v0.36.0 p5.js

Update pyproject.toml
Update Text-to-Image-app.json
2024-08-10 00:39:27 +08:00 · 2024-08-09 19:08:36 +08:00 · 2024-08-09 16:30:47 +08:00 · 2024-08-09 16:27:34 +08:00 · 2024-08-08 21:58:44 +08:00 · 2024-08-08 11:37:36 +08:00
134 changed files with 240760 additions and 2884 deletions
@@ -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 }}
@@ -0,0 +1 @@
+mixlabnodes.com
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 shadow
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
@@ -1,9 +1,48 @@
-> 适配了最新版comfyui的py3.11 ，torch 2.1.2+cu121
+![](https://img.shields.io/github/release/shadowcz007/comfyui-mixlab-nodes)

+> 适配了最新版 comfyui 的 py3.11 ，torch 2.3.1+cu121
 > [Mixlab nodes discord](https://discord.gg/cXs9vZSqeK)

-#### 
-[comfyui-sd-prompt-mixlab](https://github.com/shadowcz007/comfyui-sd-prompt-mixlab)
+
+##### `最新`：
+
+- 增加p5.js作为输入节点
+[workflow](./workflow/p5workflow.json)
+
+- App模式增加batch prompt，批量提示词，可以把动态提示词批量组成后运行
+
+![alt text](./assets/1722517810720.png)
+
+- 增加 API Key Input 节点，用于管理LLM的Key,同时优化LLM相关节点，为后续agent模式做准备
+
+- 增加 SiliconflowLLM，可以使用由Siliconflow提供的免费LLM
+
+- 增加 Edit Mask，方便在生成的时候手动绘制 mask [workflow](./workflow/edit-mask-workflow.json)
+
+- LaMaInpainting 调整为手动安装
+
+<!-- - 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) -->
+
+
+#### `相关插件推荐`
+
+[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)

@@ -13,25 +52,15 @@

 <!-- [comfyui-CLIPSeg](https://github.com/shadowcz007/comfyui-CLIPSeg) -->

-#### 
-最新：ChatGPT节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。
+## 🚀🚗🚚🏃 Workflow-to-APP

-Model download,move to :```models/llamafile/```
-
-强烈推荐：[Phi-3-mini-4k-instruct-GGUF](https://huggingface.co/lmstudio-community/Phi-3-mini-4k-instruct-GGUF/tree/main)
-
-备选：[llama3_if_ai_sdpromptmkr_q2k](https://hf-mirror.com/impactframes/llama3_if_ai_sdpromptmkr_q2k/tree/main)
-
-
-
-
-
-## 🚀🚗🚚🏃 Workflow-to-APP 
- 新增AppInfo节点，可以通过简单的配置，把workflow转变为一个Web APP。
- 支持多个web app 切换
- 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
+- 新增 AppInfo 节点，可以通过简单的配置，把 workflow 转变为一个 Web APP。
+- 支持多个 web app 切换
+- 发布为 app 的 workflow，可以在右键里再次编辑了
+- web app 可以设置分类，在 comfyui 右键菜单可以编辑更新 web app
 - 支持动态提示
+- 支持把输出显示到comfyui背景（TouchDesigner 风格）
+- 如果转为web app打开是空白的，注意检查下插件目录的名字需要是：comfyui-mixlab-nodes(如果是zip包下载会多了个-main的后缀，需要去掉)

 ![](./assets/微信图片_20240421205440.png)

@@ -40,7 +69,6 @@ Model download,move to :```models/llamafile/```
 - The workflow, which is now released as an app, can also be edited again by right-clicking.
 - The web app can be configured with categories, and the web app can be edited and updated in the right-click menu of ComfyUI.

-
 ![](./assets/0-m-app.png)

 ![](./assets/appinfo-readme.png)
@@ -48,69 +76,96 @@ Model download,move to :```models/llamafile/```
 ![](./assets/appinfo-2.png)

 Example:
+
 - workflow
-![APP info](./workflow/appinfo-workflow.svg)
-[text-to-image](./workflow/Text-to-Image-app.json)
+  ![APP info](./workflow/appinfo-workflow.svg)
+  [text-to-image](./workflow/Text-to-Image-app.json)

 APP-JSON:
+
 - [text-to-image](./example/Text-to-Image_3.json)
 - [image-to-image](./example/Image-to-Image_2.json)
 - text-to-text

-> 暂时支持 9 种节点作为界面上的输入节点：Load Image、VHS_LoadVideo、CLIPTextEncode、PromptSlide、TextInput_、Color、FloatSlider、IntNumber、CheckpointLoaderSimple、LoraLoader
+> 暂时支持 9 种节点作为界面上的输入节点：Load Image、VHS*LoadVideo、CLIPTextEncode、PromptSlide、TextInput*、Color、FloatSlider、IntNumber、CheckpointLoaderSimple、LoraLoader

 > 输出节点：PreviewImage 、SaveImage、ShowTextForGPT、VHS_VideoCombine、PromptImage

-> seed统一输入控件，支持：SamplerCustom、KSampler
+> seed 统一输入控件，支持：SamplerCustom、KSampler

-> 配套[ps插件](https://github.com/shadowcz007/comfyui-ps-plugin)
+> 配套[ps 插件](https://github.com/shadowcz007/comfyui-ps-plugin)

-> 如果遇到上传图片不成功，请检查下：局域网或者是云服务，请使用https，端口8189这个服务（ 感谢 @Damien 反馈问题）
+> 如果遇到上传图片不成功，请检查下：局域网或者是云服务，请使用 https，端口 8189 这个服务（ 感谢 @Damien 反馈问题）

 > If you encounter difficulties in uploading images, please check the following: for local network or cloud services, please use HTTPS and the service on port 8189. (Thanks to @Damien for reporting the issue.)

+## 🏃🚗🚚🚀 Real-time Design

-
-## 🏃🚗🚚🚀  Real-time Design
 > ScreenShareNode & FloatingVideoNode. Now comfyui supports capturing screen pixel streams from any software and can be used for LCM-Lora integration. Let's get started with implementation and design! 💻🌐

 ![screenshare](./assets/screenshare.png)

 https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43e-410a-ab3a-1952b7b4e7da

-
 <!-- [ScreenShareNode](./workflow/2-screeshare.json) -->
+
 [ScreenShareNode & FloatingVideoNode](./workflow/3-FloatVideo-workflow.json)

 !! Please use the address with HTTPS (https://127.0.0.1).

-
 ### SpeechRecognition & SpeechSynthesis
+
 ![f](./assets/audio-workflow.svg)

 [Voice + Real-time Face Swap Workflow](./workflow/语音+实时换脸workflow.json)

+- Preview Audio
+
+[text-to-audio](./workflow/text-to-audio-base-workflow.json)
+
 ### GPT
-> Support for calling multiple GPTs.Local LLM（llama.cpp）、 ChatGPT、ChatGLM3 、ChatGLM4 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 .  Azure OpenAI:https://xxxx.openai.azure.com 

-![gpt-workflow.svg](./assets/gpt-workflow.svg)
+> Support for calling multiple GPTs.Local LLM 、 ChatGPT、ChatGLM3 、ChatGLM4 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 . Azure OpenAI:https://xxxx.openai.azure.com

-[workflow-5](./workflow/5-gpt-workflow.json)
+[LLM_base_workflow](./workflow/LLM_base_workflow.json)

+- SiliconflowLLM
+- ChatGPTOpenAI

-最新：ChatGPT节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。
+<!-- 最新：ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。

-Model download,move to :```models/llamafile/```
+Model download,move to :`models/llamafile/`

 强烈推荐：[Phi-3-mini-4k-instruct-GGUF](https://huggingface.co/lmstudio-community/Phi-3-mini-4k-instruct-GGUF/tree/main)

 备选：[llama3_if_ai_sdpromptmkr_q2k](https://hf-mirror.com/impactframes/llama3_if_ai_sdpromptmkr_q2k/tree/main)

+> 如果碰到安装失败，可以尝试手动安装

+```
+../../../python_embeded/python.exe -s -m pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
+
+../../../python_embeded/python.exe -s -m pip install llama-cpp-python[server]
+
+```
+
+> [Mac](https://llama-cpp-python.readthedocs.io/en/latest/install/macos/)
+
+```
+pip uninstall llama-cpp-python -y
+CMAKE_ARGS="-DLLAMA_METAL=on" pip install -U llama-cpp-python --no-cache-dir
+pip install 'llama-cpp-python[server]'
+```
+
+```
+pip install llama-cpp-python \
+  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
+``` -->

 ## Prompt
+
 > PromptSlide
-![](./assets/prompt_weight.png)
+> ![](./assets/prompt_weight.png)

 <!-- ![](./workflow/promptslide-appinfo-workflow.svg) -->

@@ -124,20 +179,23 @@ Model download,move to :```models/llamafile/```

 > PromptImage & PromptSimplification,Assist in simplifying prompt words, comparing images and prompt word nodes.

-> ChinesePrompt && PromptGenerate，中文prompt节点，直接用中文书写你的prompt
+> ChinesePrompt && PromptGenerate，中文 prompt 节点，直接用中文书写你的 prompt

 ![](./assets/ChinesePrompt_workflow.svg)

-
 ### Layers
+
 > 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)

-
 ### 3D
+
 ![](./assets/3d-workflow.png)
 ![](./assets/3d_app.png)
 [workflow](./assets/Image-to-3D_1.json)
@@ -145,44 +203,50 @@ Model download,move to :```models/llamafile/```
 ![](./assets/3dimage.png)
 [workflow](./workflow/3D-workflow.json)

-
 ### Image

 #### LoadImagesToBatch
-> Upload multiple images for batch input into the IP adapter. 
+
+> Upload multiple images for batch input into the IP adapter.

 #### LoadImagesFromLocal
-> Monitor changes to images in a local folder, and trigger real-time execution of workflows, supporting common image formats, especially PSD format, in conjunction with Photoshop. 
+
+> Monitor changes to images in a local folder, and trigger real-time execution of workflows, supporting common image formats, especially PSD format, in conjunction with Photoshop.

 ![watch](./assets/4-loadfromlocal-watcher-workflow.svg)

 [workflow-4](./workflow/4-loadfromlocal-watcher-workflow.json)

 #### LoadImagesFromURL
+
 > 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) 
+
+> Apply VisualStyle Prompting , Modified from [ComfyUI_VisualStylePrompting](https://github.com/ExponentialML/ComfyUI_VisualStylePrompting)

 ![](./assets/VisualStylePrompting.png)

-> StyleAligned , Modified from [style_aligned_comfy](https://github.com/brianfitzgerald/style_aligned_comfy) 
-
+> StyleAligned , Modified from [style_aligned_comfy](https://github.com/brianfitzgerald/style_aligned_comfy)

 ### Utils
+
 > The Color node provides a color picker for easy color selection, the Font node offers built-in font selection for use with TextImage to generate text images, and the DynamicDelayByText node allows delayed execution based on the length of the input text.

- [添加了DynamicDelayByText功能，可以根据输入文本的长度进行延迟执行。](./workflow/audio-chatgpt-workflow.json)
+- [添加了 DynamicDelayByText 功能，可以根据输入文本的长度进行延迟执行。](./workflow/audio-chatgpt-workflow.json)

 - [Added DynamicDelayByText, enabling delayed execution based on input text length.](./workflow/audio-chatgpt-workflow.json)

- [使用CkptNames 对比不同的模型效果](./workflow/ckpts-image-workflow.json)
+- [使用 CkptNames 对比不同的模型效果](./workflow/ckpts-image-workflow.json)

 - [CkptNames compare the effects of different models.](./workflow/ckpts-image-workflow.json)

-
-
 ### Other Nodes

 ![main](./assets/all-workflow.svg)
@@ -190,33 +254,31 @@ Model download,move to :```models/llamafile/```

 [workflow-1](./workflow/1-workflow.json)

-
-
 > TransparentImage

 ![TransparentImage](./assets/TransparentImage.png)

-
 > FeatheredMask、SmoothMask

 Add edges to an image.

 ![FeatheredMask](./assets/FlVou_Y6kaGWYoEj1Tn0aTd4AjMI.jpg)

+> LaMaInpainting（需要手动安装）

-> LaMaInpainting
+*  simple-lama-inpainting 里的pillow造成冲突，暂时从依赖里移除，如果有安装 simple-lama-inpainting ，节点会自动添加，没有，则不会自动添加。

 from [simple-lama-inpainting](https://github.com/enesmsahin/simple-lama-inpainting)

+* [问题汇总](https://github.com/shadowcz007/comfyui-mixlab-nodes/issues/294)

 > rembgNode

 "briarmbg","u2net","u2netp","u2net_human_seg","u2net_cloth_seg","silueta","isnet-general-use","isnet-anime"

-*** briarmbg ***  model was developed by BRlA Al and can be used as an open-source model for non-commercial purposes
+**_ briarmbg _** model was developed by BRlA Al and can be used as an open-source model for non-commercial purposes

-
-### Improvement 
+### Improvement

 - Add "help" option to the context menu for each node.
 - Add "Nodes Map" option to the global context menu.
@@ -227,23 +289,21 @@ An improvement has been made to directly redirect to GitHub to search for missin

 ![node-not-found](./assets/node-not-found.png)

-
 ### Models

-* [Download TripoSR](https://huggingface.co/stabilityai/TripoSR/blob/main/model.ckpt) and place it in ```models/triposr```
+- [Download TripoSR](https://huggingface.co/stabilityai/TripoSR/blob/main/model.ckpt) and place it in `models/triposr`

-* [Download facebook/dino-vitb16](https://huggingface.co/facebook/dino-vitb16/tree/main)  and place it in ```models/triposr/facebook/dino-vitb16```
+- [Download facebook/dino-vitb16](https://huggingface.co/facebook/dino-vitb16/tree/main) and place it in `models/triposr/facebook/dino-vitb16`

+[Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:`models/rembg`

-[Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:```models/rembg```
+[Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : `models/lama`

-[Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : ```models/lama```
+[Download Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to :`models/clip_interrogator/Salesforce/blip-image-captioning-base`

-[Download Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to :```models/clip_interrogator/Salesforce/blip-image-captioning-base```
+[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:`models/prompt_generator/text2image-prompt-generator`

-[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:```models/prompt_generator/text2image-prompt-generator```
-
-[Download Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main),move to:```models/prompt_generator/opus-mt-zh-en```
+[Download Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main),move to:`models/prompt_generator/opus-mt-zh-en`

 ## Installation

@@ -259,40 +319,35 @@ git clone https://github.com/shadowcz007/comfyui-mixlab-nodes.git
 Install the requirements:

 run directly:
+
 ```
 cd ComfyUI/custom_nodes/comfyui-mixlab-nodes
 install.bat
 ```

 or install the requirements using:
+
 ```
 ../../../python_embeded/python.exe -s -m pip install -r requirements.txt
 ```

 If you are using a venv, make sure you have it activated before installation and use:
+
 ```
 pip3 install -r requirements.txt
 ```

-
-
-
-
 #### Chinese community
-访问 [www.mixcomfy.com](https://www.mixcomfy.com)，获得更多内测功能，关注微信公众号：Mixlab无界社区

+访问 [www.mixcomfy.com](https://www.mixcomfy.com)，获得更多内测功能，关注微信公众号：Mixlab 无界社区

+####

-
-#### 
 File / LoadImagesFromPath SaveImageToLocal LoadImagesFromURL

-
-
-
 #### discussions:
-[discussions](https://github.com/shadowcz007/comfyui-mixlab-nodes/discussions)

+[discussions](https://github.com/shadowcz007/comfyui-mixlab-nodes/discussions)

 <picture>
  <source
@@ -312,4 +367,3 @@ File / LoadImagesFromPath SaveImageToLocal LoadImagesFromURL
    src="https://api.star-history.com/svg?repos=shadowcz007/comfyui-mixlab-nodes&type=Date"
  />
 </picture>
-
@@ -3,14 +3,37 @@ 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

+#修复 sys.stdout.isatty()  object has no attribute 'isatty'
+try:
+    sys.stdout.isatty()
+except:
+    print('#fix sys.stdout.isatty')
+    sys.stdout.isatty = lambda: False
+
+_URL_=None
+
+
+# try:
+#     from .nodes.ChatGPT import get_llama_models,get_llama_model_path,llama_cpp_client
+#     llama_cpp_client("")
+
+# except:
+#     print("##nodes.ChatGPT ImportError")
+
+
+from .nodes.RembgNode import get_rembg_models,U2NET_HOME,run_briarmbg,run_rembg

 from server import PromptServer

@@ -23,27 +46,36 @@ except ImportError:
    print("or")
    print("pip install -r requirements.txt")
    sys.exit()
-   
-def is_installed(package, package_overwrite=None):
+
+
+def is_installed(package, package_overwrite=None,auto_install=True):
+    is_has=False
    try:
        spec = importlib.util.find_spec(package)
+        is_has=spec is not None
    except ModuleNotFoundError:
        pass

    package = package_overwrite or package

    if spec is None:
-        print(f"Installing {package}...")
-        # 清华源 -i https://pypi.tuna.tsinghua.edu.cn/simple
-        command = f'"{python}" -m pip install {package}'
-  
-        result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ)
+        if auto_install==True:
+            print(f"Installing {package}...")
+            # 清华源 -i https://pypi.tuna.tsinghua.edu.cn/simple
+            command = f'"{python}" -m pip install {package}'
+    
+            result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ)

-        if result.returncode != 0:
-            print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
+            is_has=True
+
+            if result.returncode != 0:
+                print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
+                is_has=False
    else:
        print(package+'## OK')
-        
+
+    return is_has
+     
 try:
    import OpenSSL
 except ImportError:
@@ -65,7 +97,6 @@ except ImportError:
    sys.exit()


-
 def install_openai():
    # Helper function to install the OpenAI module if not already installed
    try:
@@ -80,6 +111,26 @@ install_openai()
 current_path = os.path.abspath(os.path.dirname(__file__))


+def remove_base64_prefix(base64_str):
+  """
+  去除 base64 字符串中的 data:image/*;base64, 前缀
+
+  Args:
+    base64_str: base64 编码的字符串
+
+  Returns:
+    去除前缀后的 base64 字符串
+  """
+
+  # 使用正则表达式匹配常见的前缀
+  pattern = r'^data:image\/(.*);base64,(.+)$'
+  match = re.match(pattern, base64_str)
+  if match:
+    # 如果匹配到常见的前缀，则去除前缀并返回
+    return match.group(2)
+  else:
+    # 如果不匹配到常见的前缀，则直接返回
+    return base64_str

 def calculate_md5(string):
    encoded_string = string.encode()
@@ -129,15 +180,14 @@ 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:
@@ -267,31 +317,32 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
        print('app_workflow_path: ',app_workflow_path)
        try:
            with open(app_workflow_path) as json_file:
+                json_data=json.load(json_file)
                apps = [{
                    'filename':filename,
-                    'data':json.load(json_file)
+                    'data':json_data
                }]
        except Exception as e:
            print("发生异常：", str(e))
        
        # 这个代码不需要
        # if len(apps)==1 and category!='' and category!=None:
-            data=read_workflow_json_files(category_path)
+        data=read_workflow_json_files(category_path)
            
-            for item in data:
-                x=item["data"]
-                # print(apps[0]['filename'] ,item["filename"])
-                if apps[0]['filename']!=item["filename"]:
-                    category=''
-                    input=None
-                    output=None
-                    if 'category' in x['app']:
-                        category=x['app']['category']
-                    if 'input' in x['app']:
-                        input=x['app']['input']
-                    if 'output' in x['app']:
-                        output=x['app']['output']
-                    apps.append({
+        for item in data:
+            x=item["data"]
+            # print(apps[0]['filename'] ,item["filename"])
+            if apps[0]['filename']!=item["filename"]:
+                category=''
+                input=None
+                output=None
+                if 'category' in x['app']:
+                    category=x['app']['category']
+                if 'input' in x['app']:
+                    input=x['app']['input']
+                if 'output' in x['app']:
+                    output=x['app']['output']
+                apps.append({
                        "filename":item["filename"],
                        # "category":category,
                        "data":{
@@ -411,6 +462,7 @@ async def check_port_available(address, port):

 # https
 async def new_start(self, address, port, verbose=True, call_on_start=None):
+    global _URL_
    try:
        runner = web.AppRunner(self.app, access_log=None)
        await runner.setup()
@@ -437,19 +489,26 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):

        ssl_context = None
        scheme = "http"
-        # 跟着本体修改
-        if args.tls_keyfile and args.tls_certfile:
+        try:
+            # 跟着本体修改
+            if args.tls_keyfile and args.tls_certfile:
                scheme = "https"
                ssl_context = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE)
                ssl_context.load_cert_chain(certfile=args.tls_certfile,
-                                keyfile=args.tls_keyfile)
-        else:
-            # 如果没传，则自动创建
+                                    keyfile=args.tls_keyfile)
+            else:
+                # 如果没传，则自动创建
+                import ssl
+                crt, key = create_for_https()
+                ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
+                ssl_context.load_cert_chain(crt, key)
+        except:
            import ssl
            crt, key = create_for_https()
            ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
            ssl_context.load_cert_chain(crt, key)

+
        success = False
        for i in range(11):  # 尝试最多11次
            if await check_port_available(address, http_port + 1 + i):
@@ -472,18 +531,31 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
            logging.info("\n")
            logging.info("\n\nStarting server")

+            import socket
+
+            hostname = socket.gethostname()
+            ip_address = socket.gethostbyname(hostname)
+
+            # print(f"本机的IP地址是: {ip_address}")
+
+
            # print("\033[93mStarting server\n")
-            logging.info("\033[93mTo see the GUI go to: http://{}:{}".format(address, http_port))
-            logging.info("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
- 
+            logging.info("\033[93mTo see the GUI go to: http://{}:{} or http://{}:{}".format(ip_address, http_port,address,http_port))
+            logging.info("\033[93mTo see the GUI go to: https://{}:{} or https://{}:{}\033[0m".format(ip_address, https_port,address,https_port))
+
+            _URL_="http://{}:{}".format(address,http_port)
            # print("\033[93mTo see the GUI go to: http://{}:{}".format(address, http_port))
            # print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))

        if call_on_start is not None:
-            if scheme=='https':
-                call_on_start(scheme,address, https_port)
-            else:
-                call_on_start(scheme,address, http_port)
+            try:
+                if scheme=='https':
+                    call_on_start(scheme,address, https_port)
+                else:
+                    call_on_start(scheme,address, http_port)
+            except:
+                call_on_start(address,http_port)
+            

    except Exception as e:
        print(f"Error starting the server: {e}")
@@ -494,8 +566,6 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
        # webbrowser.open(f"https://{address}")
        # webbrowser.open(f"http://{address}:{port}")

-
-
 PromptServer.start=new_start

 # 创建路由表
@@ -557,13 +627,34 @@ 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:
@@ -590,60 +681,346 @@ async def nodes_map_hander(request):
 async def get_checkpoints(request):
    data = await request.json()
    t="checkpoints"
+    names=[]
    try:
        t=data['type']
+        names = folder_paths.get_filename_list(t)
    except Exception as e:
        print('/mixlab/folder_paths',False,e)
-    
-    names = folder_paths.get_filename_list(t)

+    # try:
+    #     if data['type']=='llamafile':
+    #         names=get_llama_models()
+    # except:
+    #     print("llamafile none")
+
+    try:
+        if data['type']=='rembg':
+            names=get_rembg_models(U2NET_HOME)
+    except:
+        print("rembg none")
+    
    return web.json_response({"names":names,"types":list(folder_paths.folder_names_and_paths.keys())})


-@routes.post("/mixlab/prompt_result")
-async def post_prompt_result(request):
+@routes.post('/mixlab/rembg')
+async def rembg_hander(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)
+    model=data['model']
+    result={}
+
+    data_base64=remove_base64_prefix(data['base64'])
+    image_data = base64.b64decode(data_base64)
    
-    return web.json_response({"result":res})
+    # 创建一个BytesIO对象
+    image_stream = io.BytesIO(image_data)

-# 扩展api接口
-# from server import PromptServer
-# from aiohttp import web
+    # 使用PIL Image模块读取图像
+    image = Image.open(image_stream)

-# @routes.post('/ws_image')
+    if model=='briarmbg':
+        _,rgba_images,_=run_briarmbg([image])
+    else:
+        _,rgba_images,_=run_rembg(model,[image])
+
+    with io.BytesIO() as buf:
+        rgba_images[0].save(buf, format='PNG')
+        img_bytes = buf.getvalue()
+    img_base64 = base64.b64encode(img_bytes).decode('utf-8')
+    
+    try: 
+        result={
+            'data':img_base64,
+            'model':model,
+            'status':'success',
+            }
+    except Exception as e:
+            print(e)
+
+    return web.json_response(result)
+
+# 保存运行结果？暂时去掉
+# @routes.post("/mixlab/prompt_result")
+# async def post_prompt_result(request):
+#     data = await request.json()
+#     res=None
+#     # print(data)
+#     try:
+#         action=data['action']
+#         if action=='save':
+#             result=data['data']
+#             res=save_prompt_result(result['prompt_id'],result)
+#         elif action=='all':
+#             res=get_prompt_result()
+#     except Exception as e:
+#         print('/mixlab/prompt_result',False,e)
+    
+#     return web.json_response({"result":res})
+
+# 种子设置
+def random_seed(seed, data):
+    max_seed = 4294967295
+
+    for id, value in data.items():
+        # print(seed,id)
+        if id in seed:
+            if 'seed' in value['inputs'] and not isinstance(value['inputs']['seed'], list) and seed[id] in ['increment', 'decrement', 'randomize']:
+                value['inputs']['seed'] = round(random.random() * max_seed)
+            
+            if 'noise_seed' in value['inputs'] and not isinstance(value['inputs']['noise_seed'], list) and seed[id] in ['increment', 'decrement', 'randomize']:
+                value['inputs']['noise_seed'] = round(random.random() * max_seed)
+            
+            if value.get('class_type') == "Seed_" and seed[id] in ['increment', 'decrement', 'randomize']:
+                value['inputs']['seed'] = round(random.random() * max_seed)
+            
+        print('new Seed', value)
+    
+    return data
+
+
+# 运行工作流，代替官方的prompt接口
+@routes.post("/mixlab/prompt")
+async def mixlab_post_prompt(request):
+    p_intance=PromptServer.instance
+    logging.info("/mixlab/prompt")
+    resp_code = 200
+    out_string = ""
+    json_data =  await request.json()
+    # json_data = p_intance.trigger_on_prompt(json_data)
+    # filename,category, client_id ,input
+    # workflow 的 filename,category
+
+    # 输入的参数
+    input_data=json_data['input'] if "input" in json_data else []
+    # 种子
+    seed=json_data['seed'] if "seed" in json_data else {}
+
+    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)>0:
+        # 取到prompt
+        prompt=apps[0]['data']['output']
+        # logging.info(prompt)
+        # 更新input_data到prompt里
+        '''
+          {
+                "inputs": {
+                    "number": 512,
+                    "min_value": 512,
+                    "max_value": 2048,
+                    "step": 1
+                },
+                "class_type": "IntNumber",
+                "id": "22"
+            },
+        '''
+
+        for inp in input_data:
+            id=inp['id']
+            if prompt[id]['class_type']==inp['class_type']:
+                prompt[id]['inputs'].update(inp['inputs']) 
+
+
+    if prompt==None:
+        return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
+    else:
+        # 种子更新 
+        '''
+            "seed": {
+                    "45": "randomize",
+                    "46": "randomize"
+                }
+            '''
+        json_data["prompt"]=random_seed(seed,prompt)
+
+    # print("#json_data",prompt)
+    # 需要把apps处理成 prompt
+    # 注意seed的处理
+    
+    if "number" in json_data:
+        number = float(json_data['number'])
+    else:
+        number = p_intance.number
+        if "front" in json_data:
+            if json_data['front']:
+                number = -number
+
+        p_intance.number += 1
+
+    if "prompt" in json_data:
+        prompt = json_data["prompt"]
+        valid = execution.validate_prompt(prompt)
+        extra_data = {}
+        if "extra_data" in json_data:
+            extra_data = json_data["extra_data"]
+
+        if "client_id" in json_data:
+            extra_data["client_id"] = json_data["client_id"]
+        if valid[0]:
+            prompt_id = str(uuid.uuid4())
+            outputs_to_execute = valid[2]
+            p_intance.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
+            response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
+            return web.json_response(response)
+        else:
+            logging.warning("invalid prompt: {}".format(valid[1]))
+            return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
+    else:
+        return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
+
+
+# AR页面
+# @routes.get('/mixlab/AR')
+async def handle_ar_page(request):
+    html_file = os.path.join(current_path, "web/ar.html")
+    if os.path.exists(html_file):
+        with open(html_file, 'r', encoding='utf-8', errors='ignore') as f:
+            html_data = f.read()
+            return web.Response(text=html_data, content_type='text/html')
+    else:
+        return web.Response(text="HTML file not found", status=404)
+
+
+# async def start_local_llm(data):
+#     global llama_port,llama_model,llama_chat_format
+#     if llama_port and llama_model and llama_chat_format:
+#         return {"port":llama_port,"model":llama_model,"chat_format":llama_chat_format}
+
+#     import threading
+#     import uvicorn
+#     from llama_cpp.server.app import create_app
+#     from llama_cpp.server.settings import (
+#                 Settings,
+#                 ServerSettings,
+#                 ModelSettings,
+#                 ConfigFileSettings,
+#             )
+    
+#     if not "model" in data and "model_path" in data:
+#         data['model']= os.path.basename(data["model_path"])
+#         model=data["model_path"]
+
+#     elif "model" in data:
+#         model=get_llama_model_path(data['model'])
+
+#     n_gpu_layers=-1
+
+#     if "n_gpu_layers" in data:
+#         n_gpu_layers=data['n_gpu_layers']
+
+
+#     chat_format="chatml"
+
+#     model_alias=os.path.basename(model)
+
+#     # 多模态
+#     clip_model_path=None
+    
+#     prefix = "llava-phi-3-mini"
+#     file_name = prefix+"-mmproj-"
+#     if model_alias.startswith(prefix):
+#         for file in os.listdir(os.path.dirname(model)):
+#             if file.startswith(file_name):
+#                 clip_model_path=os.path.join(os.path.dirname(model),file)
+#                 chat_format='llava-1-5'
+#     # print('#clip_model_path',chat_format,clip_model_path,model)
+
+#     address="127.0.0.1"
+#     port=9090
+#     success = False
+#     for i in range(11):  # 尝试最多11次
+#         if await check_port_available(address, port + i):
+#             port = port + i
+#             success = True
+#             break
+
+#     if success == False:
+#         return {"port":None,"model":""}
+    
+    
+#     server_settings=ServerSettings(host=address,port=port)
+
+#     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,
+#                 )
+
+#         # 创建一个子线程
+#         thread = threading.Thread(target=run_uvicorn)
+
+#         # 启动子线程
+#         thread.start()
+
+#         llama_port=port
+#         llama_model=data['model']
+#         llama_chat_format=chat_format
+
+#     return  {"port":llama_port,"model":llama_model,"chat_format":llama_chat_format}
+
+# llam服务的开启
+# @routes.post('/mixlab/start_llama')
 # async def my_hander_method(request):
-#     post = await request.post()
-#     x = post.get("something")
-#     return web.json_response({})
+#     data =await request.json()
+#     # print(data)
+#     if llama_port and llama_model and llama_chat_format:
+#         return web.json_response({"port":llama_port,"model":llama_model,"chat_format":llama_chat_format} )
+#     try:
+#         result=await start_local_llm(data)
+#     except:
+#         result= {"port":None,"model":"","llama_cpp_error":True}
+#         print('start_local_llm error')

+#     return web.json_response(result)
+
+# 重启服务
+@routes.post('/mixlab/re_start')
+def re_start(request):
+    try:
+        sys.stdout.close_log()
+    except Exception as e:
+        pass
+    return os.execv(sys.executable, [sys.executable] + sys.argv)
+
+# 状态
+@routes.get('/mixlab/status')
+def mix_status(request):
+    return web.Response(text="running#"+_URL_)

 # 导入节点
 from .nodes.PromptNode import GLIGENTextBoxApply_Advanced,EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
-from .nodes.ImageNode import LoadImages_,CompositeImages,GridDisplayAndSave,GridInput,ImagesPrompt,SaveImageAndMetadata,SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.ImageNode import DepthViewer_,ImageBatchToList_,ImageListToBatch_,ComparingTwoFrames,LoadImages_,CompositeImages,GridDisplayAndSave,GridInput,ImagesPrompt,SaveImageAndMetadata,SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 # from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo

-from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
-from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import IncrementingListNode,ListSplit,CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+from .nodes.Audio import AudioPlayNode,SpeechRecognition,SpeechSynthesis
+from .nodes.Utils import KeyInput,IncrementingListNode,ListSplit,CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
 from .nodes.Mask import PreviewMask_,MaskListReplace,MaskListMerge,OutlineMask,FeatheredMask

 from .nodes.Style import ApplyVisualStylePrompting,StyleAlignedReferenceSampler,StyleAlignedBatchAlign,StyleAlignedSampleReferenceLatents

-from .nodes.Video import VideoCombine_Adv,LoadVideoAndSegment,ImageListReplace,VAEEncodeForInpaint_Frames
-
-from .nodes.TripoSR import LoadTripoSRModel,TripoSRSampler,SaveTripoSRMesh
+from .nodes.P5 import P5Input


 # 要导出的所有节点及其名称的字典
@@ -674,7 +1051,10 @@ NODE_CLASS_MAPPINGS = {
    "ImageColorTransfer":ImageColorTransfer,
    "ShowLayer":ShowLayer,
    "NewLayer":NewLayer,
+    "ImageListToBatch_":ImageListToBatch_,
+    "ImageBatchToList_":ImageBatchToList_,
    "CompositeImages_":CompositeImages,
+    "DepthViewer": DepthViewer_,
    "SplitImage":SplitImage,
    "CenterImage":CenterImage,
    "GridOutput":GridOutput,
@@ -691,15 +1071,14 @@ NODE_CLASS_MAPPINGS = {
    # "VAELoaderConsistencyDecoder":VAELoader,
    "SaveImageToLocal":SaveImageToLocal,
    "SaveImageAndMetadata_":SaveImageAndMetadata,
+    "ComparingTwoFrames_":ComparingTwoFrames,
    # "VAEDecodeConsistencyDecoder":VAEDecode,
    "ScreenShare":ScreenShareNode,
    "FloatingVideo":FloatingVideo,
-    "ChatGPTOpenAI":ChatGPTNode,
-    "ShowTextForGPT":ShowTextForGPT,
-    "CharacterInText":CharacterInText,
-    "TextSplitByDelimiter":TextSplitByDelimiter,
+   
    "SpeechRecognition":SpeechRecognition,
    "SpeechSynthesis":SpeechSynthesis,
+    "KeyInput":KeyInput,
    "Color":ColorInput,
    "FloatSlider":FloatSlider,
    "IntNumber":IntNumber,
@@ -721,44 +1100,45 @@ NODE_CLASS_MAPPINGS = {
    "ApplyVisualStylePrompting_":ApplyVisualStylePrompting,
    "StyleAlignedReferenceSampler_": StyleAlignedReferenceSampler,
    "StyleAlignedSampleReferenceLatents_": StyleAlignedSampleReferenceLatents,
-    "StyleAlignedBatchAlign_": StyleAlignedBatchAlign,
-    "LoadVideoAndSegment_":LoadVideoAndSegment,
-    "VideoCombine_Adv":VideoCombine_Adv,
+    "StyleAlignedBatchAlign_": StyleAlignedBatchAlign, 
    "ListSplit_":ListSplit,
-    "MaskListReplace_":MaskListReplace,
-    "ImageListReplace_":ImageListReplace,
-    "VAEEncodeForInpaint_Frames":VAEEncodeForInpaint_Frames,
+    "MaskListReplace_":MaskListReplace, 
    "IncrementingListNode_":IncrementingListNode,
    "PreviewMask_":PreviewMask_,
-     "LoadTripoSRModel_": LoadTripoSRModel,
-    "TripoSRSampler_": TripoSRSampler,
-    "SaveTripoSRMesh": SaveTripoSRMesh
-    # "GamePal":GamePal
+    "AudioPlay":AudioPlayNode,
+
+    "P5Input":P5Input
 }

 # 一个包含节点友好/可读的标题的字典
 NODE_DISPLAY_NAME_MAPPINGS = {
    "AppInfo":"App Info ♾️MixlabApp",
+    "ScreenShare":"Screen Share ♾️Mixlab",
+    "FloatingVideo":"Floating Video ♾️Mixlab",
+    "TextImage":"Text Image ♾️Mixlab",
+    
    "Color":"Color Input ♾️MixlabApp",
    "TextInput_":"Text Input ♾️MixlabApp",
+    "KeyInput":"API Key Input ♾️MixlabApp",
    "FloatSlider":"Float Slider Input ♾️MixlabApp",
    "IntNumber":"Int Input ♾️MixlabApp",
    "ImagesPrompt_":"Images Input ♾️MixlabApp",
    "SaveImageAndMetadata_":"Save Image Output ♾️MixlabApp",
+    "ComparingTwoFrames_":"Comparing Two Frames ♾️MixlabApp",
    "ResizeImageMixlab":"Resize Image ♾️Mixlab",
    "RandomPrompt": "Random Prompt ♾️Mixlab",
    "PromptImage":"Output Prompt and Image ♾️Mixlab",
    "SplitLongMask":"Splitting a long image into sections",
    "VAELoaderConsistencyDecoder":"Consistency Decoder Loader",
    "VAEDecodeConsistencyDecoder":"Consistency Decoder Decode",
-    "ScreenShare":"Screen Share ♾️Mixlab",
-    "FloatingVideo":"FloatingVideo ♾️Mixlab",
-    "ChatGPTOpenAI":"ChatGPT & Local LLM ♾️Mixlab",
-    "ShowTextForGPT":"Show Text ♾️MixlabApp",
+    
+    
    "MergeLayers":"Merge Layers ♾️Mixlab",
    "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
    "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
    "3DImage":"3DImage ♾️Mixlab",
+    "ImageListToBatch_":"Image List To Batch",
+    "ImageBatchToList_":"Image Batch To List",
    "CompositeImages_":"Composite Images ♾️Mixlab",
    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
    "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
@@ -784,28 +1164,69 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "GridInput":"Grid Input ♾️Mixlab",
    "GridOutput":"Grid Output ♾️Mixlab",
    "GetImageSize_":"Get Image Size ♾️Mixlab",
-    "VAEEncodeForInpaint_Frames":"VAE Encode For Inpaint Frames ♾️Mixlab",
    "IncrementingListNode_":"Create Incrementing Number List ♾️Mixlab",
    "LoadImagesToBatch":"Load Images(base64) ♾️Mixlab",
    "PreviewMask_":"Preview Mask",
-    "LoadTripoSRModel_": "Load TripoSR Model",
-    "TripoSRSampler_": "TripoSR Sampler",
-    "SaveTripoSRMesh": "Save TripoSR Mesh"
+    "AudioPlay":"Preview Audio ♾️Mixlab",
+
+     "MultiplicationNode":"Math Operation ♾️Mixlab",
+
+     "P5Input":"P5 Input ♾️Mixlab for test"
 }

 # web ui的节点功能
 WEB_DIRECTORY = "./web"

-
 logging.info('--------------')
 logging.info('\033[91m ### Mixlab Nodes: \033[93mLoaded')
 # print('\033[91m ### Mixlab Nodes: \033[93mLoaded')

 try:
-    from .nodes.Lama import LaMaInpainting
-    logging.info('LaMaInpainting.available {}'.format(LaMaInpainting.available))
-    if LaMaInpainting.available:
-        NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
+    from .nodes.ChatGPT import JsonRepair,ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter,SiliconflowFreeNode
+    logging.info('ChatGPT.available True')
+
+    NODE_CLASS_MAPPINGS_V = { 
+       "ChatGPTOpenAI":ChatGPTNode,
+       "SiliconflowLLM":SiliconflowFreeNode,
+        "ShowTextForGPT":ShowTextForGPT,
+        "CharacterInText":CharacterInText,
+        "TextSplitByDelimiter":TextSplitByDelimiter,
+        "JsonRepair":JsonRepair
+    }
+
+    # 一个包含节点友好/可读的标题的字典
+    NODE_DISPLAY_NAME_MAPPINGS_V = { 
+        "ChatGPTOpenAI":"ChatGPT & Local LLM ♾️Mixlab",
+        "SiliconflowLLM":"LLM Siliconflow ♾️Mixlab",
+        "ShowTextForGPT":"Show Text ♾️MixlabApp",
+        "CharacterInText":"Character In Text",
+        "TextSplitByDelimiter":"Text Split By Delimiter",
+        "JsonRepair":"Json Repair"
+    }
+
+
+    NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_V)
+    NODE_DISPLAY_NAME_MAPPINGS.update(NODE_DISPLAY_NAME_MAPPINGS_V)
+
+except Exception as e:
+    logging.info('ChatGPT.available False')
+
+
+try:
+    from .nodes.edit_mask import EditMask
+    logging.info('edit_mask.available True')
+    NODE_CLASS_MAPPINGS['EditMask']=EditMask
+    NODE_DISPLAY_NAME_MAPPINGS['EditMask']="Edit Mask ♾️Mixlab"
+except Exception as e:
+    logging.info('edit_mask.available False')
+
+try:
+    is_has=is_installed('simple_lama_inpainting',None,False)
+    if is_has:
+        from .nodes.Lama import LaMaInpainting
+        logging.info('LaMaInpainting.available {}'.format(LaMaInpainting.available))
+        if LaMaInpainting.available:
+            NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
 except Exception as e:
    logging.info('LaMaInpainting.available False')

@@ -836,4 +1257,57 @@ try:
 except Exception as e:
    logging.info('RembgNode_.available False' )

-logging.info('\033[93m -------------- \033[0m')
+
+try:
+    from .nodes.Video import GenerateFramesByCount,scenesNode_,CombineAudioVideo,VideoCombine_Adv,LoadVideoAndSegment,ImageListReplace,VAEEncodeForInpaint_Frames,LoadAndCombinedAudio_
+    
+    NODE_CLASS_MAPPINGS_V = {
+        "VAEEncodeForInpaint_Frames":VAEEncodeForInpaint_Frames,
+        "ImageListReplace_":ImageListReplace,
+        "LoadVideoAndSegment_":LoadVideoAndSegment,
+        "VideoCombine_Adv":VideoCombine_Adv,
+        "LoadAndCombinedAudio_":LoadAndCombinedAudio_,
+        "CombineAudioVideo":CombineAudioVideo,
+        "ScenesNode_":scenesNode_,
+        "GenerateFramesByCount":GenerateFramesByCount 
+    }
+
+    # 一个包含节点友好/可读的标题的字典
+    NODE_DISPLAY_NAME_MAPPINGS_V = {
+        "VAEEncodeForInpaint_Frames":"VAE Encode For Inpaint Frames ♾️Mixlab",
+        "ImageListReplace_":"Image List Replace",
+        "LoadVideoAndSegment_":"Load Video And Segment",
+        "VideoCombine_Adv":"Video Combine",
+        "LoadAndCombinedAudio_":"Load And Combined Audio",
+        "CombineAudioVideo":"Combine Audio Video",
+        "ScenesNode_":"Select Scene", 
+        "GenerateFramesByCount":"Generate Frames By Count"
+    }
+
+
+    NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_V)
+    NODE_DISPLAY_NAME_MAPPINGS.update(NODE_DISPLAY_NAME_MAPPINGS_V)
+
+except:
+    logging.info('Video.available False')
+
+
+try:
+    from .nodes.TripoSR import LoadTripoSRModel,TripoSRSampler,SaveTripoSRMesh
+    logging.info('TripoSR.available')
+    # logging.info( folder_paths.get_temp_directory())
+    NODE_CLASS_MAPPINGS['LoadTripoSRModel_']=LoadTripoSRModel
+    NODE_DISPLAY_NAME_MAPPINGS["LoadTripoSRModel_"]= "Load TripoSR Model"
+    
+    NODE_CLASS_MAPPINGS['TripoSRSampler_']=TripoSRSampler
+    NODE_DISPLAY_NAME_MAPPINGS["TripoSRSampler_"]= "TripoSR Sampler"
+
+    NODE_CLASS_MAPPINGS['SaveTripoSRMesh']=SaveTripoSRMesh
+    NODE_DISPLAY_NAME_MAPPINGS["SaveTripoSRMesh"]= "Save TripoSR Mesh"
+    
+
+except Exception as e:
+    logging.info('TripoSR.available False' )
+
+
+logging.info('\033[93m -------------- \033[0m')
@@ -10,6 +10,12 @@ if exist "%python_exec%" (
    for /f "delims=" %%i in (%requirements_txt%) do (
        %python_exec% -s -m pip install "%%i" -i https://pypi.tuna.tsinghua.edu.cn/simple
    )
+
+    @REM %python_exec% -s -m pip install --upgrade --force llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
+
+    @REM %python_exec% -s -m pip install --upgrade --force llama-cpp-python[server]
+    
+
 ) else (
    echo Installing with system Python
    for /f "delims=" %%i in (%requirements_txt%) do (
@@ -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}}
@@ -6,14 +6,69 @@ import folder_paths
 import hashlib
 import codecs,sys
 import importlib.util
+import subprocess
+
+python = sys.executable
+
+# 从文本中提取json
+def extract_json_strings(text):
+    json_strings = []
+    brace_level = 0
+    json_str = ''
+    in_json = False
+    
+    for char in text:
+        if char == '{':
+            brace_level += 1
+            in_json = True
+        if in_json:
+            json_str += char
+        if char == '}':
+            brace_level -= 1
+        if in_json and brace_level == 0:
+            json_strings.append(json_str)
+            json_str = ''
+            in_json = False
+
+    return json_strings[0] if len(json_strings)>0 else "{}"


-def is_installed(package):
+def is_installed(package, package_overwrite=None,auto_install=True):
+    is_has=False
    try:
        spec = importlib.util.find_spec(package)
+        is_has=spec is not None
    except ModuleNotFoundError:
-        return False
-    return spec is not None
+        pass
+
+    package = package_overwrite or package
+
+    if spec is None:
+        if auto_install==True:
+            print(f"Installing {package}...")
+            # 清华源 -i https://pypi.tuna.tsinghua.edu.cn/simple
+            command = f'"{python}" -m pip install {package}'
+    
+            result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ)
+
+            is_has=True
+
+            if result.returncode != 0:
+                print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
+                is_has=False
+    else:
+        print(package+'## OK')
+
+    return is_has
+  
+
+
+# def is_installed(package):
+#     try:
+#         spec = importlib.util.find_spec(package)
+#     except ModuleNotFoundError:
+#         return False
+#     return spec is not None


 def get_unique_hash(string):
@@ -53,30 +108,14 @@ def azure_client(key,url):

 def openai_client(key,url):
    client = openai.OpenAI(
-    api_key=key,
-    base_url=url
+        api_key=key,
+        base_url=url
    )
    return client

 def ZhipuAI_client(key):
-
    try:
-        if is_installed('zhipuai')==False:
-            import subprocess
-
-            # 安装
-            print('#pip install zhipuai')
-
-            result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'zhipuai'], capture_output=True, text=True)
-
-            #检查命令执行结果
-            if result.returncode == 0:
-                print("#install success")
-                from zhipuai import ZhipuAI
-            else:
-                print("#install error")
-            
-        else:
+        if is_installed('zhipuai')==True:
            from zhipuai import ZhipuAI
    except:
        print("#install zhipuai error")
@@ -91,66 +130,82 @@ def ZhipuAI_client(key):
 def phi_sort(lst):
    return sorted(lst, key=lambda x: x.lower().count('phi'), reverse=True)

-def get_llama_models():
-    res=[]
-
-    model_path=os.path.join(folder_paths.models_dir, "llamafile")
-    if os.path.exists(model_path):
-        files = os.listdir(model_path)
-        for file in files:
-            if os.path.isfile(os.path.join(model_path, file)):
-                res.append(file)
-        res=phi_sort(res)
-    return res
-
-llama_modes_list=get_llama_models()
-
-def get_llama_model_path(file_name):
-    model_path=os.path.join(folder_paths.models_dir, "llamafile")
-    mp=os.path.join(model_path,file_name)
-    return mp
-
-def llama_cpp_client(file_name):
+def get_llama_path():
    try:
-        if is_installed('llama_cpp')==False:
-            import subprocess
-
-            # 安装
-            print('#pip install llama-cpp-python')
- 
-            result = subprocess.run([sys.executable, '-s', '-m', 'pip', 
-                                     'install', 
-                                     'llama-cpp-python',
-                                     '--extra-index-url',
-                                     'https://abetlen.github.io/llama-cpp-python/whl/cu121'
-                                     ], capture_output=True, text=True)
-
-            #检查命令执行结果
-            if result.returncode == 0:
-                print("#install success")
-                from llama_cpp import Llama
-            else:
-                print("#install error")
-            
-        else:
-            from llama_cpp import Llama
+        return folder_paths.get_folder_paths('llamafile')[0]
    except:
-        print("#install llama-cpp-python error")
+        return os.path.join(folder_paths.models_dir, "llamafile")

-    mp=get_llama_model_path(file_name)
-    # file_name=get_llama_models()[0]
-    # model_path=os.path.join(folder_paths.models_dir, "llamafile")
-    # mp=os.path.join(model_path,file_name)
+# def get_llama_models():
+#     res=[]

-    llm = Llama(model_path=mp, chat_format="chatml")
+#     model_path=get_llama_path()
+#     if os.path.exists(model_path):
+#         files = os.listdir(model_path)
+#         for file in files:
+#             if os.path.isfile(os.path.join(model_path, file)):
+#                 res.append(file)
+#         res=phi_sort(res)
+#     return res

-    return llm
+# llama_modes_list=get_llama_models()
+# llama_modes_list=[]

-    
+# def get_llama_model_path(file_name):
+#     model_path=get_llama_path()
+#     mp=os.path.join(model_path,file_name)
+#     return mp
+
+# def llama_cpp_client(file_name):
+#     try:
+#         if is_installed('llama_cpp')==False:
+#             import subprocess
+
+#             # 安装
+#             print('#pip install llama-cpp-python')
+ 
+#             result = subprocess.run([sys.executable, '-s', '-m', 'pip', 
+#                                      'install', 
+#                                      'llama-cpp-python',
+#                                      '--extra-index-url',
+#                                      'https://abetlen.github.io/llama-cpp-python/whl/cu121'
+#                                      ], capture_output=True, text=True)
+
+#             #检查命令执行结果
+#             if result.returncode == 0:
+#                 print("#install success")
+#                 from llama_cpp import Llama
+
+#                 subprocess.run([sys.executable, '-s', '-m', 'pip', 
+#                                      'install', 
+#                                      'llama-cpp-python[server]'
+#                                      ], capture_output=True, text=True)
+
+#             else:
+#                 print("#install error")
+            
+#         else:
+#             from llama_cpp import Llama
+#     except:
+#         print("#install llama-cpp-python error")
+
+#     if file_name:
+#         mp=get_llama_model_path(file_name)
+#         # file_name=get_llama_models()[0]
+#         # model_path=os.path.join(folder_paths.models_dir, "llamafile")
+#         # mp=os.path.join(model_path,file_name)
+
+#         llm = Llama(model_path=mp, chat_format="chatml",n_gpu_layers=-1,n_ctx=512)
+
+#         return llm
+
+
+if is_installed('json_repair'):
+    from json_repair import repair_json


 def chat(client, model_name,messages ):
-
+        print('#chat',model_name,messages)
        try_count = 0
        while True:
            try_count += 1
@@ -193,6 +248,36 @@ def chat(client, model_name,messages ):
        return content


+llm_apis=[
+            {
+                "value": "https://api.openai.com/v1",
+                "label": "openai"
+            },
+            {
+                "value": "https://openai.api2d.net/v1",
+                "label": "api2d"
+            },
+            # {
+            #     "value": "https://docs-test-001.openai.azure.com",
+            #     "label": "https://docs-test-001.openai.azure.com"
+            # },
+             
+            {
+                "value": "https://api.moonshot.cn/v1",
+                "label": "Kimi"
+            },
+            {
+                "value": "https://api.deepseek.com/v1",
+                "label": "DeepSeek-V2"
+            },
+            {
+                "value": "https://api.siliconflow.cn/v1",
+                "label": "SiliconCloud"
+            }]
+        
+llm_apis_dict = {api["label"]: api["value"] for api in llm_apis}
+
+
 class ChatGPTNode:
    def __init__(self):
        # self.__client = OpenAI()
@@ -202,35 +287,60 @@ class ChatGPTNode:

    @classmethod
    def INPUT_TYPES(cls):
-        model_list=llama_modes_list+[
-                    "gpt-3.5-turbo",
-                    "gpt-3.5-turbo-0125",
-                    "gpt-35-turbo",
-                    "gpt-3.5-turbo-16k", 
-                    "gpt-3.5-turbo-16k-0613", 
-                    "gpt-4-0613",
-                    "gpt-4-1106-preview",
-                    "glm-4"
+
+        model_list=[ 
+            "gpt-3.5-turbo",
+            "gpt-3.5-turbo-16k",
+            "gpt-4o",
+            "gpt-4o-2024-05-13",
+            "gpt-4",
+            "gpt-4-0314",
+            "gpt-4-0613",
+            "gpt-3.5-turbo-0301",
+            "gpt-3.5-turbo-0613",
+            "gpt-3.5-turbo-16k-0613",
+            "qwen-turbo",
+            "qwen-plus",
+            "qwen-long",
+            "qwen-max",
+            "qwen-max-longcontext",
+            "glm-4",
+            "glm-3-turbo",
+            "moonshot-v1-8k",
+            "moonshot-v1-32k",
+            "moonshot-v1-128k",
+            "deepseek-chat",
+            "Qwen/Qwen2-7B-Instruct",
+            "THUDM/glm-4-9b-chat",
+            "01-ai/Yi-1.5-9B-Chat-16K",
+            "meta-llama/Meta-Llama-3.1-8B-Instruct"
                    ]
+        
        return {
            "required": {
-                "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
-                "api_url":("URL", {"default": "", "multiline": True,"dynamicPrompts": False}),
+                # "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
+                # "api_key":("STRING", {"forceInput": True,}),
+               
                "prompt": ("STRING", {"multiline": True,"dynamicPrompts": False}),
                "system_content": ("STRING", 
                                   {
                                       "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
                                       "multiline": True,"dynamicPrompts": False
                                       }),
+                 
                "model": ( model_list, 
                    {"default": model_list[0]}),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
                "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
+                "api_url":(list(llm_apis_dict.keys()), 
+                    {"default": list(llm_apis_dict.keys())[0]}),
            },
-             "hidden": {
-                "unique_id": "UNIQUE_ID",
-                "extra_pnginfo": "EXTRA_PNGINFO",
-            },
+             "optional":{
+                    "api_key":("STRING", {"forceInput": True,}),
+                    "custom_model_name":("STRING", {"forceInput": True,}), #适合自定义model
+                     "custom_api_url":("STRING", {"forceInput": True,}), #适合自定义model
+                },
+             
        }

    RETURN_TYPES = ("STRING","STRING","STRING",)
@@ -242,12 +352,29 @@ class ChatGPTNode:

    
    def generate_contextual_text(self,
-                                 api_key,
-                                 api_url, 
+                                #  api_key,
                                 prompt, 
                                 system_content,
-                                model, 
-                                seed,context_size,unique_id = None, extra_pnginfo=None):
+                                 model, 
+                                seed,
+                                context_size,
+                                api_url,
+                                api_key=None,
+                                custom_model_name=None,
+                                custom_api_url=None,
+                                ):
+        
+        if custom_model_name!=None:
+            model=custom_model_name
+
+        api_url=llm_apis_dict[api_url] if api_url in llm_apis_dict else ""
+
+        if custom_api_url!=None:
+            api_url=custom_api_url
+
+        if api_key==None:
+            api_key="lm_studio"
+
        # print(api_key!='',api_url,prompt,system_content,model,seed)
        # 可以选择保留会话历史以维持上下文记忆
        # 或者在此处清除会话历史 self.session_history.clear()
@@ -260,7 +387,7 @@ class ChatGPTNode:
            self.system_content=system_content
            # self.session_history=[]
            # self.session_history.append({"role": "system", "content": system_content})
-        
+        print("api_key,api_url",api_key,api_url)
        # 
        if is_azure_url(api_url):
            client=azure_client(api_key,api_url)
@@ -269,12 +396,12 @@ class ChatGPTNode:
            if model == "glm-4" :
                client = ZhipuAI_client(api_key)  # 使用 Zhipuai 的接口
                print('using Zhipuai interface')
-            elif model in llama_modes_list:
-                #
-                client=llama_cpp_client(model)
+            # elif model in llama_modes_list:
+            #     #
+            #     client=llama_cpp_client(model)
            else :
                client = openai_client(api_key,api_url)  # 使用 ChatGPT  的接口
-                print('using ChatGPT interface')
+                # print('using ChatGPT interface',api_key,api_url)

        # 把用户的提示添加到会话历史中
        # 调用API时传递整个会话历史
@@ -290,6 +417,7 @@ class ChatGPTNode:
        session_history=crop_list_tail(self.session_history,context_size)

        messages=[{"role": "system", "content": self.system_content}]+session_history+[{"role": "user", "content": prompt}]
+
        response_content = chat(client,model,messages)
        
        self.session_history=self.session_history+[{"role": "user", "content": prompt}]+[{'role':'assistant',"content":response_content}]
@@ -310,6 +438,93 @@ class ChatGPTNode:
        return (response_content,json.dumps(messages, indent=4),json.dumps(self.session_history, indent=4),)


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

 class ShowTextForGPT:
    @classmethod
@@ -471,3 +686,41 @@ class TextSplitByDelimiter:
        arr= arr[start_index:start_index + max_count * (skip_every+1):(skip_every+1)]

        return (arr,)
+
+
+class JsonRepair:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "json_string":("STRING", {"forceInput": True,}), 
+                 "key":("STRING", {"multiline": False,"dynamicPrompts": False,"default": ""}),
+            }
+        }
+
+    INPUT_IS_LIST = False
+    RETURN_TYPES = ("STRING","STRING",)
+    RETURN_NAMES = ("json_string","value",)
+    FUNCTION = "run"
+    # OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (False,False,)
+
+    CATEGORY = "♾️Mixlab/GPT"
+
+    def run(self, json_string,key=""):
+
+        json_string=extract_json_strings(json_string)
+        # print(json_string)
+        good_json_string = repair_json(json_string)
+
+        # 将 JSON 字符串解析为 Python 对象
+        data = json.loads(good_json_string)
+
+        v=""
+        if key!="" and (key in data):
+            v=data[key]
+
+        # 将 Python 对象转换回 JSON 字符串，确保中文字符不被转义
+        json_str_with_chinese = json.dumps(data, ensure_ascii=False)
+
+        return (json_str_with_chinese,v,)
@@ -70,14 +70,20 @@ def load_caption_model(model_path,config,t='blip-base'):
    return (caption_model,caption_processor)
    

+def get_clip_interrogator_path():
+    try:
+        return folder_paths.get_folder_paths('clip_interrogator')[0]
+    except:
+        return os.path.join(folder_paths.models_dir, "clip_interrogator")

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

-cache_path=os.path.join(folder_paths.models_dir, "clip_interrogator")
-

 # Tensor to PIL
 def tensor2pil(image):
@@ -1,12 +1,14 @@
 import numpy as np
 import requests
 import torch
+import torchvision.transforms.v2 as T
 # from PIL import Image, ImageDraw
 from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
 from PIL.PngImagePlugin import PngInfo
 import base64,os,random
 from io import BytesIO
 import folder_paths
+import node_helpers
 import json,io
 import comfy.utils
 from comfy.cli_args import args
@@ -14,8 +16,8 @@ import cv2
 import string
 import math,glob
 from .Watcher import FolderWatcher
-import hashlib

+from itertools import product


 # 将PIL图片转换为OpenCV格式
@@ -28,43 +30,105 @@ def opencv_to_pil(image):
    pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    return pil_image

+# 列出目录下面的所有文件
+def get_files_with_extension(directory, extensions):
+    file_list = []
+    # 确保extensions参数是一个list，即使只有一个元素
+    if not isinstance(extensions, (tuple, list)):
+        extensions = [extensions]
+    for root, dirs, files in os.walk(directory):
+        # print(f"Files at {root}: {files}")  # 确认files是一个字符串列表
+        for file in files:
+            # 检查文件是否以任何一个提供的扩展名结尾
+            if any(file.endswith(ext) for ext in extensions):
+                # 直接将文件名添加到列表中
+                file_list.append(file)
+    return file_list
+	
+def composite_images(foreground, background, mask, is_multiply_blend=False, position="overall", scale=0.25):
+    width, height = foreground.size
+    bg_image = background
+    bwidth, bheight = bg_image.size

-def composite_images(foreground, background, mask,is_multiply_blend=False):
-    width,height=foreground.size
- 
-    bg_image=background
-    # 按z-index排序
-    layer = {
-            "x":0,
-            "y":0,
-            "width":width,
-            "height":height,
-            "z_index":88,
-            "scale_option":'overall',
-            "image":foreground,
-            "mask":mask
-    }
-                
-    width, height = bg_image.size
+    scale=max(scale,1/bwidth)
+    scale=max(scale,1/bheight)
+
+    def determine_scale_option(width, height):
+        return 'height' if height > width else 'width'
+
+    if position == "overall":
+        layer = {
+            "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
+
+        new_width = int(width * scale)
+        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": 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']
+
+    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')

-    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):
@@ -101,7 +165,8 @@ class AnyType(str):
 any_type = AnyType("*")


-FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
+FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),"..","assets","fonts"))
+

 MAX_RESOLUTION=8192

@@ -427,6 +492,53 @@ def load_image(fp,white_bg=False):
        
    return images

+
+# 读取图片数据，转成tensor
+def load_image_to_tensor( image):
+    image_path = folder_paths.get_annotated_filepath(image)
+        
+    img = node_helpers.pillow(Image.open, image_path)
+        
+    output_images = []
+    output_masks = []
+    w, h = None, None
+
+    excluded_formats = ['MPO']
+        
+    for i in ImageSequence.Iterator(img):
+        i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+        if i.mode == 'I':
+            i = i.point(lambda i: i * (1 / 255))
+        image = i.convert("RGB")
+
+        if len(output_images) == 0:
+            w = image.size[0]
+            h = image.size[1]
+            
+        if image.size[0] != w or image.size[1] != h:
+            continue
+            
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        if 'A' in i.getbands():
+            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+            mask = 1. - torch.from_numpy(mask)
+        else:
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        output_images.append(image)
+        output_masks.append(mask.unsqueeze(0))
+
+    if len(output_images) > 1 and img.format not in excluded_formats:
+        output_image = torch.cat(output_images, dim=0)
+        output_mask = torch.cat(output_masks, dim=0)
+    else:
+        output_image = output_images[0]
+        output_mask = output_masks[0]
+
+    return (output_image, output_mask)
+
+
 def load_image_and_mask_from_url(url, timeout=10):
    # Load the image from the URL
    response = requests.get(url, timeout=timeout)
@@ -703,63 +815,78 @@ def multiply_blend(image1, image2):

 # cv2.imwrite('result.jpg', result)

+# 使用gpt4o优化代码
+# 为了消除图像合并时出现的灰色描边，可以使用以下方法：
+# 调整透明度：确保透明像素不会引入不需要的颜色。
+# 预处理图像：在缩放图像之前，可以先将图像的边缘进行预处理，例如扩展边缘颜色，减少抗锯齿带来的过渡效果。

-def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option,is_multiply_blend=False):
+def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option, is_multiply_blend=False):
    # 打开底图
    bg_image = bg_image.convert("RGBA")

    # 打开图层
    layer_image = layer_image.convert("RGBA")
-    # layer_image = layer_image.resize((width, height))
-
+   
    # 根据缩放选项调整图像大小
    if scale_option == "height":
        # 按照高度比例缩放
        original_width, original_height = layer_image.size
        scale = height / original_height
        new_width = int(original_width * scale)
-        layer_image = layer_image.resize((new_width, height))
+        layer_image = layer_image.resize((new_width, height), Image.NEAREST)
    elif scale_option == "width":
        # 按照宽度比例缩放
        original_width, original_height = layer_image.size
        scale = width / original_width
        new_height = int(original_height * scale)
-        layer_image = layer_image.resize((width, new_height))
+        layer_image = layer_image.resize((width, new_height), Image.NEAREST)
    elif scale_option == "overall":
        # 整体缩放
-        layer_image = layer_image.resize((width, height))
+        layer_image = layer_image.resize((width, height), Image.NEAREST)
+    elif scale_option == "longest":
+        original_width, original_height = layer_image.size
+        if original_width > original_height:
+            new_width = width
+            scale = width / original_width
+            new_height = int(original_height * scale)
+            x = 0
+            y = int((height - new_height) * 0.5)
+        else:
+            new_height = height
+            scale = height / original_height
+            new_width = int(original_height * scale)
+            x = int((width - new_width) * 0.5)
+            y = 0

    # 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, (0, 0, 0, 0))
-        transparent_img.paste(layer_image,(0, 0), mask)
-        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
@@ -803,58 +930,43 @@ def resize_image(layer_image, scale_option, width, height,color="white"):
        resized_image = resized_image.convert("RGB")
        resized_image=resize_2(resized_image)
        return resized_image
+    elif scale_option == "longest":
+    #暂时不用，  
+        if original_width > original_height:
+            new_width=width
+            scale = width / original_width
+            new_height = int(original_height * scale)
+            x=0
+            y=int((new_height-height)*0.5)
+            resized_image = Image.new("RGB", (new_width, new_height), color=color)
+            resized_image.paste(layer_image.resize((new_width, new_height)), (x,y))
+            resized_image = resized_image.convert("RGB")
+            resized_image=resize_2(resized_image)
+            return resized_image
+        else:
+            new_height=height
+            scale = height / original_height
+            new_width = int(original_height * scale)
+            x=int((new_width-width)*0.5)
+            y=0
+            resized_image = Image.new("RGB", (new_width, new_height), color=color)
+            resized_image.paste(layer_image.resize((new_width, new_height)), (x,y))
+            resized_image = resized_image.convert("RGB")
+            resized_image=resize_2(resized_image)
+            return resized_image
+
    
    layer_image=resize_2(layer_image)
    return layer_image


-
-
-
-
-# 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"
@@ -863,49 +975,54 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s

    # 2. Calculate absolute coordinates for each character
    char_coordinates = []
-    if layout == "vertical":
-        x = 0
-        y = 0
-        for i in range(len(lines)):
-            line = lines[i]
-            for char in line:
-                char_coordinates.append((x, y))
-                y += font_size + spacing
-            x += font_size + spacing
-            y = 0
-    else:
-        x = 0
-        y = 0
-        for line in lines:
-            for char in line:
-                char_coordinates.append((x, y))
-                x += font_size + spacing
-            y += font_size + spacing
-            x = 0
+    x, y = padding, padding
+    max_width, max_height = 0, 0

-    # 3. Calculate image width and height
    if layout == "vertical":
-        width = (len(lines) * (font_size + spacing)) - spacing
-        height = ((len(max(lines, key=len)) + 1) * (font_size + spacing)) + spacing
+        for line in lines:
+            max_char_width = max(font.getsize(char)[0] for char in line)
+            for char in line:
+                char_width, char_height = font.getsize(char)
+                char_coordinates.append((x, y))
+                y += char_height + spacing
+                max_height = max(max_height, y + padding)
+            x += max_char_width + line_spacing
+            y = padding
+        max_width = x
+        total_line_width = sum(font.getsize(line)[1] for line in lines)
+        total_spacing = line_spacing * (len(lines) - 1)
+        # 确保左边和右边的padding都被计入max_width
+        max_width = total_line_width + total_spacing + padding * 2
    else:
-        width = (len(max(lines, key=len)) * (font_size + spacing)) - spacing
-        height = ((len(lines) - 1) * (font_size + spacing)) + font_size
+        for line in lines:
+            line_width, line_height = font.getsize(line)
+            for char in line:
+                char_width, char_height = font.getsize(char)
+                char_coordinates.append((x, y))
+                x += char_width + spacing
+                max_width = max(max_width, x + padding)
+            y += line_height + line_spacing
+            x = padding
+        # max_height = y
+        total_line_heights = sum(font.getsize(line)[1] for line in lines)
+        total_spacing = line_spacing * (len(lines) - 1)
+        # 确保顶部和底部的padding都被计入max_height
+        max_height = total_line_heights + total_spacing + padding * 2
+
+    # 3. Create image with calculated width and height
+    image = Image.new('RGBA', (max_width, max_height), (255, 255, 255, 0))
+    draw = ImageDraw.Draw(image)

    # 4. Draw each character on the image
-    image = Image.new('RGBA', (width, height), (255, 255, 255, 0))
-    draw = ImageDraw.Draw(image)
-    font = ImageFont.truetype(font_path, font_size)
-
    index = 0
-    for i, line in enumerate(lines):
-        for j, char in enumerate(line):
+    for line in lines:
+        for char in line:
            x, y = char_coordinates[index]
-            
            if stroke:
-                draw.text((x-stroke_width, y), char, font=font, fill=stroke_color)
-                draw.text((x+stroke_width, y), char, font=font, fill=stroke_color)
-                draw.text((x, y-stroke_width), char, font=font, fill=stroke_color)
-                draw.text((x, y+stroke_width), char, font=font, fill=stroke_color)
+                draw.text((x-stroke_width, y), char, font=font, fill=text_color)
+                draw.text((x+stroke_width, y), char, font=font, fill=text_color)
+                draw.text((x, y-stroke_width), char, font=font, fill=text_color)
+                draw.text((x, y+stroke_width), char, font=font, fill=text_color)
            
            draw.text((x, y), char, font=font, fill=text_color)
            index += 1
@@ -1230,6 +1347,9 @@ class LoadImages_:
            image=pil2tensor(image)
            ims.append(image)

+        if len(ims)==0:
+            image1 = Image.new('RGB', (512, 512), color='black')
+            return (pil2tensor(image1),)
        image1 = ims[0]
        for image2 in ims[1:]:
            if image1.shape[1:] != image2.shape[1:]:
@@ -1432,7 +1552,7 @@ class ImageCropByAlpha:



-
+# get_files_with_extension(FONT_PATH,'.ttf')

 class TextImage:
    @classmethod
@@ -1440,18 +1560,32 @@ class TextImage:
        return {"required": { 
            
                    "text": ("STRING",{"multiline": True,"default": "龍馬精神迎新歲","dynamicPrompts": False}),
-                    "font_path": ("STRING",{"multiline": False,"default": FONT_PATH,"dynamicPrompts": False}),
+                    "font": (get_files_with_extension(FONT_PATH,['.ttf','.otf']),),#后缀为 ttf
                    "font_size": ("INT",{
                                "default":100, 
-                                "min": 100, #Minimum value
-                                "max": 1000, #Maximum value
+                                "min": 1, #Minimum value
+                                "max": 10000000, #Maximum value
                                "step": 1, #Slider's step
                                "display": "number" # Cosmetic only: display as "number" or "slider"
                                }), 
                    "spacing": ("INT",{
                                "default":12, 
-                                "min": -200, #Minimum value
-                                "max": 200, #Maximum value
+                                "min": -2000000000, #Minimum value
+                                "max": 2000000000, #Maximum value
+                                "step": 1, #Slider's step
+                                "display": "number" # Cosmetic only: display as "number" or "slider"
+                                }), 
+                    "line_spacing": ("INT",{
+                                "default":12, 
+                                "min": -2000000000, #Minimum value
+                                "max": 2000000000, #Maximum value
+                                "step": 1, #Slider's step
+                                "display": "number" # Cosmetic only: display as "number" or "slider"
+                                }), 
+                    "padding": ("INT",{
+                                "default":8, 
+                                "min": 0, #Minimum value
+                                "max": 2000000000, #Maximum value
                                "step": 1, #Slider's step
                                "display": "number" # Cosmetic only: display as "number" or "slider"
                                }), 
@@ -1462,7 +1596,7 @@ class TextImage:
                }
    
    RETURN_TYPES = ("IMAGE","MASK",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_NAMES = ("image","mask",)

    FUNCTION = "run"

@@ -1471,11 +1605,14 @@ class TextImage:
    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,False,)

-    def run(self,text,font_path,font_size,spacing,text_color,vertical,stroke):
+    def run(self,text,font,font_size,spacing,line_spacing,padding,text_color,vertical,stroke):
        
-        # text_list=list(text)
+        font_path=os.path.join(FONT_PATH,font)
+
+        if text=="":
+            text=" "
        # stroke=False, stroke_color=(0, 0, 0), stroke_width=1, spacing=0
-        img,mask=generate_text_image(text,font_path,font_size,text_color,vertical,stroke,(0, 0, 0),1,spacing)
+        img,mask=generate_text_image(text,font_path,font_size,text_color,vertical,stroke,(0, 0, 0),1,spacing,line_spacing,padding)
        
        img=pil2tensor(img)
        mask=pil2tensor(mask)
@@ -1509,7 +1646,7 @@ class LoadImagesFromURL:

    def run(self,url,seed=0):
        global urls_image
-        print(urls_image)
+        # print(urls_image)
        def filter_http_urls(urls):
            filtered_urls = []
            for url in urls.split('\n'):
@@ -1598,28 +1735,51 @@ class Image3D:
    def run(self,upload,material=None):
        # print('material',material)
        # print(upload )
-        image = base64_to_image(upload['image'])

-        mat=None
-        if 'material' in upload and upload['material']:
-            mat=base64_to_image(upload['material'])
-            mat=mat.convert('RGB')
-            mat=pil2tensor(mat)
+        # 截取的系列角度截图
+        images=upload['images'] if "images" in upload else []

-        mask = image.split()[3]
-        image=image.convert('RGB')
+        ims=[]
+        for im in images:
+            if 'type' in im and (not f"[{im['type']}]" in im['name']):
+                im['name']=im['name']+" "+f"[{im['type']}]"
+            output_image, output_mask = load_image_to_tensor(im['name'])
+            ims.append(output_image)
        
-        mask=mask.convert('L')
-
+        
+        mask=None
        bg_image=None
-        if 'bg_image' in upload and upload['bg_image']:
-            bg_image = base64_to_image(upload['bg_image'])
-            bg_image=bg_image.convert('RGB')
-            bg_image=pil2tensor(bg_image)
+        mat=None
+
+        # 如果没有系列截图
+        if len(ims)==0:
+            # 这个是3d模型当前截图
+            image = base64_to_image(upload['image'])
+
+            
+            if 'material' in upload and upload['material']:
+                mat=base64_to_image(upload['material'])
+                mat=mat.convert('RGB')
+                mat=pil2tensor(mat)
+
+            mask = image.split()[3]
+            image=image.convert('RGB')
+            
+            mask=mask.convert('L')
+
+            
+            if 'bg_image' in upload and upload['bg_image']:
+                bg_image = base64_to_image(upload['bg_image'])
+                bg_image=bg_image.convert('RGB')
+                bg_image=pil2tensor(bg_image)


-        mask=pil2tensor(mask)
-        image=pil2tensor(image)
+            mask=pil2tensor(mask)
+            image=pil2tensor(image)
+        else:
+            
+            image = torch.cat(ims, dim=0)
+
        
        m=[]
        if not material is None:
@@ -1704,14 +1864,20 @@ class CompositeImages:
    def INPUT_TYPES(s):
        return {
            "required": { 
-                    "foreground": ("IMAGE",),
+                    "foreground": (any_type,),
                    "mask":("MASK",),
                    "background": ("IMAGE",),
                    },
-             "optional":{
-                           
+             "optional":{ 
                  "is_multiply_blend":  ("BOOLEAN", {"default": False}),
-                            
+                  "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"
+                            }), 
                    }
                }
    
@@ -1724,15 +1890,30 @@ class CompositeImages:

    # OUTPUT_IS_LIST = (True,)

-    def run(self, foreground,mask,background,is_multiply_blend):
-        foreground= tensor2pil(foreground)
-        mask= tensor2pil(mask)
-        background= tensor2pil(background)
-        res=composite_images(foreground,background,mask,is_multiply_blend)
+    # 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:
@@ -2837,6 +3018,65 @@ class SaveImageAndMetadata:

        return { "ui": { "images": results } }

+class ComparingTwoFrames:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = "ComparingTwoFrames"
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": 
+                    {"before_image": ("IMAGE", ),
+                    "after_image": ("IMAGE", )
+                     }, 
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "comparingImages"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "♾️Mixlab/Output"
+
+    def comparingImages(self, before_image,after_image):
+        filename_prefix = self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+            filename_prefix, self.output_dir, after_image[0].shape[1], after_image[0].shape[0])
+        
+        bresults = list()
+        
+        for bimage in before_image:
+            i = 255. * bimage.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+         
+            file = f"{filename}_{counter:05}_.png"
+            img.save(os.path.join(full_output_folder, file), pnginfo=None, compress_level=self.compress_level)
+            bresults.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            counter += 1
+
+
+        results = list()
+        for aimage in after_image:
+            i = 255. * aimage.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+         
+            file = f"{filename}_{counter:05}_.png"
+            img.save(os.path.join(full_output_folder, file), pnginfo=None, compress_level=self.compress_level)
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            counter += 1
+
+        return { "ui": { "after_images": results,"before_images":bresults } }
+
 class ImageColorTransfer:
    @classmethod
    def INPUT_TYPES(s):
@@ -3002,3 +3242,144 @@ class SaveImageToLocal:
            counter += 1

        return ()
+
+
+class ImageBatchToList_:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"image_batch": ("IMAGE",), }}
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image_list",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Image"
+
+    def run(self, image_batch):
+        images = [image_batch[i:i + 1, ...] for i in range(image_batch.shape[0])]
+        return (images, )
+
+class ImageListToBatch_:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "run"
+    INPUT_IS_LIST = True
+    CATEGORY = "♾️Mixlab/Image"
+
+    def run(self, images):
+        shape = images[0].shape[1:3]
+        out = []
+
+        for i in range(len(images)):
+            img = images[i].permute([0,3,1,2])
+            if images[i].shape[1:3] != shape:
+                transforms = T.Compose([
+                    T.CenterCrop(min(img.shape[2], img.shape[3])),
+                    T.Resize((shape[0], shape[1]), interpolation=T.InterpolationMode.BICUBIC),
+                ])
+                img = transforms(img)
+            out.append(img.permute([0,2,3,1]))
+
+        out = torch.cat(out, dim=0)
+
+        return (out,)
+
+
+# https://github.com/gokayfem/ComfyUI-Depth-Visualization?tab=readme-ov-file
+class DepthViewer_:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "depth_map": ("IMAGE",),
+
+            },
+             "optional":{
+                  "frames":("IMAGEBASE64",), 
+                },
+        }
+
+    def __init__(self):
+        self.saved_reference = []
+        self.saved_depth = []
+
+        self.full_output_folder,self.filename,self.counter, self.subfolder, self.filename_prefix = folder_paths.get_save_image_path(
+            "imagesave", 
+            folder_paths.get_output_directory())
+
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("frames",)
+    
+    OUTPUT_NODE = True
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/3D"
+    def run(self, image, depth_map,frames=None):
+        self.saved_reference.clear()
+        self.saved_depth.clear()
+        image = image[0].detach().cpu().numpy()
+        depth = depth_map[0].detach().cpu().numpy()
+
+        image = Image.fromarray(np.clip(255. * image, 0, 255).astype(np.uint8)).convert('RGB')
+        depth = Image.fromarray(np.clip(255. * depth, 0, 255).astype(np.uint8))
+
+        return self.display([image], [depth],frames)
+
+    def display(self, reference_image, depth_map,frames):
+        for (batch_number, (single_image, single_depth)) in enumerate(zip(reference_image, depth_map)):
+            filename_with_batch_num = self.filename.replace("%batch_num%", str(batch_number))
+
+            image_file = f"{filename_with_batch_num}_{self.counter:05}_reference.png"
+            single_image.save(os.path.join(self.full_output_folder, image_file))
+
+            depth_file = f"{filename_with_batch_num}_{self.counter:05}_depth.png"
+            single_depth.save(os.path.join(self.full_output_folder, depth_file))
+
+            self.saved_reference.append({
+                "filename": image_file,
+                "subfolder": self.subfolder,
+                "type": "output"
+            })
+
+            self.saved_depth.append({
+                "filename": depth_file,
+                "subfolder": self.subfolder,
+                "type": "output"
+            })
+            self.counter += 1
+
+
+        ims=[]
+        image1 = Image.new('RGB', (512, 512), color='black')
+        image1=pil2tensor(image1)
+
+        if frames!=None:
+            for im in frames['images']:
+                # print(im)
+                if 'type' in im and (not f"[{im['type']}]" in im['name']):
+                    im['name']=im['name']+" "+f"[{im['type']}]"
+                    
+                output_image, output_mask = load_image_to_tensor(im['name'])
+                ims.append(output_image)
+
+            if len(ims)>0:
+                image1 = ims[0]
+                for image2 in ims[1:]:
+                    if image1.shape[1:] != image2.shape[1:]:
+                        image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
+                    image1 = torch.cat((image1, image2), dim=0)
+
+        return {"ui": {"reference_image": self.saved_reference, "depth_map": self.saved_depth}, "result": (image1,)}
@@ -42,8 +42,13 @@ else:
    _available=True


- 
-llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
+def get_lama_path():
+    try:
+        return folder_paths.get_folder_paths('lama')[0]
+    except:
+        return os.path.join(folder_paths.models_dir, "lama")
+
+llma_model_path=os.path.join(get_lama_path(), "big-lama.pt")
 if not os.path.exists(llma_model_path):
    os.environ['LAMA_MODEL']=''
    print(f"## lama torchscript model not found: {llma_model_path},pls download from https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt")
@@ -80,8 +85,6 @@ class LaMaInpainting:
            "image": ("IMAGE",),
            "mask": ("MASK",),
                             },
-
-            
                }
    
    RETURN_TYPES = ("IMAGE",)
@@ -0,0 +1,104 @@
+import torch
+import numpy as np
+from PIL import Image,ImageSequence,ImageOps
+import base64
+import io
+import comfy.utils
+import folder_paths
+import node_helpers
+
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+def load_image_to_tensor( image):
+    image_path = folder_paths.get_annotated_filepath(image)
+        
+    img = node_helpers.pillow(Image.open, image_path)
+        
+    output_images = []
+    output_masks = []
+    w, h = None, None
+
+    excluded_formats = ['MPO']
+        
+    for i in ImageSequence.Iterator(img):
+        i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+        if i.mode == 'I':
+            i = i.point(lambda i: i * (1 / 255))
+        image = i.convert("RGB")
+
+        if len(output_images) == 0:
+            w = image.size[0]
+            h = image.size[1]
+            
+        if image.size[0] != w or image.size[1] != h:
+            continue
+            
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        if 'A' in i.getbands():
+            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+            mask = 1. - torch.from_numpy(mask)
+        else:
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        output_images.append(image)
+        output_masks.append(mask.unsqueeze(0))
+
+    if len(output_images) > 1 and img.format not in excluded_formats:
+        output_image = torch.cat(output_images, dim=0)
+        output_mask = torch.cat(output_masks, dim=0)
+    else:
+        output_image = output_images[0]
+        output_mask = output_masks[0]
+
+    return (output_image, output_mask)
+
+          
+
+class P5Input:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "frames":("IMAGEBASE64",), 
+                             },
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("frames",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Input"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self, frames):
+        ims=[]
+        for im in frames['images']:
+            # print(im)
+            if 'type' in im and (not f"[{im['type']}]" in im['name']):
+                im['name']=im['name']+" "+f"[{im['type']}]"
+                
+            output_image, output_mask = load_image_to_tensor(im['name'])
+            ims.append(output_image)
+
+        if len(ims)==0:
+            image1 = Image.new('RGB', (512, 512), color='black')
+            return (pil2tensor(image1),)
+        image1 = ims[0]
+        for image2 in ims[1:]:
+            if image1.shape[1:] != image2.shape[1:]:
+                image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
+            image1 = torch.cat((image1, image2), dim=0)
+
+        # 用于节点提示:p5节点提示有多少帧
+        return {"ui": {"_info": [len(frames['images'])]}, "result": (image1,)}
@@ -467,15 +467,37 @@ 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
 _available=False

+
+def get_rembg_models(path):
+    """从目录中获取文件并提取文件名
+    Args:
+        path: 目录路径
+    Returns:
+        文件名列表
+    """
+    filenames = []
+    for root, _, files in os.walk(path):
+        for filename in files:
+        # 过滤隐藏文件
+            if not filename.startswith('.'):
+                name, ext = os.path.splitext(os.path.basename(filename))
+                filenames.append(name)
+    return filenames
+
+
 def is_installed(package):
    try:
        spec = importlib.util.find_spec(package)
@@ -509,8 +531,8 @@ except:
    _available=False


-def briarmbg_run(images=[]):
-    mroot=os.path.join(folder_paths.models_dir, "rembg")
+def run_briarmbg(images=[]):
+    mroot=U2NET_HOME
    m=os.path.join(mroot,'briarmbg.pth')
    if os.path.exists(m)==False:
        # 下载
@@ -573,14 +595,15 @@ def briarmbg_run(images=[]):
    return (masks,rgba_images,rgb_images)   


-def run_bg(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)
@@ -620,8 +643,9 @@ def run_bg(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)


@@ -643,17 +667,7 @@ class RembgNode_:
    def INPUT_TYPES(s):
        return {"required": {
            "image": ("IMAGE",),
-            "model_name": ([
-                            "briarmbg",
-                            "u2net",
-                            "u2netp",
-                            "u2net_human_seg",
-                            "u2net_cloth_seg",
-                            "silueta",
-                            "isnet-general-use",
-                            "isnet-anime",
-                            
-                            ],),
+            "model_name": (get_rembg_models(U2NET_HOME),),
            
                             },
                }
@@ -681,9 +695,9 @@ class RembgNode_:
                images.append(im)

        if model_name=='briarmbg':
-            masks,rgba_images,rgb_images=briarmbg_run(images)
+            masks,rgba_images,rgb_images=run_briarmbg(images)
        else:
-            masks,rgba_images,rgb_images=run_bg(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]

@@ -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)
@@ -18,19 +18,25 @@ from lark import Lark, Transformer, v_args
 global _available
 _available=True

+def get_text_generator_path():
+    try:
+        return folder_paths.get_folder_paths('prompt_generator')[0]
+    except:
+        return os.path.join(folder_paths.models_dir, "prompt_generator")

-text_generator_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/text2image-prompt-generator")
+prompt_generator=get_text_generator_path()
+
+text_generator_model_path=os.path.join(prompt_generator, "text2image-prompt-generator")
 if not os.path.exists(text_generator_model_path):
    print(f"## text_generator_model not found: {text_generator_model_path}, pls download from https://huggingface.co/succinctly/text2image-prompt-generator/tree/main")
    text_generator_model_path='succinctly/text2image-prompt-generator'

-zh_en_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/opus-mt-zh-en")
+zh_en_model_path=os.path.join(prompt_generator, "opus-mt-zh-en")
 if not os.path.exists(zh_en_model_path):
    print(f"## zh_en_model not found: {zh_en_model_path}, pls download from https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main")
    zh_en_model_path='Helsinki-NLP/opus-mt-zh-en'


-
 def is_installed(package):
    try:
        spec = importlib.util.find_spec(package)
@@ -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
@@ -133,7 +133,7 @@ def get_font_files(directory):

    return font_files

-r_directory = os.path.join(os.path.dirname(__file__), '../assets/')
+r_directory = os.path.join(os.path.dirname(__file__), '..','assets','/')

 font_files = get_font_files(r_directory)
 # print(font_files)
@@ -181,6 +181,28 @@ class ColorInput:
        return (h,r,g,b,a,)


+class KeyInput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "key":("KEY",), 
+                             },
+                }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("key",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Input"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,key):
+        return (key,)
+
+

 class FontInput:
    @classmethod
@@ -284,7 +306,7 @@ class FloatSlider:
                }
    
    RETURN_TYPES = ("FLOAT",) 
-    RETURN_NAMES = ('weight(0-1)',)
+    RETURN_NAMES = ('FLOAT',)
    FUNCTION = "run"

    CATEGORY = "♾️Mixlab/Input"
@@ -297,9 +319,7 @@ 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 +588,7 @@ class AppInfo:
                             },

                "optional":{
-                    "IMAGE": ("IMAGE",),
+                    "image": ("IMAGE",),
                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
                    "version":("INT", {
                        "default": 1, 
@@ -596,12 +616,12 @@ class AppInfo:
    INPUT_IS_LIST = True
    # OUTPUT_IS_LIST = (True,)

-    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
+    def run(self,name,input_ids,output_ids,image,description,version,share_prefix,link,category,auto_save):
        name=name[0]
        
        im=None
-        if IMAGE:
-            im=IMAGE[0][0]
+        if image:
+            im=image[0][0]
            #TODO batch 的方式需要处理
            im=create_temp_file(im)
        # image [img,] img[batch,w,h,a] 列表里面是batch，
@@ -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)
@@ -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)
@@ -0,0 +1,10 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libsvtav1",
+        "-pix_fmt", "yuv420p10le",
+        "-crf", "23"
+    ],
+     "extension": "webm",
+     "environment": {"SVT_LOG": "1"}
+}
@@ -0,0 +1,9 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libx264",
+        "-pix_fmt", "yuv420p",
+        "-crf", "19"
+    ],
+     "extension": "mp4"
+}
@@ -0,0 +1,11 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libx265",
+        "-pix_fmt", "yuv420p10le",
+        "-preset", "medium",
+        "-crf", "22",
+        "-x265-params", "log-level=quiet"
+    ],
+     "extension": "mp4"
+}
@@ -0,0 +1,9 @@
+{
+    "main_pass":
+    [
+        "-n",
+        "-pix_fmt", "yuv420p",
+        "-crf", "23"
+    ],
+     "extension": "webm"
+}
@@ -0,0 +1,15 @@
+[project]
+name = "comfyui-mixlab-nodes"
+description = "3D, ScreenShareNode & FloatingVideoNode, SpeechRecognition & SpeechSynthesis, GPT, LoadImagesFromLocal, Layers, Other Nodes, ..."
+version = "0.36.0"
+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 = ""
@@ -4,7 +4,7 @@ watchdog
 opencv-python-headless
 matplotlib
 openai
-simple-lama-inpainting
+# simple-lama-inpainting
 clip-interrogator==0.6.0
 transformers>=4.36.0
 lark-parser
@@ -15,4 +15,7 @@ Pillow>=9.5.0
 einops==0.7.0
 trimesh>=4.0.5
 huggingface-hub
-scikit-image
+scikit-image
+torchaudio
+soundfile>=0.12.1
+json-repair
@@ -0,0 +1,22 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Mixlab AR</title>
+</head>
+
+<body>
+
+    <script type="module">
+
+        import { api } from "../../../scripts/api.js";
+        import Command from '/extensions/comfyui-mixlab-nodes/javascript/command.js'
+
+
+    </script>
+
+</body>
+
+</html>
@@ -26,7 +26,8 @@ const setLocalDataOfWin = (key, value) => {
  localStorage.setItem(key, JSON.stringify(value))
  // window[key] = value
 }
-async function uploadImage (blob, fileType = '.svg', filename) {
+
+async function uploadImage_ (blob, fileType = '.svg', filename) {
  // const blob = await (await fetch(src)).blob();
  const body = new FormData()
  body.append(
@@ -41,13 +42,17 @@ async function uploadImage (blob, fileType = '.svg', filename) {

  // console.log(resp)
  let data = await resp.json()
+  return data
+}
+
+async function uploadImage (blob, fileType = '.svg', filename) {
+  let data = await uploadImage_(blob, fileType, filename)
  let { name, subfolder } = data
  let src = api.apiURL(
    `/view?filename=${encodeURIComponent(
      name
    )}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
  )
-
  return src
 }

@@ -171,6 +176,42 @@ async function changeMaterial (
  targetMaterial.pbrMetallicRoughness.baseColorTexture.setTexture(targetTexture)
 }

+function inputFileClick (isFileURL = false, isGlb = false) {
+  return new Promise((res, rej) => {
+    // 创建一个input元素
+    var input = document.createElement('input')
+    input.type = 'file'
+    input.accept = isGlb ? '.glb' : 'image/*'
+
+    // 监听input的change事件
+    input.addEventListener('change', function () {
+      // 获取上传的文件
+      var file = input.files[0]
+
+      if (isFileURL) {
+        res(URL.createObjectURL(file))
+        return
+      }
+
+      // 创建一个FileReader对象来读取文件
+      var reader = new FileReader()
+
+      // 监听FileReader的load事件
+      reader.addEventListener('load', async () => {
+        let base64 = reader.result
+        input.remove()
+        res(base64)
+      })
+
+      // 读取文件
+      reader.readAsDataURL(file)
+    })
+
+    // 触发input的点击事件
+    input.click()
+  })
+}
+
 app.registerExtension({
  name: 'Mixlab.3D.3DImage',
  async getCustomWidgets (app) {
@@ -189,7 +230,7 @@ app.registerExtension({
            let d = getLocalData('_mixlab_3d_image')
            // console.log('serializeValue', node)
            if (d && d[node.id]) {
-              let { url, bg, material } = d[node.id]
+              let { url, bg, material, images } = d[node.id]
              let data = {}
              if (url) {
                data.image = await parseImage(url)
@@ -205,6 +246,10 @@ app.registerExtension({
                data.material = await parseImage(material)
              }

+              if (images) {
+                data.images = images
+              }
+
              return JSON.parse(JSON.stringify(data))
            } else {
              return {}
@@ -243,39 +288,29 @@ app.registerExtension({

        const inputDiv = (key, placeholder, preview) => {
          let div = document.createElement('div')
-          const ip = document.createElement('input')
-          ip.type = 'file'
+          const ip = document.createElement('button')
          ip.className = `${'comfy-multiline-input'} ${placeholder}`
          div.style = `display: flex;
            align-items: center; 
            margin: 6px 8px;
            margin-top: 0;`
-          ip.placeholder = placeholder
-          // ip.value = value

          ip.style = `outline: none;
            border: none;
            padding: 4px;
-            width: 60%;cursor: pointer;
+            width: 100px;cursor: pointer;
            height: 32px;`
-          const label = document.createElement('label')
-          label.style = 'font-size: 10px;min-width:32px'
-          label.innerText = placeholder
-          div.appendChild(label)
+          ip.innerText = placeholder
          div.appendChild(ip)

-          let that = this,
-            filename = new Date().getTime()
+          let that = this

-          ip.addEventListener('change', async event => {
-            const file = event.target.files[0]
-            const reader = new FileReader()
-            filename = new Date().getTime()
-            // 读取文件内容
-            reader.onload = async e => {
-              const fileURL = URL.createObjectURL(file)
-              // console.log('文件URL: ', fileURL)
-              let html = `<model-viewer  src="${fileURL}" 
+          ip.addEventListener('click', async event => {
+            let fileURL = await inputFileClick(true, true)
+
+            // console.log('文件URL: ', fileURL)
+            let html = `<model-viewer  src="${fileURL}" 
+                oncontextmenu="return false;"
                min-field-of-view="0deg" max-field-of-view="180deg"
                 shadow-intensity="1" 
                 camera-controls 
@@ -285,230 +320,303 @@ app.registerExtension({
                  <div>Variant: <select class="variant"></select></div>
                  <div>Material: <select class="material"></select></div>
                  <div>Material: <div class="material_img"> </div></div>
-                  <div><button class="bg">BG</button></div>
+                  <div>
+                    <button class="bg">BG</button>
+                   
+                  </div>
+                  <div>
+                   <input class="ddcap_step" type="number" min="1" max="20" step="1" value="1">
+                   <input class="total_images" type="number" min="1" max="180" step="1" value="40">
+                   <input class="ddcap_range" type="range" min="-180" max="180" step="1" value="0">
+                   <input class="ddcap_range_top" type="range" min="-180" max="180" step="1" value="0">
+                  <button class="ddcap">Capture Rotational Screenshots</button></div>
+                  
                  <div><button class="export">Export GLB</button></div>
                  
                </div></model-viewer>`

-              preview.innerHTML = html
-              if (that.size[1] < 400) {
-                that.setSize([that.size[0], that.size[1] + 300])
-                app.canvas.draw(true, true)
-              }
-
-              const modelViewerVariants = preview.querySelector('model-viewer')
-              const select = preview.querySelector('.variant')
-              const selectMaterial = preview.querySelector('.material')
-              const material_img = preview.querySelector('.material_img')
-              const bg = preview.querySelector('.bg')
-              const exportGLB = preview.querySelector('.export')
-
-              if (modelViewerVariants) {
-                modelViewerVariants.style.width = `${that.size[0] - 24}px`
-                modelViewerVariants.style.height = `${that.size[1] - 48}px`
-              }
-
-              modelViewerVariants.addEventListener('load', async () => {
-                const names = modelViewerVariants.availableVariants
-
-                // 变量
-                for (const name of names) {
-                  const option = document.createElement('option')
-                  option.value = name
-                  option.textContent = name
-                  select.appendChild(option)
-                }
-                // Adds a default option.
-                if (names.length === 0) {
-                  const option = document.createElement('option')
-                  option.value = 'default'
-                  option.textContent = 'Default'
-                  select.appendChild(option)
-                }
-
-                // 材质
-                extractMaterial(
-                  modelViewerVariants,
-                  selectMaterial,
-                  material_img
-                )
-              })
-
-              let timer = null
-              const delay = 500 // 延迟时间，单位为毫秒
-
-              async function checkCameraChange () {
-                let dd = getLocalData(key)
-                let base64Data = modelViewerVariants.toDataURL()
-
-                const contentType = getContentTypeFromBase64(base64Data)
-
-                const blob = await base64ToBlobFromURL(base64Data, contentType)
-
-                //  const fileBlob = new Blob([e.target.result], { type: file.type });
-                let url = await uploadImage(blob, '.png')
-                // console.log(url)
-
-                let bg_blob = await base64ToBlobFromURL(
-                  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mN88uXrPQAFwwK/6xJ6CQAAAABJRU5ErkJggg=='
-                )
-                let url_bg = await uploadImage(bg_blob, '.png')
-                // console.log('url_bg',url_bg)
-
-                if (!dd[that.id]) {
-                  dd[that.id] = { url, bg: url_bg }
-                } else {
-                  dd[that.id] = { ...dd[that.id], url }
-                }
-
-                //  材质贴图
-                let thumbUrl = material_img.getAttribute('src')
-                if (thumbUrl) {
-                  let tb = await base64ToBlobFromURL(thumbUrl)
-                  let tUrl = await uploadImage(tb, '.png')
-                  // console.log('材质贴图', tUrl, thumbUrl)
-                  dd[that.id].material = tUrl
-                }
-
-                setLocalDataOfWin(key, dd)
-              }
-
-              function startTimer () {
-                if (timer) clearTimeout(timer)
-                timer = setTimeout(checkCameraChange, delay)
-              }
-
-              modelViewerVariants.addEventListener('camera-change', startTimer)
-
-              select.addEventListener('input', async event => {
-                modelViewerVariants.variantName =
-                  event.target.value === 'default' ? null : event.target.value
-                // 材质
-                await extractMaterial(
-                  modelViewerVariants,
-                  selectMaterial,
-                  material_img
-                )
-                checkCameraChange()
-              })
-
-              selectMaterial.addEventListener('input', event => {
-                // console.log(selectMaterial.value)
-                material_img.setAttribute('src', selectMaterial.value)
-
-                if (selectMaterial.getAttribute('data-new-material')) {
-                  let index =
-                    ~~selectMaterial.selectedOptions[0].getAttribute(
-                      'data-index'
-                    )
-                  changeMaterial(
-                    modelViewerVariants,
-                    modelViewerVariants.model.materials[index],
-                    selectMaterial.getAttribute('data-new-material')
-                  )
-                }
-
-                checkCameraChange()
-              })
-
-              bg.addEventListener('click', () => {
-                // 创建一个input元素
-                var input = document.createElement('input')
-                input.type = 'file'
-
-                // 监听input的change事件
-                input.addEventListener('change', function () {
-                  // 获取上传的文件
-                  var file = input.files[0]
-
-                  // 创建一个FileReader对象来读取文件
-                  var reader = new FileReader()
-
-                  // 监听FileReader的load事件
-                  reader.addEventListener('load', async () => {
-                    let base64 = reader.result
-                    // 将读取的文件内容设置为div的背景
-                    preview.style.backgroundImage = 'url(' + base64 + ')'
-
-                    const contentType = getContentTypeFromBase64(base64)
-
-                    const blob = await base64ToBlobFromURL(base64, contentType)
-
-                    //  const fileBlob = new Blob([e.target.result], { type: file.type });
-                    let bg_url = await uploadImage(blob, '.png')
-                    let bg_img = await createImage(base64)
-
-                    let dd = getLocalData(key)
-                    // console.log(dd[that.id],bg_url)
-                    if (!dd[that.id]) dd[that.id] = { url: '', bg: bg_url }
-                    dd[that.id] = {
-                      ...dd[that.id],
-                      bg: bg_url,
-                      bg_w: bg_img.naturalWidth,
-                      bg_h: bg_img.naturalHeight
-                    }
-
-                    setLocalDataOfWin(key, dd)
-
-                    // 更新尺寸
-                    let w = that.size[0] - 24,
-                      h = (w * bg_img.naturalHeight) / bg_img.naturalWidth
-
-                    if (modelViewerVariants) {
-                      modelViewerVariants.style.width = `${w}px`
-                      modelViewerVariants.style.height = `${h}px`
-                    }
-                    preview.style.width = `${w}px`
-                  })
-
-                  // 读取文件
-                  reader.readAsDataURL(file)
-                })
-
-                // 触发input的点击事件
-                input.click()
-              })
-
-              exportGLB.addEventListener('click', async () => {
-                const glTF = await modelViewerVariants.exportScene()
-                const file = new File([glTF], 'export.glb')
-                const link = document.createElement('a')
-                link.download = file.name
-                link.href = URL.createObjectURL(file)
-                link.click()
-              })
-
-              uploadWidget.value = await uploadWidget.serializeValue()
-
-              // 更新尺寸
-              let dd = getLocalData(key)
-              // console.log(dd[that.id],bg_url)
-              if (dd[that.id]) {
-                const { bg_w, bg_h } = dd[that.id]
-                if (bg_h && bg_w) {
-                  let w = that.size[0] - 24,
-                    h = (w * bg_h) / bg_w
-
-                  if (modelViewerVariants) {
-                    modelViewerVariants.style.width = `${w}px`
-                    modelViewerVariants.style.height = `${h}px`
-                  }
-                  preview.style.width = `${w}px`
-                }
-              }
+            preview.innerHTML = html
+            if (that.size[1] < 400) {
+              that.setSize([that.size[0], that.size[1] + 300])
+              app.canvas.draw(true, true)
            }

-            // 以文本形式读取文件
-            reader.readAsDataURL(file)
+            const modelViewerVariants = preview.querySelector('model-viewer')
+            const select = preview.querySelector('.variant')
+            const selectMaterial = preview.querySelector('.material')
+            const material_img = preview.querySelector('.material_img')
+            const bg = preview.querySelector('.bg')
+
+            const exportGLB = preview.querySelector('.export')
+
+            const ddcap_step = preview.querySelector('.ddcap_step')
+            const total_images = preview.querySelector('.total_images')
+            const ddcap_range = preview.querySelector('.ddcap_range')
+            const ddcap_range_top = preview.querySelector('.ddcap_range_top')
+            const ddCap = preview.querySelector('.ddcap')
+            const sleep = (t = 1000) => {
+              return new Promise((res, rej) => {
+                return setTimeout(() => {
+                  res(t)
+                }, t)
+              })
+            }
+
+            async function captureImage (isUrl = true) {
+              let base64Data = modelViewerVariants.toDataURL()
+
+              const contentType = getContentTypeFromBase64(base64Data)
+
+              const blob = await base64ToBlobFromURL(base64Data, contentType)
+
+              if (isUrl) return await uploadImage(blob, '.png')
+              return await uploadImage_(blob, '.png')
+            }
+
+            async function captureImages (angleIncrement = 1, totalImages = 12) {
+              // 记录初始旋转角度
+              const initialCameraOrbit =
+                modelViewerVariants.cameraOrbit.split(' ')
+              console.log(
+                '#captureImages',
+                initialCameraOrbit,
+                angleIncrement * totalImages
+              )
+              // const totalImages = 12
+              // const angleIncrement = totalRotation / totalImages // Each increment in degrees
+              let currentAngle =
+                Number(initialCameraOrbit[0].replace('deg', '')) -
+                (angleIncrement * totalImages) / 2 // Start from the leftmost angle
+              let frames = []
+
+              modelViewerVariants.removeAttribute('camera-controls')
+
+              for (let i = 0; i < totalImages; i++) {
+                modelViewerVariants.cameraOrbit = `${currentAngle}deg ${initialCameraOrbit[1]} ${initialCameraOrbit[2]}`
+                await sleep(1000)
+                console.log(`Capturing image at angle: ${currentAngle}deg`)
+                let file = await captureImage(false)
+                frames.push(file)
+                currentAngle += angleIncrement
+              }
+              await sleep(1000)
+              // 恢复到初始旋转角度
+              modelViewerVariants.cameraOrbit = initialCameraOrbit.join(' ')
+              modelViewerVariants.setAttribute('camera-controls', '')
+              return frames
+            }
+            ddCap.addEventListener('click', async e => {
+              const angleIncrement = Number(ddcap_step.value),
+                totalImages = Number(total_images.value)
+
+              let images = await captureImages(angleIncrement, totalImages)
+              // console.log(images)
+              let dd = getLocalData(key)
+              dd[that.id].images = images
+              setLocalDataOfWin(key, dd)
+            })
+
+            ddcap_range.addEventListener('input', async e => {
+              // console.log(ddcap_range.value)
+              const initialCameraOrbit =
+                modelViewerVariants.cameraOrbit.split(' ')
+              modelViewerVariants.cameraOrbit = `${ddcap_range.value}deg ${initialCameraOrbit[1]} ${initialCameraOrbit[2]}`
+              modelViewerVariants.setAttribute('camera-controls', '')
+            })
+
+            ddcap_range_top.addEventListener('input', async e => {
+              // console.log(ddcap_range.value)
+              const initialCameraOrbit =
+                modelViewerVariants.cameraOrbit.split(' ')
+              modelViewerVariants.cameraOrbit = `${initialCameraOrbit[0]} ${ddcap_range_top.value}deg ${initialCameraOrbit[2]}`
+              modelViewerVariants.setAttribute('camera-controls', '')
+            })
+
+            if (modelViewerVariants) {
+              modelViewerVariants.style.width = `${that.size[0] - 48}px`
+              modelViewerVariants.style.height = `${that.size[1] - 48}px`
+            }
+
+            modelViewerVariants.addEventListener('load', async () => {
+              const names = modelViewerVariants.availableVariants
+
+              // 变量
+              for (const name of names) {
+                const option = document.createElement('option')
+                option.value = name
+                option.textContent = name
+                select.appendChild(option)
+              }
+              // Adds a default option.
+              if (names.length === 0) {
+                const option = document.createElement('option')
+                option.value = 'default'
+                option.textContent = 'Default'
+                select.appendChild(option)
+              }
+
+              // 材质
+              extractMaterial(modelViewerVariants, selectMaterial, material_img)
+            })
+
+            let timer = null
+            const delay = 500 // 延迟时间，单位为毫秒
+
+            async function checkCameraChange () {
+              let dd = getLocalData(key)
+
+              //  const fileBlob = new Blob([e.target.result], { type: file.type });
+              let url = await captureImage()
+
+              let bg_blob = await base64ToBlobFromURL(
+                'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mN88uXrPQAFwwK/6xJ6CQAAAABJRU5ErkJggg=='
+              )
+              let url_bg = await uploadImage(bg_blob, '.png')
+              // console.log('url_bg',url_bg)
+
+              if (!dd[that.id]) {
+                dd[that.id] = { url, bg: url_bg }
+              } else {
+                dd[that.id] = { ...dd[that.id], url }
+              }
+
+              //  材质贴图
+              let thumbUrl = material_img.getAttribute('src')
+              if (thumbUrl) {
+                let tb = await base64ToBlobFromURL(thumbUrl)
+                let tUrl = await uploadImage(tb, '.png')
+                // console.log('材质贴图', tUrl, thumbUrl)
+                dd[that.id].material = tUrl
+              }
+
+              setLocalDataOfWin(key, dd)
+            }
+
+            function startTimer () {
+              if (timer) clearTimeout(timer)
+              timer = setTimeout(checkCameraChange, delay)
+            }
+
+            modelViewerVariants.addEventListener('camera-change', startTimer)
+
+            select.addEventListener('input', async event => {
+              modelViewerVariants.variantName =
+                event.target.value === 'default' ? null : event.target.value
+              // 材质
+              await extractMaterial(
+                modelViewerVariants,
+                selectMaterial,
+                material_img
+              )
+              checkCameraChange()
+            })
+
+            selectMaterial.addEventListener('input', event => {
+              // console.log(selectMaterial.value)
+              material_img.setAttribute('src', selectMaterial.value)
+
+              if (selectMaterial.getAttribute('data-new-material')) {
+                let index =
+                  ~~selectMaterial.selectedOptions[0].getAttribute('data-index')
+                changeMaterial(
+                  modelViewerVariants,
+                  modelViewerVariants.model.materials[index],
+                  selectMaterial.getAttribute('data-new-material')
+                )
+              }
+
+              checkCameraChange()
+            })
+
+            //更新bg
+            const updateBgData = (id, key, url, w, h) => {
+              let dd = getLocalData(key)
+              // console.log(dd[that.id],url)
+              if (!dd[id]) dd[id] = { url: '', bg: url }
+              dd[id] = {
+                ...dd[id],
+                bg: url,
+                bg_w: w,
+                bg_h: h
+              }
+              setLocalDataOfWin(key, dd)
+            }
+
+            bg.addEventListener('click', async () => {
+              //更新bg
+              updateBgData(that.id, key, '', 0, 0)
+              preview.style.backgroundImage = 'none'
+
+              let base64 = await inputFileClick(false, false)
+              // 将读取的文件内容设置为div的背景
+              preview.style.backgroundImage = 'url(' + base64 + ')'
+
+              const contentType = getContentTypeFromBase64(base64)
+
+              const blob = await base64ToBlobFromURL(base64, contentType)
+
+              //  const fileBlob = new Blob([e.target.result], { type: file.type });
+              let bg_url = await uploadImage(blob, '.png')
+              let bg_img = await createImage(base64)
+
+              //更新bg
+              updateBgData(
+                that.id,
+                key,
+                bg_url,
+                bg_img.naturalWidth,
+                bg_img.naturalHeight
+              )
+
+              // 更新尺寸
+              let w = that.size[0] - 48,
+                h = (w * bg_img.naturalHeight) / bg_img.naturalWidth
+
+              if (modelViewerVariants) {
+                modelViewerVariants.style.width = `${w}px`
+                modelViewerVariants.style.height = `${h}px`
+              }
+              preview.style.width = `${w}px`
+            })
+
+            exportGLB.addEventListener('click', async () => {
+              const glTF = await modelViewerVariants.exportScene()
+              const file = new File([glTF], 'export.glb')
+              const link = document.createElement('a')
+              link.download = file.name
+              link.href = URL.createObjectURL(file)
+              link.click()
+            })
+
+            uploadWidget.value = await uploadWidget.serializeValue()
+
+            // 更新尺寸
+            let dd = getLocalData(key)
+            // console.log(dd[that.id],bg_url)
+            if (dd[that.id]) {
+              const { bg_w, bg_h } = dd[that.id]
+              if (bg_h && bg_w) {
+                let w = that.size[0] - 48,
+                  h = (w * bg_h) / bg_w
+
+                if (modelViewerVariants) {
+                  modelViewerVariants.style.width = `${w}px`
+                  modelViewerVariants.style.height = `${h}px`
+                }
+                preview.style.width = `${w}px`
+              }
+            }
          })
          return div
        }

        let preview = document.createElement('div')
        preview.className = 'preview'
-        preview.style = `margin-top: 12px;display: flex;
+        preview.style = `margin-top: 12px;
+          display: flex;
          justify-content: center;
-          align-items: center;background-repeat: no-repeat;background-size: contain;`
+          align-items: center;background-repeat: no-repeat;
+          background-size: contain;`

        let upload = inputDiv('_mixlab_3d_image', '3D Model', preview)

@@ -527,7 +635,7 @@ app.registerExtension({
          if (dd[that.id]) {
            const { bg_w, bg_h } = dd[that.id]
            if (bg_h && bg_w) {
-              let w = that.size[0] - 24,
+              let w = that.size[0] - 48,
                h = (w * bg_h) / bg_w

              if (modelViewerVariants) {
@@ -561,7 +669,7 @@ app.registerExtension({
        const r = onExecuted?.apply?.(this, arguments)

        let div = this.widgets.filter(d => d.div)[0]?.div
-        console.log('Test', this.widgets)
+        // console.log('Test', this.widgets)

        let material = message.material[0]
        if (material) {
@@ -2,6 +2,9 @@ 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

@@ -70,7 +73,8 @@ function get_position_style (ctx, widget_width, y, node_height) {
    display: 'flex',
    flexDirection: 'column',
    // alignItems: 'center',
-    justifyContent: 'flex-start'
+    justifyContent: 'flex-start',
+    zIndex: 9999999
  }
 }

@@ -184,6 +188,25 @@ 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]
@@ -192,7 +215,7 @@ async function extractInputAndOutputData (
            options.hasMask = true
          }
          // loadImage的默认图，转为base64
-          let imgurl = app.graph.getNodeById(id).imgs[0].src
+          let imgurl = app.graph.getNodeById(id).imgs[0].src + '&channel=rgb'

          options.defaultImage = await drawImageToCanvas(imgurl, 512)
          console.log('#loadImage的默认图', options)
@@ -207,16 +230,36 @@ async function extractInputAndOutputData (
        // input.push()
      }
      if (outputIds.includes(id)) {
+        let options = {}
+        //输出的默认图
+        if (
+          node.type === 'SaveImageAndMetadata_' &&
+          app.graph.getNodeById(id).imgs
+        ) {
+          // SaveImageAndMetadata_的默认图，转为base64
+          let imgurl = app.graph.getNodeById(id).imgs[0].src
+
+          options.defaultImage = await drawImageToCanvas(imgurl, 512)
+          console.log('#SaveImageAndMetadata_的默认图', options)
+        }
+
        // let node = app.graph.getNodeById(id)
        // output.push()
-        output[outputIds.indexOf(id)] = { ...data[id], title: node.title, id }
+        output[outputIds.indexOf(id)] = {
+          ...data[id],
+          title: node.title,
+          id,
+          options
+        }
      }

      if (
        node.type === 'KSampler' ||
        node.type == 'SamplerCustom' ||
        node.type === 'ChinesePrompt_Mix' ||
-        node.type === 'Seed_'
+        node.type === 'Seed_'||
+        node.type==='SiliconflowLLM'||
+        node.type==='ChatGPTOpenAI'
      ) {
        // seed 的类型收集
        try {
@@ -378,11 +421,11 @@ 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(
        ','
      )

@@ -426,19 +469,18 @@ 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}
            )
          }
        }
@@ -484,6 +526,21 @@ 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`
@@ -640,6 +697,7 @@ app.registerExtension({

        btns.appendChild(btn)
        btns.appendChild(download)
+        btns.appendChild(tdBG)

        document.body.appendChild(widget.div)
        this.addCustomWidget(widget)
@@ -653,6 +711,7 @@ app.registerExtension({
        this.serialize_widgets = true //需要保存参数

        window._mixlab_app_json = null
+ 
      }

      const onExecuted = nodeType.prototype.onExecuted
@@ -668,9 +727,8 @@ 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) {}
      }
@@ -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)
+      }
+    }
+  }
+})
@@ -0,0 +1,106 @@
+async function* completion (url, messages, controller) {
+  let data = {
+    model: 'gpt-3.5-turbo-16k',
+    messages,
+    temperature: 0.05,
+    stream: true
+  }
+  // if (imageNode) {
+  //   data = { ...data, image_data: [imageNode] }
+  // }
+
+  // let controller = new AbortController()
+
+  let response = await fetch(url, {
+    method: 'POST',
+    body: JSON.stringify(data),
+    headers: {
+      Connection: 'keep-alive',
+      'Content-Type': 'application/json',
+      Accept: 'text/event-stream'
+    },
+    signal: controller.signal
+  })
+
+  const reader = response.body.getReader()
+  const decoder = new TextDecoder()
+
+  let content = ''
+  let leftover = '' // Buffer for partially read lines
+
+  try {
+    let cont = true
+    while (cont) {
+      let result = await reader.read()
+      if (result.done) {
+        break
+      }
+
+      // Add any leftover data to the current chunk of data
+      const text = leftover + decoder.decode(result.value)
+
+      // Check if the last character is a line break
+      const endsWithLineBreak = text.endsWith('\n')
+
+      // Split the text into lines
+      let lines = text.split('\n')
+
+      // If the text doesn't end with a line break, then the last line is incomplete
+      // Store it in leftover to be added to the next chunk of data
+      if (!endsWithLineBreak) {
+        leftover = lines.pop()
+      } else {
+        leftover = '' // Reset leftover if we have a line break at the end
+      }
+
+      // Parse all sse events and add them to result
+      const regex = /^(\S+):\s(.*)$/gm
+      for (const line of lines) {
+        const match = regex.exec(line)
+        if (match) {
+          result[match[1]] = match[2]
+          // since we know this is llama.cpp, let's just decode the json in data
+          if (result.data) {
+            result.data = JSON.parse(result.data)
+            // console.log('#result.data',result.data)
+
+            content += result.data.choices[0].delta?.content || ''
+
+            // yield
+            yield result
+
+            // if we got a stop token from server, we will break here
+            if (result.data.choices[0].finish_reason == 'stop') {
+              if (result.data.generation_settings) {
+                // generation_settings = result.data.generation_settings;
+              }
+              cont = false
+              break
+            }
+          }
+        }
+      }
+    }
+  } catch (e) {
+    console.error('llama error: ', e)
+    throw e
+  } finally {
+    controller.abort()
+  }
+
+  return content
+  // return (await response.json()).content
+}
+
+export async function completion_ (url, messages, controller, callback) {
+  let request = await completion(url, messages, controller)
+  for await (const chunk of request) {
+    let content = chunk.data.choices[0].delta.content || ''
+    if (chunk.data.choices[0].role == 'assistant') {
+      //开始
+      content = ''
+    }
+
+    if (callback) callback(content)
+  }
+}
@@ -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 = 'v0.24.0'
+const version = 'v0.36.0'

 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
  .then(response => response.json())
@@ -0,0 +1,689 @@
+function get_url () {
+  // 如果有缓存记录
+  let hostUrl = localStorage.getItem('_hostUrl') || ''
+  if (hostUrl) {
+    return hostUrl
+  }
+  let api_host = `${window.location.hostname}:${window.location.port}`
+  let api_base = ''
+  let url = `${window.location.protocol}//${api_host}${api_base}`
+  return url
+}
+
+function getFilenameAndCategoryFromUrl (url) {
+  const queryString = url.split('?')[1]
+  if (!queryString) {
+    return {}
+  }
+
+  const params = new URLSearchParams(queryString)
+
+  const filename = params.get('filename')
+    ? decodeURIComponent(params.get('filename'))
+    : null
+  const category = params.get('category')
+    ? decodeURIComponent(params.get('category') || '')
+    : ''
+
+  return { category, filename }
+}
+
+async function get_my_app (category = '', filename = null) {
+  let url = get_url()
+  const res = await fetch(`${url}/mixlab/workflow`, {
+    method: 'POST',
+    mode: 'cors', // 允许跨域请求
+    headers: {
+      'Content-Type': 'application/json'
+    },
+    body: JSON.stringify({
+      task: 'my_app',
+      filename,
+      category
+    })
+  })
+  let result = await res.json()
+  let data = []
+  try {
+    for (const res of result.data) {
+      let { output, app } = res.data
+      if (app.filename)
+        data.push({
+          ...app,
+          data: output,
+          date: res.date
+        })
+    }
+  } catch (error) {}
+
+  return data
+}
+
+async function getAppInit () {
+  const { category, filename } = getFilenameAndCategoryFromUrl(
+    window.location.href
+  )
+  return await get_my_app(category, filename)
+}
+
+function success (isSuccess, btn, text) {
+  isSuccess ? (btn.innerText = 'success') : text
+  setTimeout(() => {
+    btn.innerText = text
+  }, 5000)
+}
+
+async function interrupt () {
+  try {
+    await fetch(`${get_url()}/interrupt`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: undefined
+    })
+  } catch (error) {
+    console.error(error)
+  }
+  return true
+}
+
+async function getQueue (clientId) {
+  try {
+    const res = await fetch(`${get_url()}/queue`)
+    const data = await res.json()
+    return {
+      // Running action uses a different endpoint for cancelling
+      Running: Array.from(data.queue_running, prompt => {
+        if (prompt[3].client_id === clientId) {
+          let prompt_id = prompt[1]
+          return {
+            prompt_id,
+            remove: () => interrupt()
+          }
+        }
+      }),
+      Pending: data.queue_pending.map(prompt => ({ prompt }))
+    }
+  } catch (error) {
+    console.error(error)
+    return { Running: [], Pending: [] }
+  }
+}
+
+// 请求历史数据
+async function getPromptResult (category) {
+  let url = get_url()
+  try {
+    const response = await fetch(`${url}/mixlab/prompt_result`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        action: 'all'
+      })
+    })
+
+    if (response.ok) {
+      const data = await response.json()
+      console.log('#getPromptResult:', category, data)
+
+      return data.result.filter(r => r.appInfo.category == category)
+      // 处理返回的数据
+    } else {
+      console.log('Error:', response.status)
+      // 处理错误情况
+    }
+  } catch (error) {
+    console.log('Error:', error)
+    // 处理异常情况
+  }
+}
+
+// 新的运行工作流的接口
+function queuePromptNew (filename, category, seed, input, client_id,apps=null) {
+  let url = get_url()
+  // var filename = "Text-to-Image_1.json", category = "";
+
+  // 随机seed
+  //  promptWorkflow = randomSeed(seed, promptWorkflow);
+  let d = { filename, category, seed, input, client_id }
+  if (apps) {
+    d.apps = apps
+  }
+
+  const data = JSON.stringify(d)
+  return new Promise((res, rej) => {
+    fetch(`${url}/mixlab/prompt`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: data
+    })
+      .then(response => {
+        if (!response.ok) {
+          // Handle HTTP error responses
+          if (response.status === 400) {
+            return response.json().then(errorData => {
+              // Process the error data
+              console.error('Error 400:', errorData)
+              alert(JSON.stringify(errorData, null, 2))
+              res(null)
+            })
+          }
+          throw new Error('Network response was not ok')
+        }
+        return response.json() // Process the response data
+      })
+      .then(data => {
+        // Handle the response data
+        console.log('Success:', data)
+        res(true)
+      })
+      .catch(error => {
+        // Handle fetch errors
+        console.error('Fetch error:', error)
+        res(null)
+      })
+  })
+}
+
+// 保存历史数据
+async function savePromptResult (data) {
+  let url = get_url()
+  try {
+    const response = await fetch(`${url}/mixlab/prompt_result`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        action: 'save',
+        data
+      })
+    })
+
+    if (response.ok) {
+      const res = await response.json()
+      console.log('Response:', res)
+      return res
+      // 处理返回的数据
+    } else {
+      console.log('Error:', response.status)
+      // 处理错误情况
+    }
+  } catch (error) {
+    console.log('Error:', error)
+    // 处理异常情况
+  }
+}
+
+async function uploadImage (blob, fileType = '.png', filename) {
+  const body = new FormData()
+  body.append(
+    'image',
+    new File([blob], (filename || new Date().getTime()) + fileType)
+  )
+
+  const url = get_url()
+
+  const resp = await fetch(`${url}/upload/image`, {
+    method: 'POST',
+    body
+  })
+
+  let data = await resp.json()
+  // console.log(data)
+  let { name, subfolder } = data
+  let src = `${url}/view?filename=${encodeURIComponent(
+    name
+  )}&type=input&subfolder=${subfolder}&rand=${Math.random()}`
+
+  return { url: src, name }
+}
+
+async function uploadMask (arrayBuffer, imgurl) {
+  const body = new FormData()
+  const filename = 'clipspace-mask-' + performance.now() + '.png'
+
+  let original_url = new URL(imgurl)
+
+  const original_ref = { filename: original_url.searchParams.get('filename') }
+
+  let original_subfolder = original_url.searchParams.get('subfolder')
+  if (original_subfolder) original_ref.subfolder = original_subfolder
+
+  let original_type = original_url.searchParams.get('type')
+  if (original_type) original_ref.type = original_type
+
+  body.append('image', arrayBuffer, filename)
+  body.append('original_ref', JSON.stringify(original_ref))
+  body.append('type', 'input')
+  body.append('subfolder', 'clipspace')
+
+  const url = get_url()
+
+  const resp = await fetch(`${url}/upload/mask`, {
+    method: 'POST',
+    body
+  })
+
+  // console.log(resp)
+  let data = await resp.json()
+  let { name, subfolder, type } = data
+  let src = `${url}/view?filename=${encodeURIComponent(
+    name
+  )}&type=${type}&subfolder=${subfolder}&rand=${Math.random()}`
+
+  return { url: src, name: 'clipspace/' + name }
+}
+
+const parseImageToBase64 = url => {
+  return new Promise((res, rej) => {
+    fetch(url)
+      .then(response => response.blob())
+      .then(blob => {
+        const reader = new FileReader()
+        reader.onloadend = () => {
+          const base64data = reader.result
+          res(base64data)
+          // 在这里可以将base64数据用于进一步处理或显示图片
+        }
+        reader.readAsDataURL(blob)
+      })
+      .catch(error => {
+        console.log('发生错误:', error)
+      })
+  })
+}
+
+function createImage (url) {
+  let im = new Image()
+  return new Promise((res, rej) => {
+    im.onload = () => res(im)
+    im.src = url
+  })
+}
+
+function convertImageToBlackBasedOnAlpha (image) {
+  const canvas = document.createElement('canvas')
+  const ctx = canvas.getContext('2d')
+
+  // Draw the image onto the canvas
+  canvas.width = image.width
+  canvas.height = image.height
+  ctx.drawImage(image, 0, 0)
+
+  // Get the image data from the canvas
+  const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height)
+  const pixels = imageData.data
+
+  // Modify the RGB values based on the alpha channel
+  for (let i = 0; i < pixels.length; i += 4) {
+    const alpha = pixels[i + 3]
+    if (alpha !== 0) {
+      // Set non-transparent pixels to black
+      pixels[i] = 0 // Red
+      pixels[i + 1] = 0 // Green
+      pixels[i + 2] = 0 // Blue
+    }
+  }
+
+  // Put the modified image data back onto the canvas
+  ctx.putImageData(imageData, 0, 0)
+
+  // Convert the modified canvas to base64 data URL
+  const base64ImageData = canvas.toDataURL('image/png') // Replace 'png' with your desired image format
+
+  return base64ImageData
+}
+
+const blobToBase64 = blob => {
+  return new Promise((res, rej) => {
+    const reader = new FileReader()
+    reader.onloadend = () => {
+      const base64data = reader.result
+      res(base64data)
+      // 在这里可以将base64数据用于进一步处理或显示图片
+    }
+    reader.readAsDataURL(blob)
+  })
+}
+
+function base64ToBlob (base64) {
+  // 去除base64编码中的前缀
+  const base64WithoutPrefix = base64.replace(/^data:image\/\w+;base64,/, '')
+
+  // 将base64编码转换为字节数组
+  const byteCharacters = atob(base64WithoutPrefix)
+
+  // 创建一个存储字节数组的数组
+  const byteArrays = []
+
+  // 将字节数组放入数组中
+  for (let offset = 0; offset < byteCharacters.length; offset += 1024) {
+    const slice = byteCharacters.slice(offset, offset + 1024)
+
+    const byteNumbers = new Array(slice.length)
+    for (let i = 0; i < slice.length; i++) {
+      byteNumbers[i] = slice.charCodeAt(i)
+    }
+
+    const byteArray = new Uint8Array(byteNumbers)
+    byteArrays.push(byteArray)
+  }
+
+  // 创建blob对象
+  const blob = new Blob(byteArrays, { type: 'image/png' }) // 根据实际情况设置MIME类型
+
+  return blob
+}
+
+async function calculateImageHash (blob) {
+  const buffer = await blob.arrayBuffer()
+  const hashBuffer = await crypto.subtle.digest('SHA-256', buffer)
+  const hashArray = Array.from(new Uint8Array(hashBuffer))
+  const hashHex = hashArray
+    .map(byte => byte.toString(16).padStart(2, '0'))
+    .join('')
+  return hashHex
+}
+
+// 获取 rembg 模型
+async function get_rembg_models () {
+  try {
+    const response = await fetch(`${get_url()}/mixlab/folder_paths`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        type: 'rembg'
+      })
+    })
+
+    const data = await response.json()
+    // console.log(data)
+    return data.names
+  } catch (error) {
+    console.error(error)
+  }
+}
+
+//自动抠图
+async function run_rembg (model, base64) {
+  try {
+    const response = await fetch(`${get_url()}/mixlab/rembg`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        model,
+        base64
+      })
+    })
+
+    const data = await response.json()
+    // console.log(data)
+    return data.data
+  } catch (error) {
+    console.error(error)
+  }
+}
+
+function copyHtmlWithImagesToClipboard (data, cb) {
+  // 创建一个临时div元素
+  const tempDiv = document.createElement('div')
+
+  // 将HTML字符串赋值给div的innerHTML属性
+  tempDiv.innerHTML = data
+
+  // 获取div中的所有图像元素
+  const images = tempDiv.getElementsByTagName('img')
+
+  // 遍历图像元素，并将图像数据转换为Base64编码
+  for (let i = 0; i < images.length; i++) {
+    const image = images[i]
+    const canvas = document.createElement('canvas')
+    const context = canvas.getContext('2d')
+
+    // 设置canvas尺寸与图像尺寸相同
+    canvas.width = image.width
+    canvas.height = image.height
+
+    // 在canvas上绘制图像
+    context.drawImage(image, 0, 0)
+
+    // 将canvas转换为Base64编码
+    const imageData = canvas.toDataURL()
+
+    // 将Base64编码替换图像元素的src属性
+    image.src = imageData
+  }
+
+  let richText = tempDiv.innerHTML
+
+  // 创建一个新的Blob对象，并将富文本字符串作为数据传递进去
+  const blob = new Blob([richText], { type: 'text/html' })
+
+  // 创建一个ClipboardItem对象，并将Blob对象添加到其中
+  const clipboardItem = new ClipboardItem({ 'text/html': blob })
+
+  // 使用Clipboard API将内容复制到剪贴板
+  navigator.clipboard
+    .write([clipboardItem])
+    .then(() => {
+      console.log('富文本已成功复制到剪贴板')
+      tempDiv.remove()
+      if (cb) cb(true)
+    })
+    .catch(error => {
+      console.error('复制到剪贴板失败:', error)
+      tempDiv.remove()
+      if (cb) cb(false)
+    })
+}
+
+function copyImagesToClipboard (html, cb) {
+  const tempDiv = document.createElement('div')
+  tempDiv.innerHTML = html
+  const images = tempDiv.querySelectorAll('img')
+  const promises = Array.from(images).map(image => {
+    return new Promise(resolve => {
+      const img = new Image()
+      img.src = image.src
+      img.onload = () => {
+        const canvas = document.createElement('canvas')
+        const context = canvas.getContext('2d')
+        canvas.width = img.width
+        canvas.height = img.height
+        context.drawImage(img, 0, 0)
+        canvas.toBlob(blob => {
+          const clipboardItem = new ClipboardItem({ 'image/png': blob })
+          navigator.clipboard
+            .write([clipboardItem])
+            .then(() => {
+              resolve()
+              tempDiv.remove()
+              if (cb) cb(true)
+            })
+            .catch(error => {
+              reject(error)
+              tempDiv.remove()
+              if (cb) cb(false)
+            })
+        })
+      }
+    })
+  })
+  Promise.all([...promises])
+    .then(() => {
+      console.log('所有图片已成功复制到剪贴板')
+      if (cb) cb(true)
+      tempDiv.remove()
+    })
+    .catch(error => {
+      console.error('复制到剪贴板失败:', error)
+      if (cb) cb(false)
+      tempDiv.remove()
+    })
+}
+
+function copyTextToClipboard (html, cb) {
+  const tempDiv = document.createElement('div')
+  tempDiv.innerHTML = html
+
+  const text = tempDiv.innerText
+  const textData = new ClipboardItem({
+    'text/plain': new Blob([text], { type: 'text/plain' })
+  })
+
+  navigator.clipboard
+    .write([textData])
+    .then(() => {
+      console.log('所有文本已成功复制到剪贴板', text)
+      if (cb) cb(true)
+      tempDiv.remove()
+    })
+    .catch(error => {
+      console.error('复制到剪贴板失败:', error)
+      if (cb) cb(false)
+      tempDiv.remove()
+    })
+}
+
+// ComfyUI\web\extensions\core\dynamicPrompts.js
+// 官方实现修改
+// Allows for simple dynamic prompt replacement
+// Inputs in the format {a|b} will have a random value of a or b chosen when the prompt is queued.
+
+/*
+ * Strips C-style line and block comments from a string
+ */
+function dynamicPrompts (prompt) {
+  prompt = prompt.replace(/\/\*[\s\S]*?\*\/|\/\/.*/g, '')
+  while (
+    prompt.replace('\\{', '').includes('{') &&
+    prompt.replace('\\}', '').includes('}')
+  ) {
+    const startIndex = prompt.replace('\\{', '00').indexOf('{')
+    const endIndex = prompt.replace('\\}', '00').indexOf('}')
+
+    const optionsString = prompt.substring(startIndex + 1, endIndex)
+    const options = optionsString.split('|')
+
+    const randomIndex = Math.floor(Math.random() * options.length)
+    const randomOption = options[randomIndex]
+
+    prompt =
+      prompt.substring(0, startIndex) +
+      randomOption +
+      prompt.substring(endIndex + 1)
+  }
+  return prompt
+}
+
+// 遍历所有组合,语法同 动态提示
+function generateAllCombinations (prompt) {
+  prompt = prompt.replace(/\/\*[\s\S]*?\*\/|\/\/.*/g, '')
+
+  // Helper function to get all combinations
+  function getAllCombinations (parts) {
+    if (parts.length === 0) return ['']
+    const [firstPart, ...restParts] = parts
+    const restCombinations = getAllCombinations(restParts)
+    const allCombinations = []
+
+    firstPart.forEach(option => {
+      restCombinations.forEach(combination => {
+        allCombinations.push(option + combination)
+      })
+    })
+
+    return allCombinations
+  }
+
+  // Split prompt into static parts and dynamic parts
+  let parts = []
+  let startIndex = 0
+
+  while (
+    prompt.replace('\\{', '').includes('{') &&
+    prompt.replace('\\}', '').includes('}')
+  ) {
+    startIndex = prompt.replace('\\{', '00').indexOf('{')
+    const endIndex = prompt.replace('\\}', '00').indexOf('}')
+    const staticPart = prompt.substring(0, startIndex)
+    const optionsString = prompt.substring(startIndex + 1, endIndex)
+    const options = optionsString.split('|')
+
+    parts.push([staticPart])
+    parts.push(options)
+
+    prompt = prompt.substring(endIndex + 1)
+  }
+
+  // Add the remaining static part
+  parts.push([prompt])
+
+  // Get all combinations
+  const combinations = getAllCombinations(parts)
+
+  return combinations
+}
+
+const _textNodes = [
+    'TextInput_',
+    'CLIPTextEncode',
+    'PromptSimplification',
+    'ChinesePrompt_Mix'
+  ],
+  _loraNodes = ['CheckpointLoaderSimple', 'LoraLoader'],
+  _numberNodes = ['FloatSlider', 'IntNumber'],
+  _slideNodes = ['PromptSlide'],
+  _imageNodes = [
+    'LoadImage',
+    'VHS_LoadVideo',
+    'ImagesPrompt_',
+    'LoadImagesToBatch'
+  ],
+  _colorNodes = ['Color'],
+  _audioNodes = ['LoadAndCombinedAudio_']
+
+export default {
+  get_url,
+  get_my_app,
+  getAppInit,
+  getFilenameAndCategoryFromUrl,
+  success,
+  interrupt,
+  getQueue,
+  queuePromptNew,
+  savePromptResult,
+  uploadImage,
+  uploadMask,
+  run_rembg,
+  get_rembg_models,
+  parseImageToBase64,
+  createImage,
+  convertImageToBlackBasedOnAlpha,
+  blobToBase64,
+  base64ToBlob,
+  calculateImageHash,
+  copyHtmlWithImagesToClipboard,
+  copyImagesToClipboard,
+  copyTextToClipboard,
+  dynamicPrompts,
+  generateAllCombinations,
+
+  _textNodes,
+  _loraNodes,
+  _numberNodes,
+  _slideNodes,
+  _imageNodes,
+  _colorNodes,
+  _audioNodes
+}
@@ -1,205 +1,5 @@
 import { app } from '../../../scripts/app.js'
-// import { api } from '../../../scripts/api.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
-import { $el } from '../../../scripts/ui.js'
-
-async function getConfig () {
-  let api_host = `${window.location.hostname}:${window.location.port}`
-  let api_base = ''
-  let url = `${window.location.protocol}//${api_host}${api_base}`
-
-  const res = await fetch(`${url}/mixlab`, {
-    method: 'POST'
-  })
-  return await res.json()
-}
-
-function get_position_style (ctx, widget_width, y, node_height) {
-  const MARGIN = 4 // the margin around the html element
-
-  /* Create a transform that deals with all the scrolling and zooming */
-  const elRect = ctx.canvas.getBoundingClientRect()
-  const transform = new DOMMatrix()
-    .scaleSelf(
-      elRect.width / ctx.canvas.width,
-      elRect.height / ctx.canvas.height
-    )
-    .multiplySelf(ctx.getTransform())
-    .translateSelf(MARGIN, MARGIN + y)
-
-  return {
-    transformOrigin: '0 0',
-    transform: transform,
-    left: `0`,
-    top: `0`,
-    cursor: 'pointer',
-    position: 'absolute',
-    maxWidth: `${widget_width - MARGIN * 2}px`,
-    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
-    width: `${widget_width - MARGIN * 2}px`,
-    // height: `${node_height * 0.3 - MARGIN * 2}px`,
-    // background: '#EEEEEE',
-    display: 'flex',
-    flexDirection: 'column',
-    // alignItems: 'center',
-    justifyContent: 'space-around'
-  }
-}
-
-const getLocalData = key => {
-  let data = {}
-  try {
-    data = JSON.parse(localStorage.getItem(key)) || {}
-  } catch (error) {
-    return {}
-  }
-  return data
-}
-
-app.registerExtension({
-  name: 'Mixlab.GPT.ChatGPTOpenAI',
-  async getCustomWidgets (app) {
-    return {
-      KEY (node, inputName, inputData, app) {
-        // console.log('##inputData', inputData)
-        const widget = {
-          type: inputData[0], // the type, CHEESE
-          name: inputName, // the name, slice
-          size: [128, 32], // a default size
-          draw (ctx, node, width, y) {},
-          computeSize (...args) {
-            return [128, 32] // a method to compute the current size of the widget
-          },
-          async serializeValue (nodeId, widgetIndex) {
-            let data = getLocalData('_mixlab_api_key')
-            return data[node.id] || 'by Mixlab'
-          }
-        }
-        //  widget.something = something;          // maybe adds stuff to it
-        node.addCustomWidget(widget) // adds it to the node
-        return widget // and returns it.
-      },
-      URL (node, inputName, inputData, app) {
-        // console.log('node', inputName, inputData[0])
-        const widget = {
-          type: inputData[0], // the type, CHEESE
-          name: inputName, // the name, slice
-          size: [128, 32], // a default size
-          draw (ctx, node, width, y) {
-            // a method to draw the widget (ctx is a CanvasRenderingContext2D)
-          },
-          computeSize (...args) {
-            return [128, 32] // a method to compute the current size of the widget
-          },
-          async serializeValue (nodeId, widgetIndex) {
-            let data = getLocalData('_mixlab_api_url')
-            return data[node.id] || 'https://api.openai.com/v1'
-          }
-        }
-        //  widget.something = something;          // maybe adds stuff to it
-        node.addCustomWidget(widget) // adds it to the node
-        return widget // and returns it.
-      }
-    }
-  },
-
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeType.comfyClass == 'ChatGPTOpenAI') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated
-      nodeType.prototype.onNodeCreated = function () {
-        orig_nodeCreated?.apply(this, arguments)
-
-        const api_key = this.widgets.filter(w => w.name == 'api_key')[0]
-        const api_url = this.widgets.filter(w => w.name == 'api_url')[0]
-
-        console.log('ChatGPTOpenAI nodeData', this.widgets)
-
-        const widget = {
-          type: 'div',
-          name: 'chatgptdiv',
-          draw (ctx, node, widget_width, y, widget_height) {
-            Object.assign(
-              this.div.style,
-              get_position_style(ctx, widget_width, api_key.y, node.size[1])
-            )
-          }
-        }
-
-        widget.div = $el('div', {})
-
-        document.body.appendChild(widget.div)
-
-        const inputDiv = (key, placeholder) => {
-          let div = document.createElement('div')
-          const ip = document.createElement('input')
-          ip.type = placeholder === 'Key' ? 'password' : 'text'
-          ip.className = `${'comfy-multiline-input'} ${placeholder}`
-          div.style = `display: flex;
-          align-items: center; 
-          margin: 6px 8px;
-          margin-top: 0;`
-          ip.placeholder = placeholder
-          ip.value = placeholder
-
-          ip.style = `margin-left: 24px;
-          outline: none;
-          border: none;
-          padding: 4px;width: 100%;`
-          const label = document.createElement('label')
-          label.style = 'font-size: 10px;min-width:32px'
-          label.innerText = placeholder
-          div.appendChild(label)
-          div.appendChild(ip)
-
-          ip.addEventListener('change', () => {
-            let data = getLocalData(key)
-            data[this.id] = ip.value.trim()
-            localStorage.setItem(key, JSON.stringify(data))
-            console.log(this.id, key)
-          })
-          return div
-        }
-
-        let inputKey = inputDiv('_mixlab_api_key', 'Key')
-        let inputUrl = inputDiv('_mixlab_api_url', 'URL')
-
-        widget.div.appendChild(inputKey)
-        widget.div.appendChild(inputUrl)
-
-        this.addCustomWidget(widget)
-
-        const onRemoved = this.onRemoved
-        this.onRemoved = () => {
-          inputUrl.remove()
-          inputKey.remove()
-          widget.div.remove()
-          return onRemoved?.()
-        }
-
-        this.serialize_widgets = true //需要保存参数
-      }
-    }
-  },
-  async loadedGraphNode (node, app) {
-    // Fires every time a node is constructed
-    // You can modify widgets/add handlers/etc here
-
-    if (node.type === 'ChatGPTOpenAI') {
-      let widget = node.widgets.filter(w => w.div)[0]
-
-      let apiKey = getLocalData('_mixlab_api_key'),
-        url = getLocalData('_mixlab_api_url')
-
-      let id = node.id
-
-      // console.log('ChatGPTOpenAI serialize_widgets', this)
-
-      widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
-      widget.div.querySelector('.URL').value =
-        url[id] || 'https://api.openai.com/v1'
-    }
-  }
-})

 app.registerExtension({
  name: 'Mixlab.GPT.ShowTextForGPT',
@@ -209,13 +9,16 @@ app.registerExtension({
        text = text.filter(t => t && t?.trim())

        if (this.widgets) {
+          // console.log('#ShowTextForGPT',this.widgets)
          // const pos = this.widgets.findIndex(w => w.name === 'text')
          for (let i = 0; i < this.widgets.length; i++) {
-            if (this.widgets[i].name == 'show_text') this.widgets[i].onRemove?.()
+            if (this.widgets[i].name == 'show_text')
+              this.widgets[i].onRemove?.()
+       
          }
-          this.widgets.length = 1
+          this.widgets.length = 2
        }
-        // console.log('ShowTextForGPT',text)
+
        for (let list of text) {
          if (list) {
            // console.log('#####', list)
@@ -228,6 +31,8 @@ app.registerExtension({
            w.inputEl.readOnly = true
            w.inputEl.style.opacity = 0.6

+            // w.inputEl.style.display='none'
+
            try {
              if (typeof list != 'string') {
                let data = JSON.parse(list)
@@ -675,6 +675,18 @@ const createInputImageForBatch = (base64, widget) => {
  return im
 }

+// 添加新图片
+const addBase64ToWidgetForLoadImagesToBatch = (
+  base64,
+  imagesWidget,
+  imagesDiv
+) => {
+  if(!imagesWidget.value.base64) imagesWidget.value.base64=[]
+  imagesWidget.value.base64.push(base64)
+  let im = createInputImageForBatch(base64, imagesWidget)
+  imagesDiv.appendChild(im)
+}
+
 app.registerExtension({
  name: 'Mixlab.Comfy.LoadImagesToBatch',
  async getCustomWidgets (app) {
@@ -706,6 +718,7 @@ app.registerExtension({
  async beforeRegisterNodeDef (nodeType, nodeData, app) {
    if (nodeType.comfyClass == 'LoadImagesToBatch') {
      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+
      nodeType.prototype.onNodeCreated = function () {
        orig_nodeCreated?.apply(this, arguments)

@@ -749,13 +762,18 @@ app.registerExtension({
            base64 = await loadImageToCanvas(base64)
            // console.log(base64)
            if (!imagesWidget.value) imagesWidget.value = { base64: [] }
-            imagesWidget.value.base64.push(base64)
-            let im = createInputImageForBatch(base64, imagesWidget)
-            imagesDiv.appendChild(im)
+            addBase64ToWidgetForLoadImagesToBatch(
+              base64,
+              imagesWidget,
+              imagesDiv
+            )
          }
          reader.readAsDataURL(file)
        })

+        // 如果是复制的，有数据 , 这个不生效，取不到数据， 需要在nodeCreated里获取
+        // console.log('#LoadImagesToBatch', imagesWidget.value?.base64)
+
        const btn = document.createElement('button')
        btn.innerText = 'Upload Image'

@@ -827,17 +845,176 @@ app.registerExtension({
      }
    }
  },
+
  async loadedGraphNode (node, app) {
    if (node.type === 'LoadImagesToBatch') {
-      // await sleep(0)
      let imagesWidget = node.widgets.filter(w => w.name === 'images')[0]
      let imagePreview = node.widgets.filter(w => w.name == 'image_base64')[0]
+      // console.log('#LoadImagesToBatch', imagesWidget.value?.base64)
+      let imagesDiv = imagePreview.div.querySelector('.images_preview')

-      let pre = imagePreview.div.querySelector('.images_preview')
      for (const d of imagesWidget.value?.base64 || []) {
        let im = createInputImageForBatch(d, imagesWidget)
-        pre.appendChild(im)
+        imagesDiv.appendChild(im)
      }
    }
+  },
+  nodeCreated (node, app) {
+    //数据延迟？？
+    setTimeout(() => {
+      // console.log('#LoadImagesToBatch', node.type)
+      if (node.type === 'LoadImagesToBatch') {
+        let imagesWidget = node.widgets.filter(w => w.name === 'images')[0]
+        let imagePreview = node.widgets.filter(w => w.name == 'image_base64')[0]
+
+        let imagesDiv = imagePreview?.div?.querySelector('.images_preview')
+
+        for (const d of imagesWidget.value?.base64 || []) {
+          let im = createInputImageForBatch(d, imagesWidget)
+          imagesDiv.appendChild(im)
+        }
+      }
+    }, 1000)
+  }
+})
+
+// 如何引入css
+app.registerExtension({
+  name: 'Mixlab.output.ComparingTwoFrames_',
+  init () {
+    $el('link', {
+      rel: 'stylesheet',
+      href: '/extensions/comfyui-mixlab-nodes/lib/juxtapose.css',
+      parent: document.head
+    })
+
+    $el('style', {
+      textContent: `
+      .juxtapose-name{
+        display: none!important; 
+      }
+			`,
+      parent: document.body
+    })
+  },
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'ComparingTwoFrames_') {
+      const onNodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        const r = onNodeCreated
+          ? onNodeCreated.apply(this, arguments)
+          : undefined
+
+        this.size = [400, this.size[1]]
+        console.log('##onNodeCreated', this)
+        const widget = {
+          type: 'div',
+          name: 'preview',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, 400, 44, node.size[1])
+            )
+          },
+          serialize: false
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+        this.serialize_widgets = true //需要保存参数
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        return r
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log('##onExecuted', this, message)
+
+        this.widgets[0].div.id = 'mix_comparingtowframes_' + this.id
+
+        let after_image = message.after_images[0]
+        let before_image = message.before_images[0]
+
+        after_image = `${window.location.protocol}//${
+          window.location.hostname
+        }:${window.location.port}/view?filename=${encodeURIComponent(
+          after_image.filename
+        )}&type=${after_image.type}&subfolder=${encodeURIComponent(
+          after_image.subfolder
+        )}&t=${+new Date()}`
+
+        before_image = `${window.location.protocol}//${
+          window.location.hostname
+        }:${window.location.port}/view?filename=${encodeURIComponent(
+          before_image.filename
+        )}&type=${before_image.type}&subfolder=${encodeURIComponent(
+          before_image.subfolder
+        )}&t=${+new Date()}`
+
+        this.widgets[0].div.innerHTML = ''
+
+        let slider = new juxtapose.JXSlider(
+          '#mix_comparingtowframes_' + this.id,
+          [
+            {
+              src: before_image,
+              label: 'Before'
+            },
+            {
+              src: after_image,
+              label: 'After'
+            }
+          ],
+          {
+            animate: true,
+            showLabels: true,
+            showCredits: false,
+            startingPosition: '50%',
+            makeResponsive: false
+          }
+        )
+
+        this.widgets_values = [
+          {
+            src: before_image,
+            label: 'Before'
+          },
+          {
+            src: after_image,
+            label: 'After'
+          }
+        ]
+        this.size = [this.size[0], 300]
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    // console.log('##loadedGraphNode', node)
+    if (node.type === 'ComparingTwoFrames_') {
+      // node.widgets[0].div.id = 'mix_comparingtowframes_' + node.id
+      // if (node.widgets_values && node.widgets_values[0]) {
+      //   node.widgets[0].div.innerHTML = ''
+      //   let slider = new juxtapose.JXSlider(
+      //     '#mix_comparingtowframes_' + node.id,
+      //     node.widgets_values,
+      //     {
+      //       animate: true,
+      //       showLabels: true,
+      //       showCredits: false,
+      //       startingPosition: '50%',
+      //       makeResponsive: false
+      //     }
+      //   )
+      // }
+    }
  }
 })
@@ -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',
@@ -0,0 +1,219 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { $el } from '../../../scripts/ui.js'
+
+function base64ToBlobFromURL (base64URL, contentType) {
+  return fetch(base64URL).then(response => response.blob())
+}
+
+async function uploadImage (blob, fileType = '.svg', filename) {
+  // const blob = await (await fetch(src)).blob();
+  const body = new FormData()
+  body.append(
+    'image',
+    new File([blob], (filename || new Date().getTime()) + fileType)
+  )
+
+  const resp = await api.fetchApi('/upload/image', {
+    method: 'POST',
+    body
+  })
+
+  // console.log(resp)
+  let data = await resp.json()
+  let { name, subfolder } = data
+  // let src = api.apiURL(
+  //   `/view?filename=${encodeURIComponent(
+  //     name
+  //   )}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+  // )
+
+  return data
+}
+// 上传得到url
+async function uploadBase64ToFile (base64) {
+  let bg_blob = await base64ToBlobFromURL(base64)
+  let url = await uploadImage(bg_blob, '.png')
+  return url
+}
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+const p5InputNode = {
+  name: 'Mixlab.Comfy.P5Input',
+  async getCustomWidgets (app) {
+    return {
+      IMAGEBASE64 (node, inputName, inputData, app) {
+        const widget = {
+          value: {
+            images: []
+          }, // 不能[x,x,x]
+          type: inputData[0], // the type
+          name: inputName, // the name, slice
+          size: [320, 120], // a default size
+          draw (ctx, node, width, y) {},
+          computeSize (...args) {
+            return [128, 32] // a method to compute the current size of the widget
+          }
+        }
+        node.addCustomWidget(widget)
+        return widget
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'P5Input') {
+      // console.log('P5Input')
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const widget = {
+          type: 'div',
+          name: 'image_base64',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width - 24, 44, node.size[1])
+            )
+          },
+          serialize: false
+        }
+
+        widget.div = $el('div', {})
+
+        widget.div.style = `margin:12px;width:400px;height:480px;background:white`
+
+        document.body.appendChild(widget.div)
+
+        this.addCustomWidget(widget)
+
+        // document.addEventListener('wheel', handleMouseWheel)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          // window.removeEventListener('message', ms)
+          return onRemoved?.()
+        }
+
+        // 节点的大小控制
+        this.setSize([480, 560])
+        app.canvas.draw(true, true)
+
+        const onResize = this.onResize
+        this.onResize = () => {
+          // 设置最小尺寸
+          if (
+            Math.max(this.size[0], 480) != this.size[0] &&
+            Math.max(this.size[1], 560) != this.size[1]
+          ) {
+            this.setSize([
+              Math.max(this.size[0], 480),
+              Math.max(this.size[1], 560)
+            ])
+          }
+
+          return onResize?.apply(this, arguments)
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        // console.log('##onExecuted', this, message._info)
+        //   app.graph.getNodeById(8).widgets[1].div.querySelector('iframe').contentWindow.postMessage('Hello from parent', '*');
+      }
+    }
+  },
+
+  async loadedGraphNode (node, app) {
+    if (node.type === 'P5Input') {
+    }
+  },
+  nodeCreated (node, app) {
+    //数据延迟？？
+    setTimeout(() => {
+      let widget = node.widgets?.filter(w => w.name == 'image_base64')[0]
+      let framesWidget = node.widgets?.filter(w => w.name == 'frames')[0]
+      if (node.type === 'P5Input' && widget) {
+        console.log('#nodeCreated P5Input')
+        if (framesWidget && !framesWidget.value)
+          framesWidget.value = { images: [] }
+
+        framesWidget.value._seed = Math.random()
+
+        let nodeId = node.id
+        //延迟才能获得this.id
+        widget.div.innerHTML = `<iframe src="extensions/comfyui-mixlab-nodes/p5_export/p5.html?id=${nodeId}" 
+          style="border:0;width:100%;height:100%;"
+         ></iframe>`
+
+        // 监听来自iframe的消息
+        const ms = async event => {
+          const data = event.data
+          console.log('#P5 Input #', data)
+          if (
+            data.from === 'p5.widget' &&
+            data.status === 'save' &&
+            data.frames &&
+            data.frames.length >= 0 &&
+            data.nodeId == nodeId &&
+            data.id != framesWidget.value.id
+          ) {
+            const frames = data.frames
+
+            //workflow会存储到local，会卡死
+            framesWidget.value.images = []
+            for (const f of frames) {
+              let file = await uploadBase64ToFile(f)
+              framesWidget.value.images.push(file)
+            }
+            // framesWidget.value.base64 = frames
+            // framesWidget.value._seed = Math.random()
+            node.title = 'P5 Input #' + frames.length
+            framesWidget.value.id = data.id
+          }
+        }
+
+        window.addEventListener('message', ms)
+      }
+    }, 1000)
+  }
+}
+
+app.registerExtension(p5InputNode)
@@ -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)
+    }
+  }
+})
@@ -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()
@@ -9,6 +9,179 @@ import {

 import { smart_init, addSmartMenu } from './smart_connect.js'

+import { completion_ } from './chat.js'
+
+function showTextByLanguage (key, json) {
+  // 获取浏览器语言
+  var language = navigator.language
+  // 判断是否为中文
+  if (
+    language.indexOf('zh') !== -1 ||
+    (language.indexOf('cn') !== -1 && json[key])
+  ) {
+    return json[key]
+  } else {
+    return key
+  }
+}
+
+//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`
+
+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 - ${JSON.stringify(tool,null,2)}`
+
+
+if (!localStorage.getItem('_mixlab_system_prompt')) {
+  localStorage.setItem('_mixlab_system_prompt', systemPrompt)
+}
+
+// 获取llama 模型
+async function get_llamafile_models () {
+  try {
+    const response = await fetch('/mixlab/folder_paths', {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        type: 'llamafile'
+      })
+    })
+
+    const data = await response.json()
+    // console.log(data)
+    return data.names
+  } catch (error) {
+    console.error(error)
+  }
+}
+// 运行llama
+async function start_llama (model = 'Phi-3-mini-4k-instruct-Q5_K_S.gguf') {
+  let n_gpu_layers = -1
+  try {
+    n_gpu_layers = parseInt(localStorage.getItem('_mixlab_llama_n_gpu'))
+  } catch (error) {}
+
+  try {
+    const response = await fetch('/mixlab/start_llama', {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        model,
+        n_gpu_layers
+      })
+    })
+
+    const data = await response.json()
+    if (data.llama_cpp_error||!data.port) {
+      return
+    }
+
+    return {
+      url: `http://${window.location.hostname}:${data.port}`,
+      model: data.model,
+      chat_format: data.chat_format
+    }
+  } catch (error) {
+    console.error(error)
+  }
+}
+
+function resizeImage (base64Image) {
+  var img = new Image()
+  var canvas = document.createElement('canvas')
+  var ctx = canvas.getContext('2d')
+  return new Promise((res, rej) => {
+    img.onload = function () {
+      // 等比例缩放图片
+      var width = img.width
+      var height = img.height
+      var max_width = 768
+      if (width > max_width) {
+        height *= max_width / width
+        width = max_width
+      }
+
+      // 设置canvas尺寸
+      canvas.width = width
+      canvas.height = height
+
+      // 在canvas上绘制图片
+      ctx.drawImage(img, 0, 0, width, height)
+
+      // 将canvas转换为base64图片数据
+      var canvasData = canvas.toDataURL()
+      res(canvasData) //  canvas转换后的base64图片数据
+    }
+
+    img.src = base64Image
+  })
+}
+
+// 菜单入口
+async function createMenu () {
+  const menu = document.querySelector('.comfy-menu')
+  const separator = document.createElement('div')
+  separator.style = `margin: 20px 0px;
+  width: 100%;
+  height: 1px;
+  background: var(--border-color);
+  `
+  menu.append(separator)
+
+  if (!menu.querySelector('#mixlab_chatbot_by_llamacpp')) {
+    const appsButton = document.createElement('button')
+    appsButton.id = 'mixlab_chatbot_by_llamacpp'
+    appsButton.textContent = '♾️Mixlab'
+
+    // appsButton.onclick = () =>
+    appsButton.onclick = async () => {
+      // if (window._mixlab_llamacpp&&window._mixlab_llamacpp.model&&window._mixlab_llamacpp.model.length>0) {
+      //   //显示运行的模型
+      //   createModelsModal([
+      //     window._mixlab_llamacpp.url,
+      //     window._mixlab_llamacpp.model
+      //   ])
+      // } else {
+      //   // let ms = await get_llamafile_models()
+      //   // ms = ms.filter(m => !m.match('-mmproj-'))
+      //   // if (ms.length > 0) createModelsModal(ms)
+      // }
+      createModelsModal([
+      
+      ])
+    }
+    menu.append(appsButton)
+  }
+}
+
 let isScriptLoaded = {}

 function loadExternalScript (url) {
@@ -299,8 +472,9 @@ async function get_my_app (filename = null, category = '') {
    data = []

    for (const res of result.data) {
-      let { app, workflow } = res.data;
-      if (app?.filename) data.push({
+      let { app, workflow } = res.data
+      if (app?.filename)
+        data.push({
          ...app,
          data: workflow,
          date: res.date
@@ -344,6 +518,33 @@ injectCSS(`::-webkit-scrollbar {
  width: 2px;
 }

+#mixlab_chatbot_by_llamacpp{
+  font-size:14px
+}
+
+#mixlab_chatbot_by_llamacpp::before {
+  content: attr(title);
+  position: absolute;
+  margin-top: 24px;
+  font-size: 10px;
+}
+
+.mix_tag{
+  padding:8px;cursor: pointer;font-size: 14px;
+    color: var(--input-text);
+    background-color: var(--comfy-input-bg);
+    border-radius: 8px;
+    border-color: var(--border-color);
+    border-style: solid;
+    margin-top: 2px;
+    margin-bottom: 14px;
+}
+
+.mix_tag:hover{
+  background-color: #101c19;
+  color: aquamarine;
+}
+
@keyframes loading_mixlab {
  0% {
    background-color: green;
@@ -369,7 +570,6 @@ injectCSS(`::-webkit-scrollbar {
  border-left: 2px solid var(--input-text); 
 }
 
-
 `)

 async function getCustomnodeMappings (mode = 'url') {
@@ -494,13 +694,28 @@ app.showMissingNodesError = async function (
  // console.log('#nodesMap', nodesMap)
  // console.log('###MIXLAB', missingNodeTypes, hasAddedNodes)
  this.ui.dialog.show(
-    `When loading the graph, the following node types were not found: <ul>${missingNodeGithub(
-      missingNodeTypes,
-      nodesMap
-    ).join('')}</ul>${
-      hasAddedNodes
-        ? 'Nodes that have failed to load will show as red on the graph.'
-        : ''
+    `<a style="color: white;
+    font-size: 18px;
+    font-weight: 800;
+    letter-spacing: 2px;
+    font-family: sans-serif;
+  }"
+  href="https://discord.gg/cXs9vZSqeK"  target="_blank">${showTextByLanguage(
+    'Welcome to Mixlab nodes discord, seeking help.',
+    {
+      'Welcome to Mixlab nodes discord, seeking help.':
+        '寻求帮助，加入Mixlab nodes交流频道'
+    }
+  )}</a><br><br>${showTextByLanguage(
+      'When loading the graph, the following node types were not found:',
+      {
+        'When loading the graph, the following node types were not found:':
+          '缺少以下节点：'
+      }
+    )}
+  
+   <ul>${missingNodeGithub(missingNodeTypes, nodesMap).join('')}</ul>${
+      hasAddedNodes ? '' : ''
    }`
  )
  this.logging.addEntry('Comfy.App', 'warn', {
@@ -586,13 +801,281 @@ 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')
+  div.id = 'model-modal'
+  div.innerHTML = ''
+  div.style.cssText = `
+    width: 100%;
+    z-index: 9990;
+    height: 100vh;
+    display: flex;
+    color: var(--descrip-text);
+    position: fixed;
+    top: 0;
+    left: 0;
+    background: #000000a8;
+    `
+
+  var modal = document.createElement('div')
+
+  div.addEventListener('click', e => {
+    e.stopPropagation()
+    div.remove()
+  })
+
+  div.appendChild(modal)
+  modal.classList.add('modal-body')
+  // Set modal styles
+  modal.style.cssText = `
+  color: var(--descrip-text);
+    background-color: var(--comfy-menu-bg);
+    position: fixed;
+    overflow:hidden;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    z-index: 9999;
+    border-radius: 4px;
+    box-shadow: 4px 4px 14px rgba(255,255,255,0.2);
+  `
+
+  // Create modal header
+  const headerElement = document.createElement('div')
+  headerElement.classList.add('modal-header')
+  headerElement.style.cssText = `
+      display: flex;
+      padding: 20px 24px 8px 24px;
+      justify-content: space-between;
+    `
+
+  const headTitleElement = document.createElement('a')
+  headTitleElement.classList.add('header-title')
+  headTitleElement.style.cssText = `
+      color: var(--descrip-text);
+      font-size: 18px;
+      display: flex;
+      align-items: flex-start;
+      flex: 1;
+      overflow: hidden;
+      text-decoration: none;
+      font-weight: bold;
+      justify-content: space-between;
+      padding: 20px;
+      cursor: pointer;
+      user-select: none;
+    `
+
+  // headTitleElement.href = 'https://github.com/shadowcz007/comfyui-mixlab-nodes'
+  // headTitleElement.target = '_blank'
+  const linkIcon = document.createElement('small')
+  linkIcon.textContent = showTextByLanguage('Auto Open', {
+    'Auto Open': '自动开启'
+  })
+  linkIcon.style.padding = '4px'
+
+  const 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)
+  n_gpu.setAttribute('max', 9999)
+
+  n_gpu.style = `color: var(--input-text);
+  background-color: var(--comfy-input-bg);
+  border-radius: 8px;
+  border-color: var(--border-color);
+  height: 26px;
+  padding: 4px 10px;
+  width: 48px;
+  margin-left: 12px;`
+  if (localStorage.getItem('_mixlab_llama_n_gpu')) {
+    n_gpu.value = parseInt(localStorage.getItem('_mixlab_llama_n_gpu'))
+  } else {
+    n_gpu.value = -1
+    localStorage.setItem('_mixlab_llama_n_gpu', -1)
+  }
+
+  const n_gpu_p = document.createElement('p')
+  n_gpu_p.innerText = 'n_gpu_layers'
+
+  const batchPageBtn = document.createElement('div')
+  batchPageBtn.style = `display: flex;
+  justify-content: center;
+  align-items: center;
+  font-size: 12px;`
+  batchPageBtn.innerHTML=`<a href="${get_url()}/mixlab/app" target="_blank" style="color: var(--input-text);
+  background-color: var(--comfy-input-bg);">App</a>`
+
+  const title = document.createElement('p')
+  title.innerText = 'Mixlab Nodes'
+  title.style = `font-size: 18px;
+  margin-right: 8px;
+  margin-top: 0;`
+
+  const left_d = document.createElement('div')
+  left_d.style = `display: flex;
+  justify-content: center;
+  align-items: flex-start;
+  font-size: 12px;
+  flex-direction: column; `
+  left_d.appendChild(title)
+  // title.appendChild(statusIcon)
+  // left_d.appendChild(linkIcon)
+  left_d.appendChild(batchPageBtn)
+  headTitleElement.appendChild(left_d)
+
+  // headTitleElement.appendChild(n_gpu_div)
+
+  //重启
+  const reStart = document.createElement('small')
+  reStart.textContent = showTextByLanguage('restart', {
+    restart: '重启'
+  })
+
+  reStart.style=`padding: 8px;
+  font-size: 16px;
+  outline: 1px solid;
+  padding-top: 4px;
+  padding-bottom: 4px;`
+
+  headTitleElement.appendChild(reStart)
+
+  if (localStorage.getItem('_mixlab_auto_llama_open')) {
+    linkIcon.style.backgroundColor = '#66ff6c'
+    linkIcon.style.color = 'black'
+  }
+  linkIcon.addEventListener('click', e => {
+    e.stopPropagation()
+    if (localStorage.getItem('_mixlab_auto_llama_open')) {
+      localStorage.setItem('_mixlab_auto_llama_open', '')
+      linkIcon.style.backgroundColor = ''
+      linkIcon.style.color = 'var(--descrip-text)'
+    } else {
+      localStorage.setItem('_mixlab_auto_llama_open', 'true')
+      linkIcon.style.backgroundColor = '#66ff6c'
+      linkIcon.style.color = 'black'
+    }
+  })
+
+  reStart.addEventListener('click', e => {
+    e.stopPropagation()
+    div.remove()
+    fetch('mixlab/re_start', {
+      method: 'POST'
+    })
+  })
+
+  n_gpu.addEventListener('click', e => {
+    e.stopPropagation()
+    localStorage.setItem('_mixlab_llama_n_gpu', n_gpu.value)
+  })
+
+  modal.appendChild(headTitleElement)
+
+  // Create modal content area
+  var modalContent = document.createElement('div')
+  modalContent.classList.add('modal-content')
+
+  var inputForSystemPrompt = document.createElement('textarea')
+  inputForSystemPrompt.className = 'comfy-multiline-input'
+  inputForSystemPrompt.style = `    height: 260px;
+  width: 480px;
+  font-size: 16px;
+  padding: 18px;`
+  inputForSystemPrompt.value = localStorage.getItem('_mixlab_system_prompt')
+
+  inputForSystemPrompt.addEventListener('change', e => {
+    e.stopPropagation()
+    localStorage.setItem('_mixlab_system_prompt', inputForSystemPrompt.value)
+  })
+
+  inputForSystemPrompt.addEventListener('click', e => {
+    e.stopPropagation()
+  })
+
+  // modalContent.appendChild(inputForSystemPrompt)
+
+  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`
+
+      d.addEventListener('click', async e => {
+        e.stopPropagation()
+        div.remove()
+        // startLLM(m)
+      })
+
+      // modalContent.appendChild(d)
+    }
+  }
+  modal.appendChild(modalContent)
+
+  const helpInfo = document.createElement('a')
+  helpInfo.textContent = showTextByLanguage('Help', {
+    Help: '寻求帮助'
+  })
+  helpInfo.style = `text-align: center;
+  display: block;
+  padding: 8px;
+  cursor: pointer;
+  font-size: 12px;
+  color: white;`
+  helpInfo.href = 'https://discord.gg/cXs9vZSqeK'
+  helpInfo.target = '_blank'
+  modal.appendChild(helpInfo)
+
+  document.body.appendChild(div)
+}
+
 function createModal (url, markdown, title) {
  // Create modal element
  var div =
    document.querySelector('#mix-modal') || document.createElement('div')
  div.id = 'mix-modal'
  div.innerHTML = ''
-  div.style.cssText = `width: 100%;
+  div.style.cssText = `
+    width: 100%;
    z-index: 9990;
    height: 100vh;
    display: flex;
@@ -895,16 +1378,61 @@ function drawBadge (node, orig, restArgs) {
  return r
 }

+function convertImageUrlToBase64 (imageUrl) {
+  return fetch(imageUrl)
+    .then(response => response.blob())
+    .then(blob => {
+      return new Promise((resolve, reject) => {
+        const reader = new FileReader()
+        reader.onloadend = () => resolve(reader.result)
+        reader.onerror = reject
+        reader.readAsDataURL(blob)
+      })
+    })
+}
+
+async function getSelectImageNode () {
+  var nodes = app.canvas.selected_nodes
+  let imageNode = null
+  if (Object.keys(app.canvas.selected_nodes).length == 0) return
+  for (var id in nodes) {
+    if (nodes[id].imgs) {
+      let base64 = await convertImageUrlToBase64(nodes[id].imgs[0].currentSrc)
+      imageNode = await resizeImage(base64)
+    }
+  }
+  return imageNode
+}
+
 app.registerExtension({
  name: 'Comfy.Mixlab.ui',
  init () {
+    //是否要自动加载模型
+    if (localStorage.getItem('_mixlab_auto_llama_open')) {
+      let model = localStorage.getItem('_mixlab_llama_select')
+      start_llama(model).then(res => {
+        window._mixlab_llamacpp = res
+        document.body
+          .querySelector('#mixlab_chatbot_by_llamacpp')
+          .setAttribute('title', res.url)
+      })
+    }else{
+      // startLLM('')
+    }
+
    LGraphCanvas.prototype.helpAboutNode = async function (node) {
      nodesMap =
        nodesMap && Object.keys(nodesMap).length > 0
          ? 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)
@@ -914,83 +1442,197 @@ 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()

-    const getNodeMenuOptions = LGraphCanvas.prototype.getNodeMenuOptions // store the existing method
-    LGraphCanvas.prototype.getNodeMenuOptions = function (node) {
-      // replace it
-      const options = getNodeMenuOptions.apply(this, arguments) // start by calling the stored one
-      node.setDirtyCanvas(true, true) // force a redraw of (foreground, background)
-      console.log('getNodeMenuOptions', node.type == 'CLIPTextEncode')
+    LGraphCanvas.prototype.text2text = async function (node) {
+      // console.log(node)
+      let widget = node.widgets.filter(
+        w => w.name === 'text' && typeof w.value == 'string'
+      )[0]
+      if (widget) {
+        app.canvas.centerOnNode(node)

-      let opts = [
-        {
-          content: 'Help ♾️Mixlab', // with a name
-          callback: () => {
-            LGraphCanvas.prototype.helpAboutNode(node)
-          } // and the callback
-        },
-        {
-          content: 'Fix node v2', // with a name
-          callback: () => {
-            LGraphCanvas.prototype.fixTheNode(node)
+        let controller = new AbortController()
+        let ends = [] //TODO 判断终止 <|im_start|>
+        let userInput = widget.value
+        widget.value = widget.value.trim()
+        widget.value += '\n'
+        let jsonStr="";
+        try {
+          await completion_(
+            window._mixlab_llamacpp.url + '/v1/chat/completions',
+            [
+              {
+                role: 'system',
+                content: localStorage.getItem('_mixlab_system_prompt')
+              },
+              { role: 'user', content: userInput }
+            ],
+            controller,
+            t => {
+              // console.log(t)
+              widget.value += t
+              jsonStr+=t
+            }
+          )
+        } catch (error) {
+          //是否要自动加载模型
+          if (localStorage.getItem('_mixlab_auto_llama_open')) {
+            let model = localStorage.getItem('_mixlab_llama_select')
+            start_llama(model).then(async res => {
+              window._mixlab_llamacpp = res
+              document.body
+                .querySelector('#mixlab_chatbot_by_llamacpp')
+                .setAttribute('title', res.url)
+
+              await completion_(
+                window._mixlab_llamacpp.url + '/v1/chat/completions',
+                [
+                  {
+                    role: 'system',
+                    content: localStorage.getItem('_mixlab_system_prompt')
+                  },
+                  { role: 'user', content: userInput }
+                ],
+                controller,
+                t => {
+                  // console.log(t)
+                  widget.value += t
+                  jsonStr+=t
+                }
+              )
+            })
          }
        }
-      ]

-      if (node.widgets) {
-        // let text_widget = node.widgets.filter(
-        //   w => w.name === 'text' && typeof w.value == 'string'
-        // )
-        // if (text_widget && text_widget.length == 1) {
-        //   opts.push({
-        //     content: 'Text-to-Text ♾️Mixlab', // with a name
-        //     callback: () => {
-        //       LGraphCanvas.prototype.text2text(node)
-        //     } // and the callback
-        //   })
-        // }
+        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()
+        }
+        
      }
+    }

-      opts = addSmartMenu(opts, node)
+    LGraphCanvas.prototype.image2text = async function (node) {
+      let imageBase64 = await getSelectImageNode()

-      // if (node.type == 'CLIPTextEncode') {
-      //   // 则出现 randomPrompt
-      //   // CLIPTextEncode 的widget ，name== 'text'
-      //   let node_widget_name = 'text'
-      //   const widget = node.widgets.filter(w => w.name === node_widget_name)[0]
+      if (imageBase64) {
+        // console.log('image2text')
+        // 添加note 节点
+        const NoteNode = LiteGraph.createNode('Note')
+        NoteNode.title = `Image-to-Text ${node.id}`
+        NoteNode.size = [NoteNode.size[0] + 100, NoteNode.size[1]]
+        let widget = NoteNode.widgets[0]
+        widget.value = ''

-      //  let mixlab_nodes_smart_connect= [{node_type:'CLIPTextEncode',
-      //   node_widget_name:'text',
-      //   inputNodeName:'RandomPrompt',
-      //   inputNode_output_type:'STRING'}]
+        NoteNode.pos = [node.pos[0] + node.size[0] + 24, node.pos[1] - 48]

-      //   if (widget) {
-      //     opts = [
-      //       {
-      //         content: 'RandomPrompt',
-      //         callback: () => {
-      //           LGraphCanvas.prototype._createNodeForInput(
-      //             node, //当前node
-      //             widget,//当前node里需要自动连线的widget
-      //             'RandomPrompt',//作为input的node type
-      //             'STRING'// 作为input的node的outputs的type. the input slot type of the target node
-      //           )
-      //         }
-      //       },
-      //       null,
-      //       ...opts
-      //     ]
-      //   }
-      // }
+        app.canvas.graph.add(NoteNode, false)
+        app.canvas.centerOnNode(NoteNode)

-      return [...opts, null, ...options] // and return the options
+        let controller = new AbortController()
+        let ends = []
+        let userInput = widget.value
+        widget.value = widget.value.trim()
+        widget.value += '\n'
+
+        try {
+          await completion_(
+            window._mixlab_llamacpp.url + '/v1/chat/completions',
+            [
+              {
+                role: 'system',
+                content: localStorage.getItem('_mixlab_system_prompt')
+              },
+              // { role: 'user', content: userInput }
+
+              {
+                role: 'user',
+                content: [
+                  {
+                    type: 'image_url',
+                    image_url: {
+                      url: imageBase64
+                    }
+                  },
+                  { type: 'text', text: 'What’s in this image?' }
+                ]
+              }
+            ],
+            controller,
+            t => {
+              // console.log(t)
+              widget.value += t
+
+              NoteNode.size[1] = widget.element.scrollHeight + 20
+              widget.computedHeight = NoteNode.size[1]
+              app.canvas.centerOnNode(NoteNode)
+            }
+          )
+        } catch (error) {
+          //是否要自动加载模型
+          if (localStorage.getItem('_mixlab_auto_llama_open')) {
+            let model = localStorage.getItem('_mixlab_llama_select')
+            start_llama(model).then(async res => {
+              window._mixlab_llamacpp = res
+              document.body
+                .querySelector('#mixlab_chatbot_by_llamacpp')
+                .setAttribute('title', res.url)
+
+              await completion_(
+                window._mixlab_llamacpp.url + '/v1/chat/completions',
+                [
+                  {
+                    role: 'system',
+                    content: localStorage.getItem('_mixlab_system_prompt')
+                  },
+                  {
+                    role: 'user',
+                    content: [
+                      {
+                        type: 'image_url',
+                        image_url: {
+                          url: imageBase64
+                        }
+                      },
+                      { type: 'text', text: 'What’s in this image?' }
+                    ]
+                  }
+                ],
+                controller,
+                t => {
+                  // console.log(t)
+                  widget.value += t
+                  NoteNode.size[1] = widget.element.scrollHeight + 20
+                  widget.computedHeight = NoteNode.size[1]
+                  app.canvas.centerOnNode(NoteNode)
+                }
+              )
+            })
+          }
+        }
+
+        widget.value = widget.value.trim()
+      }
    }

    const getGroupMenuOptions = LGraphCanvas.prototype.getGroupMenuOptions // store the existing method
@@ -1139,6 +1781,71 @@ app.registerExtension({
      this.setDirty(true, true)
    }

+    const getNodeMenuOptions = LGraphCanvas.prototype.getNodeMenuOptions
+    LGraphCanvas.prototype.getNodeMenuOptions = function (node) {
+      // replace it
+      const options = getNodeMenuOptions.apply(this, arguments) // start by calling the stored one
+      node.setDirtyCanvas(true, true) // force a redraw of (foreground, background)
+
+      let opts = [
+        {
+          content: 'Help ♾️Mixlab', // with a name
+          callback: () => {
+            // console.log('#data',node)
+            LGraphCanvas.prototype.helpAboutNode(node)
+          } // and the callback
+        },
+        {
+          content: 'Fix node v2', // with a name
+          callback: () => {
+            LGraphCanvas.prototype.fixTheNode(node)
+          }
+        }
+      ]
+
+      if (node.widgets) {
+        let text_widget = node.widgets.filter(
+          w => w.name === 'text' && typeof w.value == 'string'
+        )
+
+        let text_input = node.inputs?.filter(
+          inp => inp.name == 'text' && inp.type == 'STRING'
+        )
+
+        if (
+          text_input &&
+          text_input.length == 0 &&
+          text_widget &&
+          text_widget.length == 1 &&
+          window._mixlab_llamacpp &&
+          node.type != 'ShowTextForGPT'
+        ) {
+          opts.push({
+            content: 'Text-to-Text ♾️Mixlab', // with a name
+            callback: () => {
+              LGraphCanvas.prototype.text2text(node)
+            } // and the callback
+          })
+        }
+
+        if (
+          node.imgs &&
+          node.imgs.length > 0 &&
+          window._mixlab_llamacpp &&
+          window._mixlab_llamacpp.chat_format === 'llava-1-5'
+        ) {
+          opts.push({
+            content: 'Image-to-Text ♾️Mixlab', // with a name
+            callback: () => {
+              LGraphCanvas.prototype.image2text(node)
+            } // and the callback
+          })
+        }
+      }
+
+      return [...opts, null, ...options] // and return the options
+    }
+
    // 支持app模式的json
    const loadAppJson = async data => {
      let workflow
@@ -1173,8 +1880,6 @@ app.registerExtension({
        event.preventDefault()
        event.stopPropagation()

-       
-
        // Dragging from Chrome->Firefox there is a file but its a bmp, so ignore that
        if (
          event.dataTransfer.files.length &&
@@ -1182,13 +1887,14 @@ app.registerExtension({
        ) {
          const reader = new FileReader()
          reader.onload = async () => {
-       
            loadAppJson(reader.result)
          }
          reader.readAsText(event.dataTransfer.files[0])
        }
      })
    }
+
+    createMenu()
  },
  setup () {
    setTimeout(async () => {
@@ -1198,7 +1904,7 @@ app.registerExtension({
      const apps = await get_my_app()
      if (!apps) return

-      console.log('apps',apps)
+      console.log('apps', apps)

      let apps_map = { 0: [] }

@@ -1,5 +1,5 @@
 import { app } from '../../../scripts/app.js'
-import { $el } from '../../../scripts/ui.js' 
+import { $el } from '../../../scripts/ui.js'

 const getLocalData = key => {
  let data = {}
@@ -122,7 +122,7 @@ app.registerExtension({
      nodeType.prototype.onNodeCreated = function () {
        orig_nodeCreated?.apply(this, arguments)

-        // console.log('Color nodeData', this.widgets)
+        console.log('Color nodeData', this.div)

        const widget = {
          type: 'div',
@@ -273,19 +273,19 @@ app.registerExtension({
 })

 const min_max = node => {
-  if(node.widgets){
+  if (node.widgets) {
    const min_value = node.widgets.filter(w => w.name === 'min_value')[0]
    const max_value = node.widgets.filter(w => w.name === 'max_value')[0]
-  
+
    const number = node.widgets.filter(w => w.name === 'number')[0]
    if (number) {
      number.options.min = min_value.value
      number.options.max = max_value.value
-  
+
      number.value = Math.min(number.options.max, number.value)
      number.value = Math.max(number.options.min, number.value)
    }
-  
+
    if (min_value)
      min_value.callback = e => {
        number.options.min = e
@@ -297,22 +297,18 @@ const min_max = node => {
        number.value = e
      }
  }
-  
 }

 app.registerExtension({
  name: 'Mixlab.utils.FloatSlider',
  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-
    if (nodeType.comfyClass == 'FloatSlider') {
-      const orig_nodeCreated = nodeType.prototype.onNodeCreated;
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
      nodeType.prototype.onNodeCreated = function () {
        orig_nodeCreated?.apply(this, arguments)
        min_max(this)
      }
    }
-
-   
  },
  async loadedGraphNode (node, app) {
    if (node.type === 'FloatSlider') {
@@ -323,7 +319,6 @@ app.registerExtension({
 app.registerExtension({
  name: 'Mixlab.utils.IntNumber',
  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    
    if (nodeType.comfyClass == 'IntNumber') {
      const orig_nodeCreated = nodeType.prototype.onNodeCreated
      nodeType.prototype.onNodeCreated = function () {
@@ -331,7 +326,6 @@ app.registerExtension({
        min_max(this)
      }
    }
-   
  },
  async loadedGraphNode (node, app) {
    if (node.type === 'IntNumber') {
@@ -340,22 +334,145 @@ app.registerExtension({
  }
 })

-
 app.registerExtension({
  name: 'Mixlab.utils.TESTNODE_',
  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    
    if (nodeType.comfyClass == 'TESTNODE_') {
-    
-      const onExecuted = nodeType.prototype.onExecuted;
-			nodeType.prototype.onExecuted = function (message) {
-				onExecuted?.apply(this, arguments);
-        console.log('##',message)
-				 
-			};
-
-
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log('##', message)
+      }
    }
-   
-  }, 
+  }
+})
+
+app.registerExtension({
+  name: 'Mixlab.utils.KeyInput',
+  init () {},
+  async getCustomWidgets (app) {
+    return {
+      KEY (node, inputName, inputData, app) {
+        // console.log('##node', node)
+        const widget = {
+          type: inputData[0], // the type, CHEESE
+          name: inputName, // the name, slice
+          size: [128, 24], // a default size
+          draw (ctx, node, width, y) {},
+          computeSize (...args) {
+            return [128, 32] // a method to compute the current size of the widget
+          },
+          async serializeValue (nodeId, widgetIndex) {
+            let data = getLocalData('_mixlab_api_key')
+            return data[node.id] || 'by Mixlab'
+          }
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'KeyInput') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const widget = {
+          type: 'div',
+          name: 'input_key',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 24, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        const inputDiv = (key, placeholder) => {
+          let div = document.createElement('div')
+          div.style = `
+          display: flex;
+          align-items: center; 
+          margin: 6px 8px;
+          margin-top:0px;
+          height:44px;
+          width:220px; 
+          `
+
+          const ip = document.createElement('input')
+          ip.type = 'password'
+          ip.className = `${'comfy-multiline-input'} ${placeholder}`
+
+          ip.placeholder = placeholder
+          // ip.value = placeholder
+
+          ip.style = `margin-left:8px;
+          outline: none;
+          border: none;
+          padding:12px;
+          width: 100%;
+          `
+
+          div.appendChild(ip)
+
+          ip.addEventListener('change', () => {
+            let data = getLocalData(key)
+            data[this.id] = ip.value.trim()
+            localStorage.setItem(key, JSON.stringify(data))
+          })
+
+          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) {
+    if (node.type === 'KeyInput') {
+      let widget = node.widgets.filter(w => w.div)[0]
+
+      let apiKey = getLocalData('_mixlab_api_key')
+
+      let id = node.id
+      if (widget.div.querySelector('.Key'))
+        widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
+    }
+  },
+  nodeCreated (node, app) {
+    //数据延迟？？
+    setTimeout(() => {
+      // console.log('#LoadImagesToBatch', node.type)
+      if (node.type === 'KeyInput') {
+        let widget = node.widgets.filter(w => w.div)[0]
+
+        let apiKey = getLocalData('_mixlab_api_key')
+
+        let id = node.id
+
+        if (widget.div.querySelector('.Key'))
+          widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
+      }
+    }, 1000)
+  }
 })
@@ -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
            })
          }
@@ -0,0 +1,237 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+
+function base64ToBlobFromURL (base64URL, contentType) {
+  return fetch(base64URL).then(response => response.blob())
+}
+
+async function uploadImage (blob, fileType = '.svg', filename) {
+  // const blob = await (await fetch(src)).blob();
+  const body = new FormData()
+  body.append(
+    'image',
+    new File([blob], (filename || new Date().getTime()) + fileType)
+  )
+
+  const resp = await api.fetchApi('/upload/image', {
+    method: 'POST',
+    body
+  })
+
+  // console.log(resp)
+  let data = await resp.json()
+
+  return data
+}
+
+// 上传得到url
+async function uploadBase64ToFile (base64) {
+  let bg_blob = await base64ToBlobFromURL(base64)
+  let url = await uploadImage(bg_blob, '.png')
+  return url
+}
+
+class Visualizer {
+  constructor (node, container, visualSrc) {
+    this.node = node
+
+    this.iframe = document.createElement('iframe')
+    Object.assign(this.iframe, {
+      scrolling: 'no',
+      overflow: 'hidden'
+    })
+    this.iframe.src = '/extensions/comfyui-mixlab-nodes/' + visualSrc + '.html'
+    console.log('#Visualizer', container, this.iframe)
+    container.appendChild(this.iframe)
+  }
+
+  updateVisual (params) {
+    console.log('#updateVisual', params, this.iframe)
+    // const iframeDocument = this.iframe.contentWindow.document
+    // const previewScript = iframeDocument.getElementById('visualizer')
+    // previewScript.setAttribute(
+    //   'reference_image',
+    //   JSON.stringify(params.reference_image)
+    // )
+    // previewScript.setAttribute('depth_map', JSON.stringify(params.depth_map))
+    // Update the reference image and depth map
+    this.iframe.contentWindow.postMessage(params, '*')
+  }
+
+  remove () {
+    this.container.remove()
+  }
+}
+
+function createVisualizer (node, inputName, typeName, inputData, app) {
+  node.name = inputName
+
+  const widget = {
+    type: typeName,
+    name: 'preview3d',
+    callback: () => {},
+    draw: function (ctx, node, widgetWidth, widgetY, widgetHeight) {
+      const margin = 10
+      const top_offset = 5
+      const visible = app.canvas.ds.scale > 0.5 && this.type === typeName
+      const w = widgetWidth - margin * 4
+      const clientRectBound = ctx.canvas.getBoundingClientRect()
+      const transform = new DOMMatrix()
+        .scaleSelf(
+          clientRectBound.width / ctx.canvas.width,
+          clientRectBound.height / ctx.canvas.height
+        )
+        .multiplySelf(ctx.getTransform())
+        .translateSelf(margin, margin + widgetY)
+
+      Object.assign(this.visualizer.style, {
+        left: `${transform.a * margin + transform.e}px`,
+        top: `${transform.d + transform.f + top_offset}px`,
+        width: `${w * transform.a}px`,
+        height: `${
+          w * transform.d - widgetHeight - margin * 15 * transform.d
+        }px`,
+        position: 'absolute',
+        overflow: 'hidden',
+        zIndex: app.graph._nodes.indexOf(node)
+      })
+
+      Object.assign(this.visualizer.children[0].style, {
+        transformOrigin: '50% 50%',
+        width: '100%',
+        height: '100%',
+        border: '0 none'
+      })
+
+      this.visualizer.hidden = !visible
+    }
+  }
+
+  const container = document.createElement('div')
+  container.id = `Comfy3D_${inputName}`
+
+  node.visualizer = new Visualizer(node, container, typeName)
+  widget.visualizer = container
+  widget.parent = node
+
+  document.body.appendChild(widget.visualizer)
+
+  node.addCustomWidget(widget)
+
+  node.updateParameters = params => {
+    // console.log('#updateParameters', params)
+    params.id = node.id
+    // node.visualizer = new Visualizer(node, container, typeName)
+    node.visualizer.updateVisual(params)
+  }
+
+  // Events for drawing backgound
+  node.onDrawBackground = function (ctx) {
+    if (!this.flags.collapsed) {
+      node.visualizer.iframe.hidden = false
+    } else {
+      node.visualizer.iframe.hidden = true
+    }
+  }
+
+  // Make sure visualization iframe is always inside the node when resize the node
+  node.onResize = function () {
+    let [w, h] = this.size
+    if (w <= 600) w = 600
+    if (h <= 500) h = 500
+
+    if (w > 600) {
+      h = w - 100
+    }
+
+    this.size = [w, h]
+  }
+
+  // Events for remove nodes
+  node.onRemoved = () => {
+    for (let w in node.widgets) {
+      if (node.widgets[w].visualizer) {
+        node.widgets[w].visualizer.remove()
+      }
+    }
+  }
+
+  return {
+    widget: widget
+  }
+}
+
+function registerVisualizer (nodeType, nodeData, nodeClassName, typeName) {
+  if (nodeData.name == nodeClassName) {
+    const onNodeCreated = nodeType.prototype.onNodeCreated
+
+    nodeType.prototype.onNodeCreated = async function () {
+      const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined
+
+      let Preview3DNode = app.graph._nodes.filter(
+        wi => wi.type == nodeClassName
+      )
+      let nodeName = `Preview3DNode_${Preview3DNode.length}`
+
+      const result = await createVisualizer.apply(this, [
+        this,
+        nodeName,
+        typeName,
+        {},
+        app
+      ])
+
+      this.setSize([600, 500])
+
+      return r
+    }
+
+    nodeType.prototype.onExecuted = async function (message) {
+      // Check if reference image and depth map are available
+      if (message.reference_image && message.depth_map) {
+        const params = {}
+        params.reference_image = message.reference_image[0]
+        params.depth_map = message.depth_map[0]
+        this.updateParameters(params)
+      }
+    }
+  }
+}
+
+app.registerExtension({
+  name: 'Mixlab.nodes.depthviewer',
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    registerVisualizer(nodeType, nodeData, 'DepthViewer', 'threeVisualizer')
+  },
+  nodeCreated (node, app) {
+    //数据延迟？？
+    setTimeout(() => {
+      let widget = node.widgets?.filter(w => w.name == 'preview3d')[0]
+      let framesWidget = node.widgets?.filter(w => w.name == 'frames')[0]
+
+      if (node.type === 'DepthViewer' && widget) {
+        let nodeId = node.id
+        //延迟才能获得this.id
+        widget.visualizer.querySelector('iframe').src += '?id=' + nodeId
+        // console.log('DepthViewer',widget)
+        window.addEventListener('message', async event => {
+          // 检查消息的来源，确保消息来自可信的源
+          console.log(event)
+          const { id, imgs } = event.data
+          if (id == nodeId) {
+            framesWidget.value = { images: [] }
+
+            for (const f of imgs) {
+              let file = await uploadBase64ToFile(f)
+              framesWidget.value.images.push(file)
+            }
+            // framesWidget.value.base64 = frames
+            framesWidget.value._seed = Math.random()
+            node.title = 'Input #' + imgs.length
+          }
+        })
+      }
+    }, 1000)
+  }
+})
@@ -0,0 +1,347 @@
+/* juxtapose - v1.2.2 - 2020-09-03
+ * Copyright (c) 2020 Alex Duner and Northwestern University Knight Lab
+ */
+div.juxtapose {
+	width: 100%;
+	font-family: Helvetica, Arial, sans-serif;
+}
+
+div.jx-slider {
+	width: 100%;
+	height: 100%;
+	position: relative;
+	overflow: hidden;
+	cursor: pointer;
+	color: #f3f3f3;
+}
+
+
+div.jx-handle {
+	position: absolute;
+	height: 100%;
+	width: 40px;
+	cursor: col-resize;
+	z-index: 15;
+	margin-left: -20px;
+}
+
+.vertical div.jx-handle {
+	height: 40px;
+	width: 100%;
+	cursor: row-resize;
+	margin-top: -20px;
+	margin-left: 0;
+}
+
+div.jx-control {
+	height: 100%;
+	margin-right: auto;
+	margin-left: auto;
+	width: 3px;
+	background-color: currentColor;
+}
+
+.vertical div.jx-control {
+	height: 3px;
+	width: 100%;
+	background-color: currentColor;
+	position: relative;
+	top: 50%;
+	transform: translateY(-50%);
+}
+
+div.jx-controller {
+	position: absolute;
+	margin: auto;
+	top: 0;
+	bottom: 0;
+	height: 60px;
+	width: 9px;
+	margin-left: -3px;
+	background-color: currentColor;
+}
+
+.vertical div.jx-controller {
+	height: 9px;
+	width: 100px;
+	margin-left: auto;
+	margin-right: auto;
+	top: -3px;
+	position: relative;
+}
+
+div.jx-arrow {
+	position: absolute;
+	margin: auto;
+	top: 0;
+	bottom: 0;
+	width: 0;
+	height: 0;
+	transition: all .2s ease;
+}
+
+.vertical div.jx-arrow {
+	position: absolute;
+	margin: 0 auto;
+	left: 0;
+	right: 0;
+	width: 0;
+	height: 0;
+	transition: all .2s ease;
+}
+
+
+div.jx-arrow.jx-left {
+	left: 2px;
+	border-style: solid;
+	border-width: 8px 8px 8px 0;
+	border-color: transparent currentColor transparent transparent;
+}
+
+div.jx-arrow.jx-right {
+	right: 2px;
+	border-style: solid;
+	border-width: 8px 0 8px 8px;
+	border-color: transparent transparent transparent currentColor;
+}
+
+.vertical div.jx-arrow.jx-left {
+	left: 0px;
+	top: 2px;
+	border-style: solid;
+	border-width: 0px 8px 8px 8px;
+	border-color: transparent transparent currentColor transparent;
+}
+
+.vertical div.jx-arrow.jx-right {
+	right: 0px;
+	top: auto;
+	bottom: 2px;
+	border-style: solid;
+	border-width: 8px 8px 0 8px;
+	border-color: currentColor transparent transparent transparent;
+}
+
+div.jx-handle:hover div.jx-arrow.jx-left,
+div.jx-handle:active div.jx-arrow.jx-left {
+	left: -1px;
+}
+
+div.jx-handle:hover div.jx-arrow.jx-right,
+div.jx-handle:active div.jx-arrow.jx-right {
+	right: -1px;
+}
+
+.vertical div.jx-handle:hover div.jx-arrow.jx-left,
+.vertical div.jx-handle:active div.jx-arrow.jx-left {
+	left: 0px;
+	top: 0px;
+}
+
+.vertical div.jx-handle:hover div.jx-arrow.jx-right,
+.vertical div.jx-handle:active div.jx-arrow.jx-right {
+	right: 0px;
+	bottom: 0px;
+}
+
+
+div.jx-image {
+	position: absolute;
+	height: 100%;
+	display: inline-block;
+	top: 0;
+	overflow: hidden;
+	-webkit-backface-visibility: hidden;
+}
+
+.vertical div.jx-image {
+	width: 100%;
+	left: 0;
+	top: auto;
+}
+
+div.jx-image img {
+	height: 100%;
+	width: auto;
+	z-index: 5;
+	position: absolute;
+	margin-bottom: 0;
+
+	max-height: none;
+	max-width: none;
+	max-height: initial;
+	max-width: initial;
+}
+
+.vertical div.jx-image img {
+	height: auto;
+	width: 100%;
+}
+
+div.jx-image.jx-left {
+	left: 0;
+	background-position: left;
+}
+
+div.jx-image.jx-left img {
+	left: 0;
+}
+
+div.jx-image.jx-right {
+	right: 0;
+	background-position: right;
+}
+
+div.jx-image.jx-right img {
+	right: 0;
+	bottom: 0;
+}
+
+
+.veritcal div.jx-image.jx-left {
+	top: 0;
+	background-position: top;
+}
+
+.veritcal div.jx-image.jx-left img {
+	top: 0;
+}
+
+.vertical div.jx-image.jx-right {
+	bottom: 0;
+	background-position: bottom;
+}
+
+.veritcal div.jx-image.jx-right img {
+	bottom: 0;
+}
+
+
+div.jx-image div.jx-label {
+	font-size: 1em;
+	padding: .25em .75em;
+	position: relative;
+	display: inline-block;
+	top: 0;
+	background-color: #000; /* IE 8 */
+	background-color: rgba(0,0,0,.7);
+	color: white;
+	z-index: 10;
+	white-space: nowrap;
+	line-height: 18px;
+	vertical-align: middle;
+}
+
+div.jx-image.jx-left div.jx-label {
+	float: left;
+	left: 0;
+}
+
+div.jx-image.jx-right div.jx-label {
+	float: right;
+	right: 0;
+}
+
+.vertical div.jx-image div.jx-label {
+	display: table;
+	position: absolute;
+}
+
+.vertical div.jx-image.jx-right div.jx-label {
+	left: 0;
+	bottom: 0;
+	top: auto;
+}
+
+div.jx-credit {
+	line-height: 1.1;
+	font-size: 0.75em;
+}
+
+div.jx-credit em {
+	font-weight: bold;
+	font-style: normal;
+}
+
+
+/* Animation */
+
+div.jx-image.transition {
+	transition: width .5s ease;
+}
+
+div.jx-handle.transition {
+	transition: left .5s ease;
+}
+
+.vertical div.jx-image.transition {
+	transition: height .5s ease;
+}
+
+.vertical div.jx-handle.transition {
+	transition: top .5s ease;
+}
+
+/* Knight Lab Credit */
+a.jx-knightlab {
+	background-color: #000; /* IE 8 */
+	background-color: rgba(0,0,0,.25);
+	bottom: 0;
+	display: table;
+	height: 14px;
+	line-height: 14px;
+	padding: 1px 4px 1px 5px;
+	position: absolute;
+	right: 0;
+	text-decoration: none;
+	z-index: 10;
+}
+
+a.jx-knightlab div.knightlab-logo {
+	display: inline-block;
+	vertical-align: middle;
+	height: 8px;
+	width: 8px;
+	background-color: #c34528;
+	transform: rotate(45deg);
+	-ms-transform: rotate(45deg);
+	-webkit-transform: rotate(45deg);
+	top: -1.25px;
+	position: relative;
+	cursor: pointer;
+}
+
+a.jx-knightlab:hover {
+	background-color: #000; /* IE 8 */
+	background-color: rgba(0,0,0,.35);
+}
+a.jx-knightlab:hover div.knightlab-logo {
+	background-color: #ce4d28;
+}
+
+a.jx-knightlab span.juxtapose-name {
+	display: table-cell;
+	margin: 0;
+	padding: 0;
+	font-family: Helvetica, Arial, sans-serif;
+	font-weight: 300;
+	color: white;
+	font-size: 10px;
+	padding-left: 0.375em;
+	vertical-align: middle;
+	line-height: normal;
+	text-shadow: none;
+}
+
+/* keyboard accessibility */
+div.jx-controller:focus,
+div.jx-image.jx-left div.jx-label:focus,
+div.jx-image.jx-right div.jx-label:focus,
+a.jx-knightlab:focus {
+	background: #eae34a;
+	color: #000;
+}
+a.jx-knightlab:focus span.juxtapose-name{
+	color: #000;
+	border: none;
+}
@@ -0,0 +1,47 @@
+/*
+object-assign
+(c) Sindre Sorhus
+@license MIT
+*/
+
+/*!
+
+pica
+https://github.com/nodeca/pica
+
+*/
+
+/*!
+ * Block below copied from Protovis: http://mbostock.github.com/protovis/
+ * Copyright 2010 Stanford Visualization Group
+ * Licensed under the BSD License: http://www.opensource.org/licenses/bsd-license.php
+ * @license
+ */
+
+/*!
+ * jQuery JavaScript Library v3.7.1
+ * https://jquery.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2023-08-28T13:37Z
+ */
+
+/*!
+ * quantize.js Copyright 2008 Nick Rabinowitz.
+ * Licensed under the MIT license: http://www.opensource.org/licenses/mit-license.php
+ * @license
+ */
+
+/*! alertifyjs - v1.13.1 - Mohammad Younes <Mohammad@alertifyjs.com> (http://alertifyjs.com) */
+
+/*! regenerator-runtime -- Copyright (c) 2014-present, Facebook, Inc. -- license (MIT): https://github.com/facebook/regenerator/blob/main/LICENSE */
+
+/**
+ * hermite-resize - Canvas image resize/resample using Hermite filter with JavaScript.
+ * @version v2.2.10
+ * @link https://github.com/viliusle/miniPaint
+ * @license MIT
+ */
@@ -0,0 +1 @@
+{"version":3,"file":"bundle.js","sources":["webpack://miniPaint/bundle.js"],"mappings":";AAAA","sourceRoot":""}
@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 18.1.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 298.73 298.73" style="enable-background:new 0 0 298.73 298.73;" xml:space="preserve">
+<g>
+	<path style="fill:#010002;" d="M264.959,9.35H33.787C15.153,9.35,0,24.498,0,43.154v212.461c0,18.634,15.153,33.766,33.787,33.766
+		h231.171c18.634,0,33.771-15.132,33.771-33.766V43.154C298.73,24.498,283.593,9.35,264.959,9.35z M193.174,59.623
+		c18.02,0,32.634,14.615,32.634,32.634s-14.615,32.634-32.634,32.634c-18.025,0-32.634-14.615-32.634-32.634
+		S175.149,59.623,193.174,59.623z M254.363,258.149H149.362H49.039c-9.013,0-13.027-6.521-8.964-14.566l56.006-110.93
+		c4.058-8.044,11.792-8.762,17.269-1.605l56.316,73.596c5.477,7.158,15.05,7.767,21.386,1.354l13.777-13.951
+		c6.331-6.413,15.659-5.619,20.826,1.762l35.675,50.959C266.487,252.16,263.376,258.149,254.363,258.149z"/>
+</g>
+</svg>
@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 18.1.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 30.051 30.051" style="enable-background:new 0 0 30.051 30.051;" xml:space="preserve">
+<g>
+	<path d="M19.982,14.438l-6.24-4.536c-0.229-0.166-0.533-0.191-0.784-0.062c-0.253,0.128-0.411,0.388-0.411,0.669v9.069
+		c0,0.284,0.158,0.543,0.411,0.671c0.107,0.054,0.224,0.081,0.342,0.081c0.154,0,0.31-0.049,0.442-0.146l6.24-4.532
+		c0.197-0.145,0.312-0.369,0.312-0.607C20.295,14.803,20.177,14.58,19.982,14.438z"/>
+	<path d="M15.026,0.002C6.726,0.002,0,6.728,0,15.028c0,8.297,6.726,15.021,15.026,15.021c8.298,0,15.025-6.725,15.025-15.021
+		C30.052,6.728,23.324,0.002,15.026,0.002z M15.026,27.542c-6.912,0-12.516-5.601-12.516-12.514c0-6.91,5.604-12.518,12.516-12.518
+		c6.911,0,12.514,5.607,12.514,12.518C27.541,21.941,21.937,27.542,15.026,27.542z"/>
+</g>
+</svg>
@@ -0,0 +1,5 @@
+<svg width="124" height="150" viewBox="0 0 124 150" xmlns="http://www.w3.org/2000/svg">
+<rect x="55" y="14" width="14" height="63"/>
+<rect x="116" width="14" height="61" transform="rotate(90 116 0)"/>
+<path d="M62 150L8.30643 75L115.694 75L62 150Z"/>
+</svg>
@@ -0,0 +1,28 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 512 512" style="enable-background:new 0 0 512 512;" xml:space="preserve">
+<g>
+	<g>
+		<path d="M264.574,4.675C262.697,1.761,259.467,0,256,0c-3.467,0-6.697,1.761-8.574,4.675
+			c-6.532,10.14-159.966,249.362-159.966,338.784C87.459,436.393,163.066,512,256,512s168.541-75.607,168.541-168.541
+			C424.541,254.037,271.106,14.815,264.574,4.675z M256,491.602c-81.686,0-148.142-66.456-148.142-148.143
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--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
shadowcz007	8cb4507a5f	v0.36.0 p5.js	2024-08-10 00:39:27 +08:00
shadowcz007	555890d1ba	Update pyproject.toml	2024-08-09 19:08:36 +08:00
shadowcz007	e4f54e83b6	Update Text-to-Image-app.json	2024-08-09 16:30:47 +08:00
shadowcz007	692c4a709e	fixbug:web app	2024-08-09 16:27:34 +08:00
shadowcz007	cbd1961459	test	2024-08-08 21:58:44 +08:00
shadowcz007	2e31a33ebf	fixbug	2024-08-08 11:37:36 +08:00
shadowcz007	d16c6137d2	update	2024-08-06 23:07:20 +08:00
shadowcz007	0416ab79ec	Update 3d_mixlab.js	2024-08-06 21:08:52 +08:00
shadow	fc9a1c62b9	Merge pull request #295 from shadowcz007/0.36.0-py5-processing Lama 改成手动安装，新增JsonRepair	2024-08-06 11:09:27 +08:00
shadowcz007	5d4567b134	Lama 改成手动安装，新增JsonRepair	2024-08-06 11:08:50 +08:00
shadow	ae4a17d271	Merge pull request #293 from shadowcz007/0.36.0-py5-processing 0.36.0 py5 processing	2024-08-06 00:24:13 +08:00
shadowcz007	d110a08889	Update __init__.py	2024-08-06 00:23:34 +08:00
shadowcz007	e0157293cb	Update P5.py	2024-08-06 00:21:51 +08:00
shadowcz007	0d985b3b65	update	2024-08-06 00:14:05 +08:00
shadowcz007	a65ade9fda	updage	2024-08-05 21:30:30 +08:00
shadowcz007	874d6c8cb1	1	2024-08-05 21:16:19 +08:00
shadowcz007	f70ba2afa3	update	2024-08-05 21:08:32 +08:00
shadowcz007	e9f821e578	update	2024-08-05 20:49:06 +08:00
shadowcz007	8e488d4b1d	update	2024-08-05 11:55:07 +08:00
shadowcz007	77201a457d	基本打通	2024-08-04 23:48:06 +08:00
shadowcz007	076e3b1178	test	2024-08-04 22:28:16 +08:00
shadowcz007	6b13fa64dc	update	2024-08-04 20:44:56 +08:00
shadowcz007	846671a890	preview audio	2024-08-04 18:06:37 +08:00
shadowcz007	05b3088b75	0.35.1	2024-08-04 18:02:13 +08:00
shadowcz007	fe57286959	v0.34.0	2024-08-04 15:28:47 +08:00
shadowcz007	03645bbb33	image batch to list	2024-08-04 13:35:22 +08:00
shadowcz007	93dba9a399	fixbug :load image (base64)	2024-08-04 12:12:41 +08:00
shadowcz007	5627ea8073	Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes	2024-08-04 09:40:43 +08:00
shadowcz007	7ba679c9ce	fixbug	2024-08-04 09:40:40 +08:00
shadow	c7a450e6ce	Merge pull request #289 from ComfyNodePRs/licence-update Update PyProject Toml - License	2024-08-03 17:50:25 +08:00
snomiao	beda5156bf	chore(licence-update): Update PyProject Toml - License	2024-08-02 23:03:55 +00:00
shadowcz007	76a9da7163	fixbug	2024-08-02 18:32:27 +08:00
shadowcz007	edd0303f59	App模式增加batch prompt，批量提示词，可以把动态提示词批量组成后运行	2024-08-01 21:12:58 +08:00
shadowcz007	be6f47a333	batch prompt :批量提示	2024-08-01 21:03:40 +08:00
shadowcz007	4cd6a072ca	Update install.bat	2024-08-01 11:58:23 +08:00
shadowcz007	743a82efe9	fixbug	2024-07-29 18:29:16 +08:00
shadowcz007	9589f28ef7	v0.32.0	2024-07-29 18:11:57 +08:00
shadowcz007	35492c5671	add SiliconflowLLM	2024-07-29 18:06:32 +08:00
shadow	db1e695bf3	Merge pull request #284 from cd0304/main 修正text image节点的padding问题	2024-07-29 17:51:29 +08:00
shadowcz007	ecc4aec43b	Update ChatGPT.py	2024-07-29 15:17:00 +08:00
shadowcz007	fc063c2205	Update __init__.py	2024-07-29 14:17:57 +08:00
shadowcz007	4d60ce138a	Update __init__.py	2024-07-28 21:12:39 +08:00
shadowcz007	2afd24f6e4	fixbug	2024-07-28 20:52:55 +08:00
shadowcz007	437acd023a	fixbug	2024-07-28 20:28:34 +08:00
shadowcz007	b00523ae14	优化mixlab app，前端不传workflow，只传输入和输出	2024-07-28 20:21:53 +08:00
shadowcz007	4405a74993	Update Audio.py	2024-07-26 18:56:38 +08:00
cd0304	cb16090868	Update ImageNode.py	2024-07-26 13:04:17 +08:00
cd0304	396e510dce	Update ImageNode.py fix height	2024-07-26 00:32:56 +08:00
shadowcz007	3b9790b969	Update __init__.py	2024-07-25 13:39:41 +08:00
shadowcz007	a35d07a7ac	video	2024-07-17 20:49:15 +08:00
shadowcz007	6d004c61fc	Update pyproject.toml	2024-07-17 14:41:33 +08:00
shadowcz007	ffdd06da1b	Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes	2024-07-17 14:41:02 +08:00
shadowcz007	f03f34cacb	Update checkVersion_mixlab.js	2024-07-17 14:40:59 +08:00
shadow	0c86ea849e	Merge pull request #273 from cd0304/main textimge节点增加对otf后缀字体支持	2024-07-17 14:37:35 +08:00
cd0304	0efa4c38c0	Update ImageNode.py	2024-07-17 13:59:22 +08:00
cd0304	6092ab7793	Update ImageNode.py	2024-07-17 13:17:40 +08:00
shadowcz007	929def87eb	Update ui_mixlab.js	2024-07-17 11:16:43 +08:00
shadowcz007	be074ccff7	Update __init__.py	2024-07-16 22:47:10 +08:00
shadowcz007	3445199393	AUDIO	2024-07-16 21:38:54 +08:00
shadowcz007	216c7e152e	0.30.3	2024-07-08 00:03:33 +08:00
shadowcz007	cc8bc10690	update	2024-07-07 18:41:56 +08:00
shadowcz007	69b4218d60	Update __init__.py	2024-07-07 17:05:06 +08:00
shadowcz007	1dd18dc4f8	fixbug	2024-07-06 20:30:50 +08:00
shadowcz007	4ccbd999d9	fixbug	2024-07-06 00:54:19 +08:00
shadowcz007	fa8d404964	0.30.2	2024-07-06 00:39:02 +08:00
shadowcz007	30086957c9	fixbug	2024-07-06 00:37:52 +08:00
shadowcz007	0e57c620c9	Update Video.py	2024-07-04 18:19:53 +08:00
shadowcz007	3ce1c59a2d	Update README.md	2024-07-04 17:37:50 +08:00
shadowcz007	3337e20b9e	Math Operation	2024-06-23 16:50:06 +08:00
shadowcz007	e816b3626e	update	2024-06-22 21:44:33 +08:00
shadowcz007	3e0cb0f17a	Update ui_mixlab.js	2024-06-22 18:42:12 +08:00
shadowcz007	41bc606217	Update 2-screeshare.json	2024-06-22 11:56:32 +08:00
shadowcz007	5a5f4ca49a	Update pyproject.toml	2024-06-21 23:08:27 +08:00
shadowcz007	c3a8437cd1	Update ImageNode.py	2024-06-21 22:10:19 +08:00
shadowcz007	8d8a1a392d	fixbug	2024-06-21 21:54:41 +08:00
shadowcz007	5f93fb5e55	增加支持的国产大模型	2024-06-21 17:40:15 +08:00
shadowcz007	d05050d7d8	v0.30.1	2024-06-20 20:39:43 +08:00
shadowcz007	8e9744100d	优化composite images节点	2024-06-20 17:46:46 +08:00
shadowcz007	1e4e7e287d	Update ImageNode.py	2024-06-20 16:33:58 +08:00
shadowcz007	e8f0c73f08	优化text image节点，更为精准控制空白间距，字体修改为选择方式	2024-06-20 16:32:04 +08:00
shadowcz007	e923e28f8d	Canvas Mode	2024-06-20 14:59:51 +08:00
shadowcz007	5cc75bfa7c	Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes	2024-06-20 12:04:29 +08:00
shadowcz007	d6701769b8	fixbug：showtext	2024-06-20 12:04:23 +08:00
shadow	0ddc67bdab	Create CNAME	2024-06-20 11:13:12 +08:00
shadowcz007	38b62b7a68	Update pyproject.toml	2024-06-19 11:10:27 +08:00
shadowcz007	7e726000c7	v0.30.0	2024-06-18 16:57:14 +08:00
shadowcz007	d8dfb292ec	增加 Edit Mask & SD3 示例	2024-06-18 16:55:59 +08:00
shadowcz007	826975241d	Audio Play	2024-06-17 10:50:26 +08:00
shadowcz007	743637ceaf	Update Video.py	2024-06-14 11:50:05 +08:00
shadowcz007	e350c7e31e	CombineAudioVideo、LoadAndCombinedAudio	2024-06-14 11:38:10 +08:00
shadowcz007	66b1e0ab9f	Update __init__.py	2024-06-14 08:17:04 +08:00
shadowcz007	7b0374d110	Update requirements.txt	2024-06-13 09:04:48 +08:00
shadowcz007	e86ef8cbb0	ImageBatchToList、LoadAndCombinedAudio、combine_audio_video、GenerateFramesByCount	2024-06-12 20:54:16 +08:00
shadowcz007	8c901c54bc	Update extension-node-map.json	2024-06-08 17:41:40 +08:00
shadowcz007	408d85691e	v0.29.0 支持把输出显示到comfyui背景（TouchDesigner 风格）	2024-06-08 16:58:21 +08:00
shadowcz007	c66cd6901b	appinfo add performance features Appinfo supports outputting to the background, enhancing the performance features of ComfyUI.	2024-06-08 16:03:10 +08:00
shadowcz007	aeadbc4f6d	fixbug	2024-06-06 15:17:40 +08:00
shadowcz007	224136890e	fixbug	2024-06-06 08:02:51 +08:00
shadowcz007	3669a1e86d	0.28.3	2024-06-01 23:21:33 +08:00
shadowcz007	d588b5b327	Update index.html	2024-05-29 22:37:52 +08:00
shadowcz007	b705679098	Update index.html	2024-05-29 21:49:32 +08:00
shadowcz007	f71a0b0da5	Update index.html	2024-05-29 20:20:19 +08:00
shadowcz007	ebc2c76b6b	fixbug	2024-05-25 22:50:19 +08:00
shadow	2e3fff278e	Merge pull request #240 from audioscavenger/patch-1 Update extension-node-map.json	2024-05-24 11:12:17 +08:00
Eric	1f4bc5e089	Update extension-node-map.json i'm the new maintainer, thanks	2024-05-23 16:41:33 -07:00
shadowcz007	52c38b10dd	v0.28.2	2024-05-23 18:19:30 +08:00
shadowcz007	7047aa5456	add video format	2024-05-23 16:59:04 +08:00
shadowcz007	33fe4019f7	Update ui_mixlab.js	2024-05-23 16:43:50 +08:00
shadowcz007	80b9d97690	Update Video.py	2024-05-23 15:58:24 +08:00
shadowcz007	3c3c92723f	Update pyproject.toml	2024-05-23 10:34:36 +08:00
shadowcz007	037bd87006	Update pyproject.toml	2024-05-23 10:26:48 +08:00
shadowcz007	f688310d28	Update Utils.py	2024-05-23 10:14:25 +08:00
shadow	c4d65e7a45	Merge pull request #234 from haohaocreates/publish Add Github Action for Publishing to Comfy Registry	2024-05-22 23:14:33 +08:00
shadow	6f208b710d	Merge pull request #235 from haohaocreates/pyproject Add pyproject.toml for Custom Node Registry	2024-05-22 23:14:17 +08:00
haohaocreates	b599faaf85	Update pyproject.toml desc	2024-05-21 15:24:07 -04:00
haohaocreates	6d991d20dc	chore(publish): Add Github Action for Publishing to Comfy Registry	2024-05-21 19:19:01 +00:00
haohaocreates	c87e0296f6	chore(pyproject): Add pyproject.toml for Custom Node Registry	2024-05-21 19:19:01 +00:00
shadow	16cdb4c5b4	Merge pull request #231 from 295958090/main 修复FloatSlider的bug	2024-05-21 21:57:10 +08:00
Bai Shui	7631b8924d	修复bug	2024-05-21 13:34:29 +08:00
shadowcz007	785d307ff3	Update index.html	2024-05-18 16:13:30 +08:00
shadowcz007	8c713ff35e	Update index.html	2024-05-18 16:08:25 +08:00
shadowcz007	7d80493bef	Update index.html	2024-05-18 16:05:39 +08:00
shadowcz007	bff2760c3d	v0.28.1 修复bug	2024-05-18 11:38:50 +08:00
shadowcz007	a0f8848367	修复当上传新的图片，编辑mask的bug	2024-05-18 11:38:28 +08:00
shadowcz007	5b1cbcd8d5	修复bug	2024-05-16 13:20:06 +08:00
shadowcz007	05857a92d5	v0.28.0 add rembg api & webapp rembg	2024-05-16 11:49:45 +08:00
shadowcz007	6bdc811286	add rembg api & webapp rembg	2024-05-16 11:49:14 +08:00
shadowcz007	469d50a5b8	Update index.html	2024-05-16 09:02:13 +08:00
shadowcz007	ef86904bfb	Update ui_mixlab.js	2024-05-16 09:02:08 +08:00
shadowcz007	d4181ea67c	v0.27.1 fixbug	2024-05-16 08:49:30 +08:00
shadowcz007	1c6d17309f	Update index.html	2024-05-16 08:49:09 +08:00
shadowcz007	db293ec41d	fixbug css	2024-05-16 08:47:17 +08:00
shadowcz007	db8d468f29	0.27.0 增加webapp的mask绘制	2024-05-16 00:11:16 +08:00
shadowcz007	d7d7af7265	add mask edit for webapp	2024-05-16 00:06:42 +08:00
shadowcz007	bd763cadc1	fixbug	2024-05-16 00:05:24 +08:00
shadowcz007	22799fc549	fixbug for mask	2024-05-16 00:05:16 +08:00
shadowcz007	0f231d1271	add minPaint for mask	2024-05-16 00:04:54 +08:00
shadowcz007	8c0c911020	Create LICENSE	2024-05-14 10:02:46 +08:00
shadowcz007	6cb9df700b	增加ComparingTwoFrames、右键image-to-text	2024-05-14 09:59:39 +08:00
shadowcz007	4fdda537b9	Update README.md	2024-05-14 09:57:51 +08:00
shadowcz007	cb6f32465a	Update README.md	2024-05-14 09:55:34 +08:00
shadowcz007	c235e36cb4	Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes	2024-05-14 09:54:27 +08:00
shadowcz007	aaca440a94	Update README.md	2024-05-14 09:54:24 +08:00
shadow	1f57950a29	Update README.md	2024-05-14 09:39:57 +08:00
shadow	3d8855ec72	Update README.md	2024-05-14 09:39:45 +08:00
shadowcz007	ac9231d9f3	Update image_mixlab.js	2024-05-13 21:43:20 +08:00
shadowcz007	37c0a56d89	add ComparingTwoFrames	2024-05-13 21:33:44 +08:00
shadowcz007	010915dac4	add help	2024-05-13 11:37:17 +08:00
shadowcz007	83a8b3b970	Update ui_mixlab.js	2024-05-13 10:58:36 +08:00
shadowcz007	f130202aa1	resizeImage	2024-05-12 22:25:08 +08:00
shadowcz007	c8f6800bcd	add image-to-text :llava-phi-3-mini-gguf	2024-05-12 17:34:53 +08:00
shadowcz007	078f9f5dd4	Update ui_mixlab.js	2024-05-12 00:03:11 +08:00
shadowcz007	c0de178c7d	add re_start	2024-05-11 23:58:05 +08:00
shadowcz007	1c767b538d	set n_gpu_layers	2024-05-11 17:53:18 +08:00
shadowcz007	51aab44b5d	Update ui_mixlab.js	2024-05-11 14:43:35 +08:00
shadowcz007	b8a0d4a67b	Update ui_mixlab.js	2024-05-11 14:17:01 +08:00
shadowcz007	cd0dcfbb8c	v0.25.1	2024-05-11 14:12:00 +08:00
shadow	fcc9e30eae	Update __init__.py	2024-05-11 12:55:14 +08:00
shadowcz007	5f66218a43	修复 sys.stdout.isatty() object has no attribute 'isatty'	2024-05-11 12:28:14 +08:00
shadowcz007	61ef4f9a0f	Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes	2024-05-11 12:13:34 +08:00
shadowcz007	f0a8734b42	修复 sys.stdout.isatty() object has no attribute 'isatty'	2024-05-11 12:13:31 +08:00
shadow	4fe95ef4ec	Update README.md	2024-05-11 08:53:33 +08:00
shadowcz007	2a5148845b	yaml	2024-05-10 14:38:32 +08:00
shadowcz007	8fa562caaf	Update install.bat	2024-05-09 09:55:34 +08:00
shadowcz007	cab5620cd5	Update index.html	2024-05-08 23:42:48 +08:00
shadowcz007	be38d36677	Update index.html	2024-05-08 23:32:20 +08:00
shadowcz007	69236fca89	Update __init__.py	2024-05-08 22:55:42 +08:00
shadowcz007	4a4f376bfd	Update __init__.py	2024-05-08 22:53:39 +08:00
shadowcz007	fd9718fe24	Update __init__.py	2024-05-08 22:50:30 +08:00
shadowcz007	26a6e11212	llama_cpp	2024-05-08 22:42:34 +08:00
shadowcz007	de1a669f6e	Update README.md	2024-05-08 10:11:16 +08:00
shadowcz007	e482c9e5c4	0.25.0 text-to-text for prompt	2024-05-07 21:26:47 +08:00
shadowcz007	4e96a77a41	Update README.md	2024-05-07 21:12:27 +08:00
shadowcz007	3346290e5c	Update ImageNode.py	2024-05-07 20:59:22 +08:00
shadowcz007	dcac593efe	Update install.bat	2024-05-07 12:49:03 +08:00
shadowcz007	7248d0de02	update	2024-05-07 12:14:42 +08:00
shadowcz007	8ad3ce632c	Update index.html	2024-05-07 00:08:35 +08:00
shadowcz007	9398b02562	Update ImageNode.py	2024-05-06 22:13:09 +08:00
shadowcz007	164e4da99d	Update ImageNode.py	2024-05-05 18:04:00 +08:00
shadowcz007	0025ea6119	output defaultImage	2024-05-05 13:16:14 +08:00
shadowcz007	eef53a5165	Update ImageNode.py	2024-05-05 12:59:21 +08:00
shadowcz007	9ea066d948	composite_images add position	2024-05-05 12:20:57 +08:00
shadowcz007	4bd900c4a1	Update __init__.py	2024-05-04 09:44:01 +08:00
shadowcz007	736cd2bebd	兼容旧版comfyui	2024-05-04 09:37:25 +08:00
shadowcz007	cd658c2a60	Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes	2024-05-03 22:08:43 +08:00
shadowcz007	9730658f21	Update __init__.py	2024-05-03 22:08:40 +08:00
shadow	3fa107acb1	Update ui_mixlab.js	2024-05-03 18:35:39 +08:00
shadowcz007	7da22179a0	text-to-text	2024-05-03 00:30:16 +08:00
shadowcz007	fc7b71ee78	Update ui_mixlab.js	2024-05-02 19:02:33 +08:00
shadowcz007	b79573bf1f	Update __init__.py	2024-05-02 19:02:28 +08:00
shadowcz007	a01db6f7c0	Update README.md	2024-05-02 12:09:47 +08:00
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