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

修正text image节点的padding问题

README.md

@@ -6,13 +6,18 @@
 
 ##### `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)
 
+<!-- - ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。模型下载后，放置到 `models/llamafile/` -->
 
-- ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。模型下载后，放置到 `models/llamafile/`
-
-- 右键菜单支持 text-to-text，方便对 prompt 词补全
-
+<!-- - 右键菜单支持 text-to-text，方便对 prompt 词补全 -->
+<!-- 
 强烈推荐：
 [Phi-3-mini-4k-instruct-function-calling-GGUF](https://huggingface.co/nold/Phi-3-mini-4k-instruct-function-calling-GGUF)
 
@@ -21,7 +26,7 @@
 - 右键菜单支持 image-to-text，使用多模态模型，多模态使用 [llava-phi-3-mini-gguf](https://huggingface.co/xtuner/llava-phi-3-mini-gguf/tree/main)，注意需要把llava-phi-3-mini-mmproj-f16.gguf也下载
 
 ![](./assets/prompt_ai_setup.png)
-![](./assets/prompt-ai.png)
+![](./assets/prompt-ai.png) -->
 
 
 #### `相关插件推荐`
@@ -114,7 +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/`
 
@@ -142,7 +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

@@ -3,12 +3,14 @@ import os
 import subprocess
 import importlib.util
 import sys,json
-import urllib
+import execution
+import uuid
 import hashlib
 import datetime
 import folder_paths
 import logging
 import base64,io,re
+import random
 from PIL import Image
 from comfy.cli_args import args
 python = sys.executable
@@ -24,12 +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
@@ -171,8 +173,7 @@ def create_for_https():
         os.mkdir(https_key_path)
     if not os.path.exists(crt):
         create_key(key,crt)
-
-    print('https_key OK: ', crt,key)
+    # print('https_key OK: ', crt,key)
     return (crt,key)
 
 
@@ -309,9 +310,10 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
         print('app_workflow_path: ',app_workflow_path)
         try:
             with open(app_workflow_path) as json_file:
+                json_data=json.load(json_file)
                 apps = [{
                     'filename':filename,
-                    'data':json.load(json_file)
+                    'data':json_data
                 }]
         except Exception as e:
             print("发生异常：", str(e))
@@ -521,9 +523,17 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
             logging.info("\n")
             logging.info("\n\nStarting server")
 
+            import socket
+
+            hostname = socket.gethostname()
+            ip_address = socket.gethostbyname(hostname)
+
+            # print(f"本机的IP地址是: {ip_address}")
+
+
             # print("\033[93mStarting server\n")
-            logging.info("\033[93mTo see the GUI go to: http://{}:{}".format(address, http_port))
-            logging.info("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
+            logging.info("\033[93mTo see the GUI go to: http://{}:{} or http://{}:{}".format(ip_address, http_port,address,http_port))
+            logging.info("\033[93mTo see the GUI go to: https://{}:{} or https://{}:{}\033[0m".format(ip_address, https_port,address,https_port))
  
             # 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))
@@ -608,13 +618,34 @@ async def mixlab_workflow_hander(request):
                     category=data['category']
                 if 'admin' in data:
                     admin=data['admin']
+
+                ds=get_my_workflow_for_app(filename,category,admin)
+                data=[]           
+                for json_data in ds:
+                    # 不传给前端
+                    if 'output' in json_data['data']:
+                        del json_data['data']['output']
+                    if 'workflow' in json_data['data']:
+                        del json_data['data']['workflow']
+                    data.append(json_data)
+
                 result={
-                    'data':get_my_workflow_for_app(filename,category,admin),
+                    'data':data,
                     'status':'success',
                 }
             elif data['task']=='list':
+                ds=get_workflows()
+                data=[]
+                for json_data in ds:
+                    # 不传给前端
+                    if 'output' in json_data['data']:
+                        del json_data['data']['output']
+                    if 'workflow' in json_data['data']:
+                        del json_data['data']['workflow']
+                    data.append(json_data)
+
                 result={
-                    'data':get_workflows(),
+                    'data':data,
                     'status':'success',
                 }
     except Exception as e:
@@ -648,11 +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':
@@ -699,135 +730,258 @@ async def rembg_hander(request):
 
     return web.json_response(result)
 
-
-@routes.post("/mixlab/prompt_result")
-async def post_prompt_result(request):
-    data = await request.json()
-    res=None
-    # print(data)
-    try:
-        action=data['action']
-        if action=='save':
-            result=data['data']
-            res=save_prompt_result(result['prompt_id'],result)
-        elif action=='all':
-            res=get_prompt_result()
-    except Exception as e:
-        print('/mixlab/prompt_result',False,e)
+# 保存运行结果？暂时去掉
+# @routes.post("/mixlab/prompt_result")
+# async def post_prompt_result(request):
+#     data = await request.json()
+#     res=None
+#     # print(data)
+#     try:
+#         action=data['action']
+#         if action=='save':
+#             result=data['data']
+#             res=save_prompt_result(result['prompt_id'],result)
+#         elif action=='all':
+#             res=get_prompt_result()
+#     except Exception as e:
+#         print('/mixlab/prompt_result',False,e)
     
-    return web.json_response({"result":res})
+#     return web.json_response({"result":res})
 
+# 种子设置
+def random_seed(seed, data):
+    max_seed = 4294967295
 
-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,
-            )
+    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)
+        
+        if 'noise_seed' in value['inputs'] and not isinstance(value['inputs']['noise_seed'], list) and seed[id] in ['increment', 'decrement', 'randomize']:
+            value['inputs']['noise_seed'] = round(random.random() * max_seed)
+        
+        if value.get('class_type') == "Seed_" and seed[id] in ['increment', 'decrement', 'randomize']:
+            value['inputs']['seed'] = round(random.random() * max_seed)
+        
+        print('new Seed', value)
     
-    if not "model" in data and "model_path" in data:
-        data['model']= os.path.basename(data["model_path"])
-        model=data["model_path"]
-
-    elif "model" in data:
-        model=get_llama_model_path(data['model'])
-
-    n_gpu_layers=-1
-
-    if "n_gpu_layers" in data:
-        n_gpu_layers=data['n_gpu_layers']
+    return data
 
 
-    chat_format="chatml"
+# 运行工作流，代替官方的prompt接口
+@routes.post("/mixlab/prompt")
+async def mixlab_post_prompt(request):
+    p_intance=PromptServer.instance
+    logging.info("got prompt")
+    resp_code = 200
+    out_string = ""
+    json_data =  await request.json()
+    # json_data = p_intance.trigger_on_prompt(json_data)
+    # filename,category, client_id ,input
+    # workflow 的 filename,category
 
-    model_alias=os.path.basename(model)
+    # 输入的参数
+    input_data=json_data['input'] if "input" in json_data else []
+    # 种子
+    seed=json_data['seed'] if "seed" in json_data else {}
 
-    # 多模态
-    clip_model_path=None
+    apps=get_my_workflow_for_app(json_data['filename'],json_data['category'],False)
     
-    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)
+    prompt=json_data['prompt'] if 'prompt' in json_data else None
 
-    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 len(apps)==1:
+        # 取到prompt
+        prompt=apps[0]['data']['output']
+        # 更新input_data到prompt里
+        '''
+          {
+                "inputs": {
+                    "number": 512,
+                    "min_value": 512,
+                    "max_value": 2048,
+                    "step": 1
+                },
+                "class_type": "IntNumber",
+                "id": "22"
+            },
+        '''
 
-    if success == False:
-        return {"port":None,"model":""}
+        for inp in input_data:
+            id=inp['id']
+            if prompt[id]['class_type']==inp['class_type']:
+                prompt[id]['inputs'].update(inp['inputs']) 
+
+
+    if prompt==None:
+        return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
+    else:
+        # 种子更新 
+        '''
+            "seed": {
+                    "45": "randomize",
+                    "46": "randomize"
+                }
+            '''
+        json_data["prompt"]=random_seed(seed,prompt)
+
+    # print("#json_data",prompt)
+    # 需要把apps处理成 prompt
+    # 注意seed的处理
+    
+    if "number" in json_data:
+        number = float(json_data['number'])
+    else:
+        number = p_intance.number
+        if "front" in json_data:
+            if json_data['front']:
+                number = -number
+
+        p_intance.number += 1
+
+    if "prompt" in json_data:
+        prompt = json_data["prompt"]
+        valid = execution.validate_prompt(prompt)
+        extra_data = {}
+        if "extra_data" in json_data:
+            extra_data = json_data["extra_data"]
+
+        if "client_id" in json_data:
+            extra_data["client_id"] = json_data["client_id"]
+        if valid[0]:
+            prompt_id = str(uuid.uuid4())
+            outputs_to_execute = valid[2]
+            p_intance.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
+            response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
+            return web.json_response(response)
+        else:
+            logging.warning("invalid prompt: {}".format(valid[1]))
+            return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
+    else:
+        return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
+
+
+# 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,
+#             )
+    
+#     if not "model" in data and "model_path" in data:
+#         data['model']= os.path.basename(data["model_path"])
+#         model=data["model_path"]
+
+#     elif "model" in data:
+#         model=get_llama_model_path(data['model'])
+
+#     n_gpu_layers=-1
+
+#     if "n_gpu_layers" in data:
+#         n_gpu_layers=data['n_gpu_layers']
+
+
+#     chat_format="chatml"
+
+#     model_alias=os.path.basename(model)
+
+#     # 多模态
+#     clip_model_path=None
+    
+#     prefix = "llava-phi-3-mini"
+#     file_name = prefix+"-mmproj-"
+#     if model_alias.startswith(prefix):
+#         for file in os.listdir(os.path.dirname(model)):
+#             if file.startswith(file_name):
+#                 clip_model_path=os.path.join(os.path.dirname(model),file)
+#                 chat_format='llava-1-5'
+#     # print('#clip_model_path',chat_format,clip_model_path,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)
+#     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')
@@ -991,7 +1145,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "IncrementingListNode_":"Create Incrementing Number List ♾️Mixlab",
     "LoadImagesToBatch":"Load Images(base64) ♾️Mixlab",
     "PreviewMask_":"Preview Mask",
-    "AudioPlay":"Audio Play ♾️Mixlab",
+    "AudioPlay":"Preview Audio ♾️Mixlab",
 
      "MultiplicationNode":"Math Operation ♾️Mixlab",
 }
@@ -1005,11 +1159,12 @@ logging.info('\033[91m ### Mixlab Nodes: \033[93mLoaded')
 # print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
 
 try:
-    from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
+    from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter,SiliconflowFreeNode
     logging.info('ChatGPT.available True')
 
     NODE_CLASS_MAPPINGS_V = {
        "ChatGPTOpenAI":ChatGPTNode,
+       "SiliconflowLLM":SiliconflowFreeNode,
         "ShowTextForGPT":ShowTextForGPT,
         "CharacterInText":CharacterInText,
         "TextSplitByDelimiter":TextSplitByDelimiter,
@@ -1018,6 +1173,7 @@ try:
     # 一个包含节点友好/可读的标题的字典
     NODE_DISPLAY_NAME_MAPPINGS_V = {
         "ChatGPTOpenAI":"ChatGPT & Local LLM ♾️Mixlab",
+        "SiliconflowLLM":"LLM Siliconflow ♾️Mixlab",
         "ShowTextForGPT":"Show Text ♾️MixlabApp",
         "CharacterInText":"Character In Text",
         "TextSplitByDelimiter":"Text Split By Delimiter",

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/Audio.py

@@ -90,7 +90,7 @@ class AudioPlayNode:
             # {'waveform': tensor([], size=(1, 1, 0)), 'sample_rate': 44100}
             is_tensor=True
 
-        if is_tensor:
+        if is_tensor and (not 'audio_path' in audio):
             filename_prefix=""
             # 保存
             filename_prefix += self.prefix_append
@@ -108,7 +108,12 @@ class AudioPlayNode:
                 })
             
         else:
-            results=[audio]
+            results=[{
+                    "filename": audio['filename'],
+                    "subfolder":audio['subfolder'],
+                    "type": audio['type'],
+                    "audio_path":audio['audio_path']
+                }]
                 
 
         # print(audio)

nodes/ChatGPT.py

@@ -53,8 +53,8 @@ def azure_client(key,url):
 
 def openai_client(key,url):
     client = openai.OpenAI(
-    api_key=key,
-    base_url=url
+        api_key=key,
+        base_url=url
     )
     return client
 
@@ -97,73 +97,74 @@ 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
 
     
 
 
 def chat(client, model_name,messages ):
-
+        print('#chat',model_name,messages)
         try_count = 0
         while True:
             try_count += 1
@@ -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",
@@ -236,7 +237,11 @@ class ChatGPTNode:
             "moonshot-v1-8k",
             "moonshot-v1-32k",
             "moonshot-v1-128k",
-            "deepseek-chat" 
+            "deepseek-chat",
+            "Qwen/Qwen2-7B-Instruct",
+            "THUDM/glm-4-9b-chat",
+            "01-ai/Yi-1.5-9B-Chat-16K",
+            "meta-llama/Meta-Llama-3.1-8B-Instruct"
                     ]
         return {
             "required": {
@@ -295,12 +300,12 @@ class ChatGPTNode:
             if model == "glm-4" :
                 client = ZhipuAI_client(api_key)  # 使用 Zhipuai 的接口
                 print('using Zhipuai interface')
-            elif model in llama_modes_list:
-                #
-                client=llama_cpp_client(model)
+            # elif model in llama_modes_list:
+            #     #
+            #     client=llama_cpp_client(model)
             else :
                 client = openai_client(api_key,api_url)  # 使用 ChatGPT  的接口
-                print('using ChatGPT interface')
+                # print('using ChatGPT interface',api_key,api_url)
 
         # 把用户的提示添加到会话历史中
         # 调用API时传递整个会话历史
@@ -316,6 +321,7 @@ class ChatGPTNode:
         session_history=crop_list_tail(self.session_history,context_size)
 
         messages=[{"role": "system", "content": self.system_content}]+session_history+[{"role": "user", "content": prompt}]
+
         response_content = chat(client,model,messages)
         
         self.session_history=self.session_history+[{"role": "user", "content": prompt}]+[{'role':'assistant',"content":response_content}]
@@ -336,6 +342,91 @@ class ChatGPTNode:
         return (response_content,json.dumps(messages, indent=4),json.dumps(self.session_history, indent=4),)
 
 
+class SiliconflowFreeNode:
+    def __init__(self):
+        # self.__client = OpenAI()
+        self.session_history = []  # 用于存储会话历史的列表
+        # self.seed=0
+        self.system_content="You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible."
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        model_list= [ 
+            "Qwen/Qwen2-7B-Instruct",
+            "THUDM/glm-4-9b-chat",
+            "01-ai/Yi-1.5-9B-Chat-16K",
+            "meta-llama/Meta-Llama-3.1-8B-Instruct"
+            ]
+        return {
+            "required": {
+                "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
+                "prompt": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                "system_content": ("STRING", 
+                                   {
+                                       "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
+                                       "multiline": True,"dynamicPrompts": False
+                                       }),
+                "model": ( model_list, 
+                    {"default": model_list[0]}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
+                "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
+            },
+             "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ("STRING","STRING","STRING",)
+    RETURN_NAMES = ("text","messages","session_history",)
+    FUNCTION = "generate_contextual_text"
+    CATEGORY = "♾️Mixlab/GPT"
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,False,False,)
+
+    
+    def generate_contextual_text(self,
+                                 api_key,
+                                 prompt, 
+                                 system_content,
+                                model, 
+                                seed,context_size,unique_id = None, extra_pnginfo=None):
+    
+        api_url="https://api.siliconflow.cn/v1"
+        
+        # 把系统信息和初始信息添加到会话历史中
+        if system_content:
+            self.system_content=system_content
+            # self.session_history=[]
+            # self.session_history.append({"role": "system", "content": system_content})
+        
+        # 
+        client = openai_client(api_key,api_url)  # 使用 ChatGPT  的接口
+        # print('using ChatGPT interface',api_key,api_url)
+
+        # 把用户的提示添加到会话历史中
+        # 调用API时传递整个会话历史
+
+        def crop_list_tail(lst, size):
+            if size >= len(lst):
+                return lst
+            elif size==0:
+                return []
+            else:
+                return lst[-size:]
+            
+        session_history=crop_list_tail(self.session_history,context_size)
+
+        messages=[{"role": "system", "content": self.system_content}]+session_history+[{"role": "user", "content": prompt}]
+
+        response_content = chat(client,model,messages)
+        
+        self.session_history=self.session_history+[{"role": "user", "content": prompt}]+[{'role':'assistant',"content":response_content}]
+
+        return (response_content,json.dumps(messages, indent=4),json.dumps(self.session_history, indent=4),)
+
+
+
 
 class ShowTextForGPT:
     @classmethod

nodes/ImageNode.py

@@ -30,17 +30,20 @@ def opencv_to_pil(image):
     return pil_image
 
 # 列出目录下面的所有文件
-def get_files_with_extension(directory, extension):
+def get_files_with_extension(directory, extensions):
     file_list = []
+    # 确保extensions参数是一个list，即使只有一个元素
+    if not isinstance(extensions, (tuple, list)):
+        extensions = [extensions]
     for root, dirs, files in os.walk(directory):
+        # print(f"Files at {root}: {files}")  # 确认files是一个字符串列表
         for file in files:
-            if file.endswith(extension):
-                file = os.path.splitext(file)[0]
-                file_path = os.path.join(root, file)
-                file_name = os.path.relpath(file_path, directory)
-                file_list.append(file_name)
+            # 检查文件是否以任何一个提供的扩展名结尾
+            if any(file.endswith(ext) for ext in extensions):
+                # 直接将文件名添加到列表中
+                file_list.append(file)
     return file_list
-
+	
 def composite_images(foreground, background, mask, is_multiply_blend=False, position="overall", scale=0.25):
     width, height = foreground.size
     bg_image = background
@@ -909,7 +912,7 @@ def resize_image(layer_image, scale_option, width, height,color="white"):
     return layer_image
 
 
-def generate_text_image(text, font_path, font_size, text_color, vertical=True, stroke=False, stroke_color=(0, 0, 0), stroke_width=1, spacing=0, padding=4):
+def generate_text_image(text, font_path, font_size, text_color, vertical=True, stroke=False, stroke_color=(0, 0, 0), stroke_width=1, spacing=0, line_spacing=0,padding=4):
     # Split text into lines based on line breaks
     lines = text.split("\n")
 
@@ -935,9 +938,13 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s
                 char_coordinates.append((x, y))
                 y += char_height + spacing
                 max_height = max(max_height, y + padding)
-            x += max_char_width + spacing
+            x += max_char_width + line_spacing
             y = padding
         max_width = x
+        total_line_width = sum(font.getsize(line)[1] for line in lines)
+        total_spacing = line_spacing * (len(lines) - 1)
+        # 确保左边和右边的padding都被计入max_width
+        max_width = total_line_width + total_spacing + padding * 2
     else:
         for line in lines:
             line_width, line_height = font.getsize(line)
@@ -946,9 +953,13 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s
                 char_coordinates.append((x, y))
                 x += char_width + spacing
                 max_width = max(max_width, x + padding)
-            y += line_height + spacing
+            y += line_height + line_spacing
             x = padding
-        max_height = y
+        # max_height = y
+        total_line_heights = sum(font.getsize(line)[1] for line in lines)
+        total_spacing = line_spacing * (len(lines) - 1)
+        # 确保顶部和底部的padding都被计入max_height
+        max_height = total_line_heights + total_spacing + padding * 2
 
     # 3. Create image with calculated width and height
     image = Image.new('RGBA', (max_width, max_height), (255, 255, 255, 0))
@@ -1288,6 +1299,9 @@ class LoadImages_:
             image=pil2tensor(image)
             ims.append(image)
 
+        if len(ims)==0:
+            image1 = Image.new('RGB', (512, 512), color='black')
+            return (pil2tensor(image1),)
         image1 = ims[0]
         for image2 in ims[1:]:
             if image1.shape[1:] != image2.shape[1:]:
@@ -1498,7 +1512,7 @@ class TextImage:
         return {"required": { 
             
                     "text": ("STRING",{"multiline": True,"default": "龍馬精神迎新歲","dynamicPrompts": False}),
-                    "font": (get_files_with_extension(FONT_PATH,'.ttf'),),#后缀为 ttf
+                    "font": (get_files_with_extension(FONT_PATH,['.ttf','.otf']),),#后缀为 ttf
                     "font_size": ("INT",{
                                 "default":100, 
                                 "min": 100, #Minimum value
@@ -1513,6 +1527,13 @@ class TextImage:
                                 "step": 1, #Slider's step
                                 "display": "number" # Cosmetic only: display as "number" or "slider"
                                 }), 
+                    "line_spacing": ("INT",{
+                                "default":12, 
+                                "min": -200, #Minimum value
+                                "max": 200, #Maximum value
+                                "step": 1, #Slider's step
+                                "display": "number" # Cosmetic only: display as "number" or "slider"
+                                }), 
                     "padding": ("INT",{
                                 "default":8, 
                                 "min": 0, #Minimum value
@@ -1536,14 +1557,14 @@ class TextImage:
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,)
 
-    def run(self,text,font,font_size,spacing,padding,text_color,vertical,stroke):
+    def run(self,text,font,font_size,spacing,line_spacing,padding,text_color,vertical,stroke):
         
-        font_path=os.path.join(FONT_PATH,font+'.ttf')
+        font_path=os.path.join(FONT_PATH,font)
 
         if text=="":
             text=" "
         # stroke=False, stroke_color=(0, 0, 0), stroke_width=1, spacing=0
-        img,mask=generate_text_image(text,font_path,font_size,text_color,vertical,stroke,(0, 0, 0),1,spacing,padding)
+        img,mask=generate_text_image(text,font_path,font_size,text_color,vertical,stroke,(0, 0, 0),1,spacing,line_spacing,padding)
         
         img=pil2tensor(img)
         mask=pil2tensor(mask)
@@ -3201,4 +3222,4 @@ class ImageListToBatch_:
 
         out = torch.cat(out, dim=0)
 
-        return (out,)
+        return (out,)

nodes/Video.py

@@ -545,20 +545,24 @@ class LoadAndCombinedAudio_:
         if duration > -1:
             crop_audio(audio_file, start_time, duration)
 
-        return (audio_file, {
-                "filename": audio_file_name,
-                "subfolder": "",
-                "type": "output",
-                "audio_path":audio_file
-                } ,)
+        waveform, sample_rate = torchaudio.load(audio_file)
+        audio = {
+            "filename": audio_file_name,
+            "subfolder": "",
+            "type": "output",
+            "audio_path":audio_file,
+            "waveform": waveform.unsqueeze(0), 
+            "sample_rate": sample_rate}
+
+        return (audio_file,audio ,)
 
 class CombineAudioVideo:
     @classmethod
     def INPUT_TYPES(s):
        
         return {"required": {
-                     "video_file_path": ("STRING",  {"forceInput": True}),
-                     "audio_file_path": ("STRING",  {"forceInput": True}), 
+                     "video": ("SCENE_VIDEO",),
+                     "audio": ("AUDIO", ), 
                      },
                 }
 
@@ -566,23 +570,46 @@ class CombineAudioVideo:
 
     OUTPUT_NODE = True
     FUNCTION = "run" 
-    RETURN_TYPES = ()
-    RETURN_NAMES = ()
+    RETURN_TYPES = ("SCENE_VIDEO",)
+    RETURN_NAMES = ("SCENE_VIDEO",)
 
-    def run(self,video_file_path, audio_file_path):
+    def run(self,video, audio):
 
         output_dir = folder_paths.get_output_directory()
 
-        counter=get_new_counter(output_dir,'video_final_')
-        
+        # 判断是否是 Tensor 类型
+        is_tensor = not isinstance(audio, dict)
+        # print('#判断是否是 Tensor 类型',is_tensor,audio)
+        if not is_tensor and 'waveform' in audio and 'sample_rate' in audio:
+            # {'waveform': tensor([], size=(1, 1, 0)), 'sample_rate': 44100}
+            is_tensor=True
+
+        if "audio_path" in audio:
+            is_tensor=False
+            audio_file_path=audio["audio_path"]
+
+        if is_tensor:
+            filename_prefix="audio_tmp"
+            full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+                filename_prefix, 
+                folder_paths.get_temp_directory())
+            
+            filename_with_batch_num = filename.replace("%batch_num%", str(1))
+            file = f"{filename_with_batch_num}_{counter:05}_.wav"
+            
+            audio_file_path=os.path.join(full_output_folder, file)
+
+            torchaudio.save(audio_file_path, audio['waveform'].squeeze(0), audio["sample_rate"])
+            
         # 获取文件名和扩展名
-        base, ext = os.path.splitext(video_file_path)
+        base, ext = os.path.splitext(video)
+        counter=get_new_counter(output_dir,'video_final_')
 
         v_file = f"video_final_{counter:05}{ext}"
         
         v_file_path=os.path.join(output_dir, v_file)
 
-        combine_audio_video(audio_file_path,video_file_path,v_file_path)
+        combine_audio_video(audio_file_path,video,v_file_path)
 
         previews = [
             {
@@ -592,7 +619,8 @@ class CombineAudioVideo:
                 "format": get_mime_type(v_file),
             }
         ]
-        return {"ui": {"gifs": previews}}
+
+        return {"ui": {"gifs": previews},"result":(v_file_path,)}
 
 # The code is based on ComfyUI-VideoHelperSuite modification.
 class VideoCombine_Adv:

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-mixlab-nodes"
 description = "3D, ScreenShareNode & FloatingVideoNode, SpeechRecognition & SpeechSynthesis, GPT, LoadImagesFromLocal, Layers, Other Nodes, ..."
-version = "0.30.3"
+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 {
@@ -466,19 +468,18 @@ app.registerExtension({
         const { input, output } = getInputsAndOutputs()
         input_ids.value = input.join('\n')
         output_ids.value = output.join('\n')
-
         const widget = {
           type: 'div',
           name: 'AppInfoRun',
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(
+              {...get_position_style(
                 ctx,
                 widget_width,
                 node.size[1] - widget_height,
                 node.size[1]
-              )
+              ),zIndex:1}
             )
           }
         }

web/javascript/audio_mixlab.js

@@ -555,6 +555,7 @@ app.registerExtension({
           e.preventDefault()
           let inputAudio = document.createElement('input')
           inputAudio.type = 'file'
+          inputAudio.accept = "audio/*"
           inputAudio.style.display = 'none'
           inputAudio.addEventListener('change', async e => {
             e.preventDefault()

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 = 'v0.30.3'
+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/gpt_mixlab.js

@@ -199,6 +199,120 @@ app.registerExtension({
         url[id] || 'https://api.openai.com/v1'
     }
   }
+});
+
+
+app.registerExtension({
+  name: 'Mixlab.GPT.SiliconflowLLM',
+  async getCustomWidgets (app) {
+    return {
+      KEY (node, inputName, inputData, app) {
+        // console.log('##inputData', inputData)
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          size: [128, 32], // a default size
+          draw (ctx, node, width, y) {},
+          computeSize (...args) {
+            return [128, 32] // a method to compute the current size of the widget
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let data = getLocalData('_mixlab_api_key')
+            return data[node.id] || 'by Mixlab'
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      },
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'SiliconflowLLM') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const api_key = this.widgets.filter(w => w.name == 'api_key')[0]
+       
+        const widget = {
+          type: 'div',
+          name: 'chatgptdiv',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, api_key.y, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        const inputDiv = (key, placeholder) => {
+          let div = document.createElement('div')
+          const ip = document.createElement('input')
+          ip.type = placeholder === 'Key' ? 'password' : 'text'
+          ip.className = `${'comfy-multiline-input'} ${placeholder}`
+          div.style = `display: flex;
+          align-items: center; 
+          margin: 6px 8px;
+          margin-top: 0;`
+          ip.placeholder = placeholder
+          ip.value = placeholder
+
+          ip.style = `margin-left: 24px;
+          outline: none;
+          border: none;
+          padding: 4px;width: 100%;`
+          const label = document.createElement('label')
+          label.style = 'font-size: 10px;min-width:32px'
+          label.innerText = placeholder
+          div.appendChild(label)
+          div.appendChild(ip)
+
+          ip.addEventListener('change', () => {
+            let data = getLocalData(key)
+            data[this.id] = ip.value.trim()
+            localStorage.setItem(key, JSON.stringify(data))
+            console.log(this.id, key)
+          })
+          return div
+        }
+
+        let inputKey = inputDiv('_mixlab_api_key', 'Key') 
+
+        widget.div.appendChild(inputKey) 
+
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => { 
+          inputKey.remove()
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    // Fires every time a node is constructed
+    // You can modify widgets/add handlers/etc here
+
+    if (node.type === 'SiliconflowLLM') {
+      let widget = node.widgets.filter(w => w.div)[0]
+
+      let apiKey = getLocalData('_mixlab_api_key');
+
+      let id = node.id
+ 
+      widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab' 
+    }
+  }
 })
 
 app.registerExtension({

web/javascript/ui_mixlab.js

@@ -100,7 +100,7 @@ async function start_llama (model = 'Phi-3-mini-4k-instruct-Q5_K_S.gguf') {
     })
 
     const data = await response.json()
-    if (data.llama_cpp_error) {
+    if (data.llama_cpp_error||!data.port) {
       return
     }
 
@@ -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) {

web/javascript/video_mixlab.js

@@ -468,6 +468,8 @@ app.registerExtension({
         const prefix = 'vhs_gif_preview_'
         const r = onExecuted ? onExecuted.apply(this, message) : undefined
 
+        if(!this.widgets) this.widgets=[]
+
         if (this.widgets) {
           const pos = this.widgets.findIndex(w => w.name === `${prefix}_0`)
           if (pos !== -1) {
@@ -488,6 +490,7 @@ app.registerExtension({
                   params.format || 'image/gif'
                 )
               )
+              console.log(w)
               w.parent = this
             })
           }