0.30.2

fixbug
Update Video.py
2024-07-06 00:39:02 +08:00 · 2024-07-06 00:37:52 +08:00 · 2024-07-04 18:19:53 +08:00 · 2024-07-04 17:37:50 +08:00 · 2024-06-23 16:50:06 +08:00 · 2024-06-22 21:44:33 +08:00
120 changed files with 23003 additions and 2242 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.
@@ -1,30 +1,61 @@
-> 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 2.1.2+cu121
 > [Mixlab nodes discord](https://discord.gg/cXs9vZSqeK)

-#### 
+
+##### `最新`：
+
+- 增加 Edit Mask，方便在生成的时候手动绘制 mask [workflow](./workflow/edit-mask-workflow.json)
+
+
+- ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。模型下载后，放置到 `models/llamafile/`
+
+- 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)

 [comfyui-ultralytics-yolo](https://github.com/shadowcz007/comfyui-ultralytics-yolo)

 [comfyui-moondream](https://github.com/shadowcz007/comfyui-moondream)

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

+## 🚀🚗🚚🏃 Workflow-to-APP

-## 🚀🚗🚚🏃 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）

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

 - Support multiple web app switching.
 - Add the AppInfo node, which allows you to transform the workflow into a web app by simple configuration.
 - 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)
@@ -32,59 +63,91 @@
 ![](./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)

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

 ![gpt-workflow.svg](./assets/gpt-workflow.svg)

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

+最新：ChatGPT 节点支持 Local LLM（llama.cpp），Phi3、llama3 都可以直接一个节点运行了。
+
+Model download,move to :`models/llamafile/`
+
+强烈推荐：[Phi-3-mini-4k-instruct-GGUF](https://huggingface.co/lmstudio-community/Phi-3-mini-4k-instruct-GGUF/tree/main)
+
+备选：[llama3_if_ai_sdpromptmkr_q2k](https://hf-mirror.com/impactframes/llama3_if_ai_sdpromptmkr_q2k/tree/main)
+
+> 如果碰到安装失败，可以尝试手动安装
+
+```
+../../../python_embeded/python.exe -s -m pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
+
+../../../python_embeded/python.exe -s -m pip install llama-cpp-python[server]
+
+```
+
+> [Mac](https://llama-cpp-python.readthedocs.io/en/latest/install/macos/)
+
+```
+pip uninstall llama-cpp-python -y
+CMAKE_ARGS="-DLLAMA_METAL=on" pip install -U llama-cpp-python --no-cache-dir
+pip install 'llama-cpp-python[server]'
+```
+
+```
+pip install llama-cpp-python \
+  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
+```

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

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

@@ -98,90 +161,102 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43

 > 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)
+
 ![](./assets/3dimage.png)
 [workflow](./workflow/3D-workflow.json)

+### Image

-### 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. 
+#### LoadImagesToBatch
+
+> 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.

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

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

-### LoadImagesFromURL
+#### LoadImagesFromURL
+
 > Conveniently load images from a fixed address on the internet to ensure that default images in the workflow can be executed.

+#### TextImage

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

 ![](./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

-## 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
+### Other Nodes

 ![main](./assets/all-workflow.svg)
 ![main2](./assets/detect-face-all.png)

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

-
-
 > TransparentImage

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

-
 > FeatheredMask、SmoothMask

 Add edges to an image.

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

-
 > LaMaInpainting

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

-
 > 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.
@@ -192,18 +267,21 @@ An improvement has been made to directly redirect to GitHub to search for missin

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

-
 ### Models

-[Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:models/rembg
+- [Download TripoSR](https://huggingface.co/stabilityai/TripoSR/blob/main/model.ckpt) and place it in `models/triposr`

-[Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : models/lama
+- [Download facebook/dino-vitb16](https://huggingface.co/facebook/dino-vitb16/tree/main) and place it in `models/triposr/facebook/dino-vitb16`

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

-[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:prompt_generator/text2image-prompt-generator
+[Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : `models/lama`

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

@@ -219,40 +297,35 @@ 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
@@ -272,4 +345,3 @@ File / LoadImagesFromPath SaveImageToLocal LoadImagesFromURL
    src="https://api.star-history.com/svg?repos=shadowcz007/comfyui-mixlab-nodes&type=Date"
  />
 </picture>
-
@@ -7,9 +7,32 @@ import urllib
 import hashlib
 import datetime
 import folder_paths
-
+import logging
+import base64,io,re
+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
+
+llama_port=None
+llama_model=""
+llama_chat_format=""
+
+try:
+    from .nodes.ChatGPT import get_llama_models,get_llama_model_path,llama_cpp_client
+    llama_cpp_client("")
+
+except:
+    print("##nodes.ChatGPT ImportError")
+
+
+from .nodes.RembgNode import get_rembg_models,U2NET_HOME,run_briarmbg,run_rembg

 from server import PromptServer

@@ -42,7 +65,7 @@ def is_installed(package, package_overwrite=None):
            print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
    else:
        print(package+'## OK')
-        
+     
 try:
    import OpenSSL
 except ImportError:
@@ -79,6 +102,26 @@ 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()
@@ -263,7 +306,7 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                print("发生异常：", str(e))
    else:
        app_workflow_path=os.path.join(category_path, filename)
-        # print('app_workflow_path: ',app_workflow_path)
+        print('app_workflow_path: ',app_workflow_path)
        try:
            with open(app_workflow_path) as json_file:
                apps = [{
@@ -273,7 +316,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
        except Exception as e:
            print("发生异常：", str(e))
        
-        if len(apps)==1 and category!='' and category!=None:
+        # 这个代码不需要
+        # if len(apps)==1 and category!='' and category!=None:
            data=read_workflow_json_files(category_path)
            
            for item in data:
@@ -413,37 +457,86 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
        runner = web.AppRunner(self.app, access_log=None)
        await runner.setup()

-        if not await check_port_available(address, port):
-            raise RuntimeError(f"Port {port} is already in use.")
+        # if not await check_port_available(address, port):
+        #     raise RuntimeError(f"Port {port} is already in use.")
+        
+        http_success = False
+        http_port=port
+        for i in range(11):  # 尝试最多11次
+            if await check_port_available(address, port + i):
+                http_port = port + i
+                site = web.TCPSite(runner, address, http_port)
+                await site.start()
+                http_success = True
+                break

-        site = web.TCPSite(runner, address, port)
-        await site.start()
+        if not http_success:
+            raise RuntimeError(f"Ports {port} to {port + 10} are all in use.")
+        
+
+        # site = web.TCPSite(runner, address, port)
+        # await site.start()
+
+        ssl_context = None
+        scheme = "http"
+        try:
+            # 跟着本体修改
+            if args.tls_keyfile and args.tls_certfile:
+                scheme = "https"
+                ssl_context = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE)
+                ssl_context.load_cert_chain(certfile=args.tls_certfile,
+                                    keyfile=args.tls_keyfile)
+            else:
+                # 如果没传，则自动创建
+                import ssl
+                crt, key = create_for_https()
+                ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
+                ssl_context.load_cert_chain(crt, key)
+        except:
+            import ssl
+            crt, key = create_for_https()
+            ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
+            ssl_context.load_cert_chain(crt, key)

-        import ssl
-        crt, key = create_for_https()
-        ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
-        ssl_context.load_cert_chain(crt, key)

        success = False
-        for i in range(10):  # 尝试最多10次
-            if await check_port_available(address, port + 1 + i):
-                https_port = port + 1 + i
+        for i in range(11):  # 尝试最多11次
+            if await check_port_available(address, http_port + 1 + i):
+                https_port = http_port + 1 + i
                site2 = web.TCPSite(runner, address, https_port, ssl_context=ssl_context)
                await site2.start()
                success = True
                break

        if not success:
-            raise RuntimeError(f"Ports {port + 1} to {port + 10} are all in use.")
+            raise RuntimeError(f"Ports {http_port + 1} to {http_port + 10} are all in use.")

        if address == '':
-            address = '0.0.0.0'
+            address = '127.0.0.1'
+        if address=='0.0.0.0':
+            address = '127.0.0.1'
+            
        if verbose:
-            print("\033[93mStarting server\n")
-            print("\033[93mTo see the GUI go to: http://{}:{}".format(address, port))
-            print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
+
+            logging.info("\n")
+            logging.info("\n\nStarting server")
+
+            # print("\033[93mStarting server\n")
+            logging.info("\033[93mTo see the GUI go to: http://{}:{}".format(address, http_port))
+            logging.info("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
+ 
+            # print("\033[93mTo see the GUI go to: http://{}:{}".format(address, http_port))
+            # print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
+
        if call_on_start is not None:
-            call_on_start(address, port)
+            try:
+                if scheme=='https':
+                    call_on_start(scheme,address, https_port)
+                else:
+                    call_on_start(scheme,address, http_port)
+            except:
+                call_on_start(address,http_port)
+            

    except Exception as e:
        print(f"Error starting the server: {e}")
@@ -454,7 +547,6 @@ 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
@@ -549,16 +641,65 @@ 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/rembg')
+async def rembg_hander(request):
+    data = await request.json()
+    model=data['model']
+    result={}
+
+    data_base64=remove_base64_prefix(data['base64'])
+    image_data = base64.b64decode(data_base64)
+    
+    # 创建一个BytesIO对象
+    image_stream = io.BytesIO(image_data)
+
+    # 使用PIL Image模块读取图像
+    image = Image.open(image_stream)
+
+    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)
+
+
@routes.post("/mixlab/prompt_result")
 async def post_prompt_result(request):
    data = await request.json()
@@ -577,33 +718,140 @@ async def post_prompt_result(request):
    return web.json_response({"result":res})


+async def start_local_llm(data):
+    global llama_port,llama_model,llama_chat_format
+    if llama_port and llama_model and llama_chat_format:
+        return {"port":llama_port,"model":llama_model,"chat_format":llama_chat_format}
+
+    import threading
+    import uvicorn
+    from llama_cpp.server.app import create_app
+    from llama_cpp.server.settings import (
+                Settings,
+                ServerSettings,
+                ModelSettings,
+                ConfigFileSettings,
+            )
+    
+    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']


-# 扩展api接口
-# from server import PromptServer
-# from aiohttp import web
+    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)
+
+
+    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))
+    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):
+    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)

-# @routes.post('/ws_image')
-# async def my_hander_method(request):
-#     post = await request.post()
-#     x = post.get("something")
-#     return web.json_response({})


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

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

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

-from .nodes.Video import LoadVideoAndSegment


 # 要导出的所有节点及其名称的字典
@@ -626,6 +874,7 @@ NODE_CLASS_MAPPINGS = {
    "ResizeImageMixlab":ResizeImage,
    "LoadImagesFromPath":LoadImagesFromPath,
    "LoadImagesFromURL":LoadImagesFromURL,
+    "LoadImagesToBatch":LoadImages_,
    "TextImage":TextImage,
    "EnhanceImage":EnhanceImage,
    "SvgImage":SvgImage,
@@ -633,6 +882,8 @@ NODE_CLASS_MAPPINGS = {
    "ImageColorTransfer":ImageColorTransfer,
    "ShowLayer":ShowLayer,
    "NewLayer":NewLayer,
+    "ImageListToBatch_":ImageListToBatch_,
+    "CompositeImages_":CompositeImages,
    "SplitImage":SplitImage,
    "CenterImage":CenterImage,
    "GridOutput":GridOutput,
@@ -649,13 +900,11 @@ 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,
    "Color":ColorInput,
@@ -679,100 +928,207 @@ NODE_CLASS_MAPPINGS = {
    "ApplyVisualStylePrompting_":ApplyVisualStylePrompting,
    "StyleAlignedReferenceSampler_": StyleAlignedReferenceSampler,
    "StyleAlignedSampleReferenceLatents_": StyleAlignedSampleReferenceLatents,
-    "StyleAlignedBatchAlign_": StyleAlignedBatchAlign,
-    "LoadVideoAndSegment_":LoadVideoAndSegment,
+    "StyleAlignedBatchAlign_": StyleAlignedBatchAlign, 
    "ListSplit_":ListSplit,
-    "MaskListReplace_":MaskListReplace
-    # "LaMaInpainting":LaMaInpainting
-    # "GamePal":GamePal
+    "MaskListReplace_":MaskListReplace, 
+    "IncrementingListNode_":IncrementingListNode,
+    "PreviewMask_":PreviewMask_,
+    "AudioPlay":AudioPlayNode
 }

 # 一个包含节点友好/可读的标题的字典
 NODE_DISPLAY_NAME_MAPPINGS = {
    "AppInfo":"App Info ♾️MixlabApp",
+    "ScreenShare":"Screen Share ♾️Mixlab",
+    "FloatingVideo":"Floating Video ♾️Mixlab",
+    "TextImage":"Text Image ♾️Mixlab",
+    
    "Color":"Color Input ♾️MixlabApp",
    "TextInput_":"Text Input ♾️MixlabApp",
    "FloatSlider":"Float Slider Input ♾️MixlabApp",
    "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",
+    "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 ♾️Mixlab",
-    "ShowTextForGPT":"Show Text ♾️MixlabApp",
+    
+    
    "MergeLayers":"Merge Layers ♾️Mixlab",
    "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
    "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
    "3DImage":"3DImage ♾️Mixlab",
+    "ImageListToBatch_":"Image List To Batch",
+    "CompositeImages_":"Composite Images ♾️Mixlab",
    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
    "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
    "PromptSlide":"Prompt Slide ♾️Mixlab",
    "PromptGenerate_Mix":"Prompt Generate ♾️Mixlab",
    "ChinesePrompt_Mix":"Chinese Prompt ♾️Mixlab",
    "GamePal":"GamePal ♾️Mixlab",
-    "RembgNode_Mix":"Remove Background",
-    "LoraNames_":"LoraName",
-    "ApplyVisualStylePrompting_":"Apply VisualStyle Prompting",
-    "StyleAlignedReferenceSampler_": "StyleAligned Reference Sampler",
-    "StyleAlignedSampleReferenceLatents_": "StyleAligned Sample Reference Latents",
-    "StyleAlignedBatchAlign_": "StyleAligned Batch Align",
-    "LoadVideoAndSegment_":"Load Video And Segment",
-    "MaskListMerge_":"MaskList to Mask",
-    "ListSplit_":"Split List",
-    "MaskListReplace_":"MaskList Replace",
-    "SwitchByIndex":"List Switch By Index",
+    "RembgNode_Mix":"Remove Background ♾️Mixlab",
+    "LoraNames_":"LoraName ♾️Mixlab",
+    "ApplyVisualStylePrompting_":"Apply VisualStyle Prompting ♾️Mixlab",
+    "StyleAlignedReferenceSampler_": "StyleAligned Reference Sampler ♾️Mixlab",
+    "StyleAlignedSampleReferenceLatents_": "StyleAligned Sample Reference Latents ♾️Mixlab",
+    "StyleAlignedBatchAlign_": "StyleAligned Batch Align ♾️Mixlab",
+    "LoadVideoAndSegment_":"Load Video And Segment ♾️Mixlab",
+    "VideoCombine_Adv":"Video Combine ♾️Mixlab",
+    "MaskListMerge_":"MaskList to Mask ♾️Mixlab",
+    "ListSplit_":"Split List ♾️Mixlab",
+    "MaskListReplace_":"MaskList Replace ♾️Mixlab",
+    "ImageListReplace_":"ImageList Replace ♾️Mixlab",
+    "SwitchByIndex":"List Switch By Index ♾️Mixlab",
    "GLIGENTextBoxApply_Advanced":"GLIGEN TextBox Apply ♾️Mixlab",
-    "GridDisplayAndSave":"Grid Display And Save",
-    "GridInput":"Grid Input",
-    "GridOutput":"Grid Output",
-    "GetImageSize_":"Get Image Size"
+    "GridDisplayAndSave":"Grid Display And Save ♾️Mixlab",
+    "GridInput":"Grid Input ♾️Mixlab",
+    "GridOutput":"Grid Output ♾️Mixlab",
+    "GetImageSize_":"Get Image Size ♾️Mixlab",
+    "IncrementingListNode_":"Create Incrementing Number List ♾️Mixlab",
+    "LoadImagesToBatch":"Load Images(base64) ♾️Mixlab",
+    "PreviewMask_":"Preview Mask",
+    "AudioPlay":"Audio Play ♾️Mixlab",
+
+     "MultiplicationNode":"Math Operation ♾️Mixlab",
 }

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

-print('--------------')
-print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
+
+logging.info('--------------')
+logging.info('\033[91m ### Mixlab Nodes: \033[93mLoaded')
+# print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
+
+try:
+    from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
+    logging.info('ChatGPT.available True')
+
+    NODE_CLASS_MAPPINGS_V = {
+       "ChatGPTOpenAI":ChatGPTNode,
+        "ShowTextForGPT":ShowTextForGPT,
+        "CharacterInText":CharacterInText,
+        "TextSplitByDelimiter":TextSplitByDelimiter,
+    }
+
+    # 一个包含节点友好/可读的标题的字典
+    NODE_DISPLAY_NAME_MAPPINGS_V = {
+        "ChatGPTOpenAI":"ChatGPT & Local LLM ♾️Mixlab",
+        "ShowTextForGPT":"Show Text ♾️MixlabApp",
+        "CharacterInText":"Character In Text",
+        "TextSplitByDelimiter":"Text Split By Delimiter",
+    }
+
+
+    NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_V)
+    NODE_DISPLAY_NAME_MAPPINGS.update(NODE_DISPLAY_NAME_MAPPINGS_V)
+
+except Exception as e:
+    logging.info('ChatGPT.available False')
+
+
+try:
+    from .nodes.edit_mask import EditMask
+    logging.info('edit_mask.available True')
+    NODE_CLASS_MAPPINGS['EditMask']=EditMask
+    NODE_DISPLAY_NAME_MAPPINGS['EditMask']="Edit Mask ♾️Mixlab"
+except Exception as e:
+    logging.info('edit_mask.available False')

 try:
    from .nodes.Lama import LaMaInpainting
-    print('LaMaInpainting.available',LaMaInpainting.available)
+    logging.info('LaMaInpainting.available {}'.format(LaMaInpainting.available))
    if LaMaInpainting.available:
        NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
 except Exception as e:
-    print('LaMaInpainting.available',False,e)
+    logging.info('LaMaInpainting.available False')

 try:
    from .nodes.ClipInterrogator import ClipInterrogator
-    print('ClipInterrogator.available',ClipInterrogator.available)
+    logging.info('ClipInterrogator.available {}'.format(ClipInterrogator.available))
    if ClipInterrogator.available:
        NODE_CLASS_MAPPINGS['ClipInterrogator']=ClipInterrogator
 except Exception as e:
-    print('ClipInterrogator.available',False,e)
+    logging.info('ClipInterrogator.available False')

 try:
    from .nodes.TextGenerateNode import PromptGenerate,ChinesePrompt
-    print('PromptGenerate.available',PromptGenerate.available)
+    logging.info('PromptGenerate.available {}'.format(PromptGenerate.available))
    if PromptGenerate.available:
        NODE_CLASS_MAPPINGS['PromptGenerate_Mix']=PromptGenerate
-    print('ChinesePrompt.available',ChinesePrompt.available)
+    logging.info('ChinesePrompt.available {}'.format(ChinesePrompt.available))
    if ChinesePrompt.available:
        NODE_CLASS_MAPPINGS['ChinesePrompt_Mix']=ChinesePrompt
 except Exception as e:
-    print('TextGenerateNode.available',False,e)
+    logging.info('TextGenerateNode.available False')

 try:
    from .nodes.RembgNode import RembgNode_
-    print('RembgNode_.available',RembgNode_.available)
+    logging.info('RembgNode_.available {}'.format(RembgNode_.available))
    if RembgNode_.available:
        NODE_CLASS_MAPPINGS['RembgNode_Mix']=RembgNode_
 except Exception as e:
-    print('RembgNode_.available',False,e)
+    logging.info('RembgNode_.available False' )

-print('\033[93m -------------- \033[0m')
+
+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_":"Scenes Node", 
+        "GenerateFramesByCount":"Generate Frames By Count"
+    }
+
+
+    NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_V)
+    NODE_DISPLAY_NAME_MAPPINGS.update(NODE_DISPLAY_NAME_MAPPINGS_V)
+
+except:
+    logging.info('Video.available False')
+
+
+try:
+    from .nodes.TripoSR import LoadTripoSRModel,TripoSRSampler,SaveTripoSRMesh
+    logging.info('TripoSR.available')
+
+    NODE_CLASS_MAPPINGS['LoadTripoSRModel_']=LoadTripoSRModel
+    NODE_DISPLAY_NAME_MAPPINGS["LoadTripoSRModel_"]= "Load TripoSR Model"
+    
+    NODE_CLASS_MAPPINGS['TripoSRSampler_']=TripoSRSampler
+    NODE_DISPLAY_NAME_MAPPINGS["TripoSRSampler_"]= "TripoSR Sampler"
+
+    NODE_CLASS_MAPPINGS['SaveTripoSRMesh']=SaveTripoSRMesh
+    NODE_DISPLAY_NAME_MAPPINGS["SaveTripoSRMesh"]= "Save TripoSR Mesh"
+    
+
+except Exception as e:
+    logging.info('TripoSR.available False' )
+
+
+
+
+
+
+logging.info('\033[93m -------------- \033[0m')
@@ -10,6 +10,12 @@ if exist "%python_exec%" (
    for /f "delims=" %%i in (%requirements_txt%) do (
        %python_exec% -s -m pip install "%%i" -i https://pypi.tuna.tsinghua.edu.cn/simple
    )
+
+    %python_exec% -s -m pip install --upgrade --force llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
+
+    %python_exec% -s -m pip install --upgrade --force llama-cpp-python[server]
+    
+
 ) else (
    echo Installing with system Python
    for /f "delims=" %%i in (%requirements_txt%) do (
@@ -1,6 +1,7 @@

-
-
+import os
+import folder_paths
+import torchaudio

 class SpeechRecognition:
    @classmethod
@@ -55,46 +56,60 @@ 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:
+            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=[audio]
+                
+
+        # print(audio)
+        return {"ui": {"audio":results}}
@@ -4,7 +4,18 @@ import urllib.error
 import re,json,os,string,random
 import folder_paths
 import hashlib
-from zhipuai import ZhipuAI
+import codecs,sys
+import importlib.util
+
+
+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):
    hash_object = hashlib.sha1(string.encode())
    unique_hash = hash_object.hexdigest()
@@ -46,13 +57,110 @@ def openai_client(key,url):
    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:
+            from zhipuai import ZhipuAI
+    except:
+        print("#install zhipuai error")
+
    client = ZhipuAI(
        api_key=key, # 填写您的 APIKey
    ) 
    return client


+# 优先使用phi
+def phi_sort(lst):
+    return sorted(lst, key=lambda x: x.lower().count('phi'), reverse=True)
+
+def get_llama_path():
+    try:
+        return folder_paths.get_folder_paths('llamafile')[0]
+    except:
+        return os.path.join(folder_paths.models_dir, "llamafile")
+
+def get_llama_models():
+    res=[]
+
+    model_path=get_llama_path()
+    if os.path.exists(model_path):
+        files = os.listdir(model_path)
+        for file in files:
+            if os.path.isfile(os.path.join(model_path, file)):
+                res.append(file)
+        res=phi_sort(res)
+    return res
+
+llama_modes_list=get_llama_models()
+
+def get_llama_model_path(file_name):
+    model_path=get_llama_path()
+    mp=os.path.join(model_path,file_name)
+    return mp
+
+def llama_cpp_client(file_name):
+    try:
+        if is_installed('llama_cpp')==False:
+            import subprocess
+
+            # 安装
+            print('#pip install llama-cpp-python')
+ 
+            result = subprocess.run([sys.executable, '-s', '-m', 'pip', 
+                                     'install', 
+                                     'llama-cpp-python',
+                                     '--extra-index-url',
+                                     'https://abetlen.github.io/llama-cpp-python/whl/cu121'
+                                     ], capture_output=True, text=True)
+
+            #检查命令执行结果
+            if result.returncode == 0:
+                print("#install success")
+                from llama_cpp import Llama
+
+                subprocess.run([sys.executable, '-s', '-m', 'pip', 
+                                     'install', 
+                                     'llama-cpp-python[server]'
+                                     ], capture_output=True, text=True)
+
+            else:
+                print("#install error")
+            
+        else:
+            from llama_cpp import Llama
+    except:
+        print("#install llama-cpp-python error")
+
+    if file_name:
+        mp=get_llama_model_path(file_name)
+        # file_name=get_llama_models()[0]
+        # model_path=os.path.join(folder_paths.models_dir, "llamafile")
+        # mp=os.path.join(model_path,file_name)
+
+        llm = Llama(model_path=mp, chat_format="chatml",n_gpu_layers=-1,n_ctx=512)
+
+        return llm
+
+    
+

 def chat(client, model_name,messages ):

@@ -60,10 +168,21 @@ def chat(client, model_name,messages ):
        while True:
            try_count += 1
            try:
-                response = client.chat.completions.create(
-                    model=model_name,
-                    messages=messages
-                )
+                if hasattr(client, "chat"):
+                    response = client.chat.completions.create(
+                        model=model_name,
+                        messages=messages
+                    )
+                else:
+                    # 是llama的
+                    response = client.create_chat_completion_openai_v1(
+                        messages=messages,
+                        # response_format={
+                        #     "type": "json_object",
+                        # },
+                        # temperature=0.7,
+                    )
+
                break
            except openai.AuthenticationError as ex:
                raise ex
@@ -72,7 +191,8 @@ def chat(client, model_name,messages ):
                    raise ex
                time.sleep(3)
                continue
-
+        
+        # print(response.keys())
        finish_reason = response.choices[0].finish_reason
        if finish_reason != "stop":
            raise RuntimeError("API finished with unexpected reason: " + finish_reason)
@@ -95,6 +215,29 @@ class ChatGPTNode:

    @classmethod
    def INPUT_TYPES(cls):
+        model_list=llama_modes_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" 
+                    ]
        return {
            "required": {
                "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
@@ -105,16 +248,8 @@ class ChatGPTNode:
                                       "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
                                       "multiline": True,"dynamicPrompts": False
                                       }),
-                "model": ([
-                    "gpt-3.5-turbo",
-                    "gpt-3.5-turbo-0125",
-                    "gpt-35-turbo",
-                    "gpt-3.5-turbo-16k", 
-                    "gpt-3.5-turbo-16k-0613", 
-                    "gpt-4-0613",
-                    "gpt-4-1106-preview",
-                    "glm-4"], 
-                          {"default": "gpt-3.5-turbo"}),
+                "model": ( model_list, 
+                    {"default": model_list[0]}),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
                "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
            },
@@ -137,8 +272,8 @@ class ChatGPTNode:
                                 api_url, 
                                 prompt, 
                                 system_content,
-                                   model, 
-                                   seed,context_size,unique_id = None, extra_pnginfo=None):
+                                model, 
+                                seed,context_size,unique_id = None, extra_pnginfo=None):
        # print(api_key!='',api_url,prompt,system_content,model,seed)
        # 可以选择保留会话历史以维持上下文记忆
        # 或者在此处清除会话历史 self.session_history.clear()
@@ -160,6 +295,9 @@ class ChatGPTNode:
            if model == "glm-4" :
                client = ZhipuAI_client(api_key)  # 使用 Zhipuai 的接口
                print('using Zhipuai interface')
+            elif model in llama_modes_list:
+                #
+                client=llama_cpp_client(model)
            else :
                client = openai_client(api_key,api_url)  # 使用 ChatGPT  的接口
                print('using ChatGPT interface')
@@ -314,8 +452,8 @@ class TextSplitByDelimiter:
    def INPUT_TYPES(s):
        return {
            "required": {
-                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
-                "delimiter":(["newline","comma"],),
+                "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                "delimiter":("STRING", {"multiline": False,"default":",","dynamicPrompts": False}),
                "start_index": ("INT", {
                    "default": 0,
                    "min": 0, #Minimum value
@@ -349,12 +487,13 @@ class TextSplitByDelimiter:
    CATEGORY = "♾️Mixlab/Text"

    def run(self, text,delimiter,start_index,skip_every,max_count):
-        arr=[]
-        if delimiter=='newline':
-            arr = [line for line in text.split('\n') if line.strip()]
-        elif delimiter=='comma':
-            arr = [line for line in text.split(',') if line.strip()]
-        
+         
+        if delimiter=="":
+            arr=[text.strip()]
+        else:
+            delimiter=codecs.decode(delimiter, 'unicode_escape')
+            arr= [line for line in text.split(delimiter) if line.strip()]
+
        arr= arr[start_index:start_index + max_count * (skip_every+1):(skip_every+1)]

        return (arr,)
@@ -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):
@@ -1,6 +1,7 @@
 import numpy as np
 import requests
 import torch
+import torchvision.transforms.v2 as T
 # from PIL import Image, ImageDraw
 from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
 from PIL.PngImagePlugin import PngInfo
@@ -8,12 +9,121 @@ import base64,os,random
 from io import BytesIO
 import folder_paths
 import json,io
+import comfy.utils
 from comfy.cli_args import args
 import cv2
 import string
 import math,glob
 from .Watcher import FolderWatcher

+from itertools import product
+
+
+# 将PIL图片转换为OpenCV格式
+def pil_to_opencv(image):
+    open_cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    return open_cv_image
+
+# 将OpenCV格式图片转换为PIL格式
+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, extension):
+    file_list = []
+    for root, dirs, files in os.walk(directory):
+        for file in files:
+            if file.endswith(extension):
+                file = os.path.splitext(file)[0]
+                file_path = os.path.join(root, file)
+                file_name = os.path.relpath(file_path, directory)
+                file_list.append(file_name)
+    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
+
+    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')
+
+    return bg_image
+
+

 def count_files_in_directory(directory):
    file_count = 0
@@ -51,7 +161,8 @@ 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

@@ -583,7 +694,7 @@ def detect_faces(image):

 def areaToMask(x,y,w,h,image):
    # 创建一个与原图片大小相同的空白图片
-    mask = Image.new('1', image.size)
+    mask = Image.new('L', image.size)

    # 创建一个可用于绘制的对象
    draw = ImageDraw.Draw(mask)
@@ -616,41 +727,126 @@ def areaToMask(x,y,w,h,image):
 #     # bg_image.save("output.jpg")
 #     return bg_image

-def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option):
+
+import cv2
+import numpy as np
+
+# ps的正片叠底
+# 可以基于https://www.cnblogs.com/jsxyhelu/p/16947810.html ，用gpt写python代码
+def multiply_blend(image1, image2):
+    image1=pil_to_opencv(image1)
+    image2=pil_to_opencv(image2)
+    # 将图像转换为浮点型
+    image1 = image1.astype(float)
+    image2 = image2.astype(float)
+    if image1.shape != image2.shape:
+        image1 = cv2.resize(image1, (image2.shape[1], image2.shape[0]))
+        
+    # 归一化图像
+    image1 /= 255.0
+    image2 /= 255.0
+
+    # 正片叠底混合
+    blended = image1 * image2
+
+    # 将图像还原为8位无符号整数
+    blended = (blended * 255).astype(np.uint8)
+
+    blended=opencv_to_pil(blended)
+    return blended
+
+# # 读取图像
+# image1 = cv2.imread('1.png')
+# image2 = cv2.imread('3.png')
+
+# # 进行正片叠底混合
+# result = 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):
    # 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)

-    # 在底图上粘贴图层
-    bg_image.paste(layer_image, (x, y), mask=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.paste(layer_image, (x, y), mask=mask)
+        bg_image = multiply_blend(bg_image_white, bg_image)
+        bg_image = bg_image.convert("RGBA")
+    else:
+        transparent_img = Image.new("RGBA", layer_image.size, (255, 255, 255, 0))
+        # 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
+    if img.height % 2 != 0:
+        img = img.resize((img.width, img.height + 1))
+
+    # 检查图像的宽度是否是2的倍数，如果不是，则调整宽度
+    if img.width % 2 != 0:
+        img = img.resize((img.width + 1, img.height))
+
+    return img

 # TODO 几个像素点的底
 def resize_image(layer_image, scale_option, width, height,color="white"):
@@ -681,58 +877,45 @@ def resize_image(layer_image, scale_option, width, height,color="white"):
        resized_image = Image.new("RGB", (width, height), color=color)
        resized_image.paste(layer_image.resize((new_width, new_height)), ((width - new_width) // 2, (height - new_height) // 2))
        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, 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"
@@ -741,49 +924,46 @@ 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 + spacing
+            y = padding
+        max_width = x
    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 + spacing
+            x = padding
+        max_height = y
+
+    # 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
@@ -1082,6 +1262,42 @@ class EnhanceImage:



+class LoadImages_:
+    @classmethod
+    def INPUT_TYPES(s):
+        
+        return {"required":
+                    {"images": ("IMAGEBASE64",), 
+                     },
+                }
+
+    CATEGORY = "♾️Mixlab/Image"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_image"
+    def load_image(self, images):
+
+        # print(images)
+        ims=[]
+        for im in images['base64']:
+            image = base64_to_image(im)
+            image=image.convert('RGB')
+            image=pil2tensor(image)
+            ims.append(image)
+
+        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 (image1,)
+        
+ 
+
+
 ''' 
 ("STRING",{"multiline": False,"default": "Hello World!"})
 对应 widgets.js 里：
@@ -1274,7 +1490,7 @@ class ImageCropByAlpha:



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

 class TextImage:
    @classmethod
@@ -1282,7 +1498,7 @@ class TextImage:
        return {"required": { 
            
                    "text": ("STRING",{"multiline": True,"default": "龍馬精神迎新歲","dynamicPrompts": False}),
-                    "font_path": ("STRING",{"multiline": False,"default": FONT_PATH,"dynamicPrompts": False}),
+                    "font": (get_files_with_extension(FONT_PATH,'.ttf'),),#后缀为 ttf
                    "font_size": ("INT",{
                                "default":100, 
                                "min": 100, #Minimum value
@@ -1297,6 +1513,13 @@ class TextImage:
                                "step": 1, #Slider's step
                                "display": "number" # Cosmetic only: display as "number" or "slider"
                                }), 
+                    "padding": ("INT",{
+                                "default":8, 
+                                "min": 0, #Minimum value
+                                "max": 200, #Maximum value
+                                "step": 1, #Slider's step
+                                "display": "number" # Cosmetic only: display as "number" or "slider"
+                                }), 
                    "text_color":("STRING",{"multiline": False,"default": "#000000","dynamicPrompts": False}),
                    "vertical":("BOOLEAN", {"default": True},),
                    "stroke":("BOOLEAN", {"default": False},),
@@ -1304,7 +1527,7 @@ class TextImage:
                }
    
    RETURN_TYPES = ("IMAGE","MASK",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_NAMES = ("image","mask",)

    FUNCTION = "run"

@@ -1313,11 +1536,14 @@ 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,padding,text_color,vertical,stroke):
        
-        # text_list=list(text)
+        font_path=os.path.join(FONT_PATH,font+'.ttf')
+
+        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,padding)
        
        img=pil2tensor(img)
        mask=pil2tensor(mask)
@@ -1431,7 +1657,7 @@ class Image3D:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Image"
+    CATEGORY = "♾️Mixlab/3D"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,False,False,False,)
@@ -1541,6 +1767,66 @@ class FaceToMask:
        return (mask,)


+class CompositeImages:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": { 
+                    "foreground": (any_type,),
+                    "mask":("MASK",),
+                    "background": ("IMAGE",),
+                    },
+             "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"
+                            }), 
+                    }
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    # OUTPUT_IS_LIST = (True,)
+
+    # 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),)
+
+    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:
    @classmethod
    def INPUT_TYPES(s):
@@ -1636,7 +1922,7 @@ class NewLayer:
                    "display": "number" # Cosmetic only: display as "number" or "slider"
                }),
                "scale_option": (["width","height",'overall'],),
-                "image": ("IMAGE",),
+                "image": (any_type,),
            },
             "optional":{
                    "mask": ("MASK",{"default": None}),
@@ -1693,21 +1979,20 @@ def createMask(image,x,y,w,h):
    # mask.save("mask.png")
    return mask

-
 def splitImage(image, num):
    width, height = image.size

    num_rows = int(num ** 0.5)
    num_cols = int(num / num_rows)
    
-    grid_width = width // num_cols
-    grid_height = height // num_rows
+    grid_width = int(width // num_cols)
+    grid_height = int(height // num_rows)

    grid_coordinates = []
    for i in range(num_rows):
        for j in range(num_cols):
-            x = j * grid_width
-            y = i * grid_height
+            x = int(j * grid_width)
+            y = int(i * grid_height)
            grid_coordinates.append((x, y, grid_width, grid_height))

    return grid_coordinates
@@ -2039,12 +2324,16 @@ class GridOutput:
    def INPUT_TYPES(s):
        return { 
            "required": {
-                "grid": ("_GRID",)
-            }
+                "grid": ("_GRID",),
+                
+            },
+            "optional":{
+                  "bg_image":("IMAGE",)
+                }
                }
    
-    RETURN_TYPES = ("INT","INT","INT","INT",)
-    RETURN_NAMES = ("x","y","width","height",)
+    RETURN_TYPES = ("INT","INT","INT","INT","MASK",)
+    RETURN_NAMES = ("x","y","width","height","mask",)

    FUNCTION = "run"

@@ -2053,9 +2342,29 @@ class GridOutput:
    INPUT_IS_LIST = False
    # OUTPUT_IS_LIST = (True,)

-    def run(self,grid):
+    def run(self,grid,bg_image=None):
        x,y,w,h=grid
-        return (x,y,w,h,)
+        x=int(x)
+        y=int(y)
+        w=int(w)
+        h=int(h)
+
+        masks=[]
+        if bg_image!=None:
+            for i in range(len(bg_image)):
+                im=bg_image[i]
+                #增加输出mask
+                im=tensor2pil(im)
+                mask=areaToMask(x,y,w,h,im)
+                mask=pil2tensor(mask)
+                masks.append(mask)
+        out=None
+        if len(masks)>0:
+            out = torch.cat(masks, dim=0)
+        return (x,y,w,h,out,)
+
+
+

 class ShowLayer:
    @classmethod
@@ -2147,10 +2456,14 @@ class MergeLayers:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { 
-            "layers": ("LAYER",),
-            "images": ("IMAGE",),
-                             },
-
+                            "layers": ("LAYER",),
+                            "images": ("IMAGE",),
+                                            },
+            "optional":{
+                           
+                            "is_multiply_blend":  ("BOOLEAN", {"default": False}),
+                            
+                    }
                }
    
    RETURN_TYPES = ("IMAGE","MASK",)
@@ -2163,11 +2476,12 @@ class MergeLayers:
    INPUT_IS_LIST = True
    # OUTPUT_IS_LIST = (False,)

-    def run(self,layers,images):
+    def run(self,layers,images,is_multiply_blend):

        bg_images=[]
        masks=[]
- 
+        
+        is_multiply_blend=is_multiply_blend[0]
        # print(len(images),images[0].shape)
        # 1  torch.Size([2, 512, 512, 3])
        # 4  torch.Size([1, 1024, 768, 3])
@@ -2198,6 +2512,8 @@ class MergeLayers:
                    
                    layer_image=tensor2pil(image)
                    layer_mask=tensor2pil(mask)
+                    # t=layer_image.convert("RGBA")
+                    # t.save('test.png') 如果layerimage传入的是rgba，则是透明的
                    bg_image=merge_images(bg_image,
                                        layer_image,
                                        layer_mask,
@@ -2205,7 +2521,8 @@ class MergeLayers:
                                        layer['y'],
                                        layer['width'],
                                        layer['height'],
-                                        layer['scale_option']
+                                        layer['scale_option'],
+                                        is_multiply_blend
                                        )
                    
                    final_mask=merge_images(final_mask,
@@ -2612,12 +2929,72 @@ 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):
        return {"required": {
                "source": ("IMAGE",),
                "target": ("IMAGE",),
+                "weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
                },
                }
    
@@ -2631,25 +3008,45 @@ class ImageColorTransfer:
    CATEGORY = "♾️Mixlab/Color"

    # 输入是否为列表
-    INPUT_IS_LIST = True
+    # INPUT_IS_LIST = True

    # 输出是否为列表
-    OUTPUT_IS_LIST = (True,)
+    # OUTPUT_IS_LIST = (True,)

-    def run(self,source,target):
+    def run(self,source,target,weight):

        res=[]

-        target=target[0][0]
-        print(target.shape)
-        target=tensor2pil(target)
+        #batch-list
+        source_list = [source[i:i + 1, ...] for i in range(source.shape[0])]
+        target_list = [target[i:i + 1, ...] for i in range(target.shape[0])]

-        for ims in source:
-            for im in ims:
-                image=tensor2pil(im)
-                image=color_transfer(image,target)
-                image=pil2tensor(image)
-                res.append(image)
+        # 长度纠正为相等
+        if len(target_list) != len(source_list):
+            target_list = target_list * (len(source_list) // len(target_list)) + target_list[:len(source_list) % len(target_list)]
+        
+        for i in range(len(source_list)):
+            target=target_list[i]
+            source=source_list[i]
+            target=tensor2pil(target)
+
+            image=tensor2pil(source)
+
+            image_res=color_transfer(image,target)
+
+            # weight Blend image # contributors:@ning
+            blend_mask = Image.new(mode="L", size=image.size,
+                                    color=(round(weight * 255)))
+            blend_mask = ImageOps.invert(blend_mask)
+            img_result = Image.composite(image, image_res, blend_mask)
+            del image, image_res, blend_mask
+            
+            img_result=pil2tensor(img_result)
+
+            res.append(img_result)
+
+        # list - batch
+        res=torch.cat(res, dim=0)

        return (res,)

@@ -2756,3 +3153,52 @@ class SaveImageToLocal:
            counter += 1

        return ()
+
+
+class ImageBatchToList_:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"image_batch": ("IMAGE",), }}
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image_list",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Image"
+
+    def run(self, image_batch):
+        images = [image_batch[i:i + 1, ...] for i in range(image_batch.shape[0])]
+        return (images, )
+
+class ImageListToBatch_:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "run"
+    INPUT_IS_LIST = True
+    CATEGORY = "♾️Mixlab/Image"
+
+    def run(self, images):
+        shape = images[0].shape[1:3]
+        out = []
+
+        for i in range(len(images)):
+            img = images[i].permute([0,3,1,2])
+            if images[i].shape[1:3] != shape:
+                transforms = T.Compose([
+                    T.CenterCrop(min(img.shape[2], img.shape[3])),
+                    T.Resize((shape[0], shape[1]), interpolation=T.InterpolationMode.BICUBIC),
+                ])
+                img = transforms(img)
+            out.append(img.permute([0,2,3,1]))
+
+        out = torch.cat(out, dim=0)
+
+        return (out,)
@@ -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",)
@@ -2,16 +2,14 @@
 import scipy.ndimage
 import torch

-from nodes import MAX_RESOLUTION
-
 import numpy as np 
 # from PIL import Image, ImageDraw
 from PIL import Image, ImageOps 

 from comfy.cli_args import args
-import cv2
-
-
+import cv2,os
+from nodes import MAX_RESOLUTION, SaveImage, common_ksampler
+import folder_paths,random

 # Tensor to PIL
 def tensor2pil(image):
@@ -71,6 +69,35 @@ def combine(destination, source, x, y):

    return output

+
+class PreviewMask_(SaveImage):
+    def __init__(self):
+        self.output_dir = folder_paths.get_temp_directory()
+        self.type = "temp"
+        self.prefix_append =''.join(random.choice("abcdehijklmnopqrstupvxyzfg") for x in range(5))
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                                "mask": ("MASK",), 
+                            }
+            }
+    
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/Mask"
+
+    # 运行的函数
+    def run(self, mask ):
+        img=tensor2pil(mask)
+        img=img.convert('RGB')
+        img=pil2tensor(img) 
+        return self.save_images(img, 'temp_', None,  None)
+
+
 class OutlineMask:

    @classmethod
@@ -108,32 +135,32 @@ class MaskListReplace:
            "mask_replace": ("MASK",),
            "start_index":("INT", {"default": 0, "min": 0, "step": 1}),
            "end_index":("INT", {"default": 0, "min": 0, "step": 1}),
-            "reverse": ("BOOLEAN", {"default": False}),
+            "invert": ("BOOLEAN", {"default": False}),
        }
        }

    RETURN_TYPES = ("MASK",)
    FUNCTION = "run"
-    CATEGORY = "♾️Mixlab/Mask"
+    CATEGORY = "♾️Mixlab/Video"

    INPUT_IS_LIST = True
    OUTPUT_IS_LIST = (True,)

-    def run(self, masks,mask_replace,start_index,end_index,reverse):
+    def run(self, masks,mask_replace,start_index,end_index,invert):
        mask_replace=mask_replace[0]
        start_index=start_index[0]
        end_index=end_index[0]
-        reverse=reverse[0]
+        invert=invert[0]
        
        new_masks=[]
        for i in range(len(masks)):
            if i>=start_index and i<=end_index:
-                if reverse:
+                if invert:
                    new_masks.append(masks[i])
                else:
                    new_masks.append(mask_replace)
            else:
-                if reverse:
+                if invert:
                    new_masks.append(mask_replace)
                else:
                    new_masks.append(masks[i])
@@ -580,16 +580,17 @@ class GLIGENTextBoxApply_Advanced:
    RETURN_NAMES = ("CONDITIONING","label",)

    FUNCTION = "run"
-    INPUT_IS_LIST = True
+    # INPUT_IS_LIST = True
    CATEGORY = "♾️Mixlab/Prompt"

    def run(self, conditioning, clip, gligen_textbox_model, grids, labels, index,max_size,random_shuffle,seed=0):
-        conditioning=conditioning[0]
-        clip=clip[0]
-        gligen_textbox_model=gligen_textbox_model[0]
-        index=index[0]
-        max_size=max_size[0]
-        random_shuffle=random_shuffle[0]
+        # print('grids',grids)
+        # conditioning=conditioning[0]
+        # clip=clip[0]
+        # gligen_textbox_model=gligen_textbox_model[0]
+        # index=index[0]
+        # max_size=max_size[0]
+        # random_shuffle=random_shuffle[0]

        texts=labels
        
@@ -618,7 +619,7 @@ class GLIGENTextBoxApply_Advanced:
                text=texts[i]
                grid=grids[i]
                x,y,width,height=grid
-                print(text)
+                # print(text)
                cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
                position_params =position_params+ [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]

@@ -628,6 +629,7 @@ class GLIGENTextBoxApply_Advanced:
                prev = n[1]['gligen'][2]

            n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params)
+            # print('gligen',n)
            c.append(n)
        
        #  下面这个写法有bug
@@ -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)
@@ -0,0 +1,180 @@
+import sys
+from os import path
+sys.path.insert(0, path.dirname(__file__))
+from PIL import Image
+import numpy as np
+import torch
+
+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
+
+
+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
+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 fill_background(image):
+    im = np.array(image).astype(np.float32) / 255.0
+    im = im[:, :, :3] * im[:, :, 3:4] + (1 - im[:, :, 3:4]) * 0.5
+    im = Image.fromarray((im * 255.0).astype(np.uint8))
+    return im
+
+
+class LoadTripoSRModel:
+    def __init__(self):
+        self.initialized_model = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                # "model": (get_filename_list("checkpoints"),),
+                "chunk_size": ("INT", {"default": 8192, "min": 0, "max": 10000})
+            }
+        }
+
+    RETURN_TYPES = ("TRIPOSR_MODEL",)
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/3D/TripoSR"
+
+    def run(self, chunk_size):
+        device = get_torch_device()
+
+        if not torch.cuda.is_available():
+            device = "cpu"
+
+        if not self.initialized_model:
+            # triposr_model_path
+            print("#Loading TripoSR model",triposr_model_path)
+            self.initialized_model = TSR.from_pretrained_custom(
+                weight_path=triposr_model_path,
+                config_path=path.join(path.dirname(__file__), "tsr/config.yaml")
+            )
+            self.initialized_model.renderer.set_chunk_size(chunk_size)
+            self.initialized_model.to(device)
+
+        return (self.initialized_model,)
+
+
+class TripoSRSampler:
+    def __init__(self):
+        self.initialized_model = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("TRIPOSR_MODEL",),
+                "image": ("IMAGE",),
+                "resolution": ("INT", {"default": 256, "min": 128, "max": 12288}),
+                "threshold": ("FLOAT", {"default": 25.0, "min": 0.0, "step": 0.01}),
+                "device":(["auto","cpu"],), 
+            },
+            "optional": {
+                "mask": ("MASK",)
+            }
+        }
+
+    RETURN_TYPES = ("MESH",)
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/3D/TripoSR"
+
+    def run(self, model, image, resolution, threshold,device='auto', mask=None):
+
+        reference_image=image
+        reference_mask=mask
+
+        device = get_torch_device()
+
+        if not torch.cuda.is_available():
+            device = "cpu"
+
+        if device=='cpu':
+            device = "cpu"
+
+        print('#TripoSRSampler device',device)
+
+        to_images=[]
+
+        for i in range(len(reference_image)):
+
+            image = reference_image[i]
+            
+            if reference_mask is not None:
+                mask = reference_mask[i].unsqueeze(2)
+                image = torch.cat((image, mask), dim=2).detach().cpu().numpy()
+                image = Image.fromarray(np.clip(255. * image, 0, 255).astype(np.uint8))
+                image = fill_background(image)
+            else:
+                image = tensor2pil(image)
+
+            image = image.convert('RGB')
+
+            to_images.append(image)
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(to_images))
+        def callback(c):
+            pbar.update(1)    
+
+        scene_codes = model(to_images, device)
+        meshes = model.extract_mesh(scene_codes, resolution=resolution, threshold=threshold,callback=callback)
+
+        del model
+        return (meshes,)
+
+
+class SaveTripoSRMesh:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mesh": ("MESH",),
+                # "format":(["glb","obj"],), 
+                "filename_prefix":("STRING", {"multiline": False,"default": "TripoSR_"})
+            }
+        }
+
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/3D/TripoSR"
+
+    def run(self, mesh,filename_prefix):
+        format='glb'
+        saved = list()
+        full_output_folder, filename, counter, subfolder, filename_prefix = get_save_image_path(filename_prefix,
+                                                                                                get_output_directory())
+
+        for (index, single_mesh) in enumerate(mesh):
+            filename_with_batch_num = filename.replace("%batch_num%", str(index))
+            file = f"{filename_with_batch_num}_{counter:05}_.{format}"
+            single_mesh.apply_transform(np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]))
+            single_mesh.export(path.join(full_output_folder, file))
+            saved.append({
+                "filename": file,
+                "type": "output",
+                "subfolder": subfolder
+            })
+
+        return {"ui": {"mesh": saved}}
+
+
+
@@ -8,6 +8,10 @@ import matplotlib.font_manager as fm
 import torch
 import importlib.util

+def create_incrementing_list(min_value, max_value, step, count):
+    l1 = [int(min_value + i * step) for i in range(count) if min_value + i * step <= max_value]
+    l2 = [float(min_value + i * step) for i in range(count) if min_value + i * step <= max_value]
+    return (l1,l2)

 def split_list(lst, chunk_size, transition_size):
    result = []
@@ -154,7 +158,6 @@ class ColorInput:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { 
-             
                    "color":("TCOLOR",), 
                             },
                }
@@ -281,7 +284,7 @@ class FloatSlider:
                }
    
    RETURN_TYPES = ("FLOAT",) 
-
+    RETURN_NAMES = ('FLOAT',)
    FUNCTION = "run"

    CATEGORY = "♾️Mixlab/Input"
@@ -294,9 +297,7 @@ class FloatSlider:
            number = min_value
        elif number > max_value:
            number = max_value
-        scaled_number = (number - min_value) / (max_value - min_value)
-        return (scaled_number,)
-    
+        return (number,)    
  
 class IntNumber:
    @classmethod
@@ -406,6 +407,61 @@ class TextInput:
       
        return (text,)
    
+
+class IncrementingListNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "min_value": ("FLOAT", {
+                        "default": 0, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                    "max_value": ("FLOAT", {
+                        "default": 10, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                    "step": ("FLOAT", {
+                        "default": 0, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                    "count": ("INT", {
+                        "default": 1, 
+                        "min": 1, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step":1, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    })
+                             },
+                             "optional":{
+                    "seed":("INT", {"default": -1, "min": -1, "max": 1000000}), 
+                    
+                },
+                }
+    
+    RETURN_TYPES = ("INT","FLOAT",) 
+    RETURN_NAMES = ('int_list','float_list',)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Video"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,True,)
+
+    def run(self,min_value,max_value,step,count,seed):
+        print('create_incrementing_list',seed)
+        l1,l2=create_incrementing_list(min_value,max_value,step,count)
+        return (l1,l2,)
+    
 # 接收一个值，然后根据字符串或数值长度计算延迟时间，用户可以自定义延迟"字/s"，延迟之后将转化

 import comfy.samplers
@@ -510,7 +566,7 @@ class AppInfo:
                             },

                "optional":{
-                    "IMAGE": ("IMAGE",),
+                    "image": ("IMAGE",),
                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
                    "version":("INT", {
                        "default": 1, 
@@ -538,12 +594,12 @@ class AppInfo:
    INPUT_IS_LIST = True
    # OUTPUT_IS_LIST = (True,)

-    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
+    def run(self,name,input_ids,output_ids,image,description,version,share_prefix,link,category,auto_save):
        name=name[0]
        
        im=None
-        if IMAGE:
-            im=IMAGE[0][0]
+        if image:
+            im=image[0][0]
            #TODO batch 的方式需要处理
            im=create_temp_file(im)
        # image [img,] img[batch,w,h,a] 列表里面是batch，
@@ -585,14 +641,14 @@ class SwitchByIndex:
        }

    RETURN_TYPES = (any_type,"INT",)
-    RETURN_NAMES = ("C","count",)
+    RETURN_NAMES = ("list", "count",)

    FUNCTION = "run"

    CATEGORY = "♾️Mixlab/Utils"

    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,False,)
+    OUTPUT_IS_LIST = (True, False,)

    def run(self, A=[],B=[],index=-1,flat='on'):

@@ -613,9 +669,9 @@ class SwitchByIndex:
            try:
                C=[C[index]]
            except Exception as e:
-                C=[]
+                C=[C[-1]] #最后一个

-        return (C,len(C),)
+        return (C, len(C),)

 class ListSplit:
    @classmethod
@@ -755,9 +811,13 @@ class TESTNODE_:

    def run(self,ANY):
        print(type(ANY))
-        print(ANY[0].shape)
-        img= tensor2pil(ANY[0])
-        print(img.size)
+        try:
+            print(ANY[0].shape)
+            img= tensor2pil(ANY[0])
+            print(img.size)
+        except:
+            print('')
+        
        # data=ANY
        list_stats = ListStatistics()

@@ -4,23 +4,213 @@ import json
 import subprocess
 import shutil
 import re
-import time
+import time,math
 import numpy as np
 from typing import List
 import torch
 from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
-import cv2
+import cv2,random,string
 from pathlib import Path

 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)
+    # Generate a random string of lowercase letters and digits
+    random_string = ''.join(random.choices(string.ascii_lowercase + string.digits, k=8))
+    # Create the folder name by combining the random string and the filename
+    folder_name = random_string + '_' + filename
+    # Create the full folder path by joining the directory and the folder name
+    folder_path = os.path.join(directory, folder_name)
+    return folder_path
+
+def create_folder(directory,video_path):
+    folder_path = generate_folder_name(directory,video_path)
+    os.makedirs(folder_path)
+    return folder_path
+
+
+def split_video(video_path, video_segment_frames, transition_frames, output_dir):
+    # 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)
+    
+    # 计算每个视频片段的总帧数，包括过渡帧
+    segment_total_frames = video_segment_frames + transition_frames
+    
+    # 计算可以分割的片段数量，向上取整
+    num_segments = (total_frames + transition_frames - 1) // segment_total_frames
+    
+    vs=[]
+    # 计算每个片段的起始帧和结束帧
+    start_frame = 0
+    for i in range(num_segments):
+        # 计算当前片段的结束帧，注意最后一个片段可能没有过渡帧
+        end_frame = min(start_frame + segment_total_frames, total_frames)
+        
+        # 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"
+
+        fourcc = cv2.VideoWriter_fourcc(*'XVID')
+        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:
+                segment_video.write(frame)
+            else:
+                break  # 如果读取失败，则退出循环
+        
+        # 更新起始帧为下一个片段的起始位置
+        start_frame = end_frame + transition_frames
+        vs.append(segment_video_path)
+    
+    # 释放视频捕获对象
+    video_capture.release()
+    # print(vs)
+    return (vs,total_frames,fps)
+

 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"]
 )
@@ -35,6 +225,67 @@ if ffmpeg_path is None:
        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))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+def count_files(directory):
+    count = 0
+    for root, dirs, files in os.walk(directory):
+        count += len(files)
+    return count
+
+def create_temp_file(image):
+    output_dir = folder_paths.get_temp_directory()
+
+    c=count_files(output_dir)
+
+    (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('temp_', output_dir)
+
+    
+    image=tensor2pil(image)
+ 
+    image_file = f"{filename}_{c}_{counter:05}.png"
+     
+    image_path=os.path.join(full_output_folder, image_file)
+
+    image.save(image_path,compress_level=4)
+
+    return [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type": "temp"
+                }]
+
+
 def split_list(lst, chunk_size, transition_size):
    result = []
    for i in range(0, len(lst), chunk_size):
@@ -50,7 +301,96 @@ def split_list(lst, chunk_size, transition_size):
 # result = split_list(images, chunk_size, transition_size)
 # print(result)

+class ImageListReplace:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "images": ("IMAGE",),
+            "start_index":("INT", {"default": 0, "min": 0, "step": 1}),
+            "end_index":("INT", {"default": 0, "min": 0, "step": 1}),
+            "invert": ("BOOLEAN", {"default": False}),
+        },
+         "optional":{
+                    "image_replace": ("IMAGE",),
+                    "images_replace": ("IMAGE",),
+                    
+                    }
+        }

+    RETURN_TYPES = ("IMAGE","IMAGE",)
+    RETURN_NAMES = ("images","select_images",)
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/Video"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,True,)
+
+    def run(self, images,start_index=[0],end_index=[0],invert=[False],image_replace=None,images_replace=None):
+        start_index=start_index[0]
+        end_index=end_index[0]
+        invert=invert[0]
+
+        image_rs=[]
+
+        if image_replace!=None:
+            for i in range(end_index-start_index+1):
+                image_rs.append(image_replace[0])
+
+        if images_replace!=None:
+            image_rs=images_replace
+
+        # 如果image replace 为空
+        if image_replace==None and images_replace==None:
+            # print('如果image replace 为空',images[0])
+            # [[tensor(
+            # tensor([[[[0.
+            first_image=tensor2pil(images[0][0])
+            width, height = first_image.size
+            image_replace=Image.new("RGB", (width, height), (0, 0, 0))
+            image_replace=pil2tensor(image_replace)
+            for i in range(end_index-start_index+1):
+                image_rs.append(image_replace)
+
+        
+        new_images=[]
+        select_images=[]
+        k=0
+        for i in range(len(images)):
+            if i>=start_index and i<=end_index:
+                if invert:
+                    new_images.append(images[i])
+                else:
+                    new_images.append(image_rs[k])
+                    select_images.append(images[i])
+                    k+=1
+            else:
+                if invert:
+                    new_images.append(image_rs[k])
+                    select_images.append(images[i])
+                    k+=1
+                else:
+                    new_images.append(images[i])
+
+        imss=[]
+        # print(len(images))
+        for i in range(len(images)):
+            t=images[i][0]
+            t=tensor2pil(t)
+            t = t.convert("RGB")
+            original_width, original_height = t.size
+            scale = 300 / original_width
+            new_height = int(original_height * scale)
+            t = t.resize((300, new_height))
+
+            ims=create_temp_file(pil2tensor(t))
+            imss.append(ims[0])
+
+        # image_replace=create_temp_file(image_replace)
+
+        return {"ui":{"_images": imss},"result": (new_images,select_images,)}
+
+# The code is based on ComfyUI-VideoHelperSuite modification.
 class LoadVideoAndSegment:
    @classmethod
    def INPUT_TYPES(s):
@@ -64,17 +404,17 @@ 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}), 
                     },}

    CATEGORY = "♾️Mixlab/Video"

-    RETURN_TYPES = ("IMAGE", "INT",)
-    RETURN_NAMES = ("image_batch", "frame_count",)
+    RETURN_TYPES = ("SCENE_VIDEO","INT", "INT","INT",)
+    RETURN_NAMES = ("scenes_video","scenes_count","frame_count","fps",)
    FUNCTION = "load_video"
    OUTPUT_NODE = True
-    OUTPUT_IS_LIST = (True,False,)
+    OUTPUT_IS_LIST = (True,False,False,False,)


    def is_gif(self, filename):
@@ -131,70 +471,34 @@ class LoadVideoAndSegment:
        return (images, frames_added)

    def load_video(self, video,video_segment_frames,transition_frames ):
-        frame_load_cap=0
-        skip_first_frames=0
-        # 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
-        video_path = folder_paths.get_annotated_filepath(video)
-        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)]
+        video_path = folder_paths.get_annotated_filepath(video)
+        
+        # temp path 
+        tp=folder_paths.get_temp_directory()
+        basename = os.path.basename(video_path)  # 获取文件名
+        name_without_extension = os.path.splitext(basename)[0]  # 去掉文件后缀

-        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)
+        folder_path = create_folder(tp,name_without_extension)
+        
+        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)

-        imgs=split_list(images,video_segment_frames,transition_frames)
+        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 (imgs, len(imgs))
+        return (scenes_video,len(scenes_video), total_frames,fps,)

    @classmethod
    def IS_CHANGED(s, video, **kwargs):
@@ -210,5 +514,460 @@ 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)
+
+        return (audio_file, {
+                "filename": audio_file_name,
+                "subfolder": "",
+                "type": "output",
+                "audio_path":audio_file
+                } ,)
+
+class CombineAudioVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+       
+        return {"required": {
+                     "video_file_path": ("STRING",  {"forceInput": True}),
+                     "audio_file_path": ("STRING",  {"forceInput": True}), 
+                     },
+                }
+
+    CATEGORY = "♾️Mixlab/Video"
+
+    OUTPUT_NODE = True
+    FUNCTION = "run" 
+    RETURN_TYPES = ()
+    RETURN_NAMES = ()
+
+    def run(self,video_file_path, audio_file_path):
+
+        output_dir = folder_paths.get_output_directory()
+
+        counter=get_new_counter(output_dir,'video_final_')
+        
+        # 获取文件名和扩展名
+        base, ext = os.path.splitext(video_file_path)
+
+        v_file = f"video_final_{counter:05}{ext}"
+        
+        v_file_path=os.path.join(output_dir, v_file)
+
+        combine_audio_video(audio_file_path,video_file_path,v_file_path)
+
+        previews = [
+            {
+                "filename": v_file,
+                "subfolder": "",
+                "type": "output",
+                "format": get_mime_type(v_file),
+            }
+        ]
+        return {"ui": {"gifs": previews}}
+
+# The code is based on ComfyUI-VideoHelperSuite modification.
+class VideoCombine_Adv:
+    @classmethod
+    def INPUT_TYPES(s):
+        #Hide ffmpeg formats if ffmpeg isn't available
+        if ffmpeg_path is not None:
+            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",),
+                "frame_rate": (
+                    "INT",
+                    {"default": 8, "min": 1, "step": 1},
+                ),
+                "loop_count": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
+                "filename_prefix": ("STRING", {"default": "Comfyui"}),
+                "format": (["image/gif", "image/webp"] + ffmpeg_formats,),
+                "pingpong": ("BOOLEAN", {"default": False}),
+                "save_image": ("BOOLEAN", {"default": True}),
+                "metadata": ("BOOLEAN", {"default": False}),
+            },
+            "hidden": {
+                "prompt": "PROMPT",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ("SCENE_VIDEO",)
+    RETURN_NAMES = ("scenes_video",)
+    OUTPUT_NODE = True
+    CATEGORY = "♾️Mixlab/Video"
+    FUNCTION = "run"
+
+    def save_with_tempfile(self, args, metadata, file_path, frames, env):
+        #Ensure temp directory exists
+        os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
+
+        metadata_path = os.path.join(folder_paths.get_temp_directory(), "metadata.txt")
+        #metadata from file should  escape = ; # \ and newline
+        #From my testing, though, only backslashes need escapes and = in particular causes problems
+        #It is likely better to prioritize future compatibility with containers that don't support
+        #or shouldn't use the comment tag for embedding metadata
+        metadata = metadata.replace("\\","\\\\")
+        metadata = metadata.replace(";","\\;")
+        metadata = metadata.replace("#","\\#")
+        #metadata = metadata.replace("=","\\=")
+        metadata = metadata.replace("\n","\\\n")
+        with open(metadata_path, "w") as f:
+            f.write(";FFMETADATA1\n")
+            f.write(metadata)
+        args = args[:1] + ["-i", metadata_path] + args[1:] + [file_path]
+        with subprocess.Popen(args, stdin=subprocess.PIPE, env=env) as proc:
+            for frame in frames:
+                proc.stdin.write(frame.tobytes())
+
+    def run(
+        self,
+        image_batch,
+        frame_rate: int,
+        loop_count: int,
+        filename_prefix="AnimateDiff",
+        format="image/gif",
+        pingpong=False,
+        save_image=True,
+        metadata=False,
+        prompt=None,
+        extra_pnginfo=None,
+    ):
+        images=image_batch
+
+        frames: List[Image.Image] = []
+        for image in images:
+            img = 255.0 * image.cpu().numpy()
+            img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
+            # resize 保证
+            # 检查图像的高度是否是2的倍数，如果不是，则调整高度
+            if img.height % 2 != 0:
+                img = img.resize((img.width, img.height + 1))
+
+            # 检查图像的宽度是否是2的倍数，如果不是，则调整宽度
+            if img.width % 2 != 0:
+                img = img.resize((img.width + 1, img.height))
+                
+            frames.append(img)
+
+        # get output information
+        output_dir = (
+            folder_paths.get_output_directory()
+            if save_image
+            else folder_paths.get_temp_directory()
+        )
+        (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path(filename_prefix, output_dir)
+
+        metadata = PngInfo()
+        video_metadata = {}
+        if prompt is not None:
+            metadata.add_text("prompt", json.dumps(prompt))
+            video_metadata["prompt"] = prompt
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+                video_metadata[x] = extra_pnginfo[x]
+
+        # 取消保存metadata
+        if metadata==False:
+            metadata = PngInfo()
+
+        # save first frame as png to keep metadata
+        file = f"{filename}_{counter:05}_.png"
+        file_path = os.path.join(full_output_folder, file)
+        frames[0].save(
+            file_path,
+            pnginfo=metadata,
+            compress_level=4,
+        )
+        if pingpong:
+            frames = frames + frames[-2:0:-1]
+
+        format_type, format_ext = format.split("/")
+        file = f"{filename}_{counter:05}_.{format_ext}"
+        file_path = os.path.join(full_output_folder, file)
+        if format_type == "image":
+            # Use pillow directly to save an animated image
+            frames[0].save(
+                file_path,
+                format=format_ext.upper(),
+                save_all=True,
+                append_images=frames[1:],
+                duration=round(1000 / frame_rate),
+                loop=loop_count,
+                compress_level=4,
+            )
+        else:
+            # Use ffmpeg to save a video
+            if ffmpeg_path is None:
+                #Should never be reachable
+                raise ProcessLookupError("Could not find ffmpeg")
+
+            video_format_path = folder_paths.get_full_path("video_formats", format_ext + ".json")
+            with open(video_format_path, 'r') as stream:
+                video_format = json.load(stream)
+            file = f"{filename}_{counter:05}_.{video_format['extension']}"
+            file_path = os.path.join(full_output_folder, file)
+            dimensions = f"{frames[0].width}x{frames[0].height}"
+            metadata_args = ["-metadata", "comment=" + json.dumps(video_metadata)]
+            args = [ffmpeg_path, "-v", "error", "-f", "rawvideo", "-pix_fmt", "rgb24",
+                    "-s", dimensions, "-r", str(frame_rate), "-i", "-"] \
+                    + video_format['main_pass']
+            # On linux, max arg length is Pagesize * 32 -> 131072
+            # On windows, this around 32767 but seems to vary wildly by > 500
+            # in a manor not solely related to other arguments
+            if os.name == 'posix':
+                max_arg_length = 4096*32
+            else:
+                max_arg_length = 32767 - len(" ".join(args + [metadata_args[0]] + [file_path])) - 1
+            #test max limit
+            #metadata_args[1] = metadata_args[1] + "a"*(max_arg_length - len(metadata_args[1])-1)
+
+            env=os.environ.copy()
+            if  "environment" in video_format:
+                env.update(video_format["environment"])
+            if len(metadata_args[1]) >= max_arg_length:
+                print(f"Using fallback file for extremely long metadata: {len(metadata_args[1])}/{max_arg_length}")
+                self.save_with_tempfile(args, metadata_args[1], file_path, frames, env)
+            else:
+                try:
+                    with subprocess.Popen(args + metadata_args + [file_path],
+                                          stdin=subprocess.PIPE, env=env) as proc:
+                        for frame in frames:
+                            proc.stdin.write(frame.tobytes())
+                except FileNotFoundError as e:
+                    if "winerror" in dir(e) and e.winerror == 206:
+                        print("Metadata was too long. Retrying with fallback file")
+                        self.save_with_tempfile(args, metadata_args[1], file_path, frames, env)
+                    else:
+                        raise
+                except OSError as e:
+                    if "errno" in dir(e) and e.errno == 7:
+                        print("Metadata was too long. Retrying with fallback file")
+                        self.save_with_tempfile(args, metadata_args[1], file_path, frames, env)
+                    else:
+                        raise
+
+        previews = [
+            {
+                "filename": file,
+                "subfolder": subfolder,
+                "type": "output" if save_image else "temp",
+                "format": format,
+            }
+        ]
+        return {"ui": {"gifs": previews},"result":(file_path,)}
+
+
+class VAEEncodeForInpaint_Frames:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "vae": ("VAE", ), 
+            "images": ("IMAGE", ), 
+            "masks": ("MASK", ), 
+            "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),
+            }}
+    
+    FUNCTION = "encode"
+
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("LATENT",)
+ 
+    CATEGORY = "♾️Mixlab/Video"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+
+    def encode(self, vae, images, masks, grow_mask_by=[6]):
+        vae=vae[0]
+        grow_mask_by=grow_mask_by[0]
+
+        result=[]
+
+        for i in range(len(images)):
+            pixels=images[i]
+            mask=masks[i]
+
+
+            x = (pixels.shape[1] // 8) * 8
+            y = (pixels.shape[2] // 8) * 8
+            mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
+
+            pixels = pixels.clone()
+            if pixels.shape[1] != x or pixels.shape[2] != y:
+                x_offset = (pixels.shape[1] % 8) // 2
+                y_offset = (pixels.shape[2] % 8) // 2
+                pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+                mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
+
+            #grow mask by a few pixels to keep things seamless in latent space
+            if grow_mask_by == 0:
+                mask_erosion = mask
+            else:
+                kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
+                padding = math.ceil((grow_mask_by - 1) / 2)
+
+                mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)
+
+            m = (1.0 - mask.round()).squeeze(1)
+            for i in range(3):
+                pixels[:,:,:,i] -= 0.5
+                pixels[:,:,:,i] *= m
+                pixels[:,:,:,i] += 0.5
+            t = vae.encode(pixels)
+
+            result.append({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())})
+
+
+        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 = ("frames","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]
+
+            return self.load_video_cv_fallback(vp,0,0)
+        
+        return ([], 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 +1,38 @@
+cond_image_size: 512
+
+image_tokenizer_cls: tsr.models.tokenizers.image.DINOSingleImageTokenizer
+image_tokenizer:
+  pretrained_model_name_or_path: "facebook/dino-vitb16"
+
+tokenizer_cls: tsr.models.tokenizers.triplane.Triplane1DTokenizer
+tokenizer:
+  plane_size: 32
+  num_channels: 1024
+
+backbone_cls: tsr.models.transformer.transformer_1d.Transformer1D
+backbone:
+  in_channels: ${tokenizer.num_channels}
+  num_attention_heads: 16
+  attention_head_dim: 64
+  num_layers: 16
+  cross_attention_dim: 768
+
+post_processor_cls: tsr.models.network_utils.TriplaneUpsampleNetwork
+post_processor:
+  in_channels: 1024
+  out_channels: 40
+
+decoder_cls: tsr.models.network_utils.NeRFMLP
+decoder:
+  in_channels: 120 # 3 * 40
+  n_neurons: 64
+  n_hidden_layers: 9
+  activation: silu
+
+renderer_cls: tsr.models.nerf_renderer.TriplaneNeRFRenderer
+renderer:
+  radius: 0.87 # slightly larger than 0.5 * sqrt(3)
+  feature_reduction: concat
+  density_activation: exp
+  density_bias: -1.0
+  num_samples_per_ray: 128
@@ -0,0 +1,51 @@
+from typing import Callable, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+from skimage import measure
+
+
+class IsosurfaceHelper(nn.Module):
+    points_range: Tuple[float, float] = (0, 1)
+
+    @property
+    def grid_vertices(self) -> torch.FloatTensor:
+        raise NotImplementedError
+
+
+class MarchingCubeHelper(IsosurfaceHelper):
+    def __init__(self, resolution: int) -> None:
+        super().__init__()
+        self.resolution = resolution
+        #self.mc_func: Callable = marching_cubes
+        self._grid_vertices: Optional[torch.FloatTensor] = None
+
+    @property
+    def grid_vertices(self) -> torch.FloatTensor:
+        if self._grid_vertices is None:
+            # keep the vertices on CPU so that we can support very large resolution
+            x, y, z = (
+                torch.linspace(*self.points_range, self.resolution),
+                torch.linspace(*self.points_range, self.resolution),
+                torch.linspace(*self.points_range, self.resolution),
+            )
+            x, y, z = torch.meshgrid(x, y, z, indexing="ij")
+            verts = torch.cat(
+                [x.reshape(-1, 1), y.reshape(-1, 1), z.reshape(-1, 1)], dim=-1
+            ).reshape(-1, 3)
+            self._grid_vertices = verts
+        return self._grid_vertices
+
+    def forward(
+        self,
+        level: torch.FloatTensor,
+    ) -> Tuple[torch.FloatTensor, torch.LongTensor]:
+        level = -level.view(self.resolution, self.resolution, self.resolution)
+        v_pos, t_pos_idx, _, __ = measure.marching_cubes((level.detach().cpu() if level.is_cuda else level.detach()).numpy(), 0.0) #self.mc_func(level.detach(), 0.0)
+        v_pos = torch.from_numpy(v_pos.copy()).type(torch.FloatTensor).to(level.device)
+        t_pos_idx = torch.from_numpy(t_pos_idx.copy()).type(torch.LongTensor).to(level.device)
+        v_pos = v_pos[..., [0, 1, 2]]
+        t_pos_idx = t_pos_idx[..., [1, 0, 2]]
+        v_pos = v_pos / (self.resolution - 1.0)
+        return v_pos, t_pos_idx
@@ -0,0 +1,180 @@
+from dataclasses import dataclass
+from typing import Dict
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, reduce
+
+from ..utils import (
+    BaseModule,
+    chunk_batch,
+    get_activation,
+    rays_intersect_bbox,
+    scale_tensor,
+)
+
+
+class TriplaneNeRFRenderer(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        radius: float
+
+        feature_reduction: str = "concat"
+        density_activation: str = "trunc_exp"
+        density_bias: float = -1.0
+        color_activation: str = "sigmoid"
+        num_samples_per_ray: int = 128
+        randomized: bool = False
+
+    cfg: Config
+
+    def configure(self) -> None:
+        assert self.cfg.feature_reduction in ["concat", "mean"]
+        self.chunk_size = 0
+
+    def set_chunk_size(self, chunk_size: int):
+        assert (
+            chunk_size >= 0
+        ), "chunk_size must be a non-negative integer (0 for no chunking)."
+        self.chunk_size = chunk_size
+
+    def query_triplane(
+        self,
+        decoder: torch.nn.Module,
+        positions: torch.Tensor,
+        triplane: torch.Tensor,
+    ) -> Dict[str, torch.Tensor]:
+        input_shape = positions.shape[:-1]
+        positions = positions.view(-1, 3)
+
+        # positions in (-radius, radius)
+        # normalized to (-1, 1) for grid sample
+        positions = scale_tensor(
+            positions, (-self.cfg.radius, self.cfg.radius), (-1, 1)
+        )
+
+        def _query_chunk(x):
+            indices2D: torch.Tensor = torch.stack(
+                (x[..., [0, 1]], x[..., [0, 2]], x[..., [1, 2]]),
+                dim=-3,
+            )
+            out: torch.Tensor = F.grid_sample(
+                rearrange(triplane, "Np Cp Hp Wp -> Np Cp Hp Wp", Np=3),
+                rearrange(indices2D, "Np N Nd -> Np () N Nd", Np=3),
+                align_corners=False,
+                mode="bilinear",
+            )
+            if self.cfg.feature_reduction == "concat":
+                out = rearrange(out, "Np Cp () N -> N (Np Cp)", Np=3)
+            elif self.cfg.feature_reduction == "mean":
+                out = reduce(out, "Np Cp () N -> N Cp", Np=3, reduction="mean")
+            else:
+                raise NotImplementedError
+
+            net_out: Dict[str, torch.Tensor] = decoder(out)
+            return net_out
+
+        if self.chunk_size > 0:
+            net_out = chunk_batch(_query_chunk, self.chunk_size, positions)
+        else:
+            net_out = _query_chunk(positions)
+
+        net_out["density_act"] = get_activation(self.cfg.density_activation)(
+            net_out["density"] + self.cfg.density_bias
+        )
+        net_out["color"] = get_activation(self.cfg.color_activation)(
+            net_out["features"]
+        )
+
+        net_out = {k: v.view(*input_shape, -1) for k, v in net_out.items()}
+
+        return net_out
+
+    def _forward(
+        self,
+        decoder: torch.nn.Module,
+        triplane: torch.Tensor,
+        rays_o: torch.Tensor,
+        rays_d: torch.Tensor,
+        **kwargs,
+    ):
+        rays_shape = rays_o.shape[:-1]
+        rays_o = rays_o.view(-1, 3)
+        rays_d = rays_d.view(-1, 3)
+        n_rays = rays_o.shape[0]
+
+        t_near, t_far, rays_valid = rays_intersect_bbox(rays_o, rays_d, self.cfg.radius)
+        t_near, t_far = t_near[rays_valid], t_far[rays_valid]
+
+        t_vals = torch.linspace(
+            0, 1, self.cfg.num_samples_per_ray + 1, device=triplane.device
+        )
+        t_mid = (t_vals[:-1] + t_vals[1:]) / 2.0
+        z_vals = t_near * (1 - t_mid[None]) + t_far * t_mid[None]  # (N_rays, N_samples)
+
+        xyz = (
+            rays_o[:, None, :] + z_vals[..., None] * rays_d[..., None, :]
+        )  # (N_rays, N_sample, 3)
+
+        mlp_out = self.query_triplane(
+            decoder=decoder,
+            positions=xyz,
+            triplane=triplane,
+        )
+
+        eps = 1e-10
+        # deltas = z_vals[:, 1:] - z_vals[:, :-1] # (N_rays, N_samples)
+        deltas = t_vals[1:] - t_vals[:-1]  # (N_rays, N_samples)
+        alpha = 1 - torch.exp(
+            -deltas * mlp_out["density_act"][..., 0]
+        )  # (N_rays, N_samples)
+        accum_prod = torch.cat(
+            [
+                torch.ones_like(alpha[:, :1]),
+                torch.cumprod(1 - alpha[:, :-1] + eps, dim=-1),
+            ],
+            dim=-1,
+        )
+        weights = alpha * accum_prod  # (N_rays, N_samples)
+        comp_rgb_ = (weights[..., None] * mlp_out["color"]).sum(dim=-2)  # (N_rays, 3)
+        opacity_ = weights.sum(dim=-1)  # (N_rays)
+
+        comp_rgb = torch.zeros(
+            n_rays, 3, dtype=comp_rgb_.dtype, device=comp_rgb_.device
+        )
+        opacity = torch.zeros(n_rays, dtype=opacity_.dtype, device=opacity_.device)
+        comp_rgb[rays_valid] = comp_rgb_
+        opacity[rays_valid] = opacity_
+
+        comp_rgb += 1 - opacity[..., None]
+        comp_rgb = comp_rgb.view(*rays_shape, 3)
+
+        return comp_rgb
+
+    def forward(
+        self,
+        decoder: torch.nn.Module,
+        triplane: torch.Tensor,
+        rays_o: torch.Tensor,
+        rays_d: torch.Tensor,
+    ) -> Dict[str, torch.Tensor]:
+        if triplane.ndim == 4:
+            comp_rgb = self._forward(decoder, triplane, rays_o, rays_d)
+        else:
+            comp_rgb = torch.stack(
+                [
+                    self._forward(decoder, triplane[i], rays_o[i], rays_d[i])
+                    for i in range(triplane.shape[0])
+                ],
+                dim=0,
+            )
+
+        return comp_rgb
+
+    def train(self, mode=True):
+        self.randomized = mode and self.cfg.randomized
+        return super().train(mode=mode)
+
+    def eval(self):
+        self.randomized = False
+        return super().eval()
@@ -0,0 +1,124 @@
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+from ..utils import BaseModule
+
+
+class TriplaneUpsampleNetwork(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        in_channels: int
+        out_channels: int
+
+    cfg: Config
+
+    def configure(self) -> None:
+        self.upsample = nn.ConvTranspose2d(
+            self.cfg.in_channels, self.cfg.out_channels, kernel_size=2, stride=2
+        )
+
+    def forward(self, triplanes: torch.Tensor) -> torch.Tensor:
+        triplanes_up = rearrange(
+            self.upsample(
+                rearrange(triplanes, "B Np Ci Hp Wp -> (B Np) Ci Hp Wp", Np=3)
+            ),
+            "(B Np) Co Hp Wp -> B Np Co Hp Wp",
+            Np=3,
+        )
+        return triplanes_up
+
+
+class NeRFMLP(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        in_channels: int
+        n_neurons: int
+        n_hidden_layers: int
+        activation: str = "relu"
+        bias: bool = True
+        weight_init: Optional[str] = "kaiming_uniform"
+        bias_init: Optional[str] = None
+
+    cfg: Config
+
+    def configure(self) -> None:
+        layers = [
+            self.make_linear(
+                self.cfg.in_channels,
+                self.cfg.n_neurons,
+                bias=self.cfg.bias,
+                weight_init=self.cfg.weight_init,
+                bias_init=self.cfg.bias_init,
+            ),
+            self.make_activation(self.cfg.activation),
+        ]
+        for i in range(self.cfg.n_hidden_layers - 1):
+            layers += [
+                self.make_linear(
+                    self.cfg.n_neurons,
+                    self.cfg.n_neurons,
+                    bias=self.cfg.bias,
+                    weight_init=self.cfg.weight_init,
+                    bias_init=self.cfg.bias_init,
+                ),
+                self.make_activation(self.cfg.activation),
+            ]
+        layers += [
+            self.make_linear(
+                self.cfg.n_neurons,
+                4,  # density 1 + features 3
+                bias=self.cfg.bias,
+                weight_init=self.cfg.weight_init,
+                bias_init=self.cfg.bias_init,
+            )
+        ]
+        self.layers = nn.Sequential(*layers)
+
+    def make_linear(
+        self,
+        dim_in,
+        dim_out,
+        bias=True,
+        weight_init=None,
+        bias_init=None,
+    ):
+        layer = nn.Linear(dim_in, dim_out, bias=bias)
+
+        if weight_init is None:
+            pass
+        elif weight_init == "kaiming_uniform":
+            torch.nn.init.kaiming_uniform_(layer.weight, nonlinearity="relu")
+        else:
+            raise NotImplementedError
+
+        if bias:
+            if bias_init is None:
+                pass
+            elif bias_init == "zero":
+                torch.nn.init.zeros_(layer.bias)
+            else:
+                raise NotImplementedError
+
+        return layer
+
+    def make_activation(self, activation):
+        if activation == "relu":
+            return nn.ReLU(inplace=True)
+        elif activation == "silu":
+            return nn.SiLU(inplace=True)
+        else:
+            raise NotImplementedError
+
+    def forward(self, x):
+        inp_shape = x.shape[:-1]
+        x = x.reshape(-1, x.shape[-1])
+
+        features = self.layers(x)
+        features = features.reshape(*inp_shape, -1)
+        out = {"density": features[..., 0:1], "features": features[..., 1:4]}
+
+        return out
@@ -0,0 +1,72 @@
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from huggingface_hub import hf_hub_download
+from transformers.models.vit.modeling_vit import ViTModel
+
+from ...utils import BaseModule
+import os
+import folder_paths
+model_path=os.path.join(folder_paths.models_dir,'triposr')
+
+
+class DINOSingleImageTokenizer(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        pretrained_model_name_or_path: str = "facebook/dino-vitb16"
+        enable_gradient_checkpointing: bool = False
+
+    cfg: Config
+
+    def configure(self) -> None:
+        print('#Loading ViTModel:',os.path.join(model_path,self.cfg.pretrained_model_name_or_path))
+        self.model: ViTModel = ViTModel(
+            ViTModel.config_class.from_pretrained(
+                hf_hub_download(
+                    repo_id=self.cfg.pretrained_model_name_or_path,
+                    filename="config.json",
+                    local_dir=model_path,
+                    endpoint='https://hf-mirror.com'
+                )
+            )
+        )
+
+        if self.cfg.enable_gradient_checkpointing:
+            self.model.encoder.gradient_checkpointing = True
+
+        self.register_buffer(
+            "image_mean",
+            torch.as_tensor([0.485, 0.456, 0.406]).reshape(1, 1, 3, 1, 1),
+            persistent=False,
+        )
+        self.register_buffer(
+            "image_std",
+            torch.as_tensor([0.229, 0.224, 0.225]).reshape(1, 1, 3, 1, 1),
+            persistent=False,
+        )
+
+    def forward(self, images: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
+        packed = False
+        if images.ndim == 4:
+            packed = True
+            images = images.unsqueeze(1)
+
+        batch_size, n_input_views = images.shape[:2]
+        images = (images - self.image_mean) / self.image_std
+        out = self.model(
+            rearrange(images, "B N C H W -> (B N) C H W"), interpolate_pos_encoding=True
+        )
+        local_features, global_features = out.last_hidden_state, out.pooler_output
+        local_features = local_features.permute(0, 2, 1)
+        local_features = rearrange(
+            local_features, "(B N) Ct Nt -> B N Ct Nt", B=batch_size
+        )
+        if packed:
+            local_features = local_features.squeeze(1)
+
+        return local_features
+
+    def detokenize(self, *args, **kwargs):
+        raise NotImplementedError
@@ -0,0 +1,45 @@
+import math
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+
+from ...utils import BaseModule
+
+
+class Triplane1DTokenizer(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        plane_size: int
+        num_channels: int
+
+    cfg: Config
+
+    def configure(self) -> None:
+        self.embeddings = nn.Parameter(
+            torch.randn(
+                (3, self.cfg.num_channels, self.cfg.plane_size, self.cfg.plane_size),
+                dtype=torch.float32,
+            )
+            * 1
+            / math.sqrt(self.cfg.num_channels)
+        )
+
+    def forward(self, batch_size: int) -> torch.Tensor:
+        return rearrange(
+            repeat(self.embeddings, "Np Ct Hp Wp -> B Np Ct Hp Wp", B=batch_size),
+            "B Np Ct Hp Wp -> B Ct (Np Hp Wp)",
+        )
+
+    def detokenize(self, tokens: torch.Tensor) -> torch.Tensor:
+        batch_size, Ct, Nt = tokens.shape
+        assert Nt == self.cfg.plane_size**2 * 3
+        assert Ct == self.cfg.num_channels
+        return rearrange(
+            tokens,
+            "B Ct (Np Hp Wp) -> B Np Ct Hp Wp",
+            Np=3,
+            Hp=self.cfg.plane_size,
+            Wp=self.cfg.plane_size,
+        )
@@ -0,0 +1,653 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# --------
+#
+# Modified 2024 by the Tripo AI and Stability AI Team.
+#
+# Copyright (c) 2024 Tripo AI & Stability AI
+#
+# 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.
+
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+class Attention(nn.Module):
+    r"""
+    A cross attention layer.
+
+    Parameters:
+        query_dim (`int`):
+            The number of channels in the query.
+        cross_attention_dim (`int`, *optional*):
+            The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
+        heads (`int`,  *optional*, defaults to 8):
+            The number of heads to use for multi-head attention.
+        dim_head (`int`,  *optional*, defaults to 64):
+            The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability to use.
+        bias (`bool`, *optional*, defaults to False):
+            Set to `True` for the query, key, and value linear layers to contain a bias parameter.
+        upcast_attention (`bool`, *optional*, defaults to False):
+            Set to `True` to upcast the attention computation to `float32`.
+        upcast_softmax (`bool`, *optional*, defaults to False):
+            Set to `True` to upcast the softmax computation to `float32`.
+        cross_attention_norm (`str`, *optional*, defaults to `None`):
+            The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
+        cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups to use for the group norm in the cross attention.
+        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
+            The number of channels to use for the added key and value projections. If `None`, no projection is used.
+        norm_num_groups (`int`, *optional*, defaults to `None`):
+            The number of groups to use for the group norm in the attention.
+        spatial_norm_dim (`int`, *optional*, defaults to `None`):
+            The number of channels to use for the spatial normalization.
+        out_bias (`bool`, *optional*, defaults to `True`):
+            Set to `True` to use a bias in the output linear layer.
+        scale_qk (`bool`, *optional*, defaults to `True`):
+            Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
+        only_cross_attention (`bool`, *optional*, defaults to `False`):
+            Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
+            `added_kv_proj_dim` is not `None`.
+        eps (`float`, *optional*, defaults to 1e-5):
+            An additional value added to the denominator in group normalization that is used for numerical stability.
+        rescale_output_factor (`float`, *optional*, defaults to 1.0):
+            A factor to rescale the output by dividing it with this value.
+        residual_connection (`bool`, *optional*, defaults to `False`):
+            Set to `True` to add the residual connection to the output.
+        _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
+            Set to `True` if the attention block is loaded from a deprecated state dict.
+        processor (`AttnProcessor`, *optional*, defaults to `None`):
+            The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
+            `AttnProcessor` otherwise.
+    """
+
+    def __init__(
+        self,
+        query_dim: int,
+        cross_attention_dim: Optional[int] = None,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        bias: bool = False,
+        upcast_attention: bool = False,
+        upcast_softmax: bool = False,
+        cross_attention_norm: Optional[str] = None,
+        cross_attention_norm_num_groups: int = 32,
+        added_kv_proj_dim: Optional[int] = None,
+        norm_num_groups: Optional[int] = None,
+        out_bias: bool = True,
+        scale_qk: bool = True,
+        only_cross_attention: bool = False,
+        eps: float = 1e-5,
+        rescale_output_factor: float = 1.0,
+        residual_connection: bool = False,
+        _from_deprecated_attn_block: bool = False,
+        processor: Optional["AttnProcessor"] = None,
+        out_dim: int = None,
+    ):
+        super().__init__()
+        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
+        self.query_dim = query_dim
+        self.cross_attention_dim = (
+            cross_attention_dim if cross_attention_dim is not None else query_dim
+        )
+        self.upcast_attention = upcast_attention
+        self.upcast_softmax = upcast_softmax
+        self.rescale_output_factor = rescale_output_factor
+        self.residual_connection = residual_connection
+        self.dropout = dropout
+        self.fused_projections = False
+        self.out_dim = out_dim if out_dim is not None else query_dim
+
+        # we make use of this private variable to know whether this class is loaded
+        # with an deprecated state dict so that we can convert it on the fly
+        self._from_deprecated_attn_block = _from_deprecated_attn_block
+
+        self.scale_qk = scale_qk
+        self.scale = dim_head**-0.5 if self.scale_qk else 1.0
+
+        self.heads = out_dim // dim_head if out_dim is not None else heads
+        # for slice_size > 0 the attention score computation
+        # is split across the batch axis to save memory
+        # You can set slice_size with `set_attention_slice`
+        self.sliceable_head_dim = heads
+
+        self.added_kv_proj_dim = added_kv_proj_dim
+        self.only_cross_attention = only_cross_attention
+
+        if self.added_kv_proj_dim is None and self.only_cross_attention:
+            raise ValueError(
+                "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
+            )
+
+        if norm_num_groups is not None:
+            self.group_norm = nn.GroupNorm(
+                num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True
+            )
+        else:
+            self.group_norm = None
+
+        self.spatial_norm = None
+
+        if cross_attention_norm is None:
+            self.norm_cross = None
+        elif cross_attention_norm == "layer_norm":
+            self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
+        elif cross_attention_norm == "group_norm":
+            if self.added_kv_proj_dim is not None:
+                # The given `encoder_hidden_states` are initially of shape
+                # (batch_size, seq_len, added_kv_proj_dim) before being projected
+                # to (batch_size, seq_len, cross_attention_dim). The norm is applied
+                # before the projection, so we need to use `added_kv_proj_dim` as
+                # the number of channels for the group norm.
+                norm_cross_num_channels = added_kv_proj_dim
+            else:
+                norm_cross_num_channels = self.cross_attention_dim
+
+            self.norm_cross = nn.GroupNorm(
+                num_channels=norm_cross_num_channels,
+                num_groups=cross_attention_norm_num_groups,
+                eps=1e-5,
+                affine=True,
+            )
+        else:
+            raise ValueError(
+                f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
+            )
+
+        linear_cls = nn.Linear
+
+        self.linear_cls = linear_cls
+        self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias)
+
+        if not self.only_cross_attention:
+            # only relevant for the `AddedKVProcessor` classes
+            self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
+            self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
+        else:
+            self.to_k = None
+            self.to_v = None
+
+        if self.added_kv_proj_dim is not None:
+            self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
+            self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
+
+        self.to_out = nn.ModuleList([])
+        self.to_out.append(linear_cls(self.inner_dim, self.out_dim, bias=out_bias))
+        self.to_out.append(nn.Dropout(dropout))
+
+        # set attention processor
+        # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
+        # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
+        # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
+        if processor is None:
+            processor = (
+                AttnProcessor2_0()
+                if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
+                else AttnProcessor()
+            )
+        self.set_processor(processor)
+
+    def set_processor(self, processor: "AttnProcessor") -> None:
+        self.processor = processor
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        **cross_attention_kwargs,
+    ) -> torch.Tensor:
+        r"""
+        The forward method of the `Attention` class.
+
+        Args:
+            hidden_states (`torch.Tensor`):
+                The hidden states of the query.
+            encoder_hidden_states (`torch.Tensor`, *optional*):
+                The hidden states of the encoder.
+            attention_mask (`torch.Tensor`, *optional*):
+                The attention mask to use. If `None`, no mask is applied.
+            **cross_attention_kwargs:
+                Additional keyword arguments to pass along to the cross attention.
+
+        Returns:
+            `torch.Tensor`: The output of the attention layer.
+        """
+        # The `Attention` class can call different attention processors / attention functions
+        # here we simply pass along all tensors to the selected processor class
+        # For standard processors that are defined here, `**cross_attention_kwargs` is empty
+        return self.processor(
+            self,
+            hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+
+    def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
+        r"""
+        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
+        is the number of heads initialized while constructing the `Attention` class.
+
+        Args:
+            tensor (`torch.Tensor`): The tensor to reshape.
+
+        Returns:
+            `torch.Tensor`: The reshaped tensor.
+        """
+        head_size = self.heads
+        batch_size, seq_len, dim = tensor.shape
+        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(
+            batch_size // head_size, seq_len, dim * head_size
+        )
+        return tensor
+
+    def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
+        r"""
+        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
+        the number of heads initialized while constructing the `Attention` class.
+
+        Args:
+            tensor (`torch.Tensor`): The tensor to reshape.
+            out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
+                reshaped to `[batch_size * heads, seq_len, dim // heads]`.
+
+        Returns:
+            `torch.Tensor`: The reshaped tensor.
+        """
+        head_size = self.heads
+        batch_size, seq_len, dim = tensor.shape
+        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
+        tensor = tensor.permute(0, 2, 1, 3)
+
+        if out_dim == 3:
+            tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
+
+        return tensor
+
+    def get_attention_scores(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        attention_mask: torch.Tensor = None,
+    ) -> torch.Tensor:
+        r"""
+        Compute the attention scores.
+
+        Args:
+            query (`torch.Tensor`): The query tensor.
+            key (`torch.Tensor`): The key tensor.
+            attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
+
+        Returns:
+            `torch.Tensor`: The attention probabilities/scores.
+        """
+        dtype = query.dtype
+        if self.upcast_attention:
+            query = query.float()
+            key = key.float()
+
+        if attention_mask is None:
+            baddbmm_input = torch.empty(
+                query.shape[0],
+                query.shape[1],
+                key.shape[1],
+                dtype=query.dtype,
+                device=query.device,
+            )
+            beta = 0
+        else:
+            baddbmm_input = attention_mask
+            beta = 1
+
+        attention_scores = torch.baddbmm(
+            baddbmm_input,
+            query,
+            key.transpose(-1, -2),
+            beta=beta,
+            alpha=self.scale,
+        )
+        del baddbmm_input
+
+        if self.upcast_softmax:
+            attention_scores = attention_scores.float()
+
+        attention_probs = attention_scores.softmax(dim=-1)
+        del attention_scores
+
+        attention_probs = attention_probs.to(dtype)
+
+        return attention_probs
+
+    def prepare_attention_mask(
+        self,
+        attention_mask: torch.Tensor,
+        target_length: int,
+        batch_size: int,
+        out_dim: int = 3,
+    ) -> torch.Tensor:
+        r"""
+        Prepare the attention mask for the attention computation.
+
+        Args:
+            attention_mask (`torch.Tensor`):
+                The attention mask to prepare.
+            target_length (`int`):
+                The target length of the attention mask. This is the length of the attention mask after padding.
+            batch_size (`int`):
+                The batch size, which is used to repeat the attention mask.
+            out_dim (`int`, *optional*, defaults to `3`):
+                The output dimension of the attention mask. Can be either `3` or `4`.
+
+        Returns:
+            `torch.Tensor`: The prepared attention mask.
+        """
+        head_size = self.heads
+        if attention_mask is None:
+            return attention_mask
+
+        current_length: int = attention_mask.shape[-1]
+        if current_length != target_length:
+            if attention_mask.device.type == "mps":
+                # HACK: MPS: Does not support padding by greater than dimension of input tensor.
+                # Instead, we can manually construct the padding tensor.
+                padding_shape = (
+                    attention_mask.shape[0],
+                    attention_mask.shape[1],
+                    target_length,
+                )
+                padding = torch.zeros(
+                    padding_shape,
+                    dtype=attention_mask.dtype,
+                    device=attention_mask.device,
+                )
+                attention_mask = torch.cat([attention_mask, padding], dim=2)
+            else:
+                # TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
+                #       we want to instead pad by (0, remaining_length), where remaining_length is:
+                #       remaining_length: int = target_length - current_length
+                # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
+                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
+
+        if out_dim == 3:
+            if attention_mask.shape[0] < batch_size * head_size:
+                attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
+        elif out_dim == 4:
+            attention_mask = attention_mask.unsqueeze(1)
+            attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
+
+        return attention_mask
+
+    def norm_encoder_hidden_states(
+        self, encoder_hidden_states: torch.Tensor
+    ) -> torch.Tensor:
+        r"""
+        Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
+        `Attention` class.
+
+        Args:
+            encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
+
+        Returns:
+            `torch.Tensor`: The normalized encoder hidden states.
+        """
+        assert (
+            self.norm_cross is not None
+        ), "self.norm_cross must be defined to call self.norm_encoder_hidden_states"
+
+        if isinstance(self.norm_cross, nn.LayerNorm):
+            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
+        elif isinstance(self.norm_cross, nn.GroupNorm):
+            # Group norm norms along the channels dimension and expects
+            # input to be in the shape of (N, C, *). In this case, we want
+            # to norm along the hidden dimension, so we need to move
+            # (batch_size, sequence_length, hidden_size) ->
+            # (batch_size, hidden_size, sequence_length)
+            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
+            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
+            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
+        else:
+            assert False
+
+        return encoder_hidden_states
+
+    @torch.no_grad()
+    def fuse_projections(self, fuse=True):
+        is_cross_attention = self.cross_attention_dim != self.query_dim
+        device = self.to_q.weight.data.device
+        dtype = self.to_q.weight.data.dtype
+
+        if not is_cross_attention:
+            # fetch weight matrices.
+            concatenated_weights = torch.cat(
+                [self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data]
+            )
+            in_features = concatenated_weights.shape[1]
+            out_features = concatenated_weights.shape[0]
+
+            # create a new single projection layer and copy over the weights.
+            self.to_qkv = self.linear_cls(
+                in_features, out_features, bias=False, device=device, dtype=dtype
+            )
+            self.to_qkv.weight.copy_(concatenated_weights)
+
+        else:
+            concatenated_weights = torch.cat(
+                [self.to_k.weight.data, self.to_v.weight.data]
+            )
+            in_features = concatenated_weights.shape[1]
+            out_features = concatenated_weights.shape[0]
+
+            self.to_kv = self.linear_cls(
+                in_features, out_features, bias=False, device=device, dtype=dtype
+            )
+            self.to_kv.weight.copy_(concatenated_weights)
+
+        self.fused_projections = fuse
+
+
+class AttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.Tensor:
+        residual = hidden_states
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(
+            attention_mask, sequence_length, batch_size
+        )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class AttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
@@ -0,0 +1,334 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# --------
+#
+# Modified 2024 by the Tripo AI and Stability AI Team.
+#
+# Copyright (c) 2024 Tripo AI & Stability AI
+#
+# 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.
+
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from .attention import Attention
+
+
+class BasicTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        upcast_attention (`bool`, *optional*):
+            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
+            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        double_self_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_elementwise_affine: bool = True,
+        norm_type: str = "layer_norm",
+        final_dropout: bool = False,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+
+        assert norm_type == "layer_norm"
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+        )
+
+        # 2. Cross-Attn
+        if cross_attention_dim is not None or double_self_attention:
+            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
+            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
+            # the second cross attention block.
+            self.norm2 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+
+            self.attn2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=(
+                    cross_attention_dim if not double_self_attention else None
+                ),
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )  # is self-attn if encoder_hidden_states is none
+        else:
+            self.norm2 = None
+            self.attn2 = None
+
+        # 3. Feed-forward
+        self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,
+            activation_fn=activation_fn,
+            final_dropout=final_dropout,
+        )
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+        norm_hidden_states = self.norm1(hidden_states)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=(
+                encoder_hidden_states if self.only_cross_attention else None
+            ),
+            attention_mask=attention_mask,
+        )
+
+        hidden_states = attn_output + hidden_states
+
+        # 3. Cross-Attention
+        if self.attn2 is not None:
+            norm_hidden_states = self.norm2(hidden_states)
+
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+            )
+            hidden_states = attn_output + hidden_states
+
+        # 4. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+                raise ValueError(
+                    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+                )
+
+            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+            ff_output = torch.cat(
+                [
+                    self.ff(hid_slice)
+                    for hid_slice in norm_hidden_states.chunk(
+                        num_chunks, dim=self._chunk_dim
+                    )
+                ],
+                dim=self._chunk_dim,
+            )
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        hidden_states = ff_output + hidden_states
+
+        return hidden_states
+
+
+class FeedForward(nn.Module):
+    r"""
+    A feed-forward layer.
+
+    Parameters:
+        dim (`int`): The number of channels in the input.
+        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
+        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        mult: int = 4,
+        dropout: float = 0.0,
+        activation_fn: str = "geglu",
+        final_dropout: bool = False,
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+        linear_cls = nn.Linear
+
+        if activation_fn == "gelu":
+            act_fn = GELU(dim, inner_dim)
+        if activation_fn == "gelu-approximate":
+            act_fn = GELU(dim, inner_dim, approximate="tanh")
+        elif activation_fn == "geglu":
+            act_fn = GEGLU(dim, inner_dim)
+        elif activation_fn == "geglu-approximate":
+            act_fn = ApproximateGELU(dim, inner_dim)
+
+        self.net = nn.ModuleList([])
+        # project in
+        self.net.append(act_fn)
+        # project dropout
+        self.net.append(nn.Dropout(dropout))
+        # project out
+        self.net.append(linear_cls(inner_dim, dim_out))
+        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
+        if final_dropout:
+            self.net.append(nn.Dropout(dropout))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        for module in self.net:
+            hidden_states = module(hidden_states)
+        return hidden_states
+
+
+class GELU(nn.Module):
+    r"""
+    GELU activation function with tanh approximation support with `approximate="tanh"`.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int, approximate: str = "none"):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out)
+        self.approximate = approximate
+
+    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
+        if gate.device.type != "mps":
+            return F.gelu(gate, approximate=self.approximate)
+        # mps: gelu is not implemented for float16
+        return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(
+            dtype=gate.dtype
+        )
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states = self.gelu(hidden_states)
+        return hidden_states
+
+
+class GEGLU(nn.Module):
+    r"""
+    A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int):
+        super().__init__()
+        linear_cls = nn.Linear
+
+        self.proj = linear_cls(dim_in, dim_out * 2)
+
+    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
+        if gate.device.type != "mps":
+            return F.gelu(gate)
+        # mps: gelu is not implemented for float16
+        return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
+
+    def forward(self, hidden_states, scale: float = 1.0):
+        args = ()
+        hidden_states, gate = self.proj(hidden_states, *args).chunk(2, dim=-1)
+        return hidden_states * self.gelu(gate)
+
+
+class ApproximateGELU(nn.Module):
+    r"""
+    The approximate form of Gaussian Error Linear Unit (GELU). For more details, see section 2:
+    https://arxiv.org/abs/1606.08415.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        return x * torch.sigmoid(1.702 * x)
@@ -0,0 +1,219 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# --------
+#
+# Modified 2024 by the Tripo AI and Stability AI Team.
+#
+# Copyright (c) 2024 Tripo AI & Stability AI
+#
+# 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.
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ...utils import BaseModule
+from .basic_transformer_block import BasicTransformerBlock
+
+
+class Transformer1D(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        num_attention_heads: int = 16
+        attention_head_dim: int = 88
+        in_channels: Optional[int] = None
+        out_channels: Optional[int] = None
+        num_layers: int = 1
+        dropout: float = 0.0
+        norm_num_groups: int = 32
+        cross_attention_dim: Optional[int] = None
+        attention_bias: bool = False
+        activation_fn: str = "geglu"
+        only_cross_attention: bool = False
+        double_self_attention: bool = False
+        upcast_attention: bool = False
+        norm_type: str = "layer_norm"
+        norm_elementwise_affine: bool = True
+        gradient_checkpointing: bool = False
+
+    cfg: Config
+
+    def configure(self) -> None:
+        self.num_attention_heads = self.cfg.num_attention_heads
+        self.attention_head_dim = self.cfg.attention_head_dim
+        inner_dim = self.num_attention_heads * self.attention_head_dim
+
+        linear_cls = nn.Linear
+
+        # 2. Define input layers
+        self.in_channels = self.cfg.in_channels
+
+        self.norm = torch.nn.GroupNorm(
+            num_groups=self.cfg.norm_num_groups,
+            num_channels=self.cfg.in_channels,
+            eps=1e-6,
+            affine=True,
+        )
+        self.proj_in = linear_cls(self.cfg.in_channels, inner_dim)
+
+        # 3. Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    self.num_attention_heads,
+                    self.attention_head_dim,
+                    dropout=self.cfg.dropout,
+                    cross_attention_dim=self.cfg.cross_attention_dim,
+                    activation_fn=self.cfg.activation_fn,
+                    attention_bias=self.cfg.attention_bias,
+                    only_cross_attention=self.cfg.only_cross_attention,
+                    double_self_attention=self.cfg.double_self_attention,
+                    upcast_attention=self.cfg.upcast_attention,
+                    norm_type=self.cfg.norm_type,
+                    norm_elementwise_affine=self.cfg.norm_elementwise_affine,
+                )
+                for d in range(self.cfg.num_layers)
+            ]
+        )
+
+        # 4. Define output layers
+        self.out_channels = (
+            self.cfg.in_channels
+            if self.cfg.out_channels is None
+            else self.cfg.out_channels
+        )
+
+        self.proj_out = linear_cls(inner_dim, self.cfg.in_channels)
+
+        self.gradient_checkpointing = self.cfg.gradient_checkpointing
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+    ):
+        """
+        The [`Transformer1DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
+                Input `hidden_states`.
+            encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
+                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+                self-attention.
+            attention_mask ( `torch.Tensor`, *optional*):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+            encoder_attention_mask ( `torch.Tensor`, *optional*):
+                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
+
+                    * Mask `(batch, sequence_length)` True = keep, False = discard.
+                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
+
+                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
+                above. This bias will be added to the cross-attention scores.
+
+        Returns:
+            torch.FloatTensor
+        """
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
+        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
+        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None and attention_mask.ndim == 2:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
+            encoder_attention_mask = (
+                1 - encoder_attention_mask.to(hidden_states.dtype)
+            ) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 1. Input
+        batch, _, seq_len = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 1).reshape(
+            batch, seq_len, inner_dim
+        )
+        hidden_states = self.proj_in(hidden_states)
+
+        # 2. Blocks
+        for block in self.transformer_blocks:
+            if self.training and self.gradient_checkpointing:
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    block,
+                    hidden_states,
+                    attention_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+
+        # 3. Output
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = (
+            hidden_states.reshape(batch, seq_len, inner_dim)
+            .permute(0, 2, 1)
+            .contiguous()
+        )
+
+        output = hidden_states + residual
+
+        return output
@@ -0,0 +1,218 @@
+import math
+import os
+from dataclasses import dataclass, field
+from typing import List, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+import trimesh
+from einops import rearrange
+from huggingface_hub import hf_hub_download
+from omegaconf import OmegaConf
+from PIL import Image
+
+from .models.isosurface import MarchingCubeHelper
+from .utils import (
+    BaseModule,
+    ImagePreprocessor,
+    find_class,
+    get_spherical_cameras,
+    scale_tensor,
+)
+
+
+class TSR(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        cond_image_size: int
+
+        image_tokenizer_cls: str
+        image_tokenizer: dict
+
+        tokenizer_cls: str
+        tokenizer: dict
+
+        backbone_cls: str
+        backbone: dict
+
+        post_processor_cls: str
+        post_processor: dict
+
+        decoder_cls: str
+        decoder: dict
+
+        renderer_cls: str
+        renderer: dict
+
+    cfg: Config
+
+    @classmethod
+    def from_pretrained(
+        cls, pretrained_model_name_or_path: str, config_name: str, weight_name: str
+    ):
+        if os.path.isdir(pretrained_model_name_or_path):
+            config_path = os.path.join(pretrained_model_name_or_path, config_name)
+            weight_path = os.path.join(pretrained_model_name_or_path, weight_name)
+        else:
+            config_path = hf_hub_download(
+                repo_id=pretrained_model_name_or_path, filename=config_name
+            )
+            weight_path = hf_hub_download(
+                repo_id=pretrained_model_name_or_path, filename=weight_name
+            )
+
+        cfg = OmegaConf.load(config_path)
+        OmegaConf.resolve(cfg)
+        model = cls(cfg)
+        ckpt = torch.load(weight_path, map_location="cpu")
+        model.load_state_dict(ckpt)
+        return model
+
+    @classmethod
+    def from_pretrained_custom(
+            cls, weight_path: str, config_path: str
+    ):
+        cfg = OmegaConf.load(config_path)
+        OmegaConf.resolve(cfg)
+        model = cls(cfg)
+        ckpt = torch.load(weight_path, map_location="cpu")
+        model.load_state_dict(ckpt)
+        return model
+
+    def configure(self):
+        self.image_tokenizer = find_class(self.cfg.image_tokenizer_cls)(
+            self.cfg.image_tokenizer
+        )
+        self.tokenizer = find_class(self.cfg.tokenizer_cls)(self.cfg.tokenizer)
+        self.backbone = find_class(self.cfg.backbone_cls)(self.cfg.backbone)
+        self.post_processor = find_class(self.cfg.post_processor_cls)(
+            self.cfg.post_processor
+        )
+        self.decoder = find_class(self.cfg.decoder_cls)(self.cfg.decoder)
+        self.renderer = find_class(self.cfg.renderer_cls)(self.cfg.renderer)
+        self.image_processor = ImagePreprocessor()
+        self.isosurface_helper = None
+
+    def forward(
+        self,
+        image: Union[
+            PIL.Image.Image,
+            np.ndarray,
+            torch.FloatTensor,
+            List[PIL.Image.Image],
+            List[np.ndarray],
+            List[torch.FloatTensor],
+        ],
+        device: str,
+    ) -> torch.FloatTensor:
+        rgb_cond = self.image_processor(image, self.cfg.cond_image_size)[:, None].to(
+            device
+        )
+        batch_size = rgb_cond.shape[0]
+
+        input_image_tokens: torch.Tensor = self.image_tokenizer(
+            rearrange(rgb_cond, "B Nv H W C -> B Nv C H W", Nv=1),
+        )
+
+        input_image_tokens = rearrange(
+            input_image_tokens, "B Nv C Nt -> B (Nv Nt) C", Nv=1
+        )
+
+        tokens: torch.Tensor = self.tokenizer(batch_size)
+
+        tokens = self.backbone(
+            tokens,
+            encoder_hidden_states=input_image_tokens,
+        )
+
+        scene_codes = self.post_processor(self.tokenizer.detokenize(tokens))
+        return scene_codes
+
+    def render(
+        self,
+        scene_codes,
+        n_views: int,
+        elevation_deg: float = 0.0,
+        camera_distance: float = 1.9,
+        fovy_deg: float = 40.0,
+        height: int = 256,
+        width: int = 256,
+        return_type: str = "pil",
+    ):
+        rays_o, rays_d = get_spherical_cameras(
+            n_views, elevation_deg, camera_distance, fovy_deg, height, width
+        )
+        rays_o, rays_d = rays_o.to(scene_codes.device), rays_d.to(scene_codes.device)
+
+        def process_output(image: torch.FloatTensor):
+            if return_type == "pt":
+                return image
+            elif return_type == "np":
+                return image.detach().cpu().numpy()
+            elif return_type == "pil":
+                return Image.fromarray(
+                    (image.detach().cpu().numpy() * 255.0).astype(np.uint8)
+                )
+            else:
+                raise NotImplementedError
+
+        images = []
+        for scene_code in scene_codes:
+            images_ = []
+            for i in range(n_views):
+                with torch.no_grad():
+                    image = self.renderer(
+                        self.decoder, scene_code, rays_o[i], rays_d[i]
+                    )
+                images_.append(process_output(image))
+            images.append(images_)
+
+        return images
+
+    def set_marching_cubes_resolution(self, resolution: int):
+        if (
+            self.isosurface_helper is not None
+            and self.isosurface_helper.resolution == resolution
+        ):
+            return
+        self.isosurface_helper = MarchingCubeHelper(resolution)
+
+    def extract_mesh(self, scene_codes, resolution: int = 256, threshold: float = 25.0,callback=None):
+        self.set_marching_cubes_resolution(resolution)
+        meshes = []
+        for scene_code in scene_codes:
+            with torch.no_grad():
+                density = self.renderer.query_triplane(
+                    self.decoder,
+                    scale_tensor(
+                        self.isosurface_helper.grid_vertices.to(scene_codes.device),
+                        self.isosurface_helper.points_range,
+                        (-self.renderer.cfg.radius, self.renderer.cfg.radius),
+                    ),
+                    scene_code,
+                )["density_act"]
+            v_pos, t_pos_idx = self.isosurface_helper(-(density - threshold))
+            v_pos = scale_tensor(
+                v_pos,
+                self.isosurface_helper.points_range,
+                (-self.renderer.cfg.radius, self.renderer.cfg.radius),
+            )
+            with torch.no_grad():
+                color = self.renderer.query_triplane(
+                    self.decoder,
+                    v_pos,
+                    scene_code,
+                )["color"]
+            mesh = trimesh.Trimesh(
+                vertices=v_pos.cpu().numpy(),
+                faces=t_pos_idx.cpu().numpy(),
+                vertex_colors=color.cpu().numpy(),
+            )
+            meshes.append(mesh)
+
+            if callback:
+                callback(len(meshes))
+
+        return meshes
@@ -0,0 +1,475 @@
+import importlib
+import math
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import imageio
+import numpy as np
+import PIL.Image
+#import rembg
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import trimesh
+from omegaconf import DictConfig, OmegaConf
+#from PIL import Image
+
+
+def parse_structured(fields: Any, cfg: Optional[Union[dict, DictConfig]] = None) -> Any:
+    scfg = OmegaConf.merge(OmegaConf.structured(fields), cfg)
+    return scfg
+
+
+def find_class(cls_string):
+    module_string = ".".join(cls_string.split(".")[:-1])
+    cls_name = cls_string.split(".")[-1]
+    module = importlib.import_module(module_string, package=None)
+    cls = getattr(module, cls_name)
+    return cls
+
+
+def get_intrinsic_from_fov(fov, H, W, bs=-1):
+    focal_length = 0.5 * H / np.tan(0.5 * fov)
+    intrinsic = np.identity(3, dtype=np.float32)
+    intrinsic[0, 0] = focal_length
+    intrinsic[1, 1] = focal_length
+    intrinsic[0, 2] = W / 2.0
+    intrinsic[1, 2] = H / 2.0
+
+    if bs > 0:
+        intrinsic = intrinsic[None].repeat(bs, axis=0)
+
+    return torch.from_numpy(intrinsic)
+
+
+class BaseModule(nn.Module):
+    @dataclass
+    class Config:
+        pass
+
+    cfg: Config  # add this to every subclass of BaseModule to enable static type checking
+
+    def __init__(
+        self, cfg: Optional[Union[dict, DictConfig]] = None, *args, **kwargs
+    ) -> None:
+        super().__init__()
+        self.cfg = parse_structured(self.Config, cfg)
+        self.configure(*args, **kwargs)
+
+    def configure(self, *args, **kwargs) -> None:
+        raise NotImplementedError
+
+
+class ImagePreprocessor:
+    def convert_and_resize(
+        self,
+        image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],
+        size: int,
+    ):
+        if isinstance(image, PIL.Image.Image):
+            image = torch.from_numpy(np.array(image).astype(np.float32) / 255.0)
+        elif isinstance(image, np.ndarray):
+            if image.dtype == np.uint8:
+                image = torch.from_numpy(image.astype(np.float32) / 255.0)
+            else:
+                image = torch.from_numpy(image)
+        elif isinstance(image, torch.Tensor):
+            pass
+
+        batched = image.ndim == 4
+
+        if not batched:
+            image = image[None, ...]
+        image = F.interpolate(
+            image.permute(0, 3, 1, 2),
+            (size, size),
+            mode="bilinear",
+            align_corners=False,
+            antialias=True,
+        ).permute(0, 2, 3, 1)
+        if not batched:
+            image = image[0]
+        return image
+
+    def __call__(
+        self,
+        image: Union[
+            PIL.Image.Image,
+            np.ndarray,
+            torch.FloatTensor,
+            List[PIL.Image.Image],
+            List[np.ndarray],
+            List[torch.FloatTensor],
+        ],
+        size: int,
+    ) -> Any:
+        if isinstance(image, (np.ndarray, torch.FloatTensor)) and image.ndim == 4:
+            image = self.convert_and_resize(image, size)
+        else:
+            if not isinstance(image, list):
+                image = [image]
+            image = [self.convert_and_resize(im, size) for im in image]
+            image = torch.stack(image, dim=0)
+        return image
+
+
+def rays_intersect_bbox(
+    rays_o: torch.Tensor,
+    rays_d: torch.Tensor,
+    radius: float,
+    near: float = 0.0,
+    valid_thresh: float = 0.01,
+):
+    input_shape = rays_o.shape[:-1]
+    rays_o, rays_d = rays_o.view(-1, 3), rays_d.view(-1, 3)
+    rays_d_valid = torch.where(
+        rays_d.abs() < 1e-6, torch.full_like(rays_d, 1e-6), rays_d
+    )
+    if type(radius) in [int, float]:
+        radius = torch.FloatTensor(
+            [[-radius, radius], [-radius, radius], [-radius, radius]]
+        ).to(rays_o.device)
+    radius = (
+        1.0 - 1.0e-3
+    ) * radius  # tighten the radius to make sure the intersection point lies in the bounding box
+    interx0 = (radius[..., 1] - rays_o) / rays_d_valid
+    interx1 = (radius[..., 0] - rays_o) / rays_d_valid
+    t_near = torch.minimum(interx0, interx1).amax(dim=-1).clamp_min(near)
+    t_far = torch.maximum(interx0, interx1).amin(dim=-1)
+
+    # check wheter a ray intersects the bbox or not
+    rays_valid = t_far - t_near > valid_thresh
+
+    t_near[torch.where(~rays_valid)] = 0.0
+    t_far[torch.where(~rays_valid)] = 0.0
+
+    t_near = t_near.view(*input_shape, 1)
+    t_far = t_far.view(*input_shape, 1)
+    rays_valid = rays_valid.view(*input_shape)
+
+    return t_near, t_far, rays_valid
+
+
+def chunk_batch(func: Callable, chunk_size: int, *args, **kwargs) -> Any:
+    if chunk_size <= 0:
+        return func(*args, **kwargs)
+    B = None
+    for arg in list(args) + list(kwargs.values()):
+        if isinstance(arg, torch.Tensor):
+            B = arg.shape[0]
+            break
+    assert (
+        B is not None
+    ), "No tensor found in args or kwargs, cannot determine batch size."
+    out = defaultdict(list)
+    out_type = None
+    # max(1, B) to support B == 0
+    for i in range(0, max(1, B), chunk_size):
+        out_chunk = func(
+            *[
+                arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg
+                for arg in args
+            ],
+            **{
+                k: arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg
+                for k, arg in kwargs.items()
+            },
+        )
+        if out_chunk is None:
+            continue
+        out_type = type(out_chunk)
+        if isinstance(out_chunk, torch.Tensor):
+            out_chunk = {0: out_chunk}
+        elif isinstance(out_chunk, tuple) or isinstance(out_chunk, list):
+            chunk_length = len(out_chunk)
+            out_chunk = {i: chunk for i, chunk in enumerate(out_chunk)}
+        elif isinstance(out_chunk, dict):
+            pass
+        else:
+            print(
+                f"Return value of func must be in type [torch.Tensor, list, tuple, dict], get {type(out_chunk)}."
+            )
+            exit(1)
+        for k, v in out_chunk.items():
+            v = v if torch.is_grad_enabled() else v.detach()
+            out[k].append(v)
+
+    if out_type is None:
+        return None
+
+    out_merged: Dict[Any, Optional[torch.Tensor]] = {}
+    for k, v in out.items():
+        if all([vv is None for vv in v]):
+            # allow None in return value
+            out_merged[k] = None
+        elif all([isinstance(vv, torch.Tensor) for vv in v]):
+            out_merged[k] = torch.cat(v, dim=0)
+        else:
+            raise TypeError(
+                f"Unsupported types in return value of func: {[type(vv) for vv in v if not isinstance(vv, torch.Tensor)]}"
+            )
+
+    if out_type is torch.Tensor:
+        return out_merged[0]
+    elif out_type in [tuple, list]:
+        return out_type([out_merged[i] for i in range(chunk_length)])
+    elif out_type is dict:
+        return out_merged
+
+
+ValidScale = Union[Tuple[float, float], torch.FloatTensor]
+
+
+def scale_tensor(dat: torch.FloatTensor, inp_scale: ValidScale, tgt_scale: ValidScale):
+    if inp_scale is None:
+        inp_scale = (0, 1)
+    if tgt_scale is None:
+        tgt_scale = (0, 1)
+    if isinstance(tgt_scale, torch.FloatTensor):
+        assert dat.shape[-1] == tgt_scale.shape[-1]
+    dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])
+    dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]
+    return dat
+
+
+def get_activation(name) -> Callable:
+    if name is None:
+        return lambda x: x
+    name = name.lower()
+    if name == "none":
+        return lambda x: x
+    elif name == "exp":
+        return lambda x: torch.exp(x)
+    elif name == "sigmoid":
+        return lambda x: torch.sigmoid(x)
+    elif name == "tanh":
+        return lambda x: torch.tanh(x)
+    elif name == "softplus":
+        return lambda x: F.softplus(x)
+    else:
+        try:
+            return getattr(F, name)
+        except AttributeError:
+            raise ValueError(f"Unknown activation function: {name}")
+
+
+def get_ray_directions(
+    H: int,
+    W: int,
+    focal: Union[float, Tuple[float, float]],
+    principal: Optional[Tuple[float, float]] = None,
+    use_pixel_centers: bool = True,
+    normalize: bool = True,
+) -> torch.FloatTensor:
+    """
+    Get ray directions for all pixels in camera coordinate.
+    Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/
+               ray-tracing-generating-camera-rays/standard-coordinate-systems
+
+    Inputs:
+        H, W, focal, principal, use_pixel_centers: image height, width, focal length, principal point and whether use pixel centers
+    Outputs:
+        directions: (H, W, 3), the direction of the rays in camera coordinate
+    """
+    pixel_center = 0.5 if use_pixel_centers else 0
+
+    if isinstance(focal, float):
+        fx, fy = focal, focal
+        cx, cy = W / 2, H / 2
+    else:
+        fx, fy = focal
+        assert principal is not None
+        cx, cy = principal
+
+    i, j = torch.meshgrid(
+        torch.arange(W, dtype=torch.float32) + pixel_center,
+        torch.arange(H, dtype=torch.float32) + pixel_center,
+        indexing="xy",
+    )
+
+    directions = torch.stack([(i - cx) / fx, -(j - cy) / fy, -torch.ones_like(i)], -1)
+
+    if normalize:
+        directions = F.normalize(directions, dim=-1)
+
+    return directions
+
+
+def get_rays(
+    directions,
+    c2w,
+    keepdim=False,
+    normalize=False,
+) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
+    # Rotate ray directions from camera coordinate to the world coordinate
+    assert directions.shape[-1] == 3
+
+    if directions.ndim == 2:  # (N_rays, 3)
+        if c2w.ndim == 2:  # (4, 4)
+            c2w = c2w[None, :, :]
+        assert c2w.ndim == 3  # (N_rays, 4, 4) or (1, 4, 4)
+        rays_d = (directions[:, None, :] * c2w[:, :3, :3]).sum(-1)  # (N_rays, 3)
+        rays_o = c2w[:, :3, 3].expand(rays_d.shape)
+    elif directions.ndim == 3:  # (H, W, 3)
+        assert c2w.ndim in [2, 3]
+        if c2w.ndim == 2:  # (4, 4)
+            rays_d = (directions[:, :, None, :] * c2w[None, None, :3, :3]).sum(
+                -1
+            )  # (H, W, 3)
+            rays_o = c2w[None, None, :3, 3].expand(rays_d.shape)
+        elif c2w.ndim == 3:  # (B, 4, 4)
+            rays_d = (directions[None, :, :, None, :] * c2w[:, None, None, :3, :3]).sum(
+                -1
+            )  # (B, H, W, 3)
+            rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape)
+    elif directions.ndim == 4:  # (B, H, W, 3)
+        assert c2w.ndim == 3  # (B, 4, 4)
+        rays_d = (directions[:, :, :, None, :] * c2w[:, None, None, :3, :3]).sum(
+            -1
+        )  # (B, H, W, 3)
+        rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape)
+
+    if normalize:
+        rays_d = F.normalize(rays_d, dim=-1)
+    if not keepdim:
+        rays_o, rays_d = rays_o.reshape(-1, 3), rays_d.reshape(-1, 3)
+
+    return rays_o, rays_d
+
+
+def get_spherical_cameras(
+    n_views: int,
+    elevation_deg: float,
+    camera_distance: float,
+    fovy_deg: float,
+    height: int,
+    width: int,
+):
+    azimuth_deg = torch.linspace(0, 360.0, n_views + 1)[:n_views]
+    elevation_deg = torch.full_like(azimuth_deg, elevation_deg)
+    camera_distances = torch.full_like(elevation_deg, camera_distance)
+
+    elevation = elevation_deg * math.pi / 180
+    azimuth = azimuth_deg * math.pi / 180
+
+    # convert spherical coordinates to cartesian coordinates
+    # right hand coordinate system, x back, y right, z up
+    # elevation in (-90, 90), azimuth from +x to +y in (-180, 180)
+    camera_positions = torch.stack(
+        [
+            camera_distances * torch.cos(elevation) * torch.cos(azimuth),
+            camera_distances * torch.cos(elevation) * torch.sin(azimuth),
+            camera_distances * torch.sin(elevation),
+        ],
+        dim=-1,
+    )
+
+    # default scene center at origin
+    center = torch.zeros_like(camera_positions)
+    # default camera up direction as +z
+    up = torch.as_tensor([0, 0, 1], dtype=torch.float32)[None, :].repeat(n_views, 1)
+
+    fovy = torch.full_like(elevation_deg, fovy_deg) * math.pi / 180
+
+    lookat = F.normalize(center - camera_positions, dim=-1)
+    right = F.normalize(torch.cross(lookat, up), dim=-1)
+    up = F.normalize(torch.cross(right, lookat), dim=-1)
+    c2w3x4 = torch.cat(
+        [torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]],
+        dim=-1,
+    )
+    c2w = torch.cat([c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1)
+    c2w[:, 3, 3] = 1.0
+
+    # get directions by dividing directions_unit_focal by focal length
+    focal_length = 0.5 * height / torch.tan(0.5 * fovy)
+    directions_unit_focal = get_ray_directions(
+        H=height,
+        W=width,
+        focal=1.0,
+    )
+    directions = directions_unit_focal[None, :, :, :].repeat(n_views, 1, 1, 1)
+    directions[:, :, :, :2] = (
+        directions[:, :, :, :2] / focal_length[:, None, None, None]
+    )
+    # must use normalize=True to normalize directions here
+    rays_o, rays_d = get_rays(directions, c2w, keepdim=True, normalize=True)
+
+    return rays_o, rays_d
+
+
+# def remove_background(
+#     image: PIL.Image.Image,
+#     rembg_session: Any = None,
+#     force: bool = False,
+#     **rembg_kwargs,
+# ) -> PIL.Image.Image:
+#     do_remove = True
+#     if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
+#         do_remove = False
+#     do_remove = do_remove or force
+#     if do_remove:
+#         image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
+#     return image
+
+
+def resize_foreground(
+    image: PIL.Image.Image,
+    ratio: float,
+) -> PIL.Image.Image:
+    image = np.array(image)
+    assert image.shape[-1] == 4
+    alpha = np.where(image[..., 3] > 0)
+    y1, y2, x1, x2 = (
+        alpha[0].min(),
+        alpha[0].max(),
+        alpha[1].min(),
+        alpha[1].max(),
+    )
+    # crop the foreground
+    fg = image[y1:y2, x1:x2]
+    # pad to square
+    size = max(fg.shape[0], fg.shape[1])
+    ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2
+    ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0
+    new_image = np.pad(
+        fg,
+        ((ph0, ph1), (pw0, pw1), (0, 0)),
+        mode="constant",
+        constant_values=((0, 0), (0, 0), (0, 0)),
+    )
+
+    # compute padding according to the ratio
+    new_size = int(new_image.shape[0] / ratio)
+    # pad to size, double side
+    ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2
+    ph1, pw1 = new_size - size - ph0, new_size - size - pw0
+    new_image = np.pad(
+        new_image,
+        ((ph0, ph1), (pw0, pw1), (0, 0)),
+        mode="constant",
+        constant_values=((0, 0), (0, 0), (0, 0)),
+    )
+    new_image = PIL.Image.fromarray(new_image)
+    return new_image
+
+
+def save_video(
+    frames: List[PIL.Image.Image],
+    output_path: str,
+    fps: int = 30,
+):
+    # use imageio to save video
+    frames = [np.array(frame) for frame in frames]
+    writer = imageio.get_writer(output_path, fps=fps)
+    for frame in frames:
+        writer.append_data(frame)
+    writer.close()
+
+
+def to_gradio_3d_orientation(mesh):
+    mesh.apply_transform(trimesh.transformations.rotation_matrix(-np.pi/2, [1, 0, 0]))
+    mesh.apply_scale([1, 1, -1])
+    mesh.apply_transform(trimesh.transformations.rotation_matrix(np.pi/2, [0, 1, 0]))
+    return mesh
@@ -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 = "0.30.2"
+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 = ""
@@ -7,6 +7,14 @@ openai
 simple-lama-inpainting
 clip-interrogator==0.6.0
 transformers>=4.36.0
-zhipuai
 lark-parser
-imageio-ffmpeg
+imageio-ffmpeg
+rembg[gpu]
+omegaconf==2.3.0
+Pillow>=9.5.0
+einops==0.7.0
+trimesh>=4.0.5
+huggingface-hub
+scikit-image
+torchaudio
+soundfile>=0.12.1
@@ -2,6 +2,26 @@ 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
+
+//获取当前系统的插件，节点清单
+function getObjectInfo () {
+  return new Promise(async (resolve, reject) => {
+    let url = getUrl()
+
+    try {
+      const response = await fetch(`${url}/object_info`)
+      const data = await response.json()
+      resolve(data)
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
+
 const base64Df =
  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'

@@ -52,7 +72,8 @@ function get_position_style (ctx, widget_width, y, node_height) {
    display: 'flex',
    flexDirection: 'column',
    // alignItems: 'center',
-    justifyContent: 'flex-start'
+    justifyContent: 'flex-start',
+    zIndex: 9999999
  }
 }

@@ -166,6 +187,25 @@ async function extractInputAndOutputData (
        if (node.type == 'Color') {
        }

+        // 语音输入的支持
+        if (node.type == 'LoadAndCombinedAudio_') {
+          // if (
+          //   data[id].widgets_values &&
+          //   data[id].widgets_values[0] &&
+          //   data[id].widgets_values[0].base64 &&
+          //   data[id].widgets_values[0].base64.length > 0
+          // ) {
+          //   options.defaultBase64 = data[id].widgets_values[0].base64
+          // }
+
+          input[inputIds.indexOf(id)] = {
+            ...data[id],
+            title: node.title,
+            id,
+            options
+          }
+        }
+
        if (node.type === 'LoadImage') {
          // loadImage的mask支持
          let output = node.outputs.filter(ot => ot.type == 'MASK')[0]
@@ -174,7 +214,7 @@ 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)
@@ -189,15 +229,34 @@ 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 === 'ChinesePrompt_Mix' ||
+        node.type === 'Seed_'
      ) {
        // seed 的类型收集
        try {
@@ -266,7 +325,10 @@ function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
 }

 async function save (json, download = false, showInfo = true) {
-  console.log('####SAVE', json[0])
+  let nodesAll = window._nodesAll || (await getObjectInfo())
+
+  console.log('####SAVE', nodesAll, json[0])
+
  const name = json[0],
    version = json[5],
    share_prefix = json[6], //用于分享的功能扩展
@@ -287,6 +349,13 @@ async function save (json, download = false, showInfo = true) {
  try {
    let data = await app.graphToPrompt()

+    //从output数据里把工作流的节点，插件数据统计出来
+    data.nodesMap = {}
+    for (const id in data.output) {
+      data.nodesMap[data.output[id].class_type] =
+        nodesAll[data.output[id].class_type]
+    }
+
    let { input, output, seed, seedTitle } = await extractInputAndOutputData(
      data,
      inputIds,
@@ -349,11 +418,11 @@ async function save (json, download = false, showInfo = true) {

 function getInputsAndOutputs () {
  const inputs =
-      `LoadImage 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 =
-      `PreviewImage,SaveImage,ShowTextForGPT,VHS_VideoCombine,Image Save,SaveImageAndMetadata_`.split(
+      `SaveTripoSRMesh,PreviewImage,SaveImage,TransparentImage,ShowTextForGPT,CombineAudioVideo,VHS_VideoCombine,VideoCombine_Adv,Image Save,SaveImageAndMetadata_,ClipInterrogator`.split(
        ','
      )

@@ -378,6 +447,11 @@ function getInputsAndOutputs () {

 app.registerExtension({
  name: 'Mixlab.utils.AppInfo',
+  init () {
+    if (!window._nodesAll) {
+      getObjectInfo().then(r => (window._nodesAll = r))
+    }
+  },
  async beforeRegisterNodeDef (nodeType, nodeData, app) {
    if (nodeType.comfyClass == 'AppInfo') {
      const orig_nodeCreated = nodeType.prototype.onNodeCreated
@@ -450,6 +524,21 @@ 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`
@@ -606,6 +695,7 @@ app.registerExtension({

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

        document.body.appendChild(widget.div)
        this.addCustomWidget(widget)
@@ -619,6 +709,7 @@ app.registerExtension({
        this.serialize_widgets = true //需要保存参数

        window._mixlab_app_json = null
+ 
      }

      const onExecuted = nodeType.prototype.onExecuted
@@ -634,9 +725,8 @@ 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 +396,217 @@ 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.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 = 'v0.19.0'
+const version = 'v0.30.2'

 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
  .then(response => response.json())
@@ -17,7 +17,13 @@ fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
      return
    if (latestVersion && latestVersion != version) {
      localStorage.setItem('_mixlab_nodes_vesion', latestVersion)
-      app.ui.dialog.show(`<h4 style="font-size: 18px;">${repoName} <br>
+      app.ui.dialog.show(`<a style="color: white;
+      font-size: 18px;
+      font-weight: 800;
+      letter-spacing: 2px;
+    }"
+    href="https://discord.gg/cXs9vZSqeK">Welcome to Mixlab nodes discord</a>
+    <h4 style="font-size: 18px;">${repoName} <br>
      Latest release version: ${latestVersion}</h4>
      <p>Please proceed to the official repository to download the latest version.</p>
      <a style="color: #2196F3;
@@ -61,7 +61,7 @@ app.registerExtension({
  async getCustomWidgets (app) {
    return {
      KEY (node, inputName, inputData, app) {
-        console.log('##inputData', inputData)
+        // console.log('##inputData', inputData)
        const widget = {
          type: inputData[0], // the type, CHEESE
          name: inputName, // the name, slice
@@ -209,13 +209,16 @@ app.registerExtension({
        text = text.filter(t => t && t?.trim())

        if (this.widgets) {
+          // console.log('#ShowTextForGPT',this.widgets)
          // const pos = this.widgets.findIndex(w => w.name === 'text')
          for (let i = 0; i < this.widgets.length; i++) {
-            if (this.widgets[i].name == 'show_text') this.widgets[i].onRemove?.()
+            if (this.widgets[i].name == 'show_text')
+              this.widgets[i].onRemove?.()
+            console.log('#ShowTextForGPT', this.widgets[i])
          }
-          this.widgets.length = 1
+          this.widgets.length = 2
        }
-        // console.log('ShowTextForGPT',text)
+
        for (let list of text) {
          if (list) {
            // console.log('#####', list)
@@ -228,6 +231,8 @@ app.registerExtension({
            w.inputEl.readOnly = true
            w.inputEl.style.opacity = 0.6

+            // w.inputEl.style.display='none'
+
            try {
              if (typeof list != 'string') {
                let data = JSON.parse(list)
@@ -280,5 +285,24 @@ app.registerExtension({

      this.serialize_widgets = true //需要保存参数
    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'ShowTextForGPT') {
+      let widget = node.widgets.filter(w => w.name == 'show_text')[0]
+
+      // if (widget.value) {
+      //   let [url, prompt] = widget.value
+
+      //   this[`wavesurfer_${node.id}`] = updateWaveWidgetValue(
+      //     node.widgets,
+      //     node.id,
+      //     url,
+      //     prompt,
+      //     this[`wavesurfer_${node.id}`]
+      //   )
+      // }
+
+      console.log('#loadedGraphNode', node)
+    }
  }
 })
@@ -1,7 +1,39 @@
 import { app } from '../../../scripts/app.js'
 import { api } from '../../../scripts/api.js'
-import { ComfyWidgets } from '../../../scripts/widgets.js'
+// import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
+import { applyTextReplacements } from '../../../scripts/utils.js'
+
+function loadImageToCanvas (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 = 1024
+      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
+  })
+}

 async function uploadImage (blob, fileType = '.svg', filename) {
  // const blob = await (await fetch(src)).blob();
@@ -618,9 +650,343 @@ app.registerExtension({

        if (json && json[0]) {
          uploadWidget.select.style.display = 'block'
-          createSelect(img, uploadWidget.select, json, prompt,text)
+          createSelect(img, uploadWidget.select, json, prompt, text)
        }
      } catch (error) {}
    }
  }
 })
+
+const createInputImageForBatch = (base64, widget) => {
+  let im = new Image()
+  im.src = base64
+  im.style = `width: 88px;`
+
+  im.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
+    im.remove()
+  })
+
+  return im
+}
+
+app.registerExtension({
+  name: 'Mixlab.Comfy.LoadImagesToBatch',
+  async getCustomWidgets (app) {
+    return {
+      IMAGEBASE64 (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, 32] // 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 == 'LoadImagesToBatch') {
+
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        let imagesWidget = this.widgets.filter(w => w.name == 'images')[0]
+
+        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, 44, node.size[1])
+            )
+          },
+          serialize: false
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        let imagePreview = document.createElement('div')
+        let imagesDiv = document.createElement('div') //显示图片
+        imagesDiv.className = 'images_preview'
+        imagesDiv.style = `width: calc(100% - 14px);
+        display: flex;
+        flex-wrap: wrap;
+        padding: 7px;    justify-content: space-between;
+        align-items: center;`
+
+        let inputImage = document.createElement('input')
+        inputImage.type = 'file'
+        inputImage.style.display = 'none'
+        inputImage.addEventListener('change', e => {
+          e.preventDefault()
+          const file = e.target.files[0]
+          const reader = new FileReader()
+          reader.onload = async event => {
+            let base64 = event.target.result
+            //压缩图片，控制1024以内
+            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)
+          }
+          reader.readAsDataURL(file)
+        })
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Upload Image'
+
+        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()
+          inputImage.click()
+        })
+
+        widget.div.appendChild(imagePreview)
+        imagePreview.appendChild(imagesDiv)
+        imagePreview.appendChild(btn)
+        imagePreview.appendChild(inputImage)
+
+        this.addCustomWidget(widget)
+
+        // document.addEventListener('wheel', handleMouseWheel)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          inputImage.remove()
+          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
+      }
+    }
+
+    if (nodeData.name === 'SaveImageAndMetadata_') {
+      const onNodeCreated = nodeType.prototype.onNodeCreated
+      // /web/extensions/core/saveImageExtraOutput.js
+      nodeType.prototype.onNodeCreated = function () {
+        const r = onNodeCreated
+          ? onNodeCreated.apply(this, arguments)
+          : undefined
+        const widget = this.widgets.find(w => w.name === 'filename_prefix')
+        widget.serializeValue = () => {
+          return applyTextReplacements(app, widget.value)
+        }
+
+        return r
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log('##onExecuted', this, message)
+        //TODO 是否 保存base64
+        if (message.base64) {
+          if (Array.isArray(message.base64)) {
+          }
+        }
+      }
+    }
+  },
+  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]
+
+      let pre = imagePreview.div.querySelector('.images_preview')
+      for (const d of imagesWidget.value?.base64 || []) {
+        let im = createInputImageForBatch(d, imagesWidget)
+        pre.appendChild(im)
+      }
+    }
+  }
+})
+
+// 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 +1,203 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { $el } from '../../../scripts/ui.js'
+
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 14 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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',
+    // outline: '1px solid red',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+
+app.registerExtension({
+  name: 'Mixlab.3D.SaveTripoSRMesh',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'SaveTripoSRMesh') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const widget = {
+          type: 'div',
+          name: 'preview',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 88, node.size[1])
+            )
+          }
+          //   value: [],
+          //   async serializeValue (nodeId, widgetIndex) {
+          //     return widget.value
+          //   }
+        }
+
+        widget.div = $el('div', {})
+        widget.div.style.width = `120px`
+
+        document.body.appendChild(widget.div)
+
+        // preview.style = `margin-top: 12px;display: flex;
+        //   justify-content: center;
+        //   align-items: center;background-repeat: no-repeat;background-size: contain;`
+
+        this.addCustomWidget(widget)
+
+        const onResize = this.onResize
+        this.onResize = () => {
+          widget.div.style.width = `${this.size[0]}px`
+          widget.div.style.height = `${this.size[1] - 112}px`
+          let mvs = widget.div.querySelectorAll('model-viewer')
+          for (const m of mvs) {
+            m.style.height = `${Math.round(
+              (this.size[1] - 112) / mvs.length
+            )}px`
+            // console.log(m.style.height)
+          }
+          //   console.log('resize', this.size)
+          return onResize?.apply(this, arguments)
+        }
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+        // this.isVirtualNode = true
+        this.serialize_widgets = false //需要保存参数
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        const r = onExecuted?.apply?.(this, arguments)
+
+        let widget = this.widgets.filter(d => d.name == 'preview')[0]
+        console.log('Test', widget, message)
+
+        let meshes = message.mesh
+        widget.div.innerHTML = ''
+
+        for (const mesh of meshes) {
+          if (mesh) {
+            const { filename, subfolder, type } = mesh
+            const fileURL = api.apiURL(
+              `/view?filename=${encodeURIComponent(
+                filename
+              )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+            )
+
+            let modelViewer = document.createElement('div')
+            modelViewer.innerHTML = `<model-viewer  src="${fileURL}" 
+              min-field-of-view="0deg" max-field-of-view="180deg"
+              shadow-intensity="1" 
+              camera-controls 
+              touch-action="pan-y"
+                  style="width:100%;margin:4px;min-height:88px"
+              >
+              
+              <div class="controls">
+            
+              <div><button class="export" style="
+              background-color: var(--comfy-input-bg);
+              border-radius: 8px;
+              border-color: var(--border-color);
+              border-style: solid;
+              color: var(--descrip-text);cursor: pointer;">Export GLB</button></div>
+              
+              </div></model-viewer>`
+            widget.div.appendChild(modelViewer)
+            let modelViewerVariants= modelViewer
+            .querySelector('model-viewer');
+            
+            modelViewer
+              .querySelector('.export')
+              .addEventListener('click', async e => {
+                e.preventDefault()
+                const glTF = await modelViewerVariants.exportScene()
+                const file = new File([glTF], filename)
+                const link = document.createElement('a')
+                link.download = file.name
+                link.href = URL.createObjectURL(file)
+                link.click()
+              })
+          }
+        }
+
+        // widget.value = [meshes]
+
+        this.onResize?.(this.size)
+
+        return r
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    const sleep = (t = 1000) => {
+      return new Promise((res, rej) => {
+        setTimeout(() => res(1), t)
+      })
+    }
+    // if (node.type === 'SaveTripoSRMesh') {
+    //   await sleep(0)
+    //   let widget = node.widgets.filter(w => w.name === 'preview')[0]
+    //   widget.div.innerHTML = ''
+
+    //   for (const mesh of widget.value) {
+    //     if (mesh) {
+    //       const { filename, subfolder, type } = mesh
+    //       const fileURL = api.apiURL(
+    //         `/view?filename=${encodeURIComponent(
+    //           filename
+    //         )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+    //       )
+
+    //       let modelViewer = document.createElement('div')
+    //       modelViewer.innerHTML = `<model-viewer  src="${fileURL}"
+    // 	 min-field-of-view="0deg" max-field-of-view="180deg"
+    // 	 shadow-intensity="1"
+    // 	 camera-controls
+    // 	 touch-action="pan-y">
+
+    // 	 <div class="controls">
+
+    // 	  <div><button class="export">Export GLB</button></div>
+
+    // 	</div></model-viewer>`
+    //       widget.div.appendChild(modelViewer)
+    //     }
+    //   }
+    // }
+  }
+})
@@ -46,6 +46,18 @@ const smart_connect_config_input = [
    node_widget_name: 'image',
    inputNodeName: 'LoadImage',
    inputNode_output_name: 'IMAGE'
+  },
+  {
+    node_type: 'TripoSRSampler_',
+    node_widget_name: 'image',
+    inputNodeName: 'LoadImagesToBatch',
+    inputNode_output_name: 'IMAGE'
+  },
+  {
+    node_type: 'TripoSRSampler_',
+    node_widget_name: 'mask',
+    inputNodeName: 'RembgNode_Mix',
+    inputNode_output_name: 'masks'
  }
 ]

@@ -74,6 +86,18 @@ const smart_connect_config_output = [
    outputNodeName: 'SaveImage',
    outputNode_input_name: 'images'
  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'AppInfo',
+    outputNode_input_name: 'IMAGE'
+  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'SaveImageAndMetadata_',
+    outputNode_input_name: 'images'
+  },
  {
    node_type: 'Moondream',
    node_output_name: 'STRING',
@@ -181,7 +205,10 @@ export function smart_init () {
      ]
      let node_slotType = config[0]
      // 如果input没有，则创建
-      if (!node.inputs?.filter(inp => inp.name === widget.name)[0]||!node.inputs)
+      if (
+        !node.inputs?.filter(inp => inp.name === widget.name)[0] ||
+        !node.inputs
+      )
        convertToInput(node, widget, config)
      input_node.connectByType(inputNode_slot, node, node_slotType)
    }
@@ -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()
@@ -0,0 +1,508 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+
+import { $el } from '../../../scripts/ui.js'
+
+// The code is based on ComfyUI-VideoHelperSuite modification.
+
+function injectCSS (css) {
+  // 检查页面中是否已经存在具有相同内容的style标签
+  const existingStyle = document.querySelector('style')
+  if (existingStyle && existingStyle.textContent === css) {
+    return // 如果已经存在相同的样式，则不进行注入
+  }
+
+  // 创建一个新的style标签，并将CSS内容注入其中
+  const style = document.createElement('style')
+  style.textContent = css
+
+  // 将style标签插入到页面的head元素中
+  const head = document.querySelector('head')
+  head.appendChild(style)
+}
+
+injectCSS(`
+.hidden{
+  display:none !important
+}`)
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${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'
+  }
+}
+
+function videoUpload (node, inputName, inputData, app) {
+  const imageWidget = node.widgets.find(w => w.name === 'video')
+  let uploadWidget
+
+  const widget = {
+    type: 'div',
+    name: 'upload-preview',
+    draw (ctx, node, widget_width, y, widget_height) {
+      Object.assign(
+        this.div.style,
+        get_position_style(ctx, widget_width, 220, node.size[1]),
+        {
+          outline: '1px solid'
+        }
+      )
+    }
+  }
+
+  widget.div = $el('div', {})
+  widget.div.style.width = `120px`
+  document.body.appendChild(widget.div)
+  node.addCustomWidget(widget)
+  // console.log('#imageWidget', imageWidget)
+  const displayDiv = document.createElement('video')
+  displayDiv.controls = true
+  // displayDiv.style=`width:200px;height:200px`
+  imageWidget.callback = () => {
+    displayDiv.src = `/view?filename=${
+      imageWidget.value
+    }&type=input&subfolder=${''}&rand=${Math.random()}`
+
+    // displayDiv.onloadedmetadata = function () {
+    //   var frameCount = displayDiv.duration * displayDiv.webkitDecodedFrameCount
+    //   console.log('视频帧数：' + frameCount)
+    //   node.widgets.filter(w => w.name == 'video_segment_frames')[0].value =
+    //     frameCount
+    // }
+  }
+
+  if (imageWidget.value) {
+    // console.log(imageWidget.value)
+    displayDiv.src = `/view?filename=${
+      imageWidget.value
+    }&type=input&subfolder=${''}&rand=${Math.random()}`
+  }
+
+  widget.div.appendChild(displayDiv)
+
+  const onRemoved = node.onRemoved
+  node.onRemoved = () => {
+    widget.div.remove()
+    return onRemoved?.()
+  }
+
+  var default_value = imageWidget.value
+  Object.defineProperty(imageWidget, 'value', {
+    set: function (value) {
+      this._real_value = value
+    },
+
+    get: function () {
+      let value = ''
+      if (this._real_value) {
+        value = this._real_value
+      } else {
+        return default_value
+      }
+
+      if (value.filename) {
+        let real_value = value
+        value = ''
+        if (real_value.subfolder) {
+          value = real_value.subfolder + '/'
+        }
+
+        value += real_value.filename
+
+        if (real_value.type && real_value.type !== 'input')
+          value += ` [${real_value.type}]`
+      }
+      return value
+    }
+  })
+  async function uploadFile (file, updateNode, pasted = false) {
+    try {
+      // Wrap file in formdata so it includes filename
+      const body = new FormData()
+      body.append('image', file)
+      if (pasted) body.append('subfolder', 'pasted')
+      const resp = await api.fetchApi('/upload/image', {
+        method: 'POST',
+        body
+      })
+
+      if (resp.status === 200) {
+        const data = await resp.json()
+        // Add the file to the dropdown list and update the widget value
+        let path = data.name
+        if (data.subfolder) path = data.subfolder + '/' + path
+
+        if (!imageWidget.options.values.includes(path)) {
+          imageWidget.options.values.push(path)
+        }
+
+        if (updateNode) {
+          imageWidget.value = path
+        }
+
+        return `/view?filename=${path}&type=input&subfolder=${
+          pasted ? 'pasted' : ''
+        }&rand=${Math.random()}`
+      } else {
+        alert(resp.status + ' - ' + resp.statusText)
+      }
+    } catch (error) {
+      alert(error)
+    }
+  }
+
+  const fileInput = document.createElement('input')
+  Object.assign(fileInput, {
+    type: 'file',
+    accept: 'video/*,.mkv,video/webm,video/mp4,video/x-matroska,image/gif',
+    style: 'display: none',
+    onchange: async () => {
+      if (fileInput.files.length) {
+        let file = fileInput.files[0]
+
+        const url = await uploadFile(file, true)
+
+        // console.log('fileInput', file)
+        var reader = new FileReader()
+        reader.onload = function () {
+          displayDiv.src = url
+          displayDiv.onloadedmetadata = function () {
+            // var frameCount =
+            //   displayDiv.duration * displayDiv.webkitDecodedFrameCount
+            // console.log('视频帧数：' + frameCount)
+            // node.widgets.filter(
+            //   w => w.name == 'video_segment_frames'
+            // )[0].value = frameCount
+          }
+        }
+        reader.readAsDataURL(file)
+      }
+    }
+  })
+  document.body.append(fileInput)
+
+  // Create the button widget for selecting the files
+  uploadWidget = node.addWidget('button', 'upload file', 'video', () => {
+    fileInput.click()
+  })
+  uploadWidget.serialize = false
+  return { widget: uploadWidget }
+}
+ComfyWidgets.VIDEOUPLOAD_ = videoUpload
+
+app.registerExtension({
+  name: 'Mixlab.Video.LoadVideoAndSegment_',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData?.name == 'LoadVideoAndSegment_') {
+      nodeData.input.required.upload = ['VIDEOUPLOAD_']
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'LoadVideoAndSegment_') {
+      const imageWidget = node.widgets.find(w => w.name === 'video')
+      const uploadPreview = node.widgets.find(w => w.name === 'upload-preview')
+      if (imageWidget.value) {
+        // console.log(imageWidget.value)
+        uploadPreview.div.querySelector('video').src = `/view?filename=${
+          imageWidget.value
+        }&type=input&subfolder=${''}&rand=${Math.random()}`
+      }
+    }
+  }
+})
+
+function offsetDOMWidget (widget, ctx, node, widgetWidth, widgetY, height) {
+  const margin = 10
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(0, widgetY + margin)
+
+  const scale = new DOMMatrix().scaleSelf(transform.a, transform.d)
+  Object.assign(widget.inputEl.style, {
+    transformOrigin: '0 0',
+    transform: scale,
+    left: `${transform.e}px`,
+    top: `${transform.d + transform.f}px`,
+    width: `${widgetWidth}px`,
+    height: `${(height || widget.parent?.inputHeight || 32) - margin}px`,
+    position: 'absolute',
+    background: !node.color ? '' : node.color,
+    color: !node.color ? '' : 'white',
+    zIndex: 5 //app.graph._nodes.indexOf(node),
+  })
+}
+
+export const hasWidgets = node => {
+  if (!node.widgets || !node.widgets?.[Symbol.iterator]) {
+    return false
+  }
+  return true
+}
+
+export const cleanupNode = node => {
+  if (!hasWidgets(node)) {
+    return
+  }
+
+  for (const w of node.widgets) {
+    if (w.canvas) {
+      w.canvas.remove()
+    }
+    if (w.inputEl) {
+      w.inputEl.remove()
+    }
+    // calls the widget remove callback
+    w.onRemoved?.()
+  }
+}
+
+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()
+      }
+    }
+  }
+
+  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',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData?.name == 'ImageListReplace_') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        const widget = {
+          type: 'div',
+          name: 'preview',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 188, node.size[1]),
+              {
+                outline: '1px solid',
+                display: 'flex',
+                flexWrap: 'wrap',
+                flexDirection: 'row',
+                justifyContent: 'flex-start'
+              }
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+        widget.div.style.width = `120px`
+        widget.div.className = 'hidden'
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+        // console.log('#ImageListReplace', widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+
+        // let _image_replace = message._image_replace[0]
+        // _image_replace = `/view?filename=${_image_replace.filename}&type=${
+        //   _image_replace.type
+        // }&subfolder=${_image_replace.subfolder}&rand=${Math.random()}`
+
+        let preview = this.widgets.filter(w => w.name == 'preview')[0]
+
+        if (message._images.length > 0) {
+          preview.div.className = ''
+          // console.log('#ImageListReplace', preview.div)
+        }
+
+        preview.div.innerHTML = ''
+        for (const img_ of message._images) {
+          let img = new Image()
+          img.style = `width: 100px;
+          margin: 4px;`
+          img.src = `/view?filename=${img_.filename}&type=${
+            img_.type
+          }&subfolder=${img_.subfolder}&rand=${Math.random()}`
+          preview.div.appendChild(img)
+        }
+
+        let start_index = this.widgets.filter(w => w.name == 'start_index')[0]
+        let end_index = this.widgets.filter(w => w.name == 'end_index')[0]
+        let invert = this.widgets.filter(w => w.name == 'invert')[0]
+        let _sc = start_index.callback.bind(start_index)
+        let _ec = end_index.callback.bind(end_index)
+
+        const selectImages = () => {
+          // console.log(v)
+          let s = start_index.value,
+            e = end_index.value
+          let imgs = preview.div.querySelectorAll('img')
+          for (let index = 0; index < imgs.length; index++) {
+            if (invert.value) {
+              imgs[index].style.outline =
+                index >= s && index <= e ? 'none' : '4px solid #cbd3fe'
+            } else {
+              imgs[index].style.outline =
+                index >= s && index <= e ? '4px solid #cbd3fe' : 'none'
+            }
+          }
+        }
+
+        selectImages()
+
+        start_index.callback = v => {
+          let s = v,
+            e = end_index.value
+          let imgs = preview.div.querySelectorAll('img')
+          for (let index = 0; index < imgs.length; index++) {
+            if (invert.value) {
+              imgs[index].style.outline =
+                index >= s && index <= e ? 'none' : '4px solid #cbd3fe'
+            } else {
+              imgs[index].style.outline =
+                index >= s && index <= e ? '4px solid #cbd3fe' : 'none'
+            }
+          }
+
+          _sc(v)
+        }
+
+        end_index.callback = v => {
+          let s = start_index.value,
+            e = v
+          let imgs = preview.div.querySelectorAll('img')
+          for (let index = 0; index < imgs.length; index++) {
+            if (invert.value) {
+              imgs[index].style.outline =
+                index >= s && index <= e ? 'none' : '4px solid #cbd3fe'
+            } else {
+              imgs[index].style.outline =
+                index >= s && index <= e ? '4px solid #cbd3fe' : 'none'
+            }
+          }
+
+          _ec(v)
+        }
+
+        invert.callback = v => {
+          selectImages()
+        }
+
+        try {
+        } catch (error) {}
+      }
+    }
+
+    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) {
+          const pos = this.widgets.findIndex(w => w.name === `${prefix}_0`)
+          if (pos !== -1) {
+            for (let i = pos; i < this.widgets.length; i++) {
+              this.widgets[i].onRemoved?.()
+            }
+            this.widgets.length = pos
+          }
+          if (message?.gifs) {
+            message.gifs.forEach((params, i) => {
+              const previewUrl = api.apiURL(
+                '/view?' + new URLSearchParams(params).toString()
+              )
+              const w = this.addCustomWidget(
+                createPreviewElement(
+                  `${prefix}_${i}`,
+                  previewUrl,
+                  params.format || 'image/gif'
+                )
+              )
+              w.parent = this
+            })
+          }
+          const onRemoved = this.onRemoved
+          this.onRemoved = () => {
+            cleanupNode(this)
+            return onRemoved?.()
+          }
+        }
+        this.setSize([
+          this.size[0],
+          this.computeSize([this.size[0], this.size[1]])[1]
+        ])
+        return r
+      }
+    }
+  }
+})
@@ -1,126 +0,0 @@
-import { app } from '../../../scripts/app.js'
-import { api } from '../../../scripts/api.js'
-import { ComfyWidgets } from '../../../scripts/widgets.js'
-
-function videoUpload (node, inputName, inputData, app) {
-  const imageWidget = node.widgets.find(w => w.name === 'video')
-  let uploadWidget
-
-  const displayDiv = document.createElement('video')
-  console.log('imageWidget', node)
-
-  var default_value = imageWidget.value
-  Object.defineProperty(imageWidget, 'value', {
-    set: function (value) {
-      this._real_value = value
-    },
-
-    get: function () {
-      let value = ''
-      if (this._real_value) {
-        value = this._real_value
-      } else {
-        return default_value
-      }
-
-      if (value.filename) {
-        let real_value = value
-        value = ''
-        if (real_value.subfolder) {
-          value = real_value.subfolder + '/'
-        }
-
-        value += real_value.filename
-
-        if (real_value.type && real_value.type !== 'input')
-          value += ` [${real_value.type}]`
-      }
-      return value
-    }
-  })
-  async function uploadFile (file, updateNode, pasted = false) {
-    try {
-      // Wrap file in formdata so it includes filename
-      const body = new FormData()
-      body.append('image', file)
-      if (pasted) body.append('subfolder', 'pasted')
-      const resp = await api.fetchApi('/upload/image', {
-        method: 'POST',
-        body
-      })
-
-      if (resp.status === 200) {
-        const data = await resp.json()
-        // Add the file to the dropdown list and update the widget value
-        let path = data.name
-        if (data.subfolder) path = data.subfolder + '/' + path
-
-        if (!imageWidget.options.values.includes(path)) {
-          imageWidget.options.values.push(path)
-        }
-
-        if (updateNode) {
-          imageWidget.value = path
-        }
-      } else {
-        alert(resp.status + ' - ' + resp.statusText)
-      }
-    } catch (error) {
-      alert(error)
-    }
-  }
-
-  const fileInput = document.createElement('input')
-  Object.assign(fileInput, {
-    type: 'file',
-    accept: 'video/webm,video/mp4,video/mkv,image/gif',
-    style: 'display: none',
-    onchange: async () => {
-      if (fileInput.files.length) {
-        let file = fileInput.files[0]
-        console.log(file)
-        await uploadFile(file, true)
-      }
-    }
-  })
-  document.body.append(fileInput)
-
-  // Create the button widget for selecting the files
-  uploadWidget = node.addWidget('button', 'upload file', 'video', () => {
-    fileInput.click()
-  })
-  uploadWidget.serialize = false
-  return { widget: uploadWidget }
-}
-ComfyWidgets.VIDEOUPLOAD_ = videoUpload
-
-
-app.registerExtension({
-  name: 'Mixlab.Video.LoadVideoAndSegment_',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeData?.name == 'LoadVideoAndSegment_') {
-      nodeData.input.required.upload = ['VIDEOUPLOAD_'];
-
-    //   const onExecuted = nodeType.prototype.onExecuted
-    //   nodeType.prototype.onExecuted = function (message) {
-    //     onExecuted?.apply(this, arguments)
-    //     console.log(message)
-        
-    //     // try {
-    //     //   let a = this.widgets.filter(w => w.name === 'AppInfoRun')[0]
-    //     //   if (a) {
-    //     //     if (!a.value) a.value = 0
-    //     //     a.value += 1
-    //     //   }
-
-    //     //   const div = this.widgets.filter(w => w.div)[0].div
-    //     //   Array.from(
-    //     //     div.querySelectorAll('button'),
-    //     //     b => (b.style.background = 'yellow')
-    //     //   )
-    //     // } catch (error) {}
-    //   }
-
-    }
-  }
-})
@@ -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;
+}
@@ -1,277 +0,0 @@
-* {
-    transition: all 0.6s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-#app-login {
-    width: 480px;
-    height: 90vh;
-    padding: 6vh;
-    background: white;
-    box-shadow: 0 0 2rem rgba(0, 0, 0, 0.1);
-    z-index: 999;
-    position: fixed;
-    top: 5vh;
-    left: calc(50vw - 240px);
-}
-
-.login-app-view {
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    z-index: 999;
-}
-
-.login-background {
-    background-color: #202020e6;
-    position: fixed;
-    width: 100%;
-    height: 100vh;
-    left: 0;
-    top: 0;
-    z-index: 998;
-}
-
-.app-header {
-    padding: 6vh;
-}
-
-.app-header,
-.app-header>* {
-    font-size: 1.2em;
-    margin: 0;
-    font-weight: 300;
-}
-
-.app-header>h1 {
-    font-size: 4.8vh;
-    font-weight: 400;
-    margin-bottom: 4.8vh;
-}
-
-.app-header>h2 {
-    font-size: 3vh;
-}
-
-.app-subheading {
-    color: rgba(0, 0, 0, 0.45);
-}
-
-.app-register {
-    position: absolute;
-    bottom: 0;
-    height: 10vh;
-    line-height: 10vh;
-    padding: 0 6vh;
-    color: rgba(0, 0, 0, 0.45);
-}
-
-.app-register>a {
-    font-weight: 400;
-}
-
-
-#app-login input {
-    font-size: 2.5vh;
-    width: calc(100% - 13vh);
-    height: 7.5vh;
-    margin-bottom: 2vh;
-    background: transparent;
-    position: absolute;
-    top: 0;
-    left: 6.5vh;
-    z-index: 2;
-    border: none;
-    box-shadow: inset 0 -0.5vh rgba(0, 0, 0, 0.1);
-}
-
-#app-login input:focus {
-    outline: none;
-    box-shadow: inset 0 -0.5vh transparent;
-}
-
-#app-login input[type=email] {
-    top: 58%;
-}
-
-#app-login input[type=password] {
-    top: calc(58% + 7.5vh);
-}
-
-#app-login input[type=email]:valid~* .st1 {
-    transition-timing-function: ease-in-out;
-    stroke-dasharray: 50, 153;
-    stroke-dashoffset: 25;
-}
-
-#app-login input[type=password]:focus~* .st0,
-#app-login input[type=password]:valid~* .st0,
-#login_run:focus~* .st0 {
-    stroke-dasharray: 210, 900;
-    stroke-dashoffset: -305;
-}
-
-#app-login input[type=email]:focus~* .st0 {
-    stroke-dasharray: 210, 900;
-    stroke-dashoffset: 0;
-}
-
-#app-login input:not(:valid)~#login_run {
-    /* pointer-events: none; */
-    opacity: 0.6;
-}
-
-#login_run {
-    text-decoration: none;
-    color: #0f9ede;
-    font-size: 1.5em;
-    padding: 0 6vh;
-    position: absolute;
-    bottom: 10vh;
-    font-weight: 400;
-    z-index: 998;
-    cursor: pointer;
-}
-
-#login_run:focus {
-    outline: none;
-}
-
-.login-app-view:nth-child(2) {
-    display: flex;
-    flex-direction: column;
-    pointer-events: none;
-}
-
-.login-app-view:nth-child(2)>.app-header {
-    font-size: 1rem;
-    flex-basis: 25%;
-    display: flex;
-    flex-direction: column;
-    justify-content: space-between;
-    padding: 4vh;
-    padding-bottom: 1rem;
-}
-
-.login-app-view:nth-child(2)>.app-header>h2 {
-    transform: translateY(1rem);
-}
-
-.login-app-view:nth-child(2)>.app-header>h2>em {
-    color: #0f9ede;
-    font-style: normal;
-}
-
-.login-app-view:nth-child(2)>.app-header>h2,
-.login-app-view:nth-child(2) .app-item>*:not(.app-graphic) {
-    transition-duration: 0.9s;
-    opacity: 0;
-}
-
-
-.st0,
-.st1,
-.svg-loader-segment {
-    fill: none;
-    stroke: #0f9ede;
-    stroke-width: 0.5vh;
-    stroke-alignment: inside;
-    opacity: 1;
-    transition: all 0.6s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-.svg-loader {
-    opacity: 0;
-}
-
-.st0 {
-    stroke-dasharray: 0, 900;
-    stroke-dashoffset: 0;
-}
-
-.st1 {
-    transition-delay: 0.3s;
-    stroke-dasharray: 50, 153;
-    stroke-dashoffset: -153;
-}
-
-.svg-loader-segment {
-    transition: transform 1.2s cubic-bezier(0.77, 0, 0.175, 1), opacity 0.85s cubic-bezier(0.77, 0, 0.175, 1), stroke 0.85s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-#svg-lines {
-    position: absolute;
-    top: 45%;
-    left: 0;
-    width: 100%;
-    z-index: 0;
-    overflow: visible;
-    transform-origin: center 4vh;
-}
-
-.svg-data {
-    fill: none;
-    stroke-width: 0.5vh;
-}
-
-.svg-data.-temp {
-    stroke: #f4814b;
-    stroke-dasharray: 20, 118;
-}
-
-.svg-data.-cal {
-    stroke: #08b5cf;
-    stroke-dasharray: 20, 113;
-}
-
-.svg-data.-steps-bg {
-    stroke: #e0e1e0;
-    stroke-dasharray: 40, 100;
-    stroke-dashoffset: -60;
-}
-
-.svg-data.-steps {
-    stroke: #0f9ede;
-    stroke-dasharray: 20, 73;
-    stroke-dashoffset: -53;
-}
-
-.svg-data.-heart {
-    stroke: #9965aa;
-    stroke-dasharray: 50, 200;
-    stroke-dashoffset: -150;
-}
-
-.svg-activity-fill {
-    fill: #c4e4f8;
-}
-
-.svg-activity-line {
-    fill: none;
-    stroke: #65bcea;
-    stroke-miterlimit: 10;
-    stroke-width: 0.25vh;
-}
-
-.svg-activity-avg,
-.svg-activity-indicator {
-    fill: none;
-    stroke: #d0dff0;
-    stroke-width: 0.25vh;
-    mix-blend-mode: multiply;
-}
-
-.svg-activity-fill,
-.svg-activity-line {
-    transform: translateY(10vh);
-    opacity: 0;
-}
-
-
-*,
-*:before,
-*:after {
-    box-sizing: border-box;
-    position: relative;
-}
@@ -1,67 +0,0 @@
-;(() => {
-  let div = document.createElement('div')
-
-  div.innerHTML = ` 
-    <div id="app-login">
-        <div class="login-app-view">
-            <header class="app-header">
-                <h1>Hi</h1>
-                Welcome back,<br />
-                <span class="app-subheading">
-                    sign in to continue<br />
-    
-                </span>
-            </header>
-            <input class="email" type="email" required pattern=".*\.\w{2,}" placeholder="Email Address" />
-            <input class="password" type="password" required placeholder="Password" />
-            <a class="app-button" id="login_run">登录</a>
-            <!-- <div class="app-register">
-                Don't have an account? <a>Sign Up</a>
-            </div> -->
-            <svg id="svg-lines" version="1.1" xmlns="http://www.w3.org/2000/svg"
-                xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 284.2 152.7"
-                xml:space="preserve">
-                <path class="st0"
-                    d="M37.7,107.3h222.6c12,0,21.8,9.7,21.8,21.7s-9.7,21.8-21.8,21.8c0,0-203.6,0-222.6,0S2.2,138.6,2.2,103.3   c0-52,113.5-101.5,141-101.5c13.5,0,21.8,9.7,21.8,21.8s-9.7,21.7-21.8,21.7s-21.8-9.7-21.8-21.7s9.7-21.8,21.8-21.8" />
-                <path class="st1"
-                    d="M260.2,76.3L250,87.8l-9-9c-6.2-6.2,2-24.7,17.2-24.7c15.2,0,23.9,17.7,23.9,29.7s-11.7,23.5-23.9,23.5h-10.2">
-                </path>
-                <g class="svg-loader" xmlns="http://www.w3.org/2000/svg">
-                    <path class="svg-loader-segment -cal" d="M164.7,23.5c0-12-9.7-21.8-21.8-21.8" />
-                    <path class="svg-loader-segment -heart" d="M143,45.2c12,0,21.8-9.7,21.8-21.7" />
-                    <path class="svg-loader-segment -steps" d="M121.2,23.5c0,12,9.7,21.7,21.8,21.7" />
-                    <path class="svg-loader-segment -temp" d="M143,1.7c-12,0-21.8,9.7-21.8,21.8" />
-                </g>
-            </svg>
-        </div>
-    </div>
-    <div class="login-background"></div>
-     `
-
-  document.body.appendChild(div)
-  let bg = div.querySelector('.login-background')
-  bg.addEventListener('click', e => {
-    div.style.display = 'none'
-  })
-  let login_btn = document.body.querySelector('#login_btn')
-  // login_btn.href="";
-  if (login_btn) {
-    login_btn.innerHTML =
-      '<svg stroke="currentColor" fill="none" stroke-width="0" viewBox="0 0 24 24" height="40px" width="40px" xmlns="http://www.w3.org/2000/svg"><path d="M12 17C14.2091 17 16 15.2091 16 13H8C8 15.2091 9.79086 17 12 17Z" fill="currentColor"></path><path d="M10 10C10 10.5523 9.55228 11 9 11C8.44772 11 8 10.5523 8 10C8 9.44772 8.44772 9 9 9C9.55228 9 10 9.44772 10 10Z" fill="currentColor"></path><path d="M15 11C15.5523 11 16 10.5523 16 10C16 9.44772 15.5523 9 15 9C14.4477 9 14 9.44772 14 10C14 10.5523 14.4477 11 15 11Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12ZM20 12C20 16.4183 16.4183 20 12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12Z" fill="currentColor"></path></svg>LOGIN'
-    login_btn.addEventListener('click', e => {
-      e.preventDefault()
-      div.style.display = 'block'
-    })
-  }
-
-  let login_run = div.querySelector('#login_run')
-  if (login_run) {
-    login_run.addEventListener('click', e => {
-      e.preventDefault()
-      let ps = div.querySelector('.password')
-      let email = div.querySelector('.email')
-      div.style.display = 'none'
-      console.log(ps.value, email.value)
-    })
-  }
-})()
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+/*
+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
+ */
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+{"version":3,"file":"bundle.js","sources":["webpack://miniPaint/bundle.js"],"mappings":";AAAA","sourceRoot":""}
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+	 viewBox="0 0 298.73 298.73" style="enable-background:new 0 0 298.73 298.73;" xml:space="preserve">
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+</g>
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+<g>
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+	<path d="M15.026,0.002C6.726,0.002,0,6.728,0,15.028c0,8.297,6.726,15.021,15.026,15.021c8.298,0,15.025-6.725,15.025-15.021
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--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
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
shadowcz007	117d58c58e	v0.24.0	2024-05-02 12:06:19 +08:00
shadowcz007	ff6626ed89	add llama.cpp & Local LLM -Phi-3 & llama3 Phi-3 llama3	2024-05-02 12:02:47 +08:00
shadowcz007	10c798a440	Update index.html	2024-05-02 10:54:20 +08:00
shadowcz007	9097d87819	Update index.html	2024-05-02 10:46:09 +08:00
shadowcz007	d901f503d1	fixbug	2024-05-02 10:06:23 +08:00
shadowcz007	65f2b6ce6f	update https	2024-05-02 09:07:28 +08:00
shadowcz007	d949fe8bf1	Update __init__.py	2024-05-01 22:26:46 +08:00
shadowcz007	15cfb48550	Update __init__.py	2024-05-01 20:52:50 +08:00
shadowcz007	447dc6d4c3	fixbug	2024-05-01 15:55:43 +08:00
shadowcz007	c92e43b920	Update checkVersion_mixlab.js	2024-04-29 00:16:55 +08:00
shadowcz007	be6c32b0e0	fixbug	2024-04-29 00:15:47 +08:00
shadowcz007	3d2062e810	add TripoSRModel	2024-04-29 00:15:32 +08:00
shadowcz007	dd816e95cd	Update README.md	2024-04-27 23:43:40 +08:00
shadowcz007	6d1b51890d	Update checkVersion_mixlab.js	2024-04-27 23:38:00 +08:00
shadowcz007	11f03ec99a	支持正片叠底	2024-04-25 21:23:40 +08:00
shadowcz007	bd192f43e7	优化	2024-04-24 23:10:03 +08:00
shadowcz007	36e4b11983	gridout can export mask	2024-04-23 12:14:50 +08:00
shadowcz007	97397ba8c2	Update ImageNode.py	2024-04-22 12:32:48 +08:00
shadowcz007	052eee4111	Update __init__.py	2024-04-22 12:31:40 +08:00
shadowcz007	e319496044	v0.22.0 - 优化ImageColorTransfer - 支持动态提示 - 添加更多节点支持，AppInfo自动填充id - LoadImagesToBatch加载 - zhipuai 按需安装	2024-04-22 09:52:45 +08:00
shadowcz007	b83b63c362	添加支持的节点自动填充id	2024-04-21 21:51:07 +08:00
shadowcz007	4d6b1675bb	Load Images to Batch加载文件最大宽度1024	2024-04-21 21:50:56 +08:00
shadowcz007	13110fab39	支持动态提示	2024-04-21 20:58:44 +08:00
shadowcz007	74ea509848	fixbug	2024-04-20 23:05:59 +08:00
shadowcz007	192bff9d2c	optimize ImageColorTransfer and support batching,	2024-04-20 22:23:34 +08:00
shadowcz007	44ed8812dc	output add TransparentImage	2024-04-19 22:27:53 +08:00
shadowcz007	200696ba21	Update ui_mixlab.js	2024-04-19 20:44:27 +08:00
shadowcz007	51cf3b0c04	install Zhipuai as needed	2024-04-19 11:41:56 +08:00
shadowcz007	6a4831c83b	add nodes map for appinfo	2024-04-18 18:24:58 +08:00
shadowcz007	c5e7ed95a3	Update index.html	2024-04-18 16:10:02 +08:00
shadowcz007	42a97fa4d9	Update image_mixlab.js	2024-04-18 16:00:43 +08:00
shadowcz007	45240d0012	Update image_mixlab.js	2024-04-18 16:00:18 +08:00
shadowcz007	8ed085febd	Update image_mixlab.js	2024-04-18 15:57:34 +08:00
shadowcz007	37803ea61b	Update smart_connect.js	2024-04-18 09:06:42 +08:00
shadowcz007	acd416952c	Update image_mixlab.js	2024-04-18 07:48:50 +08:00
shadowcz007	6ec46cbc44	Update ui_mixlab.js	2024-04-17 22:55:16 +08:00
shadowcz007	a9d971e476	Update __init__.py	2024-04-17 22:55:12 +08:00
shadowcz007	9fe064675d	paste appinfo data / 支持粘贴appinfo导出的数据	2024-04-17 16:28:32 +08:00
shadowcz007	c84fa467d0	Update requirements.txt	2024-04-17 15:36:49 +08:00
shadowcz007	3d7a55f6d3	SaveImageAndMetadata支持格式化文件名@bakkhos8	2024-04-17 15:01:05 +08:00
shadowcz007	d49baa1540	Update checkVersion_mixlab.js	2024-04-15 23:40:01 +08:00
shadowcz007	5c686af842	Update ImageNode.py	2024-04-15 09:02:49 +08:00
shadowcz007	22425b5bc6	Update index.html	2024-04-13 00:12:05 +08:00
shadowcz007	b6d9b338d2	Update index.html	2024-04-12 21:55:56 +08:00
shadowcz007	a191a13751	Update index.html	2024-04-12 21:48:31 +08:00
shadowcz007	3eccdbcc9b	Update index.html	2024-04-12 21:43:21 +08:00
shadowcz007	50063903f9	mixlab app add 3D	2024-04-12 16:10:05 +08:00
shadowcz007	95a1b70533	Update app_mixlab.js	2024-04-08 17:14:30 +08:00
shadowcz007	c3679ac90b	Update image_mixlab.js	2024-04-07 11:49:26 +08:00
shadowcz007	29e48eb6a2	Update ui_mixlab.js	2024-04-07 10:32:13 +08:00
shadowcz007	71d02e9651	v0.20.0	2024-04-06 21:47:07 +08:00
shadowcz007	f5193f3eec	add LoadImagesToBatch	2024-04-06 21:39:55 +08:00
shadowcz007	480c4d6919	fixbug	2024-04-06 19:56:43 +08:00
shadowcz007	1c5e030540	Update index.html	2024-04-06 18:41:06 +08:00
shadowcz007	27e83a5908	Incrementing List	2024-03-30 23:46:32 +08:00
shadowcz007	d3cbf8fa8d	Update Video.py	2024-03-30 22:46:50 +08:00
shadowcz007	74b1f8129b	Update PromptNode.py	2024-03-29 15:35:41 +08:00
shadowcz007	56ed513cfd	update	2024-03-27 17:03:28 +08:00
shadowcz007	5f412371c4	Update Utils.py	2024-03-27 13:10:30 +08:00
shadowcz007	c6b0b67585	update	2024-03-26 21:44:25 +08:00
shadowcz007	16d18e681a	add composite_images	2024-03-26 18:23:11 +08:00
shadowcz007	1fe99f33b2	add VAEEncodeForInpaint_Frames	2024-03-26 16:48:45 +08:00
shadowcz007	a2ece25ac0	update	2024-03-26 15:43:43 +08:00
shadowcz007	4865f4d148	fixbug	2024-03-26 15:14:01 +08:00
shadowcz007	41e88824cf	Update Video.py	2024-03-26 13:38:00 +08:00
shadowcz007	0961ab138e	Update videoupload.js	2024-03-26 13:35:43 +08:00
shadowcz007	fe8271a12f	fixbug	2024-03-26 13:34:52 +08:00
shadowcz007	f3866ede89	add ImageListReplace	2024-03-26 00:29:21 +08:00
shadowcz007	d938adf3cc	update TextSplitByDelimiter	2024-03-25 20:24:54 +08:00
shadowcz007	9908cff64b	Update Utils.py	2024-03-25 13:21:47 +08:00
shadowcz007	1b9b0bb4e6	fixbug	2024-03-25 13:20:30 +08:00
				`@@ -0,0 +1 @@`
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