增加 MiniCPM-V 2.6 int4

Lama 改成手动安装，新增JsonRepair

CNAME

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

README.md

@@ -1,26 +1,53 @@
 ![](https://img.shields.io/github/release/shadowcz007/comfyui-mixlab-nodes)
 
-> 适配了最新版 comfyui 的 py3.11 ，torch 2.1.2+cu121
+> 适配了最新版 comfyui 的 py3.11 ，torch 2.3.1+cu121
 > [Mixlab nodes discord](https://discord.gg/cXs9vZSqeK)
 
+商务合作请联系 389570357@qq.com
+For business cooperation, please contact email 389570357@qq.com
 
 ##### `最新`：
 
-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/`
+- 增加 MiniCPM-V 2.6 int4
 
-- 右键菜单支持 text-to-text，方便对 prompt 词补全
+This is the int4 quantized version of MiniCPM-V 2.6.
+Running with int4 version would use lower GPU memory (about 7GB).
 
-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)
+- 移动端适配、修改 app 模式的 Mask 编辑器
+
+- 增加 p5.js 作为输入节点
+  [workflow](./workflow/p5workflow.json)
+  [workflow2](./workflow/p5-video-workflow.json)
+
+- App 模式增加 batch prompt，批量提示词，可以把动态提示词批量组成后运行
+
+![alt text](./assets/1722517810720.png)
+
+- 增加 API Key Input 节点，用于管理 LLM 的 Key,同时优化 LLM 相关节点，为后续 agent 模式做准备
+
+- 增加 SiliconflowLLM，可以使用由 Siliconflow 提供的免费 LLM
+
+<!-- - ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。模型下载后，放置到 `models/llamafile/` -->
+
+<!-- - 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)
+
+[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 image-to-text，使用多模态模型，多模态使用 [llava-phi-3-mini-gguf](https://huggingface.co/xtuner/llava-phi-3-mini-gguf/tree/main)，注意需要把llava-phi-3-mini-mmproj-f16.gguf也下载
 
 ![](./assets/prompt_ai_setup.png)
-![](./assets/prompt-ai.png)
-
+![](./assets/prompt-ai.png) -->
 
 #### `相关插件推荐`
 
-<!-- [comfyui-sd-prompt-mixlab](https://github.com/shadowcz007/comfyui-sd-prompt-mixlab) -->
+[comfyui-liveportrait](https://github.com/shadowcz007/comfyui-liveportrait)
+
+[Comfyui-ChatTTS](https://github.com/shadowcz007/Comfyui-ChatTTS)
+
+[comfyui-sound-lab](https://github.com/shadowcz007/comfyui-sound-lab)
 
 [comfyui-Image-reward](https://github.com/shadowcz007/comfyui-Image-reward)
 
@@ -37,6 +64,8 @@ ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一
 - 发布为 app 的 workflow，可以在右键里再次编辑了
 - web app 可以设置分类，在 comfyui 右键菜单可以编辑更新 web app
 - 支持动态提示
+- 支持把输出显示到 comfyui 背景（TouchDesigner 风格）
+- 如果转为 web app 打开是空白的，注意检查下插件目录的名字需要是：comfyui-mixlab-nodes(如果是 zip 包下载会多了个-main 的后缀，需要去掉)
 
 ![](./assets/微信图片_20240421205440.png)
 
@@ -95,15 +124,20 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 [Voice + Real-time Face Swap Workflow](./workflow/语音+实时换脸workflow.json)
 
+- Preview Audio
+
+[text-to-audio](./workflow/text-to-audio-base-workflow.json)
+
 ### GPT
 
-> Support for calling multiple GPTs.Local LLM（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
+> Support for calling multiple GPTs.Local LLM 、 ChatGPT、ChatGLM3 、ChatGLM4 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 . Azure OpenAI:https://xxxx.openai.azure.com
 
-![gpt-workflow.svg](./assets/gpt-workflow.svg)
+[LLM_base_workflow](./workflow/LLM_base_workflow.json)
 
-[workflow-5](./workflow/5-gpt-workflow.json)
+- SiliconflowLLM
+- ChatGPTOpenAI
 
-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/`
 
@@ -131,7 +165,7 @@ pip install 'llama-cpp-python[server]'
 ```
 pip install llama-cpp-python \
   --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
-```
+``` -->
 
 ## Prompt
 
@@ -158,6 +192,8 @@ pip install llama-cpp-python \
 
 > A new layer class node has been added, allowing you to separate the image into layers. After merging the images, you can input the controlnet for further processing.
 
+> The composite images node overlays a foreground image onto a background image at specified positions and scales, with optional blending modes and masking capabilities. position : 'overall',"center_center","left_bottom","center_bottom","right_bottom","left_top","center_top","right_top"
+
 ![layers](./assets/layers-workflow.svg)
 
 ![poster](./assets/poster-workflow.svg)
@@ -189,6 +225,19 @@ pip install llama-cpp-python \
 
 > Conveniently load images from a fixed address on the internet to ensure that default images in the workflow can be executed.
 
+#### TextImage
+
+> [下载字体](https://drxie.github.io/OSFCC/)放到 `custom_nodes/comfyui-mixlab-nodes/assets/fonts`
+
+#### MiniCPM-VQA Simple
+
+This is the int4 quantized version of MiniCPM-V 2.6.
+Running with int4 version would use lower GPU memory (about 7GB).
+
+[模型](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4)
+
+![alt text](assets/1724308322276.png)
+
 ### Style
 
 > Apply VisualStyle Prompting , Modified from [ComfyUI_VisualStylePrompting](https://github.com/ExponentialML/ComfyUI_VisualStylePrompting)
@@ -211,6 +260,8 @@ pip install llama-cpp-python \
 
 ### Other Nodes
 
+- 增加 Edit Mask，方便在生成的时候手动绘制 mask [workflow](./workflow/edit-mask-workflow.json)
+
 ![main](./assets/all-workflow.svg)
 ![main2](./assets/detect-face-all.png)
 
@@ -226,10 +277,14 @@ Add edges to an image.
 
 ![FeatheredMask](./assets/FlVou_Y6kaGWYoEj1Tn0aTd4AjMI.jpg)
 
-> LaMaInpainting
+> LaMaInpainting（需要手动安装）
+
+- simple-lama-inpainting 里的 pillow 造成冲突，暂时从依赖里移除，如果有安装 simple-lama-inpainting ，节点会自动添加，没有，则不会自动添加。
 
 from [simple-lama-inpainting](https://github.com/enesmsahin/simple-lama-inpainting)
 
+- [问题汇总](https://github.com/shadowcz007/comfyui-mixlab-nodes/issues/294)
+
 > rembgNode
 
 "briarmbg","u2net","u2netp","u2net_human_seg","u2net_cloth_seg","silueta","isnet-general-use","isnet-anime"

__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
@@ -20,17 +22,17 @@ except:
     print('#fix sys.stdout.isatty')
     sys.stdout.isatty = lambda: False
 
-llama_port=None
-llama_model=""
-llama_chat_format=""
+_URL_=None
 
-try:
-    from .nodes.ChatGPT import get_llama_models,get_llama_model_path,llama_cpp_client
-    llama_cpp_client("")
 
-except:
-    print("##nodes.ChatGPT ImportError")
+# try:
+#     from .nodes.ChatGPT import get_llama_models,get_llama_model_path,llama_cpp_client
+#     llama_cpp_client("")
 
+# except:
+#     print("##nodes.ChatGPT ImportError")
+
+from .nodes.ChatGPT import openai_client
 
 from .nodes.RembgNode import get_rembg_models,U2NET_HOME,run_briarmbg,run_rembg
 
@@ -45,26 +47,35 @@ except ImportError:
     print("or")
     print("pip install -r requirements.txt")
     sys.exit()
-   
-def is_installed(package, package_overwrite=None):
+
+
+def is_installed(package, package_overwrite=None,auto_install=True):
+    is_has=False
     try:
         spec = importlib.util.find_spec(package)
+        is_has=spec is not None
     except ModuleNotFoundError:
         pass
 
     package = package_overwrite or package
 
     if spec is None:
-        print(f"Installing {package}...")
-        # 清华源 -i https://pypi.tuna.tsinghua.edu.cn/simple
-        command = f'"{python}" -m pip install {package}'
-  
-        result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ)
+        if auto_install==True:
+            print(f"Installing {package}...")
+            # 清华源 -i https://pypi.tuna.tsinghua.edu.cn/simple
+            command = f'"{python}" -m pip install {package}'
+    
+            result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ)
 
-        if result.returncode != 0:
-            print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
+            is_has=True
+
+            if result.returncode != 0:
+                print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
+                is_has=False
     else:
         print(package+'## OK')
+
+    return is_has
      
 try:
     import OpenSSL
@@ -87,7 +98,6 @@ except ImportError:
     sys.exit()
 
 
-
 def install_openai():
     # Helper function to install the OpenAI module if not already installed
     try:
@@ -171,15 +181,14 @@ def create_for_https():
         os.mkdir(https_key_path)
     if not os.path.exists(crt):
         create_key(key,crt)
-
-    print('https_key OK: ', crt,key)
+    # print('https_key OK: ', crt,key)
     return (crt,key)
 
 
 
 # workflow  目录下的所有json
 def read_workflow_json_files_all(folder_path):
-    print('#read_workflow_json_files_all',folder_path)
+    # print('#read_workflow_json_files_all',folder_path)
     json_files = []
     for root, dirs, files in os.walk(folder_path):
         for file in files:
@@ -309,31 +318,32 @@ 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))
         
         # 这个代码不需要
         # if len(apps)==1 and category!='' and category!=None:
-            data=read_workflow_json_files(category_path)
+        data=read_workflow_json_files(category_path)
             
-            for item in data:
-                x=item["data"]
-                # print(apps[0]['filename'] ,item["filename"])
-                if apps[0]['filename']!=item["filename"]:
-                    category=''
-                    input=None
-                    output=None
-                    if 'category' in x['app']:
-                        category=x['app']['category']
-                    if 'input' in x['app']:
-                        input=x['app']['input']
-                    if 'output' in x['app']:
-                        output=x['app']['output']
-                    apps.append({
+        for item in data:
+            x=item["data"]
+            # print(apps[0]['filename'] ,item["filename"])
+            if apps[0]['filename']!=item["filename"]:
+                category=''
+                input=None
+                output=None
+                if 'category' in x['app']:
+                    category=x['app']['category']
+                if 'input' in x['app']:
+                    input=x['app']['input']
+                if 'output' in x['app']:
+                    output=x['app']['output']
+                apps.append({
                         "filename":item["filename"],
                         # "category":category,
                         "data":{
@@ -453,6 +463,7 @@ async def check_port_available(address, port):
 
 # https
 async def new_start(self, address, port, verbose=True, call_on_start=None):
+    global _URL_
     try:
         runner = web.AppRunner(self.app, access_log=None)
         await runner.setup()
@@ -521,10 +532,19 @@ 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))
+
+            _URL_="http://{}:{}".format(address,http_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))
 
@@ -565,17 +585,101 @@ async def mixlab_hander(request):
             print(e)
     return web.json_response(data)
 
+# llm的api key，使用硅基流动
+@routes.post('/mixlab/llm_api_key')  
+async def mixlab_llm_api_key_handler(request):  
+    data = await request.json()  
+    api_key = data.get('key')
+
+    app_folder = os.path.join(current_path, "app")
+    key_file_path = os.path.join(app_folder, "llm_api_key.txt")
+
+    if api_key:
+        if not os.path.exists(app_folder):
+            os.makedirs(app_folder)
+        try:
+            with open(key_file_path, 'w') as f:
+                f.write(api_key)
+            return web.json_response({'message': 'API key saved successfully'})
+        except Exception as e:
+            return web.json_response({'error': str(e)}, status=500)
+    else:
+        if os.path.exists(key_file_path):
+            try:
+                with open(key_file_path, 'r') as f:
+                    saved_api_key = f.read().strip()
+                return web.json_response({'key': saved_api_key})
+            except Exception as e:
+                return web.json_response({'error': str(e)}, status=500)
+        else:
+            return web.json_response({'error': 'No API key provided and no key found in local storage'}, status=400)
+
+
+@routes.post('/chat/completions')
+async def chat_completions(request):
+    data = await request.json()
+    messages = data.get('messages')
+    key=data.get('key')
+    if not messages:
+        return web.json_response({"error": "No messages provided"}, status=400)
+
+    async def generate():
+        try:
+            client=openai_client(key,"https://api.siliconflow.cn/v1")
+
+            response = client.chat.completions.create(
+                model="01-ai/Yi-1.5-9B-Chat-16K",
+                messages=messages,
+                stream=True
+            )
+
+            for chunk in response:
+                if hasattr(chunk.choices[0].delta, 'content'):
+                    content = chunk.choices[0].delta.content
+                    if content is not None:
+                        yield content.encode('utf-8') + b"\r\n"
+
+        except Exception as e:
+            yield f"Error: {str(e)}".encode('utf-8') + b"\r\n"
+
+    return web.Response(body=generate(), content_type='text/event-stream')
+
 
 @routes.get('/mixlab/app')
 async def mixlab_app_handler(request):
-    html_file = os.path.join(current_path, "web/index.html")
+    html_file = os.path.join(current_path, "webApp/index.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)
+
+# web app模式独立
+@routes.get('/mixlab/app/{filename:.*}')
+async def static_file_handler(request):
+    filename = request.match_info['filename']
+    file_path = os.path.join(current_path, "webApp", filename)
+    print(file_path)
     
+    if os.path.exists(file_path) and os.path.isfile(file_path):
+        if filename.endswith('.js'):
+            content_type = 'application/javascript'
+        elif filename.endswith('.css'):
+            content_type = 'text/css'
+        elif filename.endswith('.html'):
+            content_type = 'text/html'
+        elif filename.endswith('.svg'):
+            content_type = 'image/svg+xml'
+        else:
+            content_type = 'application/octet-stream'
+        
+        with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
+            file_data = f.read()
+            return web.Response(text=file_data, content_type=content_type)
+    else:
+        return web.Response(text="File not found", status=404)
+
 
 @routes.post('/mixlab/workflow')
 async def mixlab_workflow_hander(request):
@@ -608,13 +712,34 @@ 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 +773,11 @@ 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 +824,263 @@ async def rembg_hander(request):
 
     return web.json_response(result)
 
+# 保存运行结果？暂时去掉
+# @routes.post("/mixlab/prompt_result")
+# async def post_prompt_result(request):
+#     data = await request.json()
+#     res=None
+#     # print(data)
+#     try:
+#         action=data['action']
+#         if action=='save':
+#             result=data['data']
+#             res=save_prompt_result(result['prompt_id'],result)
+#         elif action=='all':
+#             res=get_prompt_result()
+#     except Exception as e:
+#         print('/mixlab/prompt_result',False,e)
+    
+#     return web.json_response({"result":res})
+
+# 种子设置
+def random_seed(seed, data):
+    max_seed = 4294967295
+
+    for id, value in data.items():
+        # print(seed,id)
+        if id in seed:
+            if 'seed' in value['inputs'] and not isinstance(value['inputs']['seed'], list) and seed[id] in ['increment', 'decrement', 'randomize']:
+                value['inputs']['seed'] = round(random.random() * max_seed)
+            
+            if 'noise_seed' in value['inputs'] and not isinstance(value['inputs']['noise_seed'], list) and seed[id] in ['increment', 'decrement', 'randomize']:
+                value['inputs']['noise_seed'] = round(random.random() * max_seed)
+            
+            if value.get('class_type') == "Seed_" and seed[id] in ['increment', 'decrement', 'randomize']:
+                value['inputs']['seed'] = round(random.random() * max_seed)
+            
+        print('new Seed', value)
+    
+    return data
+
+
+# 运行工作流，代替官方的prompt接口
+@routes.post("/mixlab/prompt")
+async def mixlab_post_prompt(request):
+    p_intance=PromptServer.instance
+    logging.info("/mixlab/prompt")
+    resp_code = 200
+    out_string = ""
+    json_data =  await request.json()
+    # json_data = p_intance.trigger_on_prompt(json_data)
+    # filename,category, client_id ,input
+    # workflow 的 filename,category
+
+    # 输入的参数
+    input_data=json_data['input'] if "input" in json_data else []
+    # 种子
+    seed=json_data['seed'] if "seed" in json_data else {}
 
-@routes.post("/mixlab/prompt_result")
-async def post_prompt_result(request):
-    data = await request.json()
-    res=None
-    # print(data)
     try:
-        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)
+        apps=json_data['apps']
+    except:
+        apps=get_my_workflow_for_app(json_data['filename'],json_data['category'],False)
     
-    return web.json_response({"result":res})
-
-
-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,
-            )
+    prompt=json_data['prompt'] if 'prompt' in json_data else None
     
-    if not "model" in data and "model_path" in data:
-        data['model']= os.path.basename(data["model_path"])
-        model=data["model_path"]
+    if len(apps)>0:
+        # 取到prompt
+        prompt=apps[0]['data']['output']
+        # logging.info(prompt)
+        # 更新input_data到prompt里
+        '''
+          {
+                "inputs": {
+                    "number": 512,
+                    "min_value": 512,
+                    "max_value": 2048,
+                    "step": 1
+                },
+                "class_type": "IntNumber",
+                "id": "22"
+            },
+        '''
 
-    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']
+        for inp in input_data:
+            id=inp['id']
+            if prompt[id]['class_type']==inp['class_type']:
+                prompt[id]['inputs'].update(inp['inputs']) 
 
 
-    chat_format="chatml"
+    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)
 
-    model_alias=os.path.basename(model)
-
-    # 多模态
-    clip_model_path=None
+    # print("#json_data",prompt)
+    # 需要把apps处理成 prompt
+    # 注意seed的处理
     
-    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)
+    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)
 
 
-    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
+# 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)
 
-    if success == False:
-        return {"port":None,"model":""}
+
+# 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))
-    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)
-    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')
@@ -838,24 +1091,24 @@ def re_start(request):
         pass
     return os.execv(sys.executable, [sys.executable] + sys.argv)
 
-
+# 状态
+@routes.get('/mixlab/status')
+def mix_status(request):
+    return web.Response(text="running#"+_URL_)
 
 # 导入节点
 from .nodes.PromptNode import GLIGENTextBoxApply_Advanced,EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
-from .nodes.ImageNode import ComparingTwoFrames,LoadImages_,CompositeImages,GridDisplayAndSave,GridInput,ImagesPrompt,SaveImageAndMetadata,SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.ImageNode import DepthViewer_,ImageBatchToList_,ImageListToBatch_,ComparingTwoFrames,LoadImages_,CompositeImages,GridDisplayAndSave,GridInput,ImagesPrompt,SaveImageAndMetadata,SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 # from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
 
-from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
-from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import IncrementingListNode,ListSplit,CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+from .nodes.Audio import AudioPlayNode,SpeechRecognition,SpeechSynthesis
+from .nodes.Utils import KeyInput,IncrementingListNode,ListSplit,CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
 from .nodes.Mask import PreviewMask_,MaskListReplace,MaskListMerge,OutlineMask,FeatheredMask
 
 from .nodes.Style import ApplyVisualStylePrompting,StyleAlignedReferenceSampler,StyleAlignedBatchAlign,StyleAlignedSampleReferenceLatents
 
-from .nodes.Video import VideoCombine_Adv,LoadVideoAndSegment,ImageListReplace,VAEEncodeForInpaint_Frames
-
-from .nodes.TripoSR import LoadTripoSRModel,TripoSRSampler,SaveTripoSRMesh
+from .nodes.P5 import P5Input
 
 
 # 要导出的所有节点及其名称的字典
@@ -886,7 +1139,10 @@ NODE_CLASS_MAPPINGS = {
     "ImageColorTransfer":ImageColorTransfer,
     "ShowLayer":ShowLayer,
     "NewLayer":NewLayer,
+    "ImageListToBatch_":ImageListToBatch_,
+    "ImageBatchToList_":ImageBatchToList_,
     "CompositeImages_":CompositeImages,
+    "DepthViewer": DepthViewer_,
     "SplitImage":SplitImage,
     "CenterImage":CenterImage,
     "GridOutput":GridOutput,
@@ -907,12 +1163,10 @@ NODE_CLASS_MAPPINGS = {
     # "VAEDecodeConsistencyDecoder":VAEDecode,
     "ScreenShare":ScreenShareNode,
     "FloatingVideo":FloatingVideo,
-    "ChatGPTOpenAI":ChatGPTNode,
-    "ShowTextForGPT":ShowTextForGPT,
-    "CharacterInText":CharacterInText,
-    "TextSplitByDelimiter":TextSplitByDelimiter,
+   
     "SpeechRecognition":SpeechRecognition,
     "SpeechSynthesis":SpeechSynthesis,
+    "KeyInput":KeyInput,
     "Color":ColorInput,
     "FloatSlider":FloatSlider,
     "IntNumber":IntNumber,
@@ -934,26 +1188,26 @@ NODE_CLASS_MAPPINGS = {
     "ApplyVisualStylePrompting_":ApplyVisualStylePrompting,
     "StyleAlignedReferenceSampler_": StyleAlignedReferenceSampler,
     "StyleAlignedSampleReferenceLatents_": StyleAlignedSampleReferenceLatents,
-    "StyleAlignedBatchAlign_": StyleAlignedBatchAlign,
-    "LoadVideoAndSegment_":LoadVideoAndSegment,
-    "VideoCombine_Adv":VideoCombine_Adv,
+    "StyleAlignedBatchAlign_": StyleAlignedBatchAlign, 
     "ListSplit_":ListSplit,
-    "MaskListReplace_":MaskListReplace,
-    "ImageListReplace_":ImageListReplace,
-    "VAEEncodeForInpaint_Frames":VAEEncodeForInpaint_Frames,
+    "MaskListReplace_":MaskListReplace, 
     "IncrementingListNode_":IncrementingListNode,
     "PreviewMask_":PreviewMask_,
-     "LoadTripoSRModel_": LoadTripoSRModel,
-    "TripoSRSampler_": TripoSRSampler,
-    "SaveTripoSRMesh": SaveTripoSRMesh
-    # "GamePal":GamePal
+    "AudioPlay":AudioPlayNode,
+
+    "P5Input":P5Input
 }
 
 # 一个包含节点友好/可读的标题的字典
 NODE_DISPLAY_NAME_MAPPINGS = {
     "AppInfo":"App Info ♾️MixlabApp",
+    "ScreenShare":"Screen Share ♾️Mixlab",
+    "FloatingVideo":"Floating Video ♾️Mixlab",
+    "TextImage":"Text Image ♾️Mixlab",
+    
     "Color":"Color Input ♾️MixlabApp",
     "TextInput_":"Text Input ♾️MixlabApp",
+    "KeyInput":"API Key Input ♾️MixlabApp",
     "FloatSlider":"Float Slider Input ♾️MixlabApp",
     "IntNumber":"Int Input ♾️MixlabApp",
     "ImagesPrompt_":"Images Input ♾️MixlabApp",
@@ -965,14 +1219,14 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "SplitLongMask":"Splitting a long image into sections",
     "VAELoaderConsistencyDecoder":"Consistency Decoder Loader",
     "VAEDecodeConsistencyDecoder":"Consistency Decoder Decode",
-    "ScreenShare":"Screen Share ♾️Mixlab",
-    "FloatingVideo":"FloatingVideo ♾️Mixlab",
-    "ChatGPTOpenAI":"ChatGPT & Local LLM ♾️Mixlab",
-    "ShowTextForGPT":"Show Text ♾️MixlabApp",
+    
+    
     "MergeLayers":"Merge Layers ♾️Mixlab",
     "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
     "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
     "3DImage":"3DImage ♾️Mixlab",
+    "ImageListToBatch_":"Image List To Batch",
+    "ImageBatchToList_":"Image Batch To List",
     "CompositeImages_":"Composite Images ♾️Mixlab",
     "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
     "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
@@ -998,28 +1252,69 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "GridInput":"Grid Input ♾️Mixlab",
     "GridOutput":"Grid Output ♾️Mixlab",
     "GetImageSize_":"Get Image Size ♾️Mixlab",
-    "VAEEncodeForInpaint_Frames":"VAE Encode For Inpaint Frames ♾️Mixlab",
     "IncrementingListNode_":"Create Incrementing Number List ♾️Mixlab",
     "LoadImagesToBatch":"Load Images(base64) ♾️Mixlab",
     "PreviewMask_":"Preview Mask",
-    "LoadTripoSRModel_": "Load TripoSR Model",
-    "TripoSRSampler_": "TripoSR Sampler",
-    "SaveTripoSRMesh": "Save TripoSR Mesh"
+    "AudioPlay":"Preview Audio ♾️Mixlab",
+
+     "MultiplicationNode":"Math Operation ♾️Mixlab",
+
+     "P5Input":"P5 Input ♾️Mixlab for test"
 }
 
 # web ui的节点功能
 WEB_DIRECTORY = "./web"
 
-
 logging.info('--------------')
 logging.info('\033[91m ### Mixlab Nodes: \033[93mLoaded')
 # print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
 
 try:
-    from .nodes.Lama import LaMaInpainting
-    logging.info('LaMaInpainting.available {}'.format(LaMaInpainting.available))
-    if LaMaInpainting.available:
-        NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
+    from .nodes.ChatGPT import JsonRepair,ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter,SiliconflowFreeNode
+    logging.info('ChatGPT.available True')
+
+    NODE_CLASS_MAPPINGS_V = { 
+       "ChatGPTOpenAI":ChatGPTNode,
+       "SiliconflowLLM":SiliconflowFreeNode,
+        "ShowTextForGPT":ShowTextForGPT,
+        "CharacterInText":CharacterInText,
+        "TextSplitByDelimiter":TextSplitByDelimiter,
+        "JsonRepair":JsonRepair
+    }
+
+    # 一个包含节点友好/可读的标题的字典
+    NODE_DISPLAY_NAME_MAPPINGS_V = { 
+        "ChatGPTOpenAI":"ChatGPT & Local LLM ♾️Mixlab",
+        "SiliconflowLLM":"LLM Siliconflow ♾️Mixlab",
+        "ShowTextForGPT":"Show Text ♾️MixlabApp",
+        "CharacterInText":"Character In Text",
+        "TextSplitByDelimiter":"Text Split By Delimiter",
+        "JsonRepair":"Json Repair"
+    }
+
+
+    NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_V)
+    NODE_DISPLAY_NAME_MAPPINGS.update(NODE_DISPLAY_NAME_MAPPINGS_V)
+
+except Exception as e:
+    logging.info('ChatGPT.available False')
+
+
+try:
+    from .nodes.edit_mask import EditMask
+    logging.info('edit_mask.available True')
+    NODE_CLASS_MAPPINGS['EditMask']=EditMask
+    NODE_DISPLAY_NAME_MAPPINGS['EditMask']="Edit Mask ♾️Mixlab"
+except Exception as e:
+    logging.info('edit_mask.available False')
+
+try:
+    is_has=is_installed('simple_lama_inpainting',None,False)
+    if is_has:
+        from .nodes.Lama import LaMaInpainting
+        logging.info('LaMaInpainting.available {}'.format(LaMaInpainting.available))
+        if LaMaInpainting.available:
+            NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
 except Exception as e:
     logging.info('LaMaInpainting.available False')
 
@@ -1050,4 +1345,68 @@ try:
 except Exception as e:
     logging.info('RembgNode_.available False' )
 
+
+try:
+    from .nodes.Video import GenerateFramesByCount,scenesNode_,CombineAudioVideo,VideoCombine_Adv,LoadVideoAndSegment,ImageListReplace,VAEEncodeForInpaint_Frames,LoadAndCombinedAudio_
+    
+    NODE_CLASS_MAPPINGS_V = {
+        "VAEEncodeForInpaint_Frames":VAEEncodeForInpaint_Frames,
+        "ImageListReplace_":ImageListReplace,
+        "LoadVideoAndSegment_":LoadVideoAndSegment,
+        "VideoCombine_Adv":VideoCombine_Adv,
+        "LoadAndCombinedAudio_":LoadAndCombinedAudio_,
+        "CombineAudioVideo":CombineAudioVideo,
+        "ScenesNode_":scenesNode_,
+        "GenerateFramesByCount":GenerateFramesByCount 
+    }
+
+    # 一个包含节点友好/可读的标题的字典
+    NODE_DISPLAY_NAME_MAPPINGS_V = {
+        "VAEEncodeForInpaint_Frames":"VAE Encode For Inpaint Frames ♾️Mixlab",
+        "ImageListReplace_":"Image List Replace",
+        "LoadVideoAndSegment_":"Load Video And Segment",
+        "VideoCombine_Adv":"Video Combine",
+        "LoadAndCombinedAudio_":"Load And Combined Audio",
+        "CombineAudioVideo":"Combine Audio Video",
+        "ScenesNode_":"Select Scene", 
+        "GenerateFramesByCount":"Generate Frames By Count"
+    }
+
+
+    NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_V)
+    NODE_DISPLAY_NAME_MAPPINGS.update(NODE_DISPLAY_NAME_MAPPINGS_V)
+
+except:
+    logging.info('Video.available False')
+
+
+try:
+    from .nodes.TripoSR import LoadTripoSRModel,TripoSRSampler,SaveTripoSRMesh
+    logging.info('TripoSR.available')
+    # logging.info( folder_paths.get_temp_directory())
+    NODE_CLASS_MAPPINGS['LoadTripoSRModel_']=LoadTripoSRModel
+    NODE_DISPLAY_NAME_MAPPINGS["LoadTripoSRModel_"]= "Load TripoSR Model"
+    
+    NODE_CLASS_MAPPINGS['TripoSRSampler_']=TripoSRSampler
+    NODE_DISPLAY_NAME_MAPPINGS["TripoSRSampler_"]= "TripoSR Sampler"
+
+    NODE_CLASS_MAPPINGS['SaveTripoSRMesh']=SaveTripoSRMesh
+    NODE_DISPLAY_NAME_MAPPINGS["SaveTripoSRMesh"]= "Save TripoSR Mesh"
+    
+
+except Exception as e:
+    logging.info('TripoSR.available False' )
+
+from .nodes.MiniCPMNode import MiniCPM_VQA_Simple
+try:
+    
+    logging.info('MiniCPMNode.available')
+    # logging.info( folder_paths.get_temp_directory())
+    NODE_CLASS_MAPPINGS['MiniCPM_VQA_Simple']=MiniCPM_VQA_Simple
+    NODE_DISPLAY_NAME_MAPPINGS["MiniCPM_VQA_Simple"]= "MiniCPM VQA Simple"
+    
+except Exception as e:
+    logging.info('MiniCPMNode.available False' )
+
+
 logging.info('\033[93m -------------- \033[0m')

install.bat

@@ -11,9 +11,9 @@ if exist "%python_exec%" (
         %python_exec% -s -m pip install "%%i" -i https://pypi.tuna.tsinghua.edu.cn/simple
     )
 
-    %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

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

nodes/ChatGPT.py

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

nodes/ClipInterrogator.py

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

nodes/ImageNode.py

@@ -1,12 +1,14 @@
 import numpy as np
 import requests
 import torch
+import torchvision.transforms.v2 as T
 # from PIL import Image, ImageDraw
 from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
 from PIL.PngImagePlugin import PngInfo
 import base64,os,random
 from io import BytesIO
 import folder_paths
+import node_helpers
 import json,io
 import comfy.utils
 from comfy.cli_args import args
@@ -14,8 +16,8 @@ import cv2
 import string
 import math,glob
 from .Watcher import FolderWatcher
-import hashlib
 
+from itertools import product
 
 
 # 将PIL图片转换为OpenCV格式
@@ -28,142 +30,105 @@ def opencv_to_pil(image):
     pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
     return pil_image
 
+# 列出目录下面的所有文件
+def get_files_with_extension(directory, extensions):
+    file_list = []
+    # 确保extensions参数是一个list，即使只有一个元素
+    if not isinstance(extensions, (tuple, list)):
+        extensions = [extensions]
+    for root, dirs, files in os.walk(directory):
+        # print(f"Files at {root}: {files}")  # 确认files是一个字符串列表
+        for file in files:
+            # 检查文件是否以任何一个提供的扩展名结尾
+            if any(file.endswith(ext) for ext in extensions):
+                # 直接将文件名添加到列表中
+                file_list.append(file)
+    return file_list
+	
+def composite_images(foreground, background, mask, is_multiply_blend=False, position="overall", scale=0.25):
+    width, height = foreground.size
+    bg_image = background
+    bwidth, bheight = bg_image.size
 
-def composite_images(foreground, background, mask,is_multiply_blend=False,position="overall"):
-    width,height=foreground.size
- 
-    bg_image=background
+    scale=max(scale,1/bwidth)
+    scale=max(scale,1/bheight)
 
-    bwidth,bheight=bg_image.size
+    def determine_scale_option(width, height):
+        return 'height' if height > width else 'width'
 
-    # 按z-index排序
-    if position=="overall":
+    if position == "overall":
         layer = {
-                "x":0,
-                "y":0,
-                "width":bwidth,
-                "height":bheight,
-                "z_index":88,
-                "scale_option":'overall',
-                "image":foreground,
-                "mask":mask
+            "x": 0,
+            "y": 0,
+            "width": bwidth,
+            "height": bheight,
+            "z_index": 88,
+            "scale_option": 'overall',
+            "image": foreground,
+            "mask": mask
         }
+    else:
+        scale_option = determine_scale_option(width, height)
+        if scale_option == 'height':
+            scale = int(bheight * scale) / height
+        else:
+            scale = int(bwidth * scale) / width
 
-    elif position=='center_bottom':
-        
-        scale = int(bwidth*0.25) / width
+        new_width = int(width * scale)
         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)
+        if position == 'center_bottom':
+            x_position = int((bwidth - new_width) * 0.5)
+            y_position = bheight - new_height - 24
+        elif position == 'right_bottom':
+            x_position = bwidth - new_width - 24
+            y_position = bheight - new_height - 24
+        elif position == 'center_top':
+            x_position = int((bwidth - new_width) * 0.5)
+            y_position = 24
+        elif position == 'right_top':
+            x_position = bwidth - new_width - 24
+            y_position = 24
+        elif position == 'left_top':
+            x_position = 24
+            y_position = 24
+        elif position == 'left_bottom':
+            x_position = 24
+            y_position = bheight - new_height - 24
+        elif position == 'center_center':
+            x_position = int((bwidth - new_width) * 0.5)
+            y_position = int((bheight - new_height) * 0.5)
 
         layer = {
-                "x":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
+            "x": x_position,
+            "y": y_position,
+            "width": new_width,
+            "height": new_height,
+            "z_index": 88,
+            "scale_option": scale_option,
+            "image": foreground,
+            "mask": mask
         }
 
+    layer_image = layer['image']
+    layer_mask = layer['mask']
 
-    elif position=='center_top':
-        
-        scale = int(bwidth*0.25) / width
-        new_height = int(height * scale)
+    bg_image = merge_images(bg_image,
+                            layer_image,
+                            layer_mask,
+                            layer['x'],
+                            layer['y'],
+                            layer['width'],
+                            layer['height'],
+                            layer['scale_option'],
+                            is_multiply_blend)
 
-        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
-        }
+    bg_image = bg_image.convert('RGB')
 
-    elif position=='right_top':
-        
-        scale = int(bwidth*0.25) / width
-        new_height = int(height * scale)
-
-        layer = {
-                "x":bwidth-int(bwidth*0.25)-24,
-                "y":24,
-                "width":int(bwidth*0.25),
-                "height":int(bheight*0.25),
-                "z_index":88,
-                "scale_option":'width',
-                "image":foreground,
-                "mask":mask
-        }
-    elif position=='left_top':
-        
-        scale = int(bwidth*0.25) / width
-        new_height = int(height * scale)
-
-        layer = {
-                "x":24,
-                "y":24,
-                "width":int(bwidth*0.25),
-                "height":int(bheight*0.25),
-                "z_index":88,
-                "scale_option":'width',
-                "image":foreground,
-                "mask":mask
-        }
-    elif position=='left_bottom':
-        
-        scale = int(bwidth*0.25) / width
-        new_height = int(height * scale)
-
-        layer = {
-                "x":24,
-                "y":bheight-new_height-24,
-                "width":int(bwidth*0.25),
-                "height":int(bheight*0.25),
-                "z_index":88,
-                "scale_option":'width',
-                "image":foreground,
-                "mask":mask
-        }
-                
-    # width, height = bg_image.size
-
-    layer_image=layer['image']
-    layer_mask=layer['mask']
-    
-    bg_image=merge_images(bg_image,
-                        layer_image,
-                        layer_mask,
-                        layer['x'],
-                        layer['y'],
-                        layer['width'],
-                        layer['height'],
-                        layer['scale_option'],
-                        is_multiply_blend )
-                   
-    bg_image=bg_image.convert('RGB')
-    
     return bg_image
 
 
+
 def count_files_in_directory(directory):
     file_count = 0
     for _, _, files in os.walk(directory):
@@ -200,7 +165,8 @@ class AnyType(str):
 any_type = AnyType("*")
 
 
-FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
+FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),"..","assets","fonts"))
+
 
 MAX_RESOLUTION=8192
 
@@ -526,6 +492,53 @@ def load_image(fp,white_bg=False):
         
     return images
 
+
+# 读取图片数据，转成tensor
+def load_image_to_tensor( image):
+    image_path = folder_paths.get_annotated_filepath(image)
+        
+    img = node_helpers.pillow(Image.open, image_path)
+        
+    output_images = []
+    output_masks = []
+    w, h = None, None
+
+    excluded_formats = ['MPO']
+        
+    for i in ImageSequence.Iterator(img):
+        i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+        if i.mode == 'I':
+            i = i.point(lambda i: i * (1 / 255))
+        image = i.convert("RGB")
+
+        if len(output_images) == 0:
+            w = image.size[0]
+            h = image.size[1]
+            
+        if image.size[0] != w or image.size[1] != h:
+            continue
+            
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        if 'A' in i.getbands():
+            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+            mask = 1. - torch.from_numpy(mask)
+        else:
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        output_images.append(image)
+        output_masks.append(mask.unsqueeze(0))
+
+    if len(output_images) > 1 and img.format not in excluded_formats:
+        output_image = torch.cat(output_images, dim=0)
+        output_mask = torch.cat(output_masks, dim=0)
+    else:
+        output_image = output_images[0]
+        output_mask = output_masks[0]
+
+    return (output_image, output_mask)
+
+
 def load_image_and_mask_from_url(url, timeout=10):
     # Load the image from the URL
     response = requests.get(url, timeout=timeout)
@@ -802,85 +815,78 @@ def multiply_blend(image1, image2):
 
 # cv2.imwrite('result.jpg', result)
 
+# 使用gpt4o优化代码
+# 为了消除图像合并时出现的灰色描边，可以使用以下方法：
+# 调整透明度：确保透明像素不会引入不需要的颜色。
+# 预处理图像：在缩放图像之前，可以先将图像的边缘进行预处理，例如扩展边缘颜色，减少抗锯齿带来的过渡效果。
 
-def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option,is_multiply_blend=False):
+def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option, is_multiply_blend=False):
     # 打开底图
     bg_image = bg_image.convert("RGBA")
 
     # 打开图层
     layer_image = layer_image.convert("RGBA")
-    # layer_image = layer_image.resize((width, height))
-
+   
     # 根据缩放选项调整图像大小
     if scale_option == "height":
         # 按照高度比例缩放
         original_width, original_height = layer_image.size
         scale = height / original_height
         new_width = int(original_width * scale)
-        layer_image = layer_image.resize((new_width, height))
+        layer_image = layer_image.resize((new_width, height), Image.NEAREST)
     elif scale_option == "width":
         # 按照宽度比例缩放
         original_width, original_height = layer_image.size
         scale = width / original_width
         new_height = int(original_height * scale)
-        layer_image = layer_image.resize((width, new_height))
+        layer_image = layer_image.resize((width, new_height), Image.NEAREST)
     elif scale_option == "overall":
         # 整体缩放
-        layer_image = layer_image.resize((width, height))
-
+        layer_image = layer_image.resize((width, height), Image.NEAREST)
     elif scale_option == "longest":
         original_width, original_height = layer_image.size
         if original_width > original_height:
-            new_width=width
+            new_width = width
             scale = width / original_width
             new_height = int(original_height * scale)
-            x=0
-            y=int((height-new_height)*0.5)
+            x = 0
+            y = int((height - new_height) * 0.5)
         else:
-            new_height=height
+            new_height = height
             scale = height / original_height
             new_width = int(original_height * scale)
-            x=int((width-new_width)*0.5)
-            y=0
-    # elif side == "shortest":
-    #         if width < height:
-    #              
-    #         else:
-    #              
-    
+            x = int((width - new_width) * 0.5)
+            y = 0
 
     # 调整mask的大小
     nw, nh = layer_image.size
-    mask = mask.resize((nw, nh))
+    mask = mask.resize((nw, nh), Image.NEAREST)
 
-    # # 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)
+    # 预处理图像边缘以减少灰色描边
+    layer_image = layer_image.filter(ImageFilter.SMOOTH)
 
     if is_multiply_blend:
-        bg_image_white=Image.new("RGB", bg_image.size,(255, 255, 255))
+        bg_image_white = Image.new("RGB", bg_image.size, (255, 255, 255))
 
         bg_image_white.paste(layer_image, (x, y), mask=mask)
-        bg_image=multiply_blend(bg_image_white,bg_image)
-        bg_image=bg_image.convert("RGBA")
+        bg_image = multiply_blend(bg_image_white, bg_image)
+        bg_image = bg_image.convert("RGBA")
     else:
-        transparent_img = Image.new("RGBA",layer_image.size, (255, 255, 255, 0))
-        transparent_img.paste(layer_image,(0, 0), mask)
-        # transparent_img.save('test.png')
-        bg_image.paste(transparent_img, (x, y), transparent_img)
+        transparent_img = Image.new("RGBA", layer_image.size, (255, 255, 255, 0))
+        # 调整透明度处理
+        for i in range(transparent_img.size[0]):
+            for j in range(transparent_img.size[1]):
+                r, g, b, a = transparent_img.getpixel((i, j))
+                if a > 0:
+                    transparent_img.putpixel((i, j), (r, g, b, 255))
 
+        transparent_img.paste(layer_image, (0, 0), mask)
+        bg_image.paste(transparent_img, (x, y), transparent_img)
 
     # 输出合成后的图片
     return bg_image
 
+#MixCopilot
 
 def resize_2(img):
     # 检查图像的高度是否是2的倍数，如果不是，则调整高度
@@ -954,53 +960,13 @@ def resize_image(layer_image, scale_option, width, height,color="white"):
     return layer_image
 
 
-
-
-
-
-# def generate_text_image(text_list, font_path, font_size, text_color, vertical=True, spacing=0):
-#     # Load Chinese font
-#     font = ImageFont.truetype(font_path, font_size)
-
-#     # Calculate image size based on the number of characters and orientation
-#     if vertical:
-#         width = font_size + 100
-#         height = font_size * len(text_list) + (len(text_list) - 1) * spacing + 100
-#     else:
-#         width = font_size * len(text_list) + (len(text_list) - 1) * spacing + 100
-#         height = font_size + 100
-
-#     # Create a blank image
-#     image = Image.new('RGBA', (width, height), (255, 255, 255,0))
-#     draw = ImageDraw.Draw(image)
-
-#     # Draw text
-#     if vertical:
-#         for i, char in enumerate(text_list):
-#             char_position = (50, 50 + i * font_size)
-#             draw.text(char_position, char, font=font, fill=text_color)
-#     else:
-#         for i, char in enumerate(text_list):
-#             char_position = (50 + i * (font_size + spacing), 50)
-#             draw.text(char_position, char, font=font, fill=text_color)
-
-#     # Save the image
-#     # image.save(output_image_path)
-
-#     # 分离alpha通道
-#     alpha_channel = image.split()[3]
-
-#     # 创建一个只有alpha通道的新图像
-#     alpha_image = Image.new('L', image.size)
-#     alpha_image.putdata(alpha_channel.getdata())
-
-#     image=image.convert('RGB')
-
-#     return (image,alpha_image)
-def generate_text_image(text, font_path, font_size, text_color, vertical=True, stroke=False, stroke_color=(0, 0, 0), stroke_width=1, spacing=0):
+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")
 
+    # Load font
+    font = ImageFont.truetype(font_path, font_size)
+
     # 1. Determine layout direction
     if vertical:
         layout = "vertical"
@@ -1009,49 +975,54 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s
 
     # 2. Calculate absolute coordinates for each character
     char_coordinates = []
-    if layout == "vertical":
-        x = 0
-        y = 0
-        for i in range(len(lines)):
-            line = lines[i]
-            for char in line:
-                char_coordinates.append((x, y))
-                y += font_size + spacing
-            x += font_size + spacing
-            y = 0
-    else:
-        x = 0
-        y = 0
-        for line in lines:
-            for char in line:
-                char_coordinates.append((x, y))
-                x += font_size + spacing
-            y += font_size + spacing
-            x = 0
+    x, y = padding, padding
+    max_width, max_height = 0, 0
 
-    # 3. Calculate image width and height
     if layout == "vertical":
-        width = (len(lines) * (font_size + spacing)) - spacing
-        height = ((len(max(lines, key=len)) + 1) * (font_size + spacing)) + spacing
+        for line in lines:
+            max_char_width = max(font.getsize(char)[0] for char in line)
+            for char in line:
+                char_width, char_height = font.getsize(char)
+                char_coordinates.append((x, y))
+                y += char_height + spacing
+                max_height = max(max_height, y + padding)
+            x += max_char_width + line_spacing
+            y = padding
+        max_width = x
+        total_line_width = sum(font.getsize(line)[1] for line in lines)
+        total_spacing = line_spacing * (len(lines) - 1)
+        # 确保左边和右边的padding都被计入max_width
+        max_width = total_line_width + total_spacing + padding * 2
     else:
-        width = (len(max(lines, key=len)) * (font_size + spacing)) - spacing
-        height = ((len(lines) - 1) * (font_size + spacing)) + font_size
+        for line in lines:
+            line_width, line_height = font.getsize(line)
+            for char in line:
+                char_width, char_height = font.getsize(char)
+                char_coordinates.append((x, y))
+                x += char_width + spacing
+                max_width = max(max_width, x + padding)
+            y += line_height + line_spacing
+            x = padding
+        # max_height = y
+        total_line_heights = sum(font.getsize(line)[1] for line in lines)
+        total_spacing = line_spacing * (len(lines) - 1)
+        # 确保顶部和底部的padding都被计入max_height
+        max_height = total_line_heights + total_spacing + padding * 2
+
+    # 3. Create image with calculated width and height
+    image = Image.new('RGBA', (max_width, max_height), (255, 255, 255, 0))
+    draw = ImageDraw.Draw(image)
 
     # 4. Draw each character on the image
-    image = Image.new('RGBA', (width, height), (255, 255, 255, 0))
-    draw = ImageDraw.Draw(image)
-    font = ImageFont.truetype(font_path, font_size)
-
     index = 0
-    for i, line in enumerate(lines):
-        for j, char in enumerate(line):
+    for line in lines:
+        for char in line:
             x, y = char_coordinates[index]
-            
             if stroke:
-                draw.text((x-stroke_width, y), char, font=font, fill=stroke_color)
-                draw.text((x+stroke_width, y), char, font=font, fill=stroke_color)
-                draw.text((x, y-stroke_width), char, font=font, fill=stroke_color)
-                draw.text((x, y+stroke_width), char, font=font, fill=stroke_color)
+                draw.text((x-stroke_width, y), char, font=font, fill=text_color)
+                draw.text((x+stroke_width, y), char, font=font, fill=text_color)
+                draw.text((x, y-stroke_width), char, font=font, fill=text_color)
+                draw.text((x, y+stroke_width), char, font=font, fill=text_color)
             
             draw.text((x, y), char, font=font, fill=text_color)
             index += 1
@@ -1376,6 +1347,9 @@ 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:]:
@@ -1578,7 +1552,7 @@ class ImageCropByAlpha:
 
 
 
-
+# get_files_with_extension(FONT_PATH,'.ttf')
 
 class TextImage:
     @classmethod
@@ -1586,18 +1560,32 @@ class TextImage:
         return {"required": { 
             
                     "text": ("STRING",{"multiline": True,"default": "龍馬精神迎新歲","dynamicPrompts": False}),
-                    "font_path": ("STRING",{"multiline": False,"default": FONT_PATH,"dynamicPrompts": False}),
+                    "font": (get_files_with_extension(FONT_PATH,['.ttf','.otf']),),#后缀为 ttf
                     "font_size": ("INT",{
                                 "default":100, 
-                                "min": 100, #Minimum value
-                                "max": 1000, #Maximum value
+                                "min": 1, #Minimum value
+                                "max": 10000000, #Maximum value
                                 "step": 1, #Slider's step
                                 "display": "number" # Cosmetic only: display as "number" or "slider"
                                 }), 
                     "spacing": ("INT",{
                                 "default":12, 
-                                "min": -200, #Minimum value
-                                "max": 200, #Maximum value
+                                "min": -2000000000, #Minimum value
+                                "max": 2000000000, #Maximum value
+                                "step": 1, #Slider's step
+                                "display": "number" # Cosmetic only: display as "number" or "slider"
+                                }), 
+                    "line_spacing": ("INT",{
+                                "default":12, 
+                                "min": -2000000000, #Minimum value
+                                "max": 2000000000, #Maximum value
+                                "step": 1, #Slider's step
+                                "display": "number" # Cosmetic only: display as "number" or "slider"
+                                }), 
+                    "padding": ("INT",{
+                                "default":8, 
+                                "min": 0, #Minimum value
+                                "max": 2000000000, #Maximum value
                                 "step": 1, #Slider's step
                                 "display": "number" # Cosmetic only: display as "number" or "slider"
                                 }), 
@@ -1608,7 +1596,7 @@ class TextImage:
                 }
     
     RETURN_TYPES = ("IMAGE","MASK",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_NAMES = ("image","mask",)
 
     FUNCTION = "run"
 
@@ -1617,11 +1605,14 @@ class TextImage:
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,)
 
-    def run(self,text,font_path,font_size,spacing,text_color,vertical,stroke):
+    def run(self,text,font,font_size,spacing,line_spacing,padding,text_color,vertical,stroke):
         
-        # text_list=list(text)
+        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)
+        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)
@@ -1655,7 +1646,7 @@ class LoadImagesFromURL:
 
     def run(self,url,seed=0):
         global urls_image
-        print(urls_image)
+        # print(urls_image)
         def filter_http_urls(urls):
             filtered_urls = []
             for url in urls.split('\n'):
@@ -1744,28 +1735,51 @@ class Image3D:
     def run(self,upload,material=None):
         # print('material',material)
         # print(upload )
-        image = base64_to_image(upload['image'])
 
-        mat=None
-        if 'material' in upload and upload['material']:
-            mat=base64_to_image(upload['material'])
-            mat=mat.convert('RGB')
-            mat=pil2tensor(mat)
+        # 截取的系列角度截图
+        images=upload['images'] if "images" in upload else []
 
-        mask = image.split()[3]
-        image=image.convert('RGB')
+        ims=[]
+        for im in images:
+            if 'type' in im and (not f"[{im['type']}]" in im['name']):
+                im['name']=im['name']+" "+f"[{im['type']}]"
+            output_image, output_mask = load_image_to_tensor(im['name'])
+            ims.append(output_image)
         
-        mask=mask.convert('L')
-
+        
+        mask=None
         bg_image=None
-        if 'bg_image' in upload and upload['bg_image']:
-            bg_image = base64_to_image(upload['bg_image'])
-            bg_image=bg_image.convert('RGB')
-            bg_image=pil2tensor(bg_image)
+        mat=None
+
+        # 如果没有系列截图
+        if len(ims)==0:
+            # 这个是3d模型当前截图
+            image = base64_to_image(upload['image'])
+
+            
+            if 'material' in upload and upload['material']:
+                mat=base64_to_image(upload['material'])
+                mat=mat.convert('RGB')
+                mat=pil2tensor(mat)
+
+            mask = image.split()[3]
+            image=image.convert('RGB')
+            
+            mask=mask.convert('L')
+
+            
+            if 'bg_image' in upload and upload['bg_image']:
+                bg_image = base64_to_image(upload['bg_image'])
+                bg_image=bg_image.convert('RGB')
+                bg_image=pil2tensor(bg_image)
 
 
-        mask=pil2tensor(mask)
-        image=pil2tensor(image)
+            mask=pil2tensor(mask)
+            image=pil2tensor(image)
+        else:
+            
+            image = torch.cat(ims, dim=0)
+
         
         m=[]
         if not material is None:
@@ -1854,10 +1868,16 @@ class CompositeImages:
                     "mask":("MASK",),
                     "background": ("IMAGE",),
                     },
-             "optional":{
-                           
+             "optional":{ 
                   "is_multiply_blend":  ("BOOLEAN", {"default": False}),
-                  "position":  (['overall',"center_bottom","center_top","right_bottom","left_bottom","right_top","left_top"],),    
+                  "position":  (['overall',"center_center","left_bottom","center_bottom","right_bottom","left_top","center_top","right_top"],),
+                   "scale": ("FLOAT",{
+                                "default":0.35, 
+                                "min": 0.01, #Minimum value
+                                "max": 1, #Maximum value
+                                "step": 0.01, #Slider's step
+                                "display": "number" # Cosmetic only: display as "number" or "slider"
+                            }), 
                     }
                 }
     
@@ -1870,15 +1890,30 @@ class CompositeImages:
 
     # OUTPUT_IS_LIST = (True,)
 
-    def run(self, foreground,mask,background,is_multiply_blend,position):
-        foreground= tensor2pil(foreground)
-        mask= tensor2pil(mask)
-        background= tensor2pil(background)
-        res=composite_images(foreground,background,mask,is_multiply_blend,position)
+    # def run(self, foreground,mask,background,is_multiply_blend,position,scale):
+    #     foreground= tensor2pil(foreground)
+    #     mask= tensor2pil(mask)
+    #     background= tensor2pil(background)
+    #     res=composite_images(foreground,background,mask,is_multiply_blend,position,scale)
         
-        return (pil2tensor(res),)
+    #     return (pil2tensor(res),)
 
+    def run(self, foreground,mask,background, is_multiply_blend, position, scale):
+        results = []
+        f1=[]
+        for fg, mask in zip(foreground, mask ):
+            f1.append([fg,mask])
+        for f, bg in product(f1, background):
+            [fg,mask]=f
+            fg_pil = tensor2pil(fg)
+            mask_pil = tensor2pil(mask)
+            bg_pil = tensor2pil(bg)
+            res = composite_images(fg_pil, bg_pil, mask_pil, is_multiply_blend, position, scale)
+            results.append(pil2tensor(res))
+        
+        output_image = torch.cat(results, dim=0)
 
+        return (output_image,)
 
 
 class EmptyLayer:
@@ -3207,3 +3242,144 @@ class SaveImageToLocal:
             counter += 1
 
         return ()
+
+
+class ImageBatchToList_:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"image_batch": ("IMAGE",), }}
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image_list",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Image"
+
+    def run(self, image_batch):
+        images = [image_batch[i:i + 1, ...] for i in range(image_batch.shape[0])]
+        return (images, )
+
+class ImageListToBatch_:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "run"
+    INPUT_IS_LIST = True
+    CATEGORY = "♾️Mixlab/Image"
+
+    def run(self, images):
+        shape = images[0].shape[1:3]
+        out = []
+
+        for i in range(len(images)):
+            img = images[i].permute([0,3,1,2])
+            if images[i].shape[1:3] != shape:
+                transforms = T.Compose([
+                    T.CenterCrop(min(img.shape[2], img.shape[3])),
+                    T.Resize((shape[0], shape[1]), interpolation=T.InterpolationMode.BICUBIC),
+                ])
+                img = transforms(img)
+            out.append(img.permute([0,2,3,1]))
+
+        out = torch.cat(out, dim=0)
+
+        return (out,)
+
+
+# https://github.com/gokayfem/ComfyUI-Depth-Visualization?tab=readme-ov-file
+class DepthViewer_:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "depth_map": ("IMAGE",),
+
+            },
+             "optional":{
+                  "frames":("IMAGEBASE64",), 
+                },
+        }
+
+    def __init__(self):
+        self.saved_reference = []
+        self.saved_depth = []
+
+        self.full_output_folder,self.filename,self.counter, self.subfolder, self.filename_prefix = folder_paths.get_save_image_path(
+            "imagesave", 
+            folder_paths.get_output_directory())
+
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("frames",)
+    
+    OUTPUT_NODE = True
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/3D"
+    def run(self, image, depth_map,frames=None):
+        self.saved_reference.clear()
+        self.saved_depth.clear()
+        image = image[0].detach().cpu().numpy()
+        depth = depth_map[0].detach().cpu().numpy()
+
+        image = Image.fromarray(np.clip(255. * image, 0, 255).astype(np.uint8)).convert('RGB')
+        depth = Image.fromarray(np.clip(255. * depth, 0, 255).astype(np.uint8))
+
+        return self.display([image], [depth],frames)
+
+    def display(self, reference_image, depth_map,frames):
+        for (batch_number, (single_image, single_depth)) in enumerate(zip(reference_image, depth_map)):
+            filename_with_batch_num = self.filename.replace("%batch_num%", str(batch_number))
+
+            image_file = f"{filename_with_batch_num}_{self.counter:05}_reference.png"
+            single_image.save(os.path.join(self.full_output_folder, image_file))
+
+            depth_file = f"{filename_with_batch_num}_{self.counter:05}_depth.png"
+            single_depth.save(os.path.join(self.full_output_folder, depth_file))
+
+            self.saved_reference.append({
+                "filename": image_file,
+                "subfolder": self.subfolder,
+                "type": "output"
+            })
+
+            self.saved_depth.append({
+                "filename": depth_file,
+                "subfolder": self.subfolder,
+                "type": "output"
+            })
+            self.counter += 1
+
+
+        ims=[]
+        image1 = Image.new('RGB', (512, 512), color='black')
+        image1=pil2tensor(image1)
+
+        if frames!=None:
+            for im in frames['images']:
+                # print(im)
+                if 'type' in im and (not f"[{im['type']}]" in im['name']):
+                    im['name']=im['name']+" "+f"[{im['type']}]"
+                    
+                output_image, output_mask = load_image_to_tensor(im['name'])
+                ims.append(output_image)
+
+            if len(ims)>0:
+                image1 = ims[0]
+                for image2 in ims[1:]:
+                    if image1.shape[1:] != image2.shape[1:]:
+                        image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
+                    image1 = torch.cat((image1, image2), dim=0)
+
+        return {"ui": {"reference_image": self.saved_reference, "depth_map": self.saved_depth}, "result": (image1,)}

nodes/Lama.py

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

nodes/MiniCPMNode.py

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

nodes/P5.py

@@ -0,0 +1,104 @@
+import torch
+import numpy as np
+from PIL import Image,ImageSequence,ImageOps
+import base64
+import io
+import comfy.utils
+import folder_paths
+import node_helpers
+
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+def load_image_to_tensor( image):
+    image_path = folder_paths.get_annotated_filepath(image)
+        
+    img = node_helpers.pillow(Image.open, image_path)
+        
+    output_images = []
+    output_masks = []
+    w, h = None, None
+
+    excluded_formats = ['MPO']
+        
+    for i in ImageSequence.Iterator(img):
+        i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+        if i.mode == 'I':
+            i = i.point(lambda i: i * (1 / 255))
+        image = i.convert("RGB")
+
+        if len(output_images) == 0:
+            w = image.size[0]
+            h = image.size[1]
+            
+        if image.size[0] != w or image.size[1] != h:
+            continue
+            
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        if 'A' in i.getbands():
+            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+            mask = 1. - torch.from_numpy(mask)
+        else:
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        output_images.append(image)
+        output_masks.append(mask.unsqueeze(0))
+
+    if len(output_images) > 1 and img.format not in excluded_formats:
+        output_image = torch.cat(output_images, dim=0)
+        output_mask = torch.cat(output_masks, dim=0)
+    else:
+        output_image = output_images[0]
+        output_mask = output_masks[0]
+
+    return (output_image, output_mask)
+
+          
+
+class P5Input:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "frames":("IMAGEBASE64",), 
+                             },
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("frames",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Input"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self, frames):
+        ims=[]
+        for im in frames['images']:
+            # print(im)
+            if 'type' in im and (not f"[{im['type']}]" in im['name']):
+                im['name']=im['name']+" "+f"[{im['type']}]"
+                
+            output_image, output_mask = load_image_to_tensor(im['name'])
+            ims.append(output_image)
+
+        if len(ims)==0:
+            image1 = Image.new('RGB', (512, 512), color='black')
+            return (pil2tensor(image1),)
+        image1 = ims[0]
+        for image2 in ims[1:]:
+            if image1.shape[1:] != image2.shape[1:]:
+                image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
+            image1 = torch.cat((image1, image2), dim=0)
+
+        # 用于节点提示:p5节点提示有多少帧
+        return {"ui": {"_info": [len(frames['images'])]}, "result": (image1,)}

nodes/PromptNode.py

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

nodes/RembgNode.py

@@ -467,10 +467,14 @@ class BriaRMBG(nn.Module):
 
 
 
+def get_U2NET_model_path():
+    try:
+        return folder_paths.get_folder_paths('rembg')[0]
+    except:
+        return os.path.join(folder_paths.models_dir, "rembg")
+    
 
-
-
-U2NET_HOME=os.path.join(folder_paths.models_dir, "rembg")
+U2NET_HOME=get_U2NET_model_path()
 os.environ["U2NET_HOME"] = U2NET_HOME
 
 global _available
@@ -528,7 +532,7 @@ except:
 
 
 def run_briarmbg(images=[]):
-    mroot=os.path.join(folder_paths.models_dir, "rembg")
+    mroot=U2NET_HOME
     m=os.path.join(mroot,'briarmbg.pth')
     if os.path.exists(m)==False:
         # 下载

nodes/ScreenShareNode.py

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

nodes/TextGenerateNode.py

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

nodes/TripoSR.py

@@ -5,13 +5,20 @@ from PIL import Image
 import numpy as np
 import torch
 
-from folder_paths import get_filename_list, get_full_path, get_save_image_path, get_output_directory,models_dir
+from folder_paths import get_folder_paths, get_full_path, get_save_image_path, get_output_directory,models_dir
 from comfy.model_management import get_torch_device
 from .tsr.system import TSR
 
 import comfy.utils
 
-triposr_model_path=path.join(models_dir,'triposr/model.ckpt')
+
+def get_triposr_model_path():
+    try:
+        return path.join(get_folder_paths('triposr')[0],'model.ckpt')
+    except:
+        return path.join(path.join(models_dir, "triposr"),'model.ckpt')
+    
+triposr_model_path=get_triposr_model_path()
 
 
 # Tensor to PIL

nodes/Utils.py

@@ -133,7 +133,7 @@ def get_font_files(directory):
 
     return font_files
 
-r_directory = os.path.join(os.path.dirname(__file__), '../assets/')
+r_directory = os.path.join(os.path.dirname(__file__), '..','assets','/')
 
 font_files = get_font_files(r_directory)
 # print(font_files)
@@ -181,6 +181,28 @@ class ColorInput:
         return (h,r,g,b,a,)
 
 
+class KeyInput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "key":("KEY",), 
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("key",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Input"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,key):
+        return (key,)
+
+
 
 class FontInput:
     @classmethod
@@ -566,7 +588,7 @@ class AppInfo:
                              },
 
                 "optional":{
-                    "IMAGE": ("IMAGE",),
+                    "image": ("IMAGE",),
                     "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
                     "version":("INT", {
                         "default": 1, 
@@ -594,12 +616,12 @@ class AppInfo:
     INPUT_IS_LIST = True
     # OUTPUT_IS_LIST = (True,)
 
-    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
+    def run(self,name,input_ids,output_ids,image,description,version,share_prefix,link,category,auto_save):
         name=name[0]
         
         im=None
-        if IMAGE:
-            im=IMAGE[0][0]
+        if image:
+            im=image[0][0]
             #TODO batch 的方式需要处理
             im=create_temp_file(im)
         # image [img,] img[batch,w,h,a] 列表里面是batch，

nodes/Video.py

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

nodes/edit_mask.py

@@ -0,0 +1,172 @@
+import torch
+from PIL import Image, ImageOps, ImageSequence, ImageFile
+from PIL.PngImagePlugin import PngInfo
+
+import numpy as np
+import os
+import folder_paths 
+import node_helpers
+import hashlib
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# tensor 取hash值
+def tensor_to_hash(tensor):
+    # 将 Tensor 转换为 NumPy 数组
+    np_array = tensor.cpu().numpy()
+    
+    # 将 NumPy 数组转换为字节数据
+    byte_data = np_array.tobytes()
+    
+    # 计算哈希值
+    hash_value = hashlib.md5(byte_data).hexdigest()
+    
+    return hash_value
+
+
+def create_temp_file(image):
+    output_dir = folder_paths.get_temp_directory()
+
+    (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('material', output_dir)
+
+    
+    image=tensor2pil(image)
+ 
+    image_file = f"{filename}_{counter:05}.png"
+     
+    image_path=os.path.join(full_output_folder, image_file)
+
+    image.save(image_path,compress_level=4)
+
+    return (image_path,[{
+    "filename": image_file,
+    "subfolder": subfolder,
+    "type": "temp"
+    }])
+
+
+# image - tensor - 文件路径
+# loadImage的方法（ 文件路径 - image-mask ）
+class EditMask:
+
+    def __init__(self):
+        self.image_id = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"image": ("IMAGE",), # 表示一个张量
+                     
+                     },
+
+                    "optional":{
+                            "image_update": ("IMAGE_FILE",)
+                        },
+                   
+                }
+
+    CATEGORY = "♾️Mixlab/Mask"
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    RETURN_NAMES = ("image", "mask")
+
+    FUNCTION = "edit"
+
+    OUTPUT_NODE = True
+
+    def edit(self, image,image_update=None):
+
+        # 根据image输入来判断是否是新的图片
+        if self.image_id==None:
+            self.image_id=tensor_to_hash(image)
+            image_update=None
+        else:
+            image_id=tensor_to_hash(image)
+            if image_id!=self.image_id:
+                image_update=None
+                self.image_id=image_id
+
+
+        image_path=None
+        # print('#image_update',self.image_id,image_update)
+        if image_update==None:
+            print('--')
+        else:
+            if 'images' in image_update:
+                images=image_update['images']
+                filename=images[0]['filename']
+                subfolder=images[0]['subfolder']
+                type=images[0]['type']
+                name, base_dir=folder_paths.annotated_filepath(filename)
+                if type.endswith("output"):
+                    base_dir = folder_paths.get_output_directory() 
+                elif type.endswith("input"):
+                    base_dir = folder_paths.get_input_directory() 
+                elif type.endswith("temp"):
+                    base_dir = folder_paths.get_temp_directory() 
+                #base_dir = folder_paths.get_input_directory()  
+                # print(base_dir,subfolder, name)
+                image_path = os.path.join(base_dir,subfolder, name)
+        
+        if image_path==None:
+            image_path,images=create_temp_file(image)
+
+        print('#image_path',os.path.exists(image_path),image_path)
+        # image_path = folder_paths.get_annotated_filepath(image) #文件名
+        
+        if not os.path.exists(image_path):
+            image_path,images=create_temp_file(image)
+
+
+        img = node_helpers.pillow(Image.open, image_path)
+        
+        output_images = []
+        output_masks = []
+        w, h = None, None
+
+        excluded_formats = ['MPO']
+        
+        for i in ImageSequence.Iterator(img):
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
+            image = i.convert("RGB")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+            
+            if image.size[0] != w or image.size[1] != h:
+                continue
+            
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                # 尺寸不对，需要按照image来
+                mask = torch.zeros((h, w), dtype=torch.float32, device="cpu")
+
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
+
+        if len(output_images) > 1 and img.format not in excluded_formats:
+            output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
+        else:
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+
+        return {"ui":{"images": images},"result": (output_image, output_mask)}
+
+        # return (output_image, output_mask)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-mixlab-nodes"
 description = "3D, ScreenShareNode & FloatingVideoNode, SpeechRecognition & SpeechSynthesis, GPT, LoadImagesFromLocal, Layers, Other Nodes, ..."
-version = "0.28.1"
+version = "0.39.0"
 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"]
 

requirements.txt

@@ -4,7 +4,7 @@ watchdog
 opencv-python-headless
 matplotlib
 openai
-simple-lama-inpainting
+# simple-lama-inpainting
 clip-interrogator==0.6.0
 transformers>=4.36.0
 lark-parser
@@ -15,4 +15,11 @@ Pillow>=9.5.0
 einops==0.7.0
 trimesh>=4.0.5
 huggingface-hub
-scikit-image
+scikit-image
+torchaudio
+soundfile>=0.12.1
+json-repair
+
+decord
+bitsandbytes
+accelerate

web/javascript/3d_mixlab.js

@@ -2,6 +2,8 @@ import { app } from '../../../scripts/app.js'
 import { api } from '../../../scripts/api.js'
 import { $el } from '../../../scripts/ui.js'
 
+import { loadExternalScript } from './common.js'
+
 const getLocalData = key => {
   let data = {}
   try {
@@ -26,7 +28,8 @@ const setLocalDataOfWin = (key, value) => {
   localStorage.setItem(key, JSON.stringify(value))
   // window[key] = value
 }
-async function uploadImage (blob, fileType = '.svg', filename) {
+
+async function uploadImage_ (blob, fileType = '.svg', filename) {
   // const blob = await (await fetch(src)).blob();
   const body = new FormData()
   body.append(
@@ -41,13 +44,17 @@ async function uploadImage (blob, fileType = '.svg', filename) {
 
   // console.log(resp)
   let data = await resp.json()
+  return data
+}
+
+async function uploadImage (blob, fileType = '.svg', filename) {
+  let data = await uploadImage_(blob, fileType, filename)
   let { name, subfolder } = data
   let src = api.apiURL(
     `/view?filename=${encodeURIComponent(
       name
     )}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
   )
-
   return src
 }
 
@@ -94,7 +101,10 @@ function get_position_style (ctx, widget_width, y, node_height) {
   return {
     transformOrigin: '0 0',
     transform: transform,
-    left: `0`,
+    left:
+      document.querySelector('.comfy-menu').style.display === 'none'
+        ? `60px`
+        : `0`,
     top: `0`,
     cursor: 'pointer',
     position: 'absolute',
@@ -171,6 +181,42 @@ async function changeMaterial (
   targetMaterial.pbrMetallicRoughness.baseColorTexture.setTexture(targetTexture)
 }
 
+function inputFileClick (isFileURL = false, isGlb = false) {
+  return new Promise((res, rej) => {
+    // 创建一个input元素
+    var input = document.createElement('input')
+    input.type = 'file'
+    input.accept = isGlb ? '.glb' : 'image/*'
+
+    // 监听input的change事件
+    input.addEventListener('change', function () {
+      // 获取上传的文件
+      var file = input.files[0]
+
+      if (isFileURL) {
+        res(URL.createObjectURL(file))
+        return
+      }
+
+      // 创建一个FileReader对象来读取文件
+      var reader = new FileReader()
+
+      // 监听FileReader的load事件
+      reader.addEventListener('load', async () => {
+        let base64 = reader.result
+        input.remove()
+        res(base64)
+      })
+
+      // 读取文件
+      reader.readAsDataURL(file)
+    })
+
+    // 触发input的点击事件
+    input.click()
+  })
+}
+
 app.registerExtension({
   name: 'Mixlab.3D.3DImage',
   async getCustomWidgets (app) {
@@ -189,7 +235,7 @@ app.registerExtension({
             let d = getLocalData('_mixlab_3d_image')
             // console.log('serializeValue', node)
             if (d && d[node.id]) {
-              let { url, bg, material } = d[node.id]
+              let { url, bg, material, images } = d[node.id]
               let data = {}
               if (url) {
                 data.image = await parseImage(url)
@@ -205,6 +251,10 @@ app.registerExtension({
                 data.material = await parseImage(material)
               }
 
+              if (images) {
+                data.images = images
+              }
+
               return JSON.parse(JSON.stringify(data))
             } else {
               return {}
@@ -221,6 +271,11 @@ app.registerExtension({
     if (nodeType.comfyClass == '3DImage') {
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = async function () {
+        await loadExternalScript(
+          '/mixlab/app/lib/model-viewer.min.js',
+          'module'
+        )
+
         orig_nodeCreated?.apply(this, arguments)
 
         const uploadWidget = this.widgets.filter(w => w.name == 'upload')[0]
@@ -243,39 +298,29 @@ app.registerExtension({
 
         const inputDiv = (key, placeholder, preview) => {
           let div = document.createElement('div')
-          const ip = document.createElement('input')
-          ip.type = 'file'
+          const ip = document.createElement('button')
           ip.className = `${'comfy-multiline-input'} ${placeholder}`
           div.style = `display: flex;
             align-items: center; 
             margin: 6px 8px;
             margin-top: 0;`
-          ip.placeholder = placeholder
-          // ip.value = value
 
           ip.style = `outline: none;
             border: none;
             padding: 4px;
-            width: 60%;cursor: pointer;
+            width: 100px;cursor: pointer;
             height: 32px;`
-          const label = document.createElement('label')
-          label.style = 'font-size: 10px;min-width:32px'
-          label.innerText = placeholder
-          div.appendChild(label)
+          ip.innerText = placeholder
           div.appendChild(ip)
 
-          let that = this,
-            filename = new Date().getTime()
+          let that = this
 
-          ip.addEventListener('change', async event => {
-            const file = event.target.files[0]
-            const reader = new FileReader()
-            filename = new Date().getTime()
-            // 读取文件内容
-            reader.onload = async e => {
-              const fileURL = URL.createObjectURL(file)
-              // console.log('文件URL: ', fileURL)
-              let html = `<model-viewer  src="${fileURL}" 
+          ip.addEventListener('click', async event => {
+            let fileURL = await inputFileClick(true, true)
+
+            // console.log('文件URL: ', fileURL)
+            let html = `<model-viewer  src="${fileURL}" 
+                oncontextmenu="return false;"
                 min-field-of-view="0deg" max-field-of-view="180deg"
                  shadow-intensity="1" 
                  camera-controls 
@@ -285,230 +330,303 @@ app.registerExtension({
                   <div>Variant: <select class="variant"></select></div>
                   <div>Material: <select class="material"></select></div>
                   <div>Material: <div class="material_img"> </div></div>
-                  <div><button class="bg">BG</button></div>
+                  <div>
+                    <button class="bg">BG</button>
+                   
+                  </div>
+                  <div>
+                   <input class="ddcap_step" type="number" min="1" max="20" step="1" value="1">
+                   <input class="total_images" type="number" min="1" max="180" step="1" value="40">
+                   <input class="ddcap_range" type="range" min="-180" max="180" step="1" value="0">
+                   <input class="ddcap_range_top" type="range" min="-180" max="180" step="1" value="0">
+                  <button class="ddcap">Capture Rotational Screenshots</button></div>
+                  
                   <div><button class="export">Export GLB</button></div>
                   
                 </div></model-viewer>`
 
-              preview.innerHTML = html
-              if (that.size[1] < 400) {
-                that.setSize([that.size[0], that.size[1] + 300])
-                app.canvas.draw(true, true)
-              }
-
-              const modelViewerVariants = preview.querySelector('model-viewer')
-              const select = preview.querySelector('.variant')
-              const selectMaterial = preview.querySelector('.material')
-              const material_img = preview.querySelector('.material_img')
-              const bg = preview.querySelector('.bg')
-              const exportGLB = preview.querySelector('.export')
-
-              if (modelViewerVariants) {
-                modelViewerVariants.style.width = `${that.size[0] - 24}px`
-                modelViewerVariants.style.height = `${that.size[1] - 48}px`
-              }
-
-              modelViewerVariants.addEventListener('load', async () => {
-                const names = modelViewerVariants.availableVariants
-
-                // 变量
-                for (const name of names) {
-                  const option = document.createElement('option')
-                  option.value = name
-                  option.textContent = name
-                  select.appendChild(option)
-                }
-                // Adds a default option.
-                if (names.length === 0) {
-                  const option = document.createElement('option')
-                  option.value = 'default'
-                  option.textContent = 'Default'
-                  select.appendChild(option)
-                }
-
-                // 材质
-                extractMaterial(
-                  modelViewerVariants,
-                  selectMaterial,
-                  material_img
-                )
-              })
-
-              let timer = null
-              const delay = 500 // 延迟时间，单位为毫秒
-
-              async function checkCameraChange () {
-                let dd = getLocalData(key)
-                let base64Data = modelViewerVariants.toDataURL()
-
-                const contentType = getContentTypeFromBase64(base64Data)
-
-                const blob = await base64ToBlobFromURL(base64Data, contentType)
-
-                //  const fileBlob = new Blob([e.target.result], { type: file.type });
-                let url = await uploadImage(blob, '.png')
-                // console.log(url)
-
-                let bg_blob = await base64ToBlobFromURL(
-                  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mN88uXrPQAFwwK/6xJ6CQAAAABJRU5ErkJggg=='
-                )
-                let url_bg = await uploadImage(bg_blob, '.png')
-                // console.log('url_bg',url_bg)
-
-                if (!dd[that.id]) {
-                  dd[that.id] = { url, bg: url_bg }
-                } else {
-                  dd[that.id] = { ...dd[that.id], url }
-                }
-
-                //  材质贴图
-                let thumbUrl = material_img.getAttribute('src')
-                if (thumbUrl) {
-                  let tb = await base64ToBlobFromURL(thumbUrl)
-                  let tUrl = await uploadImage(tb, '.png')
-                  // console.log('材质贴图', tUrl, thumbUrl)
-                  dd[that.id].material = tUrl
-                }
-
-                setLocalDataOfWin(key, dd)
-              }
-
-              function startTimer () {
-                if (timer) clearTimeout(timer)
-                timer = setTimeout(checkCameraChange, delay)
-              }
-
-              modelViewerVariants.addEventListener('camera-change', startTimer)
-
-              select.addEventListener('input', async event => {
-                modelViewerVariants.variantName =
-                  event.target.value === 'default' ? null : event.target.value
-                // 材质
-                await extractMaterial(
-                  modelViewerVariants,
-                  selectMaterial,
-                  material_img
-                )
-                checkCameraChange()
-              })
-
-              selectMaterial.addEventListener('input', event => {
-                // console.log(selectMaterial.value)
-                material_img.setAttribute('src', selectMaterial.value)
-
-                if (selectMaterial.getAttribute('data-new-material')) {
-                  let index =
-                    ~~selectMaterial.selectedOptions[0].getAttribute(
-                      'data-index'
-                    )
-                  changeMaterial(
-                    modelViewerVariants,
-                    modelViewerVariants.model.materials[index],
-                    selectMaterial.getAttribute('data-new-material')
-                  )
-                }
-
-                checkCameraChange()
-              })
-
-              bg.addEventListener('click', () => {
-                // 创建一个input元素
-                var input = document.createElement('input')
-                input.type = 'file'
-
-                // 监听input的change事件
-                input.addEventListener('change', function () {
-                  // 获取上传的文件
-                  var file = input.files[0]
-
-                  // 创建一个FileReader对象来读取文件
-                  var reader = new FileReader()
-
-                  // 监听FileReader的load事件
-                  reader.addEventListener('load', async () => {
-                    let base64 = reader.result
-                    // 将读取的文件内容设置为div的背景
-                    preview.style.backgroundImage = 'url(' + base64 + ')'
-
-                    const contentType = getContentTypeFromBase64(base64)
-
-                    const blob = await base64ToBlobFromURL(base64, contentType)
-
-                    //  const fileBlob = new Blob([e.target.result], { type: file.type });
-                    let bg_url = await uploadImage(blob, '.png')
-                    let bg_img = await createImage(base64)
-
-                    let dd = getLocalData(key)
-                    // console.log(dd[that.id],bg_url)
-                    if (!dd[that.id]) dd[that.id] = { url: '', bg: bg_url }
-                    dd[that.id] = {
-                      ...dd[that.id],
-                      bg: bg_url,
-                      bg_w: bg_img.naturalWidth,
-                      bg_h: bg_img.naturalHeight
-                    }
-
-                    setLocalDataOfWin(key, dd)
-
-                    // 更新尺寸
-                    let w = that.size[0] - 24,
-                      h = (w * bg_img.naturalHeight) / bg_img.naturalWidth
-
-                    if (modelViewerVariants) {
-                      modelViewerVariants.style.width = `${w}px`
-                      modelViewerVariants.style.height = `${h}px`
-                    }
-                    preview.style.width = `${w}px`
-                  })
-
-                  // 读取文件
-                  reader.readAsDataURL(file)
-                })
-
-                // 触发input的点击事件
-                input.click()
-              })
-
-              exportGLB.addEventListener('click', async () => {
-                const glTF = await modelViewerVariants.exportScene()
-                const file = new File([glTF], 'export.glb')
-                const link = document.createElement('a')
-                link.download = file.name
-                link.href = URL.createObjectURL(file)
-                link.click()
-              })
-
-              uploadWidget.value = await uploadWidget.serializeValue()
-
-              // 更新尺寸
-              let dd = getLocalData(key)
-              // console.log(dd[that.id],bg_url)
-              if (dd[that.id]) {
-                const { bg_w, bg_h } = dd[that.id]
-                if (bg_h && bg_w) {
-                  let w = that.size[0] - 24,
-                    h = (w * bg_h) / bg_w
-
-                  if (modelViewerVariants) {
-                    modelViewerVariants.style.width = `${w}px`
-                    modelViewerVariants.style.height = `${h}px`
-                  }
-                  preview.style.width = `${w}px`
-                }
-              }
+            preview.innerHTML = html
+            if (that.size[1] < 400) {
+              that.setSize([that.size[0], that.size[1] + 300])
+              app.canvas.draw(true, true)
             }
 
-            // 以文本形式读取文件
-            reader.readAsDataURL(file)
+            const modelViewerVariants = preview.querySelector('model-viewer')
+            const select = preview.querySelector('.variant')
+            const selectMaterial = preview.querySelector('.material')
+            const material_img = preview.querySelector('.material_img')
+            const bg = preview.querySelector('.bg')
+
+            const exportGLB = preview.querySelector('.export')
+
+            const ddcap_step = preview.querySelector('.ddcap_step')
+            const total_images = preview.querySelector('.total_images')
+            const ddcap_range = preview.querySelector('.ddcap_range')
+            const ddcap_range_top = preview.querySelector('.ddcap_range_top')
+            const ddCap = preview.querySelector('.ddcap')
+            const sleep = (t = 1000) => {
+              return new Promise((res, rej) => {
+                return setTimeout(() => {
+                  res(t)
+                }, t)
+              })
+            }
+
+            async function captureImage (isUrl = true) {
+              let base64Data = modelViewerVariants.toDataURL()
+
+              const contentType = getContentTypeFromBase64(base64Data)
+
+              const blob = await base64ToBlobFromURL(base64Data, contentType)
+
+              if (isUrl) return await uploadImage(blob, '.png')
+              return await uploadImage_(blob, '.png')
+            }
+
+            async function captureImages (angleIncrement = 1, totalImages = 12) {
+              // 记录初始旋转角度
+              const initialCameraOrbit =
+                modelViewerVariants.cameraOrbit.split(' ')
+              console.log(
+                '#captureImages',
+                initialCameraOrbit,
+                angleIncrement * totalImages
+              )
+              // const totalImages = 12
+              // const angleIncrement = totalRotation / totalImages // Each increment in degrees
+              let currentAngle =
+                Number(initialCameraOrbit[0].replace('deg', '')) -
+                (angleIncrement * totalImages) / 2 // Start from the leftmost angle
+              let frames = []
+
+              modelViewerVariants.removeAttribute('camera-controls')
+
+              for (let i = 0; i < totalImages; i++) {
+                modelViewerVariants.cameraOrbit = `${currentAngle}deg ${initialCameraOrbit[1]} ${initialCameraOrbit[2]}`
+                await sleep(1000)
+                console.log(`Capturing image at angle: ${currentAngle}deg`)
+                let file = await captureImage(false)
+                frames.push(file)
+                currentAngle += angleIncrement
+              }
+              await sleep(1000)
+              // 恢复到初始旋转角度
+              modelViewerVariants.cameraOrbit = initialCameraOrbit.join(' ')
+              modelViewerVariants.setAttribute('camera-controls', '')
+              return frames
+            }
+            ddCap.addEventListener('click', async e => {
+              const angleIncrement = Number(ddcap_step.value),
+                totalImages = Number(total_images.value)
+
+              let images = await captureImages(angleIncrement, totalImages)
+              // console.log(images)
+              let dd = getLocalData(key)
+              dd[that.id].images = images
+              setLocalDataOfWin(key, dd)
+            })
+
+            ddcap_range.addEventListener('input', async e => {
+              // console.log(ddcap_range.value)
+              const initialCameraOrbit =
+                modelViewerVariants.cameraOrbit.split(' ')
+              modelViewerVariants.cameraOrbit = `${ddcap_range.value}deg ${initialCameraOrbit[1]} ${initialCameraOrbit[2]}`
+              modelViewerVariants.setAttribute('camera-controls', '')
+            })
+
+            ddcap_range_top.addEventListener('input', async e => {
+              // console.log(ddcap_range.value)
+              const initialCameraOrbit =
+                modelViewerVariants.cameraOrbit.split(' ')
+              modelViewerVariants.cameraOrbit = `${initialCameraOrbit[0]} ${ddcap_range_top.value}deg ${initialCameraOrbit[2]}`
+              modelViewerVariants.setAttribute('camera-controls', '')
+            })
+
+            if (modelViewerVariants) {
+              modelViewerVariants.style.width = `${that.size[0] - 48}px`
+              modelViewerVariants.style.height = `${that.size[1] - 48}px`
+            }
+
+            modelViewerVariants.addEventListener('load', async () => {
+              const names = modelViewerVariants.availableVariants
+
+              // 变量
+              for (const name of names) {
+                const option = document.createElement('option')
+                option.value = name
+                option.textContent = name
+                select.appendChild(option)
+              }
+              // Adds a default option.
+              if (names.length === 0) {
+                const option = document.createElement('option')
+                option.value = 'default'
+                option.textContent = 'Default'
+                select.appendChild(option)
+              }
+
+              // 材质
+              extractMaterial(modelViewerVariants, selectMaterial, material_img)
+            })
+
+            let timer = null
+            const delay = 500 // 延迟时间，单位为毫秒
+
+            async function checkCameraChange () {
+              let dd = getLocalData(key)
+
+              //  const fileBlob = new Blob([e.target.result], { type: file.type });
+              let url = await captureImage()
+
+              let bg_blob = await base64ToBlobFromURL(
+                'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mN88uXrPQAFwwK/6xJ6CQAAAABJRU5ErkJggg=='
+              )
+              let url_bg = await uploadImage(bg_blob, '.png')
+              // console.log('url_bg',url_bg)
+
+              if (!dd[that.id]) {
+                dd[that.id] = { url, bg: url_bg }
+              } else {
+                dd[that.id] = { ...dd[that.id], url }
+              }
+
+              //  材质贴图
+              let thumbUrl = material_img.getAttribute('src')
+              if (thumbUrl) {
+                let tb = await base64ToBlobFromURL(thumbUrl)
+                let tUrl = await uploadImage(tb, '.png')
+                // console.log('材质贴图', tUrl, thumbUrl)
+                dd[that.id].material = tUrl
+              }
+
+              setLocalDataOfWin(key, dd)
+            }
+
+            function startTimer () {
+              if (timer) clearTimeout(timer)
+              timer = setTimeout(checkCameraChange, delay)
+            }
+
+            modelViewerVariants.addEventListener('camera-change', startTimer)
+
+            select.addEventListener('input', async event => {
+              modelViewerVariants.variantName =
+                event.target.value === 'default' ? null : event.target.value
+              // 材质
+              await extractMaterial(
+                modelViewerVariants,
+                selectMaterial,
+                material_img
+              )
+              checkCameraChange()
+            })
+
+            selectMaterial.addEventListener('input', event => {
+              // console.log(selectMaterial.value)
+              material_img.setAttribute('src', selectMaterial.value)
+
+              if (selectMaterial.getAttribute('data-new-material')) {
+                let index =
+                  ~~selectMaterial.selectedOptions[0].getAttribute('data-index')
+                changeMaterial(
+                  modelViewerVariants,
+                  modelViewerVariants.model.materials[index],
+                  selectMaterial.getAttribute('data-new-material')
+                )
+              }
+
+              checkCameraChange()
+            })
+
+            //更新bg
+            const updateBgData = (id, key, url, w, h) => {
+              let dd = getLocalData(key)
+              // console.log(dd[that.id],url)
+              if (!dd[id]) dd[id] = { url: '', bg: url }
+              dd[id] = {
+                ...dd[id],
+                bg: url,
+                bg_w: w,
+                bg_h: h
+              }
+              setLocalDataOfWin(key, dd)
+            }
+
+            bg.addEventListener('click', async () => {
+              //更新bg
+              updateBgData(that.id, key, '', 0, 0)
+              preview.style.backgroundImage = 'none'
+
+              let base64 = await inputFileClick(false, false)
+              // 将读取的文件内容设置为div的背景
+              preview.style.backgroundImage = 'url(' + base64 + ')'
+
+              const contentType = getContentTypeFromBase64(base64)
+
+              const blob = await base64ToBlobFromURL(base64, contentType)
+
+              //  const fileBlob = new Blob([e.target.result], { type: file.type });
+              let bg_url = await uploadImage(blob, '.png')
+              let bg_img = await createImage(base64)
+
+              //更新bg
+              updateBgData(
+                that.id,
+                key,
+                bg_url,
+                bg_img.naturalWidth,
+                bg_img.naturalHeight
+              )
+
+              // 更新尺寸
+              let w = that.size[0] - 48,
+                h = (w * bg_img.naturalHeight) / bg_img.naturalWidth
+
+              if (modelViewerVariants) {
+                modelViewerVariants.style.width = `${w}px`
+                modelViewerVariants.style.height = `${h}px`
+              }
+              preview.style.width = `${w}px`
+            })
+
+            exportGLB.addEventListener('click', async () => {
+              const glTF = await modelViewerVariants.exportScene()
+              const file = new File([glTF], 'export.glb')
+              const link = document.createElement('a')
+              link.download = file.name
+              link.href = URL.createObjectURL(file)
+              link.click()
+            })
+
+            uploadWidget.value = await uploadWidget.serializeValue()
+
+            // 更新尺寸
+            let dd = getLocalData(key)
+            // console.log(dd[that.id],bg_url)
+            if (dd[that.id]) {
+              const { bg_w, bg_h } = dd[that.id]
+              if (bg_h && bg_w) {
+                let w = that.size[0] - 48,
+                  h = (w * bg_h) / bg_w
+
+                if (modelViewerVariants) {
+                  modelViewerVariants.style.width = `${w}px`
+                  modelViewerVariants.style.height = `${h}px`
+                }
+                preview.style.width = `${w}px`
+              }
+            }
           })
           return div
         }
 
         let preview = document.createElement('div')
         preview.className = 'preview'
-        preview.style = `margin-top: 12px;display: flex;
+        preview.style = `margin-top: 12px;
+          display: flex;
           justify-content: center;
-          align-items: center;background-repeat: no-repeat;background-size: contain;`
+          align-items: center;background-repeat: no-repeat;
+          background-size: contain;`
 
         let upload = inputDiv('_mixlab_3d_image', '3D Model', preview)
 
@@ -527,7 +645,7 @@ app.registerExtension({
           if (dd[that.id]) {
             const { bg_w, bg_h } = dd[that.id]
             if (bg_h && bg_w) {
-              let w = that.size[0] - 24,
+              let w = that.size[0] - 48,
                 h = (w * bg_h) / bg_w
 
               if (modelViewerVariants) {
@@ -561,7 +679,7 @@ app.registerExtension({
         const r = onExecuted?.apply?.(this, arguments)
 
         let div = this.widgets.filter(d => d.div)[0]?.div
-        console.log('Test', this.widgets)
+        // console.log('Test', this.widgets)
 
         let material = message.material[0]
         if (material) {

web/javascript/app_mixlab.js

@@ -2,27 +2,13 @@ import { app } from '../../../scripts/app.js'
 import { $el } from '../../../scripts/ui.js'
 import { api } from '../../../scripts/api.js'
 
+import { td_bg } from './td_background.js'
+// console.log('td_bg', td_bg)
+import { getUrl, base64Df, get_position_style, getObjectInfo } from './common.js'
+
 //本机安装的插件节点全集
 window._nodesAll = null
 
-//获取当前系统的插件，节点清单
-function getObjectInfo () {
-  return new Promise(async (resolve, reject) => {
-    let url = getUrl()
-
-    try {
-      const response = await fetch(`${url}/object_info`)
-      const data = await response.json()
-      resolve(data)
-    } catch (error) {
-      reject(error)
-    }
-  })
-}
-
-const base64Df =
-  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
-
 const parseImageToBase64 = url => {
   return new Promise((res, rej) => {
     fetch(url)
@@ -42,39 +28,6 @@ const parseImageToBase64 = url => {
   })
 }
 
-function get_position_style (ctx, widget_width, y, node_height) {
-  const MARGIN = 12 // the margin around the html element
-
-  /* Create a transform that deals with all the scrolling and zooming */
-  const elRect = ctx.canvas.getBoundingClientRect()
-  const transform = new DOMMatrix()
-    .scaleSelf(
-      elRect.width / ctx.canvas.width,
-      elRect.height / ctx.canvas.height
-    )
-    .multiplySelf(ctx.getTransform())
-    .translateSelf(MARGIN, MARGIN + y)
-
-  return {
-    transformOrigin: '0 0',
-    transform: transform,
-    left: `0`,
-    top: `0`,
-    cursor: 'pointer',
-    position: 'absolute',
-    maxWidth: `${widget_width - MARGIN * 2}px`,
-    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
-    width: `${widget_width - MARGIN * 2}px`,
-    // height: `${node_height * 0.3 - MARGIN * 2}px`,
-    // background: '#EEEEEE',
-    display: 'flex',
-    flexDirection: 'column',
-    // alignItems: 'center',
-    justifyContent: 'flex-start',
-    zIndex: 9999999
-  }
-}
-
 async function drawImageToCanvas (imageUrl, sFactor = 320) {
   var canvas = document.createElement('canvas')
   var ctx = canvas.getContext('2d')
@@ -185,6 +138,25 @@ async function extractInputAndOutputData (
         if (node.type == 'Color') {
         }
 
+        // 语音输入的支持
+        if (node.type == 'LoadAndCombinedAudio_') {
+          // if (
+          //   data[id].widgets_values &&
+          //   data[id].widgets_values[0] &&
+          //   data[id].widgets_values[0].base64 &&
+          //   data[id].widgets_values[0].base64.length > 0
+          // ) {
+          //   options.defaultBase64 = data[id].widgets_values[0].base64
+          // }
+
+          input[inputIds.indexOf(id)] = {
+            ...data[id],
+            title: node.title,
+            id,
+            options
+          }
+        }
+
         if (node.type === 'LoadImage') {
           // loadImage的mask支持
           let output = node.outputs.filter(ot => ot.type == 'MASK')[0]
@@ -235,7 +207,9 @@ 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 {
@@ -255,13 +229,6 @@ async function extractInputAndOutputData (
   return { input, output, seed, seedTitle }
 }
 
-function getUrl () {
-  let api_host = `${window.location.hostname}:${window.location.port}`
-  let api_base = ''
-  let url = `${window.location.protocol}//${api_host}${api_base}`
-  return url
-}
-
 const getLocalData = key => {
   let data = {}
   try {
@@ -397,11 +364,11 @@ async function save (json, download = false, showInfo = true) {
 
 function getInputsAndOutputs () {
   const inputs =
-      `LoadImage LoadImagesToBatch ImagesPrompt_ VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
+      `LoadImage LoadImagesToBatch ImagesPrompt_ LoadAndCombinedAudio_ LoadVideoAndSegment_ VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
         ' '
       ),
     outputs =
-      `SaveTripoSRMesh,PreviewImage,SaveImage,TransparentImage,ShowTextForGPT,VHS_VideoCombine,VideoCombine_Adv,Image Save,SaveImageAndMetadata_,ClipInterrogator`.split(
+      `SaveTripoSRMesh,PreviewImage,SaveImage,TransparentImage,ShowTextForGPT,CombineAudioVideo,VHS_VideoCombine,VideoCombine_Adv,Image Save,SaveImageAndMetadata_,ClipInterrogator`.split(
         ','
       )
 
@@ -445,20 +412,19 @@ app.registerExtension({
         const { input, output } = getInputsAndOutputs()
         input_ids.value = input.join('\n')
         output_ids.value = output.join('\n')
-
         const widget = {
           type: 'div',
           name: 'AppInfoRun',
           draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(
-              this.div.style,
-              get_position_style(
+            Object.assign(this.div.style, {
+              ...get_position_style(
                 ctx,
                 widget_width,
                 node.size[1] - widget_height,
                 node.size[1]
-              )
-            )
+              ),
+              zIndex: 1
+            })
           }
         }
 
@@ -503,6 +469,21 @@ app.registerExtension({
           }
         })
 
+        //td bg
+        const tdBG = document.createElement('button')
+        tdBG.innerText = 'Canvas Mode'
+        tdBG.style = style
+        tdBG.style.marginLeft = '12px'
+
+        tdBG.addEventListener('click', () => {
+          td_bg.toggle()
+          if (td_bg.running) {
+            tdBG.style.background = 'yellow'
+          } else {
+            tdBG.style.background = 'transparent'
+          }
+        })
+
         // author
         let author = document.createElement('div')
         // author.style=`display: flex`
@@ -659,6 +640,7 @@ app.registerExtension({
 
         btns.appendChild(btn)
         btns.appendChild(download)
+        btns.appendChild(tdBG)
 
         document.body.appendChild(widget.div)
         this.addCustomWidget(widget)
@@ -687,9 +669,8 @@ app.registerExtension({
           }
 
           const div = this.widgets.filter(w => w.div)[0].div
-          Array.from(
-            div.querySelectorAll('button'),
-            b => (b.style.background = 'yellow')
+          Array.from(div.querySelectorAll('button'), b =>
+            b.innerText != 'Canvas Mode' ? (b.style.background = 'yellow') : ''
           )
         } catch (error) {}
       }

web/javascript/audio_mixlab.js

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

web/javascript/chat.js

@@ -1,8 +1,10 @@
+import { getUrl } from './common.js'
+
 async function* completion (url, messages, controller) {
   let data = {
     model: 'gpt-3.5-turbo-16k',
     messages,
-    temperature: 0.6,
+    temperature: 0.05,
     stream: true
   }
   // if (imageNode) {
@@ -91,16 +93,81 @@ async function* completion (url, messages, controller) {
   return content
   // return (await response.json()).content
 }
-
-export async function completion_ (url, messages, controller, callback) {
-  let request = await completion(url, messages, controller)
+export async function completion_ (apiKey, url, messages, controller, callback) {
+  let request = await chatCompletion(apiKey, 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)
+    if (callback) callback(chunk)
   }
 }
+
+export async function* chatCompletion (apiKey, url, messages, controller) {
+  url = `${getUrl()}/chat/completions`
+
+  const requestBody = {
+    model: '01-ai/Yi-1.5-9B-Chat-16K',
+    messages: messages,
+    stream: true,
+    key: apiKey
+  }
+
+  let response = await fetch(url, {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+      Authorization: `Bearer ${apiKey}`
+    },
+    body: JSON.stringify(requestBody),
+    mode: 'cors', // This is to ensure the request is made with CORS
+    signal: controller.signal
+  })
+
+  const reader = response.body.getReader()
+  const decoder = new TextDecoder()
+
+  let content = ''
+  let leftover = '' // Buffer for partially read lines
+
+  try {
+    let cont = true
+    while (cont) {
+      let result = await reader.read()
+      if (result.done) {
+        break
+      }
+
+      // 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('\r\n')
+
+      // Split the text into lines
+      let lines = text.split('\r\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
+      }
+
+      for (const line of lines) {
+        if (line) {
+          content += line
+          yield line // Yield the trimmed line
+        } else {
+          cont = false
+          break
+        }
+      }
+    }
+  } catch (e) {
+    console.error('chat error: ', e)
+    throw e
+  } finally {
+    controller.abort()
+  }
+
+  return 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 = 'v0.28.2'
+const version = 'v0.39.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/common.js

@@ -0,0 +1,175 @@
+export const base64Df =
+  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
+
+export function getUrl () {
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+  return url
+}
+
+// 更新或者获取key
+export const updateLLMAPIKey = async key => {
+  try {
+    const res = await fetch(`${getUrl()}/mixlab/llm_api_key`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        key: key || null
+      })
+    })
+
+    const data = await res.json()
+
+    if (!res.ok) {
+      console.error('Error:', data.error)
+      return
+    }
+
+    if (key) {
+      console.log('API key saved successfully:', data.message)
+      return key
+    } else {
+      console.log('Retrieved API key:', data.key)
+      return data.key
+    }
+  } catch (error) {
+    console.error('Request failed:', error)
+  }
+}
+
+//获取当前系统的插件，节点清单
+export function getObjectInfo () {
+  return new Promise(async (resolve, reject) => {
+    let url = getUrl()
+
+    try {
+      const response = await fetch(`${url}/object_info`)
+      const data = await response.json()
+      resolve(data)
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
+
+export function get_position_style (
+  ctx,
+  widget_width,
+  y,
+  node_height,
+  left = 44
+) {
+  const MARGIN = 0 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+
+  const scaleX = elRect.width / ctx.canvas.width
+  const scaleY = elRect.height / ctx.canvas.height
+
+  const transform = new DOMMatrix()
+    .scaleSelf(scaleX, scaleY)
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+ 
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left:
+      document.querySelector('.comfy-menu').style.display === 'none'
+        ? `${left}px`
+        : `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2}px`,
+    height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'flex-start',
+    zIndex: 99
+  }
+}
+
+export function loadExternalScript (url, type) {
+  return new Promise((resolve, reject) => {
+    const existingScript = document.querySelector(`script[src="${url}"]`)
+    if (existingScript) {
+      existingScript.onload = () => {
+        resolve()
+      }
+      existingScript.onerror = reject
+      return
+    }
+
+    const script = document.createElement('script')
+    script.src = url
+    if (type) script.type = type // Add this line to load the script as an ES module
+    script.onload = () => {
+      resolve()
+    }
+    script.onerror = reject
+    document.head.appendChild(script)
+  })
+}
+
+export async function getQueue () {
+  try {
+    const res = await fetch(`${getUrl()}/queue`)
+    const data = await res.json()
+    // console.log(data.queue_running,data.queue_pending)
+    return {
+      // Running action uses a different endpoint for cancelling
+      Running: data.queue_running.length,
+      Pending: data.queue_pending.length
+    }
+  } catch (error) {
+    console.error(error)
+    return { Running: 0, Pending: 0 }
+  }
+}
+
+export async function interrupt () {
+  const resp = await fetch(`${getUrl()}/interrupt`, {
+    method: 'POST'
+  })
+}
+
+export async function sleep (t = 200) {
+  return new Promise((res, rej) => {
+    setTimeout(() => {
+      res(true)
+    }, t)
+  })
+}
+
+export function createImage (url) {
+  let im = new Image()
+  return new Promise((res, rej) => {
+    im.onload = () => res(im)
+    im.src = url
+  })
+}
+
+export const getLocalData = key => {
+  let data = {}
+  try {
+    data = JSON.parse(localStorage.getItem(key)) || {}
+  } catch (error) {
+    return {}
+  }
+  return data
+}
+
+export const saveLocalData = (key, id, val) => {
+  let data = getLocalData(key)
+  data[id] = val
+  localStorage.setItem(key, JSON.stringify(data))
+}

web/javascript/gpt_mixlab.js

@@ -1,205 +1,5 @@
 import { app } from '../../../scripts/app.js'
-// import { api } from '../../../scripts/api.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
-import { $el } from '../../../scripts/ui.js'
-
-async function getConfig () {
-  let api_host = `${window.location.hostname}:${window.location.port}`
-  let api_base = ''
-  let url = `${window.location.protocol}//${api_host}${api_base}`
-
-  const res = await fetch(`${url}/mixlab`, {
-    method: 'POST'
-  })
-  return await res.json()
-}
-
-function get_position_style (ctx, widget_width, y, node_height) {
-  const MARGIN = 4 // the margin around the html element
-
-  /* Create a transform that deals with all the scrolling and zooming */
-  const elRect = ctx.canvas.getBoundingClientRect()
-  const transform = new DOMMatrix()
-    .scaleSelf(
-      elRect.width / ctx.canvas.width,
-      elRect.height / ctx.canvas.height
-    )
-    .multiplySelf(ctx.getTransform())
-    .translateSelf(MARGIN, MARGIN + y)
-
-  return {
-    transformOrigin: '0 0',
-    transform: transform,
-    left: `0`,
-    top: `0`,
-    cursor: 'pointer',
-    position: 'absolute',
-    maxWidth: `${widget_width - MARGIN * 2}px`,
-    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
-    width: `${widget_width - MARGIN * 2}px`,
-    // height: `${node_height * 0.3 - MARGIN * 2}px`,
-    // background: '#EEEEEE',
-    display: 'flex',
-    flexDirection: 'column',
-    // alignItems: 'center',
-    justifyContent: 'space-around'
-  }
-}
-
-const getLocalData = key => {
-  let data = {}
-  try {
-    data = JSON.parse(localStorage.getItem(key)) || {}
-  } catch (error) {
-    return {}
-  }
-  return data
-}
-
-app.registerExtension({
-  name: 'Mixlab.GPT.ChatGPTOpenAI',
-  async getCustomWidgets (app) {
-    return {
-      KEY (node, inputName, inputData, app) {
-        // console.log('##inputData', inputData)
-        const widget = {
-          type: inputData[0], // the type, CHEESE
-          name: inputName, // the name, slice
-          size: [128, 32], // a default size
-          draw (ctx, node, width, y) {},
-          computeSize (...args) {
-            return [128, 32] // a method to compute the current size of the widget
-          },
-          async serializeValue (nodeId, widgetIndex) {
-            let data = getLocalData('_mixlab_api_key')
-            return data[node.id] || 'by Mixlab'
-          }
-        }
-        //  widget.something = something;          // maybe adds stuff to it
-        node.addCustomWidget(widget) // adds it to the node
-        return widget // and returns it.
-      },
-      URL (node, inputName, inputData, app) {
-        // console.log('node', inputName, inputData[0])
-        const widget = {
-          type: inputData[0], // the type, CHEESE
-          name: inputName, // the name, slice
-          size: [128, 32], // a default size
-          draw (ctx, node, width, y) {
-            // a method to draw the widget (ctx is a CanvasRenderingContext2D)
-          },
-          computeSize (...args) {
-            return [128, 32] // a method to compute the current size of the widget
-          },
-          async serializeValue (nodeId, widgetIndex) {
-            let data = getLocalData('_mixlab_api_url')
-            return data[node.id] || 'https://api.openai.com/v1'
-          }
-        }
-        //  widget.something = something;          // maybe adds stuff to it
-        node.addCustomWidget(widget) // adds it to the node
-        return widget // and returns it.
-      }
-    }
-  },
-
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeType.comfyClass == 'ChatGPTOpenAI') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = function () {
-        orig_nodeCreated?.apply(this, arguments)
-
-        const api_key = this.widgets.filter(w => w.name == 'api_key')[0]
-        const api_url = this.widgets.filter(w => w.name == 'api_url')[0]
-
-        console.log('ChatGPTOpenAI nodeData', this.widgets)
-
-        const widget = {
-          type: 'div',
-          name: 'chatgptdiv',
-          draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(
-              this.div.style,
-              get_position_style(ctx, widget_width, api_key.y, node.size[1])
-            )
-          }
-        }
-
-        widget.div = $el('div', {})
-
-        document.body.appendChild(widget.div)
-
-        const inputDiv = (key, placeholder) => {
-          let div = document.createElement('div')
-          const ip = document.createElement('input')
-          ip.type = placeholder === 'Key' ? 'password' : 'text'
-          ip.className = `${'comfy-multiline-input'} ${placeholder}`
-          div.style = `display: flex;
-          align-items: center; 
-          margin: 6px 8px;
-          margin-top: 0;`
-          ip.placeholder = placeholder
-          ip.value = placeholder
-
-          ip.style = `margin-left: 24px;
-          outline: none;
-          border: none;
-          padding: 4px;width: 100%;`
-          const label = document.createElement('label')
-          label.style = 'font-size: 10px;min-width:32px'
-          label.innerText = placeholder
-          div.appendChild(label)
-          div.appendChild(ip)
-
-          ip.addEventListener('change', () => {
-            let data = getLocalData(key)
-            data[this.id] = ip.value.trim()
-            localStorage.setItem(key, JSON.stringify(data))
-            console.log(this.id, key)
-          })
-          return div
-        }
-
-        let inputKey = inputDiv('_mixlab_api_key', 'Key')
-        let inputUrl = inputDiv('_mixlab_api_url', 'URL')
-
-        widget.div.appendChild(inputKey)
-        widget.div.appendChild(inputUrl)
-
-        this.addCustomWidget(widget)
-
-        const onRemoved = this.onRemoved
-        this.onRemoved = () => {
-          inputUrl.remove()
-          inputKey.remove()
-          widget.div.remove()
-          return onRemoved?.()
-        }
-
-        this.serialize_widgets = true //需要保存参数
-      }
-    }
-  },
-  async loadedGraphNode (node, app) {
-    // Fires every time a node is constructed
-    // You can modify widgets/add handlers/etc here
-
-    if (node.type === 'ChatGPTOpenAI') {
-      let widget = node.widgets.filter(w => w.div)[0]
-
-      let apiKey = getLocalData('_mixlab_api_key'),
-        url = getLocalData('_mixlab_api_url')
-
-      let id = node.id
-
-      // console.log('ChatGPTOpenAI serialize_widgets', this)
-
-      widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
-      widget.div.querySelector('.URL').value =
-        url[id] || 'https://api.openai.com/v1'
-    }
-  }
-})
 
 app.registerExtension({
   name: 'Mixlab.GPT.ShowTextForGPT',
@@ -209,13 +9,16 @@ app.registerExtension({
         text = text.filter(t => t && t?.trim())
 
         if (this.widgets) {
+          // console.log('#ShowTextForGPT',this.widgets)
           // const pos = this.widgets.findIndex(w => w.name === 'text')
           for (let i = 0; i < this.widgets.length; i++) {
-            if (this.widgets[i].name == 'show_text') this.widgets[i].onRemove?.()
+            if (this.widgets[i].name == 'show_text')
+              this.widgets[i].onRemove?.()
+       
           }
-          this.widgets.length = 1
+          this.widgets.length = 2
         }
-        // console.log('ShowTextForGPT',text)
+
         for (let list of text) {
           if (list) {
             // console.log('#####', list)
@@ -228,6 +31,8 @@ app.registerExtension({
             w.inputEl.readOnly = true
             w.inputEl.style.opacity = 0.6
 
+            // w.inputEl.style.display='none'
+
             try {
               if (typeof list != 'string') {
                 let data = JSON.parse(list)

web/javascript/image_mixlab.js

@@ -4,6 +4,8 @@ import { api } from '../../../scripts/api.js'
 import { $el } from '../../../scripts/ui.js'
 import { applyTextReplacements } from '../../../scripts/utils.js'
 
+import { loadExternalScript, get_position_style } from './common.js'
+
 function loadImageToCanvas (base64Image) {
   var img = new Image()
   var canvas = document.createElement('canvas')
@@ -88,37 +90,40 @@ function getContentTypeFromBase64 (base64Data) {
 // const blob = base64ToBlob(base64Data, contentType);
 // console.log(blob);
 
-function get_position_style (ctx, widget_width, y, node_height) {
-  const MARGIN = 4 // the margin around the html element
+// function get_position_style (ctx, widget_width, y, node_height) {
+//   const MARGIN = 4 // the margin around the html element
 
-  /* Create a transform that deals with all the scrolling and zooming */
-  const elRect = ctx.canvas.getBoundingClientRect()
-  const transform = new DOMMatrix()
-    .scaleSelf(
-      elRect.width / ctx.canvas.width,
-      elRect.height / ctx.canvas.height
-    )
-    .multiplySelf(ctx.getTransform())
-    .translateSelf(MARGIN, MARGIN + y)
+//   /* Create a transform that deals with all the scrolling and zooming */
+//   const elRect = ctx.canvas.getBoundingClientRect()
+//   const transform = new DOMMatrix()
+//     .scaleSelf(
+//       elRect.width / ctx.canvas.width,
+//       elRect.height / ctx.canvas.height
+//     )
+//     .multiplySelf(ctx.getTransform())
+//     .translateSelf(MARGIN, MARGIN + y)
 
-  return {
-    transformOrigin: '0 0',
-    transform: transform,
-    left: `0`,
-    top: `0`,
-    cursor: 'pointer',
-    position: 'absolute',
-    maxWidth: `${widget_width - MARGIN * 2}px`,
-    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
-    width: `${widget_width - MARGIN * 2}px`,
-    // height: `${node_height * 0.3 - MARGIN * 2}px`,
-    // background: '#EEEEEE',
-    display: 'flex',
-    flexDirection: 'column',
-    // alignItems: 'center',
-    justifyContent: 'space-around'
-  }
-}
+//   return {
+//     transformOrigin: '0 0',
+//     transform: transform,
+//     left:
+//       document.querySelector('.comfy-menu').style.display === 'none'
+//         ? `60px`
+//         : `0`,
+//     top: `0`,
+//     cursor: 'pointer',
+//     position: 'absolute',
+//     maxWidth: `${widget_width - MARGIN * 2}px`,
+//     // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+//     width: `${widget_width - MARGIN * 2}px`,
+//     // height: `${node_height * 0.3 - MARGIN * 2}px`,
+//     // background: '#EEEEEE',
+//     display: 'flex',
+//     flexDirection: 'column',
+//     // alignItems: 'center',
+//     justifyContent: 'space-around'
+//   }
+// }
 
 const getLocalData = key => {
   let data = {}
@@ -344,7 +349,7 @@ app.registerExtension({
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(ctx, widget_width, 44, node.size[1])
+              get_position_style(ctx, widget_width, 44, node.size[1], 36)
             )
           }
         }
@@ -530,7 +535,7 @@ app.registerExtension({
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(ctx, widget_width, y, node.size[1])
+              get_position_style(ctx, widget_width, y, node.size[1], 36)
             )
           }
         }
@@ -675,6 +680,18 @@ const createInputImageForBatch = (base64, widget) => {
   return im
 }
 
+// 添加新图片
+const addBase64ToWidgetForLoadImagesToBatch = (
+  base64,
+  imagesWidget,
+  imagesDiv
+) => {
+  if (!imagesWidget.value.base64) imagesWidget.value.base64 = []
+  imagesWidget.value.base64.push(base64)
+  let im = createInputImageForBatch(base64, imagesWidget)
+  imagesDiv.appendChild(im)
+}
+
 app.registerExtension({
   name: 'Mixlab.Comfy.LoadImagesToBatch',
   async getCustomWidgets (app) {
@@ -705,7 +722,6 @@ app.registerExtension({
 
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeType.comfyClass == 'LoadImagesToBatch') {
-
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
 
       nodeType.prototype.onNodeCreated = function () {
@@ -719,7 +735,7 @@ app.registerExtension({
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(ctx, widget_width, 44, node.size[1])
+              get_position_style(ctx, widget_width, 44, node.size[1], 44)
             )
           },
           serialize: false
@@ -751,13 +767,18 @@ app.registerExtension({
             base64 = await loadImageToCanvas(base64)
             // console.log(base64)
             if (!imagesWidget.value) imagesWidget.value = { base64: [] }
-            imagesWidget.value.base64.push(base64)
-            let im = createInputImageForBatch(base64, imagesWidget)
-            imagesDiv.appendChild(im)
+            addBase64ToWidgetForLoadImagesToBatch(
+              base64,
+              imagesWidget,
+              imagesDiv
+            )
           }
           reader.readAsDataURL(file)
         })
 
+        // 如果是复制的，有数据 , 这个不生效，取不到数据， 需要在nodeCreated里获取
+        // console.log('#LoadImagesToBatch', imagesWidget.value?.base64)
+
         const btn = document.createElement('button')
         btn.innerText = 'Upload Image'
 
@@ -829,18 +850,36 @@ app.registerExtension({
       }
     }
   },
+
   async loadedGraphNode (node, app) {
     if (node.type === 'LoadImagesToBatch') {
-      // await sleep(0)
       let imagesWidget = node.widgets.filter(w => w.name === 'images')[0]
       let imagePreview = node.widgets.filter(w => w.name == 'image_base64')[0]
+      // console.log('#LoadImagesToBatch', imagesWidget.value?.base64)
+      let imagesDiv = imagePreview.div.querySelector('.images_preview')
 
-      let pre = imagePreview.div.querySelector('.images_preview')
       for (const d of imagesWidget.value?.base64 || []) {
         let im = createInputImageForBatch(d, imagesWidget)
-        pre.appendChild(im)
+        imagesDiv.appendChild(im)
       }
     }
+  },
+  nodeCreated (node, app) {
+    //数据延迟？？
+    setTimeout(() => {
+      // console.log('#LoadImagesToBatch', node.type)
+      if (node.type === 'LoadImagesToBatch') {
+        let imagesWidget = node.widgets.filter(w => w.name === 'images')[0]
+        let imagePreview = node.widgets.filter(w => w.name == 'image_base64')[0]
+
+        let imagesDiv = imagePreview?.div?.querySelector('.images_preview')
+
+        for (const d of imagesWidget.value?.base64 || []) {
+          let im = createInputImageForBatch(d, imagesWidget)
+          imagesDiv.appendChild(im)
+        }
+      }
+    }, 1000)
   }
 })
 
@@ -848,9 +887,11 @@ app.registerExtension({
 app.registerExtension({
   name: 'Mixlab.output.ComparingTwoFrames_',
   init () {
+    loadExternalScript('/mixlab/app/lib/juxtapose.min.js')
+
     $el('link', {
       rel: 'stylesheet',
-      href: '/extensions/comfyui-mixlab-nodes/lib/juxtapose.css',
+      href: '/mixlab/app/lib/juxtapose.css',
       parent: document.head
     })
 
@@ -868,8 +909,8 @@ app.registerExtension({
       const onNodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = function () {
         const r = onNodeCreated
-        ? onNodeCreated.apply(this, arguments)
-        : undefined
+          ? onNodeCreated.apply(this, arguments)
+          : undefined
 
         this.size = [400, this.size[1]]
         console.log('##onNodeCreated', this)
@@ -877,10 +918,10 @@ app.registerExtension({
           type: 'div',
           name: 'preview',
           draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(
-              this.div.style,
-              get_position_style(ctx, 400, 44, node.size[1])
-            )
+            let s = get_position_style(ctx, widget_width, 44, node.size[1], 36)
+            delete s.height
+
+            Object.assign(this.div.style, s)
           },
           serialize: false
         }
@@ -891,20 +932,15 @@ app.registerExtension({
         this.addCustomWidget(widget)
         this.serialize_widgets = true //需要保存参数
 
-
         const onRemoved = this.onRemoved
         this.onRemoved = () => {
           widget.div.remove()
           return onRemoved?.()
         }
 
-
-       return r
-
+        return r
       }
 
-     
-
       const onExecuted = nodeType.prototype.onExecuted
       nodeType.prototype.onExecuted = function (message) {
         onExecuted?.apply(this, arguments)
@@ -964,7 +1000,7 @@ app.registerExtension({
             label: 'After'
           }
         ]
-        this.size=[this.size[0],300]
+        this.size = [this.size[0], 300]
       }
     }
   },
@@ -974,7 +1010,6 @@ app.registerExtension({
       // node.widgets[0].div.id = 'mix_comparingtowframes_' + node.id
       // if (node.widgets_values && node.widgets_values[0]) {
       //   node.widgets[0].div.innerHTML = ''
-
       //   let slider = new juxtapose.JXSlider(
       //     '#mix_comparingtowframes_' + node.id,
       //     node.widgets_values,

web/javascript/layer_mixlab.js

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

web/javascript/main_mixlab.js

@@ -3,31 +3,17 @@ import { app } from '../../../scripts/app.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
 
-let api_host = `${window.location.hostname}:${window.location.port}`
-let api_base = ''
-let url = `${window.location.protocol}//${api_host}${api_base}`
+import {
+  getQueue,
+  interrupt,
+  get_position_style,
+  base64Df,
+  getUrl,
+  createImage,
+  sleep
+} from './common.js'
 
-async function getQueue () {
-  try {
-    const res = await fetch(`${url}/queue`)
-    const data = await res.json()
-    // console.log(data.queue_running,data.queue_pending)
-    return {
-      // Running action uses a different endpoint for cancelling
-      Running: data.queue_running.length,
-      Pending: data.queue_pending.length
-    }
-  } catch (error) {
-    console.error(error)
-    return { Running: 0, Pending: 0 }
-  }
-}
-
-async function interrupt () {
-  const resp = await fetch(`${url}/interrupt`, {
-    method: 'POST'
-  })
-}
+// let url = getUrl()
 
 async function clipboardWriteImage (win, url) {
   const canvas = document.createElement('canvas')
@@ -208,22 +194,6 @@ async function shareScreen (
   }
 }
 
-async function sleep (t = 200) {
-  return new Promise((res, rej) => {
-    setTimeout(() => {
-      res(true)
-    }, t)
-  })
-}
-
-function createImage (url) {
-  let im = new Image()
-  return new Promise((res, rej) => {
-    im.onload = () => res(im)
-    im.src = url
-  })
-}
-
 async function compareImages (threshold, previousImage, currentImage) {
   // 将 base64 转换为 Image 对象
   var previousImg = await createImage(previousImage)
@@ -458,44 +428,6 @@ async function requestCamera () {
   return false
 }
 
-/* 
-A method that returns the required style for the html 
-*/
-function get_position_style (ctx, widget_width, y, node_height, top) {
-  const MARGIN = 4 // the margin around the html element
-
-  /* Create a transform that deals with all the scrolling and zooming */
-  const elRect = ctx.canvas.getBoundingClientRect()
-  const transform = new DOMMatrix()
-    .scaleSelf(
-      elRect.width / ctx.canvas.width,
-      elRect.height / ctx.canvas.height
-    )
-    .multiplySelf(ctx.getTransform())
-    .translateSelf(MARGIN, MARGIN + y)
-
-  return {
-    transformOrigin: '0 0',
-    transform: transform,
-    left: `0`,
-    top: `${top}px`,
-    cursor: 'pointer',
-    position: 'absolute',
-    maxWidth: `${widget_width - MARGIN * 2}px`,
-    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
-    width: `${widget_width - MARGIN * 2}px`,
-    // height: `${node_height - MARGIN * 2}px`,
-    // background: '#EEEEEE',
-    display: 'flex',
-    flexDirection: 'column',
-    // alignItems: 'center',
-    justifyContent: 'space-around'
-  }
-}
-
-const base64Df =
-  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
-
 app.registerExtension({
   name: 'Mixlab.image.ScreenShareNode',
   async getCustomWidgets (app) {
@@ -593,17 +525,12 @@ app.registerExtension({
           type: 'HTML', // whatever
           name: 'sreen_share', // whatever
           draw (ctx, node, widget_width, y, widget_height) {
-            // console.log('ScreenSHare', y, widget_height)
+            // console.log('ScreenSHare', node)
             Object.assign(
               this.card.style,
-              get_position_style(
-                ctx,
-                widget_width,
-                widget_height * 5,
-                node.size[1],
-                40
-              )
+              get_position_style(ctx, widget_width, y, node.size[1], 40)
             )
+ 
           }
         }
 
@@ -1043,12 +970,13 @@ async function setArea (src) {
   div.innerHTML = `
     <div id='ml_overlay' style='position: absolute;top:0;background: #251f1fc4;
     height: 100vh;
-    z-index:999999;
+    z-index:99999999999999;
     width: 100%;'>
       <img id='ml_video' style='position: absolute; 
       height: ${displayHeight}px;user-select: none; 
       -webkit-user-drag: none;
       outline: 2px solid #eaeaea;
+      left: 0;
       box-shadow: 8px 9px 17px #575757;' />
       <div id='ml_selection' style='position: absolute; 
       border: 2px dashed red; 
@@ -1267,7 +1195,7 @@ app.registerExtension({
         })
 
         widget.PictureInPicture = $el('button', {
-          innerText: 'PictureInPicture',
+          innerText: 'Picture In Picture',
           style: {
             display: 'pictureInPictureEnabled' in document ? 'block' : 'none',
             cursor: 'pointer',

web/javascript/p5.js

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

web/javascript/prompt_mixlab.js

@@ -3,7 +3,7 @@ import { api } from '../../../scripts/api.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
 
-import PhotoSwipeLightbox from '/extensions/comfyui-mixlab-nodes/lib/photoswipe-lightbox.esm.min.js'
+import PhotoSwipeLightbox from '/mixlab/app/lib/photoswipe-lightbox.esm.min.js'
 function loadCSS (url) {
   var link = document.createElement('link')
   link.rel = 'stylesheet'
@@ -40,14 +40,14 @@ function loadCSS (url) {
   // Append the style element to the document head
   document.head.appendChild(style)
 }
-loadCSS('/extensions/comfyui-mixlab-nodes/lib/photoswipe.min.css')
+loadCSS('/mixlab/app/lib/photoswipe.min.css')
 
 function initLightBox () {
   const lightbox = new PhotoSwipeLightbox({
     gallery: '.prompt_image_output',
     children: 'a',
     pswpModule: () =>
-      import('/extensions/comfyui-mixlab-nodes/lib/photoswipe.esm.min.js')
+      import('/mixlab/app/lib/photoswipe.esm.min.js')
   })
 
   lightbox.on('uiRegister', function () {
@@ -100,7 +100,10 @@ function get_position_style (ctx, widget_width, y, node_height) {
   return {
     transformOrigin: '0 0',
     transform: transform,
-    left: `0`,
+    left:
+    document.querySelector('.comfy-menu').style.display === 'none'
+      ? `60px`
+      : `0`,
     top: `0`,
     cursor: 'pointer',
     position: 'absolute',
@@ -178,7 +181,7 @@ app.registerExtension({
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = async function () {
         orig_nodeCreated?.apply(this, arguments)
-         
+
         const mutable_prompt = this.widgets.filter(
           w => w.name == 'mutable_prompt'
         )[0]
@@ -190,7 +193,12 @@ app.registerExtension({
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(ctx, widget_width, y, node.size[1])
+              get_position_style(
+                ctx,
+                widget_width,
+                y + widget_height + 24,
+                node.size[1]
+              )
             )
           }
         }
@@ -207,7 +215,7 @@ app.registerExtension({
         background-color: var(--comfy-input-bg);
         border-radius: 8px;
         border-color: var(--border-color);
-        border-style: solid;    height: 30px;min-width: 122px;
+        border-style: solid;height: 30px;min-width: 122px;
        `
 
         // const btn=document.createElement('button');
@@ -266,7 +274,6 @@ app.registerExtension({
   },
   async loadedGraphNode (node, app) {
     if (node.type === 'RandomPrompt') {
-     
     }
   }
 })
@@ -408,7 +415,7 @@ const _createResult = async (node, widget, message) => {
   const width = node.size[0] * 0.5 - 12
 
   let height_add = 0
-  
+
   for (let index = 0; index < message._images.length; index++) {
     const imgs = message._images[index]
 
@@ -559,7 +566,7 @@ app.registerExtension({
 
       let cards = widget.div.querySelectorAll('.card')
       if (cards.length == 0) node.size = [280, 120]
-      if(widget.value) _createResult(node, widget, widget.value)
+      if (widget.value) _createResult(node, widget, widget.value)
     }
   }
 })

web/javascript/saveTripoSrMesh.js

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

web/javascript/sound-lab.js

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

web/javascript/td_background.js

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

web/javascript/ui_mixlab.js

@@ -11,6 +11,10 @@ import { smart_init, addSmartMenu } from './smart_connect.js'
 
 import { completion_ } from './chat.js'
 
+import { getLocalData, saveLocalData, updateLLMAPIKey } from './common.js'
+
+const BIZYAIR_SERVER_ADDRESS = 'https://api.siliconflow.cn'
+
 function showTextByLanguage (key, json) {
   // 获取浏览器语言
   var language = navigator.language
@@ -26,7 +30,50 @@ function showTextByLanguage (key, json) {
 }
 
 //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`
+// 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`
+
+let tool = {
+  name: 'create_prompt',
+  description:
+    'Create a prompt with a given subject, content, and style based on user input for image descriptions.',
+  parameter: {
+    type: 'object',
+    properties: {
+      subject: {
+        type: 'string',
+        description:
+          'The subject of the prompt, included verbatim from the input request.',
+        required: true
+      },
+      content: {
+        type: 'string',
+        description:
+          'The content of the prompt, primarily focusing on the scene and objects, including keywords for adjective, type of image, framing/composition, subject appearance/action, and environment.',
+        required: true
+      },
+      style: {
+        type: 'string',
+        description:
+          'The style of the prompt, including lighting situation, details of the shoot/illustration, visual aesthetics, and artists. Ensure it is high quality, non-verbose, coherent, brief, concise, and not superfluous.',
+        required: true
+      }
+    }
+  }
+}
+
+const systemPrompt = `
+Prompt:
+
+Describe a scene with a specific theme in fluent and highly detailed English, focusing on the content and style. The description should be within 100 words.
+
+Theme: [Insert Theme Here]
+
+Example:
+
+Theme: Sunset
+
+The sun sets in a blaze of orange and pink, casting a warm glow over a tranquil lake. Silhouetted trees line the shore, their reflections shimmering in the water. A lone figure sits at the end of a wooden pier, feet dangling above the mirrored surface, lost in thought. The scene exudes peacefulness and quiet beauty.
+`
 
 if (!localStorage.getItem('_mixlab_system_prompt')) {
   localStorage.setItem('_mixlab_system_prompt', systemPrompt)
@@ -72,7 +119,7 @@ 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
     }
 
@@ -117,6 +164,31 @@ function resizeImage (base64Image) {
   })
 }
 
+const createMixlabBtn = () => {
+  const appsButton = document.createElement('button')
+  appsButton.id = 'mixlab_chatbot_by_llamacpp'
+  appsButton.className = 'comfyui-button'
+  appsButton.textContent = '♾️Mixlab'
+
+  // appsButton.onclick = () =>
+  appsButton.onclick = async () => {
+    let llm_key = await updateLLMAPIKey()
+    // 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([], llm_key)
+  }
+  return appsButton
+}
+
 // 菜单入口
 async function createMenu () {
   const menu = document.querySelector('.comfy-menu')
@@ -128,26 +200,16 @@ async function createMenu () {
   `
   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)
-      }
+  if (
+    menu.style.display === 'none' &&
+    document.querySelector('.comfyui-menu-push')
+  ) {
+    //新版ui
+    document.querySelector('.comfyui-menu-push').append(createMixlabBtn())
+  } else {
+    if (!menu.querySelector('#mixlab_chatbot_by_llamacpp')) {
+      menu.append(createMixlabBtn())
     }
-    menu.append(appsButton)
   }
 }
 
@@ -160,6 +222,16 @@ function loadExternalScript (url) {
       return
     }
 
+    const existingScript = document.querySelector(`script[src="${url}"]`)
+    if (existingScript) {
+      existingScript.onload = () => {
+        isScriptLoaded[url] = true
+        resolve()
+      }
+      existingScript.onerror = reject
+      return
+    }
+
     const script = document.createElement('script')
     script.src = url
     script.onload = () => {
@@ -202,9 +274,7 @@ function createChart (chartDom, nodes) {
 }
 
 async function createNodesCharts () {
-  await loadExternalScript(
-    '/extensions/comfyui-mixlab-nodes/lib/echarts.min.js'
-  )
+  await loadExternalScript('/mixlab/app/lib/echarts.min.js')
   const templates = await loadTemplate()
   var nodes = {}
   Array.from(templates, t => {
@@ -635,7 +705,10 @@ function get_position_style (ctx, widget_width, y, node_height) {
   return {
     transformOrigin: '0 0',
     transform: transform,
-    left: `0`,
+    left:
+      document.querySelector('.comfy-menu').style.display === 'none'
+        ? `60px`
+        : `0`,
     top: `0`,
     cursor: 'pointer',
     position: 'absolute',
@@ -770,7 +843,69 @@ async function fetchReadmeContent (url) {
   }
 }
 
-function createModelsModal (models) {
+async function startLLM (model) {
+  let res = await start_llama(model)
+  window._mixlab_llamacpp = res || { model: [] }
+
+  localStorage.setItem('_mixlab_llama_select', res?.model || '')
+
+  if (
+    document.body.querySelector('#mixlab_chatbot_by_llamacpp') &&
+    window._mixlab_llamacpp?.url
+  ) {
+    document.body
+      .querySelector('#mixlab_chatbot_by_llamacpp')
+      .setAttribute('title', window._mixlab_llamacpp.url)
+  }
+  if (
+    document.body.querySelector('#llm_status_btn') &&
+    window._mixlab_llamacpp
+  ) {
+    document.body.querySelector('#llm_status_btn').innerText =
+      window._mixlab_llamacpp.model
+  }
+}
+
+function createInputOfLabel (labelText, key, id) {
+  const label = document.createElement('p')
+  label.innerText = labelText
+
+  const input = document.createElement('input')
+  input.type = 'text'
+  input.style = `color: var(--input-text);
+  background-color: var(--comfy-input-bg);
+  border-radius: 8px;
+  border-color: var(--border-color);
+  height: 26px;
+  padding: 4px 10px;
+  width: 150px;
+  margin-left: 12px;`
+
+  input.value =
+    getLocalData(key)['-'] || Object.values(getLocalData(key))[0] || 'by Mixlab'
+
+  input.addEventListener('change', e => {
+    e.stopPropagation()
+    e.preventDefault()
+
+    saveLocalData(key, '-', input.value)
+  })
+
+  const div = document.createElement('div')
+  div.style = `display: flex;
+    justify-content: flex-start;
+    align-items: baseline;padding: 0 18px;`
+
+  div.addEventListener('click', e => {
+    e.stopPropagation()
+  })
+
+  div.appendChild(label)
+  div.appendChild(input)
+  return div
+}
+
+function createModelsModal (models, llmKey) {
   var div =
     document.querySelector('#model-modal') || document.createElement('div')
   div.id = 'model-modal'
@@ -825,7 +960,7 @@ function createModelsModal (models) {
       color: var(--descrip-text);
       font-size: 18px;
       display: flex;
-      align-items: center;
+      align-items: flex-start;
       flex: 1;
       overflow: hidden;
       text-decoration: none;
@@ -836,70 +971,81 @@ function createModelsModal (models) {
       user-select: none;
     `
 
-  // headTitleElement.href = 'https://github.com/shadowcz007/comfyui-mixlab-nodes'
-  // headTitleElement.target = '_blank'
   const linkIcon = document.createElement('small')
   linkIcon.textContent = showTextByLanguage('Auto Open', {
     'Auto Open': '自动开启'
   })
   linkIcon.style.padding = '4px'
 
-  const n_gpu = document.createElement('input')
-  n_gpu.type = 'number'
-  n_gpu.setAttribute('min', -1)
-  n_gpu.setAttribute('max', 9999)
-
-  n_gpu.style = `color: var(--input-text);
-  background-color: var(--comfy-input-bg);
-  border-radius: 8px;
-  border-color: var(--border-color);
-  height: 26px;
-  padding: 4px 10px;
-  width: 48px;
+  const statusIcon = document.createElement('small')
+  statusIcon.textContent = showTextByLanguage('Status', {
+    Status: 'OFF'
+  })
+  statusIcon.id = 'llm_status_btn'
+  statusIcon.style = `padding: 4px;
+  background-color: rgb(102, 255, 108);
+  color: black;
+  font-size: 12px;
   margin-left: 12px;`
-  if (localStorage.getItem('_mixlab_llama_n_gpu')) {
-    n_gpu.value = parseInt(localStorage.getItem('_mixlab_llama_n_gpu'))
+  if (window._mixlab_llamacpp?.url) {
+    statusIcon.textContent = window._mixlab_llamacpp.model
+    statusIcon.style.backgroundColor = '#66ff6c'
+    statusIcon.style.color = 'black'
   } else {
-    n_gpu.value = -1
-    localStorage.setItem('_mixlab_llama_n_gpu', -1)
   }
+  statusIcon.addEventListener('click', e => {
+    e.stopPropagation()
+    // startLLM()
+  })
 
-  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 siliconflowHelp = document.createElement('a')
+  siliconflowHelp.textContent = showTextByLanguage('Siliconflow', {
+    Siliconflow: '硅基流动'
+  })
+  siliconflowHelp.style = `color: var(--input-text);
+  background-color: var(--comfy-input-bg);margin-top:14px`
+  siliconflowHelp.href = 'https://cloud.siliconflow.cn/s/mixlabs'
+  siliconflowHelp.target = '_blank' 
+
 
   const title = document.createElement('p')
-  title.innerText = 'Models'
+  title.innerText = 'Mixlab Nodes'
   title.style = `font-size: 18px;
-  margin-right: 8px;`
+  margin-right: 8px;
+  margin-top: 0;`
 
   const left_d = document.createElement('div')
   left_d.style = `display: flex;
   justify-content: center;
-  align-items: center;
-  font-size: 12px;`
+  align-items: flex-start;
+  font-size: 12px;
+  flex-direction: column; `
   left_d.appendChild(title)
-
-  left_d.appendChild(linkIcon)
+  left_d.appendChild(batchPageBtn)
+  left_d.appendChild(siliconflowHelp)
 
   headTitleElement.appendChild(left_d)
 
-  headTitleElement.appendChild(n_gpu_div)
-
   //重启
   const reStart = document.createElement('small')
   reStart.textContent = showTextByLanguage('restart', {
     restart: '重启'
   })
 
-  reStart.style.padding = '4px'
+  reStart.style = `padding: 8px;
+  font-size: 16px;
+  outline: 1px solid;
+  padding-top: 4px;
+  padding-bottom: 4px;`
 
   headTitleElement.appendChild(reStart)
 
@@ -928,60 +1074,36 @@ function createModelsModal (models) {
     })
   })
 
-  n_gpu.addEventListener('click', e => {
-    e.stopPropagation()
-    localStorage.setItem('_mixlab_llama_n_gpu', n_gpu.value)
-  })
-
   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')
+  let llmKeyDiv = createInputOfLabel('LLM Key', '_mixlab_llm_api_key', '-')
 
-  input.addEventListener('change', e => {
+  saveLocalData('_mixlab_llm_api_url', '-', BIZYAIR_SERVER_ADDRESS)
+  let llmAPIDiv = createInputOfLabel('LLM API', '_mixlab_llm_api_url', '-')
+
+  modalContent.appendChild(llmKeyDiv)
+  modalContent.appendChild(llmAPIDiv)
+
+  var inputForSystemPrompt = document.createElement('textarea')
+  inputForSystemPrompt.className = 'comfy-multiline-input'
+  inputForSystemPrompt.style = `height: 260px;width: 480px;font-size: 16px;padding: 18px;`
+  inputForSystemPrompt.value = localStorage.getItem('_mixlab_system_prompt')
+
+  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)
 
-  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)
 
   const helpInfo = document.createElement('a')
@@ -994,8 +1116,8 @@ function createModelsModal (models) {
   cursor: pointer;
   font-size: 12px;
   color: white;`
-  helpInfo.href="https://discord.gg/cXs9vZSqeK"
-  helpInfo.target="_blank"
+  helpInfo.href = 'https://discord.gg/cXs9vZSqeK'
+  helpInfo.target = '_blank'
   modal.appendChild(helpInfo)
 
   document.body.appendChild(div)
@@ -1349,6 +1471,8 @@ app.registerExtension({
           .querySelector('#mixlab_chatbot_by_llamacpp')
           .setAttribute('title', res.url)
       })
+    } else {
+      // startLLM('')
     }
 
     LGraphCanvas.prototype.helpAboutNode = async function (node) {
@@ -1357,7 +1481,13 @@ app.registerExtension({
           ? nodesMap
           : await getCustomnodeMappings('url')
 
-      console.log("%c### node & node map", "background: yellow; color: black", node, nodesMap, nodesMap[node.type])
+      console.log(
+        '%c### node & node map',
+        'background: yellow; color: black',
+        node,
+        nodesMap,
+        nodesMap[node.type]
+      )
       let repo = nodesMap[node.type]
       if (repo) {
         let markdown = await fetchReadmeContent(repo.url)
@@ -1367,16 +1497,18 @@ app.registerExtension({
 
     LGraphCanvas.prototype.fixTheNode = function (node) {
       let new_node = LiteGraph.createNode(node.comfyClass)
-      new_node.pos = [node.pos[0], node.pos[1]]
-      app.canvas.graph.add(new_node, false)
-      copyNodeValues(node, new_node)
-      app.canvas.graph.remove(node)
+      console.log(node)
+      if (new_node) {
+        new_node.pos = [node.pos[0], node.pos[1]]
+        app.canvas.graph.add(new_node, false)
+        copyNodeValues(node, new_node)
+        app.canvas.graph.remove(node)
+      }
     }
 
     smart_init()
 
     LGraphCanvas.prototype.text2text = async function (node) {
-      // console.log(node)
       let widget = node.widgets.filter(
         w => w.name === 'text' && typeof w.value == 'string'
       )[0]
@@ -1388,9 +1520,14 @@ app.registerExtension({
         let userInput = widget.value
         widget.value = widget.value.trim()
         widget.value += '\n'
+        let jsonStr = ''
         try {
           await completion_(
-            window._mixlab_llamacpp.url + '/v1/chat/completions',
+            getLocalData('_mixlab_llm_api_key')['-'] ||
+              Object.values(getLocalData('_mixlab_llm_api_key'))[0],
+            getLocalData('_mixlab_llm_api_url')['-'] ||
+              Object.values(getLocalData('_mixlab_llm_api_url'))[0],
+
             [
               {
                 role: 'system',
@@ -1400,40 +1537,15 @@ app.registerExtension({
             ],
             controller,
             t => {
-              // console.log(t)
+              // console.log(t.endsWith('\r'))
               widget.value += t
+              jsonStr += 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)
-
-              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
-                }
-              )
-            })
-          }
+          console.log(error)
         }
-
-        widget.value = widget.value.trim()
+ 
       }
     }
 
@@ -1696,6 +1808,7 @@ app.registerExtension({
         {
           content: 'Help ♾️Mixlab', // with a name
           callback: () => {
+            // console.log('#data',node)
             LGraphCanvas.prototype.helpAboutNode(node)
           } // and the callback
         },
@@ -1716,12 +1829,17 @@ app.registerExtension({
           inp => inp.name == 'text' && inp.type == 'STRING'
         )
 
+        const llm_api_key =
+            getLocalData('_mixlab_llm_api_key')['-'] ||
+            Object.values(getLocalData('_mixlab_llm_api_key'))[0],
+          llm_api_url =
+            getLocalData('_mixlab_llm_api_url')['-'] ||
+            Object.values(getLocalData('_mixlab_llm_api_url'))[0]
+
         if (
-          text_input &&
-          text_input.length == 0 &&
           text_widget &&
           text_widget.length == 1 &&
-          window._mixlab_llamacpp &&
+          llm_api_key &&llm_api_url&&
           node.type != 'ShowTextForGPT'
         ) {
           opts.push({
@@ -1732,19 +1850,19 @@ app.registerExtension({
           })
         }
 
-        if (
-          node.imgs &&
-          node.imgs.length > 0 &&
-          window._mixlab_llamacpp &&
-          window._mixlab_llamacpp.chat_format === 'llava-1-5'
-        ) {
-          opts.push({
-            content: 'Image-to-Text ♾️Mixlab', // with a name
-            callback: () => {
-              LGraphCanvas.prototype.image2text(node)
-            } // and the callback
-          })
-        }
+        // if (
+        //   node.imgs &&
+        //   node.imgs.length > 0 &&
+        //   window._mixlab_llamacpp &&
+        //   window._mixlab_llamacpp.chat_format === 'llava-1-5'
+        // ) {
+        //   opts.push({
+        //     content: 'Image-to-Text ♾️Mixlab', // with a name
+        //     callback: () => {
+        //       LGraphCanvas.prototype.image2text(node)
+        //     } // and the callback
+        //   })
+        // }
       }
 
       return [...opts, null, ...options] // and return the options
@@ -1805,127 +1923,127 @@ app.registerExtension({
       // Add canvas menu options
       const orig = LGraphCanvas.prototype.getCanvasMenuOptions
 
-      const apps = await get_my_app()
-      if (!apps) return
+      // const apps = await get_my_app()
+      // if (!apps) return
 
-      console.log('apps', apps)
+      // console.log('apps', apps)
 
-      let apps_map = { 0: [] }
+      // let apps_map = { 0: [] }
 
-      for (const app of apps) {
-        if (app.category) {
-          if (!apps_map[app.category]) apps_map[app.category] = []
-          apps_map[app.category].push(app)
-        } else {
-          apps_map['0'].push(app)
-        }
-      }
+      // for (const app of apps) {
+      //   if (app.category) {
+      //     if (!apps_map[app.category]) apps_map[app.category] = []
+      //     apps_map[app.category].push(app)
+      //   } else {
+      //     apps_map['0'].push(app)
+      //   }
+      // }
 
       let apps_opts = []
-      for (const category in apps_map) {
-        // console.log('category', typeof category)
-        if (category === '0') {
-          apps_opts.push(
-            ...Array.from(apps_map[category], a => {
-              // console.log('#1级',a)
-              return {
-                content: `${a.name}_${a.version}`,
-                has_submenu: false,
-                callback: async () => {
-                  try {
-                    let ddd = await get_my_app(a.filename)
-                    if (!ddd) return
-                    let item = ddd[0]
-                    if (item) {
-                      if (item.author) {
-                        // 有作者信息
-                        if (item.author.avatar)
-                          localStorage.setItem(
-                            '_mixlab_author_avatar',
-                            item.author.avatar
-                          )
-                        if (item.author.name)
-                          localStorage.setItem(
-                            '_mixlab_author_name',
-                            item.author.name
-                          )
+      // for (const category in apps_map) {
+      //   // console.log('category', typeof category)
+      //   if (category === '0') {
+      //     apps_opts.push(
+      //       ...Array.from(apps_map[category], a => {
+      //         // console.log('#1级',a)
+      //         return {
+      //           content: `${a.name}_${a.version}`,
+      //           has_submenu: false,
+      //           callback: async () => {
+      //             try {
+      //               let ddd = await get_my_app(a.filename)
+      //               if (!ddd) return
+      //               let item = ddd[0]
+      //               if (item) {
+      //                 if (item.author) {
+      //                   // 有作者信息
+      //                   if (item.author.avatar)
+      //                     localStorage.setItem(
+      //                       '_mixlab_author_avatar',
+      //                       item.author.avatar
+      //                     )
+      //                   if (item.author.name)
+      //                     localStorage.setItem(
+      //                       '_mixlab_author_name',
+      //                       item.author.name
+      //                     )
 
-                        if (item.author.link)
-                          localStorage.setItem(
-                            '_mixlab_author_link',
-                            item.author.link
-                          )
-                      }
+      //                   if (item.author.link)
+      //                     localStorage.setItem(
+      //                       '_mixlab_author_link',
+      //                       item.author.link
+      //                     )
+      //                 }
 
-                      // console.log(item.data)
-                      app.loadGraphData(item.data)
-                      setTimeout(() => {
-                        const node = app.graph._nodes_in_order[0]
-                        if (!node) return
-                        app.canvas.centerOnNode(node)
-                        app.canvas.setZoom(0.5)
-                      }, 1000)
-                    }
-                  } catch (error) {}
-                }
-              }
-            })
-          )
-        } else {
-          // 二级
-          apps_opts.push({
-            content: '🚀 ' + category,
-            has_submenu: true,
-            disabled: false,
-            submenu: {
-              options: Array.from(apps_map[category], a => {
-                // console.log('#二级',a)
-                return {
-                  content: `${a.name}_${a.version}`,
-                  callback: async () => {
-                    try {
-                      let ddd = await get_my_app(a.filename, a.category)
+      //                 // console.log(item.data)
+      //                 app.loadGraphData(item.data)
+      //                 setTimeout(() => {
+      //                   const node = app.graph._nodes_in_order[0]
+      //                   if (!node) return
+      //                   app.canvas.centerOnNode(node)
+      //                   app.canvas.setZoom(0.5)
+      //                 }, 1000)
+      //               }
+      //             } catch (error) {}
+      //           }
+      //         }
+      //       })
+      //     )
+      //   } else {
+      //     // 二级
+      //     apps_opts.push({
+      //       content: '🚀 ' + category,
+      //       has_submenu: true,
+      //       disabled: false,
+      //       submenu: {
+      //         options: Array.from(apps_map[category], a => {
+      //           // console.log('#二级',a)
+      //           return {
+      //             content: `${a.name}_${a.version}`,
+      //             callback: async () => {
+      //               try {
+      //                 let ddd = await get_my_app(a.filename, a.category)
 
-                      if (!ddd) return
-                      let item = ddd[0]
-                      if (item) {
-                        console.log(item)
-                        if (item.author) {
-                          // 有作者信息
-                          if (item.author.avatar)
-                            localStorage.setItem(
-                              '_mixlab_author_avatar',
-                              item.author.avatar
-                            )
-                          if (item.author.name)
-                            localStorage.setItem(
-                              '_mixlab_author_name',
-                              item.author.name
-                            )
-                          if (item.author.link)
-                            localStorage.setItem(
-                              '_mixlab_author_link',
-                              item.author.link
-                            )
-                        }
+      //                 if (!ddd) return
+      //                 let item = ddd[0]
+      //                 if (item) {
+      //                   console.log(item)
+      //                   if (item.author) {
+      //                     // 有作者信息
+      //                     if (item.author.avatar)
+      //                       localStorage.setItem(
+      //                         '_mixlab_author_avatar',
+      //                         item.author.avatar
+      //                       )
+      //                     if (item.author.name)
+      //                       localStorage.setItem(
+      //                         '_mixlab_author_name',
+      //                         item.author.name
+      //                       )
+      //                     if (item.author.link)
+      //                       localStorage.setItem(
+      //                         '_mixlab_author_link',
+      //                         item.author.link
+      //                       )
+      //                   }
 
-                        // console.log(item.data)
-                        app.loadGraphData(item.data)
-                        setTimeout(() => {
-                          const node = app.graph._nodes_in_order[0]
-                          if (!node) return
-                          app.canvas.centerOnNode(node)
-                          app.canvas.setZoom(0.5)
-                        }, 1000)
-                      }
-                    } catch (error) {}
-                  }
-                }
-              })
-            }
-          })
-        }
-      }
+      //                   // console.log(item.data)
+      //                   app.loadGraphData(item.data)
+      //                   setTimeout(() => {
+      //                     const node = app.graph._nodes_in_order[0]
+      //                     if (!node) return
+      //                     app.canvas.centerOnNode(node)
+      //                     app.canvas.setZoom(0.5)
+      //                   }, 1000)
+      //                 }
+      //               } catch (error) {}
+      //             }
+      //           }
+      //         })
+      //       }
+      //     })
+      //   }
+      // }
 
       // console.log('apps',apps_map, apps_opts,apps)
       LGraphCanvas.prototype.getCanvasMenuOptions = function () {

web/javascript/utils_mixlab.js

@@ -1,46 +1,13 @@
 import { app } from '../../../scripts/app.js'
-import { $el } from '../../../scripts/ui.js' 
+import { $el } from '../../../scripts/ui.js'
+import {
+  loadExternalScript,
+  updateLLMAPIKey,
+  get_position_style,
+  getLocalData
+} from './common.js'
 
-const getLocalData = key => {
-  let data = {}
-  try {
-    data = JSON.parse(localStorage.getItem(key)) || {}
-  } catch (error) {
-    return {}
-  }
-  return data
-}
-function get_position_style (ctx, widget_width, y, node_height) {
-  const MARGIN = 4 // the margin around the html element
-
-  /* Create a transform that deals with all the scrolling and zooming */
-  const elRect = ctx.canvas.getBoundingClientRect()
-  const transform = new DOMMatrix()
-    .scaleSelf(
-      elRect.width / ctx.canvas.width,
-      elRect.height / ctx.canvas.height
-    )
-    .multiplySelf(ctx.getTransform())
-    .translateSelf(MARGIN, MARGIN + y)
-
-  return {
-    transformOrigin: '0 0',
-    transform: transform,
-    left: `0`,
-    top: `0`,
-    cursor: 'pointer',
-    position: 'absolute',
-    maxWidth: `${widget_width - MARGIN * 2}px`,
-    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
-    width: `${widget_width - MARGIN * 2}px`,
-    // height: `${node_height * 0.3 - MARGIN * 2}px`,
-    // background: '#EEEEEE',
-    display: 'flex',
-    flexDirection: 'column',
-    // alignItems: 'center',
-    justifyContent: 'space-around'
-  }
-}
+loadExternalScript('/mixlab/app/lib/pickr.min.js')
 
 function hexToRGBA (hexColor) {
   var hex = hexColor.replace('#', '')
@@ -62,7 +29,7 @@ app.registerExtension({
   init () {
     $el('link', {
       rel: 'stylesheet',
-      href: '/extensions/comfyui-mixlab-nodes/lib/classic.min.css',
+      href: '/mixlab/app/lib/classic.min.css',
       parent: document.head
     })
 
@@ -122,7 +89,7 @@ app.registerExtension({
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
 
-        // console.log('Color nodeData', this.widgets)
+        // console.log('Color nodeData', this.div)
 
         const widget = {
           type: 'div',
@@ -273,19 +240,19 @@ app.registerExtension({
 })
 
 const min_max = node => {
-  if(node.widgets){
+  if (node.widgets) {
     const min_value = node.widgets.filter(w => w.name === 'min_value')[0]
     const max_value = node.widgets.filter(w => w.name === 'max_value')[0]
-  
+
     const number = node.widgets.filter(w => w.name === 'number')[0]
     if (number) {
       number.options.min = min_value.value
       number.options.max = max_value.value
-  
+
       number.value = Math.min(number.options.max, number.value)
       number.value = Math.max(number.options.min, number.value)
     }
-  
+
     if (min_value)
       min_value.callback = e => {
         number.options.min = e
@@ -297,22 +264,18 @@ const min_max = node => {
         number.value = e
       }
   }
-  
 }
 
 app.registerExtension({
   name: 'Mixlab.utils.FloatSlider',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
-
     if (nodeType.comfyClass == 'FloatSlider') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated;
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
         min_max(this)
       }
     }
-
-   
   },
   async loadedGraphNode (node, app) {
     if (node.type === 'FloatSlider') {
@@ -323,7 +286,6 @@ app.registerExtension({
 app.registerExtension({
   name: 'Mixlab.utils.IntNumber',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    
     if (nodeType.comfyClass == 'IntNumber') {
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = function () {
@@ -331,7 +293,6 @@ app.registerExtension({
         min_max(this)
       }
     }
-   
   },
   async loadedGraphNode (node, app) {
     if (node.type === 'IntNumber') {
@@ -340,22 +301,157 @@ app.registerExtension({
   }
 })
 
-
 app.registerExtension({
   name: 'Mixlab.utils.TESTNODE_',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    
     if (nodeType.comfyClass == 'TESTNODE_') {
-    
-      const onExecuted = nodeType.prototype.onExecuted;
-			nodeType.prototype.onExecuted = function (message) {
-				onExecuted?.apply(this, arguments);
-        console.log('##',message)
-				 
-			};
-
-
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log('##', message)
+      }
     }
-   
-  }, 
+  }
+})
+
+app.registerExtension({
+  name: 'Mixlab.utils.KeyInput',
+  init () {},
+  async getCustomWidgets (app) {
+    return {
+      KEY (node, inputName, inputData, app) {
+        // console.log('##node', node)
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          size: [128, 24], // a default size
+          draw (ctx, node, width, y) {},
+          computeSize (...args) {
+            return [128, 32] // a method to compute the current size of the widget
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let data = getLocalData('_mixlab_llm_api_key')
+            return data[node.id] || 'by Mixlab'
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'KeyInput') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const rowHeight = this.rowHeight
+        const widget = {
+          type: 'div',
+          name: 'input_key',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 24, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        const inputDiv = (key, placeholder) => {
+          let div = document.createElement('div')
+          div.style = `
+          display: flex;
+          align-items: center; 
+          margin: 6px 8px;
+          margin-top:0px;
+          height:44px;
+          width:220px; 
+          `
+
+          const ip = document.createElement('input')
+          ip.type = 'password'
+          ip.className = `${'comfy-multiline-input'} ${placeholder}`
+
+          ip.placeholder = placeholder
+          // ip.value = placeholder
+
+          ip.style = `margin-left:8px;
+            outline: none;
+            border: none;
+            padding:12px;
+            width: 100%;
+            `
+
+          div.appendChild(ip)
+
+          ip.addEventListener('change', () => {
+            let data = getLocalData(key)
+            data[this.id] = ip.value.trim()
+            localStorage.setItem(key, JSON.stringify(data))
+            updateLLMAPIKey(data[this.id])
+          })
+
+          return div
+        }
+
+        let inputKey = inputDiv('_mixlab_llm_api_key', 'Key')
+
+        widget.div.appendChild(inputKey)
+
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          inputKey.remove()
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        // const processMouseWheel=app.canvas.processMouseWheel
+        // app.canvas.processMouseWheel=()=>{
+        //   console.log(app.canvas.ds.scale)
+        //   return processMouseWheel?.()
+        // }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'KeyInput') {
+      let widget = node.widgets.filter(w => w.div)[0]
+
+      let apiKey = getLocalData('_mixlab_llm_api_key')
+
+      let id = node.id
+      if (widget.div.querySelector('.Key'))
+        widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
+
+      if (apiKey[id]) updateLLMAPIKey(apiKey[id])
+    }
+  },
+  nodeCreated (node, app) {
+    //数据延迟？？
+    setTimeout(() => {
+      // console.log('#LoadImagesToBatch', node.type)
+      if (node.type === 'KeyInput') {
+        let widget = node.widgets.filter(w => w.div)[0]
+
+        let apiKey = getLocalData('_mixlab_llm_api_key')
+
+        let id = node.id
+
+        if (widget.div.querySelector('.Key'))
+          widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
+
+        if (apiKey[id]) updateLLMAPIKey(apiKey[id])
+      }
+    }, 1000)
+  }
 })

web/javascript/video_mixlab.js

@@ -6,8 +6,6 @@ import { $el } from '../../../scripts/ui.js'
 
 // The code is based on ComfyUI-VideoHelperSuite modification.
 
-
-
 function injectCSS (css) {
   // 检查页面中是否已经存在具有相同内容的style标签
   const existingStyle = document.querySelector('style')
@@ -45,7 +43,10 @@ function get_position_style (ctx, widget_width, y, node_height) {
   return {
     transformOrigin: '0 0',
     transform: transform,
-    left: `0`,
+    left:
+    document.querySelector('.comfy-menu').style.display === 'none'
+      ? `60px`
+      : `0`,
     top: `0`,
     cursor: 'pointer',
     position: 'absolute',
@@ -240,15 +241,7 @@ app.registerExtension({
   }
 })
 
-
-function offsetDOMWidget(
-  widget,
-  ctx,
-  node,
-  widgetWidth,
-  widgetY,
-  height
-) {
+function offsetDOMWidget (widget, ctx, node, widgetWidth, widgetY, height) {
   const margin = 10
   const elRect = ctx.canvas.getBoundingClientRect()
   const transform = new DOMMatrix()
@@ -270,18 +263,18 @@ function offsetDOMWidget(
     position: 'absolute',
     background: !node.color ? '' : node.color,
     color: !node.color ? '' : 'white',
-    zIndex: 5, //app.graph._nodes.indexOf(node),
+    zIndex: 5 //app.graph._nodes.indexOf(node),
   })
 }
 
-export const hasWidgets = (node) => {
+export const hasWidgets = node => {
   if (!node.widgets || !node.widgets?.[Symbol.iterator]) {
     return false
   }
   return true
 }
 
-export const cleanupNode = (node) => {
+export const cleanupNode = node => {
   if (!hasWidgets(node)) {
     return
   }
@@ -298,43 +291,43 @@ export const cleanupNode = (node) => {
   }
 }
 
-const CreatePreviewElement = (name, val, format) => {
-  const [type] = format.split('/')  
+const createPreviewElement = (name, val, format) => {
+  const [type] = format.split('/')
   const w = {
-      name,
-      type,
-      value: val,
-      draw: function (ctx, node, widgetWidth, widgetY, height) {
-        const [cw, ch] = this.computeSize(widgetWidth)
-        offsetDOMWidget(this, ctx, node, widgetWidth, widgetY, ch)
-      },
-      computeSize: function (_) {
-        const ratio = this.inputRatio || 1
-        const width = Math.max(220, this.parent.size[0])
-        return [width, (width / ratio + 10)]
-      },
-      onRemoved: function () {
-        if (this.inputEl) {
-          this.inputEl.remove()
-        }
-      },
+    name,
+    type,
+    value: val,
+    draw: function (ctx, node, widgetWidth, widgetY, height) {
+      const [cw, ch] = this.computeSize(widgetWidth)
+      offsetDOMWidget(this, ctx, node, widgetWidth, widgetY, ch)
+    },
+    computeSize: function (_) {
+      const ratio = this.inputRatio || 1
+      const width = Math.max(220, this.parent.size[0])
+      return [width, width / ratio + 10]
+    },
+    onRemoved: function () {
+      if (this.inputEl) {
+        this.inputEl.remove()
+      }
     }
-
-    w.inputEl = document.createElement(type === 'video' ? 'video' : 'img')
-    w.inputEl.src = w.value
-    if (type === 'video') {
-      w.inputEl.setAttribute('type', 'video/webm');
-      w.inputEl.autoplay = true
-      w.inputEl.loop = true
-      w.inputEl.controls = false;
-    }
-    w.inputEl.onload = function () {
-      w.inputRatio = w.inputEl.naturalWidth / w.inputEl.naturalHeight
-    }
-    document.body.appendChild(w.inputEl)
-    return w
   }
 
+  w.inputEl = document.createElement(type === 'video' ? 'video' : 'img')
+  w.inputEl.src = w.value
+
+  if (type === 'video' || format.match('.mp4')) {
+    w.inputEl.setAttribute('type', 'video/webm')
+    w.inputEl.autoplay = true
+    w.inputEl.loop = true
+    w.inputEl.controls = true
+  }
+  w.inputEl.onload = function () {
+    w.inputRatio = w.inputEl.naturalWidth / w.inputEl.naturalHeight
+  }
+  document.body.appendChild(w.inputEl)
+  return w
+}
 
 app.registerExtension({
   name: 'Mixlab.Video.ImageListReplace',
@@ -469,12 +462,17 @@ app.registerExtension({
       }
     }
 
-    if (nodeData?.name == 'VideoCombine_Adv') {
+    if (
+      nodeData?.name == 'VideoCombine_Adv' ||
+      nodeData?.name == 'CombineAudioVideo'
+    ) {
       const onExecuted = nodeType.prototype.onExecuted
       nodeType.prototype.onExecuted = function (message) {
         const prefix = 'vhs_gif_preview_'
         const r = onExecuted ? onExecuted.apply(this, message) : undefined
 
+        if(!this.widgets) this.widgets=[]
+
         if (this.widgets) {
           const pos = this.widgets.findIndex(w => w.name === `${prefix}_0`)
           if (pos !== -1) {
@@ -489,12 +487,13 @@ app.registerExtension({
                 '/view?' + new URLSearchParams(params).toString()
               )
               const w = this.addCustomWidget(
-                CreatePreviewElement(
+                createPreviewElement(
                   `${prefix}_${i}`,
                   previewUrl,
                   params.format || 'image/gif'
                 )
               )
+              console.log(w)
               w.parent = this
             })
           }

web/javascript/visualization.js

@@ -0,0 +1,237 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+
+function base64ToBlobFromURL (base64URL, contentType) {
+  return fetch(base64URL).then(response => response.blob())
+}
+
+async function uploadImage (blob, fileType = '.svg', filename) {
+  // const blob = await (await fetch(src)).blob();
+  const body = new FormData()
+  body.append(
+    'image',
+    new File([blob], (filename || new Date().getTime()) + fileType)
+  )
+
+  const resp = await api.fetchApi('/upload/image', {
+    method: 'POST',
+    body
+  })
+
+  // console.log(resp)
+  let data = await resp.json()
+
+  return data
+}
+
+// 上传得到url
+async function uploadBase64ToFile (base64) {
+  let bg_blob = await base64ToBlobFromURL(base64)
+  let url = await uploadImage(bg_blob, '.png')
+  return url
+}
+
+class Visualizer {
+  constructor (node, container, visualSrc) {
+    this.node = node
+
+    this.iframe = document.createElement('iframe')
+    Object.assign(this.iframe, {
+      scrolling: 'no',
+      overflow: 'hidden'
+    })
+    this.iframe.src = '/mixlab/app/' + visualSrc + '.html'
+    console.log('#Visualizer', container, this.iframe)
+    container.appendChild(this.iframe)
+  }
+
+  updateVisual (params) {
+    console.log('#updateVisual', params, this.iframe)
+    // const iframeDocument = this.iframe.contentWindow.document
+    // const previewScript = iframeDocument.getElementById('visualizer')
+    // previewScript.setAttribute(
+    //   'reference_image',
+    //   JSON.stringify(params.reference_image)
+    // )
+    // previewScript.setAttribute('depth_map', JSON.stringify(params.depth_map))
+    // Update the reference image and depth map
+    this.iframe.contentWindow.postMessage(params, '*')
+  }
+
+  remove () {
+    this.container.remove()
+  }
+}
+
+function createVisualizer (node, inputName, typeName, inputData, app) {
+  node.name = inputName
+
+  const widget = {
+    type: typeName,
+    name: 'preview3d',
+    callback: () => {},
+    draw: function (ctx, node, widgetWidth, widgetY, widgetHeight) {
+      const margin = 10
+      const top_offset = 5
+      const visible = app.canvas.ds.scale > 0.5 && this.type === typeName
+      const w = widgetWidth - margin * 4
+      const clientRectBound = ctx.canvas.getBoundingClientRect()
+      const transform = new DOMMatrix()
+        .scaleSelf(
+          clientRectBound.width / ctx.canvas.width,
+          clientRectBound.height / ctx.canvas.height
+        )
+        .multiplySelf(ctx.getTransform())
+        .translateSelf(margin, margin + widgetY)
+
+      Object.assign(this.visualizer.style, {
+        left: `${transform.a * margin + transform.e}px`,
+        top: `${transform.d + transform.f + top_offset}px`,
+        width: `${w * transform.a}px`,
+        height: `${
+          w * transform.d - widgetHeight - margin * 15 * transform.d
+        }px`,
+        position: 'absolute',
+        overflow: 'hidden',
+        zIndex: app.graph._nodes.indexOf(node)
+      })
+
+      Object.assign(this.visualizer.children[0].style, {
+        transformOrigin: '50% 50%',
+        width: '100%',
+        height: '100%',
+        border: '0 none'
+      })
+
+      this.visualizer.hidden = !visible
+    }
+  }
+
+  const container = document.createElement('div')
+  container.id = `Comfy3D_${inputName}`
+
+  node.visualizer = new Visualizer(node, container, typeName)
+  widget.visualizer = container
+  widget.parent = node
+
+  document.body.appendChild(widget.visualizer)
+
+  node.addCustomWidget(widget)
+
+  node.updateParameters = params => {
+    // console.log('#updateParameters', params)
+    params.id = node.id
+    // node.visualizer = new Visualizer(node, container, typeName)
+    node.visualizer.updateVisual(params)
+  }
+
+  // Events for drawing backgound
+  node.onDrawBackground = function (ctx) {
+    if (!this.flags.collapsed) {
+      node.visualizer.iframe.hidden = false
+    } else {
+      node.visualizer.iframe.hidden = true
+    }
+  }
+
+  // Make sure visualization iframe is always inside the node when resize the node
+  node.onResize = function () {
+    let [w, h] = this.size
+    if (w <= 600) w = 600
+    if (h <= 500) h = 500
+
+    if (w > 600) {
+      h = w - 100
+    }
+
+    this.size = [w, h]
+  }
+
+  // Events for remove nodes
+  node.onRemoved = () => {
+    for (let w in node.widgets) {
+      if (node.widgets[w].visualizer) {
+        node.widgets[w].visualizer.remove()
+      }
+    }
+  }
+
+  return {
+    widget: widget
+  }
+}
+
+function registerVisualizer (nodeType, nodeData, nodeClassName, typeName) {
+  if (nodeData.name == nodeClassName) {
+    const onNodeCreated = nodeType.prototype.onNodeCreated
+
+    nodeType.prototype.onNodeCreated = async function () {
+      const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined
+
+      let Preview3DNode = app.graph._nodes.filter(
+        wi => wi.type == nodeClassName
+      )
+      let nodeName = `Preview3DNode_${Preview3DNode.length}`
+
+      const result = await createVisualizer.apply(this, [
+        this,
+        nodeName,
+        typeName,
+        {},
+        app
+      ])
+
+      this.setSize([600, 500])
+
+      return r
+    }
+
+    nodeType.prototype.onExecuted = async function (message) {
+      // Check if reference image and depth map are available
+      if (message.reference_image && message.depth_map) {
+        const params = {}
+        params.reference_image = message.reference_image[0]
+        params.depth_map = message.depth_map[0]
+        this.updateParameters(params)
+      }
+    }
+  }
+}
+
+app.registerExtension({
+  name: 'Mixlab.nodes.depthviewer',
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    registerVisualizer(nodeType, nodeData, 'DepthViewer', 'threeVisualizer')
+  },
+  nodeCreated (node, app) {
+    //数据延迟？？
+    setTimeout(() => {
+      let widget = node.widgets?.filter(w => w.name == 'preview3d')[0]
+      let framesWidget = node.widgets?.filter(w => w.name == 'frames')[0]
+
+      if (node.type === 'DepthViewer' && widget) {
+        let nodeId = node.id
+        //延迟才能获得this.id
+        widget.visualizer.querySelector('iframe').src += '?id=' + nodeId
+        // console.log('DepthViewer',widget)
+        window.addEventListener('message', async event => {
+          // 检查消息的来源，确保消息来自可信的源
+          console.log(event)
+          const { id, imgs } = event.data
+          if (id == nodeId) {
+            framesWidget.value = { images: [] }
+
+            for (const f of imgs) {
+              let file = await uploadBase64ToFile(f)
+              framesWidget.value.images.push(file)
+            }
+            // framesWidget.value.base64 = frames
+            framesWidget.value._seed = Math.random()
+            node.title = 'Input #' + imgs.length
+          }
+        })
+      }
+    }, 1000)
+  }
+})

web/javascript/watcher_mixlab.js

@@ -34,7 +34,10 @@ function get_position_style (ctx, widget_width, y, node_height) {
   return {
     transformOrigin: '0 0',
     transform: transform,
-    left: `0`,
+    left:
+      document.querySelector('.comfy-menu').style.display === 'none'
+        ? `60px`
+        : `0`,
     top: `0`,
     cursor: 'pointer',
     position: 'absolute',
@@ -125,7 +128,7 @@ app.registerExtension({
                   window._mixlab_file_path_watcher = json.event_type
                   // widget.card.innerText = window._mixlab_file_path_watcher || ''
                   //运行
-                  // document.querySelector('#queue-button').click()
+                  if (app) app.queuePrompt()
                 }
               })
             }, 1000)

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

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

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

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

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

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

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

@@ -1,13 +0,0 @@
-<?xml version="1.0" encoding="iso-8859-1"?>
-<!-- Generator: Adobe Illustrator 18.1.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
-<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
-	 viewBox="0 0 30.051 30.051" style="enable-background:new 0 0 30.051 30.051;" xml:space="preserve">
-<g>
-	<path d="M19.982,14.438l-6.24-4.536c-0.229-0.166-0.533-0.191-0.784-0.062c-0.253,0.128-0.411,0.388-0.411,0.669v9.069
-		c0,0.284,0.158,0.543,0.411,0.671c0.107,0.054,0.224,0.081,0.342,0.081c0.154,0,0.31-0.049,0.442-0.146l6.24-4.532
-		c0.197-0.145,0.312-0.369,0.312-0.607C20.295,14.803,20.177,14.58,19.982,14.438z"/>
-	<path d="M15.026,0.002C6.726,0.002,0,6.728,0,15.028c0,8.297,6.726,15.021,15.026,15.021c8.298,0,15.025-6.725,15.025-15.021
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web/lib/miniPaint-4.14.2/images/icons/brush.svg

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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web/lib/miniPaint-4.14.2/index.html

@@ -1,139 +0,0 @@
-<!DOCTYPE html>
-<html dir="ltr" lang="en-US">
-
-<head>
-	<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-	<meta http-equiv="x-ua-compatible" content="IE=edge" />
-	<title>miniPaint - image editor</title>
-	<meta name="description"
-		content="miniPaint is free online image editor using HTML5. Edit, adjust your images, add effects online in your browser, without installing anything..." />
-	<meta name="keywords"
-		content="photo, image, picture, transparent, layers, free, edit, html5, canvas, javascript, online, photoshop, gimp, effects, sharpen, blur, magic eraser tool, clone tool, rotate, resize, photoshop online, online tools, tilt shift, sprites, keypoints" />
-	<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1.0, user-scalable=0" />
-	<link rel="icon" sizes="192x192" href="images/favicon.png">
-	<!-- <link rel="manifest" href="dist/manifest.json"> -->
-	<!-- Google -->
-	<meta itemprop="name" content="miniPaint" />
-	<meta itemprop="description"
-		content="miniPaint is free online image editor using HTML5. Edit, adjust your images, add effects online in your browser, without installing anything..." />
-	<meta itemprop="image" content="https://viliusle.github.io/miniPaint/images/preview.jpg" />
-	<!-- Twitter -->
-	<meta name="twitter:card" content="summary_large_image" />
-	<meta name="twitter:title" content="miniPaint" />
-	<meta name="twitter:description"
-		content="miniPaint is free online image editor using HTML5. Edit, adjust your images, add effects online in your browser, without installing anything..." />
-	<meta name="twitter:image" content="https://viliusle.github.io/miniPaint/images/preview.jpg" />
-	<meta name="twitter:image:alt"
-		content="miniPaint is free online image editor using HTML5. Edit, adjust your images, add effects online in your browser, without installing anything..." />
-	<!-- Facebook, Pinterest -->
-	<meta property="og:title" content="miniPaint" />
-	<meta property="og:type" content="article" />
-	<meta property="og:url" content="https://viliusle.github.io/miniPaint/" />
-	<meta property="og:image" content="https://viliusle.github.io/miniPaint/images/preview.jpg" />
-	<meta property="og:description"
-		content="miniPaint is free online image editor using HTML5. Edit, adjust your images, add effects online in your browser, without installing anything..." />
-	<meta property="og:site_name" content="miniPaint" />
-
-	<script src="dist/bundle.ejs"></script>
-</head>
-
-<body>
-	<div class="wrapper">
-
-		<nav aria-label="Main Menu" class="main_menu" id="main_menu"></nav>
-
-		<div class="submenu">
-			<!-- <a class="logo" href="#">miniPaint</a> -->
-			<div class="block attributes" id="action_attributes"></div>
-			
-			<select id="automask_models_mixlab"></select>
-
-			<button id="automask_image_mixlab" type="button" style="width: 98px;
-			height: 36px;margin-right: 18px;
-			color: white;">
-				RemoveBg
-			</button>
-
-			<button id="cancel_image_mixlab" type="button" style="width: 98px;
-			height: 36px;
-			color: white;">
-				Cancel
-			</button>
-			<button id="save_image_mixlab" type="button" style="width: 98px;
-			height: 36px;
-			color: white;">
-				Save
-			</button>
-
-			<a  
-			target="_blank" href="https://discord.gg/xbP2GZF6gn" 
-			style="width: 98px;
-			color: white;
-			text-decoration: none;"> Help/帮助 </a>
-
-			<button class="undo_button" id="undo_button" type="button">
-				<span class="sr_only">Undo</span>
-			</button>
-		</div>
-
-		<div class="sidebar_left" id="tools_container"></div>
-
-
-		<div class="middle_area" id="middle_area">
-
-			<canvas class="ruler_left" id="ruler_left"></canvas>
-			<canvas class="ruler_top" id="ruler_top"></canvas>
-
-			<div class="main_wrapper" id="main_wrapper">
-				<div class="canvas_wrapper" id="canvas_wrapper">
-					<div id="mouse"></div>
-					<div class="transparent-grid" id="canvas_minipaint_background"></div>
-					<canvas id="canvas_minipaint">
-						<div class="trn error">
-							Your browser does not support canvas or JavaScript is not enabled.
-						</div>
-					</canvas>
-				</div>
-			</div>
-		</div>
-
-		<div class="sidebar_right">
-			<div class="preview block" style="display: none;">
-				<h2 class="trn toggle" data-target="toggle_preview">Preview</h2>
-				<div id="toggle_preview"></div>
-			</div>
-
-			<div class="colors block">
-				<h2 class="trn toggle" data-target="toggle_colors">Colors</h2>
-				<div class="content" id="toggle_colors"></div>
-			</div>
-
-			<div class="block" id="info_base" style="display: none;">
-				<h2 class="trn toggle toggle-full" data-target="toggle_info">Information</h2>
-				<div class="content" id="toggle_info"></div>
-			</div>
-
-			<div class="details block" id="details_base">
-				<h2 class="trn toggle toggle-full" data-target="toggle_details">Layer details</h2>
-				<div class="content details-content" id="toggle_details"></div>
-			</div>
-
-			<div class="layers block">
-				<h2 class="trn">Layers</h2>
-				<div class="content" id="layers_base"></div>
-			</div>
-		</div>
-	</div>
-	<div class="mobile_menu">
-		<button class="left_mobile_menu" id="left_mobile_menu_button" type="button">
-			<span class="sr_only">Toggle Menu</span>
-		</button>
-		<button class="right_mobile_menu" id="mobile_menu_button" type="button">
-			<span class="sr_only">Toggle Menu</span>
-		</button>
-	</div>
-	<div class="hidden" id="tmp"></div>
-	<div id="popups"></div>
-</body>
-
-</html>