modify the pyproject.toml file

Merge pull request #182 from haohaocreates/publish
Add Github Action for Publishing to Comfy Registry
2024-05-23 13:33:03 +08:00 · 2024-05-23 13:30:16 +08:00 · 2024-05-23 13:30:00 +08:00 · 2024-05-23 12:33:45 +08:00 · 2024-05-22 18:08:19 -04:00 · 2024-05-22 18:08:16 -04:00
63 changed files with 4953 additions and 716 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 }}
@@ -10,4 +10,5 @@ autocomplete/**
 docs/**
 .vscode/
 .idea/
-mmb-preset.custom.txt
+mmb-preset.custom.txt
+config.yaml
@@ -28,10 +28,43 @@
 - Fooocus Inpaint integration
 - Integration of common logical calculations, conversion of types, display of all types, etc.
 - Background removal nodes for the RMBG-1.4 model supporting BriaAI, [BriaAI Guide](https://huggingface.co/briaai/RMBG-1.4)
-
+- Forcibly cleared the memory usage of the comfy UI model are supported
+- Stable Diffusion 3 multi-account API nodes are supported
+- 
 ## Changelog

-**v1.1.4 (2024/4/10)**
+**v1.1.7**
+
+- Added `easy prompt` - Subject and light presets, maybe adjusted later
+- Added `easy icLightApply` - Light and shadow migration, Code based on [ComfyUI-IC-Light](https://github.com/huchenlei/ComfyUI-IC-Light)
+- Added `easy imageSplitGrid`
+- `easy kSamplerInpainting` added options such as different diffusion and brushnet in **additional** widget
+- Support for brushnet model loading - [ComfyUI-BrushNet](https://github.com/nullquant/ComfyUI-BrushNet)
+- Added `easy applyFooocusInpaint` - Replace FooocusInpaintLoader
+- Removed `easy fooocusInpaintLoader`
+
+**v1.1.6**
+
+- Added **alignYourSteps** to **schedulder** widget in all `easy preSampling` and `easy fullkSampler`
+- Added **Preview&Choose** to **image_output** widget in `easy kSampler` & `easy fullkSampler`
+- Added `easy styleAlignedBatchAlign` - Credit of [style_aligned_comfy](https://github.com/brianfitzgerald/style_aligned_comfy)
+- Added `easy ckptNames`
+- Added `easy controlnetNames`
+- Added `easy imagesSplitimage` - Batch images split into single images
+- Added `easy imageCount` - Get Image Count
+- Added `easy textSwitch` - Text Switch
+
+**v1.1.5**
+
+- Rewrite `easy cleanGPUUsed` - the memory usage of the comfyUI can to be cleared
+- Added `easy humanSegmentation` - Human Part Segmentation
+- Added `easy imageColorMatch`
+- Added `easy ipadapterApplyRegional`
+- Added `easy ipadapterApplyFromParams`
+- Added `easy imageInterrogator` - Image To Prompt
+- Added `easy stableDiffusion3API` - Easy Stable Diffusion 3 Multiple accounts API Node
+
+**v1.1.4**

 - Added `easy preSamplingCustom` - Custom-PreSampling, can be supported cosXL-edit
 - Added `easy ipadapterStyleComposition`
@@ -39,7 +72,7 @@
 - Fixed `easy preSamplingNoiseIn`、`easy latentNoisy`、`east Unsampler` compatible with ComfyUI Revision>=2098 [0542088e] or later
 

-**v1.1.3 (2024/4/4)**
+**v1.1.3**

 - `easy ipadapterApply` Added **COMPOSITION** preset
 - Supported [ResAdapter](https://huggingface.co/jiaxiangc/res-adapter) when load ResAdapter lora
@@ -48,7 +81,7 @@
 - Added `easy promptConcat`
 - `easy wildcards` Added **multiline_mode**  

-**v1.1.2 (39c5ccf)**
+**v1.1.2**

 - Optimized some of the recommended nodes for slots related to EasyUse
 - Added **Enable ContextMenu Auto Nest Subdirectories** The setting item is enabled by default, and it can be classified into subdirectories, checkpoints and loras previews
@@ -64,7 +97,7 @@
 - Fixed layerDiffuse xyplot bug
 - `easy kSamplerInpainting` add *additional* widget，you can choose 'Differential Diffusion' or 'Only InpaintModelConditioning'

-**v1.1.1 (2024/3/16)**
+**v1.1.1**

 - The issue that the seed is 0 when a node with a seed control is added and **control before generate** is fixed for the first time run queue prompt.
 - `easy preSamplingAdvanced` Added **return_with_leftover_noise**
@@ -74,7 +107,7 @@
 - Remove forced **control_before_generate** settings。 If you want to use control_before_generate, change widget_value_control_mode to before in system settings
 - Added `easy imageRemBg` - The default is BriaAI's RMBG-1.4 model, which removes the background effect more and faster

-**v1.1.0 (d5ff84e)**
+**v1.1.0**

 - Added `easy imageSplitList` - to split every N images
 - Added `easy preSamplingDiffusionADDTL` - It can modify foreground、background or blended additional prompt
@@ -90,15 +123,18 @@
 - Fixed `easy instantIDApply` mask not input right


-**v1.0.9 (ff1add1)**
+<details>
+<summary><b>v1.0.9</b></summary>

 - Fixed the error when ComfyUI-Impack-Pack and ComfyUI_InstantID were not installed
 - Fixed `easy pipeIn`
 - Added `easy instantIDApply` - you need installed [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID) fisrt, Workflow[Example](https://github.com/yolain/ComfyUI-Easy-Use/blob/main/README.en.md#InstantID)
 - Fixed `easy detailerFix` not added to the list of nodes available for saving images formatting extensions
 - Fixed `easy XYInputs: PromptSR` errors are reported when replacing negative prompts
+</details>

-**v1.0.8 (f28cbf7)**
+<details>
+<summary><b>v1.0.8</b></summary>

 - `easy cascadeLoader` stage_c and stage_b support the checkpoint model (Download [checkpoints](https://huggingface.co/stabilityai/stable-cascade/tree/main/comfyui_checkpoints) models) 
 - `easy styleSelector` The search box is modified to be case-insensitive
@@ -108,8 +144,10 @@
 - Fixed the error of SDXLClipModel in ComfyUI revision 2016[c2cb8e88] and above (the revision number was judged to be compatible with the old revision)
 - Fixed `easy detailerFix` generation error when batch size is greater than 1
 - Optimize the code, reduce a lot of redundant code and improve the running speed
+</details>

-**v1.0.7 (2024-02-19)**
+<details>
+<summary><b>v1.0.7</b></summary>

 - Added `easy cascadeLoader` - stable cascade Loader
 - Added `easy preSamplingCascade` - stable cascade preSampling Settings
@@ -117,22 +155,28 @@
 - Added `easy cascadeKSampler` - stable cascade stage-c ksampler simple
 - 
 - Optimize the image to image[Example](https://github.com/yolain/ComfyUI-Easy-Use/blob/main/README.en.md#image-to-image)
+</details>

-**v1.0.6**
+<details>
+<summary><b>v1.0.6</b></summary>

 - Added `easy XYInputs: Checkpoint`
 - Added `easy XYInputs: Lora`
 - `easy seed` can manually switch the random seed when increasing the fixed seed value
 - Fixed `easy fullLoader` and all loaders to automatically adjust the node size when switching LoRa
 - Removed the original ttn image saving logic and adapted to the default image saving format extension of ComfyUI
+</details>

- **v1.0.5**
+<details>
+<summary><b>v1.0.5</b></summary>

 - Added `easy isSDXL` 
 - Added prompt word control on `easy svdLoader`, which can be used with open_clip model
 - Added **populated_text** on `easy wildcards`, wildcard populated text can be output
+</details>

-**v1.0.4**
+<details>
+<summary><b>v1.0.4</b></summary>

 - `easy showAnything` added support for converting other types (e.g., tensor conditions, images, etc.)
 - Added `easy showLoaderSettingsNames` can display the model and VAE name in the output loader assembly
@@ -152,9 +196,10 @@
 - Changing the first-time install node package no longer automatically replaces the theme, you need to manually adjust and refresh the page
 - `easy imageSave` added **only_preivew**
 - Adjust the `easy latentCompositeMaskedWithCond` node
+</details>

-
-**v1.0.3**
+<details>
+<summary><b>v1.0.3</b></summary>

 - Added `easy stylesSelector`
 - Added **scale_soft_weights** in `easy controlnetLoader` and `easy controlnetLoaderADV` 
@@ -172,9 +217,10 @@

 - Adjust the UI theme, divided into two sets of styles: the official default background and the dark black background, which can be switched in the color palette in the settings
 - Modify the styles path to be compatible with other environments
+</details>

-
-**v1.0.2**
+<details>
+<summary><b>v1.0.2</b></summary>

 - Added `easy XYPlotAdvanced` and some nodes about `easy XYInputs`
 - Added **Alt+1-Alt+9** Shortcut keys to quickly paste node presets for Node templates (corresponding to 1~9 sequences)
@@ -191,7 +237,7 @@
 - Removed `easy imageRemBg`
 - Remove the introductory diagram and workflow files from the package to reduce the package size
 - Replaced the font file used in the generation of XY diagrams
-
+</details>

 <details>
 <summary><b>v1.0.1</b></summary>
@@ -286,6 +332,7 @@ Disclaimer: Opened source was not easy. I have a lot of respect for the contribu
 | easy preSamplingLayerDiffusion | [ComfyUI-layerdiffusion](https://github.com/huchenlei/ComfyUI-layerdiffusion) | LayeredDiffusionApply... | 
 | easy dynamiCrafterLoader       | [ComfyUI-layerdiffusion](https://github.com/ExponentialML/ComfyUI_Native_DynamiCrafter) | Apply Dynamicrafter    | 
 | easy imageChooser              | [cg-image-picker](https://github.com/chrisgoringe/cg-image-picker) | Preview Chooser         | 
+| easy styleAlignedBatchAlign              | [style_aligned_comfy](https://github.com/chrisgoringe/cg-image-picker) | styleAlignedBatchAlign       | 

 ## Workflow Examples

@@ -12,7 +12,7 @@

 **ComfyUI-Easy-Use** 是一个化繁为简的节点整合包, 在 [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) 的基础上进行延展，并针对了诸多主流的节点包做了整合与优化，以达到更快更方便使用ComfyUI的目的，在保证自由度的同时还原了本属于Stable Diffusion的极致畅快出图体验。

-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Docs/workflow_node_compare.png">
+[![ComfyUI-Yolain-Workflows](https://github.com/yolain/ComfyUI-Easy-Use/assets/73304135/9a3f54bc-a677-4bf1-a196-8845dd57c942)](https://github.com/yolain/ComfyUI-Yolain-Workflows)

 ## 特色介绍

@@ -23,27 +23,65 @@
 - 可多选的风格化提示词选择器，默认是Fooocus的样式json，可自定义json放在styles底下，samples文件夹里可放预览图(名称和name一致,图片文件名如有空格需转为下划线'_')
 - 加载器可开启A1111提示词风格模式，可重现与webui生成近乎相同的图像，需先安装 [ComfyUI_smZNodes](https://github.com/shiimizu/ComfyUI_smZNodes)
 - 可使用`easy latentNoisy`或`easy preSamplingNoiseIn`节点实现对潜空间的噪声注入
- 简化 SD1.x、SD2.x、SDXL、SVD、Zero123等流程 [示例参考](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#StableDiffusion)
- 简化 Stable Cascade [示例参考](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#StableCascade)
- 简化 Layer Diffuse [示例参考](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#LayerDiffusion)， 首次使用您可能需要运行 `pip install -r requirements.txt` 安装所需依赖
- 简化 InstantID [示例参考](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#InstantID), 需先保证自定义节点包中安装了 [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID)
+- 简化 SD1.x、SD2.x、SDXL、SVD、Zero123等流程 
+- 简化 Stable Cascade [示例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows?tab=readme-ov-file#1-13-stable-cascade)
+- 简化 Layer Diffuse [示例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows?tab=readme-ov-file#2-3-layerdiffusion)
+- 简化 InstantID [示例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows?tab=readme-ov-file#2-2-instantid), 需先保证自定义节点包中安装了 [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID)
 - 简化 IPAdapter, 需先保证自定义节点包中安装最新版v2的 [ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus)
 - 扩展 XYplot 的可用性
 - 整合了Fooocus Inpaint功能
 - 整合了常用的逻辑计算、转换类型、展示所有类型等
+- 支持节点上checkpoint、lora模型子目录分类及预览图 (请在设置中开启上下文菜单嵌套子目录)
 - 支持BriaAI的RMBG-1.4模型的背景去除节点，[技术参考](https://huggingface.co/briaai/RMBG-1.4)
+- 支持 强制清理comfyUI模型显存占用
+- 支持Stable Diffusion 3 多账号API节点
+- 支持IC-Light的应用 [示例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows?tab=readme-ov-file#2-5-ic-light) | [代码整合来源](https://github.com/huchenlei/ComfyUI-IC-Light) | [技术参考](https://github.com/lllyasviel/IC-Light)

 ## 更新日志

-**v1.1.4 (2024/4/13)**
+**v1.1.7**

+- 修复 一些模型(如controlnet模型等)未成功写入缓存，导致修改前置节点束参数（如提示词）需要二次载入模型的问题
+- 增加 `easy prompt` - 主体和光影预置项，后期可能会调整
+- 增加 `easy icLightApply` - 重绘光影, 从[ComfyUI-IC-Light](https://github.com/huchenlei/ComfyUI-IC-Light)优化
+- 增加 `easy imageSplitGrid` - 图像网格拆分
+- `easy kSamplerInpainting` 的 **additional** 属性增加差异扩散和brushnet等相关选项 
+- 增加 brushnet模型加载的支持 - [ComfyUI-BrushNet](https://github.com/nullquant/ComfyUI-BrushNet)
+- 增加 `easy applyFooocusInpaint` - Fooocus内补节点 替代原有的 FooocusInpaintLoader
+- 移除 `easy fooocusInpaintLoader` - 容易bug，不再使用
+- 修改 easy kSampler等采样器中并联的model 不再替换输出中pipe里的model
+
+**v1.1.6**
+
+- 增加步调齐整适配 - 在所有的预采样和全采样器节点中的 调度器(schedulder) 增加了 **alignYourSteps** 选项
+- `easy kSampler` 和 `easy fullkSampler` 的 **image_output** 增加 **Preview&Choose**选项
+- 增加 `easy styleAlignedBatchAlign` - 风格对齐 [style_aligned_comfy](https://github.com/brianfitzgerald/style_aligned_comfy)
+- 增加 `easy ckptNames`
+- 增加 `easy controlnetNames`
+- 增加 `easy imagesSplitimage` - 批次图像拆分单张
+- 增加 `easy imageCount` - 图像数量
+- 增加 `easy textSwitch` - 文字切换
+
+**v1.1.5**
+
+- 重写 `easy cleanGPUUsed` - 可强制清理comfyUI的模型显存占用
+- 增加 `easy humanSegmentation` - 多类分割、人像分割
+- 增加 `easy imageColorMatch`
+- 增加 `easy ipadapterApplyRegional`
+- 增加 `easy ipadapterApplyFromParams`
+- 增加 `easy imageInterrogator` - 图像反推
+- 增加 `easy stableDiffusion3API` - 简易的Stable Diffusion 3 多账号API节点
+
+**v1.1.4**
+
+- 增加 `easy imageChooser` - 从[cg-image-picker](https://github.com/chrisgoringe/cg-image-picker)简化的图片选择器
 - 增加 `easy preSamplingCustom` - 自定义预采样，可支持cosXL-edit
 - 增加 `easy ipadapterStyleComposition`
 - 增加 在Loaders上右键菜单可查看 checkpoints、lora 信息
 - 修复 `easy preSamplingNoiseIn`、`easy latentNoisy`、`east Unsampler` 以兼容ComfyUI Revision>=2098 [0542088e] 以上版本
 - 修复 FooocusInpaint修改ModelPatcher计算权重引发的问题，理应在生成model后重置ModelPatcher为默认值

-**v1.1.3 (2024/4/4)**
+**v1.1.3**

 - `easy ipadapterApply` 增加 **COMPOSITION** 预置项
 - 增加 对[ResAdapter](https://huggingface.co/jiaxiangc/res-adapter) lora模型 的加载支持
@@ -53,7 +91,7 @@
 - `easy wildcards` 增加 **multiline_mode**属性 
 - 增加 当节点需要下载模型时，若huggingface连接超时，会切换至镜像地址下载模型

-**v1.1.2 (39c5ccf)**
+**v1.1.2**

 - 改写 EasyUse 相关节点的部分插槽推荐节点
 - 增加 **启用上下文菜单自动嵌套子目录** 设置项，默认为启用状态，可分类子目录及checkpoints、loras预览图
@@ -69,7 +107,7 @@
 - 修复 `easy pipeEdit` 提示词输入lora时报错
 - 修复 layerDiffuse xyplot相关bug

-**v1.1.1 (5c8af8f)**
+**v1.1.1**

 - 修复首次添加含seed的节点且当前模式为control_before_generate时，seed为0的问题
 - `easy preSamplingAdvanced` 增加 **return_with_leftover_noise**
@@ -80,7 +118,7 @@
 - 去除强制**control_before_generate**设定
 - 增加 `easy imageRemBg` - 默认为BriaAI的RMBG-1.4模型, 移除背景效果更加，速度更快

-**v1.1.0 (d5ff84e)**
+**v1.1.0**

 - 增加 `easy imageSplitList` - 拆分每 N 张图像
 - 增加 `easy preSamplingDiffusionADDTL` - 可配置前景、背景、blended的additional_prompt等   
@@ -94,15 +132,18 @@
 - 修复 `easy instantIDApply` mask 未传入正确值
 - 修复 在 非a1111提示词风格下 BREAK 不生效的问题

-**v1.0.9 (ff1add1)**
+<details>
+<summary><b>v1.0.9</b></summary>

 - 修复未安装 ComfyUI-Impack-Pack 和 ComfyUI_InstantID 时报错
 - 修复 `easy pipeIn` - pipe设为可不必选
- 增加 `easy instantIDApply` - 需要先安装 [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID), 工作流参考[示例](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#InstantID)
+- 增加 `easy instantIDApply` - 需要先安装 [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID), 工作流参考[示例](https://github.com/yolain/ComfyUI-Yolain-Workflows?tab=readme-ov-file#2-2-instantid)
 - 修复 `easy detailerFix` 未添加到保存图片格式化扩展名可用节点列表
 - 修复 `easy XYInputs: PromptSR` 在替换负面提示词时报错
+</details>

-**v1.0.8 (f28cbf7)**
+<details>
+<summary><b>v1.0.8</b></summary>

 - `easy cascadeLoader` stage_c 与 stage_b 支持checkpoint模型 (需要下载[checkpoints](https://huggingface.co/stabilityai/stable-cascade/tree/main/comfyui_checkpoints)) 
 - `easy styleSelector` 搜索框修改为不区分大小写匹配
@@ -116,13 +157,16 @@

 （翻译对照已由 [AIGODLIKE-COMFYUI-TRANSLATION](https://github.com/AIGODLIKE/AIGODLIKE-ComfyUI-Translation) 统一维护啦！
 首次下载或者版本较早的朋友请更新 AIGODLIKE-COMFYUI-TRANSLATION 和本节点包至最新版本。）
+</details>

-**v1.0.7**
+<details>
+<summary><b>v1.0.7</b></summary>

 - 增加 `easy cascadeLoader` - stable cascade 加载器
 - 增加 `easy preSamplingCascade` - stabled cascade stage_c 预采样参数
 - 增加 `easy fullCascadeKSampler` - stable cascade stage_c 完整版采样器
 - 增加 `easy cascadeKSampler` - stable cascade stage-c ksampler simple
+</details>

 <details>
 <summary><b>v1.0.6</b></summary>
@@ -297,38 +341,8 @@
 | easy preSamplingLayerDiffusion | [ComfyUI-layerdiffusion](https://github.com/huchenlei/ComfyUI-layerdiffusion) | LayeredDiffusionApply等  | 
 | easy dynamiCrafterLoader       | [ComfyUI-layerdiffusion](https://github.com/ExponentialML/ComfyUI_Native_DynamiCrafter) | Apply Dynamicrafter     | 
 | easy imageChooser              | [cg-image-picker](https://github.com/chrisgoringe/cg-image-picker) | Preview Chooser         | 
-
-## 示例
-
-导入后请自行更换您目录里的大模型
-
-### StableDiffusion
-#### 文生图
-
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/Simple/text_to_image.png">
-
-#### 图生图+controlnet
-
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/Simple/image_to_image_controlnet.png">
-
-#### InstantID
-
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/Simple/instantID.png">
-
-### LayerDiffusion
-#### SD15
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/Simple/layer_diffusion_sd15.png">
-
-#### SDXL
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/Simple/layer_diffusion_example.png">
-
-### StableCascade
-#### 文生图
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/StableCascade/text_to_image.png">
-
-#### 图生图
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/StableCascade/image_to_image.png">
-
+| easy styleAlignedBatchAlign    | [style_aligned_comfy](https://github.com/chrisgoringe/cg-image-picker) | styleAlignedBatchAlign  | 
+| easy icLightApply              | [ComfyUI-IC-Light](https://github.com/huchenlei/ComfyUI-IC-Light) | ICLightApply等           |

 ## Credits

@@ -355,3 +369,5 @@
 [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) - pyssss 小蛇🐍脚本

 [cg-image-picker](https://github.com/chrisgoringe/cg-image-picker) - 图片选择器
+
+[ComfyUI-BrushNet](https://github.com/nullquant/ComfyUI-BrushNet) - BrushNet 内补节点
@@ -1,3 +1,5 @@
+__version__ = "1.1.7"
+
 import os
 import glob
 import folder_paths
@@ -85,4 +87,4 @@ WEB_DIRECTORY = "./web"
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', "WEB_DIRECTORY"]


-print('\033[34mComfy-Easy-Use (v1.1.4): \033[92mLoaded\033[0m')
+print(f'\033[34mComfy-Easy-Use v{__version__}: \033[92mLoaded\033[0m')
@@ -0,0 +1,33 @@
+import folder_paths
+import os
+def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
+    for full_folder_path in full_folder_paths:
+        folder_paths.add_model_folder_path(folder_name, full_folder_path)
+    if folder_name in folder_paths.folder_names_and_paths:
+        current_paths, current_extensions = folder_paths.folder_names_and_paths[folder_name]
+        updated_extensions = current_extensions | extensions
+        folder_paths.folder_names_and_paths[folder_name] = (current_paths, updated_extensions)
+    else:
+        folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions)
+
+image_suffixs = set([".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp", ".tiff", ".svg", ".ico", ".apng", ".tif", ".hdr", ".exr"])
+
+model_path = folder_paths.models_dir
+add_folder_path_and_extensions("ultralytics_bbox", [os.path.join(model_path, "ultralytics", "bbox")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("ultralytics_segm", [os.path.join(model_path, "ultralytics", "segm")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("ultralytics", [os.path.join(model_path, "ultralytics")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("mmdets_bbox", [os.path.join(model_path, "mmdets", "bbox")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("mmdets_segm", [os.path.join(model_path, "mmdets", "segm")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("mmdets", [os.path.join(model_path, "mmdets")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("sams", [os.path.join(model_path, "sams")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("onnx", [os.path.join(model_path, "onnx")], {'.onnx'})
+add_folder_path_and_extensions("instantid", [os.path.join(model_path, "instantid")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("layer_model", [os.path.join(model_path, "layer_model")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("rembg", [os.path.join(model_path, "rembg")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("ipadapter", [os.path.join(model_path, "ipadapter")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("dynamicrafter_models", [os.path.join(model_path, "dynamicrafter_models")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("mediapipe", [os.path.join(model_path, "mediapipe")], set(['.tflite','.pth']))
+add_folder_path_and_extensions("inpaint", [os.path.join(model_path, "inpaint")], folder_paths.supported_pt_extensions)
+
+add_folder_path_and_extensions("checkpoints_thumb", [os.path.join(model_path, "checkpoints")], image_suffixs)
+add_folder_path_and_extensions("loras_thumb", [os.path.join(model_path, "loras")], image_suffixs)
@@ -237,17 +237,17 @@ def encode_token_weights_g(model, token_weight_pairs):


 def encode_token_weights_l(model, token_weight_pairs):
-    l_out, _ = model.clip_l.encode_token_weights(token_weight_pairs)
-    return l_out, None
+    l_out, pooled = model.clip_l.encode_token_weights(token_weight_pairs)
+    return l_out, pooled


 def encode_token_weights(model, token_weight_pairs, encode_func):
    if model.layer_idx is not None:
        # 2016 [c2cb8e88] 及以上版本去除了sdxl clip的clip_layer方法
-        if compare_revision(2016):
-            model.cond_stage_model.set_clip_options({'layer': model.layer_idx})
-        else:
-            model.cond_stage_model.clip_layer(model.layer_idx)
+        # if compare_revision(2016):
+        model.cond_stage_model.set_clip_options({'layer': model.layer_idx})
+        # else:
+        # model.cond_stage_model.clip_layer(model.layer_idx)

    model_management.load_model_gpu(model.patcher)
    return encode_func(model.cond_stage_model, token_weight_pairs)
@@ -316,8 +316,8 @@ def advanced_encode(clip, text, token_normalization, weight_interpretation, w_ma
            embeddings_final, pooled = advanced_encode_from_tokens(tokenized['l'],
                                                                   token_normalization,
                                                                   weight_interpretation,
-                                                                   lambda x: (clip.encode_from_tokens({'l': x}), None),
-                                                                   w_max=w_max)
+                                                                   lambda x: encode_token_weights(clip, x, encode_token_weights_l),
+                                                                   w_max=w_max,return_pooled=True,)
            cond = [[embeddings_final, {"pooled_output": pooled}]]

        if conditioning is not None:
@@ -9,7 +9,7 @@ from server import PromptServer
 from .config import RESOURCES_DIR, FOOOCUS_STYLES_DIR, FOOOCUS_STYLES_SAMPLES
 from .logic import ConvertAnything
 from .libs.model import easyModelManager
-from .libs.utils import getMetadata
+from .libs.utils import getMetadata, cleanGPUUsedForce, get_local_filepath

 try:
    import aiohttp
@@ -19,8 +19,17 @@ except ImportError:
    print("pip install aiohttp")
    sys.exit()

+@PromptServer.instance.routes.post("/easyuse/cleangpu")
+def cleanGPU(request):
+    try:
+        cleanGPUUsedForce()
+        return web.Response(status=200)
+    except Exception as e:
+        return web.Response(status=500)
+        pass
+
@PromptServer.instance.routes.get("/easyuse/reboot")
-def reboot(self):
+def reboot(request):
    try:
        sys.stdout.close_log()
    except Exception as e:
@@ -129,7 +138,7 @@ async def getModelsThumbnail(request):
    loras = folder_paths.get_filename_list("loras_thumb")
    checkpoints_full = []
    loras_full = []
-    if len(checkpoints) + len(loras) >= 300:
+    if len(checkpoints) + len(loras) >= 500:
        return web.Response(status=400)
    for index, i in enumerate(checkpoints):
        full_path = folder_paths.get_full_path('checkpoints_thumb', str(i))
@@ -259,5 +268,19 @@ async def save_preview(request):
        "image":  type + "/" + os.path.basename(image_path)
    })

+@PromptServer.instance.routes.post("/easyuse/model/download")
+async def download_model(request):
+    post = await request.post()
+    url = post.get("url")
+    local_dir = post.get("local_dir")
+    if local_dir not in ['checkpoints', 'loras', 'controlnet', 'onnx', 'instantid', 'ipadapter', 'dynamicrafter_models', 'mediapipe', 'rembg', 'layer_model']:
+        return web.Response(status=400)
+    local_path = os.path.join(folder_paths.models_dir, local_dir)
+    try:
+        get_local_filepath(url, local_path)
+        return web.Response(status=200)
+    except:
+        return web.Response(status=500)
+
 NODE_CLASS_MAPPINGS = {}
 NODE_DISPLAY_NAME_MAPPINGS = {}
@@ -38,7 +38,7 @@ MAX_SEED_NUM = 1125899906842624

 RESOURCES_DIR = os.path.join(Path(__file__).parent.parent, "resources")

-# fooocus
+# inpaint
 INPAINT_DIR = os.path.join(folder_paths.models_dir, "inpaint")
 FOOOCUS_STYLES_DIR = os.path.join(Path(__file__).parent.parent, "styles")
 FOOOCUS_STYLES_SAMPLES = 'https://raw.githubusercontent.com/lllyasviel/Fooocus/main/sdxl_styles/samples/'
@@ -58,6 +58,24 @@ FOOOCUS_INPAINT_PATCH = {
        "model_url": "https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint.fooocus.patch"
    },
 }
+BRUSHNET_MODELS = {
+    "random_mask": {
+        "sd1": {
+            "model_url": "https://huggingface.co/Kijai/BrushNet-fp16/resolve/main/brushnet_random_mask_fp16.safetensors"
+        },
+        "sdxl": {
+            "model_url": "https://huggingface.co/yolain/brushnet/resolve/main/brushnet_random_mask_sdxl.safetensors"
+        }
+    },
+    "segmentation_mask": {
+        "sd1": {
+            "model_url": "https://huggingface.co/Kijai/BrushNet-fp16/resolve/main/brushnet_segmentation_mask_fp16.safetensors"
+        },
+        "sdxl": {
+            "model_url": "https://huggingface.co/yolain/brushnet/resolve/main/brushnet_segmentation_mask_sdxl.safetensors"
+        }
+    }
+}

 # layerDiffuse
 LAYER_DIFFUSION_DIR = os.path.join(folder_paths.models_dir, "layer_model")
@@ -68,7 +86,7 @@ LAYER_DIFFUSION_VAE = {
        }
    },
    "decode": {
-        "sd15": {
+        "sd1": {
            "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_vae_transparent_decoder.safetensors"
        },
        "sdxl": {
@@ -78,7 +96,7 @@ LAYER_DIFFUSION_VAE = {
 }
 LAYER_DIFFUSION = {
    "Attention Injection": {
-        "sd15": {
+        "sd1": {
          "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_transparent_attn.safetensors"
        },
        "sdxl": {
@@ -89,12 +107,12 @@ LAYER_DIFFUSION = {
        "sdxl": {
            "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_xl_transparent_conv.safetensors"
        },
-        "sd15": {
+        "sd1": {
            "model_url": None
        }
    },
    "Everything": {
-        "sd15": {
+        "sd1": {
          "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_joint.safetensors"
        },
        "sdxl": {
@@ -102,7 +120,7 @@ LAYER_DIFFUSION = {
        }
    },
    "Foreground": {
-        "sd15": {
+        "sd1": {
          "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_fg2bg.safetensors"
        },
        "sdxl": {
@@ -110,7 +128,7 @@ LAYER_DIFFUSION = {
        }
    },
    "Foreground to Background": {
-        "sd15": {
+        "sd1": {
          "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_fg2bg.safetensors"
        },
        "sdxl": {
@@ -118,7 +136,7 @@ LAYER_DIFFUSION = {
        }
    },
    "Background": {
-        "sd15": {
+        "sd1": {
          "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_bg2fg.safetensors"
        },
        "sdxl": {
@@ -126,7 +144,7 @@ LAYER_DIFFUSION = {
        }
    },
    "Background to Foreground": {
-        "sd15": {
+        "sd1": {
            "model_url": "https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_bg2fg.safetensors"
        },
        "sdxl": {
@@ -135,6 +153,27 @@ LAYER_DIFFUSION = {
    },
 }

+# IC Light
+IC_LIGHT_MODELS = {
+    "Foreground": {
+        "sd1": {
+            "model_url": "https://huggingface.co/huchenlei/IC-Light-ldm/resolve/main/iclight_sd15_fc_unet_ldm.safetensors"
+        },
+        "sdxl": {
+            "model_url": None
+        }
+    },
+    "Foreground&Background": {
+        "sd1": {
+            "model_url": "https://huggingface.co/huchenlei/IC-Light-ldm/resolve/main/iclight_sd15_fbc_unet_ldm.safetensors"
+        },
+        "sdxl": {
+            "model_url": None
+        }
+    }
+}
+
+
 # REMBG
 REMBG_DIR = os.path.join(folder_paths.models_dir, "rembg")
 REMBG_MODELS = {
@@ -159,7 +198,7 @@ IPADAPTER_MODELS = {
            "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.safetensors"
        },
        "sdxl": {
-            "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors"
+            "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.safetensors"
        }
    },
    "VIT-G (medium strength)": {
@@ -167,7 +206,7 @@ IPADAPTER_MODELS = {
            "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_vit-G.safetensors"
        },
        "sdxl": {
-            "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.safetensors"
+            "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors"
        }
    },
    "PLUS (high strength)": {
@@ -260,4 +299,19 @@ DYNAMICRAFTER_MODELS = {
    "dynamicrafter_unet_256 (2.98GB)": {
        "model_url": "https://huggingface.co/ExponentialML/DynamiCrafterUNet/resolve/main/dynamicrafter_unet_256.safetensors"
    },
+}
+
+#humanParsing
+HUMANPARSING_MODELS = {
+    "parsing_lip": {
+        "model_url": "https://huggingface.co/levihsu/OOTDiffusion/resolve/main/checkpoints/humanparsing/parsing_lip.onnx",
+    },
+}
+
+#mediapipe
+MEDIAPIPE_DIR = os.path.join(folder_paths.models_dir, "mediapipe")
+MEDIAPIPE_MODELS = {
+    "selfie_multiclass_256x256": {
+        "model_url": "https://huggingface.co/yolain/selfie_multiclass_256x256/resolve/main/selfie_multiclass_256x256.tflite"
+    }
 }
@@ -19,6 +19,8 @@ class InpaintWorker:
    def __init__(self, node_name):
        self.node_name = node_name if node_name is not None else ""
        self.original_calculate_weight = ModelPatcher.calculate_weight
+        if not hasattr(ModelPatcher, "original_calculate_weight"):
+            ModelPatcher.original_calculate_weight = self.original_calculate_weight
        self.injected_model_patcher_calculate_weight = False

    def load_fooocus_patch(self, lora: dict, to_load: dict):
@@ -0,0 +1,156 @@
+import torch
+import numpy as np
+import cv2
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from .simple_extractor_dataset import SimpleFolderDataset
+from .transforms import transform_logits
+from tqdm import tqdm
+from PIL import Image
+
+def get_palette(num_cls):
+    """ Returns the color map for visualizing the segmentation mask.
+    Args:
+        num_cls: Number of classes
+    Returns:
+        The color map
+    """
+    n = num_cls
+    palette = [0] * (n * 3)
+    for j in range(0, n):
+        lab = j
+        palette[j * 3 + 0] = 0
+        palette[j * 3 + 1] = 0
+        palette[j * 3 + 2] = 0
+        i = 0
+        while lab:
+            palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
+            palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
+            palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
+            i += 1
+            lab >>= 3
+    return palette
+
+
+def delete_irregular(logits_result):
+    parsing_result = np.argmax(logits_result, axis=2)
+    upper_cloth = np.where(parsing_result == 4, 255, 0)
+    contours, hierarchy = cv2.findContours(upper_cloth.astype(np.uint8),
+                                           cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
+    area = []
+    for i in range(len(contours)):
+        a = cv2.contourArea(contours[i], True)
+        area.append(abs(a))
+    if len(area) != 0:
+        top = area.index(max(area))
+        M = cv2.moments(contours[top])
+        cY = int(M["m01"] / M["m00"])
+
+    dresses = np.where(parsing_result == 7, 255, 0)
+    contours_dress, hierarchy_dress = cv2.findContours(dresses.astype(np.uint8),
+                                                       cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
+    area_dress = []
+    for j in range(len(contours_dress)):
+        a_d = cv2.contourArea(contours_dress[j], True)
+        area_dress.append(abs(a_d))
+    if len(area_dress) != 0:
+        top_dress = area_dress.index(max(area_dress))
+        M_dress = cv2.moments(contours_dress[top_dress])
+        cY_dress = int(M_dress["m01"] / M_dress["m00"])
+    wear_type = "dresses"
+    if len(area) != 0:
+        if len(area_dress) != 0 and cY_dress > cY:
+            irregular_list = np.array([4, 5, 6])
+            logits_result[:, :, irregular_list] = -1
+        else:
+            irregular_list = np.array([5, 6, 7, 8, 9, 10, 12, 13])
+            logits_result[:cY, :, irregular_list] = -1
+            wear_type = "cloth_pant"
+        parsing_result = np.argmax(logits_result, axis=2)
+    # pad border
+    parsing_result = np.pad(parsing_result, pad_width=1, mode='constant', constant_values=0)
+    return parsing_result, wear_type
+
+
+
+def hole_fill(img):
+    img_copy = img.copy()
+    mask = np.zeros((img.shape[0] + 2, img.shape[1] + 2), dtype=np.uint8)
+    cv2.floodFill(img, mask, (0, 0), 255)
+    img_inverse = cv2.bitwise_not(img)
+    dst = cv2.bitwise_or(img_copy, img_inverse)
+    return dst
+
+def refine_mask(mask):
+    contours, hierarchy = cv2.findContours(mask.astype(np.uint8),
+                                           cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
+    area = []
+    for j in range(len(contours)):
+        a_d = cv2.contourArea(contours[j], True)
+        area.append(abs(a_d))
+    refine_mask = np.zeros_like(mask).astype(np.uint8)
+    if len(area) != 0:
+        i = area.index(max(area))
+        cv2.drawContours(refine_mask, contours, i, color=255, thickness=-1)
+        # keep large area in skin case
+        for j in range(len(area)):
+          if j != i and area[i] > 2000:
+             cv2.drawContours(refine_mask, contours, j, color=255, thickness=-1)
+    return refine_mask
+
+def refine_hole(parsing_result_filled, parsing_result, arm_mask):
+    filled_hole = cv2.bitwise_and(np.where(parsing_result_filled == 4, 255, 0),
+                                  np.where(parsing_result != 4, 255, 0)) - arm_mask * 255
+    contours, hierarchy = cv2.findContours(filled_hole, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
+    refine_hole_mask = np.zeros_like(parsing_result).astype(np.uint8)
+    for i in range(len(contours)):
+        a = cv2.contourArea(contours[i], True)
+        # keep hole > 2000 pixels
+        if abs(a) > 2000:
+            cv2.drawContours(refine_hole_mask, contours, i, color=255, thickness=-1)
+    return refine_hole_mask + arm_mask
+
+def onnx_inference(lip_session, input_dir, mask_components=[0]):
+
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.406, 0.456, 0.485], std=[0.225, 0.224, 0.229])
+    ])
+    input_size = [473, 473]
+
+    dataset_lip = SimpleFolderDataset(root=input_dir, input_size=input_size, transform=transform)
+    dataloader_lip = DataLoader(dataset_lip)
+    palette = get_palette(20)
+    with torch.no_grad():
+        for _, batch in enumerate(tqdm(dataloader_lip)):
+            image, meta = batch
+            c = meta['center'].numpy()[0]
+            s = meta['scale'].numpy()[0]
+            w = meta['width'].numpy()[0]
+            h = meta['height'].numpy()[0]
+
+            output = lip_session.run(None, {"input.1": image.numpy().astype(np.float32)})
+            upsample = torch.nn.Upsample(size=input_size, mode='bilinear', align_corners=True)
+            upsample_output = upsample(torch.from_numpy(output[1][0]).unsqueeze(0))
+            upsample_output = upsample_output.squeeze()
+            upsample_output = upsample_output.permute(1, 2, 0)  # CHW -> HWC
+            logits_result_lip = transform_logits(upsample_output.data.cpu().numpy(), c, s, w, h,
+                                                 input_size=input_size)
+            parsing_result = np.argmax(logits_result_lip, axis=2)
+
+        output_img = Image.fromarray(np.asarray(parsing_result, dtype=np.uint8))
+        output_img.putpalette(palette)
+
+    mask = np.isin(output_img, mask_components).astype(np.uint8)
+    mask_image = Image.fromarray(mask * 255)
+    mask_image = mask_image.convert("RGB")
+    mask_image = torch.from_numpy(np.array(mask_image).astype(np.float32) / 255.0).unsqueeze(0)
+
+    output_img = output_img.convert('RGB')
+    output_img = torch.from_numpy(np.array(output_img).astype(np.float32) / 255.0).unsqueeze(0)
+
+    return output_img, mask_image
+
+
+
+
@@ -0,0 +1,23 @@
+from .parsing_api import onnx_inference
+from ..libs.utils import install_package
+
+class HumanParsing:
+    def __init__(self, model_path):
+        self.model_path = model_path
+        self.session = None
+
+    def __call__(self, input_image, mask_components):
+        if self.session is None:
+            install_package('onnxruntime')
+            import onnxruntime as ort
+
+            session_options = ort.SessionOptions()
+            session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+            session_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
+            # session_options.add_session_config_entry('gpu_id', str(gpu_id))
+            self.session = ort.InferenceSession(self.model_path, sess_options=session_options,
+                                                providers=['CPUExecutionProvider'])
+
+        parsed_image, mask = onnx_inference(self.session, input_image, mask_components)
+        return parsed_image, mask
+
@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+# -*- encoding: utf-8 -*-
+
+"""
+@Author  :   Peike Li
+@Contact :   peike.li@yahoo.com
+@File    :   dataset.py
+@Time    :   8/30/19 9:12 PM
+@Desc    :   Dataset Definition
+@License :   This source code is licensed under the license found in the
+             LICENSE file in the root directory of this source tree.
+"""
+
+import os
+
+import cv2
+import numpy as np
+from PIL import Image
+from torch.utils import data
+from .transforms import get_affine_transform
+
+
+class SimpleFolderDataset(data.Dataset):
+    def __init__(self, root, input_size=[512, 512], transform=None):
+        self.root = root
+        self.input_size = input_size
+        self.transform = transform
+        self.aspect_ratio = input_size[1] * 1.0 / input_size[0]
+        self.input_size = np.asarray(input_size)
+        self.is_pil_image = False
+        if isinstance(root, Image.Image):
+            self.file_list = [root]
+            self.is_pil_image = True
+        elif os.path.isfile(root):
+            self.file_list = [os.path.basename(root)]
+            self.root = os.path.dirname(root)
+        else:
+            self.file_list = os.listdir(self.root)
+
+    def __len__(self):
+        return len(self.file_list)
+
+    def _box2cs(self, box):
+        x, y, w, h = box[:4]
+        return self._xywh2cs(x, y, w, h)
+
+    def _xywh2cs(self, x, y, w, h):
+        center = np.zeros((2), dtype=np.float32)
+        center[0] = x + w * 0.5
+        center[1] = y + h * 0.5
+        if w > self.aspect_ratio * h:
+            h = w * 1.0 / self.aspect_ratio
+        elif w < self.aspect_ratio * h:
+            w = h * self.aspect_ratio
+        scale = np.array([w, h], dtype=np.float32)
+        return center, scale
+
+    def __getitem__(self, index):
+        if self.is_pil_image:
+            img = np.asarray(self.file_list[index])[:, :, [2, 1, 0]]
+        else:
+            img_name = self.file_list[index]
+            img_path = os.path.join(self.root, img_name)
+            img = cv2.imread(img_path, cv2.IMREAD_COLOR)
+        h, w, _ = img.shape
+
+        # Get person center and scale
+        person_center, s = self._box2cs([0, 0, w - 1, h - 1])
+        r = 0
+        trans = get_affine_transform(person_center, s, r, self.input_size)
+        input = cv2.warpAffine(
+            img,
+            trans,
+            (int(self.input_size[1]), int(self.input_size[0])),
+            flags=cv2.INTER_LINEAR,
+            borderMode=cv2.BORDER_CONSTANT,
+            borderValue=(0, 0, 0))
+
+        input = self.transform(input)
+        meta = {
+            'center': person_center,
+            'height': h,
+            'width': w,
+            'scale': s,
+            'rotation': r
+        }
+
+        return input, meta
@@ -0,0 +1,167 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Microsoft
+# Licensed under the MIT License.
+# Written by Bin Xiao (Bin.Xiao@microsoft.com)
+# ------------------------------------------------------------------------------
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import cv2
+import torch
+
+class BRG2Tensor_transform(object):
+    def __call__(self, pic):
+        img = torch.from_numpy(pic.transpose((2, 0, 1)))
+        if isinstance(img, torch.ByteTensor):
+            return img.float()
+        else:
+            return img
+
+class BGR2RGB_transform(object):
+    def __call__(self, tensor):
+        return tensor[[2,1,0],:,:]
+
+def flip_back(output_flipped, matched_parts):
+    '''
+    ouput_flipped: numpy.ndarray(batch_size, num_joints, height, width)
+    '''
+    assert output_flipped.ndim == 4,\
+        'output_flipped should be [batch_size, num_joints, height, width]'
+
+    output_flipped = output_flipped[:, :, :, ::-1]
+
+    for pair in matched_parts:
+        tmp = output_flipped[:, pair[0], :, :].copy()
+        output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
+        output_flipped[:, pair[1], :, :] = tmp
+
+    return output_flipped
+
+
+def fliplr_joints(joints, joints_vis, width, matched_parts):
+    """
+    flip coords
+    """
+    # Flip horizontal
+    joints[:, 0] = width - joints[:, 0] - 1
+
+    # Change left-right parts
+    for pair in matched_parts:
+        joints[pair[0], :], joints[pair[1], :] = \
+            joints[pair[1], :], joints[pair[0], :].copy()
+        joints_vis[pair[0], :], joints_vis[pair[1], :] = \
+            joints_vis[pair[1], :], joints_vis[pair[0], :].copy()
+
+    return joints*joints_vis, joints_vis
+
+
+def transform_preds(coords, center, scale, input_size):
+    target_coords = np.zeros(coords.shape)
+    trans = get_affine_transform(center, scale, 0, input_size, inv=1)
+    for p in range(coords.shape[0]):
+        target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
+    return target_coords
+
+def transform_parsing(pred, center, scale, width, height, input_size):
+
+    trans = get_affine_transform(center, scale, 0, input_size, inv=1)
+    target_pred = cv2.warpAffine(
+            pred,
+            trans,
+            (int(width), int(height)), #(int(width), int(height)),
+            flags=cv2.INTER_NEAREST,
+            borderMode=cv2.BORDER_CONSTANT,
+            borderValue=(0))
+
+    return target_pred
+
+def transform_logits(logits, center, scale, width, height, input_size):
+
+    trans = get_affine_transform(center, scale, 0, input_size, inv=1)
+    channel = logits.shape[2]
+    target_logits = []
+    for i in range(channel):
+        target_logit = cv2.warpAffine(
+            logits[:,:,i],
+            trans,
+            (int(width), int(height)), #(int(width), int(height)),
+            flags=cv2.INTER_LINEAR,
+            borderMode=cv2.BORDER_CONSTANT,
+            borderValue=(0))
+        target_logits.append(target_logit)
+    target_logits = np.stack(target_logits,axis=2)
+
+    return target_logits
+
+
+def get_affine_transform(center,
+                         scale,
+                         rot,
+                         output_size,
+                         shift=np.array([0, 0], dtype=np.float32),
+                         inv=0):
+    if not isinstance(scale, np.ndarray) and not isinstance(scale, list):
+        print(scale)
+        scale = np.array([scale, scale])
+
+    scale_tmp = scale
+
+    src_w = scale_tmp[0]
+    dst_w = output_size[1]
+    dst_h = output_size[0]
+
+    rot_rad = np.pi * rot / 180
+    src_dir = get_dir([0, src_w * -0.5], rot_rad)
+    dst_dir = np.array([0, (dst_w-1) * -0.5], np.float32)
+
+    src = np.zeros((3, 2), dtype=np.float32)
+    dst = np.zeros((3, 2), dtype=np.float32)
+    src[0, :] = center + scale_tmp * shift
+    src[1, :] = center + src_dir + scale_tmp * shift
+    dst[0, :] = [(dst_w-1) * 0.5, (dst_h-1) * 0.5]
+    dst[1, :] = np.array([(dst_w-1) * 0.5, (dst_h-1) * 0.5]) + dst_dir
+
+    src[2:, :] = get_3rd_point(src[0, :], src[1, :])
+    dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
+
+    if inv:
+        trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
+    else:
+        trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
+
+    return trans
+
+
+def affine_transform(pt, t):
+    new_pt = np.array([pt[0], pt[1], 1.]).T
+    new_pt = np.dot(t, new_pt)
+    return new_pt[:2]
+
+
+def get_3rd_point(a, b):
+    direct = a - b
+    return b + np.array([-direct[1], direct[0]], dtype=np.float32)
+
+
+def get_dir(src_point, rot_rad):
+    sn, cs = np.sin(rot_rad), np.cos(rot_rad)
+
+    src_result = [0, 0]
+    src_result[0] = src_point[0] * cs - src_point[1] * sn
+    src_result[1] = src_point[0] * sn + src_point[1] * cs
+
+    return src_result
+
+
+def crop(img, center, scale, output_size, rot=0):
+    trans = get_affine_transform(center, scale, rot, output_size)
+
+    dst_img = cv2.warpAffine(img,
+                             trans,
+                             (int(output_size[1]), int(output_size[0])),
+                             flags=cv2.INTER_LINEAR)
+
+    return dst_img
@@ -0,0 +1,185 @@
+import torch
+import numpy as np
+from typing import Tuple, TypedDict, Callable
+
+import comfy.model_management
+from comfy.sd import load_unet
+from comfy.ldm.models.autoencoder import AutoencoderKL
+from comfy.model_base import BaseModel
+from PIL import Image
+from nodes import VAEEncode
+
+from ..layer_diffuse.model import ModelPatcher, calculate_weight_adjust_channel
+from ..libs.image import np2tensor, pil2tensor
+
+class UnetParams(TypedDict):
+    input: torch.Tensor
+    timestep: torch.Tensor
+    c: dict
+    cond_or_uncond: torch.Tensor
+
+
+class VAEEncodeArgMax(VAEEncode):
+    def encode(self, vae, pixels):
+        assert isinstance(
+            vae.first_stage_model, AutoencoderKL
+        ), "ArgMax only supported for AutoencoderKL"
+        original_sample_mode = vae.first_stage_model.regularization.sample
+        vae.first_stage_model.regularization.sample = False
+        ret = super().encode(vae, pixels)
+        vae.first_stage_model.regularization.sample = original_sample_mode
+        return ret
+
+class ICLight:
+
+    @staticmethod
+    def apply_c_concat(params: UnetParams, concat_conds) -> UnetParams:
+        """Apply c_concat on unet call."""
+        sample = params["input"]
+        params["c"]["c_concat"] = torch.cat(
+            (
+                    [concat_conds.to(sample.device)]
+                    * (sample.shape[0] // concat_conds.shape[0])
+            ),
+            dim=0,
+        )
+        return params
+
+    @staticmethod
+    def create_custom_conv(
+        original_conv: torch.nn.Module,
+        dtype: torch.dtype,
+        device=torch.device,
+    ) -> torch.nn.Module:
+        with torch.no_grad():
+            new_conv_in = torch.nn.Conv2d(
+                8,
+                original_conv.out_channels,
+                original_conv.kernel_size,
+                original_conv.stride,
+                original_conv.padding,
+            )
+            new_conv_in.weight.zero_()
+            new_conv_in.weight[:, :4, :, :].copy_(original_conv.weight)
+            new_conv_in.bias = original_conv.bias
+            return new_conv_in.to(dtype=dtype, device=device)
+
+    def generate_lighting_image(self, original_image, direction):
+        _, image_height, image_width, _ = original_image.shape
+        match direction:
+            case 'Left Light':
+                gradient = np.linspace(255, 0, image_width)
+                image = np.tile(gradient, (image_height, 1))
+                input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+                return np2tensor(input_bg)
+            case 'Right Light':
+                gradient = np.linspace(0, 255, image_width)
+                image = np.tile(gradient, (image_height, 1))
+                input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+                return np2tensor(input_bg)
+            case 'Top Light':
+                gradient = np.linspace(255, 0, image_height)[:, None]
+                image = np.tile(gradient, (1, image_width))
+                input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+                return np2tensor(input_bg)
+            case 'Bottom Light':
+                gradient = np.linspace(0, 255, image_height)[:, None]
+                image = np.tile(gradient, (1, image_width))
+                input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+                return np2tensor(input_bg)
+            case 'Circle Light':
+                x = np.linspace(-1, 1, image_width)
+                y = np.linspace(-1, 1, image_height)
+                x, y = np.meshgrid(x, y)
+                r = np.sqrt(x ** 2 + y ** 2)
+                r = r / r.max()
+                color1 = np.array([0, 0, 0])[np.newaxis, np.newaxis, :]
+                color2 = np.array([255, 255, 255])[np.newaxis, np.newaxis, :]
+                gradient = (color1 * r[..., np.newaxis] + color2 * (1 - r)[..., np.newaxis]).astype(np.uint8)
+                image = pil2tensor(Image.fromarray(gradient))
+                return image
+            case _:
+                image = pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
+                return image
+
+    def generate_source_image(self, original_image, source):
+        batch_size, image_height, image_width, _ = original_image.shape
+        match source:
+            case 'Use Flipped Background Image':
+                if batch_size < 2:
+                    raise ValueError('Must be at least 2 image to use flipped background image.')
+                original_image = [img.unsqueeze(0) for img in original_image]
+                image = torch.flip(original_image[1], [2])
+                return image
+            case 'Ambient':
+                input_bg = np.zeros(shape=(image_height, image_width, 3), dtype=np.uint8) + 64
+                return np2tensor(input_bg)
+            case 'Left Light':
+                gradient = np.linspace(224, 32, image_width)
+                image = np.tile(gradient, (image_height, 1))
+                input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+                return np2tensor(input_bg)
+            case 'Right Light':
+                gradient = np.linspace(32, 224, image_width)
+                image = np.tile(gradient, (image_height, 1))
+                input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+                return np2tensor(input_bg)
+            case 'Top Light':
+                gradient = np.linspace(224, 32, image_height)[:, None]
+                image = np.tile(gradient, (1, image_width))
+                input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+                return np2tensor(input_bg)
+            case 'Bottom Light':
+                gradient = np.linspace(32, 224, image_height)[:, None]
+                image = np.tile(gradient, (1, image_width))
+                input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
+                return np2tensor(input_bg)
+            case _:
+                image = pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
+                return image
+
+
+    def apply(self, ic_model_path, model: ModelPatcher, c_concat: dict, ic_model=None) -> Tuple[ModelPatcher]:
+        try:
+            ModelPatcher.calculate_weight = calculate_weight_adjust_channel(ModelPatcher.calculate_weight)
+        except:
+            pass
+
+        device = comfy.model_management.get_torch_device()
+        dtype = comfy.model_management.unet_dtype()
+        work_model = model.clone()
+
+        # Apply scale factor.
+        base_model: BaseModel = work_model.model
+        scale_factor = base_model.model_config.latent_format.scale_factor
+
+        # [B, 4, H, W]
+        concat_conds: torch.Tensor = c_concat["samples"] * scale_factor
+        # [1, 4 * B, H, W]
+        concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1)
+
+        def unet_dummy_apply(unet_apply: Callable, params: UnetParams):
+            """A dummy unet apply wrapper serving as the endpoint of wrapper
+            chain."""
+            return unet_apply(x=params["input"], t=params["timestep"], **params["c"])
+
+        existing_wrapper = work_model.model_options.get(
+            "model_function_wrapper", unet_dummy_apply
+        )
+
+        def wrapper_func(unet_apply: Callable, params: UnetParams):
+            return existing_wrapper(unet_apply, params=self.apply_c_concat(params, concat_conds))
+
+        work_model.set_model_unet_function_wrapper(wrapper_func)
+        if not ic_model:
+            ic_model = load_unet(ic_model_path)
+        ic_model_state_dict = ic_model.model.diffusion_model.state_dict()
+
+        work_model.add_patches(
+            patches={
+                ("diffusion_model." + key): (value.to(dtype=dtype, device=device),)
+                for key, value in ic_model_state_dict.items()
+            }
+        )
+
+        return (work_model, ic_model)
@@ -1,16 +1,40 @@
-from PIL import Image
+from PIL import Image, ImageDraw, ImageFilter
 import os
 import hashlib
 import folder_paths
 import torch
 import numpy as np
 import comfy.model_management
+from comfy_extras.nodes_compositing import JoinImageWithAlpha
 from server import PromptServer
 from nodes import MAX_RESOLUTION
+from torchvision.transforms import Resize, CenterCrop, InterpolationMode
+from torchvision.transforms.functional import to_pil_image
 from .log import log_node_info
-from .libs.image import pil2tensor, tensor2pil, ResizeMode, get_new_bounds
+from .libs.image import pil2tensor, tensor2pil, ResizeMode, get_new_bounds, RGB2RGBA, image2mask, mask2image, blendImage
+from .libs.colorfix import adain_color_fix, wavelet_color_fix
 from .libs.chooser import ChooserMessage, ChooserCancelled
-from nodes import PreviewImage
+from .config import REMBG_DIR, REMBG_MODELS, HUMANPARSING_MODELS, MEDIAPIPE_MODELS, MEDIAPIPE_DIR
+
+
+# 图像数量
+class imageCount:
+  @classmethod
+  def INPUT_TYPES(s):
+    return {
+      "required": {
+        "images": ("IMAGE",),
+      }
+    }
+
+  CATEGORY = "EasyUse/Image"
+
+  RETURN_TYPES = ("INT",)
+  RETURN_NAMES = ("count",)
+  FUNCTION = "get_count"
+
+  def get_count(self, images):
+    return (images.size(0),)

 # 图像裁切
 class imageInsetCrop:
@@ -516,10 +540,90 @@ class imageSplitList:
          new_images[1].append(img)
    return new_images

+class imageSplitGrid:
+  @classmethod
+  def INPUT_TYPES(s):
+    return {
+      "required": {
+        "images": ("IMAGE",),
+        "row": ("INT", {"default": 1,"min": 1,"max": 10,"step": 1,}),
+        "column": ("INT", {"default": 1,"min": 1,"max": 10,"step": 1,}),
+      }
+    }
+
+  RETURN_TYPES = ("IMAGE",)
+  RETURN_NAMES = ("images",)
+  FUNCTION = "doit"
+  CATEGORY = "EasyUse/Image"
+
+  def crop(self, image, width, height, x, y):
+    x = min(x, image.shape[2] - 1)
+    y = min(y, image.shape[1] - 1)
+    to_x = width + x
+    to_y = height + y
+    img = image[:, y:to_y, x:to_x, :]
+    return img
+
+  def doit(self, images, row, column):
+    _, height, width, _ = images.shape
+    sub_width = width // column
+    sub_height = height // row
+    new_images = []
+    for i in range(row):
+        for j in range(column):
+          new_images.append(self.crop(images, sub_width, sub_height, j * sub_width, i * sub_height))
+
+    return (torch.cat(new_images, dim=0),)
+
+class imagesSplitImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+          "required": {
+              "images": ("IMAGE",),
+          }
+        }
+
+    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE", "IMAGE")
+    RETURN_NAMES = ("image1", "image2", "image3", "image4", "image5")
+    FUNCTION = "split"
+    CATEGORY = "EasyUse/Image"
+
+    def split(self, images,):
+      new_images = torch.chunk(images, len(images), dim=0)
+      return new_images
+
+
+class imageConcat:
+  @classmethod
+  def INPUT_TYPES(s):
+    return {"required": {
+      "image1": ("IMAGE",),
+      "image2": ("IMAGE",),
+      "direction": (['right','down','left','up',],{"default": 'right'}),
+      "match_image_size": ("BOOLEAN", {"default": False}),
+    }}
+
+  RETURN_TYPES = ("IMAGE",)
+  FUNCTION = "concat"
+  CATEGORY = "EasyUse/Image"
+
+  def concat(self, image1, image2, direction, match_image_size):
+    if match_image_size:
+      image2 = torch.nn.functional.interpolate(image2, size=(image1.shape[2], image1.shape[3]), mode="bilinear")
+    if direction == 'right':
+      row = torch.cat((image1, image2), dim=2)
+    elif direction == 'down':
+      row = torch.cat((image1, image2), dim=1)
+    elif direction == 'left':
+      row = torch.cat((image2, image1), dim=2)
+    elif direction == 'up':
+      row = torch.cat((image2, image1), dim=1)
+    return (row,)
+
 # 图片背景移除
 from .briaai.rembg import BriaRMBG, preprocess_image, postprocess_image
-from .libs.utils import get_local_filepath, easySave
-from .config import REMBG_DIR, REMBG_MODELS
+from .libs.utils import get_local_filepath, easySave, install_package
 class imageRemBg:
  @classmethod
  def INPUT_TYPES(self):
@@ -592,12 +696,12 @@ class imageChooser(PreviewImage):
  def INPUT_TYPES(self):
    return {
      "required":{
-
+        "mode": (['Always Pause', 'Keep Last Selection'], {"default": "Always Pause"}),
      },
      "optional": {
        "images": ("IMAGE",),
      },
-      "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"},
+      "hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"},
    }

  RETURN_TYPES = ("IMAGE",)
@@ -620,8 +724,7 @@ class imageChooser(PreviewImage):
    else:
      return None

-  def chooser(self, prompt=None, my_unique_id=None, **kwargs):
-
+  def chooser(self, prompt=None, my_unique_id=None, extra_pnginfo=None, **kwargs):
    id = my_unique_id[0]
    if id not in ChooserMessage.stash:
      ChooserMessage.stash[id] = {}
@@ -644,9 +747,19 @@ class imageChooser(PreviewImage):
    images = result['ui']['images']
    PromptServer.instance.send_sync("easyuse-image-choose", {"id": id, "urls": images})

+    # 获取上次选择
+    mode = kwargs.pop('mode', 'Always Pause')
+    last_choosen = None
+    if mode == 'Keep Last Selection':
+      if id and extra_pnginfo[0] and "workflow" in extra_pnginfo[0]:
+        workflow = extra_pnginfo[0]["workflow"]
+        node = next((x for x in workflow["nodes"] if str(x["id"]) == id), None)
+        if node:
+          last_choosen = node['properties']['values']
+
    # wait for selection
    try:
-      selections = ChooserMessage.waitForMessage(id, asList=True)
+      selections = ChooserMessage.waitForMessage(id, asList=True) if last_choosen is None or len(last_choosen)<1 else last_choosen
      choosen = [x for x in selections if x >= 0] if len(selections)>1 else [0]
    except ChooserCancelled:
      raise comfy.model_management.InterruptProcessingException()
@@ -654,6 +767,589 @@ class imageChooser(PreviewImage):
    return {"ui": {"images": images},
              "result": (self.tensor_bundle(images_in, choosen),)}

+class imageColorMatch(PreviewImage):
+  @classmethod
+  def INPUT_TYPES(cls):
+    return {
+      "required": {
+        "image_ref": ("IMAGE",),
+        "image_target": ("IMAGE",),
+        "method": (['wavelet', 'adain', 'mkl', 'hm', 'reinhard', 'mvgd', 'hm-mvgd-hm', 'hm-mkl-hm'],),
+        "image_output": (["Hide", "Preview", "Save", "Hide/Save"], {"default": "Preview"}),
+        "save_prefix": ("STRING", {"default": "ComfyUI"}),
+      },
+      "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+    }
+
+  CATEGORY = "EasyUse/Image"
+
+  RETURN_TYPES = ("IMAGE",)
+  RETURN_NAMES = ("image",)
+  OUTPUT_NODE = True
+  FUNCTION = "color_match"
+
+  def color_match(self, image_ref, image_target, method, image_output, save_prefix, prompt=None, extra_pnginfo=None):
+    if method in ["wavelet", "adain"]:
+      result_images = wavelet_color_fix(tensor2pil(image_target), tensor2pil(image_ref)) if method == 'wavelet' else adain_color_fix(tensor2pil(image_target), tensor2pil(image_ref))
+      new_images = pil2tensor(result_images)
+    else:
+      try:
+        from color_matcher import ColorMatcher
+      except:
+        install_package("color-matcher")
+        from color_matcher import ColorMatcher
+      image_ref = image_ref.cpu()
+      image_target = image_target.cpu()
+      batch_size = image_target.size(0)
+      out = []
+      images_target = image_target.squeeze()
+      images_ref = image_ref.squeeze()
+
+      image_ref_np = images_ref.numpy()
+      images_target_np = images_target.numpy()
+      if image_ref.size(0) > 1 and image_ref.size(0) != batch_size:
+        raise ValueError("ColorMatch: Use either single reference image or a matching batch of reference images.")
+      cm = ColorMatcher()
+      for i in range(batch_size):
+        image_target_np = images_target_np if batch_size == 1 else images_target[i].numpy()
+        image_ref_np_i = image_ref_np if image_ref.size(0) == 1 else images_ref[i].numpy()
+        try:
+          image_result = cm.transfer(src=image_target_np, ref=image_ref_np_i, method=method)
+        except BaseException as e:
+          print(f"Error occurred during transfer: {e}")
+          break
+        out.append(torch.from_numpy(image_result))
+
+      new_images = torch.stack(out, dim=0).to(torch.float32)
+
+    results = easySave(new_images, save_prefix, image_output, prompt, extra_pnginfo)
+
+    if image_output in ("Hide", "Hide/Save"):
+      return {"ui": {},
+              "result": (new_images,)}
+
+    return {"ui": {"images": results},
+            "result": (new_images,)}
+
+
+# 图像反推
+from .libs.image import ci
+class imageInterrogator:
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+          "required": {
+              "image": ("IMAGE",),
+              "mode": (['fast','classic','best','negative'],),
+              "use_lowvram": ("BOOLEAN", {"default": True}),
+          }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt",)
+    FUNCTION = "interrogate"
+    CATEGORY = "EasyUse/Image"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+
+    def interrogate(self, image, mode, use_lowvram=False):
+      prompt = ci.image_to_prompt(image, mode, low_vram=use_lowvram)
+      return {"ui":{"text":prompt},"result":(prompt,)}
+
+# 人类分割器
+class humanSegmentation:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+
+        return {
+          "required":{
+            "image": ("IMAGE",),
+            "method": (["selfie_multiclass_256x256", "human_parsing_lip"],),
+            "confidence": ("FLOAT", {"default": 0.4, "min": 0.05, "max": 0.95, "step": 0.01},),
+            "crop_multi": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001},),
+          },
+          "hidden": {
+              "prompt": "PROMPT",
+              "my_unique_id": "UNIQUE_ID",
+          }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "BBOX")
+    RETURN_NAMES = ("image", "mask", "bbox")
+    FUNCTION = "parsing"
+    CATEGORY = "EasyUse/Segmentation"
+
+    def get_mediapipe_image(self, image: Image):
+      import mediapipe as mp
+      # Convert image to NumPy array
+      numpy_image = np.asarray(image)
+      image_format = mp.ImageFormat.SRGB
+      # Convert BGR to RGB (if necessary)
+      if numpy_image.shape[-1] == 4:
+        image_format = mp.ImageFormat.SRGBA
+      elif numpy_image.shape[-1] == 3:
+        image_format = mp.ImageFormat.SRGB
+        numpy_image = cv2.cvtColor(numpy_image, cv2.COLOR_BGR2RGB)
+      return mp.Image(image_format=image_format, data=numpy_image)
+
+    def parsing(self, image, confidence, method, crop_multi, prompt=None, my_unique_id=None):
+      mask_components = []
+      if my_unique_id in prompt:
+        if prompt[my_unique_id]["inputs"]['mask_components']:
+          mask_components = prompt[my_unique_id]["inputs"]['mask_components'].split(',')
+      mask_components = list(map(int, mask_components))
+      if method == 'selfie_multiclass_256x256':
+        try:
+          import mediapipe as mp
+        except:
+          install_package("mediapipe")
+          import mediapipe as mp
+
+        from functools import reduce
+
+        model_path = get_local_filepath(MEDIAPIPE_MODELS['selfie_multiclass_256x256']['model_url'], MEDIAPIPE_DIR)
+        model_asset_buffer = None
+        with open(model_path, "rb") as f:
+            model_asset_buffer = f.read()
+        image_segmenter_base_options = mp.tasks.BaseOptions(model_asset_buffer=model_asset_buffer)
+        options = mp.tasks.vision.ImageSegmenterOptions(
+          base_options=image_segmenter_base_options,
+          running_mode=mp.tasks.vision.RunningMode.IMAGE,
+          output_category_mask=True)
+        # Create the image segmenter
+        ret_images = []
+        ret_masks = []
+
+        with mp.tasks.vision.ImageSegmenter.create_from_options(options) as segmenter:
+            for img in image:
+                _image = torch.unsqueeze(img, 0)
+                orig_image = tensor2pil(_image).convert('RGB')
+                # Convert the Tensor to a PIL image
+                i = 255. * img.cpu().numpy()
+                image_pil = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+                # create our foreground and background arrays for storing the mask results
+                mask_background_array = np.zeros((image_pil.size[0], image_pil.size[1], 4), dtype=np.uint8)
+                mask_background_array[:] = (0, 0, 0, 255)
+                mask_foreground_array = np.zeros((image_pil.size[0], image_pil.size[1], 4), dtype=np.uint8)
+                mask_foreground_array[:] = (255, 255, 255, 255)
+                # Retrieve the masks for the segmented image
+                media_pipe_image = self.get_mediapipe_image(image=image_pil)
+                segmented_masks = segmenter.segment(media_pipe_image)
+                masks = []
+                for i, com in enumerate(mask_components):
+                    masks.append(segmented_masks.confidence_masks[com])
+
+                image_data = media_pipe_image.numpy_view()
+                image_shape = image_data.shape
+                # convert the image shape from "rgb" to "rgba" aka add the alpha channel
+                if image_shape[-1] == 3:
+                    image_shape = (image_shape[0], image_shape[1], 4)
+                mask_background_array = np.zeros(image_shape, dtype=np.uint8)
+                mask_background_array[:] = (0, 0, 0, 255)
+                mask_foreground_array = np.zeros(image_shape, dtype=np.uint8)
+                mask_foreground_array[:] = (255, 255, 255, 255)
+                mask_arrays = []
+                if len(masks) == 0:
+                    mask_arrays.append(mask_background_array)
+                else:
+                    for i, mask in enumerate(masks):
+                        condition = np.stack((mask.numpy_view(),) * image_shape[-1], axis=-1) > confidence
+                        mask_array = np.where(condition, mask_foreground_array, mask_background_array)
+                        mask_arrays.append(mask_array)
+                # Merge our masks taking the maximum from each
+                merged_mask_arrays = reduce(np.maximum, mask_arrays)
+                # Create the image
+                mask_image = Image.fromarray(merged_mask_arrays)
+                # convert PIL image to tensor image
+                tensor_mask = mask_image.convert("RGB")
+                tensor_mask = np.array(tensor_mask).astype(np.float32) / 255.0
+                tensor_mask = torch.from_numpy(tensor_mask)[None,]
+                _mask = tensor_mask.squeeze(3)[..., 0]
+
+                _mask = tensor2pil(tensor_mask).convert('L')
+
+                ret_image = RGB2RGBA(orig_image, _mask)
+                ret_images.append(pil2tensor(ret_image))
+                ret_masks.append(image2mask(_mask))
+
+            output_image = torch.cat(ret_images, dim=0)
+            mask = torch.cat(ret_masks, dim=0)
+
+      elif method == "human_parsing_lip":
+        from .human_parsing.run_parsing import HumanParsing
+        onnx_path = os.path.join(folder_paths.models_dir, 'onnx')
+        model_path = get_local_filepath(HUMANPARSING_MODELS['parsing_lip']['model_url'], onnx_path)
+        parsing = HumanParsing(model_path=model_path)
+        model_image = image.squeeze(0)
+        model_image = model_image.permute((2, 0, 1))
+        model_image = to_pil_image(model_image)
+
+        map_image, mask = parsing(model_image, mask_components)
+
+        mask = mask[:, :, :, 0]
+
+        alpha = 1.0 - mask
+
+        output_image, = JoinImageWithAlpha().join_image_with_alpha(image, alpha)
+
+      # use crop
+      bbox = [[0, 0, 0, 0]]
+      if crop_multi > 0.0:
+        output_image, mask, bbox = imageCropFromMask().crop(output_image, mask, crop_multi, crop_multi, 1.0)
+
+      return (output_image, mask, bbox)
+
+
+class imageCropFromMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+          "required": {
+              "image": ("IMAGE",),
+              "mask": ("MASK",),
+              "image_crop_multi": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
+              "mask_crop_multi": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
+              "bbox_smooth_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+          },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "BBOX",)
+    RETURN_NAMES = ("crop_image", "crop_mask", "bbox",)
+    FUNCTION = "crop"
+    CATEGORY = "EasyUse/Image"
+
+    def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
+        if alpha == 0:
+            return prev_bbox_size
+        return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
+
+    def smooth_center(self, prev_center, curr_center, alpha=0.5):
+        if alpha == 0:
+            return prev_center
+        return (
+            round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
+            round(alpha * curr_center[1] + (1 - alpha) * prev_center[1])
+        )
+
+    def image2mask(self, image):
+      return image[:, :, :, 0]
+
+    def mask2image(self, mask):
+      return mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
+
+    def cropimage(self, original_images, masks, crop_size_mult, bbox_smooth_alpha):
+
+      bounding_boxes = []
+      cropped_images = []
+
+      self.max_bbox_width = 0
+      self.max_bbox_height = 0
+
+      # First, calculate the maximum bounding box size across all masks
+      curr_max_bbox_width = 0
+      curr_max_bbox_height = 0
+      for mask in masks:
+        _mask = tensor2pil(mask)
+        non_zero_indices = np.nonzero(np.array(_mask))
+        min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
+        min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
+        width = max_x - min_x
+        height = max_y - min_y
+        curr_max_bbox_width = max(curr_max_bbox_width, width)
+        curr_max_bbox_height = max(curr_max_bbox_height, height)
+
+      # Smooth the changes in the bounding box size
+      self.max_bbox_width = self.smooth_bbox_size(self.max_bbox_width, curr_max_bbox_width, bbox_smooth_alpha)
+      self.max_bbox_height = self.smooth_bbox_size(self.max_bbox_height, curr_max_bbox_height, bbox_smooth_alpha)
+
+      # Apply the crop size multiplier
+      self.max_bbox_width = round(self.max_bbox_width * crop_size_mult)
+      self.max_bbox_height = round(self.max_bbox_height * crop_size_mult)
+      bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height
+
+      # Then, for each mask and corresponding image...
+      for i, (mask, img) in enumerate(zip(masks, original_images)):
+        _mask = tensor2pil(mask)
+        non_zero_indices = np.nonzero(np.array(_mask))
+        min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
+        min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
+
+        # Calculate center of bounding box
+        center_x = np.mean(non_zero_indices[1])
+        center_y = np.mean(non_zero_indices[0])
+        curr_center = (round(center_x), round(center_y))
+
+        # If this is the first frame, initialize prev_center with curr_center
+        if not hasattr(self, 'prev_center'):
+          self.prev_center = curr_center
+
+        # Smooth the changes in the center coordinates from the second frame onwards
+        if i > 0:
+          center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha)
+        else:
+          center = curr_center
+
+        # Update prev_center for the next frame
+        self.prev_center = center
+
+        # Create bounding box using max_bbox_width and max_bbox_height
+        half_box_width = round(self.max_bbox_width / 2)
+        half_box_height = round(self.max_bbox_height / 2)
+        min_x = max(0, center[0] - half_box_width)
+        max_x = min(img.shape[1], center[0] + half_box_width)
+        min_y = max(0, center[1] - half_box_height)
+        max_y = min(img.shape[0], center[1] + half_box_height)
+
+        # Append bounding box coordinates
+        bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
+
+        # Crop the image from the bounding box
+        cropped_img = img[min_y:max_y, min_x:max_x, :]
+
+        # Calculate the new dimensions while maintaining the aspect ratio
+        new_height = min(cropped_img.shape[0], self.max_bbox_height)
+        new_width = round(new_height * bbox_aspect_ratio)
+
+        # Resize the image
+        resize_transform = Resize((new_height, new_width))
+        resized_img = resize_transform(cropped_img.permute(2, 0, 1))
+
+        # Perform the center crop to the desired size
+        crop_transform = CenterCrop((self.max_bbox_height, self.max_bbox_width))  # swap the order here if necessary
+        cropped_resized_img = crop_transform(resized_img)
+
+        cropped_images.append(cropped_resized_img.permute(1, 2, 0))
+
+      return cropped_images, bounding_boxes
+
+    def crop(self, image, mask, image_crop_multi, mask_crop_multi, bbox_smooth_alpha):
+      cropped_images, bounding_boxes = self.cropimage(image, mask, image_crop_multi, bbox_smooth_alpha)
+      cropped_mask_image, _ = self.cropimage(self.mask2image(mask), mask, mask_crop_multi, bbox_smooth_alpha)
+
+      cropped_image_out = torch.stack(cropped_images, dim=0)
+      cropped_mask_out = torch.stack(cropped_mask_image, dim=0)
+
+      return (cropped_image_out, cropped_mask_out[:, :, :, 0], bounding_boxes)
+
+
+class imageUncropFromBBOX:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+          "required": {
+              "original_image": ("IMAGE",),
+              "crop_image": ("IMAGE",),
+              "bbox": ("BBOX",),
+              "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01},),
+              "use_square_mask": ("BOOLEAN", {"default": True}),
+          },
+          "optional":{
+            "optional_mask": ("MASK",)
+          }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "uncrop"
+    CATEGORY = "EasyUse/Image"
+
+    def bbox_check(self, bbox, target_size=None):
+      if not target_size:
+        return bbox
+
+      new_bbox = (
+        bbox[0],
+        bbox[1],
+        min(target_size[0] - bbox[0], bbox[2]),
+        min(target_size[1] - bbox[1], bbox[3]),
+      )
+      return new_bbox
+
+    def bbox_to_region(self, bbox, target_size=None):
+      bbox = self.bbox_check(bbox, target_size)
+      return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
+
+    def uncrop(self, original_image, crop_image, bbox, border_blending, use_square_mask, optional_mask=None):
+      def inset_border(image, border_width=20, border_color=(0)):
+        width, height = image.size
+        bordered_image = Image.new(image.mode, (width, height), border_color)
+        bordered_image.paste(image, (0, 0))
+        draw = ImageDraw.Draw(bordered_image)
+        draw.rectangle((0, 0, width - 1, height - 1), outline=border_color, width=border_width)
+        return bordered_image
+
+      if len(original_image) != len(crop_image):
+        raise ValueError(
+          f"The number of original_images ({len(original_image)}) and cropped_images ({len(crop_image)}) should be the same")
+
+        # Ensure there are enough bboxes, but drop the excess if there are more bboxes than images
+      if len(bbox) > len(original_image):
+        print(f"Warning: Dropping excess bounding boxes. Expected {len(original_image)}, but got {len(bbox)}")
+        bbox = bbox[:len(original_image)]
+      elif len(bbox) < len(original_image):
+        raise ValueError("There should be at least as many bboxes as there are original and cropped images")
+
+
+      out_images = []
+
+      for i in range(len(original_image)):
+        img = tensor2pil(original_image[i])
+        crop = tensor2pil(crop_image[i])
+        _bbox = bbox[i]
+
+        bb_x, bb_y, bb_width, bb_height = _bbox
+        paste_region = self.bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
+        
+        # rescale the crop image to fit the paste_region
+        crop = crop.resize((round(paste_region[2] - paste_region[0]), round(paste_region[3] - paste_region[1])))
+        crop_img = crop.convert("RGB")
+
+        # border blending
+        if border_blending > 1.0:
+          border_blending = 1.0
+        elif border_blending < 0.0:
+          border_blending = 0.0
+
+        blend_ratio = (max(crop_img.size) / 2) * float(border_blending)
+        blend = img.convert("RGBA")
+
+        if use_square_mask:
+          mask = Image.new("L", img.size, 0)
+          mask_block = Image.new("L", (paste_region[2] - paste_region[0], paste_region[3] - paste_region[1]), 255)
+          mask_block = inset_border(mask_block, round(blend_ratio / 2), (0))
+          mask.paste(mask_block, paste_region)
+        else:
+          if optional_mask is None:
+            raise ValueError("optional_mask is required when use_square_mask is False")
+          original_mask = tensor2pil(optional_mask)
+          original_mask = original_mask.resize((paste_region[2] - paste_region[0], paste_region[3] - paste_region[1]))
+          mask = Image.new("L", img.size, 0)
+          mask.paste(original_mask, paste_region)
+
+        mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
+        mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))
+
+        blend.paste(crop_img, paste_region)
+        blend.putalpha(mask)
+
+        img = Image.alpha_composite(img.convert("RGBA"), blend)
+        out_images.append(img.convert("RGB"))
+
+      output_images = torch.cat([pil2tensor(img) for img in out_images], dim=0)
+      return (output_images,)
+
+
+
+import cv2
+import base64
+class loadImageBase64:
+  @classmethod
+  def INPUT_TYPES(s):
+    return {
+      "required": {
+        "base64_data": ("STRING", {"default": ""}),
+        "image_output": (["Hide", "Preview", "Save", "Hide/Save"], {"default": "Preview"}),
+        "save_prefix": ("STRING", {"default": "ComfyUI"}),
+      },
+      "optional": {
+
+      },
+      "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+    }
+
+  RETURN_TYPES = ("IMAGE", "MASK")
+  OUTPUT_NODE = True
+  FUNCTION = "load_image"
+  CATEGORY = "EasyUse/Image/LoadImage"
+
+  def convert_color(self, image,):
+    if len(image.shape) > 2 and image.shape[2] >= 4:
+      return cv2.cvtColor(image, cv2.COLOR_BGRA2RGB)
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+  def load_image(self, base64_data, image_output, save_prefix, prompt=None, extra_pnginfo=None):
+    nparr = np.frombuffer(base64.b64decode(base64_data), np.uint8)
+
+    result = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
+    channels = cv2.split(result)
+    if len(channels) > 3:
+      mask = channels[3].astype(np.float32) / 255.0
+      mask = torch.from_numpy(mask)
+    else:
+      mask = torch.ones(channels[0].shape, dtype=torch.float32, device="cpu")
+
+    result = self.convert_color(result)
+    result = result.astype(np.float32) / 255.0
+    new_images = torch.from_numpy(result)[None,]
+
+    results = easySave(new_images, save_prefix, image_output, None, None)
+    mask = mask.unsqueeze(0)
+
+    if image_output in ("Hide", "Hide/Save"):
+      return {"ui": {},
+              "result": (new_images, mask)}
+
+    return {"ui": {"images": results},
+            "result": (new_images, mask)}
+
+class imageToBase64:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+        "required": {
+            "image": ("IMAGE",),
+        },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "to_base64"
+    CATEGORY = "EasyUse/Image"
+    OUTPUT_NODE = True
+
+    def to_base64(self, image, ):
+      import base64
+      from io import BytesIO
+
+      # 将张量图像转换为PIL图像
+      pil_image = tensor2pil(image)
+
+      buffered = BytesIO()
+      pil_image.save(buffered, format="JPEG")
+      image_bytes = buffered.getvalue()
+
+      base64_str = base64.b64encode(image_bytes).decode("utf-8")
+      return {"result": (base64_str,)}
+
+class removeLocalImage:
+  @classmethod
+  def INPUT_TYPES(s):
+      return {
+        "required": {
+          "file_name": ("STRING",{"default":""}),
+        },
+      }
+
+  RETURN_TYPES = ()
+  OUTPUT_NODE = True
+  FUNCTION = "remove"
+  CATEGORY = "EasyUse/Image"
+
+  def remove(self, file_name):
+    hasFile = False
+    for file in os.listdir(folder_paths.input_directory):
+      name_without_extension, file_extension = os.path.splitext(file)
+      if name_without_extension == file_name or file == file_name:
+        os.remove(os.path.join(folder_paths.input_directory, file))
+        hasFile = True
+        break
+    if hasFile:
+      PromptServer.instance.send_sync("easyuse-toast", {"content": "Removed SuccessFully", "type":'success'})
+    else:
+      PromptServer.instance.send_sync("easyuse-toast", {"content": "Removed Failed", "type": 'error'})
+    return ()
+
+
 # 姿势编辑器
 class poseEditor:
  @classmethod
@@ -698,6 +1394,7 @@ class poseEditor:

 NODE_CLASS_MAPPINGS = {
  "easy imageInsetCrop": imageInsetCrop,
+  "easy imageCount": imageCount,
  "easy imageSize": imageSize,
  "easy imageSizeBySide": imageSizeBySide,
  "easy imageSizeByLongerSide": imageSizeByLongerSide,
@@ -707,16 +1404,28 @@ NODE_CLASS_MAPPINGS = {
  "easy imageScaleDownToSize": imageScaleDownToSize,
  "easy imageRatio": imageRatio,
  "easy imageToMask": imageToMask,
+  "easy imageConcat": imageConcat,
  "easy imageSplitList": imageSplitList,
+  "easy imageSplitGrid": imageSplitGrid,
+  "easy imagesSplitImage": imagesSplitImage,
+  "easy imageCropFromMask": imageCropFromMask,
+  "easy imageUncropFromBBOX": imageUncropFromBBOX,
  "easy imageSave": imageSaveSimple,
  "easy imageRemBg": imageRemBg,
  "easy imageChooser": imageChooser,
+  "easy imageColorMatch": imageColorMatch,
+  "easy imageInterrogator": imageInterrogator,
+  "easy loadImageBase64": loadImageBase64,
+  "easy imageToBase64": imageToBase64,
  "easy joinImageBatch": JoinImageBatch,
+  "easy humanSegmentation": humanSegmentation,
+  "easy removeLocalImage": removeLocalImage,
  "easy poseEditor": poseEditor
 }

 NODE_DISPLAY_NAME_MAPPINGS = {
  "easy imageInsetCrop": "ImageInsetCrop",
+  "easy imageCount": "ImageCount",
  "easy imageSize": "ImageSize",
  "easy imageSizeBySide": "ImageSize (Side)",
  "easy imageSizeByLongerSide": "ImageSize (LongerSide)",
@@ -727,10 +1436,21 @@ NODE_DISPLAY_NAME_MAPPINGS = {
  "easy imageRatio": "ImageRatio",
  "easy imageToMask": "ImageToMask",
  "easy imageHSVMask": "ImageHSVMask",
+  "easy imageConcat": "imageConcat",
  "easy imageSplitList": "imageSplitList",
+  "easy imageSplitGrid": "imageSplitGrid",
+  "easy imagesSplitImage": "imagesSplitImage",
+  "easy imageCropFromMask": "imageCropFromMask",
+  "easy imageUncropFromBBOX": "imageUncropFromBBOX",
  "easy imageSave": "SaveImage (Simple)",
  "easy imageRemBg": "Image Remove Bg",
  "easy imageChooser": "Image Chooser",
+  "easy imageColorMatch": "Image Color Match",
+  "easy imageInterrogator": "Image To Prompt",
  "easy joinImageBatch": "JoinImageBatch",
-  "easy poseEditor": "PoseEditor"
+  "easy loadImageBase64": "Load Image (Base64)",
+  "easy imageToBase64": "Image To Base64",
+  "easy humanSegmentation": "Human Segmentation",
+  "easy removeLocalImage": "Remove Local Image",
+  "easy poseEditor": "PoseEditor",
 }
@@ -58,15 +58,15 @@ class LayerDiffuse:
        except:
            pass

-        if method in [LayerMethod.FG_ONLY_CONV, LayerMethod.FG_ONLY_ATTN] and sd_version == 'sd15':
+        if method in [LayerMethod.FG_ONLY_CONV, LayerMethod.FG_ONLY_ATTN] and sd_version == 'sd1':
            self.frames = 1
-        elif method in [LayerMethod.BG_TO_BLEND, LayerMethod.FG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG, LayerMethod.FG_BLEND_TO_BG] and sd_version == 'sd15':
+        elif method in [LayerMethod.BG_TO_BLEND, LayerMethod.FG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG, LayerMethod.FG_BLEND_TO_BG] and sd_version == 'sd1':
            self.frames = 2
            batch_size, _, height, width = samples['samples'].shape
            if batch_size % 2 != 0:
                raise Exception(f"The batch size should be a multiple of 2. 批次大小需为2的倍数")
            control_img = image
-        elif method == LayerMethod.EVERYTHING and sd_version == 'sd15':
+        elif method == LayerMethod.EVERYTHING and sd_version == 'sd1':
            batch_size, _, height, width = samples['samples'].shape
            self.frames = 3
            if batch_size % 3 != 0:
@@ -77,7 +77,7 @@ class LayerDiffuse:
        model_path = get_local_filepath(model_url, LAYER_DIFFUSION_DIR)
        layer_lora_state_dict = load_layer_model_state_dict(model_path)
        work_model = model.clone()
-        if sd_version == 'sd15':
+        if sd_version == 'sd1':
            patcher = AttentionSharingPatcher(
                work_model, self.frames, use_control=control_img is not None
            )
@@ -97,7 +97,7 @@ class LayerDiffuse:
            else:
                c_concat = model.model.latent_format.process_in(torch.cat([samples["samples"], blend_samples["samples"]], dim=1))
            samp_model, positive, negative = (work_model,) + self.apply_layer_c_concat(positive, negative, c_concat)
-        elif sd_version == 'sd15':
+        elif sd_version == 'sd1':
            if method in [LayerMethod.BG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG]:
                additional_cond = (additional_cond[0], None)
            elif method in [LayerMethod.FG_TO_BLEND, LayerMethod.FG_BLEND_TO_BG]:
@@ -166,10 +166,10 @@ class LayerDiffuse:
        alpha = []
        if layer_diffusion_method is not None:
            sd_version = get_sd_version(model)
-            if sd_version not in ['sdxl', 'sd15']:
+            if sd_version not in ['sdxl', 'sd1']:
                raise Exception(f"Only SDXL and SD1.5 model supported for Layer Diffusion")
            method = self.get_layer_diffusion_method(layer_diffusion_method, blend_samples is not None)
-            sd15_allow = True if sd_version == 'sd15' and method in [LayerMethod.FG_ONLY_ATTN, LayerMethod.EVERYTHING, LayerMethod.BG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG] else False
+            sd15_allow = True if sd_version == 'sd1' and method in [LayerMethod.FG_ONLY_ATTN, LayerMethod.EVERYTHING, LayerMethod.BG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG] else False
            sdxl_allow = True if sd_version == 'sdxl' and method in [LayerMethod.FG_ONLY_CONV, LayerMethod.FG_ONLY_ATTN, LayerMethod.BG_BLEND_TO_FG] else False
            if sdxl_allow or sd15_allow:
                if self.vae_transparent_decoder is None:
@@ -7,18 +7,17 @@ import comfy.model_management
 from comfy.model_patcher import ModelPatcher
 from tqdm import tqdm
 from typing import Optional, Tuple
-
+from ..libs.utils import install_package
+from packaging import version

 try:
+    install_package("diffusers", "0.27.2", True, "0.25.0")
+
    from diffusers.configuration_utils import ConfigMixin, register_to_config
    from diffusers.models.modeling_utils import ModelMixin
    from diffusers import __version__
    if __version__:
-        try:
-            diffusers_version = float(__version__.replace('.', '').replace('dev','.'))
-        except ValueError:
-            diffusers_version = 270
-        if diffusers_version < 270:
+        if version.parse(__version__) < version.parse("0.26.0"):
            from diffusers.models.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
        else:
            from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
@@ -0,0 +1,115 @@
+import torch
+from PIL import Image
+from torch import Tensor
+from torch.nn import functional as F
+
+from torchvision.transforms import ToTensor, ToPILImage
+
+def adain_color_fix(target: Image, source: Image):
+    # Convert images to tensors
+    to_tensor = ToTensor()
+    target_tensor = to_tensor(target).unsqueeze(0)
+    source_tensor = to_tensor(source).unsqueeze(0)
+
+    # Apply adaptive instance normalization
+    result_tensor = adaptive_instance_normalization(target_tensor, source_tensor)
+
+    # Convert tensor back to image
+    to_image = ToPILImage()
+    result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))
+
+    return result_image
+
+def wavelet_color_fix(target: Image, source: Image):
+    source = source.resize(target.size, resample=Image.Resampling.LANCZOS)
+
+    # Convert images to tensors
+    to_tensor = ToTensor()
+    target_tensor = to_tensor(target).unsqueeze(0)
+    source_tensor = to_tensor(source).unsqueeze(0)
+
+    # Apply wavelet reconstruction
+    result_tensor = wavelet_reconstruction(target_tensor, source_tensor)
+
+    # Convert tensor back to image
+    to_image = ToPILImage()
+    result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))
+
+    return result_image
+
+def calc_mean_std(feat: Tensor, eps=1e-5):
+    """Calculate mean and std for adaptive_instance_normalization.
+    Args:
+        feat (Tensor): 4D tensor.
+        eps (float): A small value added to the variance to avoid
+            divide-by-zero. Default: 1e-5.
+    """
+    size = feat.size()
+    assert len(size) == 4, 'The input feature should be 4D tensor.'
+    b, c = size[:2]
+    feat_var = feat.view(b, c, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().view(b, c, 1, 1)
+    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
+    return feat_mean, feat_std
+
+def adaptive_instance_normalization(content_feat:Tensor, style_feat:Tensor):
+    """Adaptive instance normalization.
+    Adjust the reference features to have the similar color and illuminations
+    as those in the degradate features.
+    Args:
+        content_feat (Tensor): The reference feature.
+        style_feat (Tensor): The degradate features.
+    """
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std(style_feat)
+    content_mean, content_std = calc_mean_std(content_feat)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+def wavelet_blur(image: Tensor, radius: int):
+    """
+    Apply wavelet blur to the input tensor.
+    """
+    # input shape: (1, 3, H, W)
+    # convolution kernel
+    kernel_vals = [
+        [0.0625, 0.125, 0.0625],
+        [0.125, 0.25, 0.125],
+        [0.0625, 0.125, 0.0625],
+    ]
+    kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device)
+    # add channel dimensions to the kernel to make it a 4D tensor
+    kernel = kernel[None, None]
+    # repeat the kernel across all input channels
+    kernel = kernel.repeat(3, 1, 1, 1)
+    image = F.pad(image, (radius, radius, radius, radius), mode='replicate')
+    # apply convolution
+    output = F.conv2d(image, kernel, groups=3, dilation=radius)
+    return output
+
+def wavelet_decomposition(image: Tensor, levels=5):
+    """
+    Apply wavelet decomposition to the input tensor.
+    This function only returns the low frequency & the high frequency.
+    """
+    high_freq = torch.zeros_like(image)
+    for i in range(levels):
+        radius = 2 ** i
+        low_freq = wavelet_blur(image, radius)
+        high_freq += (image - low_freq)
+        image = low_freq
+
+    return high_freq, low_freq
+
+def wavelet_reconstruction(content_feat:Tensor, style_feat:Tensor):
+    """
+    Apply wavelet decomposition, so that the content will have the same color as the style.
+    """
+    # calculate the wavelet decomposition of the content feature
+    content_high_freq, content_low_freq = wavelet_decomposition(content_feat)
+    del content_low_freq
+    # calculate the wavelet decomposition of the style feature
+    style_high_freq, style_low_freq = wavelet_decomposition(style_feat)
+    del style_high_freq
+    # reconstruct the content feature with the style's high frequency
+    return content_high_freq + style_low_freq
@@ -7,26 +7,12 @@ class easyControlnet:
    def __init__(self):
        pass

-    def load_controlnet(self, control_net_name, control_net, scale_soft_weights):
-        if control_net is None:
-            if scale_soft_weights < 1:
-                if "ScaledSoftControlNetWeights" in NODE_CLASS_MAPPINGS:
-                    soft_weight_cls = NODE_CLASS_MAPPINGS['ScaledSoftControlNetWeights']
-                    (weights, timestep_keyframe) = soft_weight_cls().load_weights(scale_soft_weights, False)
-                    cn_adv_cls = NODE_CLASS_MAPPINGS['ControlNetLoaderAdvanced']
-                    control_net, = cn_adv_cls().load_controlnet(control_net_name, timestep_keyframe)
-                else:
-                    raise Exception(f"[Advanced-ControlNet Not Found] you need to install 'COMFYUI-Advanced-ControlNet'")
-            else:
-                controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
-                control_net = comfy.controlnet.load_controlnet(controlnet_path)
-        return control_net
-
-    def apply(self, control_net_name, image, positive, negative, strength, start_percent=0, end_percent=1, control_net=None, scale_soft_weights=1, mask=None):
+    def apply(self, control_net_name, image, positive, negative, strength, start_percent=0, end_percent=1, control_net=None, scale_soft_weights=1, mask=None, easyCache=None):
        if strength == 0:
            return (positive, negative)

-        control_net = self.load_controlnet(control_net_name, control_net, scale_soft_weights)
+        if control_net is None:
+            control_net = easyCache.load_controlnet(control_net_name, scale_soft_weights)

        if mask is not None:
            mask = mask.to(self.device)
@@ -1,7 +1,14 @@
+import os
+import base64
 import torch
 import numpy as np
 from enum import Enum
 from PIL import Image
+from io import BytesIO
+from typing import List, Union
+
+import folder_paths
+from .utils import install_package

 # PIL to Tensor
 def pil2tensor(image):
@@ -9,6 +16,28 @@ def pil2tensor(image):
 # Tensor to PIL
 def tensor2pil(image):
    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+# np to Tensor
+def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor:
+  if isinstance(img_np, list):
+    return torch.cat([np2tensor(img) for img in img_np], dim=0)
+  return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0)
+# Tensor to np
+def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]:
+  if len(tensor.shape) == 3:  # Single image
+    return np.clip(255.0 * tensor.cpu().numpy(), 0, 255).astype(np.uint8)
+  else:  # Batch of images
+    return [np.clip(255.0 * t.cpu().numpy(), 0, 255).astype(np.uint8) for t in tensor]
+
+def pil2byte(pil_image, format='PNG'):
+  byte_arr = BytesIO()
+  pil_image.save(byte_arr, format=format)
+  byte_arr.seek(0)
+  return byte_arr
+
+def image2base64(image_base64):
+    image_bytes = base64.b64decode(image_base64)
+    image_data = Image.open(BytesIO(image_bytes))
+    return image_data

 # Get new bounds
 def get_new_bounds(width, height, left, right, top, bottom):
@@ -19,6 +48,64 @@ def get_new_bounds(width, height, left, right, top, bottom):
  bottom = height - bottom
  return (left, right, top, bottom)

+def RGB2RGBA(image: Image, mask: Image) -> Image:
+  (R, G, B) = image.convert('RGB').split()
+  return Image.merge('RGBA', (R, G, B, mask.convert('L')))
+
+def image2mask(image: Image) -> torch.Tensor:
+  _image = image.convert('RGBA')
+  alpha = _image.split()[0]
+  bg = Image.new("L", _image.size)
+  _image = Image.merge('RGBA', (bg, bg, bg, alpha))
+  ret_mask = torch.tensor([pil2tensor(_image)[0, :, :, 3].tolist()])
+  return ret_mask
+
+def mask2image(mask: torch.Tensor) -> Image:
+  masks = tensor2np(mask)
+  for m in masks:
+    _mask = Image.fromarray(m).convert("L")
+    _image = Image.new("RGBA", _mask.size, color='white')
+    _image = Image.composite(
+      _image, Image.new("RGBA", _mask.size, color='black'), _mask)
+  return _image
+
+# 图像融合
+class blendImage:
+  def g(self, x):
+    return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
+
+  def blend_mode(self, img1, img2, mode):
+    if mode == "normal":
+      return img2
+    elif mode == "multiply":
+      return img1 * img2
+    elif mode == "screen":
+      return 1 - (1 - img1) * (1 - img2)
+    elif mode == "overlay":
+      return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
+    elif mode == "soft_light":
+      return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1),
+                         img1 + (2 * img2 - 1) * (self.g(img1) - img1))
+    elif mode == "difference":
+      return img1 - img2
+    else:
+      raise ValueError(f"Unsupported blend mode: {mode}")
+
+  def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str = 'normal'):
+    image2 = image2.to(image1.device)
+    if image1.shape != image2.shape:
+      image2 = image2.permute(0, 3, 1, 2)
+      image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic',
+                                          crop='center')
+      image2 = image2.permute(0, 2, 3, 1)
+
+    blended_image = self.blend_mode(image1, image2, blend_mode)
+    blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
+    blended_image = torch.clamp(blended_image, 0, 1)
+    return blended_image
+
+
+

 class ResizeMode(Enum):
  RESIZE = "Just Resize"
@@ -33,3 +120,75 @@ class ResizeMode(Enum):
      return 2
    assert False, "NOTREACHED"

+
+
+# CLIP反推
+import comfy.utils
+from torchvision import transforms
+Config, Interrogator = None, None
+class CI_Inference:
+  ci_model = None
+  cache_path: str
+
+  def __init__(self):
+    self.ci_model = None
+    self.low_vram = False
+    self.cache_path = os.path.join(folder_paths.models_dir, "clip_interrogator")
+
+  def _load_model(self, model_name, low_vram=False):
+    if not (self.ci_model and model_name == self.ci_model.config.clip_model_name and self.low_vram == low_vram):
+      self.low_vram = low_vram
+      print(f"Load model: {model_name}")
+
+      config = Config(
+        device="cuda" if torch.cuda.is_available() else "cpu",
+        download_cache=True,
+        clip_model_name=model_name,
+        clip_model_path=self.cache_path,
+        cache_path=self.cache_path,
+        caption_model_name='blip-large'
+      )
+
+      if low_vram:
+        config.apply_low_vram_defaults()
+
+      self.ci_model = Interrogator(config)
+
+  def _interrogate(self, image, mode, caption=None):
+    if mode == 'best':
+      prompt = self.ci_model.interrogate(image, caption=caption)
+    elif mode == 'classic':
+      prompt = self.ci_model.interrogate_classic(image, caption=caption)
+    elif mode == 'fast':
+      prompt = self.ci_model.interrogate_fast(image, caption=caption)
+    elif mode == 'negative':
+      prompt = self.ci_model.interrogate_negative(image)
+    else:
+      raise Exception(f"Unknown mode {mode}")
+    return prompt
+
+  def image_to_prompt(self, image, mode, model_name='ViT-L-14/openai', low_vram=False):
+    try:
+      from clip_interrogator import Config, Interrogator
+      global Config, Interrogator
+    except:
+      install_package("clip_interrogator", "0.6.0")
+      from clip_interrogator import Config, Interrogator
+
+    pbar = comfy.utils.ProgressBar(len(image))
+
+    self._load_model(model_name, low_vram)
+    prompt = []
+    for i in range(len(image)):
+      im = image[i]
+
+      im = tensor2pil(im)
+      im = im.convert('RGB')
+
+      _prompt = self._interrogate(im, mode)
+      pbar.update(1)
+      prompt.append(_prompt)
+
+    return prompt
+
+ci = CI_Inference()
@@ -1,16 +1,19 @@
 import time, os, psutil
 import comfy.utils
 import comfy.sd
+import comfy.controlnet
 import folder_paths
 from nodes import NODE_CLASS_MAPPINGS
 from collections import defaultdict
 from ..log import log_node_info, log_node_error

-stable_diffusion_loaders = ["easy a1111Loader", "easy comfyLoader", "easy zero123Loader", "easy svdLoader"]
+stable_diffusion_loaders = ["easy fullLoader", "easy a1111Loader", "easy comfyLoader", "easy zero123Loader", "easy svdLoader"]
 stable_cascade_loaders = ["easy cascadeLoader"]
+controlnet_loaders = ["easy controlnetLoader", "easy controlnetLoaderADV"]
+instant_loaders = ["easy instantIDApply", "easy instantIDApplyADV"]
 cascade_vae_node = ["easy preSamplingCascade", "easy fullCascadeKSampler"]
 model_merge_node = ["easy XYInputs: ModelMergeBlocks"]
-lora_widget = ["easy a1111Loader", "easy comfyLoader"]
+lora_widget = ["easy fullLoader", "easy a1111Loader", "easy comfyLoader"]

 class easyLoader:
    def __init__(self):
@@ -22,8 +25,9 @@ class easyLoader:
            "bvae": defaultdict(tuple),
            "vae": defaultdict(object),
            "lora": defaultdict(dict),  # {lora_name: {UID: (model_lora, clip_lora)}}
+            "controlnet": defaultdict(dict),
        }
-        self.memory_threshold = self.determine_memory_threshold(0.7)
+        self.memory_threshold = self.determine_memory_threshold(0.9)
        self.lora_name_cache = []

    def clean_values(self, values: str):
@@ -50,9 +54,17 @@ class easyLoader:
        for key in keys - desired_names:
            del self.loaded_objects[object_type][key]

-    def get_input_value(self, entry, key):
+    def get_input_value(self, entry, key, prompt=None):
        val = entry["inputs"][key]
-        return val if isinstance(val, str) else val[0]
+        if isinstance(val, str):
+            return val
+        elif isinstance(val, list):
+            if prompt is not None and val[0]:
+                return prompt[val[0]]['inputs'][key]
+            else:
+                return val[0]
+        else:
+            return str(val)

    def process_pipe_loader(self, entry, desired_ckpt_names, desired_vae_names, desired_lora_names, desired_lora_settings, num_loras=3, suffix=""):
        for idx in range(1, num_loras + 1):
@@ -71,10 +83,10 @@ class easyLoader:
        desired_vae_names = set()
        desired_lora_names = set()
        desired_lora_settings = set()
+        desired_controlnet_names = set()

        for entry in prompt.values():
            class_type = entry["class_type"]
-
            if class_type in lora_widget:
                lora_name = self.get_input_value(entry, "lora_name")
                desired_lora_names.add(lora_name)
@@ -82,7 +94,7 @@ class easyLoader:
                desired_lora_settings.add(setting)

            if class_type in stable_diffusion_loaders:
-                desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name"))
+                desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name", prompt))
                desired_vae_names.add(self.get_input_value(entry, "vae_name"))

            elif class_type in stable_cascade_loaders:
@@ -99,6 +111,16 @@ class easyLoader:
                if decode_vae_name and decode_vae_name != 'None':
                    desired_vae_names.add(decode_vae_name)

+            elif class_type in controlnet_loaders:
+                control_net_name = self.get_input_value(entry, "control_net_name", prompt)
+                scale_soft_weights = self.get_input_value(entry, "scale_soft_weights")
+                desired_controlnet_names.add(f'{control_net_name};{scale_soft_weights}')
+
+            elif class_type in instant_loaders:
+                control_net_name = self.get_input_value(entry, "control_net_name", prompt)
+                scale_soft_weights = self.get_input_value(entry, "cn_soft_weights")
+                desired_controlnet_names.add(f'{control_net_name};{scale_soft_weights}')
+
            elif class_type in model_merge_node:
                desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name_1"))
                desired_ckpt_names.add(self.get_input_value(entry, "ckpt_name_2"))
@@ -106,7 +128,7 @@ class easyLoader:
                if vae_use != 'Use Model 1' and vae_use != 'Use Model 2':
                    desired_vae_names.add(vae_use)

-        object_types = ["ckpt", "unet", "clip", "bvae", "vae", "lora"]
+        object_types = ["ckpt", "unet", "clip", "bvae", "vae", "lora", "controlnet"]
        for object_type in object_types:
            if object_type == 'unet':
                desired_names = desired_unet_names
@@ -117,6 +139,8 @@ class easyLoader:
                    desired_names = desired_ckpt_names
            elif object_type == "vae":
                desired_names = desired_vae_names
+            elif object_type == "controlnet":
+                desired_names = desired_controlnet_names
            else:
                desired_names = desired_lora_names
            self.clear_unused_objects(desired_names, object_type)
@@ -155,7 +179,7 @@ class easyLoader:
        current_memory = self.get_memory_usage()
        if current_memory < self.memory_threshold:
            return
-        eviction_order = ["vae", "lora", "bvae", "clip", "ckpt"]
+        eviction_order = ["vae", "lora", "bvae", "clip", "ckpt", "controlnet"]
        for obj_type in eviction_order:
            if current_memory < self.memory_threshold:
                break
@@ -225,6 +249,25 @@ class easyLoader:

        return model

+    def load_controlnet(self, control_net_name, scale_soft_weights=1):
+        unique_id = f'{control_net_name};{str(scale_soft_weights)}'
+        if unique_id in self.loaded_objects["controlnet"]:
+            return self.loaded_objects["controlnet"][unique_id][0]
+        if scale_soft_weights < 1:
+            if "ScaledSoftControlNetWeights" in NODE_CLASS_MAPPINGS:
+                soft_weight_cls = NODE_CLASS_MAPPINGS['ScaledSoftControlNetWeights']
+                (weights, timestep_keyframe) = soft_weight_cls().load_weights(scale_soft_weights, False)
+                cn_adv_cls = NODE_CLASS_MAPPINGS['ControlNetLoaderAdvanced']
+                control_net, = cn_adv_cls().load_controlnet(control_net_name, timestep_keyframe)
+            else:
+                raise Exception(
+                    f"[Advanced-ControlNet Not Found] you need to install 'COMFYUI-Advanced-ControlNet'")
+        else:
+            controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+            control_net = comfy.controlnet.load_controlnet(controlnet_path)
+        self.add_to_cache("controlnet", unique_id, control_net)
+        self.eviction_based_on_memory()
+        return control_net
    def load_clip(self, clip_name, type='stable_diffusion'):
        if type == 'stable_diffusion':
            clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
@@ -5,7 +5,7 @@ import latent_preview
 from nodes import MAX_RESOLUTION
 from PIL import Image
 from typing import Dict, List, Optional, Tuple, Union, Any
-
+from .utils import get_sd_version
 class easySampler:
    def __init__(self):
        self.last_helds: dict[str, list] = {
@@ -47,6 +47,14 @@ class easySampler:
            parts.append('None')
        return parts

+    def add_model_patch_option(self, model):
+        if 'transformer_options' not in model.model_options:
+            model.model_options['transformer_options'] = {}
+        to = model.model_options['transformer_options']
+        if "model_patch" not in to:
+            to["model_patch"] = {}
+        return to
+
    def common_ksampler(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
                        disable_noise=False, start_step=None, last_step=None, force_full_denoise=False,
                        preview_latent=True, disable_pbar=False, custom=None):
@@ -91,6 +99,26 @@ class easySampler:
                batch_inds = latent["batch_index"] if "batch_index" in latent else None
                noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)

+            #######################################################################################
+            # brushnet
+            transformer_options = model.model_options['transformer_options'] if "transformer_options" in model.model_options else {}
+            if 'model_patch' in transformer_options and 'brushnet' in transformer_options['model_patch']:
+                to = self.add_model_patch_option(model)
+                mp = to['model_patch']
+                if isinstance(model.model.model_config, comfy.supported_models.SD15):
+                    mp['SDXL'] = False
+                elif isinstance(model.model.model_config, comfy.supported_models.SDXL):
+                    mp['SDXL'] = True
+                else:
+                    print('Base model type: ', type(model.model.model_config))
+                    raise Exception("Unsupported model type: ", type(model.model.model_config))
+
+                mp['unet'] = model.model.diffusion_model
+                mp['step'] = 0
+                mp['total_steps'] = 1
+
+            #
+            #######################################################################################
            samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative,
                                          latent_image,
                                          denoise=denoise, disable_noise=disable_noise, start_step=start_step,
@@ -129,8 +157,31 @@ class easySampler:

        pbar = comfy.utils.ProgressBar(steps)

+        #######################################################################################
+        # brushnet
+        to = None
+        transformer_options = model.model_options['transformer_options'] if "transformer_options" in model.model_options else {}
+        if 'model_patch' in transformer_options and 'brushnet_model' in transformer_options['model_patch']:
+            to = self.add_model_patch_option(model)
+            mp = to['model_patch']
+            if isinstance(model.model.model_config, comfy.supported_models.SD15):
+                mp['SDXL'] = False
+            elif isinstance(model.model.model_config, comfy.supported_models.SDXL):
+                mp['SDXL'] = True
+            else:
+                print('Base model type: ', type(model.model.model_config))
+                raise Exception("Unsupported model type: ", type(model.model.model_config))
+
+            mp['unet'] = model.model.diffusion_model
+            mp['step'] = 0
+            mp['total_steps'] = 1
+        #
+        #######################################################################################
+
        def callback(step, x0, x, total_steps):
            preview_bytes = None
+            if to is not None and "model_patch" in to:
+                to['model_patch']['step'] = step + 1
            if previewer:
                preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
            pbar.update_absolute(step + 1, total_steps, preview_bytes)
@@ -220,4 +271,43 @@ class easySampler:
            sdxl_pipe.get("clip"),
            sdxl_pipe.get("images"),
            sdxl_pipe.get("seed")
-        )
+        )
+
+class alignYourStepsScheduler:
+
+    NOISE_LEVELS = {
+        "SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904,
+                0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582],
+        "SDXL": [14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289,
+                 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582],
+        "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]}
+
+
+    def loglinear_interp(self, t_steps, num_steps):
+        """
+        Performs log-linear interpolation of a given array of decreasing numbers.
+        """
+        xs = np.linspace(0, 1, len(t_steps))
+        ys = np.log(t_steps[::-1])
+
+        new_xs = np.linspace(0, 1, num_steps)
+        new_ys = np.interp(new_xs, xs, ys)
+
+        interped_ys = np.exp(new_ys)[::-1].copy()
+        return interped_ys
+
+    def get_sigmas(self, model_type, steps, denoise):
+
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return (torch.FloatTensor([]),)
+            total_steps = round(steps * denoise)
+
+        sigmas = self.NOISE_LEVELS[model_type][:]
+        if (steps + 1) != len(sigmas):
+            sigmas = self.loglinear_interp(sigmas, steps + 1)
+
+        sigmas = sigmas[-(total_steps + 1):]
+        sigmas[-1] = 0
+        return (torch.FloatTensor(sigmas),)
@@ -0,0 +1,201 @@
+import json
+import os
+import yaml
+import requests
+import pathlib
+from aiohttp import web
+from server import PromptServer
+from .image import tensor2pil, pil2tensor, image2base64, pil2byte
+from ..log import log_node_error
+
+
+root_path = pathlib.Path(__file__).parent.parent.parent
+config_path = os.path.join(root_path,'config.yaml')
+default_key = [{'name':'Default', 'key':''}]
+
+class StabilityAPI:
+    def __init__(self):
+        self.api_url = "https://api.stability.ai"
+        self.api_keys = None
+        self.api_current = 0
+        self.user_info = {}
+        self.getAPIKeys()
+
+    def getErrors(self, code):
+        errors = {
+            400: "Bad Request",
+            403: "ApiKey Forbidden",
+            413: "Your request was larger than 10MiB.",
+            429: "You have made more than 150 requests in 10 seconds.",
+            500: "Internal Server Error",
+        }
+        return errors.get(code, "Unknown Error")
+
+    def getAPIKeys(self):
+        if os.path.isfile(config_path):
+            with open(config_path, 'r') as f:
+                data = yaml.load(f, Loader=yaml.FullLoader)
+                if not data:
+                    data = {'STABILITY_API_KEY': default_key, 'STABILITY_API_DEFAULT':0}
+                    with open(config_path, 'w') as f:
+                        yaml.dump(data, f)
+                if 'STABILITY_API_KEY' not in data:
+                    data['STABILITY_API_KEY'] = default_key
+                    data['STABILITY_API_DEFAULT'] = 0
+                    with open(config_path, 'w') as f:
+                        yaml.dump(data, f)
+                api_keys = data['STABILITY_API_KEY']
+                self.api_current = data['STABILITY_API_DEFAULT']
+                self.api_keys = api_keys
+                return api_keys
+        else:
+            # create a yaml file
+            with open(config_path, 'w') as f:
+                data = {'STABILITY_API_KEY': default_key, 'STABILITY_API_DEFAULT':0}
+                yaml.dump(data, f)
+                return data['STABILITY_API_KEY']
+        pass
+
+    def setAPIKeys(self, api_keys):
+        if len(api_keys) > 0:
+            self.api_keys = api_keys
+            # load and save the yaml file
+            with open(config_path, 'r') as f:
+                data = yaml.load(f, Loader=yaml.FullLoader)
+                data['STABILITY_API_KEY'] = api_keys
+                with open(config_path, 'w') as f:
+                    yaml.dump(data, f)
+        return True
+
+    def setAPIDefault(self, current):
+        if current is not None:
+            self.api_current = current
+            # load and save the yaml file
+            with open(config_path, 'r') as f:
+                data = yaml.load(f, Loader=yaml.FullLoader)
+                data['STABILITY_API_DEFAULT'] = current
+                with open(config_path, 'w') as f:
+                    yaml.dump(data, f)
+        return True
+
+    def generate_sd3_image(self, prompt, negative_prompt, aspect_ratio,  model, seed, mode='text-to-image', image=None, strength=1, output_format='png', node_name='easy stableDiffusion3API'):
+        url = f"{self.api_url}/v2beta/stable-image/generate/sd3"
+        api_key = self.api_keys[self.api_current]['key']
+        files = None
+        data = {
+            "prompt": prompt,
+            "mode": mode,
+            "model": model,
+            "seed": seed,
+            "output_format": output_format,
+        }
+        if model == 'sd3':
+            data['negative_prompt'] = negative_prompt
+
+        if mode == 'text-to-image':
+            files = {"none": ''}
+            data['aspect_ratio'] = aspect_ratio
+        elif mode == 'image-to-image':
+            pil_image = tensor2pil(image)
+            image_byte = pil2byte(pil_image)
+            files = {"image": ("output.png", image_byte, 'image/png')}
+            data['strength'] = strength
+
+        response = requests.post(url,
+            headers={"authorization": f"{api_key}", "accept": "application/json"},
+            files=files,
+            data=data,
+        )
+        if response.status_code == 200:
+            PromptServer.instance.send_sync('stable-diffusion-api-generate-succeed',{"model":model})
+            json_data = response.json()
+            image_base64 = json_data['image']
+            image_data = image2base64(image_base64)
+            output_t = pil2tensor(image_data)
+            return output_t
+        else:
+            if 'application/json' in response.headers['Content-Type']:
+                error_info = response.json()
+                log_node_error(node_name,  error_info.get('name', 'No name provided'))
+                log_node_error(node_name, error_info.get('errors', ['No details provided']))
+            error_status_text = self.getErrors(response.status_code)
+            PromptServer.instance.send_sync('easyuse-toast',{"type": "error", "content": error_status_text})
+            raise Exception(f"Failed to generate image: {error_status_text}")
+
+    # get user account
+    async def getUserAccount(self, cache=True):
+        url = f"{self.api_url}/v1/user/account"
+        api_key = self.api_keys[self.api_current]['key']
+        name = self.api_keys[self.api_current]['name']
+        if cache and name in self.user_info:
+            return self.user_info[name]
+        else:
+            response = requests.get(url, headers={"Authorization": f"Bearer {api_key}"})
+            if response.status_code == 200:
+                user_info = response.json()
+                self.user_info[name] = user_info
+                return user_info
+            else:
+                PromptServer.instance.send_sync('easyuse-toast',{'type': 'error', 'content': self.getErrors(response.status_code)})
+                return None
+
+    # get user balance
+    async def getUserBalance(self):
+        url = f"{self.api_url}/v1/user/balance"
+        api_key = self.api_keys[self.api_current]['key']
+        response = requests.get(url, headers={
+            "Authorization": f"Bearer {api_key}"
+        })
+        if response.status_code == 200:
+            return response.json()
+        else:
+            PromptServer.instance.send_sync('easyuse-toast', {'type': 'error', 'content': self.getErrors(response.status_code)})
+            return None
+
+stableAPI = StabilityAPI()
+
+
+@PromptServer.instance.routes.get("/easyuse/stability/api_keys")
+async def get_stability_api_keys(request):
+    stableAPI.getAPIKeys()
+    return web.json_response({"keys": stableAPI.api_keys, "current": stableAPI.api_current})
+
+@PromptServer.instance.routes.post("/easyuse/stability/set_api_keys")
+async def set_stability_api_keys(request):
+    post = await request.post()
+    api_keys = post.get("api_keys")
+    current = post.get('current')
+    if api_keys is not None:
+        api_keys = json.loads(api_keys)
+        stableAPI.setAPIKeys(api_keys)
+        if current is not None:
+            print(current)
+            stableAPI.setAPIDefault(int(current))
+            account = await stableAPI.getUserAccount()
+            balance = await stableAPI.getUserBalance()
+            return web.json_response({'account': account, 'balance': balance})
+        else:
+            return web.json_response({'status': 'ok'})
+    else:
+        return web.Response(status=400)
+
+@PromptServer.instance.routes.post("/easyuse/stability/set_apikey_default")
+async def set_stability_api_default(request):
+    post = await request.post()
+    current = post.get("current")
+    if current is not None and current < len(stableAPI.api_keys):
+        stableAPI.api_current = current
+        return web.json_response({'status': 'ok'})
+    else:
+        return web.Response(status=400)
+
+@PromptServer.instance.routes.get("/easyuse/stability/user_info")
+async def get_account_info(request):
+    account = await stableAPI.getUserAccount()
+    balance = await stableAPI.getUserBalance()
+    return web.json_response({'account': account, 'balance': balance})
+
+@PromptServer.instance.routes.get("/easyuse/stability/balance")
+async def get_balance_info(request):
+    balance = await stableAPI.getUserBalance()
+    return web.json_response({'balance': balance})
@@ -0,0 +1,148 @@
+import torch
+import torch.nn as nn
+from comfy.model_patcher import ModelPatcher
+from typing import Union
+
+T = torch.Tensor
+
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d
+
+
+class StyleAlignedArgs:
+    def __init__(self, share_attn: str) -> None:
+        self.adain_keys = "k" in share_attn
+        self.adain_values = "v" in share_attn
+        self.adain_queries = "q" in share_attn
+
+    share_attention: bool = True
+    adain_queries: bool = True
+    adain_keys: bool = True
+    adain_values: bool = True
+
+
+def expand_first(
+    feat: T,
+    scale=1.0,
+) -> T:
+    """
+    Expand the first element so it has the same shape as the rest of the batch.
+    """
+    b = feat.shape[0]
+    feat_style = torch.stack((feat[0], feat[b // 2])).unsqueeze(1)
+    if scale == 1:
+        feat_style = feat_style.expand(2, b // 2, *feat.shape[1:])
+    else:
+        feat_style = feat_style.repeat(1, b // 2, 1, 1, 1)
+        feat_style = torch.cat([feat_style[:, :1], scale * feat_style[:, 1:]], dim=1)
+    return feat_style.reshape(*feat.shape)
+
+
+def concat_first(feat: T, dim=2, scale=1.0) -> T:
+    """
+    concat the the feature and the style feature expanded above
+    """
+    feat_style = expand_first(feat, scale=scale)
+    return torch.cat((feat, feat_style), dim=dim)
+
+
+def calc_mean_std(feat, eps: float = 1e-5) -> "tuple[T, T]":
+    feat_std = (feat.var(dim=-2, keepdims=True) + eps).sqrt()
+    feat_mean = feat.mean(dim=-2, keepdims=True)
+    return feat_mean, feat_std
+
+def adain(feat: T) -> T:
+    feat_mean, feat_std = calc_mean_std(feat)
+    feat_style_mean = expand_first(feat_mean)
+    feat_style_std = expand_first(feat_std)
+    feat = (feat - feat_mean) / feat_std
+    feat = feat * feat_style_std + feat_style_mean
+    return feat
+
+class SharedAttentionProcessor:
+    def __init__(self, args: StyleAlignedArgs, scale: float):
+        self.args = args
+        self.scale = scale
+
+    def __call__(self, q, k, v, extra_options):
+        if self.args.adain_queries:
+            q = adain(q)
+        if self.args.adain_keys:
+            k = adain(k)
+        if self.args.adain_values:
+            v = adain(v)
+        if self.args.share_attention:
+            k = concat_first(k, -2, scale=self.scale)
+            v = concat_first(v, -2)
+
+        return q, k, v
+
+
+def get_norm_layers(
+    layer: nn.Module,
+    norm_layers_: "dict[str, list[Union[nn.GroupNorm, nn.LayerNorm]]]",
+    share_layer_norm: bool,
+    share_group_norm: bool,
+):
+    if isinstance(layer, nn.LayerNorm) and share_layer_norm:
+        norm_layers_["layer"].append(layer)
+    if isinstance(layer, nn.GroupNorm) and share_group_norm:
+        norm_layers_["group"].append(layer)
+    else:
+        for child_layer in layer.children():
+            get_norm_layers(
+                child_layer, norm_layers_, share_layer_norm, share_group_norm
+            )
+
+
+def register_norm_forward(
+    norm_layer: Union[nn.GroupNorm, nn.LayerNorm],
+) -> Union[nn.GroupNorm, nn.LayerNorm]:
+    if not hasattr(norm_layer, "orig_forward"):
+        setattr(norm_layer, "orig_forward", norm_layer.forward)
+    orig_forward = norm_layer.orig_forward
+
+    def forward_(hidden_states: T) -> T:
+        n = hidden_states.shape[-2]
+        hidden_states = concat_first(hidden_states, dim=-2)
+        hidden_states = orig_forward(hidden_states)  # type: ignore
+        return hidden_states[..., :n, :]
+
+    norm_layer.forward = forward_  # type: ignore
+    return norm_layer
+
+
+def register_shared_norm(
+    model: ModelPatcher,
+    share_group_norm: bool = True,
+    share_layer_norm: bool = True,
+):
+    norm_layers = {"group": [], "layer": []}
+    get_norm_layers(model.model, norm_layers, share_layer_norm, share_group_norm)
+    print(
+        f"Patching {len(norm_layers['group'])} group norms, {len(norm_layers['layer'])} layer norms."
+    )
+    return [register_norm_forward(layer) for layer in norm_layers["group"]] + [
+        register_norm_forward(layer) for layer in norm_layers["layer"]
+    ]
+
+
+SHARE_NORM_OPTIONS = ["both", "group", "layer", "disabled"]
+SHARE_ATTN_OPTIONS = ["q+k", "q+k+v", "disabled"]
+
+
+def styleAlignBatch(model, share_norm, share_attn, scale=1.0):
+    m = model.clone()
+    share_group_norm = share_norm in ["group", "both"]
+    share_layer_norm = share_norm in ["layer", "both"]
+    register_shared_norm(model, share_group_norm, share_layer_norm)
+    args = StyleAlignedArgs(share_attn)
+    m.set_model_attn1_patch(SharedAttentionProcessor(args, scale))
+    return m
@@ -17,6 +17,53 @@ def get_comfyui_revision():
        comfy_ui_revision = "Unknown"
    return comfy_ui_revision

+
+import sys
+import importlib.util
+import importlib.metadata
+import comfy.model_management as mm
+import gc
+from packaging import version
+from server import PromptServer
+def is_package_installed(package):
+    try:
+        module = importlib.util.find_spec(package)
+        return module is not None
+    except ImportError as e:
+        print(e)
+        return False
+
+def install_package(package, v=None, compare=True, compare_version=None):
+    run_install = True
+    if is_package_installed(package):
+        try:
+            installed_version = importlib.metadata.version(package)
+            if v is not None:
+                if compare_version is None:
+                    compare_version = v
+                if not compare or version.parse(installed_version) >= version.parse(compare_version):
+                    run_install = False
+            else:
+                run_install = False
+        except:
+            run_install = False
+
+    if run_install:
+        import subprocess
+        package_command = package + '==' + v if v is not None else package
+        PromptServer.instance.send_sync("easyuse-toast", {'content': f"Installing {package_command}...", 'duration': 5000})
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', package_command], capture_output=True, text=True)
+        if result.returncode == 0:
+            PromptServer.instance.send_sync("easyuse-toast", {'content': f"{package} installed successfully", 'type': 'success', 'duration': 5000})
+            print(f"Package {package} installed successfully")
+            return True
+        else:
+            PromptServer.instance.send_sync("easyuse-toast", {'content': f"{package} installed failed", 'type': 'error', 'duration': 5000})
+            print(f"Package {package} installed failed")
+            return False
+    else:
+        return False
+
 def compare_revision(num):
    global comfy_ui_revision
    if not comfy_ui_revision:
@@ -36,16 +83,6 @@ def find_tags(string: str, sep="/") -> list[str]:
        return string.split(sep)[:-1]
    return []

-import folder_paths
-def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
-    for full_folder_path in full_folder_paths:
-        folder_paths.add_model_folder_path(folder_name, full_folder_path)
-    if folder_name in folder_paths.folder_names_and_paths:
-        current_paths, current_extensions = folder_paths.folder_names_and_paths[folder_name]
-        updated_extensions = current_extensions | extensions
-        folder_paths.folder_names_and_paths[folder_name] = (current_paths, updated_extensions)
-    else:
-        folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions)

 from comfy.model_base import BaseModel
 import comfy.supported_models
@@ -58,7 +95,11 @@ def get_sd_version(model):
    elif isinstance(
            model_config, (comfy.supported_models.SD15, comfy.supported_models.SD20)
    ):
-        return 'sd15'
+        return 'sd1'
+    elif isinstance(
+            model_config, (comfy.supported_models.SVD_img2vid)
+    ):
+        return 'svd'
    else:
        return 'unknown'

@@ -158,7 +199,7 @@ def get_local_filepath(url, dirname, local_file_name=None):
                download_url_to_file(url, destination)
            except Exception as err:
                PromptServer.instance.send_sync("easyuse-toast",
-                                                {'content': f'无法从 {url} 下载模型'}, type='error')
+                                                {'content': f'无法从 {url} 下载模型', 'type':'error'})
                raise Exception(f'无法从 {url} 下载，错误信息：{str(err.args[0])}')
    return destination

@@ -186,7 +227,7 @@ def easySave(images, filename_prefix, output_type, prompt=None, extra_pnginfo=No
    from nodes import PreviewImage, SaveImage
    if output_type == "Hide":
        return list()
-    if output_type == "Preview":
+    if output_type in ["Preview", "Preview&Choose"]:
        filename_prefix = 'easyPreview'
        results = PreviewImage().save_images(images, filename_prefix, prompt, extra_pnginfo)
        return results['ui']['images']
@@ -207,4 +248,9 @@ def getMetadata(filepath):
        if header_size <= 0:
            raise BufferError("Invalid header")

-        return header
+        return header
+
+def cleanGPUUsedForce():
+    gc.collect()
+    mm.unload_all_models()
+    mm.soft_empty_cache()
@@ -1,8 +1,7 @@
 from typing import Iterator, List, Tuple, Dict, Any, Union, Optional
 from _decimal import Context, getcontext
 from decimal import Decimal
-from .libs.utils import AlwaysEqualProxy
-import torch
+from .libs.utils import AlwaysEqualProxy, cleanGPUUsedForce
 import numpy as np
 import json

@@ -278,6 +277,30 @@ class imageSwitch:
        else:
            return (image_b, )

+class textSwitch:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input": ("INT", {"default": 1, "min": 1, "max": 2}),
+            },
+            "optional": {
+                "text1": ("STRING", {"forceInput": True}),
+                "text2": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("STRING",)
+    CATEGORY = "EasyUse/Logic/Switch"
+    FUNCTION = "switch"
+
+    def switch(self, input, text1=None, text2=None,):
+        if input == 1:
+            return (text1,)
+        else:
+            return (text2,)
+
 # ---------------------------------------------------------------运算 开始----------------------------------------------------------------------#

 COMPARE_FUNCTIONS = {
@@ -507,9 +530,7 @@ class cleanGPUUsed:
    CATEGORY = "EasyUse/Logic"

    def empty_cache(self, anything, unique_id=None, extra_pnginfo=None):
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
+        cleanGPUUsedForce()
        return ()

 from .libs.cache import remove_cache
@@ -552,6 +573,9 @@ class clearCacheAll:
        remove_cache('*')
        return ()

+
+
+
 NODE_CLASS_MAPPINGS = {
  "easy string": String,
  "easy int": Int,
@@ -561,6 +585,7 @@ NODE_CLASS_MAPPINGS = {
  "easy boolean": Boolean,
  "easy compare": Compare,
  "easy imageSwitch": imageSwitch,
+  "easy textSwitch": textSwitch,
  "easy if": If,
  "easy isSDXL": isSDXL,
  "easy xyAny": xyAny,
@@ -580,6 +605,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
  "easy boolean": "Boolean",
  "easy compare": "Compare",
  "easy imageSwitch": "Image Switch",
+  "easy textSwitch": "Text Switch",
  "easy if": "If",
  "easy isSDXL": "Is SDXL",
  "easy xyAny": "XYAny",
@@ -0,0 +1,15 @@
+[project]
+name = "comfyui-easy-use"
+description = "To enhance the usability of ComfyUI, optimizations and integrations have been implemented for several commonly used nodes."
+version = "1.1.7"
+license = "LICENSE"
+dependencies = ["diffusers>=0.25.0", "clip_interrogator>=0.6.0", "onnxruntime", "aiohttp"]
+
+[project.urls]
+Repository = "https://github.com/yolain/ComfyUI-Easy-Use"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "yolain"
+DisplayName = "ComfyUI-Easy-Use"
+Icon = ""
@@ -1,2 +1,4 @@
 diffusers>=0.25.0
+clip_interrogator>=0.6.0
+onnxruntime
 aiohttp
@@ -0,0 +1,93 @@
+.easyuse-account{
+
+}
+.easyuse-account-user{
+    font-size: 10px;
+    color:var(--descrip-text);
+    text-align: center;
+}
+.easyuse-account-user-info{
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding-bottom:10px;
+    cursor: pointer;
+}
+.easyuse-account-user-info .user{
+    display: flex;
+    align-items: center;
+}
+.easyuse-account-user-info .edit{
+    padding:5px 10px;
+    background: var(--comfy-menu-bg);
+    border-radius:4px;
+}
+.easyuse-account-user-info:hover{
+    filter:brightness(110%);
+}
+.easyuse-account-user-info h5{
+    margin:0;
+    font-size: 10px;
+    text-align: left;
+}
+.easyuse-account-user-info h6{
+    margin:0;
+    font-size: 8px;
+    text-align: left;
+    font-weight: 300;
+}
+.easyuse-account-user-info .remark{
+    margin-top: 4px;
+}
+.easyuse-account-user-info .avatar{
+    width: 36px;
+    height: 36px;
+    background: var(--comfy-input-bg);
+    border-radius: 50%;
+    margin-right: 5px;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    font-size: 16px;
+    overflow: hidden;
+}
+.easyuse-account-user-info .avatar img{
+    width: 100%;
+    height: 100%;
+}
+.easyuse-account-dialog{
+    width: 600px;
+}
+.easyuse-account-dialog-main a, .easyuse-account-dialog-main a:visited{
+    font-weight: 400;
+    color: var(--theme-color-light);
+}
+.easyuse-account-dialog-item{
+    display: flex;
+    justify-content: flex-start;
+    align-items: center;
+    padding: 10px 0;
+    border-bottom: 1px solid var(--border-color);
+}
+.easyuse-account-dialog-item input{
+    padding:5px;
+    margin-right:5px;
+}
+.easyuse-account-dialog-item input.key{
+    flex:1;
+}
+.easyuse-account-dialog-item button{
+    cursor: pointer;
+    margin-left:5px!important;
+    padding:5px!important;
+    font-size: 16px!important;
+}
+.easyuse-account-dialog-item button:hover{
+    filter:brightness(120%);
+}
+.easyuse-account-dialog-item button.choose {
+    background: var(--theme-color);
+}
+.easyuse-account-dialog-item button.delete{
+    background: var(--error-color);
+}
@@ -0,0 +1,35 @@
+.easyuse-chooser-dialog{
+    max-width: 600px;
+}
+.easyuse-chooser-dialog-title{
+    font-size: 18px;
+    font-weight: 700;
+    text-align: center;
+    color:var(--input-text);
+    margin:0;
+}
+.easyuse-chooser-dialog-images{
+    margin-top:10px;
+    display: flex;
+    flex-wrap: wrap;
+    width: 100%;
+    box-sizing: border-box;
+}
+.easyuse-chooser-dialog-images img{
+    width: 50%;
+    height: auto;
+    cursor: pointer;
+    box-sizing: border-box;
+    filter:brightness(80%);
+}
+.easyuse-chooser-dialog-images img:hover{
+    filter:brightness(100%);
+}
+.easyuse-chooser-dialog-images img.selected{
+    border: 4px solid var(--success-color);
+}
+
+.easyuse-chooser-hidden{
+    display: none;
+    height:0;
+}
@@ -10,4 +10,23 @@
  background-color: var(--comfy-menu-bg);
  padding: 10px 4px;
  border: 1px solid var(--border-color);z-index: 999999999;padding-top: 0;
+}
+#easyuse_groups_map .icon{
+  width: 12px;
+  height:12px;
+}
+#easyuse_groups_map .closeBtn{
+  float: right;
+  color: var(--input-text);
+  border-radius:30px;
+  background-color: var(--comfy-input-bg);
+  border: 1px solid var(--border-color);
+  cursor: pointer;
+  aspect-ratio: 1 / 1;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+}
+#easyuse_groups_map .closeBtn:hover{
+  filter:brightness(120%);
 }
@@ -4,4 +4,7 @@
@import "groupmap.css";
@import "contextmenu.css";
@import "modelinfo.css";
-@import "toast.css";
+@import "toast.css";
+@import "account.css";
+@import "chooser.css";
+@import "toolbar.css";
@@ -1,3 +1,6 @@
+.easyuse-prompt-styles{
+    overflow: auto;
+}
 .easyuse-prompt-styles .tools{
    display:flex;
    justify-content:space-between;
@@ -41,9 +44,13 @@
    min-height: 150px;
    height: calc(100% - 40px);
    overflow: auto;
-    // display: flex;
-    // flex-wrap: wrap;
+    /*display: flex;*/
+    /*flex-wrap: wrap;*/
 }
+.easyuse-prompt-styles-list.no-top{
+    height: auto;
+}
+
 .easyuse-prompt-styles-tag{
    display: inline-block;
    vertical-align: middle;
@@ -1,6 +1,6 @@
 :root {
   --theme-color:#3f3eed;
-   --theme-color-light:#006691;
+   --theme-color-light: #008ecb;
   --success-color: #52c41a;
   --error-color: #ff4d4f;
   --warning-color: #faad14;
@@ -0,0 +1,213 @@
+.easyuse-toolbar{
+    background: rgba(15,15,15,.5);
+    backdrop-filter: blur(4px) brightness(120%);
+    border-radius:0 12px 12px 0;
+    min-width:50px;
+    height:24px;
+    position: fixed;
+    bottom:85px;
+    left:0px;
+    display: flex;
+    align-items: center;
+    z-index:10000;
+}
+.easyuse-toolbar.disable-render-info{
+    bottom: 55px;
+}
+.easyuse-toolbar-item{
+    border-radius:20px;
+    height: 20px;
+    width:20px;
+    cursor: pointer;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    transition: all 0.3s ease-in-out;
+    margin-left:2.5px;
+}
+.easyuse-toolbar-icon{
+    width: 14px;
+    height: 14px;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    font-size: 12px;
+    color:white;
+    transition: all 0.3s ease-in-out;
+}
+.easyuse-toolbar-tips{
+    visibility: hidden;
+    opacity: 0;
+    position: absolute;
+    top: -25px;
+    left: 0;
+    color: var(--descrip-text);
+    padding: 2px 5px;
+    border-radius: 5px;
+    font-size: 11px;
+    min-width:100px;
+    transition: all 0.3s ease-in-out;
+}
+.easyuse-toolbar-item:hover{
+    background:rgba(12,12,12,1);
+}
+.easyuse-toolbar-item:hover .easyuse-toolbar-tips{
+    opacity: 1;
+    visibility: visible;
+}
+.easyuse-toolbar-item:hover .easyuse-toolbar-icon.group{
+    color:var(--warning-color);
+}
+.easyuse-toolbar-item:hover .easyuse-toolbar-icon.rocket{
+  color:var(--theme-color-light);
+}
+.easyuse-toolbar-item:hover .easyuse-toolbar-icon.question{
+  color:var(--success-color);
+}
+
+
+.easyuse-guide-dialog{
+    max-width: 300px;
+    font-family: var(--font-family);
+    position: absolute;
+    z-index:100;
+    left:0;
+    bottom:140px;
+    background: rgba(25,25,25,.85);
+    backdrop-filter: blur(8px) brightness(120%);
+    border-radius:0 12px 12px 0;
+    padding:10px;
+    transition: .5s all ease-in-out;
+    visibility: visible;
+    opacity: 1;
+    transform: translateX(0%);
+}
+.easyuse-guide-dialog.disable-render-info{
+    bottom:110px;
+}
+.easyuse-guide-dialog-top{
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+.easyuse-guide-dialog-top .icon{
+    width: 12px;
+    height:12px;
+}
+.easyuse-guide-dialog.hidden{
+    opacity: 0;
+    transform: translateX(-50%);
+    visibility: hidden;
+}
+.easyuse-guide-dialog .closeBtn{
+  float: right;
+  color: var(--input-text);
+  border-radius:30px;
+  background-color: var(--comfy-input-bg);
+  border: 1px solid var(--border-color);
+  cursor: pointer;
+  aspect-ratio: 1 / 1;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+}
+.easyuse-guide-dialog .closeBtn:hover{
+    filter:brightness(120%);
+}
+.easyuse-guide-dialog-title{
+    color:var(--input-text);
+    font-size: 16px;
+    font-weight: bold;
+    margin-bottom: 5px;
+}
+.easyuse-guide-dialog-remark{
+    color: var(--input-text);
+    font-size: 12px;
+    margin-top: 5px;
+}
+.easyuse-guide-dialog-content{
+    max-height: 600px;
+    overflow: auto;
+}
+.easyuse-guide-dialog a, .easyuse-guide-dialog a:visited{
+    color: var(--theme-color-light);
+    cursor: pointer;
+}
+.easyuse-guide-dialog-note{
+    margin-top: 20px;
+    color:white;
+}
+.easyuse-guide-dialog p{
+    margin:4px 0;
+    font-size: 12px;
+    font-weight: 300;
+}
+.markdown-body h1, .markdown-body h2, .markdown-body h3, .markdown-body h4, .markdown-body h5, .markdown-body h6 {
+    margin-top: 12px;
+    margin-bottom: 8px;
+    font-weight: 600;
+    line-height: 1.25;
+    padding-bottom: 5px;
+    border-bottom: 1px solid var(--border-color);
+    color: var(--input-text);
+}
+.markdown-body h1{
+    font-size: 18px;
+}
+.markdown-body h2{
+    font-size: 16px;
+}
+.markdown-body h3{
+    font-size: 14px;
+}
+.markdown-body h4{
+    font-size: 13px;
+}
+.markdown-body table {
+    display: block;
+    /*width: 100%;*/
+    /*width: max-content;*/
+    max-width: 300px;
+    overflow: auto;
+    color:var(--input-text);
+    box-sizing: border-box;
+    border: 1px solid var(--border-color);
+    text-align: left;
+    width: 100%;
+}
+.markdown-body table th, .markdown-body table td {
+    padding: 6px 13px;
+    font-size: 12px;
+    margin:0;
+    border-right: 1px solid var(--border-color);
+    border-bottom: 1px solid var(--border-color);
+}
+.markdown-body table td {
+    font-size: 12px;
+}
+.markdown-body table th:last-child, .markdown-body table td:last-child{
+    border-right: none;
+}
+.markdown-body table tr:last-child td{
+    border-bottom: none;
+}
+.markdown-body table th{
+    font-weight: bold;
+    width: auto;
+    min-width: 70px;
+}
+.markdown-body table th:last-child{
+    width:100%;
+}
+.markdown-body .warning{
+    color:var(--warning-color)
+}
+.markdown-body .error{
+    color:var(--error-color)
+}
+.markdown-body .success{
+    color:var(--success-color)
+}
+.markdown-body .link{
+    color:var(--theme-color-light)
+}
@@ -1,4 +1,4 @@
-import { app } from "/scripts/app.js";
+import { app } from "../../../scripts/app.js";


 app.registerExtension({
@@ -2,6 +2,9 @@ import {getLocale} from './utils.js'
 const locale = getLocale()

 const zhCN = {
+    "Workflow created by": "工作流创建者",
+    "Watch more video content": "观看更多视频内容",
+    "Workflow Guide":"工作流指南",
    // ExtraMenu
    "💎 View Checkpoint Info...": "💎 查看 Checkpoint 信息...",
    "💎 View Lora Info...": "💎 查看 Lora 信息...",
@@ -14,22 +17,30 @@ const zhCN = {
    "No notes": "当前还没有备注内容",
    "Saving Notes...": "正在保存备注...",
    "Type your notes here":"在这里输入备注内容",
+    "ModelName":"模型名称",
+    "Models Required":"所需模型",
+    "Download Model": "下载模型",
+    "Source Url": "模型源地址",
    "Notes": "备注",
    "Type": "类型",
    "Trained Words": "训练词",
    "BaseModel": "基础算法",
    "Details": "详情",
+    "Description": "描述",
    "Download": "下载量",
    "Source": "来源",
    "Saving Preview...": "正在保存预览图...",
    "Saving Succeed":"保存成功",
+    "Clean SuccessFully":"清理成功",
+    "Clean Failed": "清理失败",
    "Saving Failed":"保存失败",
    "No COMBO link": "沒有找到COMBO连接",
    "Reboot ComfyUI":"重启ComfyUI",
    "Are you sure you'd like to reboot the server?": "是否要重启ComfyUI？",
    // GroupMap
-    "Groups Map (EasyUse)": "管理组 (EasyUse)",
-    "Reboot ComfyUI (EasyUse)": "重启服务 (EasyUse)",
+    "Groups Map": "管理组",
+    "Cleanup Of GPU Usage": "清理GPU占用",
+    "Please stop all running tasks before cleaning GPU": "请在清理GPU之前停止所有运行中的任务",
    "Always": "启用中",
    "Bypass": "已忽略",
    "Never": "已停用",
@@ -39,9 +50,51 @@ const zhCN = {
    "Enable ALT+1~9 to paste nodes from nodes template (ComfyUI-Easy-Use)": "启用ALT1~9从节点模板粘贴到工作流 (ComfyUI-Easy-Use)",
    "Enable process bar in queue button (ComfyUI-Easy-Use)": "启用提示词队列进度显示条 (ComfyUI-Easy-Use）",
    "Enable ContextMenu Auto Nest Subdirectories (ComfyUI-Easy-Use)": "启用上下文菜单自动嵌套子目录 (ComfyUI-Easy-Use)",
-    "Too many thumbnails, have closed the display": "模型缩略图太多啦，为您关闭了显示"
+    "Enable tool bar fixed on the left-bottom (ComfyUI-Easy-Use)": "启用工具栏固定在左下角 (ComfyUI-Easy-Use)",
+    "Too many thumbnails, have closed the display": "模型缩略图太多啦，为您关闭了显示",
+    // selector
+    "Empty All": "清空所有",
+    "🔎 Type here to search styles ...": "🔎 在此处输入以搜索样式 ...",
+    // account
+    "Loading UserInfo...": "正在获取用户信息...",
+    "Please set the APIKEY first": "请先设置APIKEY",
+    "Setting APIKEY": "设置APIKEY",
+    "Save Account Info": "保存账号信息",
+    "Choose": "选择",
+    "Delete": "删除",
+    "Edit": "编辑",
+    "At least one account is required": "删除失败: 至少需要一个账户",
+    "APIKEY is not Empty": "APIKEY 不能为空",
+    "Add Account": "添加账号",
+    "Getting Your APIKEY": "获取您的APIKEY",
+    // choosers
+    "Choose Selected Images": "选择选中的图片",
+    "Choose images to continue": "选择图片以继续",
+    // seg
+    "Background": "背景",
+    "Hat": "帽子",
+    "Hair": "头发",
+    "Body": "身体",
+    "Face": "脸部",
+    "Clothes": "衣服",
+    "Others": "其他",
+    "Glove": "手套",
+    "Sunglasses": "太阳镜",
+    "Upper-clothes": "上衣",
+    "Dress": "连衣裙",
+    "Coat": "外套",
+    "Socks": "袜子",
+    "Pants": "裤子",
+    "Jumpsuits": "连体衣",
+    "Scarf": "围巾",
+    "Skirt": "裙子",
+    "Left-arm": "左臂",
+    "Right-arm": "右臂",
+    "Left-leg": "左腿",
+    "Right-leg": "右腿",
+    "Left-shoe": "左鞋",
+    "Right-shoe": "右鞋",
 }
-
 export const $t = (key) => {
    const cn = zhCN[key]
    return locale === 'zh-CN' && cn ? cn : key
@@ -0,0 +1,5 @@
+export const quesitonIcon = `<svg t="1714564780771" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="1489" width="200" height="200" data-spm-anchor-id="a313x.search_index.0.i2.5a663a81pw6qup"><path d="M514.048 54.272q95.232 0 178.688 36.352t145.92 98.304 98.304 145.408 35.84 178.688-35.84 178.176-98.304 145.408-145.92 98.304-178.688 35.84-178.176-35.84-145.408-98.304-98.304-145.408-35.84-178.176 35.84-178.688 98.304-145.408 145.408-98.304 178.176-36.352zM515.072 826.368q26.624 0 44.544-17.92t17.92-43.52q0-26.624-17.92-44.544t-44.544-17.92-44.544 17.92-17.92 44.544q0 25.6 17.92 43.52t44.544 17.92zM567.296 574.464q-1.024-16.384 20.48-34.816t48.128-40.96 49.152-50.688 24.576-65.024q2.048-39.936-8.192-74.752t-33.792-59.904-60.928-39.936-87.552-14.848q-62.464 0-103.936 22.016t-67.072 53.248-35.84 64.512-9.216 55.808q1.024 26.624 16.896 38.912t34.304 12.8 33.792-10.24 15.36-31.232q0-12.288 7.68-30.208t20.992-34.304 32.256-27.648 42.496-11.264q46.08 0 73.728 23.04t25.6 57.856q0 17.408-10.24 32.256t-26.112 28.672-33.792 27.648-33.792 28.672-26.624 32.256-11.776 37.888l1.024 38.912q0 15.36 14.336 29.184t37.888 14.848q23.552-1.024 37.376-15.36t12.8-32.768l0-24.576z" p-id="1490" fill="currentColor"></path></svg>`
+export const rocketIcon = `<svg t="1714565020764" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="7999" width="200" height="200"><path d="M810.438503 379.664884l-71.187166-12.777183C737.426025 180.705882 542.117647 14.602496 532.991087 7.301248c-12.777184-10.951872-32.855615-10.951872-47.45811 0-9.12656 7.301248-204.434938 175.229947-206.26025 359.586453l-67.536542 10.951871c-18.253119 3.650624-31.030303 18.253119-31.030303 36.506239v189.832442c0 10.951872 5.475936 21.903743 12.777184 27.379679 7.301248 5.475936 14.602496 9.12656 23.729055 9.12656h5.475936l133.247772-23.729055c40.156863 47.458111 91.265597 73.012478 151.500891 73.012477 60.235294 0 111.344029-27.379679 151.500891-74.837789l136.898396 23.729055h5.475936c9.12656 0 16.427807-3.650624 23.729055-9.12656 9.12656-7.301248 12.777184-16.427807 12.777184-27.379679V412.520499c1.825312-14.602496-10.951872-29.204991-27.379679-32.855615zM620.606061 766.631016H401.568627c-20.078431 0-36.506239 16.427807-36.506238 36.506239v109.518716c0 14.602496 9.12656 29.204991 23.729055 34.680927 14.602496 5.475936 31.030303 1.825312 40.156863-9.126559l16.427807-18.25312 32.855615 80.313726c5.475936 14.602496 18.253119 23.729055 34.680927 23.729055 16.427807 0 27.379679-9.12656 34.680927-23.729055l32.855615-80.313726 16.427807 18.25312c10.951872 10.951872 25.554367 14.602496 40.156863 9.126559 14.602496-5.475936 23.729055-18.253119 23.729055-34.680927v-109.518716c-3.650624-20.078431-20.078431-36.506239-40.156862-36.506239z" fill="currentColor" p-id="8000"></path></svg>`
+export const groupIcon = `<svg t="1714565543756" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="22538" width="200" height="200"><path d="M871.616 64H152.384c-31.488 0-60.416 25.28-60.416 58.24v779.52c0 32.896 26.24 58.24 60.352 58.24h719.232c34.112 0 60.352-25.344 60.352-58.24V122.24c0.128-32.96-28.8-58.24-60.288-58.24zM286.272 512c-23.616 0-44.672-20.224-44.672-43.008 0-22.784 20.992-43.008 44.608-43.008 23.616 0 44.608 20.224 44.608 43.008A43.328 43.328 0 0 1 286.272 512z m0-202.496c-23.616 0-44.608-20.224-44.608-43.008 0-22.784 20.992-43.008 44.608-43.008 23.616 0 44.608 20.224 44.608 43.008a43.456 43.456 0 0 1-44.608 43.008zM737.728 512H435.904c-23.68 0-44.672-20.224-44.672-43.008 0-22.784 20.992-43.008 44.608-43.008h299.264c23.616 0 44.608 20.224 44.608 43.008a42.752 42.752 0 0 1-41.984 43.008z m0-202.496H435.904c-23.616 0-44.608-20.224-44.608-43.008 0-22.784 20.992-43.008 44.608-43.008h299.264c23.616 0 44.608 20.224 44.608 43.008a42.88 42.88 0 0 1-42.048 43.008z" p-id="22539" fill="currentColor"></path></svg>`
+export const rebootIcon = `<svg t="1714568501931" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="4275" width="200" height="200"><path d="M511.721751 0.000278a511.999861 511.999861 0 1 0 512.277971 511.721751A511.721751 511.721751 0 0 0 511.721751 0.000278zM184.386696 511.722029A36.988583 36.988583 0 0 1 222.487718 475.011556h92.888622a36.710473 36.710473 0 0 1 0 73.420947H222.487718a36.710473 36.710473 0 0 1-38.101022-36.710474z m201.351385 158.522499l-65.911986 65.911987a36.988583 36.988583 0 0 1-62.852781-25.864197 38.101022 38.101022 0 0 1 10.846276-26.142307L333.731577 618.238024a36.710473 36.710473 0 1 1 52.006504 52.006504z m29.201513-256.138985a36.710473 36.710473 0 0 1-52.006504 0l-65.633877-65.633877a36.988583 36.988583 0 0 1 26.142307-62.85278 36.154254 36.154254 0 0 1 25.864197 10.846276L414.939594 361.54282a36.988583 36.988583 0 0 1 0 52.562723z m135.439398 373.779366a37.266693 37.266693 0 0 1-36.988583 36.988583 36.988583 36.988583 0 0 1-36.710473-36.988583V695.274397a36.988583 36.988583 0 0 1 36.710473-36.988583A37.266693 37.266693 0 0 1 550.378992 695.274397z m0-459.437137a37.266693 37.266693 0 0 1-36.988583 36.988583 36.988583 36.988583 0 0 1-36.710473-36.988583V235.559149a36.988583 36.988583 0 0 1 36.710473-36.988583 37.544802 37.544802 0 0 1 36.988583 36.988583z m63.965219 15.85225L679.978088 278.109926a36.710473 36.710473 0 0 1 52.006504 51.728394L667.463154 396.584635a37.544802 37.544802 0 0 1-52.284614 0 36.988583 36.988583 0 0 1-10.568166-26.142306 36.432364 36.432364 0 0 1 9.733837-26.142307z m122.090135 397.974905a37.544802 37.544802 0 0 1-52.284613 0l-65.355767-65.911986a36.154254 36.154254 0 0 1 0-51.728395 36.710473 36.710473 0 0 1 25.864197-10.846276 35.876145 35.876145 0 0 1 25.864197 10.846276l65.911986 65.633877a36.988583 36.988583 0 0 1 0 52.006504z m66.468206-194.676753h-92.888622a36.710473 36.710473 0 0 1 0-73.420947h92.888622a36.710473 36.710473 0 0 1 0 73.420947z" fill="currentColor" p-id="4276"></path></svg>`
+export const closeIcon = `<svg t="1714965640187" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="4264" width="200" height="200"><path d="M597.795527 511.488347 813.564755 295.718095c23.833825-23.833825 23.833825-62.47489 0.001023-86.307691-23.832801-23.832801-62.47489-23.833825-86.307691 0L511.487835 425.180656 295.717583 209.410404c-23.833825-23.833825-62.475913-23.833825-86.307691 0-23.832801 23.832801-23.833825 62.47489 0 86.308715l215.769228 215.769228L209.410915 727.258599c-23.833825 23.833825-23.833825 62.47489 0 86.307691 23.832801 23.833825 62.473867 23.833825 86.307691 0l215.768205-215.768205 215.769228 215.769228c23.834848 23.833825 62.475913 23.832801 86.308715 0 23.833825-23.833825 23.833825-62.47489 0-86.307691L597.795527 511.488347z" fill="currentColor" p-id="4265"></path></svg>`
@@ -1,5 +1,5 @@
-import { $el, ComfyDialog } from "/scripts/ui.js";
-import { api } from "/scripts/api.js";
+import { $el, ComfyDialog } from "../../../../scripts/ui.js";
+import { api } from "../../../../scripts/api.js";
 import {formatTime} from './utils.js';
 import {$t} from "./i18n.js";
 import {toast} from "./toast.js";
@@ -1,4 +1,5 @@
 import {sleep} from "./utils.js";
+import {$t} from "./i18n.js";

 class Toast{

@@ -68,6 +69,14 @@ class Toast{
        container && (container.innerHTML = "");
    }

+    async copyright(duration = 5000, actions = []) {
+        this.showToast({
+            id: `toast-info`,
+            content: `${this.info_icon} ${$t('Workflow created by')} <a href="https://github.com/yolain/">Yolain</a> , ${$t('Watch more video content')} <a href="https://space.bilibili.com/1840885116">B站乱乱呀</a>`,
+            duration,
+            actions
+        });
+    }
    async info(content, duration = 3000, actions = []) {
        this.showToast({
            id: `toast-info`,
@@ -110,4 +110,71 @@ export function isLocalNetwork(ip) {
      return range.test(ip);
    }
  });
+}
+
+
+/**
+* accAdd 高精度加法
+* @since 1.0.10
+* @param {Number} arg1
+* @param {Number} arg2
+* @return {Number}
+*/
+export function accAdd(arg1, arg2) {
+  let r1, r2, s1, s2,max;
+  s1 = typeof arg1 == 'string' ? arg1 : arg1.toString()
+  s2 = typeof arg2 == 'string' ? arg2 : arg2.toString()
+  try { r1 = s1.split(".")[1].length } catch (e) { r1 = 0 }
+  try { r2 = s2.split(".")[1].length } catch (e) { r2 = 0 }
+  max = Math.pow(10, Math.max(r1, r2))
+  return (arg1 * max + arg2 * max) / max
+}
+/**
+ * accSub 高精度减法
+ * @since 1.0.10
+ * @param {Number} arg1
+ * @param {Number} arg2
+ * @return {Number}
+ */
+export function accSub(arg1, arg2) {
+  let r1, r2, max, min,s1,s2;
+  s1 = typeof arg1 == 'string' ? arg1 : arg1.toString()
+  s2 = typeof arg2 == 'string' ? arg2 : arg2.toString()
+  try { r1 = s1.split(".")[1].length } catch (e) { r1 = 0 }
+  try { r2 = s2.split(".")[1].length } catch (e) { r2 = 0 }
+  max = Math.pow(10, Math.max(r1, r2));
+  //动态控制精度长度
+  min = (r1 >= r2) ? r1 : r2;
+  return ((arg1 * max - arg2 * max) / max).toFixed(min)
+}
+/**
+ * accMul 高精度乘法
+ * @since 1.0.10
+ * @param {Number} arg1
+ * @param {Number} arg2
+ * @return {Number}
+ */
+export function accMul(arg1, arg2) {
+  let max = 0, s1 =  typeof arg1 == 'string' ? arg1 : arg1.toString(), s2 = typeof arg2 == 'string' ? arg2 : arg2.toString();
+  try { max += s1.split(".")[1].length } catch (e) { }
+  try { max += s2.split(".")[1].length } catch (e) { }
+  return Number(s1.replace(".", "")) * Number(s2.replace(".", "")) / Math.pow(10, max)
+}
+/**
+ * accDiv 高精度除法
+ * @since 1.0.10
+ * @param {Number} arg1
+ * @param {Number} arg2
+ * @return {Number}
+ */
+export function accDiv(arg1, arg2) {
+  let t1 = 0, t2 = 0, r1, r2,s1 =  typeof arg1 == 'string' ? arg1 : arg1.toString(), s2 = typeof arg2 == 'string' ? arg2 : arg2.toString();
+  try { t1 = s1.toString().split(".")[1].length } catch (e) { }
+  try { t2 = s2.toString().split(".")[1].length } catch (e) { }
+  r1 = Number(s1.toString().replace(".", ""))
+  r2 = Number(s2.toString().replace(".", ""))
+  return (r1 / r2) * Math.pow(10, t2 - t1)
+}
+Number.prototype.div = function (arg) {
+  return accDiv(this, arg);
 }
@@ -1,8 +1,11 @@
-import { api } from "/scripts/api.js";
-import { app } from "/scripts/app.js";
+import { api } from "../../../../scripts/api.js";
+import { app } from "../../../../scripts/app.js";
 import {deepEqual, addCss, isLocalNetwork} from "../common/utils.js";
+import {quesitonIcon, rocketIcon, groupIcon, rebootIcon, closeIcon} from "../common/icon.js";
 import {$t} from '../common/i18n.js';
 import {toast} from "../common/toast.js";
+import {$el, ComfyDialog} from "../../../../scripts/ui.js";
+

 addCss('css/index.css')

@@ -21,6 +24,396 @@ api.addEventListener("easyuse-toast",event=>{
      })
    }
 })
+
+
+let draggerEl = null
+let isGroupMapcanMove = true
+function createGroupMap(){
+    let div = document.querySelector('#easyuse_groups_map')
+    if(div){
+        div.style.display = div.style.display == 'none' ? 'flex' : 'none'
+        return
+    }
+    let groups = app.canvas.graph._groups
+    let nodes = app.canvas.graph._nodes
+    let old_nodes = groups.length
+    div = document.createElement('div')
+    div.id = 'easyuse_groups_map'
+    div.innerHTML = ''
+    let btn = document.createElement('div')
+    btn.style = `display: flex;
+        width: calc(100% - 8px);
+        justify-content: space-between;
+        align-items: center;
+        padding: 0 6px;
+        height: 44px;`
+    let hideBtn = $el('button.closeBtn',{
+        innerHTML:closeIcon,
+        onclick:_=>div.style.display = 'none'
+    })
+    let textB = document.createElement('p')
+    btn.appendChild(textB)
+    btn.appendChild(hideBtn)
+    textB.style.fontSize = '11px'
+    textB.innerHTML =  `<b>${$t('Groups Map')} (EasyUse)</b>`
+    div.appendChild(btn)
+
+    div.addEventListener('mousedown', function (e) {
+        var startX = e.clientX
+        var startY = e.clientY
+        var offsetX = div.offsetLeft
+        var offsetY = div.offsetTop
+
+        function moveBox (e) {
+          var newX = e.clientX
+          var newY = e.clientY
+          var deltaX = newX - startX
+          var deltaY = newY - startY
+          div.style.left = offsetX + deltaX + 'px'
+          div.style.top = offsetY + deltaY + 'px'
+        }
+
+        function stopMoving () {
+          document.removeEventListener('mousemove', moveBox)
+          document.removeEventListener('mouseup', stopMoving)
+        }
+
+        if(isGroupMapcanMove){
+            document.addEventListener('mousemove', moveBox)
+            document.addEventListener('mouseup', stopMoving)
+        }
+    })
+
+    function updateGroups(groups, groupsDiv, autoSortDiv){
+        if(groups.length>0){
+            autoSortDiv.style.display = 'block'
+        }else autoSortDiv.style.display = 'none'
+        for (let index in groups) {
+            const group = groups[index]
+            const title = group.title
+            const show_text = $t('Always')
+            const hide_text = $t('Bypass')
+            const mute_text = $t('Never')
+            let group_item = document.createElement('div')
+            let group_item_style = `justify-content: space-between;display:flex;background-color: var(--comfy-input-bg);border-radius: 5px;border:1px solid var(--border-color);margin-top:5px;`
+            group_item.addEventListener("mouseover",event=>{
+                event.preventDefault()
+                group_item.style = group_item_style + "filter:brightness(1.2);"
+            })
+            group_item.addEventListener("mouseleave",event=>{
+                event.preventDefault()
+                group_item.style = group_item_style + "filter:brightness(1);"
+            })
+            group_item.addEventListener("dragstart",e=>{
+                draggerEl = e.currentTarget;
+                e.currentTarget.style.opacity = "0.6";
+                e.currentTarget.style.border = "1px dashed yellow";
+                e.dataTransfer.effectAllowed = 'move';
+                e.dataTransfer.setDragImage(emptyImg, 0, 0);
+            })
+            group_item.addEventListener("dragend",e=>{
+                e.target.style.opacity = "1";
+                e.currentTarget.style.border = "1px dashed transparent";
+                e.currentTarget.removeAttribute("draggable");
+                document.querySelectorAll('.easyuse-group-item').forEach((el,i) => {
+                    var prev_i = el.dataset.id;
+                    if (el == draggerEl && prev_i != i ) {
+                        groups.splice(i, 0, groups.splice(prev_i, 1)[0]);
+                    }
+                    el.dataset.id = i;
+                });
+                 isGroupMapcanMove = true
+            })
+            group_item.addEventListener("dragover",e=>{
+                e.preventDefault();
+                if (e.currentTarget == draggerEl) return;
+                let rect = e.currentTarget.getBoundingClientRect();
+                if (e.clientY > rect.top + rect.height / 2) {
+                    e.currentTarget.parentNode.insertBefore(draggerEl, e.currentTarget.nextSibling);
+                } else {
+                    e.currentTarget.parentNode.insertBefore(draggerEl, e.currentTarget);
+                }
+                isGroupMapcanMove = true
+            })
+
+
+            group_item.setAttribute('data-id',index)
+            group_item.className = 'easyuse-group-item'
+            group_item.style = group_item_style
+            // 标题
+            let text_group_title = document.createElement('div')
+            text_group_title.style = `flex:1;font-size:12px;color:var(--input-text);padding:4px;white-space: nowrap;overflow: hidden;text-overflow: ellipsis;cursor:pointer`
+            text_group_title.innerHTML = `${title}`
+            text_group_title.addEventListener('mousedown',e=>{
+                isGroupMapcanMove = false
+                e.currentTarget.parentNode.draggable = 'true';
+            })
+            text_group_title.addEventListener('mouseleave',e=>{
+                setTimeout(_=>{
+                    isGroupMapcanMove = true
+                },150)
+            })
+            group_item.append(text_group_title)
+            // 按钮组
+            let buttons = document.createElement('div')
+            group.recomputeInsideNodes();
+            const nodesInGroup = group._nodes;
+            let isGroupShow = nodesInGroup && nodesInGroup.length>0 && nodesInGroup[0].mode == 0
+            let isGroupMute = nodesInGroup && nodesInGroup.length>0 && nodesInGroup[0].mode == 2
+            let go_btn = document.createElement('button')
+            go_btn.style = "margin-right:6px;cursor:pointer;font-size:10px;padding:2px 4px;color:var(--input-text);background-color: var(--comfy-input-bg);border: 1px solid var(--border-color);border-radius:4px;"
+            go_btn.innerText = "Go"
+            go_btn.addEventListener('click', () => {
+                app.canvas.ds.offset[0] =  -group.pos[0] - group.size[0] * 0.5 + (app.canvas.canvas.width * 0.5) / app.canvas.ds.scale;
+                app.canvas.ds.offset[1] = -group.pos[1] - group.size[1] * 0.5 + (app.canvas.canvas.height * 0.5) / app.canvas.ds.scale;
+                app.canvas.setDirty(true, true);
+                app.canvas.setZoom(1)
+            })
+            buttons.append(go_btn)
+            let see_btn = document.createElement('button')
+            let defaultStyle = `cursor:pointer;font-size:10px;;padding:2px;border: 1px solid var(--border-color);border-radius:4px;width:36px;`
+            see_btn.style = isGroupMute ? `background-color:var(--error-text);color:var(--input-text);` + defaultStyle : (isGroupShow ? `background-color:#006691;color:var(--input-text);` + defaultStyle : `background-color: var(--comfy-input-bg);color:var(--descrip-text);` + defaultStyle)
+            see_btn.innerText = isGroupMute ? mute_text : (isGroupShow ? show_text : hide_text)
+            let pressTimer
+            let firstTime =0, lastTime =0
+            let isHolding = false
+            see_btn.addEventListener('click', () => {
+                if(isHolding){
+                    isHolding = false
+                    return
+                }
+                for (const node of nodesInGroup) {
+                    node.mode = isGroupShow ? 4 : 0;
+                    node.graph.change();
+                }
+                isGroupShow = nodesInGroup[0].mode == 0 ? true : false
+                isGroupMute = nodesInGroup[0].mode == 2 ? true : false
+                see_btn.style = isGroupMute ? `background-color:var(--error-text);color:var(--input-text);` + defaultStyle : (isGroupShow ? `background-color:#006691;color:var(--input-text);` + defaultStyle : `background-color: var(--comfy-input-bg);color:var(--descrip-text);` + defaultStyle)
+                see_btn.innerText = isGroupMute ? mute_text : (isGroupShow ? show_text : hide_text)
+            })
+            see_btn.addEventListener('mousedown', () => {
+                firstTime = new Date().getTime();
+                clearTimeout(pressTimer);
+                pressTimer = setTimeout(_=>{
+                    for (const node of nodesInGroup) {
+                        node.mode = isGroupMute ? 0 : 2;
+                        node.graph.change();
+                    }
+                    isGroupShow = nodesInGroup[0].mode == 0 ? true : false
+                    isGroupMute = nodesInGroup[0].mode == 2 ? true : false
+                    see_btn.style = isGroupMute ? `background-color:var(--error-text);color:var(--input-text);` + defaultStyle : (isGroupShow ? `background-color:#006691;color:var(--input-text);` + defaultStyle : `background-color: var(--comfy-input-bg);color:var(--descrip-text);` + defaultStyle)
+                    see_btn.innerText = isGroupMute ? mute_text : (isGroupShow ? show_text : hide_text)
+                },500)
+            })
+            see_btn.addEventListener('mouseup', () => {
+                lastTime = new Date().getTime();
+                if(lastTime - firstTime > 500) isHolding = true
+                clearTimeout(pressTimer);
+            })
+            buttons.append(see_btn)
+            group_item.append(buttons)
+
+            groupsDiv.append(group_item)
+        }
+
+    }
+
+    let groupsDiv =  document.createElement('div')
+    groupsDiv.id = 'easyuse-groups-items'
+    groupsDiv.style = `overflow-y: auto;max-height: 400px;height:100%;width: 100%;`
+
+    let autoSortDiv = document.createElement('button')
+    autoSortDiv.style = `cursor:pointer;font-size:10px;padding:2px 4px;color:var(--input-text);background-color: var(--comfy-input-bg);border: 1px solid var(--border-color);border-radius:4px;`
+    autoSortDiv.innerText =  $t('Auto Sorting')
+    autoSortDiv.addEventListener('click',e=>{
+        e.preventDefault()
+        groupsDiv.innerHTML = ``
+        let new_groups = groups.sort((a,b)=> a['pos'][0] - b['pos'][0]).sort((a,b)=> a['pos'][1] - b['pos'][1])
+        updateGroups(new_groups, groupsDiv, autoSortDiv)
+    })
+
+    updateGroups(groups, groupsDiv, autoSortDiv)
+
+    div.appendChild(groupsDiv)
+
+    let remarkDiv =  document.createElement('p')
+    remarkDiv.style = `text-align:center; font-size:10px; padding:0 10px;color:var(--descrip-text)`
+    remarkDiv.innerText =  $t('Toggle `Show/Hide` can set mode of group, LongPress can set group nodes to never')
+    div.appendChild(groupsDiv)
+    div.appendChild(remarkDiv)
+    div.appendChild(autoSortDiv)
+
+    let graphDiv = document.getElementById("graph-canvas")
+    graphDiv.addEventListener('mouseover', async () => {
+      groupsDiv.innerHTML = ``
+      let new_groups = app.canvas.graph._groups
+      updateGroups(new_groups, groupsDiv, autoSortDiv)
+      old_nodes = nodes
+    })
+
+    if (!document.querySelector('#easyuse_groups_map')){
+        document.body.appendChild(div)
+    }else{
+        div.style.display = 'flex'
+    }
+
+}
+
+async function cleanup(){
+    try {
+       const {Running, Pending} = await api.getQueue()
+       if(Running.length>0 || Pending.length>0){
+           toast.error($t("Clean Failed")+ ":"+ $t("Please stop all running tasks before cleaning GPU"))
+           return
+       }
+        api.fetchApi("/easyuse/cleangpu",{
+            method:"POST"
+        }).then(res=>{
+            if(res.status == 200){
+                toast.success($t("Clean SuccessFully"))
+            }else{
+                toast.error($t("Clean Failed"))
+            }
+        })
+
+    } catch (exception) {}
+}
+
+
+let guideDialog = null
+let isDownloading = false
+function download_model(url,local_dir){
+    if(isDownloading || !url || !local_dir) return
+    isDownloading = true
+    let body =  new FormData();
+    body.append('url', url);
+    body.append('local_dir', local_dir);
+    api.fetchApi("/easyuse/model/download",{
+        method:"POST",
+        body
+    }).then(res=>{
+        if(res.status == 200){
+            toast.success($t("Download SuccessFully"))
+        }else{
+            toast.error($t("Download Failed"))
+        }
+        isDownloading = false
+    })
+
+}
+class GuideDialog {
+
+    constructor(note, need_models){
+        this.dialogDiv = null
+        this.modelsDiv = null
+
+        if(need_models?.length>0){
+            let tbody = []
+
+            for(let i=0;i<need_models.length;i++){
+                tbody.push($el('tr',[
+                    $el('td',{innerHTML:need_models[i].title || need_models[i].name || ''}),
+                    $el('td',[
+                        need_models[i]['download_url'] ? $el('a',{onclick:_=>download_model(need_models[i]['download_url'],need_models[i]['local_dir']), target:"_blank", textContent:$t('Download Model')}) : '',
+                        need_models[i]['source_url'] ? $el('a',{href:need_models[i]['source_url'], target:"_blank", textContent:$t('Source Url')}) : '',
+                        need_models[i]['desciption'] ? $el('span',{textContent:need_models[i]['desciption']}) : '',
+                    ]),
+                ]))
+            }
+            this.modelsDiv = $el('div.easyuse-guide-dialog-models.markdown-body',[
+                $el('h3',{textContent:$t('Models Required')}),
+                $el('table',{cellpadding:0,cellspacing:0},[
+                    $el('thead',[
+                        $el('tr',[
+                            $el('th',{innerHTML:$t('ModelName')}),
+                            $el('th',{innerHTML:$t('Description')}),
+                        ])
+                    ]),
+                    $el('tbody',tbody)
+                ])
+            ])
+        }
+
+        this.dialogDiv = $el('div.easyuse-guide-dialog.hidden',[
+           $el('div.easyuse-guide-dialog-header',[
+                 $el('div.easyuse-guide-dialog-top',[
+                    $el('div.easyuse-guide-dialog-title',{
+                        innerHTML:$t('Workflow Guide')
+                    }),
+                    $el('button.closeBtn',{innerHTML:closeIcon,onclick:_=>this.close()})
+                 ]),
+
+                 $el('div.easyuse-guide-dialog-remark',{
+                    innerHTML:`${$t('Workflow created by')} <a href="https://github.com/yolain/" target="_blank">Yolain</a> , ${$t('Watch more video content')} <a href="https://space.bilibili.com/1840885116" target="_blank">B站乱乱呀</a>`
+                 })
+           ]),
+           $el('div.easyuse-guide-dialog-content.markdown-body',[
+               $el('div.easyuse-guide-dialog-note',{
+                   innerHTML:note
+               }),
+               ...this.modelsDiv ? [this.modelsDiv] : []
+           ])
+        ])
+
+        if(disableRenderInfo){
+            this.dialogDiv.classList.add('disable-render-info')
+        }
+        document.body.appendChild(this.dialogDiv)
+    }
+    show(){
+        if(this.dialogDiv) this.dialogDiv.classList.remove('hidden')
+    }
+
+    close(){
+        if(this.dialogDiv){
+            this.dialogDiv.classList.add('hidden')
+        }
+    }
+    toggle(){
+        if(this.dialogDiv){
+            if(this.dialogDiv.classList.contains('hidden')){
+                this.show()
+            }else{
+                this.close()
+            }
+        }
+    }
+
+    remove(){
+        if(this.dialogDiv) document.body.removeChild(this.dialogDiv)
+    }
+}
+
+const getEnableToolBar = _ => app.ui.settings.getSettingValue(toolBarId, true)
+
+const toolBarId = "Comfy.EasyUse.toolBar"
+
+let enableToolBar = getEnableToolBar()
+let disableRenderInfo = localStorage['Comfy.Settings.Comfy.EasyUse.disableRenderInfo'] ? true : false
+export function addToolBar(app) {
+	app.ui.settings.addSetting({
+		id: toolBarId,
+		name: $t("Enable tool bar fixed on the left-bottom (ComfyUI-Easy-Use)"),
+		type: "boolean",
+		defaultValue: enableToolBar,
+		onChange(value) {
+			enableToolBar = !!value;
+            if(enableToolBar){
+                showToolBar()
+            }else hideToolBar()
+		},
+	});
+}
+
+let note = null
+let toolbar = null
+function showToolBar(){
+    toolbar.style.display = 'flex'
+}
+function hideToolBar(){
+    toolbar.style.display = 'none'
+}
+
 app.registerExtension({
    name: "comfy.easyUse",
    init() {
@@ -28,252 +421,27 @@ app.registerExtension({
        const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions;
        LGraphCanvas.prototype.getCanvasMenuOptions = function () {
            const options = getCanvasMenuOptions.apply(this, arguments);
-            let draggerEl = null
-            let isGroupMapcanMove = true
-            let old_groups = []
            let emptyImg = new Image()
            emptyImg.src = "data:image/gif;base64,R0lGODlhAQABAIAAAAUEBAAAACwAAAAAAQABAAACAkQBADs=";

            options.push(null,
                // Groups Map
                {
-                    content: '📜 '+ $t('Groups Map (EasyUse)'),
+                    content: groupIcon.replace('currentColor','var(--warning-color)') + ' '+ $t('Groups Map') + ' (EasyUse)',
                    callback: async() => {
-                        let groups = app.canvas.graph._groups
-                        let nodes = app.canvas.graph._nodes
-                        let old_nodes = groups.length
-                        let div =
-                            document.querySelector('#easyuse_groups_map') ||
-                            document.createElement('div')
-                        div.id = 'easyuse_groups_map'
-                        div.innerHTML = ''
-                        let btn = document.createElement('div')
-                        btn.style = `display: flex;
-                            width: calc(100% - 8px);
-                            justify-content: space-between;
-                            align-items: center;
-                            padding: 0 6px;
-                            height: 44px;`
-                        let hideBtn = document.createElement('button')
-                        let textB = document.createElement('p')
-                        btn.appendChild(textB)
-                        btn.appendChild(hideBtn)
-                        textB.style.fontSize = '11px'
-                        textB.innerHTML =  `<b>${$t('Groups Map (EasyUse)')}</b>`
-                        hideBtn.style = `float: right;color: var(--input-text);border-radius:6px;font-size:9px;
-                            background-color: var(--comfy-input-bg); border: 1px solid var(--border-color);cursor: pointer;padding: 5px;aspect-ratio: 1 / 1;`
-                        hideBtn.addEventListener('click', () => {
-                            div.style.display = 'none'
-                        })
-                        hideBtn.innerText = '❌'
-                        div.appendChild(btn)
-
-                        div.addEventListener('mousedown', function (e) {
-                            var startX = e.clientX
-                            var startY = e.clientY
-                            var offsetX = div.offsetLeft
-                            var offsetY = div.offsetTop
-
-                            function moveBox (e) {
-                              var newX = e.clientX
-                              var newY = e.clientY
-                              var deltaX = newX - startX
-                              var deltaY = newY - startY
-                              div.style.left = offsetX + deltaX + 'px'
-                              div.style.top = offsetY + deltaY + 'px'
-                            }
-
-                            function stopMoving () {
-                              document.removeEventListener('mousemove', moveBox)
-                              document.removeEventListener('mouseup', stopMoving)
-                            }
-
-                            if(isGroupMapcanMove){
-                                document.addEventListener('mousemove', moveBox)
-                                document.addEventListener('mouseup', stopMoving)
-                            }
-                        })
-
-                        function updateGroups(groups, groupsDiv, autoSortDiv){
-                            if(groups.length>0){
-                                autoSortDiv.style.display = 'block'
-                            }else autoSortDiv.style.display = 'none'
-                            for (let index in groups) {
-                                const group = groups[index]
-                                const title = group.title
-                                const show_text = $t('Always')
-                                const hide_text = $t('Bypass')
-                                const mute_text = $t('Never')
-                                let group_item = document.createElement('div')
-                                let group_item_style = `justify-content: space-between;display:flex;background-color: var(--comfy-input-bg);border-radius: 5px;border:1px solid var(--border-color);margin-top:5px;`
-                                group_item.addEventListener("mouseover",event=>{
-                                    event.preventDefault()
-                                    group_item.style = group_item_style + "filter:brightness(1.2);"
-                                })
-                                group_item.addEventListener("mouseleave",event=>{
-                                    event.preventDefault()
-                                    group_item.style = group_item_style + "filter:brightness(1);"
-                                })
-                                group_item.addEventListener("dragstart",e=>{
-                                    draggerEl = e.currentTarget;
-                                    e.currentTarget.style.opacity = "0.6";
-									e.currentTarget.style.border = "1px dashed yellow";
-									e.dataTransfer.effectAllowed = 'move';
-									e.dataTransfer.setDragImage(emptyImg, 0, 0);
-                                })
-                                group_item.addEventListener("dragend",e=>{
-                                    e.target.style.opacity = "1";
-                                    e.currentTarget.style.border = "1px dashed transparent";
-                                    e.currentTarget.removeAttribute("draggable");
-									document.querySelectorAll('.easyuse-group-item').forEach((el,i) => {
-										var prev_i = el.dataset.id;
-										if (el == draggerEl && prev_i != i ) {
-											groups.splice(i, 0, groups.splice(prev_i, 1)[0]);
-										}
-										el.dataset.id = i;
-									});
-                                     isGroupMapcanMove = true
-                                })
-                                group_item.addEventListener("dragover",e=>{
-                                    e.preventDefault();
-									if (e.currentTarget == draggerEl) return;
-									let rect = e.currentTarget.getBoundingClientRect();
-									if (e.clientY > rect.top + rect.height / 2) {
-										e.currentTarget.parentNode.insertBefore(draggerEl, e.currentTarget.nextSibling);
-									} else {
-										e.currentTarget.parentNode.insertBefore(draggerEl, e.currentTarget);
-									}
-                                    isGroupMapcanMove = true
-                                })
-
-
-                                group_item.setAttribute('data-id',index)
-                                group_item.className = 'easyuse-group-item'
-                                group_item.style = group_item_style
-                                // 标题
-                                let text_group_title = document.createElement('div')
-                                text_group_title.style = `flex:1;font-size:12px;color:var(--input-text);padding:4px;white-space: nowrap;overflow: hidden;text-overflow: ellipsis;cursor:pointer`
-                                text_group_title.innerHTML = `${title}`
-                                text_group_title.addEventListener('mousedown',e=>{
-                                    isGroupMapcanMove = false
-                                    e.currentTarget.parentNode.draggable = 'true';
-                                })
-                                text_group_title.addEventListener('mouseleave',e=>{
-                                    setTimeout(_=>{
-                                        isGroupMapcanMove = true
-                                    },150)
-                                })
-                                group_item.append(text_group_title)
-                                // 按钮组
-                                let buttons = document.createElement('div')
-                                group.recomputeInsideNodes();
-                                const nodesInGroup = group._nodes;
-                                let isGroupShow = nodesInGroup && nodesInGroup.length>0 && nodesInGroup[0].mode == 0
-                                let isGroupMute = nodesInGroup && nodesInGroup.length>0 && nodesInGroup[0].mode == 2
-                                let go_btn = document.createElement('button')
-                                go_btn.style = "margin-right:6px;cursor:pointer;font-size:10px;padding:2px 4px;color:var(--input-text);background-color: var(--comfy-input-bg);border: 1px solid var(--border-color);border-radius:4px;"
-                                go_btn.innerText = "Go"
-                                go_btn.addEventListener('click', () => {
-                                    app.canvas.ds.offset[0] =  -group.pos[0] - group.size[0] * 0.5 + (app.canvas.canvas.width * 0.5) / app.canvas.ds.scale;
-                                    app.canvas.ds.offset[1] = -group.pos[1] - group.size[1] * 0.5 + (app.canvas.canvas.height * 0.5) / app.canvas.ds.scale;
-                                    app.canvas.setDirty(true, true);
-                                    app.canvas.setZoom(1)
-                                })
-                                buttons.append(go_btn)
-                                let see_btn = document.createElement('button')
-                                let defaultStyle = `cursor:pointer;font-size:10px;;padding:2px;border: 1px solid var(--border-color);border-radius:4px;width:36px;`
-                                see_btn.style = isGroupMute ? `background-color:var(--error-text);color:var(--input-text);` + defaultStyle : (isGroupShow ? `background-color:#006691;color:var(--input-text);` + defaultStyle : `background-color: var(--comfy-input-bg);color:var(--descrip-text);` + defaultStyle)
-                                see_btn.innerText = isGroupMute ? mute_text : (isGroupShow ? show_text : hide_text)
-                                let pressTimer
-                                let firstTime =0, lastTime =0
-                                let isHolding = false
-                                see_btn.addEventListener('click', () => {
-                                    if(isHolding){
-                                        isHolding = false
-                                        return
-                                    }
-                                    for (const node of nodesInGroup) {
-                                        node.mode = isGroupShow ? 4 : 0;
-                                        node.graph.change();
-                                    }
-                                    isGroupShow = nodesInGroup[0].mode == 0 ? true : false
-                                    isGroupMute = nodesInGroup[0].mode == 2 ? true : false
-                                    see_btn.style = isGroupMute ? `background-color:var(--error-text);color:var(--input-text);` + defaultStyle : (isGroupShow ? `background-color:#006691;color:var(--input-text);` + defaultStyle : `background-color: var(--comfy-input-bg);color:var(--descrip-text);` + defaultStyle)
-                                    see_btn.innerText = isGroupMute ? mute_text : (isGroupShow ? show_text : hide_text)
-                                })
-                                see_btn.addEventListener('mousedown', () => {
-                                    firstTime = new Date().getTime();
-                                    clearTimeout(pressTimer);
-                                    pressTimer = setTimeout(_=>{
-                                        for (const node of nodesInGroup) {
-                                            node.mode = isGroupMute ? 0 : 2;
-                                            node.graph.change();
-                                        }
-                                        isGroupShow = nodesInGroup[0].mode == 0 ? true : false
-                                        isGroupMute = nodesInGroup[0].mode == 2 ? true : false
-                                        see_btn.style = isGroupMute ? `background-color:var(--error-text);color:var(--input-text);` + defaultStyle : (isGroupShow ? `background-color:#006691;color:var(--input-text);` + defaultStyle : `background-color: var(--comfy-input-bg);color:var(--descrip-text);` + defaultStyle)
-                                        see_btn.innerText = isGroupMute ? mute_text : (isGroupShow ? show_text : hide_text)
-                                    },500)
-                                })
-                                see_btn.addEventListener('mouseup', () => {
-                                    lastTime = new Date().getTime();
-                                    if(lastTime - firstTime > 500) isHolding = true
-                                    clearTimeout(pressTimer);
-                                })
-                                buttons.append(see_btn)
-                                group_item.append(buttons)
-
-                                groupsDiv.append(group_item)
-                            }
-
-                        }
-
-                        let groupsDiv =  document.createElement('div')
-                        groupsDiv.id = 'easyuse-groups-items'
-                        groupsDiv.style = `overflow-y: auto;max-height: 400px;height:100%;width: 100%;`
-
-                        let autoSortDiv = document.createElement('button')
-                        autoSortDiv.style = `cursor:pointer;font-size:10px;padding:2px 4px;color:var(--input-text);background-color: var(--comfy-input-bg);border: 1px solid var(--border-color);border-radius:4px;`
-                        autoSortDiv.innerText =  $t('Auto Sorting')
-                        autoSortDiv.addEventListener('click',e=>{
-                            e.preventDefault()
-                            groupsDiv.innerHTML = ``
-                            let new_groups = groups.sort((a,b)=> a['pos'][0] - b['pos'][0]).sort((a,b)=> a['pos'][1] - b['pos'][1])
-                            updateGroups(new_groups, groupsDiv, autoSortDiv)
-                        })
-
-                        updateGroups(groups, groupsDiv, autoSortDiv)
-
-                        div.appendChild(groupsDiv)
-
-                        let remarkDiv =  document.createElement('p')
-                        remarkDiv.style = `text-align:center; font-size:10px; padding:0 10px;color:var(--descrip-text)`
-                        remarkDiv.innerText =  $t('Toggle `Show/Hide` can set mode of group, LongPress can set group nodes to never')
-                        div.appendChild(groupsDiv)
-                        div.appendChild(remarkDiv)
-                        div.appendChild(autoSortDiv)
-
-                        let graphDiv = document.getElementById("graph-canvas")
-                        graphDiv.addEventListener('mouseover', async () => {
-                          groupsDiv.innerHTML = ``
-                          let new_groups = app.canvas.graph._groups
-                          updateGroups(new_groups, groupsDiv, autoSortDiv)
-                          old_nodes = nodes
-                        })
-
-                        if (!document.querySelector('#easyuse_groups_map')){
-                            document.body.appendChild(div)
-                        }else{
-                            div.style.display = 'flex'
-                        }
-
+                        createGroupMap()
                    }
-                }
-            );
-            // Only show the reboot option if the server is running on a local network 仅在本地或局域网环境可重启服务
-            if(isLocalNetwork(window.location.host)){
-                options.push(null,{
-                    content: '🔴 '+ $t('Reboot ComfyUI (EasyUse)'),
+                },
+                // Force clean ComfyUI GPU Used 强制卸载模型GPU占用
+                {
+                    content: rocketIcon.replace('currentColor','var(--theme-color-light)') + ' '+ $t('Cleanup Of GPU Usage') + ' (EasyUse)',
+                    callback: async() =>{
+                       await cleanup()
+                    }
+                },
+                // Only show the reboot option if the server is running on a local network 仅在本地或局域网环境可重启服务
+                isLocalNetwork(window.location.host) ? {
+                    content: rebootIcon.replace('currentColor','var(--error-color)') + ' '+ $t('Reboot ComfyUI') + ' (EasyUse)',
                    callback: _ =>{
                        if (confirm($t("Are you sure you'd like to reboot the server?"))){
                            try {
@@ -281,10 +449,77 @@ app.registerExtension({
                            } catch (exception) {}
                        }
                    }
-                })
-            }
+                } : null,
+            );
            return options;
        };
+
+        let renderInfoEvent = LGraphCanvas.prototype.renderInfo
+        if(disableRenderInfo){
+            LGraphCanvas.prototype.renderInfo = function (ctx, x, y) {}
+        }
+
+        if(!toolbar){
+            toolbar = $el('div.easyuse-toolbar',[
+                $el('div.easyuse-toolbar-item',{
+                    onclick:_=>{
+                        createGroupMap()
+                    }
+                },[
+                    $el('div.easyuse-toolbar-icon.group', {innerHTML:groupIcon}),
+                    $el('div.easyuse-toolbar-tips',$t('Groups Map'))
+                ]),
+                $el('div.easyuse-toolbar-item',{
+                    onclick:async()=>{
+                        await cleanup()
+                    }
+                },[
+                    $el('div.easyuse-toolbar-icon.rocket',{innerHTML:rocketIcon}),
+                    $el('div.easyuse-toolbar-tips',$t('Cleanup Of GPU Usage'))
+                ]),
+            ])
+            if(disableRenderInfo){
+                toolbar.classList.add('disable-render-info')
+            }else{
+                toolbar.classList.remove('disable-render-info')
+            }
+            document.body.appendChild(toolbar)
+        }
+
+        // rewrite handleFile
+        let loadGraphDataEvent = app.loadGraphData
+        app.loadGraphData = async function (data, clean=true) {
+            // if(data?.extra?.cpr){
+            //     toast.copyright()
+            // }
+            if(data?.extra?.note){
+                 if(guideDialog) {
+                     guideDialog.remove()
+                     guideDialog = null
+                 }
+                 if(note && toolbar) toolbar.removeChild(note)
+                 const need_models = data.extra?.need_models || null
+                 guideDialog = new GuideDialog(data.extra.note, need_models)
+                 note = $el('div.easyuse-toolbar-item',{
+                    onclick:async()=>{
+                        guideDialog.toggle()
+                    }
+                },[
+                    $el('div.easyuse-toolbar-icon.question',{innerHTML:quesitonIcon}),
+                    $el('div.easyuse-toolbar-tips',$t('Workflow Guide'))
+                ])
+                if(toolbar) toolbar.insertBefore(note, toolbar.firstChild)
+            }
+            else{
+                if(note) {
+                    toolbar.removeChild(note)
+                    note = null
+                }
+            }
+            return loadGraphDataEvent.apply(this, [...arguments])
+        }
+
+        addToolBar(app)
    },
    beforeRegisterNodeDef(nodeType, nodeData, app) {
        if (nodeData.name.startsWith("easy")) {
@@ -0,0 +1,283 @@
+import { app } from "../../../../scripts/app.js";
+import { api } from "../../../../scripts/api.js";
+import { $el, ComfyDialog } from "../../../../scripts/ui.js";
+import { $t } from '../common/i18n.js'
+import { toast } from "../common/toast.js";
+import {sleep, accSub} from "../common/utils.js";
+
+let api_keys = []
+let api_current = 0
+let user_info = {}
+
+const api_cost = {
+    'sd3': 6.5,
+    'sd3-turbo': 4,
+}
+
+class AccountDialog extends ComfyDialog {
+    constructor() {
+		super();
+        this.lists = []
+        this.dialog_div = null
+        this.user_div = null
+	}
+
+    addItem(index, user_div){
+        return $el('div.easyuse-account-dialog-item',[
+          $el('input',{type:'text',placeholder:'Enter name',oninput: e=>{
+              const dataIndex = Array.prototype.indexOf.call(this.dialog_div.querySelectorAll('.easyuse-account-dialog-item'), e.target.parentNode)
+              api_keys[dataIndex]['name'] = e.target.value
+          },value:api_keys[index]['name']}),
+          $el('input.key',{type:'text',oninput: e=>{
+              const dataIndex = Array.prototype.indexOf.call(this.dialog_div.querySelectorAll('.easyuse-account-dialog-item'), e.target.parentNode)
+              api_keys[dataIndex]['key'] = e.target.value
+          },placeholder:'Enter APIKEY', value:api_keys[index]['key']}),
+          $el('button.choose',{textContent:$t('Choose'),onclick:async(e)=>{
+                const dataIndex = Array.prototype.indexOf.call(this.dialog_div.querySelectorAll('.easyuse-account-dialog-item'), e.target.parentNode)
+                let name = api_keys[dataIndex]['name']
+                let key = api_keys[dataIndex]['key']
+                if(!name){
+                    toast.error($t('Please enter the account name'))
+                    return
+                }
+                else if(!key){
+                    toast.error($t('Please enter the APIKEY'))
+                    return
+                }
+                let missing = true
+                for(let i=0;i<api_keys.length;i++){
+                    if(!api_keys[i].key) {
+                        missing = false
+                        break
+                    }
+                }
+                if(!missing){
+                    toast.error($t('APIKEY is not Empty'))
+                    return
+                }
+                // 保存记录
+                api_current = dataIndex
+                const body = new FormData();
+                body.append('api_keys', JSON.stringify(api_keys));
+                body.append('current',api_current)
+                const res = await api.fetchApi('/easyuse/stability/set_api_keys', {
+                    method: 'POST',
+                    body
+                })
+                 if (res.status == 200) {
+                    const data = await res.json()
+                    if(data?.account && data?.balance){
+                        const avatar = data.account?.profile_picture || null
+                        const email = data.account?.email || null
+                        const credits = data.balance?.credits || 0
+                        user_div.replaceChildren(
+                            $el('div.easyuse-account-user-info', {
+                                onclick:_=>{
+                                    new AccountDialog().show(user_div);
+                                }
+                            },[
+                                $el('div.user',[
+                                   $el('div.avatar', avatar ? [$el('img',{src:avatar})] : '😀'),
+                                   $el('div.info', [
+                                    $el('h5.name', email),
+                                    $el('h6.remark','Credits: '+ credits)
+                                   ])
+                                ]),
+                                $el('div.edit', {textContent:$t('Edit')})
+                            ])
+                        )
+                        toast.success($t('Save Succeed'))
+                    }
+                    else toast.success($t('Save Succeed'))
+                    this.close()
+                } else {
+                    toast.error($t('Save Failed'))
+                }
+          }}),
+          $el('button.delete',{textContent:$t('Delete'),onclick:e=>{
+              const dataIndex = Array.prototype.indexOf.call(this.dialog_div.querySelectorAll('.easyuse-account-dialog-item'), e.target.parentNode)
+              if(api_keys.length<=1){
+                toast.error($t('At least one account is required'))
+                return
+              }
+              api_keys.splice(dataIndex,1)
+              this.dialog_div.removeChild(e.target.parentNode)
+          }}),
+      ])
+    }
+
+    show(userdiv) {
+        api_keys.forEach((item,index)=>{
+          this.lists.push(this.addItem(index,userdiv))
+        })
+        this.dialog_div = $el("div.easyuse-account-dialog", this.lists)
+		super.show(
+            $el('div.easyuse-account-dialog-main',[
+               $el('div',[
+                    $el('a',{href:'https://platform.stability.ai/account/keys',target:'_blank',textContent:$t('Getting Your APIKEY')}),
+                ]),
+                this.dialog_div,
+            ])
+        );
+	}
+
+    createButtons() {
+		const btns = super.createButtons();
+        btns.unshift($el('button',{
+            type:'button',
+            textContent:$t('Save Account Info'),
+            onclick:_=>{
+                let missing = true
+                for(let i=0;i<api_keys.length;i++){
+                    if(!api_keys[i].key) {
+                        missing = false
+                        break
+                    }
+                }
+                if(!missing){
+                    toast.error($t('APIKEY is not Empty'))
+                }
+                else {
+                    const body = new FormData();
+                    body.append('api_keys', JSON.stringify(api_keys));
+                    api.fetchApi('/easyuse/stability/set_api_keys', {
+                        method: 'POST',
+                        body
+                    }).then(res => {
+                        if (res.status == 200) {
+                            toast.success($t('Save Succeed'))
+
+                        } else {
+                            toast.error($t('Save Failed'))
+                        }
+                    })
+                }
+            }
+        }))
+        btns.unshift($el('button',{
+            type:'button',
+            textContent:$t('Add Account'),
+            onclick:_=>{
+                const name = 'Account '+(api_keys.length).toString()
+                api_keys.push({name,key:''})
+                const item = this.addItem(api_keys.length - 1)
+                this.lists.push(item)
+                this.dialog_div.appendChild(item)
+            }
+        }))
+        return btns
+    }
+}
+
+
+app.registerExtension({
+    name: 'comfy.easyUse.account',
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if(nodeData.name == 'easy stableDiffusion3API'){
+
+            const onNodeCreated = nodeType.prototype.onNodeCreated;
+            nodeType.prototype.onNodeCreated = async function() {
+                onNodeCreated ? onNodeCreated?.apply(this, arguments) : undefined;
+                const seed_widget = this.widgets.find(w => ['seed_num','seed'].includes(w.name))
+				const seed_control = this.widgets.find(w=> ['control_before_generate','control_after_generate'].includes(w.name))
+                let model_widget = this.widgets.find(w => w.name == 'model')
+                model_widget.callback = value =>{
+                    cost_widget.value = '-'+api_cost[value]
+                }
+                const cost_widget = this.addWidget('text', 'cost_credit', '0', _=>{
+                },{
+                    serialize:false,
+                })
+                cost_widget.disabled = true
+                setTimeout(_=>{
+                    if(seed_control.name == 'control_before_generate' && seed_widget.value === 0){
+                        seed_widget.value = Math.floor(Math.random() * 4294967294)
+				    }
+                    cost_widget.value = '-'+api_cost[model_widget.value]
+                },100)
+                let user_div = $el('div.easyuse-account-user', [$t('Loading UserInfo...')])
+                let account = this.addDOMWidget('account',"btn",$el('div.easyuse-account',user_div));
+                // 更新balance信息
+                api.addEventListener('stable-diffusion-api-generate-succeed', async ({detail}) => {
+                    let remarkDiv = user_div.querySelectorAll('.remark')
+                    if(remarkDiv && remarkDiv[0]){
+                        const credits = detail?.model ? api_cost[detail.model] : 0
+                        if(credits) {
+                            let balance = accSub(parseFloat(remarkDiv[0].innerText.replace(/Credits: /g,'')),credits)
+                            if(balance>0){
+                                remarkDiv[0].innerText = 'Credits: '+ balance.toString()
+                            }
+                        }
+                    }
+                    await sleep(10000)
+                    const res = await api.fetchApi('/easyuse/stability/balance')
+                    if(res.status == 200){
+                        const data = await res.json()
+                        if(data?.balance){
+                            const credits = data.balance?.credits || 0
+                            if(remarkDiv && remarkDiv[0]){
+                                remarkDiv[0].innerText = 'Credits: ' + credits
+                            }
+                        }
+                    }
+                })
+                // 获取api_keys
+                const res = await api.fetchApi('/easyuse/stability/api_keys')
+                if (res.status == 200){
+                    let data = await res.json()
+                    api_keys = data.keys
+                    api_current = data.current
+                    if (api_keys.length > 0 && api_current!==undefined){
+                        const api_key = api_keys[api_current]['key']
+                        const api_name = api_keys[api_current]['name']
+                        if(!api_key){
+                            user_div.replaceChildren(
+                                $el('div.easyuse-account-user-info', {
+                                    onclick:_=>{
+                                        new AccountDialog().show(user_div);
+                                    }
+                                },[
+                                    $el('div.user',[
+                                        $el('div.avatar', '😀'),
+                                        $el('div.info', [
+                                            $el('h5.name', api_name),
+                                            $el('h6.remark',$t('Click to set the APIKEY first'))
+                                        ])
+                                    ]),
+                                    $el('div.edit', {textContent:$t('Edit')})
+                                ])
+                            )
+                        }else{
+                            // 获取账号信息
+                            const res = await  api.fetchApi('/easyuse/stability/user_info')
+                            if(res.status == 200){
+                                const data = await res.json()
+                                if(data?.account && data?.balance){
+                                    const avatar = data.account?.profile_picture || null
+                                    const email = data.account?.email || null
+                                    const credits = data.balance?.credits || 0
+                                    user_div.replaceChildren(
+                                        $el('div.easyuse-account-user-info', {
+                                            onclick:_=>{
+                                                new AccountDialog().show(user_div);
+                                            }
+                                        },[
+                                            $el('div.user',[
+                                                $el('div.avatar', avatar ? [$el('img',{src:avatar})] : '😀'),
+                                                $el('div.info', [
+                                                    $el('h5.name', email),
+                                                    $el('h6.remark','Credits: '+ credits)
+                                                ])
+                                            ]),
+                                            $el('div.edit', {textContent:$t('Edit')})
+                                        ])
+                                    )
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+})
@@ -1,6 +1,6 @@
-import {app} from "/scripts/app.js";
-import {api} from "/scripts/api.js";
-import {$el} from "/scripts/ui.js";
+import {app} from "../../../../scripts/app.js";
+import {api} from "../../../../scripts/api.js";
+import {$el} from "../../../../scripts/ui.js";
 import {$t} from "../common/i18n.js";
 import {getExtension, spliceExtension} from '../common/utils.js'
 import {toast} from "../common/toast.js";
@@ -23,6 +23,7 @@ export function addMenuNestSubSetting(app) {

 const getEnableMenuNestSub = _ => app.ui.settings.getSettingValue(setting_id, enableMenuNestSub)

+
 const Loaders = ['easy fullLoader','easy a1111Loader','easy comfyLoader']
 app.registerExtension({
    name:"comfy.easyUse.contextMenu",
@@ -39,7 +40,7 @@ app.registerExtension({
        }
        const existingContextMenu = LiteGraph.ContextMenu;
        LiteGraph.ContextMenu = function(values,options){
-            const threshold = 15;
+            const threshold = 10;
            const enabled = getEnableMenuNestSub();
            if(!enabled || (values?.length || 0) <= threshold || !(options?.callback) || values.some(i => typeof i !== 'string')){
                if(enabled){
@@ -1,6 +1,6 @@
-import { app } from "/scripts/app.js";
-import { api } from "/scripts/api.js";
-import { ComfyWidgets } from "/scripts/widgets.js";
+import { app } from "../../../../scripts/app.js";
+import { api } from "../../../../scripts/api.js";
+import { ComfyWidgets } from "../../../../scripts/widgets.js";
 import { toast} from "../common/toast.js";
 import { $t } from '../common/i18n.js';

@@ -51,18 +51,18 @@ function widgetLogic(node, widget) {
 		updateNodeHeight(node)
 	}
 	if (widget.name === 'image_output') {
-	    if (widget.value === 'Sender' || widget.value === 'Sender/Save'){
+	    if (widget.value === 'Sender' || widget.value === 'Sender&Save'){
 	        toggleWidget(node, findWidgetByName(node, 'link_id'), true)
 	    }else {
 	        toggleWidget(node, findWidgetByName(node, 'link_id'))
 	    }
-		if (widget.value === 'Hide' || widget.value === 'Preview' || widget.value == 'PreviewChooser' || widget.value === 'Sender') {
+		if (widget.value === 'Hide' || widget.value === 'Preview' || widget.value == 'Preview&Choose' || widget.value === 'Sender') {
 			toggleWidget(node, findWidgetByName(node, 'save_prefix'))
 			toggleWidget(node, findWidgetByName(node, 'output_path'))
 			toggleWidget(node, findWidgetByName(node, 'embed_workflow'))
 			toggleWidget(node, findWidgetByName(node, 'number_padding'))
 			toggleWidget(node, findWidgetByName(node, 'overwrite_existing'))
-		} else if (widget.value === 'Save' || widget.value === 'Hide/Save' || widget.value === 'Sender/Save') {
+		} else if (widget.value === 'Save' || widget.value === 'Hide&Save' || widget.value === 'Sender&Save') {
 			toggleWidget(node, findWidgetByName(node, 'save_prefix'), true)
 			toggleWidget(node, findWidgetByName(node, 'output_path'), true)
 			toggleWidget(node, findWidgetByName(node, 'embed_workflow'), true)
@@ -70,7 +70,7 @@ function widgetLogic(node, widget) {
 			toggleWidget(node, findWidgetByName(node, 'overwrite_existing'), true)
 		}

-		if(widget.value === 'Hide' || widget.value === 'Hide/Save'){
+		if(widget.value === 'Hide' || widget.value === 'Hide&Save'){
 			toggleWidget(node, findWidgetByName(node, 'decode_vae_name'))
 		}else{
 			toggleWidget(node, findWidgetByName(node, 'decode_vae_name'), true)
@@ -111,16 +111,31 @@ function widgetLogic(node, widget) {
 		updateNodeHeight(node)
 	}
 	if (widget.name === 'mode') {
-		let number_to_show = findWidgetByName(node, 'num_loras').value + 1
-		for (let i = 0; i < number_to_show; i++) {
-			if (widget.value === "simple") {
-				toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'), true)
-				toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'))
-				toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'))
-			} else {
-				toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'))
-				toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'), true)
-				toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'), true)}
+		switch (node.comfyClass) {
+			case 'easy loraStack':
+				let number_to_show = findWidgetByName(node, 'num_loras').value + 1
+				for (let i = 0; i < number_to_show; i++) {
+					if (widget.value === "simple") {
+						toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'), true)
+						toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'))
+						toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'))
+					} else {
+						toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'))
+						toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'), true)
+						toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'), true)}
+				}
+				break
+			case 'easy icLightApply':
+				if (widget.value === "Foreground") {
+					toggleWidget(node, findWidgetByName(node, 'lighting'), true)
+					toggleWidget(node, findWidgetByName(node, 'remove_bg'), true)
+					toggleWidget(node, findWidgetByName(node, 'source'))
+				} else {
+					toggleWidget(node, findWidgetByName(node, 'lighting'))
+					toggleWidget(node, findWidgetByName(node, 'source'), true)
+					toggleWidget(node, findWidgetByName(node, 'remove_bg'))
+				}
+				break
 		}
 		updateNodeHeight(node)
 	}
@@ -559,6 +574,7 @@ app.registerExtension({
 			case "easy dynamiCrafterLoader":
 			case "easy loraStack":
 			case "easy latentNoisy":
+			case "easy preSampling":
 			case "easy preSamplingAdvanced":
 			case "easy preSamplingNoiseIn":
 			case "easy preSamplingCustom":
@@ -577,6 +593,8 @@ app.registerExtension({
 			case "easy hiresFix":
 			case "easy detailerFix":
 			case "easy imageRemBg":
+			case "easy imageColorMatch":
+			case "easy loadImageBase64":
 			case "easy XYInputs: Steps":
 			case "easy XYInputs: Sampler/Scheduler":
 			case 'easy XYInputs: Checkpoint':
@@ -587,6 +605,7 @@ app.registerExtension({
 			case "easy rangeFloat":
 			case 'easy latentCompositeMaskedWithCond':
 			case 'easy pipeEdit':
+			case 'easy icLightApply':
 			case 'easy ipadapterApply':
 			case 'easy ipadapterApplyADV':
 			case 'easy ipadapterApplyEncoder':
@@ -804,6 +823,7 @@ app.registerExtension({
 					const pos = this.widgets.findIndex((w) => w.name === "spent_time");
 					if (pos !== -1 && this.widgets[pos]) {
 						const w = this.widgets[pos]
+						console.log(text)
 						w.value = text;
 					}
 				}
@@ -943,6 +963,7 @@ app.registerExtension({
 							seed_control.value = 'fixed'
 						}
 						seed_widget.value = Math.floor(Math.random() * 1125899906842624)
+						app.queuePrompt(0, 1)
 					})
 				}
 			}
@@ -951,9 +972,11 @@ app.registerExtension({
 				onAdded ? onAdded.apply(this, []) : undefined;
 				const seed_widget = this.widgets.find(w => ['seed_num','seed'].includes(w.name))
 				const seed_control = this.widgets.find(w=> ['control_before_generate','control_after_generate'].includes(w.name))
-				if(seed_control.name == 'control_before_generate' && seed_widget.value === 0){
-					seed_widget.value = Math.floor(Math.random() * 1125899906842624)
-				}
+				setTimeout(_=>{
+					if(seed_control.name == 'control_before_generate' && seed_widget.value === 0) {
+						seed_widget.value = Math.floor(Math.random() * 1125899906842624)
+					}
+				},1)
 			}
 		}

@@ -984,7 +1007,7 @@ app.registerExtension({
 			}
 		}

-		if(['easy showAnything', 'easy showTensorShape'].includes(nodeData.name)){
+		if(['easy showAnything', 'easy showTensorShape', 'easy imageInterrogator'].includes(nodeData.name)){
 			function populate(text) {
 				if (this.widgets) {
 					const pos = this.widgets.findIndex((w) => w.name === "text");
@@ -1023,13 +1046,15 @@ app.registerExtension({
 				populate.call(this, message.text);
 			};

-			const onConfigure = nodeType.prototype.onConfigure;
-			nodeType.prototype.onConfigure = function () {
-				onConfigure?.apply(this, arguments);
-				if (this.widgets_values?.length) {
-					populate.call(this, this.widgets_values);
-				}
-			};
+			if(!['easy imageInterrogator'].includes(nodeData.name)) {
+				const onConfigure = nodeType.prototype.onConfigure;
+				nodeType.prototype.onConfigure = function () {
+					onConfigure?.apply(this, arguments);
+					if (this.widgets_values?.length) {
+						populate.call(this, this.widgets_values);
+					}
+				};
+			}
 		}

 		if(nodeData.name == 'easy convertAnything'){
@@ -1,4 +1,4 @@
-import {app} from "/scripts/app.js";
+import {app} from "../../../../scripts/app.js";
 import {$t} from '../common/i18n.js'
 import {CheckpointInfoDialog, LoraInfoDialog} from "../common/model.js";

@@ -6,8 +6,9 @@ const loaders = ['easy fullLoader', 'easy a1111Loader', 'easy comfyLoader']
 const preSampling = ['easy preSampling', 'easy preSamplingAdvanced', 'easy preSamplingDynamicCFG', 'easy preSamplingNoiseIn', 'easy preSamplingCustom', 'easy preSamplingLayerDiffusion', 'easy fullkSampler']
 const kSampler = ['easy kSampler', 'easy kSamplerTiled', 'easy kSamplerInpainting', 'easy kSamplerDownscaleUnet', 'easy kSamplerLayerDiffusion']
 const controlnet = ['easy controlnetLoader', 'easy controlnetLoaderADV', 'easy instantIDApply', 'easy instantIDApplyADV']
-const ipadapter = ['easy ipadapterApply', 'easy ipadapterApplyADV', 'easy ipadapterStyleComposition']
+const ipadapter = ['easy ipadapterApply', 'easy ipadapterApplyADV', 'easy ipadapterStyleComposition', 'easy ipadapterApplyFromParams']
 const positive_prompt = ['easy positive', 'easy wildcards']
+const imageNode = ['easy loadImageBase64', 'LoadImage', 'LoadImageMask']
 const widgetMapping = {
    "positive_prompt":{
        "text": "positive",
@@ -58,6 +59,11 @@ const widgetMapping = {
        "end_at": "end_at",
        "cache_mode": "cache_mode",
        "use_tiled": "use_tiled",
+    },
+    "load_image":{
+        "image":"image",
+        "base64_data":"base64_data",
+        "channel": "channel"
    }
 }
 const inputMapping = {
@@ -131,7 +137,7 @@ const outputMapping = {
        "tiles":"tiles",
        "masks":"masks",
        "ipadapter":"ipadapter"
-    }
+    },
 };

 // 替换节点
@@ -267,11 +273,11 @@ const addMenu = (content, type, nodes_include, nodeType, has_submenu=true) => {
            has_submenu: has_submenu,
            callback: (value, options, e, menu, node) => showSwapMenu(value, options, e, menu, node, type, nodes_include)
        })
-        if(type == 'loaders'){
+        if(type == 'loaders') {
            options.unshift({
                content: $t("💎 View Lora Info..."),
                callback: (value, options, e, menu, node) => {
-                    const widget = node.widgets.find(cate=> cate.name == 'lora_name')
+                    const widget = node.widgets.find(cate => cate.name == 'lora_name')
                    let name = widget.value;
                    if (!name || name == 'None') return
                    new LoraInfoDialog(name).show('loras', name);
@@ -516,7 +522,19 @@ app.registerExtension({
                    }
                }
            })
+            // ckptNames
+            if(nodeData.name == 'easy ckptNames'){
+                options.unshift({
+                    content: $t("💎 View Checkpoint Info..."),
+                    callback: (value, options, e, menu, node) => {
+                        let name = node.widgets[0].value;
+                        if (!name || name == 'None') return
+                        new CheckpointInfoDialog(name).show('checkpoints', name);
+                    }
+                })
+            }
        })
+
        // Swap提示词
        if (positive_prompt.includes(nodeData.name)) {
            addMenu("↪️ Swap EasyPrompt", 'positive_prompt', positive_prompt, nodeType)
@@ -541,6 +559,10 @@ app.registerExtension({
        if (ipadapter.includes(nodeData.name)) {
            addMenu("↪️ Swap EasyIPAdapater", 'ipadapter', ipadapter, nodeType)
        }
+        // Swap Image
+        if (imageNode.includes(nodeData.name)) {
+            addMenu("↪️ Swap LoadImage", 'load_image', imageNode, nodeType)
+        }
    }
 });

@@ -1,17 +1,18 @@
-import { app } from "/scripts/app.js";
-import { api } from "/scripts/api.js";
-import { $el } from "/scripts/ui.js";
+import { app } from "../../../../scripts/app.js";
+import { api } from "../../../../scripts/api.js";
+import { $el } from "../../../../scripts/ui.js";
 import {addPreconnect, addCss} from "../common/utils.js";

 const locale = localStorage['AGL.Locale'] || localStorage['Comfy.Settings.AGL.Locale'] || 'en-US'

 const customThemeColor = "#3f3eed"
-const customThemeColorLight = "#006691"
+const customThemeColorLight = "#008ecb"
 // 增加Slot颜色
 const customPipeLineLink = "#7737AA"
 const customPipeLineSDXLLink = "#7737AA"
 const customIntLink = "#29699C"
 const customXYPlotLink = "#74DA5D"
+const customLoraStackLink = "#94dccd"
 const customXYLink = "#38291f"

 var customLinkColors = JSON.parse(localStorage.getItem('Comfy.Settings.ttN.customLinkColors')) || {};
@@ -20,6 +21,8 @@ if (!customLinkColors["PIPE_LINE_SDXL"] || !LGraphCanvas.link_type_colors["PIPE_
 if (!customLinkColors["INT"] || !LGraphCanvas.link_type_colors["INT"]) {customLinkColors["INT"] = customIntLink;}
 if (!customLinkColors["XYPLOT"] || !LGraphCanvas.link_type_colors["XYPLOT"]) {customLinkColors["XYPLOT"] = customXYPlotLink;}
 if (!customLinkColors["X_Y"] || !LGraphCanvas.link_type_colors["X_Y"]) {customLinkColors["X_Y"] = customXYLink;}
+if (!customLinkColors["LORA_STACK"] || !LGraphCanvas.link_type_colors["LORA_STACK"]) {customLinkColors["LORA_STACK"] = customLoraStackLink;}
+if (!customLinkColors["CONTROL_NET_STACK"] || !LGraphCanvas.link_type_colors["CONTROL_NET_STACK"]) {customLinkColors["CONTROL_NET_STACK"] = customLoraStackLink;}

 localStorage.setItem('Comfy.Settings.easyUse.customLinkColors', JSON.stringify(customLinkColors));

@@ -131,7 +134,6 @@ try{
            settings["AE.highlight"] = false
        }
        // 主题设置
-        console.log(theme_name)
        if(!theme_name && _settings['Comfy.ColorPalette']) {
            theme_name = `"${_settings['Comfy.ColorPalette']}"`
            localStorage.setItem('Comfy.Settings.Comfy.ColorPalette', theme_name)
@@ -748,6 +750,7 @@ const NODE_COLORS = {
    "easy promptReplace":"cyan",
    "easy XYInputs: Seeds++ Batch": customXYLink,
    "easy XYInputs: ModelMergeBlocks": customXYLink,
+    'easy textSwitch': "pale_blue"
 }

 function setNodeColors(node, theme) {
@@ -1,7 +1,7 @@
 // 1.0.2
-import { app } from "/scripts/app.js";
-import { GroupNodeConfig } from "/extensions/core/groupNode.js";
-import { api } from "/scripts/api.js";
+import { app } from "../../../../scripts/app.js";
+import { GroupNodeConfig } from "../../../../extensions/core/groupNode.js";
+import { api } from "../../../../scripts/api.js";
 import { $t } from "../common/i18n.js"

 const nodeTemplateShortcutId = "Comfy.EasyUse.NodeTemplateShortcut"
@@ -1,5 +1,5 @@
-import { app } from "/scripts/app.js";
-import { applyTextReplacements } from "/scripts/utils.js";
+import { app } from "../../../../scripts/app.js";
+import { applyTextReplacements } from "../../../../scripts/utils.js";

 const extraNodes = ["easy imageSave", "easy fullkSampler", "easy kSampler", "easy kSamplerTiled","easy kSamplerInpainting", "easy kSamplerDownscaleUnet", "easy kSamplerSDTurbo","easy detailerFix"]

@@ -0,0 +1,136 @@
+import {app} from "../../../../scripts/app.js";
+import {$el} from "../../../../scripts/ui.js";
+import {$t} from "../common/i18n.js";
+import {findWidgetByName, toggleWidget} from "../common/utils.js";
+
+
+const tags = {
+    "selfie_multiclass_256x256": ["Background", "Hair", "Body", "Face", "Clothes", "Others",],
+    "human_parsing_lip":["Background","Hat","Hair","Glove","Sunglasses","Upper-clothes","Dress","Coat","Socks","Pants","Jumpsuits","Scarf","Skirt","Face","Left-arm","Right-arm","Left-leg","Right-leg","Left-shoe","Right-shoe"],
+}
+function getTagList(tags) {
+    let rlist=[]
+    tags.forEach((k,i) => {
+        rlist.push($el(
+            "label.easyuse-prompt-styles-tag",
+            {
+                dataset: {
+                    tag: i,
+                    name: $t(k),
+                    index: i
+                },
+                $: (el) => {
+                    el.children[0].onclick = () => {
+                        el.classList.toggle("easyuse-prompt-styles-tag-selected");
+                    };
+                },
+            },
+            [
+                $el("input",{
+                    type: 'checkbox',
+                    name: i
+                }),
+                $el("span",{
+                    textContent: $t(k),
+                })
+            ]
+        ))
+    });
+    return rlist
+}
+
+
+app.registerExtension({
+    name: 'comfy.easyUse.seg',
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+
+        if (nodeData.name == 'easy humanSegmentation') {
+            // 创建时
+            const onNodeCreated = nodeType.prototype.onNodeCreated;
+            nodeType.prototype.onNodeCreated = function () {
+                onNodeCreated ? onNodeCreated?.apply(this, arguments) : undefined;
+                const method = this.widgets.findIndex((w) => w.name == 'method');
+                const list = $el("ul.easyuse-prompt-styles-list.no-top", []);
+                let method_values = ''
+                this.setProperty("values", [])
+
+                let selector = this.addDOMWidget('mask_components',"btn",$el('div.easyuse-prompt-styles',[list]))
+
+                Object.defineProperty(this.widgets[method],'value',{
+                    set:(value)=>{
+                        method_values = value
+                        if(method_values){
+                            selector.element.children[0].innerHTML = ''
+                            if(method_values == 'selfie_multiclass_256x256'){
+                                toggleWidget(this, findWidgetByName(this, 'confidence'), true)
+                                this.setSize([300, 260]);
+                            }else{
+                                toggleWidget(this, findWidgetByName(this, 'confidence'))
+                                this.setSize([300, 500]);
+                            }
+                            let list = getTagList(tags[method_values]);
+                            selector.element.children[0].append(...list)
+                        }
+                    },
+                    get: () => {
+                        return method_values
+                    }
+                })
+
+                let mask_select_values = ''
+
+                Object.defineProperty(selector, "value", {
+                    set: (value) => {
+                        setTimeout(_=>{
+                            selector.element.children[0].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => {
+                                let arr = value.split(',')
+                                if (arr.includes(el.dataset.tag)) {
+                                    el.classList.add("easyuse-prompt-styles-tag-selected");
+                                    el.children[0].checked = true
+                                }
+                            })
+                        },100)
+                    },
+                    get: () => {
+                        selector.element.children[0].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => {
+                            if(el.classList.value.indexOf("easyuse-prompt-styles-tag-selected")>=0){
+                                if(!this.properties["values"].includes(el.dataset.tag)){
+                                    this.properties["values"].push(el.dataset.tag);
+                                }
+                            }else{
+                                if(this.properties["values"].includes(el.dataset.tag)){
+                                    this.properties["values"]= this.properties["values"].filter(v=>v!=el.dataset.tag);
+                                }
+                            }
+                        });
+                        mask_select_values = this.properties["values"].join(',');
+                        return mask_select_values;
+                    }
+                });
+
+                let old_values = ''
+                let mask_lists_dom = selector.element.children[0]
+
+                // 初始化
+                setTimeout(_=>{
+                    if(!method_values) {
+                        method_values = 'selfie_multiclass_256x256'
+                        selector.element.children[0].innerHTML = ''
+                        // 重新排序
+                        let list = getTagList(tags[method_values]);
+                        selector.element.children[0].append(...list)
+                    }
+                    if(method_values == 'selfie_multiclass_256x256'){
+                        toggleWidget(this, findWidgetByName(this, 'confidence'), true)
+                        this.setSize([300, 260]);
+                    }else{
+                        toggleWidget(this, findWidgetByName(this, 'confidence'))
+                        this.setSize([300, 500]);
+                    }
+                },1)
+
+                return onNodeCreated;
+            }
+        }
+    }
+})
@@ -1,7 +1,8 @@
 // 1.0.3
-import { app } from "/scripts/app.js";
-import { api } from "/scripts/api.js";
-import { $el } from "/scripts/ui.js";
+import { app } from "../../../../scripts/app.js";
+import { api } from "../../../../scripts/api.js";
+import { $el } from "../../../../scripts/ui.js";
+import { $t } from "../common/i18n.js";

 // 获取风格列表
 let styles_list_cache = {}
@@ -91,10 +92,11 @@ async function displayImage(imgName, styleName) {
            img.src = empty_img
        }
    }
-    var x = e.pageX-pxy.x-100;
-    var y = e.pageY-pxy.y+25;
-    img.style.left =  x+"px";
-    img.style.top =  y+"px";
+    var scale = app?.canvas?.ds?.scale || 1;
+    var x = (e.pageX-pxy.x-100)/scale;
+    var y = (e.pageY-pxy.y+25)/scale;
+    img.style.left = x+"px";
+    img.style.top = y+"px";
    img.style.display = "block";
    img.style.borderRadius = "10px";
    img.style.borderColor = "var(--fg-color)"
@@ -118,14 +120,13 @@ app.registerExtension({
                onNodeCreated ? onNodeCreated?.apply(this, arguments) : undefined;
                const styles_id =  this.widgets.findIndex((w) => w.name == 'styles');
                const language = localStorage['AGL.Locale'] || localStorage['Comfy.Settings.AGL.Locale'] || 'en-US'
-
                const list = $el("ul.easyuse-prompt-styles-list",[]);
                let styles_values = ''
                this.setProperty("values", [])

                let selector = this.addDOMWidget('select_styles',"btn",$el('div.easyuse-prompt-styles',[$el('div.tools', [
                        $el('button.delete',{
-                            textContent: language == 'zh-CN' ? '清空所有' : 'Empty All',
+                            textContent: $t('Empty All'),
                            style:{},
                            onclick:()=>{
                                selector.element.children[0].querySelectorAll(".search").forEach(el=>{
@@ -145,7 +146,7 @@ app.registerExtension({
                            dir:"ltr",
                            style:{"overflow-y": "scroll"},
                            rows:1,
-                            placeholder:language == 'zh-CN' ? "🔎 在此处输入以搜索样式 ..."  : "🔎 Type here to search styles ...",
+                            placeholder:$t("🔎 Type here to search styles ..."),
                            oninput:(e)=>{
                                let value = e.target.value
                                selector.element.children[1].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => {
@@ -1,10 +1,10 @@
-import {app} from "/scripts/app.js";
-import {api} from "/scripts/api.js";
-import {$el} from "/scripts/ui.js";
+import {app} from "../../../../scripts/app.js";
+import {api} from "../../../../scripts/api.js";
+import {$el} from "../../../../scripts/ui.js";

 const propmts = ["easy wildcards", "easy positive", "easy negative", "easy stylesSelector", "easy promptConcat", "easy promptReplace"]
 const loaders = ["easy a1111Loader", "easy comfyLoader", "easy fullLoader", "easy svdLoader", "easy cascadeLoader", "easy sv3dLoader"]
-const preSamplingNodes = ["easy preSampling", "easy preSamplingAdvanced", "easy preSamplingNoiseIn", "preSamplingCustom", "easy preSamplingDynamicCFG","easy preSamplingSdTurbo", "easy preSamplingLayerDiffusion"]
+const preSamplingNodes = ["easy preSampling", "easy preSamplingAdvanced", "easy preSamplingNoiseIn", "easy preSamplingCustom", "easy preSamplingDynamicCFG","easy preSamplingSdTurbo", "easy preSamplingLayerDiffusion"]
 const kSampler = ["easy kSampler", "easy kSamplerTiled","easy kSamplerInpainting", "easy kSamplerDownscaleUnet", "easy kSamplerSDTurbo"]
 const controlNetNodes = ["easy controlnetLoader", "easy controlnetLoaderADV"]
 const instantIDNodes = ["easy instantIDApply", "easy instantIDApplyADV"]
@@ -1,5 +1,5 @@
-import { app } from "/scripts/app.js";
-import { ComfyWidgets } from "/scripts/widgets.js";
+import { app } from "../../../../scripts/app.js";
+import { ComfyWidgets } from "../../../../scripts/widgets.js";

 const KEY_CODES = { ENTER: 13, ESC: 27, ARROW_DOWN: 40, ARROW_UP: 38 };
 const WIDGET_GAP = -4;
@@ -150,7 +150,6 @@ const cssCode = `
 	border-radius: 7px;
 	text-align: center;
 	text-wrap: balance;
-	text-transform: uppercase;
 }
 .hideInfo-dropdown {
 	position: absolute;
@@ -1,4 +1,4 @@
-import { app } from "/scripts/app.js";
+import { app } from "../../../../scripts/app.js";
 import {removeDropdown, createDropdown} from "../common/dropdown.js";

 function generateNumList(dictionary) {
@@ -1,5 +1,4 @@
 import { app } from "../../../scripts/app.js";
-import { ComfyWidgets } from '/scripts/widgets.js'

 // Node that allows you to tunnel connections for cleaner graphs

@@ -1,15 +1,81 @@
-import { app } from "/scripts/app.js";
-import { api } from "/scripts/api.js";
+import { app } from "../../../../scripts/app.js";
+import { api } from "../../../../scripts/api.js";
+import { ComfyDialog, $el } from "../../../../scripts/ui.js";

 import { restart_from_here } from "./prompt.js";
 import { FlowState } from "./state.js";
 import { send_cancel, send_message, send_onstart, skip_next_restart_message } from "./messaging.js";
 import { display_preview_images, additionalDrawBackground, click_is_in_image } from "./preview.js";
-import {toggleWidget} from "../common/utils.js";
+import {$t} from "../common/i18n.js";
+
+
+class chooserImageDialog extends ComfyDialog {
+
+    constructor() {
+		super();
+        this.node = null
+        this.select_index = []
+        this.dialog_div = null
+	}
+
+    show(image,node){
+        this.select_index = []
+        this.node = node
+
+        const images_div = image.map((img, index) => {
+            const imgEl = $el('img', {
+                src: img.src,
+                onclick: _ => {
+                    if(this.select_index.includes(index)){
+                        this.select_index = this.select_index.filter(i => i !== index)
+                        imgEl.classList.remove('selected')
+                    } else {
+                        this.select_index.push(index)
+                        imgEl.classList.add('selected')
+                    }
+                    if (node.selected.has(index)) node.selected.delete(index);
+                    else node.selected.add(index);
+                }
+            })
+            return imgEl
+        })
+        super.show($el('div.easyuse-chooser-dialog',[
+            $el('h5.easyuse-chooser-dialog-title', $t('Choose images to continue')),
+            $el('div.easyuse-chooser-dialog-images',images_div)
+        ]))
+    }
+    createButtons() {
+        const btns = super.createButtons();
+        btns[0].onclick = _ => {
+            if (FlowState.running()) { send_cancel();}
+            super.close()
+        }
+        btns.unshift($el('button', {
+            type: 'button',
+            textContent: $t('Choose Selected Images'),
+            onclick: _ => {
+                if (FlowState.paused()) {
+                    send_message(this.node.id, [...this.node.selected, -1, ...this.node.anti_selected]);
+                }
+                if (FlowState.idle()) {
+                    skip_next_restart_message();
+                    restart_from_here(this.node.id).then(() => { send_message(this.node.id, [...this.node.selected, -1, ...this.node.anti_selected]); });
+                }
+                super.close()
+            }
+        }))
+        return btns
+    }
+
+}

 function progressButtonPressed() {
    const node = app.graph._nodes_by_id[this.node_id];
    if (node) {
+        const selected = [...node.selected]
+        if(selected?.length>0){
+            node.setProperty('values',selected)
+        }
        if (FlowState.paused()) {
            send_message(node.id, [...node.selected, -1, ...node.anti_selected]);
        }
@@ -17,10 +83,26 @@ function progressButtonPressed() {
            skip_next_restart_message();
            restart_from_here(node.id).then(() => { send_message(node.id, [...node.selected, -1, ...node.anti_selected]); });
        }
+        const maxlength = node.imgs.length;
+        if (FlowState.paused_here(node.id) && selected>0) {
+            node.send_button_widget.name = (selected>1) ? "Progress selected (" + selected + '/' + maxlength  +")" : "Progress selected image";
+        } else if (FlowState.idle() && selected>0) {
+            node.send_button_widget.name = (selected>1) ? "Progress selected (" + selected + '/' + maxlength  +")" : "Progress selected image as restart";
+        }
+        else {
+            node.send_button_widget.name = "";
+        }
    }
 }

-function cancelButtonPressed() { if (FlowState.running()) { send_cancel(); } }
+function cancelButtonPressed() {
+    if (FlowState.running()) { send_cancel();}
+    const node = app.graph._nodes_by_id[this.node_id];
+    if (node) {
+        node.send_button_widget.name = "";
+        node.cancel_button_widget.name = "";
+    }
+}

 app.registerExtension({
    name:'comfy.easyuse.imageChooser',
@@ -29,7 +111,11 @@ app.registerExtension({
    },
    setup(app) {
        function easyuseImageChooser(event) {
-            display_preview_images(event);
+            const {node,image,isKSampler} = display_preview_images(event);
+            if(isKSampler) {
+                const dialog = new chooserImageDialog();
+                dialog.show(image,node)
+            }
        }
        api.addEventListener("easyuse-image-choose", easyuseImageChooser);

@@ -60,9 +146,12 @@ app.registerExtension({
    },

    async nodeCreated(node, app) {
+
        if(node.comfyClass == 'easy imageChooser'){
            node.send_button_widget = node.addWidget("button", "", "", progressButtonPressed, {serialize: false});
            node.cancel_button_widget = node.addWidget("button", "", "", cancelButtonPressed, {serialize: false});
+            node.setProperty('values',[])
+
            /* Capture clicks */
            const org_onMouseDown = node.onMouseDown;

@@ -112,7 +201,7 @@ app.registerExtension({
                if (this.send_button_widget) {
                    this.send_button_widget.node_id = this.id;
                    const selection = ( this.selected ? this.selected.size : 0 ) + ( this.anti_selected ? this.anti_selected.size : 0 )
-                    const maxlength = this.imgs.length;
+                    const maxlength = this.imgs?.length || 0;
                    if (FlowState.paused_here(this.id) && selection>0) {
                        this.send_button_widget.name = (selection>1) ? "Progress selected (" + selection + '/' + maxlength  +")" : "Progress selected image";
                    } else if (FlowState.idle() && selection>0) {
@@ -1,4 +1,4 @@
-import { api } from "/scripts/api.js";
+import { api } from "../../../../scripts/api.js";
 import { FlowState } from "./state.js";

 function send_message_from_pausing_node(message) {
@@ -1,11 +1,14 @@
-import { app } from "/scripts/app.js";
+import { app } from "../../../../scripts/app.js";
+
+const kSampler = ['easy kSampler', 'easy kSamplerTiled', 'easy fullkSampler']

 function display_preview_images(event) {
    const node = app.graph._nodes_by_id[event.detail.id];
    if (node) {
        node.selected = new Set();
        node.anti_selected = new Set();
-        showImages(node, event.detail.urls);
+        const image = showImages(node, event.detail.urls);
+        return {node,image,isKSampler:kSampler.includes(node.type)}
    } else {
        console.log(`Image Chooser Preview - failed to find ${event.detail.id}`)
    }
@@ -20,6 +23,7 @@ function showImages(node, urls) {
        img.src = `/view?filename=${encodeURIComponent(u.filename)}&type=temp&subfolder=${app.getPreviewFormatParam()}`
    })
    node.setSizeForImage?.();
+    return node.imgs
 }

 function drawRect(node, s, ctx) {
@@ -1,4 +1,4 @@
-import { app } from "/scripts/app.js";
+import { app } from "../../../../scripts/app.js";

 function links_with(p, node_id, down, up) {
    const links_with = [];
@@ -1,4 +1,4 @@
-import { app } from "/scripts/app.js";
+import { app } from "../../../../scripts/app.js";

 export class FlowState {
    constructor(){}
Author	SHA1	Message	Date
yolain	7cbc2a2a2b	modify the pyproject.toml file	2024-05-23 13:33:03 +08:00
yolain	63811206c9	Merge pull request #182 from haohaocreates/publish Add Github Action for Publishing to Comfy Registry	2024-05-23 13:30:16 +08:00
yolain	48a4b5bfc6	Merge pull request #183 from haohaocreates/pyproject Add pyproject.toml for Custom Node Registry	2024-05-23 13:30:00 +08:00
yolain	7980f4eef4	fix:compatible with new brushnet versions #181	2024-05-23 12:33:45 +08:00
haohaocreates	b19c8b07ea	chore(pyproject): Add pyproject.toml for Custom Node Registry	2024-05-22 18:08:19 -04:00
haohaocreates	3f506d265c	chore(publish): Add Github Action for Publishing to Comfy Registry	2024-05-22 18:08:16 -04:00
yolain	d8af918e7d	fix:the modal for selecting an image can not be closed #180	2024-05-21 15:54:02 +08:00
yolain	33566e8474	fix:run error when input optional image in easy fullkSampler #178	2024-05-21 15:41:39 +08:00
yolain	ea0350c2dc	fix:preview image offset in easy stylesSelector when zooming the page #176	2024-05-18 15:35:28 +08:00
yolain	24526623fb	fix:iclight cache bug #173	2024-05-17 17:00:39 +08:00
yolain	7715ebfd06	Merge pull request #170 from chenpx976/main feat: optimize GPU memory management and model unloading	2024-05-16 12:52:09 +08:00
color	2ba814c131	feat: optimize GPU memory management and model unloading	2024-05-16 12:35:40 +08:00
yolain	d5ad332666	fix:icLightModel add to cache	2024-05-16 11:57:19 +08:00
yolain	fee7e7bf73	fix:some models were not successfully written to easyCache,resulting in slow secondary diffusion	2024-05-16 01:01:03 +08:00
yolain	01f17ff02b	remove:unnecessary import modules	2024-05-15 17:07:14 +08:00
yolain	da7120219a	fix:diffusers version>0.26.0 use different module #70	2024-05-15 10:50:19 +08:00
yolain	d616d18069	fix:compatible with cg-image-picker #169	2024-05-15 10:04:00 +08:00
yolain	dbf76f288c	Update README.md	2024-05-14 16:49:16 +08:00
yolain	05124006ba	fix:set_clip_options no longer compare version #165	2024-05-13 14:23:55 +08:00
yolain	4586af311c	fix:fooocus+dd wrong	2024-05-13 14:19:33 +08:00
yolain	1cea58c7cf	add:remove_bg in easy icLightApply	2024-05-11 02:13:55 +08:00
yolain	a84f7c4a58	fix:brushnet error in easy presamplingInpainting	2024-05-11 00:42:14 +08:00
yolain	1d9bf86560	add:easy icLightApply	2024-05-10 17:26:04 +08:00
yolain	8d352b85bc	add:easy imageSplitGrid	2024-05-09 16:08:03 +08:00
yolain	403562575c	add:easy imageCropFromMask & imageUncropFromBBOX	2024-05-06 12:52:37 +08:00
yolain	dbe2cd6569	fix:compatibility with comfyui-brushnet new commit	2024-05-05 11:30:41 +08:00
yolain	b3b0a961c5	fix:raise excenption when comfyui-brushnet is not installed	2024-05-04 19:26:18 +08:00
yolain	9dfb8b9c15	fix:layerdiffuse everything config #158	2024-05-04 19:19:01 +08:00
yolain	b4ea58946c	fix:supported brushnet for ays	2024-05-04 01:01:35 +08:00
yolain	513fc4b67e	support for brushnet model loading	2024-05-03 18:24:47 +08:00
yolain	924a16e31c	fix:easy kSamplerInpainting and js file set full relative path	2024-05-02 12:01:56 +08:00
yolain	ad43f8e0bb	fix:preview&choose return new batch #153	2024-04-30 17:24:21 +08:00
yolain	b7e1ce8a3c	fix:easy imageChooser is not working #150	2024-04-29 12:03:09 +08:00
yolain	299090d184	add:denoise value to alignYourStepsScheduler #146	2024-04-29 11:32:49 +08:00
yolain	3aa7bca86b	add:align_your_steps in all easy preSampling	2024-04-26 19:39:26 +08:00
yolain	56de6f0bc9	add:alignYourSteps of scheduler in easy preSamplingCustom #146	2024-04-26 15:35:06 +08:00
yolain	f6b5f5c99c	Upgrade to v1.1.6	2024-04-26 11:55:12 +08:00
yolain	79b81100ff	fix:easy ipadapterApply some changes	2024-04-26 09:19:03 +08:00
yolain	c885fd9bcf	fix:easy ipadapterApply bug	2024-04-25 11:02:20 +08:00
yolain	c46aaa6084	fix:incorrect string preview when refresh page	2024-04-25 00:19:37 +08:00
yolain	0562d4eb0e	add:easy humanSegmentation	2024-04-24 22:47:33 +08:00
yolain	cfa56d36d7	add:easy imageColorMatch	2024-04-24 17:37:48 +08:00
yolain	913cfe73ae	rewrite:easy cleanGPUUsed can force cleanup of models gpu usage	2024-04-24 16:56:27 +08:00
yolain	590161c560	add:easy ipadadpterApplyFromParams	2024-04-23 14:39:38 +08:00
yolain	d674313240	fix:api-key placeholder	2024-04-19 12:19:26 +08:00
yolain	44258090cd	fix:deduct credit when request successful in easy stablediffusion3API	2024-04-19 11:53:49 +08:00
yolain	1222799e8f	add:stableDiffusion3 API node	2024-04-19 11:20:15 +08:00