v0.25.1

Phi-3
llama3

README.md

@@ -1,8 +1,7 @@
 > 适配了最新版comfyui的py3.11 ，torch 2.1.2+cu121
-
 > [Mixlab nodes discord](https://discord.gg/cXs9vZSqeK)
 
-#### 
+#### `相关插件推荐`
 [comfyui-sd-prompt-mixlab](https://github.com/shadowcz007/comfyui-sd-prompt-mixlab)
 
 [comfyui-Image-reward](https://github.com/shadowcz007/comfyui-Image-reward)
@@ -13,6 +12,20 @@
 
 <!-- [comfyui-CLIPSeg](https://github.com/shadowcz007/comfyui-CLIPSeg) -->
 
+##### `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/```
+
+强烈推荐：[Phi-3-mini-4k-instruct-GGUF](https://huggingface.co/lmstudio-community/Phi-3-mini-4k-instruct-GGUF/tree/main)
+
+备选：[llama3_if_ai_sdpromptmkr_q2k](https://hf-mirror.com/impactframes/llama3_if_ai_sdpromptmkr_q2k/tree/main)
+
+
+> 右键菜单支持 text-to-text，方便对prompt词补全
+![](./assets/prompt_ai_setup.png)
+![](./assets/prompt-ai.png)
+
 
 ## 🚀🚗🚚🏃 Workflow-to-APP 
 - 新增AppInfo节点，可以通过简单的配置，把workflow转变为一个Web APP。
@@ -79,13 +92,41 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 [Voice + Real-time Face Swap Workflow](./workflow/语音+实时换脸workflow.json)
 
 ### GPT
-> Support for calling multiple GPTs.ChatGPT、ChatGLM3 、ChatGLM4 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 .  Azure OpenAI:https://xxxx.openai.azure.com 
+> Support for calling multiple GPTs.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 
 
 ![gpt-workflow.svg](./assets/gpt-workflow.svg)
 
 [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。
+
+Model download,move to :```models/llamafile/```
+
+强烈推荐：[Phi-3-mini-4k-instruct-GGUF](https://huggingface.co/lmstudio-community/Phi-3-mini-4k-instruct-GGUF/tree/main)
+
+备选：[llama3_if_ai_sdpromptmkr_q2k](https://hf-mirror.com/impactframes/llama3_if_ai_sdpromptmkr_q2k/tree/main)
+
+
+> 如果碰到安装失败，可以尝试手动安装
+```
+../../../python_embeded/python.exe -s -m pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
+
+../../../python_embeded/python.exe -s -m pip install llama-cpp-python[server]
+
+```
+
+> [Mac](https://llama-cpp-python.readthedocs.io/en/latest/install/macos/) 
+```
+pip uninstall llama-cpp-python -y
+CMAKE_ARGS="-DLLAMA_METAL=on" pip install -U llama-cpp-python --no-cache-dir
+pip install 'llama-cpp-python[server]'
+```
+```
+pip install llama-cpp-python \
+  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
+```
+
 ## Prompt
 > PromptSlide
 ![](./assets/prompt_weight.png)

__init__.py

@@ -7,9 +7,27 @@ import urllib
 import hashlib
 import datetime
 import folder_paths
-
+import logging
+from comfy.cli_args import args
 python = sys.executable
 
+#修复 sys.stdout.isatty()  object has no attribute 'isatty'
+try:
+    sys.stdout.isatty()
+except:
+    print('#fix sys.stdout.isatty')
+    sys.stdout.isatty = lambda: False
+
+llama_port=None
+llama_model=""
+
+try:
+    from .nodes.ChatGPT import get_llama_models,get_llama_model_path,llama_cpp_client
+    llama_cpp_client("")
+
+except:
+    print("##nodes.ChatGPT ImportError")
+
 
 from server import PromptServer
 
@@ -42,7 +60,7 @@ def is_installed(package, package_overwrite=None):
             print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
     else:
         print(package+'## OK')
-        
+     
 try:
     import OpenSSL
 except ImportError:
@@ -414,37 +432,86 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
         runner = web.AppRunner(self.app, access_log=None)
         await runner.setup()
 
-        if not await check_port_available(address, port):
-            raise RuntimeError(f"Port {port} is already in use.")
+        # if not await check_port_available(address, port):
+        #     raise RuntimeError(f"Port {port} is already in use.")
+        
+        http_success = False
+        http_port=port
+        for i in range(11):  # 尝试最多11次
+            if await check_port_available(address, port + i):
+                http_port = port + i
+                site = web.TCPSite(runner, address, http_port)
+                await site.start()
+                http_success = True
+                break
 
-        site = web.TCPSite(runner, address, port)
-        await site.start()
+        if not http_success:
+            raise RuntimeError(f"Ports {port} to {port + 10} are all in use.")
+        
+
+        # site = web.TCPSite(runner, address, port)
+        # await site.start()
+
+        ssl_context = None
+        scheme = "http"
+        try:
+            # 跟着本体修改
+            if args.tls_keyfile and args.tls_certfile:
+                scheme = "https"
+                ssl_context = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE)
+                ssl_context.load_cert_chain(certfile=args.tls_certfile,
+                                    keyfile=args.tls_keyfile)
+            else:
+                # 如果没传，则自动创建
+                import ssl
+                crt, key = create_for_https()
+                ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
+                ssl_context.load_cert_chain(crt, key)
+        except:
+            import ssl
+            crt, key = create_for_https()
+            ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
+            ssl_context.load_cert_chain(crt, key)
 
-        import ssl
-        crt, key = create_for_https()
-        ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
-        ssl_context.load_cert_chain(crt, key)
 
         success = False
-        for i in range(10):  # 尝试最多10次
-            if await check_port_available(address, port + 1 + i):
-                https_port = port + 1 + i
+        for i in range(11):  # 尝试最多11次
+            if await check_port_available(address, http_port + 1 + i):
+                https_port = http_port + 1 + i
                 site2 = web.TCPSite(runner, address, https_port, ssl_context=ssl_context)
                 await site2.start()
                 success = True
                 break
 
         if not success:
-            raise RuntimeError(f"Ports {port + 1} to {port + 10} are all in use.")
+            raise RuntimeError(f"Ports {http_port + 1} to {http_port + 10} are all in use.")
 
         if address == '':
-            address = '0.0.0.0'
+            address = '127.0.0.1'
+        if address=='0.0.0.0':
+            address = '127.0.0.1'
+            
         if verbose:
-            print("\033[93mStarting server\n")
-            print("\033[93mTo see the GUI go to: http://{}:{}".format(address, port))
-            print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
+
+            logging.info("\n")
+            logging.info("\n\nStarting server")
+
+            # print("\033[93mStarting server\n")
+            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))
+
         if call_on_start is not None:
-            call_on_start(address, port)
+            try:
+                if scheme=='https':
+                    call_on_start(scheme,address, https_port)
+                else:
+                    call_on_start(scheme,address, http_port)
+            except:
+                call_on_start(address,http_port)
+            
 
     except Exception as e:
         print(f"Error starting the server: {e}")
@@ -455,7 +522,6 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
         # webbrowser.open(f"https://{address}")
         # webbrowser.open(f"http://{address}:{port}")
 
-
 PromptServer.start=new_start
 
 # 创建路由表
@@ -550,13 +616,19 @@ async def nodes_map_hander(request):
 async def get_checkpoints(request):
     data = await request.json()
     t="checkpoints"
+    names=[]
     try:
         t=data['type']
+        names = folder_paths.get_filename_list(t)
     except Exception as e:
         print('/mixlab/folder_paths',False,e)
-    
-    names = folder_paths.get_filename_list(t)
 
+    try:
+        if data['type']=='llamafile':
+            names=get_llama_models()
+    except:
+        print("llamafile none")
+    
     return web.json_response({"names":names,"types":list(folder_paths.folder_names_and_paths.keys())})
 
 
@@ -577,15 +649,98 @@ async def post_prompt_result(request):
     
     return web.json_response({"result":res})
 
-# 扩展api接口
-# from server import PromptServer
-# from aiohttp import web
 
-# @routes.post('/ws_image')
-# async def my_hander_method(request):
-#     post = await request.post()
-#     x = post.get("something")
-#     return web.json_response({})
+async def start_local_llm(data):
+    global llama_port,llama_model
+    if llama_port and llama_model:
+        return {"port":llama_port,"model":llama_model}
+
+    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'])
+    
+
+    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)
+
+    app = create_app(
+                server_settings=server_settings,
+                model_settings=[
+                    ModelSettings(
+                    model=model,
+                    n_gpu_layers=-1,
+                    n_ctx=4098,
+                    chat_format="chatml"
+                    )])
+
+    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']
+
+    return  {"port":llama_port,"model":llama_model}
+
+# llam服务的开启
+@routes.post('/mixlab/start_llama')
+async def my_hander_method(request):
+    data =await request.json()
+    # print(data)
+    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)
+
+# parser.add_argument("--llm-model", type=str, help="Path to Model file (gguf). Run the Local LLM by MixlabNodes")
+# if args.llm_model and os.path.exists(args.llm_model):
+#     start_local_llm({
+#         "model_path":args.llm_model
+#     })
+#     print("Local LLM Start")
+
+
 
 
 # 导入节点
@@ -713,7 +868,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "VAEDecodeConsistencyDecoder":"Consistency Decoder Decode",
     "ScreenShare":"Screen Share ♾️Mixlab",
     "FloatingVideo":"FloatingVideo ♾️Mixlab",
-    "ChatGPTOpenAI":"ChatGPT ♾️Mixlab",
+    "ChatGPTOpenAI":"ChatGPT & Local LLM ♾️Mixlab",
     "ShowTextForGPT":"Show Text ♾️MixlabApp",
     "MergeLayers":"Merge Layers ♾️Mixlab",
     "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
@@ -756,42 +911,44 @@ NODE_DISPLAY_NAME_MAPPINGS = {
 # web ui的节点功能
 WEB_DIRECTORY = "./web"
 
-print('--------------')
-print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
+
+logging.info('--------------')
+logging.info('\033[91m ### Mixlab Nodes: \033[93mLoaded')
+# print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
 
 try:
     from .nodes.Lama import LaMaInpainting
-    print('LaMaInpainting.available',LaMaInpainting.available)
+    logging.info('LaMaInpainting.available {}'.format(LaMaInpainting.available))
     if LaMaInpainting.available:
         NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
 except Exception as e:
-    print('LaMaInpainting.available',False,e)
+    logging.info('LaMaInpainting.available False')
 
 try:
     from .nodes.ClipInterrogator import ClipInterrogator
-    print('ClipInterrogator.available',ClipInterrogator.available)
+    logging.info('ClipInterrogator.available {}'.format(ClipInterrogator.available))
     if ClipInterrogator.available:
         NODE_CLASS_MAPPINGS['ClipInterrogator']=ClipInterrogator
 except Exception as e:
-    print('ClipInterrogator.available',False,e)
+    logging.info('ClipInterrogator.available False')
 
 try:
     from .nodes.TextGenerateNode import PromptGenerate,ChinesePrompt
-    print('PromptGenerate.available',PromptGenerate.available)
+    logging.info('PromptGenerate.available {}'.format(PromptGenerate.available))
     if PromptGenerate.available:
         NODE_CLASS_MAPPINGS['PromptGenerate_Mix']=PromptGenerate
-    print('ChinesePrompt.available',ChinesePrompt.available)
+    logging.info('ChinesePrompt.available {}'.format(ChinesePrompt.available))
     if ChinesePrompt.available:
         NODE_CLASS_MAPPINGS['ChinesePrompt_Mix']=ChinesePrompt
 except Exception as e:
-    print('TextGenerateNode.available',False,e)
+    logging.info('TextGenerateNode.available False')
 
 try:
     from .nodes.RembgNode import RembgNode_
-    print('RembgNode_.available',RembgNode_.available)
+    logging.info('RembgNode_.available {}'.format(RembgNode_.available))
     if RembgNode_.available:
         NODE_CLASS_MAPPINGS['RembgNode_Mix']=RembgNode_
 except Exception as e:
-    print('RembgNode_.available',False,e)
+    logging.info('RembgNode_.available False' )
 
-print('\033[93m -------------- \033[0m')
+logging.info('\033[93m -------------- \033[0m')

install.bat

@@ -10,6 +10,12 @@ if exist "%python_exec%" (
     for /f "delims=" %%i in (%requirements_txt%) do (
         %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
+
+    %python_exec% -s -m pip install --upgrade --force llama-cpp-python[server]
+    
+
 ) else (
     echo Installing with system Python
     for /f "delims=" %%i in (%requirements_txt%) do (

nodes/ChatGPT.py

@@ -87,16 +87,102 @@ def ZhipuAI_client(key):
     return client
 
 
+# 优先使用phi
+def phi_sort(lst):
+    return sorted(lst, key=lambda x: x.lower().count('phi'), reverse=True)
+
+def get_llama_path():
+    try:
+        return folder_paths.get_folder_paths('llamafile')[0]
+    except:
+        return os.path.join(folder_paths.models_dir, "llamafile")
+
+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
+
+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 llama_cpp_client(file_name):
+    try:
+        if is_installed('llama_cpp')==False:
+            import subprocess
+
+            # 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)
+
+            #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)
+
+            else:
+                print("#install 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)
+
+        llm = Llama(model_path=mp, chat_format="chatml",n_gpu_layers=-1,n_ctx=512)
+
+        return llm
+
+    
+
+
 def chat(client, model_name,messages ):
 
         try_count = 0
         while True:
             try_count += 1
             try:
-                response = client.chat.completions.create(
-                    model=model_name,
-                    messages=messages
-                )
+                if hasattr(client, "chat"):
+                    response = client.chat.completions.create(
+                        model=model_name,
+                        messages=messages
+                    )
+                else:
+                    # 是llama的
+                    response = client.create_chat_completion_openai_v1(
+                        messages=messages,
+                        # response_format={
+                        #     "type": "json_object",
+                        # },
+                        # temperature=0.7,
+                    )
+
                 break
             except openai.AuthenticationError as ex:
                 raise ex
@@ -105,7 +191,8 @@ def chat(client, model_name,messages ):
                     raise ex
                 time.sleep(3)
                 continue
-
+        
+        # print(response.keys())
         finish_reason = response.choices[0].finish_reason
         if finish_reason != "stop":
             raise RuntimeError("API finished with unexpected reason: " + finish_reason)
@@ -128,6 +215,16 @@ class ChatGPTNode:
 
     @classmethod
     def INPUT_TYPES(cls):
+        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}),
@@ -138,16 +235,8 @@ class ChatGPTNode:
                                        "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
                                        "multiline": True,"dynamicPrompts": False
                                        }),
-                "model": ([
-                    "gpt-3.5-turbo",
-                    "gpt-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"], 
-                          {"default": "gpt-3.5-turbo"}),
+                "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}),
             },
@@ -170,8 +259,8 @@ class ChatGPTNode:
                                  api_url, 
                                  prompt, 
                                  system_content,
-                                   model, 
-                                   seed,context_size,unique_id = None, extra_pnginfo=None):
+                                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()
@@ -193,6 +282,9 @@ 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)
             else :
                 client = openai_client(api_key,api_url)  # 使用 ChatGPT  的接口
                 print('using ChatGPT interface')

nodes/ClipInterrogator.py

@@ -70,14 +70,20 @@ def load_caption_model(model_path,config,t='blip-base'):
     return (caption_model,caption_processor)
     
 
+def get_clip_interrogator_path():
+    try:
+        return folder_paths.get_folder_paths('clip_interrogator')[0]
+    except:
+        return os.path.join(folder_paths.models_dir, "clip_interrogator")
 
-caption_model_path=os.path.join(folder_paths.models_dir, "clip_interrogator/Salesforce/blip-image-captioning-base")
+
+cache_path=get_clip_interrogator_path()
+
+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'
 
-cache_path=os.path.join(folder_paths.models_dir, "clip_interrogator")
-
 
 # Tensor to PIL
 def tensor2pil(image):

nodes/ImageNode.py

@@ -29,23 +29,122 @@ def opencv_to_pil(image):
     return pil_image
 
 
-def composite_images(foreground, background, mask):
+def composite_images(foreground, background, mask,is_multiply_blend=False,position="overall"):
     width,height=foreground.size
  
     bg_image=background
+
+    bwidth,bheight=bg_image.size
+
     # 按z-index排序
-    layer = {
-            "x":0,
-            "y":0,
-            "width":width,
-            "height":height,
-            "z_index":88,
-            "scale_option":'overall',
-            "image":foreground,
-            "mask":mask
-    }
+    if position=="overall":
+        layer = {
+                "x":0,
+                "y":0,
+                "width":bwidth,
+                "height":bheight,
+                "z_index":88,
+                "scale_option":'overall',
+                "image":foreground,
+                "mask":mask
+        }
+
+    elif position=='center_bottom':
+        
+        scale = int(bwidth*0.25) / width
+        new_height = int(height * scale)
+
+        layer = {
+                "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
+        }
+
+    elif position=='right_bottom':
+        
+        scale = int(bwidth*0.25) / width
+        new_height = int(height * scale)
+
+        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
+        }
+
+
+    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
+    # width, height = bg_image.size
 
     layer_image=layer['image']
     layer_mask=layer['mask']
@@ -53,12 +152,12 @@ def composite_images(foreground, background, mask):
     bg_image=merge_images(bg_image,
                         layer_image,
                         layer_mask,
-                                        layer['x'],
-                                        layer['y'],
-                                        layer['width'],
-                                        layer['height'],
-                                        layer['scale_option']
-                                        )
+                        layer['x'],
+                        layer['y'],
+                        layer['width'],
+                        layer['height'],
+                        layer['scale_option'],
+                        is_multiply_blend )
                    
     bg_image=bg_image.convert('RGB')
     
@@ -704,7 +803,6 @@ def multiply_blend(image1, image2):
 # cv2.imwrite('result.jpg', result)
 
 
-
 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")
@@ -730,21 +828,42 @@ def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option,
         # 整体缩放
         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
+            scale = width / original_width
+            new_height = int(original_height * scale)
+            x=0
+            y=int((height-new_height)*0.5)
+        else:
+            new_height=height
+            scale = height / original_height
+            new_width = int(original_height * scale)
+            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))
 
-    # 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)
+    # # 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)
+    # 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))
@@ -753,8 +872,11 @@ def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option,
         bg_image=multiply_blend(bg_image_white,bg_image)
         bg_image=bg_image.convert("RGBA")
     else:
-        # 在底图上粘贴图层
-        bg_image.paste(layer_image, (x, y), mask=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
@@ -802,6 +924,31 @@ def resize_image(layer_image, scale_option, width, height,color="white"):
         resized_image = resized_image.convert("RGB")
         resized_image=resize_2(resized_image)
         return resized_image
+    elif scale_option == "longest":
+    #暂时不用，  
+        if original_width > original_height:
+            new_width=width
+            scale = width / original_width
+            new_height = int(original_height * scale)
+            x=0
+            y=int((new_height-height)*0.5)
+            resized_image = Image.new("RGB", (new_width, new_height), color=color)
+            resized_image.paste(layer_image.resize((new_width, new_height)), (x,y))
+            resized_image = resized_image.convert("RGB")
+            resized_image=resize_2(resized_image)
+            return resized_image
+        else:
+            new_height=height
+            scale = height / original_height
+            new_width = int(original_height * scale)
+            x=int((new_width-width)*0.5)
+            y=0
+            resized_image = Image.new("RGB", (new_width, new_height), color=color)
+            resized_image.paste(layer_image.resize((new_width, new_height)), (x,y))
+            resized_image = resized_image.convert("RGB")
+            resized_image=resize_2(resized_image)
+            return resized_image
+
     
     layer_image=resize_2(layer_image)
     return layer_image
@@ -1703,10 +1850,15 @@ class CompositeImages:
     def INPUT_TYPES(s):
         return {
             "required": { 
-            "foreground": ("IMAGE",),
-            "mask":("MASK",),
-            "background": ("IMAGE",),
-            },
+                    "foreground": (any_type,),
+                    "mask":("MASK",),
+                    "background": ("IMAGE",),
+                    },
+             "optional":{
+                           
+                  "is_multiply_blend":  ("BOOLEAN", {"default": False}),
+                  "position":  (['overall',"center_bottom","center_top","right_bottom","left_bottom","right_top","left_top"],),    
+                    }
                 }
     
     RETURN_TYPES = ("IMAGE",)
@@ -1718,11 +1870,11 @@ class CompositeImages:
 
     # OUTPUT_IS_LIST = (True,)
 
-    def run(self, foreground,mask,background):
+    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)
+        res=composite_images(foreground,background,mask,is_multiply_blend,position)
         
         return (pil2tensor(res),)
 

nodes/Lama.py

@@ -42,8 +42,13 @@ else:
     _available=True
 
 
- 
-llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
+def get_lama_path():
+    try:
+        return folder_paths.get_folder_paths('lama')[0]
+    except:
+        return os.path.join(folder_paths.models_dir, "lama")
+
+llma_model_path=os.path.join(get_lama_path(), "big-lama.pt")
 if not os.path.exists(llma_model_path):
     os.environ['LAMA_MODEL']=''
     print(f"## lama torchscript model not found: {llma_model_path},pls download from https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt")

nodes/TextGenerateNode.py

@@ -18,19 +18,25 @@ from lark import Lark, Transformer, v_args
 global _available
 _available=True
 
+def get_text_generator_path():
+    try:
+        return folder_paths.get_folder_paths('prompt_generator')[0]
+    except:
+        return os.path.join(folder_paths.models_dir, "prompt_generator")
 
-text_generator_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/text2image-prompt-generator")
+prompt_generator=get_text_generator_path()
+
+text_generator_model_path=os.path.join(prompt_generator, "text2image-prompt-generator")
 if not os.path.exists(text_generator_model_path):
     print(f"## text_generator_model not found: {text_generator_model_path}, pls download from https://huggingface.co/succinctly/text2image-prompt-generator/tree/main")
     text_generator_model_path='succinctly/text2image-prompt-generator'
 
-zh_en_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/opus-mt-zh-en")
+zh_en_model_path=os.path.join(prompt_generator, "opus-mt-zh-en")
 if not os.path.exists(zh_en_model_path):
     print(f"## zh_en_model not found: {zh_en_model_path}, pls download from https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main")
     zh_en_model_path='Helsinki-NLP/opus-mt-zh-en'
 
 
-
 def is_installed(package):
     try:
         spec = importlib.util.find_spec(package)

web/index.html

@@ -247,10 +247,10 @@
 
         .card textarea {
             width: 100%;
-            /* height: 200px; */
+            height:'fit-content';
             /* min-width: 300px; */
             margin-top: 12px;
-            resize: vertical;
+            resize: none;
             overflow: hidden;
         }
 
@@ -404,9 +404,9 @@
             display: none;
         }
 
-         /* 定义滚动条轨道的背景颜色 */
+        /* 定义滚动条轨道的背景颜色 */
 
-         ::-webkit-scrollbar-track {
+        ::-webkit-scrollbar-track {
             background-color: #f1f1f1;
             /* 轨道背景颜色 */
         }
@@ -434,14 +434,18 @@
             /* 角落颜色 */
         }
 
-
+/* 
         .dynamic_prompt::after {
             content: attr(title);
-            position: absolute; 
+            position: absolute;
             color: black;
             padding: 4px;
             padding-left: 25px;
             border-radius: 4px;
+        } */
+
+        summary {
+            user-select: none;
         }
     </style>
     <!-- <script src="../../../scripts/api.js" type="module"></script> -->
@@ -948,6 +952,10 @@
 
 
         function createOutputs(outputData, link) {
+            const url = new URL(window.location.href);
+            const params = new URLSearchParams(url.search);
+            const innerApp = params.get("innerApp");
+
             const container = document.createElement('div');
             container.className = "output";
 
@@ -956,11 +964,11 @@
 
             const copyHTML = document.createElement('button');
             copyHTML.innerText = 'copy as html'
-            action.appendChild(copyHTML)
+            if (!innerApp) action.appendChild(copyHTML)
 
             const copyImage = document.createElement('button');
             copyImage.innerText = 'copy image'
-            action.appendChild(copyImage)
+            if (!innerApp) action.appendChild(copyImage)
             copyImage.style.marginLeft = '18px';
 
             let isURL = false;
@@ -1062,18 +1070,19 @@
                     "Image Save",
                     "SaveImageAndMetadata_",
                     "TransparentImage"].includes(node.class_type)) {
-                    console.log('output#image', node)
+
+                    const url = node.options?.defaultImage || window._appData?.icon || base64Df;
                     let a = document.createElement('a');
                     a.id = `output_${node.id}`
                     a.setAttribute('data-pswp-width', "200");
                     a.setAttribute('data-pswp-height', "200");
                     a.setAttribute('target', "_blank");
-                    a.setAttribute('href', base64Df);
+                    a.setAttribute('href', url);
                     a.setAttribute('title', node.title);
 
                     let img = new Image();
                     // img;
-                    img.src = window._appData?.icon || base64Df;
+                    img.src = url;
                     a.appendChild(img)
                     output_card.appendChild(a);
                     isShowImageFn = true;
@@ -2267,7 +2276,8 @@
             // leftDiv.appendChild(des);
             leftDiv.appendChild(statusDiv);
             leftDiv.appendChild(input1);
-            mainDiv.appendChild(submitButton);
+
+            if (typeof (data.data) == 'object') mainDiv.appendChild(submitButton);
 
             rightDiv.appendChild(output);
 
@@ -2624,7 +2634,7 @@
             ui.title.update(appData.name || 'Mixlab APP');
 
             // 更新应用图标
-            ui.icon.update(appData.icon || base64Df);
+            ui.icon.update(appData.icon || appData.output[0]?.options?.defaultImage || base64Df);
 
             ui.des.update(appData.description || '-');
 

web/javascript/app_mixlab.js

@@ -207,9 +207,27 @@ async function extractInputAndOutputData (
         // input.push()
       }
       if (outputIds.includes(id)) {
+        let options = {}
+        //输出的默认图
+        if (
+          node.type === 'SaveImageAndMetadata_' &&
+          app.graph.getNodeById(id).imgs
+        ) {
+          // SaveImageAndMetadata_的默认图，转为base64
+          let imgurl = app.graph.getNodeById(id).imgs[0].src
+
+          options.defaultImage = await drawImageToCanvas(imgurl, 512)
+          console.log('#SaveImageAndMetadata_的默认图', options)
+        }
+
         // let node = app.graph.getNodeById(id)
         // output.push()
-        output[outputIds.indexOf(id)] = { ...data[id], title: node.title, id }
+        output[outputIds.indexOf(id)] = {
+          ...data[id],
+          title: node.title,
+          id,
+          options
+        }
       }
 
       if (

web/javascript/chat.js

@@ -0,0 +1,108 @@
+async function* completion (url, messages, controller) {
+  let data = {
+    model: 'gpt-3.5-turbo-16k',
+    messages,
+    temperature: 0.6,
+    stream: true
+  }
+  // if (imageNode) {
+  //   data = { ...data, image_data: [imageNode] }
+  // }
+
+  // let controller = new AbortController()
+
+  let response = await fetch(url, {
+    method: 'POST',
+    body: JSON.stringify(data),
+    headers: {
+      Connection: 'keep-alive',
+      'Content-Type': 'application/json',
+      Accept: 'text/event-stream'
+    },
+    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
+      }
+
+     
+      // Add any leftover data to the current chunk of data
+      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)
+            // console.log('#result.data',result.data)
+            
+            content += result.data.choices[0].delta?.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
+            }
+          }
+        }
+      }
+    }
+  } catch (e) {
+    console.error('llama error: ', e)
+    throw e
+  } finally {
+    controller.abort()
+  }
+
+  return content
+  // return (await response.json()).content
+}
+
+export async function completion_ (url, messages, controller, callback) {
+  let request = await completion(url, messages, controller)
+  for await (const chunk of request) {
+    
+    let content=chunk.data.choices[0].delta.content||""
+    if(chunk.data.choices[0].role=="assistant"){
+      //开始
+      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 = 'v0.23.0'
+const version = 'v0.25.1'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/ui_mixlab.js

@@ -9,6 +9,94 @@ import {
 
 import { smart_init, addSmartMenu } from './smart_connect.js'
 
+import { completion_ } from './chat.js'
+
+//系统prompt
+const systemPrompt = `You are a prompt creator, your task is to create prompts for the user input request, the prompts are image descriptions that include keywords for (an adjective, type of image, framing/composition, subject, subject appearance/action, environment, lighting situation, details of the shoot/illustration, visuals aesthetics and artists), brake keywords by comas, provide high quality, non-verboose, coherent, brief, concise, and not superfluous prompts, the subject from the input request must be included verbatim on the prompt,the prompt is english`
+
+if (!localStorage.getItem('_mixlab_system_prompt')) {
+  localStorage.setItem('_mixlab_system_prompt', systemPrompt)
+}
+
+// 获取llama 模型
+async function get_llamafile_models () {
+  try {
+    const response = await fetch('/mixlab/folder_paths', {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        type: 'llamafile'
+      })
+    })
+
+    const data = await response.json()
+    console.log(data)
+    return data.names
+  } catch (error) {
+    console.error(error)
+  }
+}
+// 运行llama
+async function start_llama (model = 'Phi-3-mini-4k-instruct-Q5_K_S.gguf') {
+  try {
+    const response = await fetch('/mixlab/start_llama', {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        model
+      })
+    })
+
+    const data = await response.json();
+    if(data.llama_cpp_error){
+      return 
+    }
+    
+    return { url: `http://127.0.0.1:${data.port}`, model: data.model }
+    
+  } catch (error) {
+    console.error(error)
+  }
+}
+
+// 菜单入口
+async function createMenu () {
+  const menu = document.querySelector('.comfy-menu')
+  const separator = document.createElement('div')
+  separator.style = `margin: 20px 0px;
+  width: 100%;
+  height: 1px;
+  background: var(--border-color);
+  `
+  menu.append(separator)
+
+  if (!menu.querySelector('#mixlab_chatbot_by_llamacpp')) {
+    const appsButton = document.createElement('button')
+    appsButton.id = 'mixlab_chatbot_by_llamacpp'
+    appsButton.textContent = '♾️Mixlab'
+
+    // appsButton.onclick = () =>
+    appsButton.onclick = async () => {
+      if (window._mixlab_llamacpp) {
+        //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)
+      }
+    }
+    menu.append(appsButton)
+  }
+}
+
 let isScriptLoaded = {}
 
 function loadExternalScript (url) {
@@ -299,8 +387,9 @@ async function get_my_app (filename = null, category = '') {
     data = []
 
     for (const res of result.data) {
-      let { app, workflow } = res.data;
-      if (app?.filename) data.push({
+      let { app, workflow } = res.data
+      if (app?.filename)
+        data.push({
           ...app,
           data: workflow,
           date: res.date
@@ -344,6 +433,33 @@ injectCSS(`::-webkit-scrollbar {
   width: 2px;
 }
 
+#mixlab_chatbot_by_llamacpp{
+  font-size:14px
+}
+
+#mixlab_chatbot_by_llamacpp::before {
+  content: attr(title);
+  position: absolute;
+  margin-top: 24px;
+  font-size: 10px;
+}
+
+.mix_tag{
+  padding:8px;cursor: pointer;font-size: 14px;
+    color: var(--input-text);
+    background-color: var(--comfy-input-bg);
+    border-radius: 8px;
+    border-color: var(--border-color);
+    border-style: solid;
+    margin-top: 2px;
+    margin-bottom: 14px;
+}
+
+.mix_tag:hover{
+  background-color: #101c19;
+  color: aquamarine;
+}
+
 @keyframes loading_mixlab {
   0% {
     background-color: green;
@@ -586,13 +702,160 @@ async function fetchReadmeContent (url) {
   }
 }
 
+function createModelsModal (models) {
+  var div =
+    document.querySelector('#model-modal') || document.createElement('div')
+  div.id = 'model-modal'
+  div.innerHTML = ''
+  div.style.cssText = `
+    width: 100%;
+    z-index: 9990;
+    height: 100vh;
+    display: flex;
+    color: var(--descrip-text);
+    position: fixed;
+    top: 0;
+    left: 0;
+    background: #000000a8;
+    `
+
+  var modal = document.createElement('div')
+
+  div.addEventListener('click', e => {
+    e.stopPropagation()
+    div.remove()
+  })
+
+  div.appendChild(modal)
+  modal.classList.add('modal-body')
+  // Set modal styles
+  modal.style.cssText = `
+  color: var(--descrip-text);
+    background-color: var(--comfy-menu-bg);
+    position: fixed;
+    overflow:hidden;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    z-index: 9999;
+    border-radius: 4px;
+    box-shadow: 4px 4px 14px rgba(255,255,255,0.2);
+  `
+
+  // Create modal header
+  const headerElement = document.createElement('div')
+  headerElement.classList.add('modal-header')
+  headerElement.style.cssText = `
+      display: flex;
+      padding: 20px 24px 8px 24px;
+      justify-content: space-between;
+    `
+
+  const headTitleElement = document.createElement('a')
+  headTitleElement.classList.add('header-title')
+  headTitleElement.style.cssText = `
+      color: var(--descrip-text);
+      font-size: 18px;
+      display: flex;
+      align-items: center;
+      flex: 1;
+      overflow: hidden;
+      text-decoration: none;
+      font-weight: bold;
+      justify-content: space-between;
+      padding: 20px;
+      cursor: pointer;
+      user-select: none;
+    `
+
+  headTitleElement.textContent = 'Models'
+  // headTitleElement.href = 'https://github.com/shadowcz007/comfyui-mixlab-nodes'
+  // headTitleElement.target = '_blank'
+  const linkIcon = document.createElement('small')
+  linkIcon.textContent = '自动开启'
+  linkIcon.style.padding = '4px'
+
+  headTitleElement.appendChild(linkIcon)
+  headerElement.appendChild(headTitleElement)
+  if (localStorage.getItem('_mixlab_auto_llama_open')) {
+    linkIcon.style.backgroundColor = '#66ff6c'
+    linkIcon.style.color = 'black'
+  }
+  linkIcon.addEventListener('click', e => {
+    e.stopPropagation()
+    if (localStorage.getItem('_mixlab_auto_llama_open')) {
+      localStorage.setItem('_mixlab_auto_llama_open', '')
+      linkIcon.style.backgroundColor = ''
+      linkIcon.style.color = 'var(--descrip-text)'
+    } else {
+      localStorage.setItem('_mixlab_auto_llama_open', 'true')
+      linkIcon.style.backgroundColor = '#66ff6c'
+      linkIcon.style.color = 'black'
+    }
+  })
+
+  modal.appendChild(headTitleElement)
+
+  // Create modal content area
+  var modalContent = document.createElement('div')
+  modalContent.classList.add('modal-content')
+
+  var input = document.createElement('textarea')
+  input.className = 'comfy-multiline-input'
+  input.style = `    height: 260px;
+  width: 480px;
+  font-size: 16px;
+  padding: 18px;`
+  input.value = localStorage.getItem('_mixlab_system_prompt')
+
+  input.addEventListener('change', e => {
+    e.stopPropagation()
+    localStorage.setItem('_mixlab_system_prompt', input.value)
+  })
+
+  input.addEventListener('click', e => {
+    e.stopPropagation()
+  })
+
+  modalContent.appendChild(input)
+
+  for (const m of models) {
+    let d = document.createElement('div')
+    d.innerText = m
+    d.className = `mix_tag`
+
+    if (!window._mixlab_llamacpp) {
+      d.addEventListener('click', async e => {
+        e.stopPropagation()
+        div.remove()
+        let res = await start_llama(m)
+        window._mixlab_llamacpp = res
+
+        localStorage.setItem('_mixlab_llama_select', res.model)
+
+        if (document.body.querySelector('#mixlab_chatbot_by_llamacpp')) {
+          document.body
+            .querySelector('#mixlab_chatbot_by_llamacpp')
+            .setAttribute('title', window._mixlab_llamacpp.url)
+        }
+      })
+    }
+
+    modalContent.appendChild(d)
+  }
+  modal.appendChild(modalContent)
+
+  document.body.appendChild(div)
+}
+
 function createModal (url, markdown, title) {
   // Create modal element
   var div =
     document.querySelector('#mix-modal') || document.createElement('div')
   div.id = 'mix-modal'
   div.innerHTML = ''
-  div.style.cssText = `width: 100%;
+  div.style.cssText = `
+    width: 100%;
     z-index: 9990;
     height: 100vh;
     display: flex;
@@ -898,6 +1161,17 @@ function drawBadge (node, orig, restArgs) {
 app.registerExtension({
   name: 'Comfy.Mixlab.ui',
   init () {
+    //是否要自动加载模型
+    if (localStorage.getItem('_mixlab_auto_llama_open')) {
+      let model = localStorage.getItem('_mixlab_llama_select')
+      start_llama(model).then(res => {
+        window._mixlab_llamacpp = res
+        document.body
+          .querySelector('#mixlab_chatbot_by_llamacpp')
+          .setAttribute('title', res.url)
+      })
+    }
+
     LGraphCanvas.prototype.helpAboutNode = async function (node) {
       nodesMap =
         nodesMap && Object.keys(nodesMap).length > 0
@@ -922,75 +1196,64 @@ app.registerExtension({
 
     smart_init()
 
-    const getNodeMenuOptions = LGraphCanvas.prototype.getNodeMenuOptions // store the existing method
-    LGraphCanvas.prototype.getNodeMenuOptions = function (node) {
-      // replace it
-      const options = getNodeMenuOptions.apply(this, arguments) // start by calling the stored one
-      node.setDirtyCanvas(true, true) // force a redraw of (foreground, background)
-      console.log('getNodeMenuOptions', node.type == 'CLIPTextEncode')
+    LGraphCanvas.prototype.text2text = async function (node) {
+      // console.log(node)
+      let widget = node.widgets.filter(
+        w => w.name === 'text' && typeof w.value == 'string'
+      )[0]
+      if (widget) {
+        let controller = new AbortController()
+        let ends = []
+        let userInput = widget.value
+        widget.value = widget.value.trim()
+        widget.value += '\n'
+        try {
+          await completion_(
+            window._mixlab_llamacpp.url + '/v1/chat/completions',
+            [
+              {
+                role: 'system',
+                content: localStorage.getItem('_mixlab_system_prompt')
+              },
+              { role: 'user', content: userInput }
+            ],
+            controller,
+            t => {
+              // console.log(t)
+              widget.value += t
+            }
+          )
+        } catch (error) {
+          //是否要自动加载模型
+          if (localStorage.getItem('_mixlab_auto_llama_open')) {
+            let model = localStorage.getItem('_mixlab_llama_select')
+            start_llama(model).then(async res => {
+              window._mixlab_llamacpp = res
+              document.body
+                .querySelector('#mixlab_chatbot_by_llamacpp')
+                .setAttribute('title', res.url)
 
-      let opts = [
-        {
-          content: 'Help ♾️Mixlab', // with a name
-          callback: () => {
-            LGraphCanvas.prototype.helpAboutNode(node)
-          } // and the callback
-        },
-        {
-          content: 'Fix node v2', // with a name
-          callback: () => {
-            LGraphCanvas.prototype.fixTheNode(node)
+              await completion_(
+                window._mixlab_llamacpp.url + '/v1/chat/completions',
+                [
+                  {
+                    role: 'system',
+                    content: localStorage.getItem('_mixlab_system_prompt')
+                  },
+                  { role: 'user', content: userInput }
+                ],
+                controller,
+                t => {
+                  // console.log(t)
+                  widget.value += t
+                }
+              )
+            })
           }
         }
-      ]
 
-      if (node.widgets) {
-        // let text_widget = node.widgets.filter(
-        //   w => w.name === 'text' && typeof w.value == 'string'
-        // )
-        // if (text_widget && text_widget.length == 1) {
-        //   opts.push({
-        //     content: 'Text-to-Text ♾️Mixlab', // with a name
-        //     callback: () => {
-        //       LGraphCanvas.prototype.text2text(node)
-        //     } // and the callback
-        //   })
-        // }
+        widget.value = widget.value.trim()
       }
-
-      opts = addSmartMenu(opts, node)
-
-      // if (node.type == 'CLIPTextEncode') {
-      //   // 则出现 randomPrompt
-      //   // CLIPTextEncode 的widget ，name== 'text'
-      //   let node_widget_name = 'text'
-      //   const widget = node.widgets.filter(w => w.name === node_widget_name)[0]
-
-      //  let mixlab_nodes_smart_connect= [{node_type:'CLIPTextEncode',
-      //   node_widget_name:'text',
-      //   inputNodeName:'RandomPrompt',
-      //   inputNode_output_type:'STRING'}]
-
-      //   if (widget) {
-      //     opts = [
-      //       {
-      //         content: 'RandomPrompt',
-      //         callback: () => {
-      //           LGraphCanvas.prototype._createNodeForInput(
-      //             node, //当前node
-      //             widget,//当前node里需要自动连线的widget
-      //             'RandomPrompt',//作为input的node type
-      //             'STRING'// 作为input的node的outputs的type. the input slot type of the target node
-      //           )
-      //         }
-      //       },
-      //       null,
-      //       ...opts
-      //     ]
-      //   }
-      // }
-
-      return [...opts, null, ...options] // and return the options
     }
 
     const getGroupMenuOptions = LGraphCanvas.prototype.getGroupMenuOptions // store the existing method
@@ -1139,6 +1402,56 @@ app.registerExtension({
       this.setDirty(true, true)
     }
 
+    const getNodeMenuOptions=LGraphCanvas.prototype.getNodeMenuOptions;
+    LGraphCanvas.prototype.getNodeMenuOptions = function (node) {
+      // replace it
+      const options = getNodeMenuOptions.apply(this, arguments) // start by calling the stored one
+      node.setDirtyCanvas(true, true) // force a redraw of (foreground, background)
+
+      let opts = [
+        {
+          content: 'Help ♾️Mixlab', // with a name
+          callback: () => {
+            LGraphCanvas.prototype.helpAboutNode(node)
+          } // and the callback
+        },
+        {
+          content: 'Fix node v2', // with a name
+          callback: () => {
+            LGraphCanvas.prototype.fixTheNode(node)
+          }
+        }
+      ]
+
+      if (node.widgets) {
+        let text_widget = node.widgets.filter(
+          w => w.name === 'text' && typeof w.value == 'string'
+        )
+
+        let text_input = node.inputs?.filter(
+          inp => inp.name == 'text' && inp.type == 'STRING'
+        )
+
+        if (
+          text_input&&
+          text_input.length == 0 &&
+          text_widget &&
+          text_widget.length == 1 &&
+          window._mixlab_llamacpp &&
+          node.type != 'ShowTextForGPT'
+        ) {
+          opts.push({
+            content: 'Text-to-Text ♾️Mixlab', // with a name
+            callback: () => {
+              LGraphCanvas.prototype.text2text(node)
+            } // and the callback
+          })
+        }
+      }
+
+      return [...opts, null, ...options] // and return the options
+    }
+
     // 支持app模式的json
     const loadAppJson = async data => {
       let workflow
@@ -1173,8 +1486,6 @@ app.registerExtension({
         event.preventDefault()
         event.stopPropagation()
 
-       
-
         // Dragging from Chrome->Firefox there is a file but its a bmp, so ignore that
         if (
           event.dataTransfer.files.length &&
@@ -1182,13 +1493,14 @@ app.registerExtension({
         ) {
           const reader = new FileReader()
           reader.onload = async () => {
-       
             loadAppJson(reader.result)
           }
           reader.readAsText(event.dataTransfer.files[0])
         }
       })
     }
+
+    createMenu()
   },
   setup () {
     setTimeout(async () => {
@@ -1198,7 +1510,7 @@ app.registerExtension({
       const apps = await get_my_app()
       if (!apps) return
 
-      console.log('apps',apps)
+      console.log('apps', apps)
 
       let apps_map = { 0: [] }