+
+    // 设置canvas尺寸与图像尺寸相同
+    canvas.width = image.width
+    canvas.height = image.height
+
+    // 在canvas上绘制图像
+    context.drawImage(image, 0, 0)
+
+    // 将canvas转换为Base64编码
+    const imageData = canvas.toDataURL()
+
+    // 将Base64编码替换图像元素的src属性
+    image.src = imageData
+  }
+
+  let richText = tempDiv.innerHTML
+
+  // 创建一个新的Blob对象，并将富文本字符串作为数据传递进去
+  const blob = new Blob([richText], { type: 'text/html' })
+
+  // 创建一个ClipboardItem对象，并将Blob对象添加到其中
+  const clipboardItem = new ClipboardItem({ 'text/html': blob })
+
+  // 使用Clipboard API将内容复制到剪贴板
+  navigator.clipboard
+    .write([clipboardItem])
+    .then(() => {
+      console.log('富文本已成功复制到剪贴板')
+      tempDiv.remove()
+      if (cb) cb(true)
+    })
+    .catch(error => {
+      console.error('复制到剪贴板失败:', error)
+      tempDiv.remove()
+      if (cb) cb(false)
+    })
+}
+
+function copyImagesToClipboard (html, cb) {
+  const tempDiv = document.createElement('div')
+  tempDiv.innerHTML = html
+  const images = tempDiv.querySelectorAll('img')
+  const promises = Array.from(images).map(image => {
+    return new Promise(resolve => {
+      const img = new Image()
+      img.src = image.src
+      img.onload = () => {
+        const canvas = document.createElement('canvas')
+        const context = canvas.getContext('2d')
+        canvas.width = img.width
+        canvas.height = img.height
+        context.drawImage(img, 0, 0)
+        canvas.toBlob(blob => {
+          const clipboardItem = new ClipboardItem({ 'image/png': blob })
+          navigator.clipboard
+            .write([clipboardItem])
+            .then(() => {
+              resolve()
+              tempDiv.remove()
+              if (cb) cb(true)
+            })
+            .catch(error => {
+              reject(error)
+              tempDiv.remove()
+              if (cb) cb(false)
+            })
+        })
+      }
+    })
+  })
+  Promise.all([...promises])
+    .then(() => {
+      console.log('所有图片已成功复制到剪贴板')
+      if (cb) cb(true)
+      tempDiv.remove()
+    })
+    .catch(error => {
+      console.error('复制到剪贴板失败:', error)
+      if (cb) cb(false)
+      tempDiv.remove()
+    })
+}
+
+function copyTextToClipboard (html, cb) {
+  const tempDiv = document.createElement('div')
+  tempDiv.innerHTML = html
+
+  const text = tempDiv.innerText
+  const textData = new ClipboardItem({
+    'text/plain': new Blob([text], { type: 'text/plain' })
+  })
+
+  navigator.clipboard
+    .write([textData])
+    .then(() => {
+      console.log('所有文本已成功复制到剪贴板', text)
+      if (cb) cb(true)
+      tempDiv.remove()
+    })
+    .catch(error => {
+      console.error('复制到剪贴板失败:', error)
+      if (cb) cb(false)
+      tempDiv.remove()
+    })
+}
+
+// ComfyUI\web\extensions\core\dynamicPrompts.js
+// 官方实现修改
+// Allows for simple dynamic prompt replacement
+// Inputs in the format {a|b} will have a random value of a or b chosen when the prompt is queued.
+
+/*
+ * Strips C-style line and block comments from a string
+ */
+function dynamicPrompts (prompt) {
+  prompt = prompt.replace(/\/\*[\s\S]*?\*\/|\/\/.*/g, '')
+  while (
+    prompt.replace('\\{', '').includes('{') &&
+    prompt.replace('\\}', '').includes('}')
+  ) {
+    const startIndex = prompt.replace('\\{', '00').indexOf('{')
+    const endIndex = prompt.replace('\\}', '00').indexOf('}')
+
+    const optionsString = prompt.substring(startIndex + 1, endIndex)
+    const options = optionsString.split('|')
+
+    const randomIndex = Math.floor(Math.random() * options.length)
+    const randomOption = options[randomIndex]
+
+    prompt =
+      prompt.substring(0, startIndex) +
+      randomOption +
+      prompt.substring(endIndex + 1)
+  }
+  return prompt
+}
+
+// 遍历所有组合,语法同 动态提示
+function generateAllCombinations (prompt) {
+  prompt = prompt.replace(/\/\*[\s\S]*?\*\/|\/\/.*/g, '')
+
+  // Helper function to get all combinations
+  function getAllCombinations (parts) {
+    if (parts.length === 0) return ['']
+    const [firstPart, ...restParts] = parts
+    const restCombinations = getAllCombinations(restParts)
+    const allCombinations = []
+
+    firstPart.forEach(option => {
+      restCombinations.forEach(combination => {
+        allCombinations.push(option + combination)
+      })
+    })
+
+    return allCombinations
+  }
+
+  // Split prompt into static parts and dynamic parts
+  let parts = []
+  let startIndex = 0
+
+  while (
+    prompt.replace('\\{', '').includes('{') &&
+    prompt.replace('\\}', '').includes('}')
+  ) {
+    startIndex = prompt.replace('\\{', '00').indexOf('{')
+    const endIndex = prompt.replace('\\}', '00').indexOf('}')
+    const staticPart = prompt.substring(0, startIndex)
+    const optionsString = prompt.substring(startIndex + 1, endIndex)
+    const options = optionsString.split('|')
+
+    parts.push([staticPart])
+    parts.push(options)
+
+    prompt = prompt.substring(endIndex + 1)
+  }
+
+  // Add the remaining static part
+  parts.push([prompt])
+
+  // Get all combinations
+  const combinations = getAllCombinations(parts)
+
+  return combinations
+}
+
+const _textNodes = [
+    'TextInput_',
+    'CLIPTextEncode',
+    'PromptSimplification',
+    'ChinesePrompt_Mix'
+  ],
+  _loraNodes = ['CheckpointLoaderSimple', 'LoraLoader'],
+  _numberNodes = ['FloatSlider', 'IntNumber'],
+  _slideNodes = ['PromptSlide'],
+  _imageNodes = [
+    'LoadImage',
+    'VHS_LoadVideo',
+    'ImagesPrompt_',
+    'LoadImagesToBatch'
+  ],
+  _colorNodes = ['Color'],
+  _audioNodes = ['LoadAndCombinedAudio_']
+
+export default {
+  get_url,
+  get_my_app,
+  getAppInit,
+  getFilenameAndCategoryFromUrl,
+  success,
+  interrupt,
+  getQueue,
+  queuePromptNew,
+  savePromptResult,
+  uploadImage,
+  uploadMask,
+  run_rembg,
+  get_rembg_models,
+  parseImageToBase64,
+  createImage,
+  convertImageToBlackBasedOnAlpha,
+  blobToBase64,
+  base64ToBlob,
+  calculateImageHash,
+  copyHtmlWithImagesToClipboard,
+  copyImagesToClipboard,
+  copyTextToClipboard,
+  dynamicPrompts,
+  generateAllCombinations,
+
+  _textNodes,
+  _loraNodes,
+  _numberNodes,
+  _slideNodes,
+  _imageNodes,
+  _colorNodes,
+  _audioNodes
+}

web/javascript/ui_mixlab.js

@@ -163,17 +163,20 @@ async function createMenu () {
 
     // appsButton.onclick = () =>
     appsButton.onclick = async () => {
-      if (window._mixlab_llamacpp&&window._mixlab_llamacpp.model&&window._mixlab_llamacpp.model.length>0) {
-        //显示运行的模型
-        createModelsModal([
-          window._mixlab_llamacpp.url,
-          window._mixlab_llamacpp.model
-        ])
-      } else {
-        let ms = await get_llamafile_models()
-        ms = ms.filter(m => !m.match('-mmproj-'))
-        if (ms.length > 0) createModelsModal(ms)
-      }
+      // if (window._mixlab_llamacpp&&window._mixlab_llamacpp.model&&window._mixlab_llamacpp.model.length>0) {
+      //   //显示运行的模型
+      //   createModelsModal([
+      //     window._mixlab_llamacpp.url,
+      //     window._mixlab_llamacpp.model
+      //   ])
+      // } else {
+      //   // let ms = await get_llamafile_models()
+      //   // ms = ms.filter(m => !m.match('-mmproj-'))
+      //   // if (ms.length > 0) createModelsModal(ms)
+      // }
+      createModelsModal([
+      
+      ])
     }
     menu.append(appsButton)
   }
@@ -932,16 +935,16 @@ function createModelsModal (models) {
   const n_gpu_p = document.createElement('p')
   n_gpu_p.innerText = 'n_gpu_layers'
 
-  const n_gpu_div = document.createElement('div')
-  n_gpu_div.style = `display: flex;
+  const batchPageBtn = document.createElement('div')
+  batchPageBtn.style = `display: flex;
   justify-content: center;
   align-items: center;
   font-size: 12px;`
-  n_gpu_div.appendChild(n_gpu_p)
-  n_gpu_div.appendChild(n_gpu)
+  batchPageBtn.innerHTML=`<a href="${get_url()}/mixlab/app" target="_blank" style="color: var(--input-text);
+  background-color: var(--comfy-input-bg);">App</a>`
 
   const title = document.createElement('p')
-  title.innerText = 'Models'
+  title.innerText = 'Mixlab Nodes'
   title.style = `font-size: 18px;
   margin-right: 8px;
   margin-top: 0;`
@@ -953,9 +956,9 @@ function createModelsModal (models) {
   font-size: 12px;
   flex-direction: column; `
   left_d.appendChild(title)
-  title.appendChild(statusIcon)
-  left_d.appendChild(linkIcon)
-  left_d.appendChild(n_gpu_div)
+  // title.appendChild(statusIcon)
+  // left_d.appendChild(linkIcon)
+  left_d.appendChild(batchPageBtn)
   headTitleElement.appendChild(left_d)
 
   // headTitleElement.appendChild(n_gpu_div)
@@ -1010,24 +1013,24 @@ function createModelsModal (models) {
   var modalContent = document.createElement('div')
   modalContent.classList.add('modal-content')
 
-  var input = document.createElement('textarea')
-  input.className = 'comfy-multiline-input'
-  input.style = `    height: 260px;
+  var inputForSystemPrompt = document.createElement('textarea')
+  inputForSystemPrompt.className = 'comfy-multiline-input'
+  inputForSystemPrompt.style = `    height: 260px;
   width: 480px;
   font-size: 16px;
   padding: 18px;`
-  input.value = localStorage.getItem('_mixlab_system_prompt')
+  inputForSystemPrompt.value = localStorage.getItem('_mixlab_system_prompt')
 
-  input.addEventListener('change', e => {
+  inputForSystemPrompt.addEventListener('change', e => {
     e.stopPropagation()
-    localStorage.setItem('_mixlab_system_prompt', input.value)
+    localStorage.setItem('_mixlab_system_prompt', inputForSystemPrompt.value)
   })
 
-  input.addEventListener('click', e => {
+  inputForSystemPrompt.addEventListener('click', e => {
     e.stopPropagation()
   })
 
-  modalContent.appendChild(input)
+  // modalContent.appendChild(inputForSystemPrompt)
 
   if (!window._mixlab_llamacpp||(window._mixlab_llamacpp?.model?.length==0)) {
     for (const m of models) {
@@ -1040,10 +1043,10 @@ function createModelsModal (models) {
       d.addEventListener('click', async e => {
         e.stopPropagation()
         div.remove()
-        startLLM(m)
+        // startLLM(m)
       })
 
-      modalContent.appendChild(d)
+      // modalContent.appendChild(d)
     }
   }
   modal.appendChild(modalContent)
@@ -1414,7 +1417,7 @@ app.registerExtension({
           .setAttribute('title', res.url)
       })
     }else{
-      startLLM('')
+      // startLLM('')
     }
 
     LGraphCanvas.prototype.helpAboutNode = async function (node) {