v0.32.0

修正text image节点的padding问题

.github/workflows/publish.yml

@@ -0,0 +1,21 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

CNAME

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

README.md

@@ -6,21 +6,32 @@
 
 ##### `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 SiliconflowLLM，可以使用由Siliconflow提供的免费LLM
 
-- 右键菜单支持 text-to-text，方便对 prompt 词补全
+- 增加 Edit Mask，方便在生成的时候手动绘制 mask [workflow](./workflow/edit-mask-workflow.json)
 
-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)
+<!-- - 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 +48,7 @@ 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）
 
 ![](./assets/微信图片_20240421205440.png)
 
@@ -158,6 +170,9 @@ 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 +204,12 @@ 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```
+
+
+
 ### Style
 
 > Apply VisualStyle Prompting , Modified from [ComfyUI_VisualStylePrompting](https://github.com/ExponentialML/ComfyUI_VisualStylePrompting)

__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
@@ -171,15 +173,14 @@ 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:
@@ -309,9 +310,10 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
         print('app_workflow_path: ',app_workflow_path)
         try:
             with open(app_workflow_path) as json_file:
+                json_data=json.load(json_file)
                 apps = [{
                     'filename':filename,
-                    'data':json.load(json_file)
+                    'data':json_data
                 }]
         except Exception as e:
             print("发生异常：", str(e))
@@ -521,9 +523,17 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
             logging.info("\n")
             logging.info("\n\nStarting server")
 
+            import socket
+
+            hostname = socket.gethostname()
+            ip_address = socket.gethostbyname(hostname)
+
+            # print(f"本机的IP地址是: {ip_address}")
+
+
             # print("\033[93mStarting server\n")
-            logging.info("\033[93mTo see the GUI go to: http://{}:{}".format(address, http_port))
-            logging.info("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
+            logging.info("\033[93mTo see the GUI go to: http://{}:{} or http://{}:{}".format(ip_address, http_port,address,http_port))
+            logging.info("\033[93mTo see the GUI go to: https://{}:{} or https://{}:{}\033[0m".format(ip_address, https_port,address,https_port))
  
             # print("\033[93mTo see the GUI go to: http://{}:{}".format(address, http_port))
             # print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
@@ -608,13 +618,34 @@ async def mixlab_workflow_hander(request):
                     category=data['category']
                 if 'admin' in data:
                     admin=data['admin']
+
+                ds=get_my_workflow_for_app(filename,category,admin)
+                data=[]           
+                for json_data in ds:
+                    # 不传给前端
+                    if 'output' in json_data['data']:
+                        del json_data['data']['output']
+                    if 'workflow' in json_data['data']:
+                        del json_data['data']['workflow']
+                    data.append(json_data)
+
                 result={
-                    'data':get_my_workflow_for_app(filename,category,admin),
+                    'data':data,
                     'status':'success',
                 }
             elif data['task']=='list':
+                ds=get_workflows()
+                data=[]
+                for json_data in ds:
+                    # 不传给前端
+                    if 'output' in json_data['data']:
+                        del json_data['data']['output']
+                    if 'workflow' in json_data['data']:
+                        del json_data['data']['workflow']
+                    data.append(json_data)
+
                 result={
-                    'data':get_workflows(),
+                    'data':data,
                     'status':'success',
                 }
     except Exception as e:
@@ -699,23 +730,134 @@ async def rembg_hander(request):
 
     return web.json_response(result)
 
-
-@routes.post("/mixlab/prompt_result")
-async def post_prompt_result(request):
-    data = await request.json()
-    res=None
-    # print(data)
-    try:
-        action=data['action']
-        if action=='save':
-            result=data['data']
-            res=save_prompt_result(result['prompt_id'],result)
-        elif action=='all':
-            res=get_prompt_result()
-    except Exception as e:
-        print('/mixlab/prompt_result',False,e)
+# 保存运行结果？暂时去掉
+# @routes.post("/mixlab/prompt_result")
+# async def post_prompt_result(request):
+#     data = await request.json()
+#     res=None
+#     # print(data)
+#     try:
+#         action=data['action']
+#         if action=='save':
+#             result=data['data']
+#             res=save_prompt_result(result['prompt_id'],result)
+#         elif action=='all':
+#             res=get_prompt_result()
+#     except Exception as e:
+#         print('/mixlab/prompt_result',False,e)
     
-    return web.json_response({"result":res})
+#     return web.json_response({"result":res})
+
+# 种子设置
+def random_seed(seed, data):
+    max_seed = 4294967295
+
+    for id, value in data.items():
+        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("got prompt")
+    resp_code = 200
+    out_string = ""
+    json_data =  await request.json()
+    # json_data = p_intance.trigger_on_prompt(json_data)
+    # filename,category, client_id ,input
+    # workflow 的 filename,category
+
+    # 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 {}
+
+    apps=get_my_workflow_for_app(json_data['filename'],json_data['category'],False)
+    
+    prompt=json_data['prompt'] if 'prompt' in json_data else None
+
+    if len(apps)==1:
+        # 取到prompt
+        prompt=apps[0]['data']['output']
+        # 更新input_data到prompt里
+        '''
+          {
+                "inputs": {
+                    "number": 512,
+                    "min_value": 512,
+                    "max_value": 2048,
+                    "step": 1
+                },
+                "class_type": "IntNumber",
+                "id": "22"
+            },
+        '''
+
+        for inp in input_data:
+            id=inp['id']
+            if prompt[id]['class_type']==inp['class_type']:
+                prompt[id]['inputs'].update(inp['inputs']) 
+
+
+    if prompt==None:
+        return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
+    else:
+        # 种子更新 
+        '''
+            "seed": {
+                    "45": "randomize",
+                    "46": "randomize"
+                }
+            '''
+        json_data["prompt"]=random_seed(seed,prompt)
+
+    # print("#json_data",prompt)
+    # 需要把apps处理成 prompt
+    # 注意seed的处理
+    
+    if "number" in json_data:
+        number = float(json_data['number'])
+    else:
+        number = p_intance.number
+        if "front" in json_data:
+            if json_data['front']:
+                number = -number
+
+        p_intance.number += 1
+
+    if "prompt" in json_data:
+        prompt = json_data["prompt"]
+        valid = execution.validate_prompt(prompt)
+        extra_data = {}
+        if "extra_data" in json_data:
+            extra_data = json_data["extra_data"]
+
+        if "client_id" in json_data:
+            extra_data["client_id"] = json_data["client_id"]
+        if valid[0]:
+            prompt_id = str(uuid.uuid4())
+            outputs_to_execute = valid[2]
+            p_intance.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
+            response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
+            return web.json_response(response)
+        else:
+            logging.warning("invalid prompt: {}".format(valid[1]))
+            return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
+    else:
+        return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
+
 
 
 async def start_local_llm(data):
@@ -760,8 +902,7 @@ async def start_local_llm(data):
             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)
-
+    # print('#clip_model_path',chat_format,clip_model_path,model)
 
     address="127.0.0.1"
     port=9090
@@ -778,37 +919,40 @@ async def start_local_llm(data):
     
     server_settings=ServerSettings(host=address,port=port)
 
-    app = create_app(
-                server_settings=server_settings,
-                model_settings=[
-                    ModelSettings(
-                    model=model,
-                    model_alias=os.path.basename(model),
-                    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}
 
@@ -817,12 +961,12 @@ async def start_local_llm(data):
 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}
-        }
+        result= {"port":None,"model":"","llama_cpp_error":True}
         print('start_local_llm error')
 
     return web.json_response(result)
@@ -840,20 +984,16 @@ def re_start(request):
 
 # 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 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.Audio import AudioPlayNode,SpeechRecognition,SpeechSynthesis
 from .nodes.Utils import IncrementingListNode,ListSplit,CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
 from .nodes.Mask import PreviewMask_,MaskListReplace,MaskListMerge,OutlineMask,FeatheredMask
 
 from .nodes.Style import ApplyVisualStylePrompting,StyleAlignedReferenceSampler,StyleAlignedBatchAlign,StyleAlignedSampleReferenceLatents
 
-from .nodes.Video import VideoCombine_Adv,LoadVideoAndSegment,ImageListReplace,VAEEncodeForInpaint_Frames
-
-from .nodes.TripoSR import LoadTripoSRModel,TripoSRSampler,SaveTripoSRMesh
 
 
 # 要导出的所有节点及其名称的字典
@@ -884,6 +1024,7 @@ NODE_CLASS_MAPPINGS = {
     "ImageColorTransfer":ImageColorTransfer,
     "ShowLayer":ShowLayer,
     "NewLayer":NewLayer,
+    "ImageListToBatch_":ImageListToBatch_,
     "CompositeImages_":CompositeImages,
     "SplitImage":SplitImage,
     "CenterImage":CenterImage,
@@ -905,10 +1046,7 @@ NODE_CLASS_MAPPINGS = {
     # "VAEDecodeConsistencyDecoder":VAEDecode,
     "ScreenShare":ScreenShareNode,
     "FloatingVideo":FloatingVideo,
-    "ChatGPTOpenAI":ChatGPTNode,
-    "ShowTextForGPT":ShowTextForGPT,
-    "CharacterInText":CharacterInText,
-    "TextSplitByDelimiter":TextSplitByDelimiter,
+   
     "SpeechRecognition":SpeechRecognition,
     "SpeechSynthesis":SpeechSynthesis,
     "Color":ColorInput,
@@ -932,24 +1070,21 @@ 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
 }
 
 # 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",
     "FloatSlider":"Float Slider Input ♾️MixlabApp",
@@ -963,14 +1098,13 @@ 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",
     "CompositeImages_":"Composite Images ♾️Mixlab",
     "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
     "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
@@ -996,13 +1130,12 @@ 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",
 }
 
 # web ui的节点功能
@@ -1013,6 +1146,43 @@ logging.info('--------------')
 logging.info('\033[91m ### Mixlab Nodes: \033[93mLoaded')
 # print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
 
+try:
+    from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter,SiliconflowFreeNode
+    logging.info('ChatGPT.available True')
+
+    NODE_CLASS_MAPPINGS_V = {
+       "ChatGPTOpenAI":ChatGPTNode,
+       "SiliconflowLLM":SiliconflowFreeNode,
+        "ShowTextForGPT":ShowTextForGPT,
+        "CharacterInText":CharacterInText,
+        "TextSplitByDelimiter":TextSplitByDelimiter,
+    }
+
+    # 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",
+    }
+
+
+    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:
     from .nodes.Lama import LaMaInpainting
     logging.info('LaMaInpainting.available {}'.format(LaMaInpainting.available))
@@ -1048,4 +1218,61 @@ 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')
+
+    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' )
+
+
+
+
+
+
 logging.info('\033[93m -------------- \033[0m')

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

@@ -53,8 +53,8 @@ def azure_client(key,url):
 
 def openai_client(key,url):
     client = openai.OpenAI(
-    api_key=key,
-    base_url=url
+        api_key=key,
+        base_url=url
     )
     return client
 
@@ -163,7 +163,7 @@ def llama_cpp_client(file_name):
 
 
 def chat(client, model_name,messages ):
-
+        print('#chat',model_name,messages)
         try_count = 0
         while True:
             try_count += 1
@@ -215,15 +215,32 @@ class ChatGPTNode:
 
     @classmethod
     def INPUT_TYPES(cls):
-        model_list=llama_modes_list+[
+        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"
+            "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": {
@@ -287,7 +304,7 @@ class ChatGPTNode:
                 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 +320,7 @@ 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 +341,91 @@ class ChatGPTNode:
         return (response_content,json.dumps(messages, indent=4),json.dumps(self.session_history, indent=4),)
 
 
+class SiliconflowFreeNode:
+    def __init__(self):
+        # self.__client = OpenAI()
+        self.session_history = []  # 用于存储会话历史的列表
+        # self.seed=0
+        self.system_content="You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible."
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        model_list= [ 
+            "Qwen/Qwen2-7B-Instruct",
+            "THUDM/glm-4-9b-chat",
+            "01-ai/Yi-1.5-9B-Chat-16K",
+            "meta-llama/Meta-Llama-3.1-8B-Instruct"
+            ]
+        return {
+            "required": {
+                "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
+                "prompt": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                "system_content": ("STRING", 
+                                   {
+                                       "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
+                                       "multiline": True,"dynamicPrompts": False
+                                       }),
+                "model": ( model_list, 
+                    {"default": model_list[0]}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
+                "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
+            },
+             "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ("STRING","STRING","STRING",)
+    RETURN_NAMES = ("text","messages","session_history",)
+    FUNCTION = "generate_contextual_text"
+    CATEGORY = "♾️Mixlab/GPT"
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,False,False,)
+
+    
+    def generate_contextual_text(self,
+                                 api_key,
+                                 prompt, 
+                                 system_content,
+                                model, 
+                                seed,context_size,unique_id = None, extra_pnginfo=None):
+    
+        api_url="https://api.siliconflow.cn/v1"
+        
+        # 把系统信息和初始信息添加到会话历史中
+        if system_content:
+            self.system_content=system_content
+            # self.session_history=[]
+            # self.session_history.append({"role": "system", "content": system_content})
+        
+        # 
+        client = openai_client(api_key,api_url)  # 使用 ChatGPT  的接口
+        # print('using ChatGPT interface',api_key,api_url)
+
+        # 把用户的提示添加到会话历史中
+        # 调用API时传递整个会话历史
+
+        def crop_list_tail(lst, size):
+            if size >= len(lst):
+                return lst
+            elif size==0:
+                return []
+            else:
+                return lst[-size:]
+            
+        session_history=crop_list_tail(self.session_history,context_size)
+
+        messages=[{"role": "system", "content": self.system_content}]+session_history+[{"role": "user", "content": prompt}]
+
+        response_content = chat(client,model,messages)
+        
+        self.session_history=self.session_history+[{"role": "user", "content": prompt}]+[{'role':'assistant',"content":response_content}]
+
+        return (response_content,json.dumps(messages, indent=4),json.dumps(self.session_history, indent=4),)
+
+
+
 
 class ShowTextForGPT:
     @classmethod

nodes/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

@@ -1,6 +1,7 @@
 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
@@ -14,8 +15,8 @@ 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 +29,105 @@ 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 +164,8 @@ 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
 
@@ -802,85 +767,78 @@ 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 +912,13 @@ 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 +927,54 @@ 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 +1299,9 @@ class LoadImages_:
             image=pil2tensor(image)
             ims.append(image)
 
+        if len(ims)==0:
+            image1 = Image.new('RGB', (512, 512), color='black')
+            return (pil2tensor(image1),)
         image1 = ims[0]
         for image2 in ims[1:]:
             if image1.shape[1:] != image2.shape[1:]:
@@ -1578,7 +1504,7 @@ class ImageCropByAlpha:
 
 
 
-
+# get_files_with_extension(FONT_PATH,'.ttf')
 
 class TextImage:
     @classmethod
@@ -1586,7 +1512,7 @@ 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
@@ -1601,6 +1527,20 @@ class TextImage:
                                 "step": 1, #Slider's step
                                 "display": "number" # Cosmetic only: display as "number" or "slider"
                                 }), 
+                    "line_spacing": ("INT",{
+                                "default":12, 
+                                "min": -200, #Minimum value
+                                "max": 200, #Maximum value
+                                "step": 1, #Slider's step
+                                "display": "number" # Cosmetic only: display as "number" or "slider"
+                                }), 
+                    "padding": ("INT",{
+                                "default":8, 
+                                "min": 0, #Minimum value
+                                "max": 200, #Maximum value
+                                "step": 1, #Slider's step
+                                "display": "number" # Cosmetic only: display as "number" or "slider"
+                                }), 
                     "text_color":("STRING",{"multiline": False,"default": "#000000","dynamicPrompts": False}),
                     "vertical":("BOOLEAN", {"default": True},),
                     "stroke":("BOOLEAN", {"default": False},),
@@ -1608,7 +1548,7 @@ class TextImage:
                 }
     
     RETURN_TYPES = ("IMAGE","MASK",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_NAMES = ("image","mask",)
 
     FUNCTION = "run"
 
@@ -1617,11 +1557,14 @@ 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)
@@ -1854,10 +1797,16 @@ 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 +1819,33 @@ 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 +3174,52 @@ 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,)

nodes/Lama.py

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

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
@@ -591,14 +595,15 @@ def run_briarmbg(images=[]):
     return (masks,rgba_images,rgb_images)   
 
 
-def run_rembg(model_name= "unet",images=[]):
+def run_rembg(model_name= "unet",images=[],callback=None):
     # model_name = "unet" # "isnet-general-use"
+    # print('#run_rembg',model_name)
     rembg_session = new_session(model_name)
     masks=[]
     rgba_images=[]
     rgb_images=[]
     # 进度条
-    pbar = comfy.utils.ProgressBar(len(images) )
+    pbar=callback 
     for img in images:
         # use the post_process_mask argument to post process the mask to get better results.
         mask = remove(img, session=rembg_session,only_mask=True,post_process_mask=True)
@@ -638,8 +643,9 @@ def run_rembg(model_name= "unet",images=[]):
             rgb_image = Image.new("RGB", image_rgba.size, (0, 0, 0))
             rgb_image.paste(image_rgba, mask=image_rgba.split()[3])
             rgb_images.append(rgb_image)
-            
-        pbar.update(1)
+
+        if pbar:
+            pbar.update(1)
     return (masks,rgba_images,rgb_images)
 
 
@@ -691,7 +697,7 @@ class RembgNode_:
         if model_name=='briarmbg':
             masks,rgba_images,rgb_images=run_briarmbg(images)
         else:
-            masks,rgba_images,rgb_images=run_rembg(model_name,images)
+            masks,rgba_images,rgb_images=run_rembg(model_name,images, comfy.utils.ProgressBar(len(images) ))
 
         masks=[pil2tensor(m) for m in masks]
 

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/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)
@@ -284,7 +284,7 @@ class FloatSlider:
                 }
     
     RETURN_TYPES = ("FLOAT",) 
-    RETURN_NAMES = ('weight(0-1)',)
+    RETURN_NAMES = ('FLOAT',)
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/Input"
@@ -297,9 +297,7 @@ class FloatSlider:
             number = min_value
         elif number > max_value:
             number = max_value
-        scaled_number = (number - min_value) / (max_value - min_value)
-        return (scaled_number,)
-    
+        return (number,)    
   
 class IntNumber:
     @classmethod
@@ -568,7 +566,7 @@ class AppInfo:
                              },
 
                 "optional":{
-                    "IMAGE": ("IMAGE",),
+                    "image": ("IMAGE",),
                     "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
                     "version":("INT", {
                         "default": 1, 
@@ -596,12 +594,12 @@ 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:
@@ -87,7 +210,7 @@ def split_video(video_path, video_segment_frames, transition_frames, output_dir)
 
 folder_paths.folder_names_and_paths["video_formats"] = (
     [
-        os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "video_formats"),
+        os.path.join(os.path.dirname(os.path.abspath(__file__)), ".", "video_formats"),
     ],
     [".json"]
 )
@@ -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:
@@ -433,6 +631,7 @@ class VideoCombine_Adv:
             ffmpeg_formats = ["video/"+x[:-5] for x in folder_paths.get_filename_list("video_formats")]
         else:
             ffmpeg_formats = []
+        # ffmpeg_formats =["video/"+x for x in  ['webm', 'mp4', 'mkv']]
         return {
             "required": {
                 "image_batch": ("IMAGE",),
@@ -453,7 +652,8 @@ class VideoCombine_Adv:
             },
         }
 
-    RETURN_TYPES = ()
+    RETURN_TYPES = ("SCENE_VIDEO",)
+    RETURN_NAMES = ("scenes_video",)
     OUTPUT_NODE = True
     CATEGORY = "♾️Mixlab/Video"
     FUNCTION = "run"
@@ -622,7 +822,7 @@ class VideoCombine_Adv:
                 "format": format,
             }
         ]
-        return {"ui": {"gifs": previews}}
+        return {"ui": {"gifs": previews},"result":(file_path,)}
 
 
 class VAEEncodeForInpaint_Frames:
@@ -689,4 +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)

nodes/video_formats/av1-webm.json

@@ -0,0 +1,10 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libsvtav1",
+        "-pix_fmt", "yuv420p10le",
+        "-crf", "23"
+    ],
+     "extension": "webm",
+     "environment": {"SVT_LOG": "1"}
+}

nodes/video_formats/h264-mp4.json

@@ -0,0 +1,9 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libx264",
+        "-pix_fmt", "yuv420p",
+        "-crf", "19"
+    ],
+     "extension": "mp4"
+}

nodes/video_formats/h265-mp4.json

@@ -0,0 +1,11 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libx265",
+        "-pix_fmt", "yuv420p10le",
+        "-preset", "medium",
+        "-crf", "22",
+        "-x265-params", "log-level=quiet"
+    ],
+     "extension": "mp4"
+}

nodes/video_formats/webm.json

@@ -0,0 +1,9 @@
+{
+    "main_pass":
+    [
+        "-n",
+        "-pix_fmt", "yuv420p",
+        "-crf", "23"
+    ],
+     "extension": "webm"
+}

pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "comfyui-mixlab-nodes"
+description = "3D, ScreenShareNode & FloatingVideoNode, SpeechRecognition & SpeechSynthesis, GPT, LoadImagesFromLocal, Layers, Other Nodes, ..."
+version = "0.32.0"
+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"]
+
+[project.urls]
+Repository = "https://github.com/shadowcz007/comfyui-mixlab-nodes"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "shadow"
+DisplayName = "comfyui-mixlab-nodes"
+Icon = ""

requirements.txt

@@ -15,4 +15,6 @@ Pillow>=9.5.0
 einops==0.7.0
 trimesh>=4.0.5
 huggingface-hub
-scikit-image
+scikit-image
+torchaudio
+soundfile>=0.12.1

web/index.html

@@ -24,6 +24,7 @@
             margin-left: 5%;
             user-select: none;
             margin-top: 32px;
+            margin-bottom: 120px;
         }
 
         .apps {
@@ -65,9 +66,9 @@
             box-shadow: 0px 0px 10px 10px #fbe9f0
         }
 
-        .card:hover {
+        /* .card:hover {
             box-shadow: 0px 0px 10px 10px #e9fbfa
-        }
+        } */
 
         .card h5 {
             font-size: 14px;
@@ -145,15 +146,6 @@
             margin-top: 4px;
         }
 
-        .status {
-            background: black;
-            color: white;
-            display: flex;
-            width: fit-content;
-            padding: 4px;
-            font-size: 12px;
-
-        }
 
         .description {
             display: flex;
@@ -263,18 +255,44 @@
             margin-top: 12px;
         }
 
+        .run_div {
+            position: fixed;
+            bottom: 28px;
+            display: flex;
+            left: 144px;
+            z-index: 100;
+            /* align-items: center; */
+            /* justify-content: space-around; */
+            /* width: calc(50% - 100px); */
+            flex-direction: column;
+            background-color: #f7f7f7;
+            box-shadow: 3px 3px 8px #cacaca;
+            border-radius: 8px;
+        }
+
+        .status {
+            /* background: #e7e7e7; */
+            color: black;
+            display: flex;
+            width: 120px;
+            padding-left: 11px;
+            font-size: 12px;
+            border-radius: 8px;
+            justify-content: flex-start;
+            align-items: center;
+            height: 48px;
+        }
+
         .run_btn {
             background: black;
             color: white;
-            width: 88px;
-            height: 88px;
-            position: fixed;
-            bottom: 72px;
-            left: calc(50% - 44px);
-            border-radius: 100%;
+            /* width: calc(50% - 100px); */
+            min-width: 200px;
+            max-width: 460px;
+            height: 48px;
+            border-radius: 8px;
             cursor: pointer;
             border: 3px solid;
-            z-index: 100;
         }
 
         button:hover {
@@ -474,8 +492,7 @@
             <a class="link" href="https://www.mixcomfy.com" target="_blank">ComfyUI中文爱好者社区推荐</a>
         </div>
 
-        <a id="login_btn" target="_blank" href="https://discord.gg/xbP2GZF6gn" 
-        style="text-decoration: none;
+        <a id="login_btn" target="_blank" href="https://discord.gg/xbP2GZF6gn" style="text-decoration: none;
         color: black;font-size:12px">
             <svg height="32" aria-hidden="true" viewBox="0 0 16 16" version="1.1" width="32" data-view-component="true"
                 class="octicon octicon-mark-github v-align-middle color-fg-default">
@@ -624,8 +641,13 @@
             imageElement.appendChild(im);
 
             let base64s = imageElement.querySelectorAll('.base64')
-            //更新输入
-            window._appData.data[nodeId].inputs.images.base64 = Array.from(base64s, (b) => b.src)
+            //更新输入 //兼容 ，如果data没有则不更新data
+            if (window._appData.data) window._appData.data[nodeId].inputs.images.base64 = Array.from(base64s, (b) => b.src)
+            //更新输入参数
+            updateInputData(nodeId, (inputs) => {
+                inputs.images.base64 = Array.from(base64s, (b) => b.src)
+                return inputs
+            })
 
             // 删除
             im.addEventListener('click', e => {
@@ -633,7 +655,11 @@
                 im.remove();
                 let base64s = imageElement.querySelectorAll('.base64')
                 //更新输入
-                window._appData.data[nodeId].inputs.images.base64 = Array.from(base64s, (b) => b.src)
+                if (window._appData.data) window._appData.data[nodeId].inputs.images.base64 = Array.from(base64s, (b) => b.src)
+                updateInputData(nodeId, (inputs) => {
+                    inputs.images.base64 = Array.from(base64s, (b) => b.src)
+                    return inputs
+                })
             })
         }
 
@@ -762,7 +788,6 @@
         async function editImage(image, data) {
             //判断mask是否有输出
             let isMask = data.options.hasMask;
-            console.log(data)
 
             //app
             document.body.querySelector('.app').style.display = 'none'
@@ -772,6 +797,22 @@
             editor.style.display = 'block';
             const iframe = editor.querySelector('iframe');
 
+            const sleep = (t = 1000) => {
+                return new Promise((res, rej) => {
+                    setTimeout(() => {
+                        res(true)
+                    }, t)
+                })
+            }
+
+            //清空 图层
+            const removeAllLayer = () => {
+                let Layers = iframe.contentWindow.Layers;
+                //清空
+                Layers.reset_layers()
+                Layers.refresh_gui()
+            }
+
             // 复原
             const resetLayer = () => {
                 let Layers = iframe.contentWindow.Layers;
@@ -841,17 +882,19 @@
             //自动抠图
             let autoMaskSelect = iframe.contentDocument.getElementById('automask_image_mixlab');
             const select = iframe.contentDocument.getElementById('automask_models_mixlab');
-            let rembgModels = await get_rembg_models()
-            // 遍历模型列表并创建选项
-            for (const model of rembgModels) {
-                const option = document.createElement('option');
-                option.value = model;
-                option.textContent = model;
-                select.appendChild(option);
+            if (select.children.length === 0) {
+                let rembgModels = await get_rembg_models()
+                // 遍历模型列表并创建选项
+                for (const model of rembgModels) {
+                    const option = document.createElement('option');
+                    option.value = model;
+                    option.textContent = model;
+                    select.appendChild(option);
+                }
             }
 
             let autoMaskBtn = iframe.contentDocument.getElementById('automask_image_mixlab');
-            autoMaskBtn.addEventListener('click', async e => {
+            if (!autoMaskBtn.getAttribute('init')) autoMaskBtn.addEventListener('click', async e => {
                 //api请求
                 let base64 = getImageBase64FromLayer()
                 resetLayer()
@@ -866,16 +909,19 @@
 
                 let mb = convertImageToBlackBasedOnAlpha(image)
                 let mask = await createImage(mb)
-                addImage((Layers.get_layers()).length, 'Mask_' + data.id, mask)
+                let id = Layers.auto_increment;
+                addImage(id, 'Mask_' + data.id + id, mask)
             })
 
-
+            autoMaskBtn.setAttribute('init', 1)
             let cancelImageBtn = iframe.contentDocument.getElementById('cancel_image_mixlab');
-            cancelImageBtn.addEventListener('click', e => {
+            if (!cancelImageBtn.getAttribute('init')) cancelImageBtn.addEventListener('click', e => {
                 editor.style.display = 'none';
                 document.body.querySelector('.app').style.display = 'flex'
                 document.body.querySelector('#author').style.display = 'block'
             })
+
+            cancelImageBtn.setAttribute('init', 1)
             // 获取 id 为 "mix" 的 button 元素
             let saveImageBtn = iframe.contentDocument.getElementById('save_image_mixlab');
             saveImageBtn.style = `width: 98px;
@@ -883,7 +929,7 @@
             margin: 0 12px;
             background-color: var(--background-color-active);
             color: var(--text-color-active);`
-            saveImageBtn.addEventListener('click', async e => {
+            if (!saveImageBtn.getAttribute('init')) saveImageBtn.addEventListener('click', async e => {
                 //保存，并更新图片
                 e.preventDefault();
 
@@ -897,6 +943,7 @@
                     tempCanvas.width = dim.width;
                     tempCanvas.height = dim.height;
 
+                    //todo 获取Image更新后的数据(暂不支持image的修改)
 
                     for (const layer of Layers.get_layers()) {
                         if (layer.name !== 'Image_' + data.id) {
@@ -936,19 +983,29 @@
                     // // 获取读取的文件内容，即 Blob 对象
                     let hashId = await calculateImageHash(fileBlob)
 
-                    if (hashId == window._appData.data[data.id].hashId) return;
+                    if (window._appData.data && hashId == window._appData.data[data.id].hashId) {
+                        document.body.querySelector('.app').style.display = 'flex'
+                        document.body.querySelector('#author').style.display = 'block'
+                        return;
+                    }
 
+                    //底图
                     const { url: imgurl } = await uploadImage(base64ToBlob(data.options.defaultImage))
-
+                    //mask
                     let { url, name } = await uploadMask(fileBlob, imgurl);
                     // 在这里可以对 Blob 对象进行进一步处理
                     // imageElement.src = url;
-                    window._appData.data[data.id].inputs.image = name;
-                    window._appData.data[data.id].hashId = hashId;
+                    if (window._appData.data && window._appData.data[data.id]) window._appData.data[data.id].inputs.image = name;
+                    if (window._appData.data && window._appData.data[data.id]) window._appData.data[data.id].hashId = hashId;
+
+                    //更新输入参数
+                    updateInputData(data.id, (inputs) => {
+                        inputs.image = name
+                        return inputs
+                    })
+
 
                     // console.log("上传的文件：", url, data.id, name);
-
-
                     //更新图片
                     const canvas = document.createElement("canvas");
                     canvas.width = dim.width;
@@ -956,7 +1013,6 @@
 
                     const ctx = canvas.getContext('2d');
 
-
                     const defaultImage = await createImage(data.options.defaultImage)
                     ctx.drawImage(defaultImage, 0, 0, dim.width, dim.height);
 
@@ -973,22 +1029,34 @@
                 document.body.querySelector('#author').style.display = 'block'
             })
 
+            saveImageBtn.setAttribute('init', 1)
 
             var Layers = iframe.contentWindow.Layers;
             // console.log(Layers)
 
             //判断是否已经存在
-            let layers1 = Layers.get_layers()
+            let layers1 = Layers.get_layers();
 
+            //通过layer.link.src 判断是否图片更新
+            console.log(layers1.filter(l => l.name == 'Image_' + data.id)[0]?.link?.currentSrc !== data.options.defaultImage)
+            if (layers1.filter(l => l.name == 'Image_' + data.id)[0]
+                && layers1.filter(l => l.name == 'Image_' + data.id)[0].link.src !== data.options.defaultImage) {
+                //清空图层
+                removeAllLayer();
+                layers1 = [];
+                await sleep()
+            }
+
+            let im = await createImage(data.options.defaultImage)
+            // console.log(layers1, layers1.length)
             if (!layers1.filter(l => l.name == 'Image_' + data.id)[0]) {
-                addImage((Layers.get_layers()).length, 'Image_' + data.id, image)
+                addImage(0, 'Image_' + data.id, im)
             }
 
             if (isMask) {
                 if (!layers1.filter(l => l.name == 'Mask_' + data.id)[0]) {
-                    addMask((Layers.get_layers()).length, 'Mask_' + data.id, image)
+                    addMask(1, 'Mask_' + data.id, im)
                 }
-
             }
 
         }
@@ -1064,10 +1132,31 @@
 
         function updateSeed(id, val) {
             // console.log(val)
-            if (window._appData.data[id].inputs.seed && !Array.isArray(window._appData.data[id].inputs.seed)) window._appData.data[id].inputs.seed = Math.round(val);
-            if (window._appData.data[id].inputs.noise_seed && !Array.isArray(window._appData.data[id].inputs.noise_seed)) window._appData.data[id].inputs.noise_seed = Math.round(val);
+            if (window._appData.data
+                && window._appData.data[id].inputs.seed
+                && !Array.isArray(window._appData.data[id].inputs.seed)) window._appData.data[id].inputs.seed = Math.round(val);
+            if (window._appData.data
+                && window._appData.data[id].inputs.noise_seed
+                && !Array.isArray(window._appData.data[id].inputs.noise_seed)) window._appData.data[id].inputs.noise_seed = Math.round(val);
+
+            //更新输入参数
+            updateInputData(id, (inputs) => {
+                inputs.seed = Math.round(val);
+                inputs.noise_seed = Math.round(val);
+                return inputs
+            })
+
         }
 
+        //更新输入参数
+        function updateInputData(nodeId, callback) {
+            for (let index = 0; index < window._appData.input.length; index++) {
+                const inp = window._appData.input[index];
+                if (inp.id === nodeId) {
+                    window._appData.input[index].inputs = callback(window._appData.input[index].inputs);
+                }
+            }
+        }
 
         function queuePrompt(appInfo, promptWorkflow, seed, client_id) {
             // appinfo升级后 兼容，补丁
@@ -1079,21 +1168,6 @@
             // 随机seed
             promptWorkflow = randomSeed(seed, promptWorkflow);
 
-            // //动态提示,改为输入的时候，手动触发
-            // for (const id in promptWorkflow) {
-            //     let node = promptWorkflow[id]
-            //     if (["TextInput_", "CLIPTextEncode", "PromptSimplification", "ChinesePrompt_Mix"].includes(
-            //         node.class_type
-            //     )) {
-            //         if (node.class_type == "PromptSimplification") {
-            //             promptWorkflow[id].inputs.prompt = dynamicPrompts(node.inputs.prompt);
-            //         } else {
-            //             promptWorkflow[id].inputs.text = dynamicPrompts(node.inputs.text);
-            //         }
-            //         console.log('#动态提示', promptWorkflow[id].inputs)
-            //     }
-            // }
-
             let url = get_url()
             const data = JSON.stringify({ prompt: promptWorkflow, client_id });
             fetch(`${url}/prompt`, {
@@ -1118,6 +1192,24 @@
         }
 
 
+        // 新的运行工作流的接口
+        function queuePromptNew(filename, category, seed, input, client_id) {
+            let url = get_url()
+            // var filename = "Text-to-Image_1.json", category = "";
+
+            // 随机seed
+            //  promptWorkflow = randomSeed(seed, promptWorkflow);
+
+            const data = JSON.stringify({ filename, category, seed, input, client_id });
+            fetch(`${url}/mixlab/prompt`, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: data,
+            })
+        }
+
         function success(isSuccess, btn, text) {
             isSuccess ? btn.innerText = 'success' : text;
             setTimeout(() => {
@@ -1181,7 +1273,7 @@
         }
 
 
-        function createOutputs(outputData, link) {
+        async function createOutputs(outputData, link) {
             const url = new URL(window.location.href);
             const params = new URLSearchParams(url.search);
             const innerApp = params.get("innerApp");
@@ -1302,20 +1394,21 @@
                     "TransparentImage"].includes(node.class_type)) {
 
                     const url = node.options?.defaultImage || window._appData?.icon || base64Df;
-                    let a = document.createElement('a');
+                    let a = document.createElement('div');
                     a.id = `output_${node.id}`
-                    a.setAttribute('data-pswp-width', "200");
-                    a.setAttribute('data-pswp-height', "200");
-                    a.setAttribute('target', "_blank");
-                    a.setAttribute('href', url);
-                    a.setAttribute('title', node.title);
 
-                    let img = new Image();
-                    // img;
-                    img.src = url;
+                    let img = await createImage(url)
+
                     a.appendChild(img)
+                    // a.setAttribute('data-pswp-width', img.naturalWidth);
+                    // a.setAttribute('data-pswp-height', img.naturalHeight);
+                    // a.setAttribute('target', "_blank");
+                    // a.setAttribute('href', url);
+                    // a.setAttribute('title', node.title);
+
                     output_card.appendChild(a);
                     isShowImageFn = true;
+
                 }
 
                 //3d 
@@ -1335,7 +1428,7 @@
                 }
 
                 // video ,gif
-                if (["VHS_VideoCombine", "VideoCombine_Adv"].includes(node.class_type)) {
+                if (["VHS_VideoCombine", "VideoCombine_Adv", "CombineAudioVideo"].includes(node.class_type)) {
 
                     let a = document.createElement('a');
                     a.id = `output_${node.id}`
@@ -1435,18 +1528,33 @@
                             // // 获取读取的文件内容，即 Blob 对象
                             let hashId = await calculateImageHash(fileBlob)
 
-                            if (hashId == window._appData.data[data.id].hashId) return
+                            if (window._appData.data && hashId == window._appData.data[data.id].hashId) return
+
+                            let base64 = await blobToBase64(fileBlob)
 
                             if (data.class_type === 'LoadImagesToBatch') {
-                                let base64 = await blobToBase64(fileBlob)
                                 createBase64ImageForLoadImageToBatch(imageElement, data.id, base64)
                             } else {
                                 let { url, name } = await uploadImage(fileBlob);
                                 // 在这里可以对 Blob 对象进行进一步处理
                                 imageElement.src = url;
-                                window._appData.data[data.id].inputs.image = name;
-                                window._appData.data[data.id].hashId = hashId;
+                                if (window._appData.data && window._appData.data[data.id]) window._appData.data[data.id].inputs.image = name;
+                                if (window._appData.data && window._appData.data[data.id]) window._appData.data[data.id].hashId = hashId;
                                 console.log("上传的文件：", url, data.id, name);
+
+                                //更新输入参数
+                                updateInputData(data.id, (inputs) => {
+                                    inputs.image = name;
+                                    return inputs
+                                })
+
+                                //更换option里的default image
+                                window._appData.input = Array.from(window._appData.input, inp => {
+                                    if (inp.id === data.id) {
+                                        inp.options.defaultImage = base64;
+                                    }
+                                    return inp
+                                })
                             }
                         }
                     }
@@ -1583,6 +1691,42 @@
         // copyImagesToClipboard(htmlString);
 
 
+        // 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)
+            })
+        }
 
         function createInputs(inputData) {
             // Assuming you have an HTML element with the id "container" to hold the UI
@@ -1694,11 +1838,26 @@
                             data.options.images,
                             data.inputs.imageIndex,
                             (base64, text) => {
-                                window._appData.data[data.id].inputs.image_base64 = base64;
-                                window._appData.data[data.id].inputs.text = text;
+                                if (window._appData.data && window._appData.data[data.id]) window._appData.data[data.id].inputs.image_base64 = base64;
+                                if (window._appData.data && window._appData.data[data.id]) window._appData.data[data.id].inputs.text = text;
+
+                                //更新输入参数
+                                updateInputData(data.id, (inputs) => {
+                                    inputs.image_base64 = base64;
+                                    inputs.text = text;
+                                    return inputs
+                                })
+
                             })
                         uploadContainer.appendChild(imgDiv);
-                        window._appData.data[data.id].inputs.image_base64 = mainImage.querySelector('.images_prompt_main').src;
+                        if (window._appData.data && window._appData.data[data.id]) window._appData.data[data.id].inputs.image_base64 = mainImage.querySelector('.images_prompt_main').src;
+
+                        //更新输入参数
+                        updateInputData(data.id, (inputs) => {
+                            inputs.image_base64 = mainImage.querySelector('.images_prompt_main').src;
+                            return inputs
+                        })
+
                     } else if (data.class_type === 'LoadImagesToBatch') {
                         // 多张base64 图片
                         let base64 = data.inputs.images.base64
@@ -1738,7 +1897,7 @@
                             // console.log( file.type.split('/')[1])
                             let hashId = await calculateImageHash(fileBlob)
 
-                            if (hashId == window._appData.data[data.id].hashId) return
+                            if (window._appData.data && hashId == window._appData.data[data.id].hashId) return
 
                             if (data.class_type === 'LoadImagesToBatch') {
                                 // 上传 ，转为base64
@@ -1748,11 +1907,19 @@
                                 //上传，返回url
                                 let { url, name } = await uploadImage(fileBlob, '.' + file.type.split('/')[1])
 
+                                let base64 = await parseImageToBase64(url);
+
                                 if (data.class_type === 'ImagesPrompt_') {
-                                    // 
-                                    let base64 = await parseImageToBase64(url);
-                                    uploadContainer.querySelector('.images_prompt_main').src = base64
-                                    window._appData.data[data.id].inputs.image_base64 = base64;
+                                    uploadContainer.querySelector('.images_prompt_main').src = base64;
+
+                                    if (window._appData.data && window._appData.data[data.id]) window._appData.data[data.id].inputs.image_base64 = base64;
+
+                                    //更新输入参数
+                                    updateInputData(data.id, (inputs) => {
+                                        inputs.image_base64 = base64;
+                                        return inputs
+                                    })
+
                                 } else {
                                     if (isVideoUpload) {
                                         imageElement.srcObject = null;
@@ -1760,17 +1927,37 @@
                                     // 在这里可以对 Blob 对象进行进一步处理
                                     imageElement.src = url;
 
-
                                     if (isVideoUpload) {
-                                        window._appData.data[data.id].inputs.video = name;
+                                        if (window._appData.data && window._appData.data[data.id]) window._appData.data[data.id].inputs.video = name;
+
+                                        //更新输入参数
+                                        updateInputData(data.id, (inputs) => {
+                                            inputs.video = name;
+                                            return inputs
+                                        })
+
                                     } else {
-                                        window._appData.data[data.id].inputs.image = name;
+                                        //更换option里的default image
+                                        window._appData.input = Array.from(window._appData.input, inp => {
+                                            if (inp.id === data.id) {
+                                                inp.options.defaultImage = base64;
+                                            }
+                                            return inp
+                                        })
+
+                                        if (window._appData.data && window._appData.data[data.id]) window._appData.data[data.id].inputs.image = name;
+                                        //更新输入参数
+                                        updateInputData(data.id, (inputs) => {
+                                            inputs.image = name;
+                                            return inputs
+                                        })
+
                                     }
 
                                 }
 
 
-                                window._appData.data[data.id].hashId = hashId;
+                                if (window._appData.data) window._appData.data[data.id].hashId = hashId;
 
                                 console.log("上传的文件：", url, data.id, name);
                             }
@@ -1810,7 +1997,12 @@
                     let silde = createNumSlide(label,
                         data.inputs.weight,
                         (v) => {
-                            window._appData.data[data.id].inputs.weight = parseFloat(v);
+                            if (window._appData.data) window._appData.data[data.id].inputs.weight = parseFloat(v);
+                            //更新输入参数
+                            updateInputData(data.id, (inputs) => {
+                                inputs.weight = parseFloat(v);
+                                return inputs
+                            })
                         },
                         options.min,
                         options.max,
@@ -1834,7 +2026,14 @@
                         data.inputs.number,
                         (v) => {
                             // console.log(data.id,window._appData.data[data.id])
-                            window._appData.data[data.id].inputs.number = data.class_type === 'IntNumber' ? parseInt(v) : parseFloat(v);
+                            if (window._appData.data) window._appData.data[data.id].inputs.number = data.class_type === 'IntNumber' ? parseInt(v) : parseFloat(v);
+
+                            //更新输入参数
+                            updateInputData(data.id, (inputs) => {
+                                inputs.number = data.class_type === 'IntNumber' ? parseInt(v) : parseFloat(v);
+                                return inputs
+                            })
+
                         },
                         options.min,
                         options.max,
@@ -1874,9 +2073,21 @@
                         textInput.style.height = height;
 
                         if (data.class_type == "PromptSimplification") {
-                            window._appData.data[data.id].inputs.prompt = textInput.value;
+                            if (window._appData.data) window._appData.data[data.id].inputs.prompt = textInput.value;
+                            //更新输入参数
+                            updateInputData(data.id, (inputs) => {
+                                inputs.prompt = textInput.value;
+                                return inputs
+                            })
                         } else {
-                            window._appData.data[data.id].inputs.text = textInput.value;
+                            if (window._appData.data) window._appData.data[data.id].inputs.text = textInput.value;
+
+                            //更新输入参数
+                            updateInputData(data.id, (inputs) => {
+                                inputs.text = textInput.value;
+                                return inputs
+                            })
+
                         }
 
                     } catch (error) {
@@ -1900,9 +2111,19 @@
                         textInput.setAttribute('title', prompt)
                         dynamicPromptsBtn.setAttribute('title', prompt)
                         if (data.class_type == "PromptSimplification") {
-                            window._appData.data[data.id].inputs.prompt = prompt;
+                            if (window._appData.data) window._appData.data[data.id].inputs.prompt = prompt;
+                            //更新输入参数
+                            updateInputData(data.id, (inputs) => {
+                                inputs.prompt = prompt;
+                                return inputs
+                            })
                         } else {
-                            window._appData.data[data.id].inputs.text = prompt;
+                            if (window._appData.data) window._appData.data[data.id].inputs.text = prompt;
+                            //更新输入参数
+                            updateInputData(data.id, (inputs) => {
+                                inputs.text = prompt;
+                                return inputs
+                            })
                         }
                     })
 
@@ -1917,9 +2138,19 @@
                         autoResize(textInput);
 
                         if (data.class_type == "PromptSimplification") {
-                            window._appData.data[data.id].inputs.prompt = textInput.value;
+                            if (window._appData.data) window._appData.data[data.id].inputs.prompt = textInput.value;
+                            //更新输入参数
+                            updateInputData(data.id, (inputs) => {
+                                inputs.prompt = textInput.value;
+                                return inputs
+                            })
                         } else {
-                            window._appData.data[data.id].inputs.text = textInput.value;
+                            if (window._appData.data) window._appData.data[data.id].inputs.text = textInput.value;
+                            //更新输入参数
+                            updateInputData(data.id, (inputs) => {
+                                inputs.text = textInput.value;
+                                return inputs
+                            })
                         }
                         localStorage.setItem(`t_${data.id}`, JSON.stringify({
                             value: textInput.value,
@@ -1966,10 +2197,24 @@
                         if (v) {
                             value = v;
                             if (data.class_type === 'CheckpointLoaderSimple') {
-                                window._appData.data[data.id].inputs.ckpt_name = value;
+
+                                if (window._appData.data) window._appData.data[data.id].inputs.ckpt_name = value;
+                                //更新输入参数
+                                updateInputData(data.id, (inputs) => {
+                                    inputs.ckpt_name = value;
+                                    return inputs
+                                })
+
                             }
                             if (data.class_type === 'LoraLoader') {
-                                window._appData.data[data.id].inputs.lora_name = value;
+
+                                if (window._appData.data) window._appData.data[data.id].inputs.lora_name = value;
+                                //更新输入参数
+                                updateInputData(data.id, (inputs) => {
+                                    inputs.lora_name = value;
+                                    return inputs
+                                })
+
                             }
                         }
                     } catch (error) {
@@ -1989,10 +2234,20 @@
                         e.preventDefault();
                         // console.log(selectDom.value)
                         if (data.class_type === 'CheckpointLoaderSimple') {
-                            window._appData.data[data.id].inputs.ckpt_name = selectDom.value;
+                            if (window._appData.data) window._appData.data[data.id].inputs.ckpt_name = selectDom.value;
+                            //更新输入参数
+                            updateInputData(nodeId, (inputs) => {
+                                inputs.ckpt_name = selectDom.value;
+                                return inputs
+                            })
                         }
                         if (data.class_type === 'LoraLoader') {
-                            window._appData.data[data.id].inputs.lora_name = selectDom.value;
+                            if (window._appData.data) window._appData.data[data.id].inputs.lora_name = selectDom.value;
+                            //更新输入参数
+                            updateInputData(nodeId, (inputs) => {
+                                inputs.lora_name = selectDom.value;
+                                return inputs
+                            })
                         }
 
                         localStorage.setItem(`_model_${data.id}_${data.class_type}`, selectDom.value)
@@ -2022,7 +2277,61 @@
                     container.appendChild(d);
                 }
 
+                // 加载音频 base64
+                if (['LoadAndCombinedAudio_'].includes(data.class_type)) {
+                    let inpAudio = document.createElement('div');
 
+                    let inputAudio = document.createElement('button');
+
+                    inputAudio.innerText = data.title || 'Upload Audio'
+                    inputAudio.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;
+                    `;
+
+                    let audioE = document.createElement('audio');
+                    audioE.setAttribute('controls', 'on')
+                    //暂时只支持一个
+                    audioE.src = data.inputs.audios.base64[0];
+
+                    inputAudio.addEventListener('click', e => {
+                        e.preventDefault()
+                        let inp = document.createElement('input')
+                        inp.type = 'file'
+                        inp.style.display = 'none'
+                        inp.addEventListener('change', async e => {
+                            e.preventDefault()
+                            const file = e.target.files[0]
+                            let base64 = await uploadAndConvertAudio(file)
+
+                            audioE.src = base64;
+
+                            if (window._appData.data) window._appData.data[data.id].inputs.audios.base64 = [base64];
+
+                            //更新输入参数
+                            updateInputData(data.id, (inputs) => {
+                                inputs.audios.base64 = [base64];
+                                return inputs
+                            })
+
+
+                        })
+
+                        inp.click()
+                        inp.remove()
+                    })
+
+                    inpAudio.appendChild(inputAudio)
+                    inpAudio.appendChild(audioE)
+
+                    container.appendChild(inpAudio);
+
+                }
             });
             return container
         }
@@ -2074,11 +2383,26 @@
                         // console.log(color)
                         // window._appData.data[data.id].inputs.color.hex = color.toHEXA().toString();
                         let [r, g, b, a] = color.toRGBA();
-                        window._appData.data[nodeId].inputs.color = {
-                            ...window._appData.data[nodeId].inputs.color,
+                        r = parseInt(r);
+                        g = parseInt(g);
+                        b = parseInt(b);
+
+                        if (window._appData.data) window._appData.data[nodeId].inputs.color = {
+                            // ...window._appData.data[nodeId].inputs.color,
                             r, g, b, a,
                             hex: color.toHEXA().toString()
-                        }
+                        };
+
+                        //更新输入参数 //todo color里的参数
+                        updateInputData(nodeId, (inputs) => {
+                            inputs.color = {
+                                // ...window._appData.data[nodeId].inputs.color,
+                                r, g, b, a,
+                                hex: color.toHEXA().toString()
+                            };
+                            return inputs
+                        })
+
                     } catch (error) { }
                 })
                 .on('cancel', instance => {
@@ -2157,7 +2481,14 @@
 
                 let defaultValue = (targetId ? localStorage.getItem(`_slide_${targetId}`) : '') || keywords[0];
 
-                window._appData.data[targetId].inputs.prompt_keyword = defaultValue;
+                if (window._appData.data) window._appData.data[targetId].inputs.prompt_keyword = defaultValue;
+
+                //更新输入参数 //todo color里的参数
+                updateInputData(targetId, (inputs) => {
+                    inputs.prompt_keyword = defaultValue;
+                    return inputs
+                })
+
 
                 let selectTag = createSelect(Array.from(keywords, (k, i) => {
                     return {
@@ -2174,7 +2505,13 @@
                 // selectTag.setAttribute('data-content',labelText);
                 selectTag.addEventListener('change', e => {
                     e.preventDefault();
-                    window._appData.data[targetId].inputs.prompt_keyword = selectTag.value;
+                    if (window._appData.data) window._appData.data[targetId].inputs.prompt_keyword = selectTag.value;
+
+                    //更新输入参数 //todo color里的参数
+                    updateInputData(targetId, (inputs) => {
+                        inputs.prompt_keyword = selectTag.value;
+                        return inputs
+                    })
 
                     targetId ? localStorage.setItem(`_slide_${targetId}`, selectTag.value) : ''
                 })
@@ -2347,9 +2684,10 @@
             })
         }
 
-        function createUI(data, share = true) {
+        async function createUI(data, share = true) {
             // appData.input, appData.output, appData.seed, share, appData.link
             if (!data) return
+            // console.log('#createUI', data)
             const { input: inputData, output: outputData, data: workflow, seed, seedTitle, link, name } = data;
 
             let mainDiv = document.createElement('div');
@@ -2433,6 +2771,7 @@
             // seeds.textContent = 'Status';
             seeds.className = 'seeds';
 
+            console.log('#createUI', seed, data.data)
             try {
                 if (Object.keys(seed).length > 0) {
                     seeds.innerHTML = `<summary>SEED</summary>
@@ -2440,7 +2779,8 @@
                     const content = seeds.querySelector('.content')
                     for (const id in seed) {
                         const s = seed[id];
-                        if (!Array.isArray(workflow[id].inputs.seed)) {
+                        console.log('#createUI', !!(workflow && !Array.isArray(workflow[id].inputs.seed) || !workflow))
+                        if (!!(workflow && !Array.isArray(workflow[id].inputs.seed) || !workflow)) {
 
                             let seedInput = document.createElement('div');
                             content.appendChild(seedInput)
@@ -2488,30 +2828,44 @@
             }
 
 
-            statusDiv.appendChild(status);
+            // statusDiv.appendChild(status);
             statusDiv.appendChild(seeds);
 
             // 创建输入框
             var input1 = createInputs(inputData)
 
-            var output = createOutputs(outputData, link)
+            var output = await createOutputs(outputData, link)
 
             // 创建提交按钮
+            let submitDiv = document.createElement('div');
+            submitDiv.appendChild(statusDiv);
+
+            let submitDivBtn = document.createElement('div');
+            submitDivBtn.style.display = 'flex'
+            submitDiv.appendChild(submitDivBtn);
+
+            submitDivBtn.appendChild(status);
+
+            submitDiv.className = 'run_div';
             var submitButton = document.createElement('button');
             submitButton.textContent = 'Create';
             submitButton.className = 'run_btn'
 
             // 将所有UI元素添加到页面中
-            leftDiv.appendChild(titleDiv);
-            leftDiv.appendChild(iconDes);
+            // leftDiv.appendChild(titleDiv);
+            // leftDiv.appendChild(iconDes);
             // leftDiv.appendChild(des);
-            leftDiv.appendChild(statusDiv);
+            // leftDiv.appendChild(statusDiv);
             leftDiv.appendChild(input1);
 
-            if (typeof (data.data) == 'object') mainDiv.appendChild(submitButton);
+            submitDivBtn.appendChild(submitButton);
+            mainDiv.appendChild(submitDiv);
 
             rightDiv.appendChild(output);
 
+            // mainDiv.appendChild(titleDiv);
+            // mainDiv.appendChild(iconDes);
+
             mainDiv.appendChild(leftDetails);
             mainDiv.appendChild(rightDiv);
 
@@ -2859,7 +3213,7 @@
         async function createApp(appData, share = true) {
             // console.log(appData)
             // 使用示例：
-            var ui = createUI(appData, share);
+            var ui = await createUI(appData, share);
 
             // 更新标题
             ui.title.update(appData.name || 'Mixlab APP');
@@ -2876,13 +3230,22 @@
             ui.submitButton.update(
                 () => {
                     // 在提交按钮点击时执行的逻辑
-                    queuePrompt({
-                        name: window._appData.name,
-                        id: window._appData.id,
-                        icon: window._appData.icon,
-                        category: window._appData.category,
-                        filename: window._appData.filename
-                    }, window._appData.data, window._appData.seed, api.clientId);
+                    // queuePrompt({
+                    //     name: window._appData.name,
+                    //     id: window._appData.id,
+                    //     icon: window._appData.icon,
+                    //     category: window._appData.category,
+                    //     filename: window._appData.filename
+                    // }, window._appData.data, window._appData.seed, api.clientId);
+
+                    queuePromptNew(
+                        window._appData.filename,
+                        window._appData.category,
+                        window._appData.seed,
+                        window._appData.input.filter(inp => inp),
+                        api.clientId
+                    )
+
                 }, () => {
                     // 取消
                     if (api.runningCancel) {
@@ -2999,7 +3362,8 @@
             const lightbox = new PhotoSwipeLightbox({
                 gallery: '.output_card',
                 children: 'a',
-                pswpModule: () => import('/extensions/comfyui-mixlab-nodes/lib/photoswipe.esm.min.js')
+                pswpModule: () => import('/extensions/comfyui-mixlab-nodes/lib/photoswipe.esm.min.js'),
+                // appendToEl: document.querySelector('#result')
             });
             lightbox.on('uiRegister', function () {
                 lightbox.pswp.ui.registerElement({
@@ -3044,8 +3408,8 @@
             if (appData.author) {
                 let div = document.body.querySelector('#author');
                 if (appData.author.link) div.href = appData.author.link
-                div.style = `z-index:20;display: flex;flex-direction: column;position: fixed;bottom: 12px;right: 24px;cursor: pointer;text-decoration: none;color: black;`
-                div.innerHTML = `<p style="font-size:12px">Author:</p>
+                div.style = `z-index:20;display: flex;flex-direction: column;position: fixed;bottom: 24px;left: 24px;cursor: pointer;text-decoration: none;color: black;`
+                div.innerHTML = `<p style="font-size:12px;margin: 8px 0;">Author:</p>
                 <div style="display: flex;"> <img style="width:32px;height:32px;border-radius: 100%;" 
                 src="${appData.author.avatar || base64Df}"/>
                 <p style="margin-left:8px;font-size:12px;font-weight:800">${appData.author.name || '-'}</p></div>`
@@ -3294,7 +3658,7 @@
 
                 createAppList(window._apps);
 
-                if (window._apps.length > 0) await createHistoryList(category || '');
+                // if (window._apps.length > 0) await createHistoryList(category || '');
 
                 createApp(window._appData);
             }

web/javascript/app_mixlab.js

@@ -2,6 +2,8 @@ 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)
 //本机安装的插件节点全集
 window._nodesAll = null
 
@@ -185,6 +187,25 @@ 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]
@@ -397,11 +418,11 @@ 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(
         ','
       )
 
@@ -445,19 +466,18 @@ app.registerExtension({
         const { input, output } = getInputsAndOutputs()
         input_ids.value = input.join('\n')
         output_ids.value = output.join('\n')
-
         const widget = {
           type: 'div',
           name: 'AppInfoRun',
           draw (ctx, node, widget_width, y, widget_height) {
             Object.assign(
               this.div.style,
-              get_position_style(
+              {...get_position_style(
                 ctx,
                 widget_width,
                 node.size[1] - widget_height,
                 node.size[1]
-              )
+              ),zIndex:1}
             )
           }
         }
@@ -503,6 +523,21 @@ 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 +694,7 @@ app.registerExtension({
 
         btns.appendChild(btn)
         btns.appendChild(download)
+        btns.appendChild(tdBG)
 
         document.body.appendChild(widget.div)
         this.addCustomWidget(widget)
@@ -672,6 +708,7 @@ app.registerExtension({
         this.serialize_widgets = true //需要保存参数
 
         window._mixlab_app_json = null
+ 
       }
 
       const onExecuted = nodeType.prototype.onExecuted
@@ -687,9 +724,8 @@ 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

@@ -396,3 +396,218 @@ 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

@@ -2,7 +2,7 @@ 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) {

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 = 'v0.28.0'
+const version = 'v0.32.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/gpt_mixlab.js

@@ -199,6 +199,120 @@ app.registerExtension({
         url[id] || 'https://api.openai.com/v1'
     }
   }
+});
+
+
+app.registerExtension({
+  name: 'Mixlab.GPT.SiliconflowLLM',
+  async getCustomWidgets (app) {
+    return {
+      KEY (node, inputName, inputData, app) {
+        // console.log('##inputData', inputData)
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          size: [128, 32], // a default size
+          draw (ctx, node, width, y) {},
+          computeSize (...args) {
+            return [128, 32] // a method to compute the current size of the widget
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let data = getLocalData('_mixlab_api_key')
+            return data[node.id] || 'by Mixlab'
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      },
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'SiliconflowLLM') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const api_key = this.widgets.filter(w => w.name == 'api_key')[0]
+       
+        const widget = {
+          type: 'div',
+          name: 'chatgptdiv',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, api_key.y, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        const inputDiv = (key, placeholder) => {
+          let div = document.createElement('div')
+          const ip = document.createElement('input')
+          ip.type = placeholder === 'Key' ? 'password' : 'text'
+          ip.className = `${'comfy-multiline-input'} ${placeholder}`
+          div.style = `display: flex;
+          align-items: center; 
+          margin: 6px 8px;
+          margin-top: 0;`
+          ip.placeholder = placeholder
+          ip.value = placeholder
+
+          ip.style = `margin-left: 24px;
+          outline: none;
+          border: none;
+          padding: 4px;width: 100%;`
+          const label = document.createElement('label')
+          label.style = 'font-size: 10px;min-width:32px'
+          label.innerText = placeholder
+          div.appendChild(label)
+          div.appendChild(ip)
+
+          ip.addEventListener('change', () => {
+            let data = getLocalData(key)
+            data[this.id] = ip.value.trim()
+            localStorage.setItem(key, JSON.stringify(data))
+            console.log(this.id, key)
+          })
+          return div
+        }
+
+        let inputKey = inputDiv('_mixlab_api_key', 'Key') 
+
+        widget.div.appendChild(inputKey) 
+
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => { 
+          inputKey.remove()
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    // Fires every time a node is constructed
+    // You can modify widgets/add handlers/etc here
+
+    if (node.type === 'SiliconflowLLM') {
+      let widget = node.widgets.filter(w => w.div)[0]
+
+      let apiKey = getLocalData('_mixlab_api_key');
+
+      let id = node.id
+ 
+      widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab' 
+    }
+  }
 })
 
 app.registerExtension({
@@ -209,13 +323,16 @@ 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?.()
+            console.log('#ShowTextForGPT', this.widgets[i])
           }
-          this.widgets.length = 1
+          this.widgets.length = 2
         }
-        // console.log('ShowTextForGPT',text)
+
         for (let list of text) {
           if (list) {
             // console.log('#####', list)
@@ -228,6 +345,8 @@ 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)
@@ -280,5 +399,24 @@ app.registerExtension({
 
       this.serialize_widgets = true //需要保存参数
     }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'ShowTextForGPT') {
+      let widget = node.widgets.filter(w => w.name == 'show_text')[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/main_mixlab.js

@@ -1267,7 +1267,7 @@ app.registerExtension({
         })
 
         widget.PictureInPicture = $el('button', {
-          innerText: 'PictureInPicture',
+          innerText: 'Picture In Picture',
           style: {
             display: 'pictureInPictureEnabled' in document ? 'block' : 'none',
             cursor: 'pointer',

web/javascript/sound-lab.js

@@ -0,0 +1,295 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js'
+
+import WaveSurfer from 'https://cdn.jsdelivr.net/npm/wavesurfer.js@7/dist/wavesurfer.esm.js'
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: '0',
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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

@@ -26,7 +26,35 @@ 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=`You are a helpful assistant with access to the following functions. Use them if required - ${JSON.stringify(tool,null,2)}`
+
 
 if (!localStorage.getItem('_mixlab_system_prompt')) {
   localStorage.setItem('_mixlab_system_prompt', systemPrompt)
@@ -72,7 +100,7 @@ async function start_llama (model = 'Phi-3-mini-4k-instruct-Q5_K_S.gguf') {
     })
 
     const data = await response.json()
-    if (data.llama_cpp_error) {
+    if (data.llama_cpp_error||!data.port) {
       return
     }
 
@@ -135,7 +163,7 @@ async function createMenu () {
 
     // appsButton.onclick = () =>
     appsButton.onclick = async () => {
-      if (window._mixlab_llamacpp) {
+      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,
@@ -770,6 +798,22 @@ async function fetchReadmeContent (url) {
   }
 }
 
+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 createModelsModal (models) {
   var div =
     document.querySelector('#model-modal') || document.createElement('div')
@@ -825,7 +869,7 @@ 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;
@@ -844,6 +888,27 @@ function createModelsModal (models) {
   })
   linkIcon.style.padding = '4px'
 
+  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 (window._mixlab_llamacpp?.url) {
+    statusIcon.textContent = window._mixlab_llamacpp.model
+    statusIcon.style.backgroundColor = '#66ff6c'
+    statusIcon.style.color = 'black'
+  } else {
+  }
+  statusIcon.addEventListener('click', e => {
+    e.stopPropagation()
+    // startLLM()
+  })
+
   const n_gpu = document.createElement('input')
   n_gpu.type = 'number'
   n_gpu.setAttribute('min', -1)
@@ -878,20 +943,22 @@ function createModelsModal (models) {
   const title = document.createElement('p')
   title.innerText = 'Models'
   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)
-
+  title.appendChild(statusIcon)
   left_d.appendChild(linkIcon)
-
+  left_d.appendChild(n_gpu_div)
   headTitleElement.appendChild(left_d)
 
-  headTitleElement.appendChild(n_gpu_div)
+  // headTitleElement.appendChild(n_gpu_div)
 
   //重启
   const reStart = document.createElement('small')
@@ -899,7 +966,11 @@ function createModelsModal (models) {
     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)
 
@@ -958,29 +1029,22 @@ function createModelsModal (models) {
 
   modalContent.appendChild(input)
 
-  for (const m of models) {
-    let d = document.createElement('div')
-    d.innerText = m
-    d.className = `mix_tag`
+  if (!window._mixlab_llamacpp||(window._mixlab_llamacpp?.model?.length==0)) {
+    for (const m of models) {
+      let d = document.createElement('div')
+      d.innerText = `${showTextByLanguage('Run', {
+        Run: '运行'
+      })} ${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)
-        }
+        startLLM(m)
       })
-    }
 
-    modalContent.appendChild(d)
+      modalContent.appendChild(d)
+    }
   }
   modal.appendChild(modalContent)
 
@@ -994,8 +1058,8 @@ 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 +1413,8 @@ app.registerExtension({
           .querySelector('#mixlab_chatbot_by_llamacpp')
           .setAttribute('title', res.url)
       })
+    }else{
+      startLLM('')
     }
 
     LGraphCanvas.prototype.helpAboutNode = async function (node) {
@@ -1357,7 +1423,13 @@ app.registerExtension({
           ? nodesMap
           : await getCustomnodeMappings('url')
 
-      console.log('node & node map', 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)
@@ -1367,10 +1439,14 @@ 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()
@@ -1388,6 +1464,7 @@ 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',
@@ -1402,6 +1479,7 @@ app.registerExtension({
             t => {
               // console.log(t)
               widget.value += t
+              jsonStr+=t
             }
           )
         } catch (error) {
@@ -1427,13 +1505,27 @@ app.registerExtension({
                 t => {
                   // console.log(t)
                   widget.value += t
+                  jsonStr+=t
                 }
               )
             })
           }
         }
 
-        widget.value = widget.value.trim()
+        let json=null;
+
+        try {
+          json=JSON.parse(jsonStr.trim())
+        } catch (error) {
+          json=JSON.parse(jsonStr.trim()+"}")
+        }
+
+        if(json){
+          widget.value = [json.subject,json.content,json.style].join('\n')
+        }else{
+          widget.value = widget.value.trim()
+        }
+        
       }
     }
 
@@ -1696,6 +1788,7 @@ app.registerExtension({
         {
           content: 'Help ♾️Mixlab', // with a name
           callback: () => {
+            // console.log('#data',node)
             LGraphCanvas.prototype.helpAboutNode(node)
           } // and the callback
         },

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')
@@ -240,15 +238,7 @@ 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 +260,18 @@ 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 +288,43 @@ 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 +459,17 @@ 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 === `${prefix}_0`)
           if (pos !== -1) {
@@ -489,12 +484,13 @@ app.registerExtension({
                 '/view?' + new URLSearchParams(params).toString()
               )
               const w = this.addCustomWidget(
-                CreatePreviewElement(
+                createPreviewElement(
                   `${prefix}_${i}`,
                   previewUrl,
                   params.format || 'image/gif'
                 )
               )
+              console.log(w)
               w.parent = this
             })
           }