Upgrade 1.2.1 to comfy regsitry

Update PyProject Toml - License

.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+custom: ["https://afdian.net/a/yolain"]

.gitignore

@@ -7,8 +7,10 @@ wildcards/**
 styles/**
 workflow/**
 autocomplete/**
+web_beta/**
 docs/**
 .vscode/
 .idea/
 mmb-preset.custom.txt
-config.yaml
+config.yaml
+node.tar.gz

README.en.md

@@ -9,9 +9,9 @@
 
 **ComfyUI-Easy-Use** is a simplified node integration package, which is extended on the basis of [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes), and has been integrated and optimized for many mainstream node packages to achieve the purpose of faster and more convenient use of ComfyUI. While ensuring the degree of freedom, it restores the ultimate smooth image production experience that belongs to Stable Diffusion.
 
-<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)
 
-## Introduce
+## 👨🏻‍🎨 Introduce
 
 - Inspire by [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes), which greatly reduces the time cost of tossing workflows。
 - UI interface beautification, the first time you install the user, if you need to use the UI theme, please switch the theme in Settings -> Color Palette and refresh page.
@@ -30,8 +30,58 @@
 - 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
+- Support Stable Diffusion 3 model
+- Support Kolors model
+
+## 👨🏻‍🔧 Installation
+Clone the repo into the **custom_nodes** directory and install the requirements:
+```shell
+#1. Clone the repo
+git clone https://github.com/yolain/ComfyUI-Easy-Use
+#2. Install the requirements
+Double-click install.bat to install the required dependencies
+```
+
+## ☕️ Plan
+
+- [ ] Updated new front-end code for easier maintenance
+  - [x] Maintain css styles using sass
+  - [ ] Optimize existing extensions
+  - [ ] Add new components
+  - [ ] Add light theme
+- [ ] Upload new workflows to [ComfyUI-Yolain-Workflows](https://github.com/yolain/ComfyUI-Yolain-Workflows) and translate readme to english version.
+- [ ] Write gitbook with more detailed function introdution
+
+## 📜 Changelog
+
+**v1.2.1**
+
+- Added `easy ipadapterApplyFaceIDKolors`
+- Added **inspyrenet** to `easy imageRemBg` 
+- Added `easy controlnetLoader++`
+- Added **PLUS (kolors genernal)** and **FACEID PLUS KOLORS** preset to `easy ipadapterApply` and `easy ipadapterApplyADV` (Supported kolors ipadapter）
+- Added `easy kolorsLoader` - Code based on [MinusZoneAI](https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ)'s and [kijai](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper)'s repo, thanks for their contribution.
+
+**v1.2.0**
+
+- Added `easy pulIDApply` and `easy pulIDApplyADV`
+- Added `easy huanyuanDiTLoader` and `easy pixArtLoader`
+- Added **easy sliderControl** - Slider control node, which can currently be used to control the parameters of ipadapterMS (double-click the slider to reset to default)
+- Added **layer_weights** in `easy ipadapterApplyADV`
+
+**v1.1.9**
+
+- Added **gitsScheduler**
+- Added `easy imageBatchToImageList` and `easy imageListToImageBatch` 
+- Recursive subcategories nested for models
+- Support for Stable Diffusion 3 model
+- Added `easy applyInpaint` - All inpainting mode in this node
+
+**v1.1.8**
+
+- Added `easy controlnetStack`
+- Added `easy applyBrushNet` - [Workflow Example](https://github.com/yolain/ComfyUI-Yolain-Workflows/blob/main/workflows/2_advanced/2-4inpainting/2-4brushnet_1.1.8.json)
+- Added `easy applyPowerPaint` - [Workflow Example](https://github.com/yolain/ComfyUI-Yolain-Workflows/blob/main/workflows/2_advanced/2-4inpainting/2-4powerpaint_outpaint_1.1.8.json)
 
 **v1.1.7**
 
@@ -107,7 +157,8 @@
 - 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**
+<details>
+<summary><b>v1.1.0</b></summary>
 
 - Added `easy imageSplitList` - to split every N images
 - Added `easy preSamplingDiffusionADDTL` - It can modify foreground、background or blended additional prompt
@@ -121,7 +172,7 @@
 - Fixed `easy wildcards` When LoRa is not filled in completely, LoRa is not automatically retrieved, resulting in failure to load LoRa
 - Fixed the issue that 'BREAK' non-initiation when didn't use a1111 prompt style
 - Fixed `easy instantIDApply` mask not input right
-
+</details>
 
 <details>
 <summary><b>v1.0.9</b></summary>
@@ -333,34 +384,8 @@ Disclaimer: Opened source was not easy. I have a lot of respect for the contribu
 | 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       | 
+| easy kolorsLoader              | [ComfyUI-Kolors-MZ](https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ) | kolorsLoader            |
 
-## Workflow Examples
-
-### Text to image
-
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/Simple/text_to_image.png">
-
-### Image to image + controlnet
-
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/Simple/image_to_image_controlnet.png">
-
-### SDTurbo + HiresFix + SVD
-
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/Simple/sdturbo_hiresfix_svd.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
-#### Text to image
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/StableCascade/text_to_image.png">
-
-#### Image to image
-<img src="https://raw.githubusercontent.com/yolain/yolain-comfyui-workflow/main/Workflows/StableCascade/image_to_image.png">
 
 ## Credits
 
@@ -382,6 +407,17 @@ Disclaimer: Opened source was not easy. I have a lot of respect for the contribu
 
 [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID) - Face migration
 
+[ComfyUI_PuLID](https://github.com/cubiq/PuLID_ComfyUI) - Face migration
+
 [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) - pyssss🐍
 
 [cg-image-picker](https://github.com/chrisgoringe/cg-image-picker) - Image Preview Chooser
+
+[ComfyUI_ExtraModels](https://github.com/city96/ComfyUI_ExtraModels) - DiT custom nodes
+
+
+##  🌟Stargazers
+
+My gratitude extends to the generous souls who bestow a star. Your support is much appreciated!
+
+[![Stargazers repo roster for @yolain/ComfyUI-Easy-Use](https://reporoster.com/stars/yolain/ComfyUI-Easy-Use)](https://github.com/yolain/ComfyUI-Easy-Use/stargazers)

README.md

@@ -6,7 +6,7 @@
 
 # ComfyUI Easy Use
 
-[![Bilibili Badge](https://img.shields.io/badge/1.0版本-00A1D6?style=for-the-badge&logo=bilibili&logoColor=white&link=https://www.bilibili.com/video/BV1Wi4y1h76G)](https://www.bilibili.com/video/BV1Wi4y1h76G)
+[![Bilibili Badge](https://img.shields.io/badge/1.1版本-00A1D6?style=for-the-badge&logo=bilibili&logoColor=white&link=https://www.bilibili.com/video/BV1w6421F7Uv)](https://www.bilibili.com/video/BV1w6421F7Uv)
 [![Bilibili Badge](https://img.shields.io/badge/基本介绍-00A1D6?style=for-the-badge&logo=bilibili&logoColor=white&link=https://www.bilibili.com/video/BV1vQ4y1G7z7)](https://www.bilibili.com/video/BV1vQ4y1G7z7/)
 </div>
 
@@ -14,7 +14,7 @@
 
 [![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
+## 👨🏻‍🎨 特色介绍
 
 - 沿用了 [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) 的思路，大大减少了折腾工作流的时间成本。
 - UI界面美化，首次安装的用户，如需使用UI主题，请在 Settings -> Color Palette 中自行切换主题并**刷新页面**即可
@@ -36,8 +36,63 @@
 - 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)
+- 中文提示词自动识别，使用[opus-mt-zh-en模型](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en)
+- 支持 sd3 模型
+- 支持 kolors 模型
 
-## 更新日志
+## 👨🏻‍🔧 安装
+
+1. 将存储库克隆到 **custom_nodes** 目录并安装依赖
+```shell
+#1. git下载
+git clone https://github.com/yolain/ComfyUI-Easy-Use
+#2. 安装依赖
+双击install.bat安装依赖
+```
+
+## ☕️ 计划
+
+- [ ] 更新便于维护的新前端代码
+  - [x] 使用sass维护css样式
+  - [ ] 对原有扩展进行优化
+  - [ ] 增加新的组件（如节点时间统计等） 
+  - [ ] 增加浅色主题
+- [ ] 在[ComfyUI-Yolain-Workflows](https://github.com/yolain/ComfyUI-Yolain-Workflows)中上传更多的工作流（如kolors,sd3等），并更新english版本的readme  
+- [ ] 更详细功能介绍的 gitbook   
+
+## 📜 更新日志
+
+**v1.2.1**
+
+- 增加 `easy ipadapterApplyFaceIDKolors`
+- `easy ipadapterApply` 和 `easy ipadapterApplyADV` 增加 **PLUS (kolors genernal)** 和 **FACEID PLUS KOLORS** 预置项
+- `easy imageRemBg` 增加 **inspyrenet** 选项
+- 增加 `easy controlnetLoader++`
+- 去除 `easy positive` `easy negative` 等prompt节点的自动将中文翻译功能，自动翻译仅在 `easy a1111Loader` 等不支持中文TE的加载器中生效
+- 增加 `easy kolorsLoader` - 可灵加载器，参考了 [MinusZoneAI](https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ) 和 [kijai](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper) 的代码。
+
+**v1.2.0**
+
+- 增加 `easy pulIDApply` 和 `easy pulIDApplyADV`
+- 增加 `easy hunyuanDiTLoader` 和 `easy pixArtLoader`
+- 当新菜单的位置在上或者下时增加上 crystools 的显示，推荐开两个就好（如果后续crystools有更新UI适配我可能会删除掉）
+- 增加 **easy sliderControl** - 滑块控制节点，当前可用于控制ipadapterMS的参数 (双击滑块可重置为默认值)
+- 增加 **layer_weights** 属性在 `easy ipadapterApplyADV` 节点
+
+**v1.1.9**
+
+- 增加 新的调度器 **gitsScheduler**
+- 增加 `easy imageBatchToImageList` 和 `easy imageListToImageBatch` (修复Impact版的一点小问题)
+- 递归模型子目录嵌套
+- 支持 sd3 模型 
+- 增加 `easy applyInpaint` - 局部重绘全模式节点 (相比与之前的kSamplerInpating节点逻辑会更合理些)
+
+**v1.1.8**
+
+- 增加中文提示词自动翻译，使用[opus-mt-zh-en模型](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en), 默认已对wildcard、lora正则处理, 其他需要保留的中文，可使用`@你的提示词@`包裹 (若依赖安装完成后报错, 请重启)，测算大约会占0.3GB显存
+- 增加 `easy controlnetStack` - controlnet堆
+- 增加 `easy applyBrushNet` - [示例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows/blob/main/workflows/2_advanced/2-4inpainting/2-4brushnet_1.1.8.json)
+- 增加 `easy applyPowerPaint` - [示例参考](https://github.com/yolain/ComfyUI-Yolain-Workflows/blob/main/workflows/2_advanced/2-4inpainting/2-4powerpaint_outpaint_1.1.8.json)
 
 **v1.1.7**
 
@@ -118,7 +173,9 @@
 - 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**
+
+<details>
+<summary><b>v1.1.0</b></summary>
 
 - 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   
@@ -131,6 +188,7 @@
 - 修复 `easy wildcards` 读取lora未填写完整路径时未自动检索导致加载lora失败的问题
 - 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
+</details>
 
 <details>
 <summary><b>v1.0.9</b></summary>
@@ -343,6 +401,7 @@
 | 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  | 
 | easy icLightApply              | [ComfyUI-IC-Light](https://github.com/huchenlei/ComfyUI-IC-Light) | ICLightApply等           |
+| easy kolorsLoader              | [ComfyUI-Kolors-MZ](https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ) | kolorsLoader            |
 
 ## Credits
 
@@ -366,8 +425,18 @@
 
 [ComfyUI_InstantID](https://github.com/cubiq/ComfyUI_InstantID) - 人脸迁移
 
+[ComfyUI_PuLID](https://github.com/cubiq/PuLID_ComfyUI) - 人脸迁移
+
 [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
+
+[ComfyUI_ExtraModels](https://github.com/city96/ComfyUI_ExtraModels) - DiT架构相关节点（Pixart、混元DiT等）
+
+##  🌟Stargazers
+
+My gratitude extends to the generous souls who bestow a star. Your support is much appreciated!
+
+[![Stargazers repo roster for @yolain/ComfyUI-Easy-Use](https://reporoster.com/stars/yolain/ComfyUI-Easy-Use)](https://github.com/yolain/ComfyUI-Easy-Use/stargazers)

__init__.py

@@ -1,7 +1,6 @@
-__version__ = "1.1.7"
+__version__ = "1.2.1"
 
 import os
-import glob
 import folder_paths
 import importlib
 from pathlib import Path
@@ -26,7 +25,7 @@ cwd_path = os.path.dirname(os.path.realpath(__file__))
 comfy_path = folder_paths.base_path
 
 #Wildcards读取
-from .py.wildcards import read_wildcard_dict
+from .py.libs.wildcards import read_wildcard_dict
 wildcards_path = os.path.join(os.path.dirname(__file__), "wildcards")
 if os.path.exists(wildcards_path):
     read_wildcard_dict(wildcards_path)
@@ -43,36 +42,7 @@ else:
     os.mkdir(styles_path)
     os.mkdir(samples_path)
 
-#合并autocomplete覆盖到pyssss包
-pyssss_path = os.path.join(comfy_path, "custom_nodes", "ComfyUI-Custom-Scripts", "user")
-combine_folder = os.path.join(cwd_path, "autocomplete")
-if os.path.exists(combine_folder):
-    pass
-else:
-    os.mkdir(combine_folder)
-if os.path.exists(pyssss_path):
-    output_file = os.path.join(pyssss_path, "autocomplete.txt")
-    # 遍历 combine 目录下的所有 txt 文件，读取内容并合并
-    merged_content = ''
-    for file_path in glob.glob(os.path.join(combine_folder, '*.txt')):
-        with open(file_path, 'r', encoding='utf-8', errors='ignore') as file:
-            try:
-                file_content = file.read()
-                merged_content += file_content + '\n'
-            except UnicodeDecodeError:
-                pass
-    # 备份之前的autocomplete
-    # bak_file = os.path.join(pyssss_path, "autocomplete.txt.bak")
-    # if os.path.exists(bak_file):
-    #     pass
-    # elif os.path.exists(output_file):
-    #     shutil.copy(output_file, bak_file)
-    if merged_content != '':
-        # 将合并的内容写入目标文件 autocomplete.txt，并指定编码为 utf-8
-        with open(output_file, 'w', encoding='utf-8') as target_file:
-            target_file.write(merged_content)
-
-# ComfyUI-Easy-PS相关 (需要把模型预览图暴露给PS读取，此处借鉴了 AIGODLIKE-ComfyUI-Studio 的部分代码)
+# Model thumbnails
 from .py.libs.add_resources import add_static_resource
 from .py.libs.model import easyModelManager
 model_config = easyModelManager().models_config

install.bat

@@ -0,0 +1,26 @@
+@echo off
+
+set "requirements_txt=%~dp0\requirements.txt"
+set "requirements_repair_txt=%~dp0\repair_dependency_list.txt"
+set "python_exec=..\..\..\python_embeded\python.exe"
+set "aki_python_exec=..\..\python\python.exe"
+
+echo Installing EasyUse Requirements...
+
+if exist "%python_exec%" (
+    echo Installing with ComfyUI Portable
+    "%python_exec%" -s -m pip install -r "%requirements_txt%"
+)^
+else if exist "%aki_python_exec%" (
+    echo Installing with ComfyUI Aki
+    "%aki_python_exec%" -s -m pip install -r "%requirements_txt%"
+    for /f "delims=" %%i in (%requirements_repair_txt%) do (
+        %aki_python_exec% -s -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple "%%i"
+        )
+)^
+else (
+    echo Installing with system Python
+    pip install -r "%requirements_txt%"
+)
+
+pause

prestartup_script.py

@@ -22,12 +22,16 @@ add_folder_path_and_extensions("mmdets", [os.path.join(model_path, "mmdets")], f
 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("pulid", [os.path.join(model_path, "pulid")], 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("prompt_generator", [os.path.join(model_path, "prompt_generator")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("t5", [os.path.join(model_path, "t5")], folder_paths.supported_pt_extensions)
+add_folder_path_and_extensions("llm", [os.path.join(model_path, "LLM")], 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)

py/api.py

@@ -10,6 +10,8 @@ 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, cleanGPUUsedForce, get_local_filepath
+from .libs.cache import remove_cache
+from .libs.translate import has_chinese, zh_to_en
 
 try:
     import aiohttp
@@ -23,11 +25,21 @@ except ImportError:
 def cleanGPU(request):
     try:
         cleanGPUUsedForce()
+        remove_cache('*')
         return web.Response(status=200)
     except Exception as e:
         return web.Response(status=500)
         pass
 
+@PromptServer.instance.routes.post("/easyuse/translate")
+async def translate(request):
+    post = await request.post()
+    text = post.get("text")
+    if has_chinese(text):
+        return web.json_response({"text": zh_to_en([text])[0]})
+    else:
+        return web.json_response({"text": text})
+
 @PromptServer.instance.routes.get("/easyuse/reboot")
 def reboot(request):
     try:
@@ -87,6 +99,10 @@ async def getStylesList(request):
                         nd['name_cn'] = cn_data[key] if key in cn_data else key
                     nd["name"] = d['name']
                     nd['imgName'] = img_name
+                    if "prompt" in d:
+                        nd['prompt'] = d['prompt']
+                    if "negative_prompt" in d:
+                        nd['negative_prompt'] = d['negative_prompt']
                     ndata.append(nd)
                 return web.json_response(ndata)
     return web.Response(status=400)

py/brushnet/__init__.py

@@ -0,0 +1,806 @@
+#credit to nullquant for this module
+#from https://github.com/nullquant/ComfyUI-BrushNet
+
+import os
+import types
+
+import torch
+from accelerate import init_empty_weights, load_checkpoint_and_dispatch
+
+import comfy
+
+from .model import BrushNetModel, PowerPaintModel
+from .model_patch import add_model_patch_option, patch_model_function_wrapper
+from .powerpaint_utils import TokenizerWrapper, add_tokens
+
+cwd_path = os.path.dirname(os.path.realpath(__file__))
+brushnet_config_file = os.path.join(cwd_path, 'config', 'brushnet.json')
+brushnet_xl_config_file = os.path.join(cwd_path, 'config', 'brushnet_xl.json')
+powerpaint_config_file = os.path.join(cwd_path, 'config', 'powerpaint.json')
+
+sd15_scaling_factor = 0.18215
+sdxl_scaling_factor = 0.13025
+
+ModelsToUnload = [comfy.sd1_clip.SD1ClipModel, comfy.ldm.models.autoencoder.AutoencoderKL]
+
+class BrushNet:
+
+    # Check models compatibility
+    def check_compatibilty(self, model, brushnet):
+        is_SDXL = False
+        is_PP = False
+        if isinstance(model.model.model_config, comfy.supported_models.SD15):
+            print('Base model type: SD1.5')
+            is_SDXL = False
+            if brushnet["SDXL"]:
+                raise Exception("Base model is SD15, but BrushNet is SDXL type")
+            if brushnet["PP"]:
+                is_PP = True
+        elif isinstance(model.model.model_config, comfy.supported_models.SDXL):
+            print('Base model type: SDXL')
+            is_SDXL = True
+            if not brushnet["SDXL"]:
+                raise Exception("Base model is SDXL, but BrushNet is SD15 type")
+        else:
+            print('Base model type: ', type(model.model.model_config))
+            raise Exception("Unsupported model type: " + str(type(model.model.model_config)))
+
+        return (is_SDXL, is_PP)
+
+    def check_image_mask(self, image, mask, name):
+        if len(image.shape) < 4:
+            # image tensor shape should be [B, H, W, C], but batch somehow is missing
+            image = image[None, :, :, :]
+
+        if len(mask.shape) > 3:
+            # mask tensor shape should be [B, H, W] but we get [B, H, W, C], image may be?
+            # take first mask, red channel
+            mask = (mask[:, :, :, 0])[:, :, :]
+        elif len(mask.shape) < 3:
+            # mask tensor shape should be [B, H, W] but batch somehow is missing
+            mask = mask[None, :, :]
+
+        if image.shape[0] > mask.shape[0]:
+            print(name, "gets batch of images (%d) but only %d masks" % (image.shape[0], mask.shape[0]))
+            if mask.shape[0] == 1:
+                print(name, "will copy the mask to fill batch")
+                mask = torch.cat([mask] * image.shape[0], dim=0)
+            else:
+                print(name, "will add empty masks to fill batch")
+                empty_mask = torch.zeros([image.shape[0] - mask.shape[0], mask.shape[1], mask.shape[2]])
+                mask = torch.cat([mask, empty_mask], dim=0)
+        elif image.shape[0] < mask.shape[0]:
+            print(name, "gets batch of images (%d) but too many (%d) masks" % (image.shape[0], mask.shape[0]))
+            mask = mask[:image.shape[0], :, :]
+
+        return (image, mask)
+
+    # Prepare image and mask
+    def prepare_image(self, image, mask):
+
+        image, mask = self.check_image_mask(image, mask, 'BrushNet')
+
+        print("BrushNet image.shape =", image.shape, "mask.shape =", mask.shape)
+
+        if mask.shape[2] != image.shape[2] or mask.shape[1] != image.shape[1]:
+            raise Exception("Image and mask should be the same size")
+
+        # As a suggestion of inferno46n2 (https://github.com/nullquant/ComfyUI-BrushNet/issues/64)
+        mask = mask.round()
+
+        masked_image = image * (1.0 - mask[:, :, :, None])
+
+        return (masked_image, mask)
+
+    # Get origin of the mask
+    def cut_with_mask(self, mask, width, height):
+        iy, ix = (mask == 1).nonzero(as_tuple=True)
+
+        h0, w0 = mask.shape
+
+        if iy.numel() == 0:
+            x_c = w0 / 2.0
+            y_c = h0 / 2.0
+        else:
+            x_min = ix.min().item()
+            x_max = ix.max().item()
+            y_min = iy.min().item()
+            y_max = iy.max().item()
+
+            if x_max - x_min > width or y_max - y_min > height:
+                raise Exception("Mask is bigger than provided dimensions")
+
+            x_c = (x_min + x_max) / 2.0
+            y_c = (y_min + y_max) / 2.0
+
+        width2 = width / 2.0
+        height2 = height / 2.0
+
+        if w0 <= width:
+            x0 = 0
+            w = w0
+        else:
+            x0 = max(0, x_c - width2)
+            w = width
+            if x0 + width > w0:
+                x0 = w0 - width
+
+        if h0 <= height:
+            y0 = 0
+            h = h0
+        else:
+            y0 = max(0, y_c - height2)
+            h = height
+            if y0 + height > h0:
+                y0 = h0 - height
+
+        return (int(x0), int(y0), int(w), int(h))
+
+    # Prepare conditioning_latents
+    @torch.inference_mode()
+    def get_image_latents(self, masked_image, mask, vae, scaling_factor):
+        processed_image = masked_image.to(vae.device)
+        image_latents = vae.encode(processed_image[:, :, :, :3]) * scaling_factor
+        processed_mask = 1. - mask[:, None, :, :]
+        interpolated_mask = torch.nn.functional.interpolate(
+            processed_mask,
+            size=(
+                image_latents.shape[-2],
+                image_latents.shape[-1]
+            )
+        )
+        interpolated_mask = interpolated_mask.to(image_latents.device)
+
+        conditioning_latents = [image_latents, interpolated_mask]
+
+        print('BrushNet CL: image_latents shape =', image_latents.shape, 'interpolated_mask shape =',
+              interpolated_mask.shape)
+
+        return conditioning_latents
+
+    def brushnet_blocks(self, sd):
+        brushnet_down_block = 0
+        brushnet_mid_block = 0
+        brushnet_up_block = 0
+        for key in sd:
+            if 'brushnet_down_block' in key:
+                brushnet_down_block += 1
+            if 'brushnet_mid_block' in key:
+                brushnet_mid_block += 1
+            if 'brushnet_up_block' in key:
+                brushnet_up_block += 1
+        return (brushnet_down_block, brushnet_mid_block, brushnet_up_block, len(sd))
+
+    def get_model_type(self, brushnet_file):
+        sd = comfy.utils.load_torch_file(brushnet_file)
+        brushnet_down_block, brushnet_mid_block, brushnet_up_block, keys = self.brushnet_blocks(sd)
+        del sd
+        if brushnet_down_block == 24 and brushnet_mid_block == 2 and brushnet_up_block == 30:
+            is_SDXL = False
+            if keys == 322:
+                is_PP = False
+                print('BrushNet model type: SD1.5')
+            else:
+                is_PP = True
+                print('PowerPaint model type: SD1.5')
+        elif brushnet_down_block == 18 and brushnet_mid_block == 2 and brushnet_up_block == 22:
+            print('BrushNet model type: Loading SDXL')
+            is_SDXL = True
+            is_PP = False
+        else:
+            raise Exception("Unknown BrushNet model")
+        return is_SDXL, is_PP
+
+    def load_brushnet_model(self, brushnet_file, dtype='float16'):
+        is_SDXL, is_PP = self.get_model_type(brushnet_file)
+        with init_empty_weights():
+            if is_SDXL:
+                brushnet_config = BrushNetModel.load_config(brushnet_xl_config_file)
+                brushnet_model = BrushNetModel.from_config(brushnet_config)
+            elif is_PP:
+                brushnet_config = PowerPaintModel.load_config(powerpaint_config_file)
+                brushnet_model = PowerPaintModel.from_config(brushnet_config)
+            else:
+                brushnet_config = BrushNetModel.load_config(brushnet_config_file)
+                brushnet_model = BrushNetModel.from_config(brushnet_config)
+        if is_PP:
+            print("PowerPaint model file:", brushnet_file)
+        else:
+            print("BrushNet model file:", brushnet_file)
+
+        if dtype == 'float16':
+            torch_dtype = torch.float16
+        elif dtype == 'bfloat16':
+            torch_dtype = torch.bfloat16
+        elif dtype == 'float32':
+            torch_dtype = torch.float32
+        else:
+            torch_dtype = torch.float64
+
+        brushnet_model = load_checkpoint_and_dispatch(
+            brushnet_model,
+            brushnet_file,
+            device_map="sequential",
+            max_memory=None,
+            offload_folder=None,
+            offload_state_dict=False,
+            dtype=torch_dtype,
+            force_hooks=False,
+        )
+
+        if is_PP:
+            print("PowerPaint model is loaded")
+        elif is_SDXL:
+            print("BrushNet SDXL model is loaded")
+        else:
+            print("BrushNet SD1.5 model is loaded")
+
+        return ({"brushnet": brushnet_model, "SDXL": is_SDXL, "PP": is_PP, "dtype": torch_dtype},)
+
+    def brushnet_model_update(self, model, vae, image, mask, brushnet, positive, negative, scale, start_at, end_at):
+
+        is_SDXL, is_PP = self.check_compatibilty(model, brushnet)
+
+        if is_PP:
+            raise Exception("PowerPaint model was loaded, please use PowerPaint node")
+
+            # Make a copy of the model so that we're not patching it everywhere in the workflow.
+        model = model.clone()
+
+        # prepare image and mask
+        # no batches for original image and mask
+        masked_image, mask = self.prepare_image(image, mask)
+
+        batch = masked_image.shape[0]
+        width = masked_image.shape[2]
+        height = masked_image.shape[1]
+
+        if hasattr(model.model.model_config, 'latent_format') and hasattr(model.model.model_config.latent_format,
+                                                                          'scale_factor'):
+            scaling_factor = model.model.model_config.latent_format.scale_factor
+        elif is_SDXL:
+            scaling_factor = sdxl_scaling_factor
+        else:
+            scaling_factor = sd15_scaling_factor
+
+        torch_dtype = brushnet['dtype']
+
+        # prepare conditioning latents
+        conditioning_latents = self.get_image_latents(masked_image, mask, vae, scaling_factor)
+        conditioning_latents[0] = conditioning_latents[0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
+        conditioning_latents[1] = conditioning_latents[1].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
+
+        # unload vae
+        del vae
+        for loaded_model in comfy.model_management.current_loaded_models:
+            if type(loaded_model.model.model) in ModelsToUnload:
+                comfy.model_management.current_loaded_models.remove(loaded_model)
+                loaded_model.model_unload()
+                del loaded_model
+
+        # prepare embeddings
+        prompt_embeds = positive[0][0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
+        negative_prompt_embeds = negative[0][0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
+
+        max_tokens = max(prompt_embeds.shape[1], negative_prompt_embeds.shape[1])
+        if prompt_embeds.shape[1] < max_tokens:
+            multiplier = max_tokens // 77 - prompt_embeds.shape[1] // 77
+            prompt_embeds = torch.concat([prompt_embeds] + [prompt_embeds[:, -77:, :]] * multiplier, dim=1)
+            print('BrushNet: negative prompt more than 75 tokens:', negative_prompt_embeds.shape,
+                  'multiplying prompt_embeds')
+        if negative_prompt_embeds.shape[1] < max_tokens:
+            multiplier = max_tokens // 77 - negative_prompt_embeds.shape[1] // 77
+            negative_prompt_embeds = torch.concat(
+                [negative_prompt_embeds] + [negative_prompt_embeds[:, -77:, :]] * multiplier, dim=1)
+            print('BrushNet: positive prompt more than 75 tokens:', prompt_embeds.shape,
+                  'multiplying negative_prompt_embeds')
+
+        if len(positive[0]) > 1 and 'pooled_output' in positive[0][1] and positive[0][1]['pooled_output'] is not None:
+            pooled_prompt_embeds = positive[0][1]['pooled_output'].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
+        else:
+            print('BrushNet: positive conditioning has not pooled_output')
+            if is_SDXL:
+                print('BrushNet will not produce correct results')
+            pooled_prompt_embeds = torch.empty([2, 1280], device=brushnet['brushnet'].device).to(dtype=torch_dtype)
+
+        if len(negative[0]) > 1 and 'pooled_output' in negative[0][1] and negative[0][1]['pooled_output'] is not None:
+            negative_pooled_prompt_embeds = negative[0][1]['pooled_output'].to(dtype=torch_dtype).to(
+                brushnet['brushnet'].device)
+        else:
+            print('BrushNet: negative conditioning has not pooled_output')
+            if is_SDXL:
+                print('BrushNet will not produce correct results')
+            negative_pooled_prompt_embeds = torch.empty([1, pooled_prompt_embeds.shape[1]],
+                                                        device=brushnet['brushnet'].device).to(dtype=torch_dtype)
+
+        time_ids = torch.FloatTensor([[height, width, 0., 0., height, width]]).to(dtype=torch_dtype).to(
+            brushnet['brushnet'].device)
+
+        if not is_SDXL:
+            pooled_prompt_embeds = None
+            negative_pooled_prompt_embeds = None
+            time_ids = None
+
+        # apply patch to model
+        brushnet_conditioning_scale = scale
+        control_guidance_start = start_at
+        control_guidance_end = end_at
+
+        add_brushnet_patch(model,
+                           brushnet['brushnet'],
+                           torch_dtype,
+                           conditioning_latents,
+                           (brushnet_conditioning_scale, control_guidance_start, control_guidance_end),
+                           prompt_embeds, negative_prompt_embeds,
+                           pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids,
+                           False)
+
+        latent = torch.zeros([batch, 4, conditioning_latents[0].shape[2], conditioning_latents[0].shape[3]],
+                             device=brushnet['brushnet'].device)
+
+        return (model, positive, negative, {"samples": latent},)
+
+    #powperpaint
+    def load_powerpaint_clip(self, base_clip_file, pp_clip_file):
+        pp_clip = comfy.sd.load_clip(ckpt_paths=[base_clip_file])
+
+        print('PowerPaint base CLIP file: ', base_clip_file)
+
+        pp_tokenizer = TokenizerWrapper(pp_clip.tokenizer.clip_l.tokenizer)
+        pp_text_encoder = pp_clip.patcher.model.clip_l.transformer
+
+        add_tokens(
+            tokenizer=pp_tokenizer,
+            text_encoder=pp_text_encoder,
+            placeholder_tokens=["P_ctxt", "P_shape", "P_obj"],
+            initialize_tokens=["a", "a", "a"],
+            num_vectors_per_token=10,
+        )
+
+        pp_text_encoder.load_state_dict(comfy.utils.load_torch_file(pp_clip_file), strict=False)
+
+        print('PowerPaint CLIP file: ', pp_clip_file)
+
+        pp_clip.tokenizer.clip_l.tokenizer = pp_tokenizer
+        pp_clip.patcher.model.clip_l.transformer = pp_text_encoder
+
+        return (pp_clip,)
+
+    def powerpaint_model_update(self, model, vae, image, mask, powerpaint, clip, positive, negative, fitting, function, scale, start_at, end_at, save_memory):
+        is_SDXL, is_PP = self.check_compatibilty(model, powerpaint)
+        if not is_PP:
+            raise Exception("BrushNet model was loaded, please use BrushNet node")
+
+            # Make a copy of the model so that we're not patching it everywhere in the workflow.
+        model = model.clone()
+
+        # prepare image and mask
+        # no batches for original image and mask
+        masked_image, mask = self.prepare_image(image, mask)
+
+        batch = masked_image.shape[0]
+        # width = masked_image.shape[2]
+        # height = masked_image.shape[1]
+
+        if hasattr(model.model.model_config, 'latent_format') and hasattr(model.model.model_config.latent_format,
+                                                                          'scale_factor'):
+            scaling_factor = model.model.model_config.latent_format.scale_factor
+        else:
+            scaling_factor = sd15_scaling_factor
+
+        torch_dtype = powerpaint['dtype']
+
+        # prepare conditioning latents
+        conditioning_latents = self.get_image_latents(masked_image, mask, vae, scaling_factor)
+        conditioning_latents[0] = conditioning_latents[0].to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
+        conditioning_latents[1] = conditioning_latents[1].to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
+
+        # prepare embeddings
+
+        if function == "object removal":
+            promptA = "P_ctxt"
+            promptB = "P_ctxt"
+            negative_promptA = "P_obj"
+            negative_promptB = "P_obj"
+            print('You should add to positive prompt: "empty scene blur"')
+            # positive = positive + " empty scene blur"
+        elif function == "context aware":
+            promptA = "P_ctxt"
+            promptB = "P_ctxt"
+            negative_promptA = ""
+            negative_promptB = ""
+            # positive = positive + " empty scene"
+            print('You should add to positive prompt: "empty scene"')
+        elif function == "shape guided":
+            promptA = "P_shape"
+            promptB = "P_ctxt"
+            negative_promptA = "P_shape"
+            negative_promptB = "P_ctxt"
+        elif function == "image outpainting":
+            promptA = "P_ctxt"
+            promptB = "P_ctxt"
+            negative_promptA = "P_obj"
+            negative_promptB = "P_obj"
+            # positive = positive + " empty scene"
+            print('You should add to positive prompt: "empty scene"')
+        else:
+            promptA = "P_obj"
+            promptB = "P_obj"
+            negative_promptA = "P_obj"
+            negative_promptB = "P_obj"
+
+        tokens = clip.tokenize(promptA)
+        prompt_embedsA = clip.encode_from_tokens(tokens, return_pooled=False)
+
+        tokens = clip.tokenize(negative_promptA)
+        negative_prompt_embedsA = clip.encode_from_tokens(tokens, return_pooled=False)
+
+        tokens = clip.tokenize(promptB)
+        prompt_embedsB = clip.encode_from_tokens(tokens, return_pooled=False)
+
+        tokens = clip.tokenize(negative_promptB)
+        negative_prompt_embedsB = clip.encode_from_tokens(tokens, return_pooled=False)
+
+        prompt_embeds_pp = (prompt_embedsA * fitting + (1.0 - fitting) * prompt_embedsB).to(dtype=torch_dtype).to(
+            powerpaint['brushnet'].device)
+        negative_prompt_embeds_pp = (negative_prompt_embedsA * fitting + (1.0 - fitting) * negative_prompt_embedsB).to(
+            dtype=torch_dtype).to(powerpaint['brushnet'].device)
+
+        # unload vae and CLIPs
+        del vae
+        del clip
+        for loaded_model in comfy.model_management.current_loaded_models:
+            if type(loaded_model.model.model) in ModelsToUnload:
+                comfy.model_management.current_loaded_models.remove(loaded_model)
+                loaded_model.model_unload()
+                del loaded_model
+
+        # apply patch to model
+
+        brushnet_conditioning_scale = scale
+        control_guidance_start = start_at
+        control_guidance_end = end_at
+
+        if save_memory != 'none':
+            powerpaint['brushnet'].set_attention_slice(save_memory)
+
+        add_brushnet_patch(model,
+                           powerpaint['brushnet'],
+                           torch_dtype,
+                           conditioning_latents,
+                           (brushnet_conditioning_scale, control_guidance_start, control_guidance_end),
+                           negative_prompt_embeds_pp, prompt_embeds_pp,
+                           None, None, None,
+                           False)
+
+        latent = torch.zeros([batch, 4, conditioning_latents[0].shape[2], conditioning_latents[0].shape[3]],
+                             device=powerpaint['brushnet'].device)
+
+        return (model, positive, negative, {"samples": latent},)
+@torch.inference_mode()
+def brushnet_inference(x, timesteps, transformer_options, debug):
+    if 'model_patch' not in transformer_options:
+        print('BrushNet inference: there is no model_patch key in transformer_options')
+        return ([], 0, [])
+    mp = transformer_options['model_patch']
+    if 'brushnet' not in mp:
+        print('BrushNet inference: there is no brushnet key in mdel_patch')
+        return ([], 0, [])
+    bo = mp['brushnet']
+    if 'model' not in bo:
+        print('BrushNet inference: there is no model key in brushnet')
+        return ([], 0, [])
+    brushnet = bo['model']
+    if not (isinstance(brushnet, BrushNetModel) or isinstance(brushnet, PowerPaintModel)):
+        print('BrushNet model is not a BrushNetModel class')
+        return ([], 0, [])
+
+    torch_dtype = bo['dtype']
+    cl_list = bo['latents']
+    brushnet_conditioning_scale, control_guidance_start, control_guidance_end = bo['controls']
+    pe = bo['prompt_embeds']
+    npe = bo['negative_prompt_embeds']
+    ppe, nppe, time_ids = bo['add_embeds']
+
+    #do_classifier_free_guidance = mp['free_guidance']
+    do_classifier_free_guidance = len(transformer_options['cond_or_uncond']) > 1
+
+    x = x.detach().clone()
+    x = x.to(torch_dtype).to(brushnet.device)
+
+    timesteps = timesteps.detach().clone()
+    timesteps = timesteps.to(torch_dtype).to(brushnet.device)
+
+    total_steps = mp['total_steps']
+    step = mp['step']
+
+    added_cond_kwargs = {}
+
+    if do_classifier_free_guidance and step == 0:
+        print('BrushNet inference: do_classifier_free_guidance is True')
+
+    sub_idx = None
+    if 'ad_params' in transformer_options and 'sub_idxs' in transformer_options['ad_params']:
+        sub_idx = transformer_options['ad_params']['sub_idxs']
+
+    # we have batch input images
+    batch = cl_list[0].shape[0]
+    # we have incoming latents
+    latents_incoming = x.shape[0]
+    # and we already got some
+    latents_got = bo['latent_id']
+    if step == 0 or batch > 1:
+        print('BrushNet inference, step = %d: image batch = %d, got %d latents, starting from %d' \
+                % (step, batch, latents_incoming, latents_got))
+
+    image_latents = []
+    masks = []
+    prompt_embeds = []
+    negative_prompt_embeds = []
+    pooled_prompt_embeds = []
+    negative_pooled_prompt_embeds = []
+    if sub_idx:
+        # AnimateDiff indexes detected
+        if step == 0:
+            print('BrushNet inference: AnimateDiff indexes detected and applied')
+
+        batch = len(sub_idx)
+
+        if do_classifier_free_guidance:
+            for i in sub_idx:
+                image_latents.append(cl_list[0][i][None,:,:,:])
+                masks.append(cl_list[1][i][None,:,:,:])
+                prompt_embeds.append(pe)
+                negative_prompt_embeds.append(npe)
+                pooled_prompt_embeds.append(ppe)
+                negative_pooled_prompt_embeds.append(nppe)
+            for i in sub_idx:
+                image_latents.append(cl_list[0][i][None,:,:,:])
+                masks.append(cl_list[1][i][None,:,:,:])
+        else:
+            for i in sub_idx:
+                image_latents.append(cl_list[0][i][None,:,:,:])
+                masks.append(cl_list[1][i][None,:,:,:])
+                prompt_embeds.append(pe)
+                pooled_prompt_embeds.append(ppe)
+    else:
+        # do_classifier_free_guidance = 2 passes, 1st pass is cond, 2nd is uncond
+        continue_batch = True
+        for i in range(latents_incoming):
+            number = latents_got + i
+            if number < batch:
+                # 1st pass, cond
+                image_latents.append(cl_list[0][number][None,:,:,:])
+                masks.append(cl_list[1][number][None,:,:,:])
+                prompt_embeds.append(pe)
+                pooled_prompt_embeds.append(ppe)
+            elif do_classifier_free_guidance and number < batch * 2:
+                # 2nd pass, uncond
+                image_latents.append(cl_list[0][number-batch][None,:,:,:])
+                masks.append(cl_list[1][number-batch][None,:,:,:])
+                negative_prompt_embeds.append(npe)
+                negative_pooled_prompt_embeds.append(nppe)
+            else:
+                # latent batch
+                image_latents.append(cl_list[0][0][None,:,:,:])
+                masks.append(cl_list[1][0][None,:,:,:])
+                prompt_embeds.append(pe)
+                pooled_prompt_embeds.append(ppe)
+                latents_got = -i
+                continue_batch = False
+
+        if continue_batch:
+            # we don't have full batch yet
+            if do_classifier_free_guidance:
+                if number < batch * 2 - 1:
+                    bo['latent_id'] = number + 1
+                else:
+                    bo['latent_id'] = 0
+            else:
+                if number < batch - 1:
+                    bo['latent_id'] = number + 1
+                else:
+                    bo['latent_id'] = 0
+        else:
+            bo['latent_id'] = 0
+
+    cl = []
+    for il, m in zip(image_latents, masks):
+        cl.append(torch.concat([il, m], dim=1))
+    cl2apply = torch.concat(cl, dim=0)
+
+    conditioning_latents = cl2apply.to(torch_dtype).to(brushnet.device)
+
+    prompt_embeds.extend(negative_prompt_embeds)
+    prompt_embeds = torch.concat(prompt_embeds, dim=0).to(torch_dtype).to(brushnet.device)
+
+    if ppe is not None:
+        added_cond_kwargs = {}
+        added_cond_kwargs['time_ids'] = torch.concat([time_ids] * latents_incoming, dim = 0).to(torch_dtype).to(brushnet.device)
+
+        pooled_prompt_embeds.extend(negative_pooled_prompt_embeds)
+        pooled_prompt_embeds = torch.concat(pooled_prompt_embeds, dim=0).to(torch_dtype).to(brushnet.device)
+        added_cond_kwargs['text_embeds'] = pooled_prompt_embeds
+    else:
+        added_cond_kwargs = None
+
+    if x.shape[2] != conditioning_latents.shape[2] or x.shape[3] != conditioning_latents.shape[3]:
+        if step == 0:
+            print('BrushNet inference: image', conditioning_latents.shape, 'and latent', x.shape, 'have different size, resizing image')
+        conditioning_latents = torch.nn.functional.interpolate(
+            conditioning_latents, size=(
+                x.shape[2],
+                x.shape[3],
+            ), mode='bicubic',
+        ).to(torch_dtype).to(brushnet.device)
+
+    if step == 0:
+        print('BrushNet inference: sample', x.shape, ', CL', conditioning_latents.shape, 'dtype', torch_dtype)
+
+    if debug: print('BrushNet: step =', step)
+
+    if step < control_guidance_start or step > control_guidance_end:
+        cond_scale = 0.0
+    else:
+        cond_scale = brushnet_conditioning_scale
+
+    return brushnet(x,
+        encoder_hidden_states=prompt_embeds,
+        brushnet_cond=conditioning_latents,
+        timestep = timesteps,
+        conditioning_scale=cond_scale,
+        guess_mode=False,
+        added_cond_kwargs=added_cond_kwargs,
+        return_dict=False,
+        debug=debug,
+    )
+
+def add_brushnet_patch(model, brushnet, torch_dtype, conditioning_latents,
+                       controls,
+                       prompt_embeds, negative_prompt_embeds,
+                       pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids,
+                       debug):
+
+    is_SDXL = isinstance(model.model.model_config, comfy.supported_models.SDXL)
+
+    if is_SDXL:
+        input_blocks = [[0, comfy.ops.disable_weight_init.Conv2d],
+                        [1, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
+                        [2, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
+                        [3, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
+                        [4, comfy.ldm.modules.attention.SpatialTransformer],
+                        [5, comfy.ldm.modules.attention.SpatialTransformer],
+                        [6, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
+                        [7, comfy.ldm.modules.attention.SpatialTransformer],
+                        [8, comfy.ldm.modules.attention.SpatialTransformer]]
+        middle_block = [0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]
+        output_blocks = [[0, comfy.ldm.modules.attention.SpatialTransformer],
+                         [1, comfy.ldm.modules.attention.SpatialTransformer],
+                         [2, comfy.ldm.modules.attention.SpatialTransformer],
+                         [2, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
+                         [3, comfy.ldm.modules.attention.SpatialTransformer],
+                         [4, comfy.ldm.modules.attention.SpatialTransformer],
+                         [5, comfy.ldm.modules.attention.SpatialTransformer],
+                         [5, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
+                         [6, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
+                         [7, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
+                         [8, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]]
+    else:
+        input_blocks = [[0, comfy.ops.disable_weight_init.Conv2d],
+                        [1, comfy.ldm.modules.attention.SpatialTransformer],
+                        [2, comfy.ldm.modules.attention.SpatialTransformer],
+                        [3, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
+                        [4, comfy.ldm.modules.attention.SpatialTransformer],
+                        [5, comfy.ldm.modules.attention.SpatialTransformer],
+                        [6, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
+                        [7, comfy.ldm.modules.attention.SpatialTransformer],
+                        [8, comfy.ldm.modules.attention.SpatialTransformer],
+                        [9, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
+                        [10, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
+                        [11, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]]
+        middle_block = [0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]
+        output_blocks = [[0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
+                         [1, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
+                         [2, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
+                         [2, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
+                         [3, comfy.ldm.modules.attention.SpatialTransformer],
+                         [4, comfy.ldm.modules.attention.SpatialTransformer],
+                         [5, comfy.ldm.modules.attention.SpatialTransformer],
+                         [5, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
+                         [6, comfy.ldm.modules.attention.SpatialTransformer],
+                         [7, comfy.ldm.modules.attention.SpatialTransformer],
+                         [8, comfy.ldm.modules.attention.SpatialTransformer],
+                         [8, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
+                         [9, comfy.ldm.modules.attention.SpatialTransformer],
+                         [10, comfy.ldm.modules.attention.SpatialTransformer],
+                         [11, comfy.ldm.modules.attention.SpatialTransformer]]
+
+    def last_layer_index(block, tp):
+        layer_list = []
+        for layer in block:
+            layer_list.append(type(layer))
+        layer_list.reverse()
+        if tp not in layer_list:
+            return -1, layer_list.reverse()
+        return len(layer_list) - 1 - layer_list.index(tp), layer_list
+
+    def brushnet_forward(model, x, timesteps, transformer_options, control):
+        if 'brushnet' not in transformer_options['model_patch']:
+            input_samples = []
+            mid_sample = 0
+            output_samples = []
+        else:
+            # brushnet inference
+            input_samples, mid_sample, output_samples = brushnet_inference(x, timesteps, transformer_options, debug)
+
+        # give additional samples to blocks
+        for i, tp in input_blocks:
+            idx, layer_list = last_layer_index(model.input_blocks[i], tp)
+            if idx < 0:
+                print("BrushNet can't find", tp, "layer in", i, "input block:", layer_list)
+                continue
+            model.input_blocks[i][idx].add_sample_after = input_samples.pop(0) if input_samples else 0
+
+        idx, layer_list = last_layer_index(model.middle_block, middle_block[1])
+        if idx < 0:
+            print("BrushNet can't find", middle_block[1], "layer in middle block", layer_list)
+        model.middle_block[idx].add_sample_after = mid_sample
+
+        for i, tp in output_blocks:
+            idx, layer_list = last_layer_index(model.output_blocks[i], tp)
+            if idx < 0:
+                print("BrushNet can't find", tp, "layer in", i, "outnput block:", layer_list)
+                continue
+            model.output_blocks[i][idx].add_sample_after = output_samples.pop(0) if output_samples else 0
+
+    patch_model_function_wrapper(model, brushnet_forward)
+
+    to = add_model_patch_option(model)
+    mp = to['model_patch']
+    if 'brushnet' not in mp:
+        mp['brushnet'] = {}
+    bo = mp['brushnet']
+
+    bo['model'] = brushnet
+    bo['dtype'] = torch_dtype
+    bo['latents'] = conditioning_latents
+    bo['controls'] = controls
+    bo['prompt_embeds'] = prompt_embeds
+    bo['negative_prompt_embeds'] = negative_prompt_embeds
+    bo['add_embeds'] = (pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids)
+    bo['latent_id'] = 0
+
+    # patch layers `forward` so we can apply brushnet
+    def forward_patched_by_brushnet(self, x, *args, **kwargs):
+        h = self.original_forward(x, *args, **kwargs)
+        if hasattr(self, 'add_sample_after') and type(self):
+            to_add = self.add_sample_after
+            if torch.is_tensor(to_add):
+                # interpolate due to RAUNet
+                if h.shape[2] != to_add.shape[2] or h.shape[3] != to_add.shape[3]:
+                    to_add = torch.nn.functional.interpolate(to_add, size=(h.shape[2], h.shape[3]), mode='bicubic')
+                h += to_add.to(h.dtype).to(h.device)
+            else:
+                h += self.add_sample_after
+            self.add_sample_after = 0
+        return h
+
+    for i, block in enumerate(model.model.diffusion_model.input_blocks):
+        for j, layer in enumerate(block):
+            if not hasattr(layer, 'original_forward'):
+                layer.original_forward = layer.forward
+            layer.forward = types.MethodType(forward_patched_by_brushnet, layer)
+            layer.add_sample_after = 0
+
+    for j, layer in enumerate(model.model.diffusion_model.middle_block):
+        if not hasattr(layer, 'original_forward'):
+            layer.original_forward = layer.forward
+        layer.forward = types.MethodType(forward_patched_by_brushnet, layer)
+        layer.add_sample_after = 0
+
+    for i, block in enumerate(model.model.diffusion_model.output_blocks):
+        for j, layer in enumerate(block):
+            if not hasattr(layer, 'original_forward'):
+                layer.original_forward = layer.forward
+            layer.forward = types.MethodType(forward_patched_by_brushnet, layer)
+            layer.add_sample_after = 0

py/brushnet/config/brushnet.json

@@ -0,0 +1,58 @@
+{
+  "_class_name": "BrushNetModel",
+  "_diffusers_version": "0.27.0.dev0",
+  "_name_or_path": "runs/logs/brushnet_randommask/checkpoint-100000",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": 8,
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "brushnet_conditioning_channel_order": "rgb",
+  "class_embed_type": null,
+  "conditioning_channels": 5,
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "cross_attention_dim": 768,
+  "down_block_types": [
+    "DownBlock2D",
+    "DownBlock2D",
+    "DownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "global_pool_conditions": false,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "MidBlock2D",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": 1,
+  "up_block_types": [
+    "UpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D"
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": false
+}

py/brushnet/config/brushnet_xl.json

@@ -0,0 +1,63 @@
+{
+  "_class_name": "BrushNetModel",
+  "_diffusers_version": "0.27.0.dev0",
+  "_name_or_path": "runs/logs/brushnetsdxl_randommask/checkpoint-80000",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "block_out_channels": [
+    320,
+    640,
+    1280
+  ],
+  "brushnet_conditioning_channel_order": "rgb",
+  "class_embed_type": null,
+  "conditioning_channels": 5,
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "cross_attention_dim": 2048,
+  "down_block_types": [
+    "DownBlock2D",
+    "DownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "global_pool_conditions": false,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "MidBlock2D",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "up_block_types": [
+    "UpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D"
+  ],
+  "upcast_attention": null,
+  "use_linear_projection": true
+}

py/brushnet/config/powerpaint.json

@@ -0,0 +1,57 @@
+{
+  "_class_name": "BrushNetModel",
+  "_diffusers_version": "0.27.2",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": 8,
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "brushnet_conditioning_channel_order": "rgb",
+  "class_embed_type": null,
+  "conditioning_channels": 5,
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "cross_attention_dim": 768,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "global_pool_conditions": false,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": 1,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": false
+}

py/brushnet/model_patch.py

@@ -0,0 +1,137 @@
+import torch
+import comfy
+
+# Check and add 'model_patch' to model.model_options['transformer_options']
+def add_model_patch_option(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
+
+
+# Patch model with model_function_wrapper
+def patch_model_function_wrapper(model, forward_patch, remove=False):
+    def brushnet_model_function_wrapper(apply_model_method, options_dict):
+        to = options_dict['c']['transformer_options']
+
+        control = None
+        if 'control' in options_dict['c']:
+            control = options_dict['c']['control']
+
+        x = options_dict['input']
+        timestep = options_dict['timestep']
+
+        # check if there are patches to execute
+        if 'model_patch' not in to or 'forward' not in to['model_patch']:
+            return apply_model_method(x, timestep, **options_dict['c'])
+
+        mp = to['model_patch']
+        unet = mp['unet']
+
+        all_sigmas = mp['all_sigmas']
+        sigma = to['sigmas'][0].item()
+        total_steps = all_sigmas.shape[0] - 1
+        step = torch.argmin((all_sigmas - sigma).abs()).item()
+
+        mp['step'] = step
+        mp['total_steps'] = total_steps
+
+        # comfy.model_base.apply_model
+        xc = model.model.model_sampling.calculate_input(timestep, x)
+        if 'c_concat' in options_dict['c'] and options_dict['c']['c_concat'] is not None:
+            xc = torch.cat([xc] + [options_dict['c']['c_concat']], dim=1)
+        t = model.model.model_sampling.timestep(timestep).float()
+        # execute all patches
+        for method in mp['forward']:
+            method(unet, xc, t, to, control)
+
+        return apply_model_method(x, timestep, **options_dict['c'])
+
+    if "model_function_wrapper" in model.model_options and model.model_options["model_function_wrapper"]:
+        print('BrushNet is going to replace existing model_function_wrapper:',
+              model.model_options["model_function_wrapper"])
+    model.set_model_unet_function_wrapper(brushnet_model_function_wrapper)
+
+    to = 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))
+
+    if 'forward' not in mp:
+        mp['forward'] = []
+
+    if remove:
+        if forward_patch in mp['forward']:
+            mp['forward'].remove(forward_patch)
+    else:
+        mp['forward'].append(forward_patch)
+
+    mp['unet'] = model.model.diffusion_model
+    mp['step'] = 0
+    mp['total_steps'] = 1
+
+    # apply patches to code
+    if comfy.samplers.sample.__doc__ is None or 'BrushNet' not in comfy.samplers.sample.__doc__:
+        comfy.samplers.original_sample = comfy.samplers.sample
+        comfy.samplers.sample = modified_sample
+
+    if comfy.ldm.modules.diffusionmodules.openaimodel.apply_control.__doc__ is None or \
+            'BrushNet' not in comfy.ldm.modules.diffusionmodules.openaimodel.apply_control.__doc__:
+        comfy.ldm.modules.diffusionmodules.openaimodel.original_apply_control = comfy.ldm.modules.diffusionmodules.openaimodel.apply_control
+        comfy.ldm.modules.diffusionmodules.openaimodel.apply_control = modified_apply_control
+
+
+# Model needs current step number and cfg at inference step. It is possible to write a custom KSampler but I'd like to use ComfyUI's one.
+# The first versions had modified_common_ksampler, but it broke custom KSampler nodes
+def modified_sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={},
+                    latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+    ''' Modified by BrushNet nodes'''
+    cfg_guider = comfy.samplers.CFGGuider(model)
+    cfg_guider.set_conds(positive, negative)
+    cfg_guider.set_cfg(cfg)
+
+    ### Modified part ######################################################################
+    to = add_model_patch_option(model)
+    to['model_patch']['all_sigmas'] = sigmas
+    #######################################################################################
+
+    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+
+# To use Controlnet with RAUNet it is much easier to modify apply_control a little
+def modified_apply_control(h, control, name):
+    '''Modified by BrushNet nodes'''
+    if control is not None and name in control and len(control[name]) > 0:
+        ctrl = control[name].pop()
+        if ctrl is not None:
+            if h.shape[2] != ctrl.shape[2] or h.shape[3] != ctrl.shape[3]:
+                ctrl = torch.nn.functional.interpolate(ctrl, size=(h.shape[2], h.shape[3]), mode='bicubic').to(
+                    h.dtype).to(h.device)
+            try:
+                h += ctrl
+            except:
+                print.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape))
+    return h
+
+def add_model_patch(model):
+    to = add_model_patch_option(model)
+    mp = to['model_patch']
+    if "brushnet" in mp:
+        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

py/brushnet/powerpaint_utils.py

@@ -0,0 +1,467 @@
+import copy
+import random
+
+import torch
+import torch.nn as nn
+from transformers import CLIPTokenizer
+from typing import Any, List, Optional, Union
+
+
+class TokenizerWrapper:
+    """Tokenizer wrapper for CLIPTokenizer. Only support CLIPTokenizer
+    currently. This wrapper is modified from https://github.com/huggingface/dif
+    fusers/blob/e51f19aee82c8dd874b715a09dbc521d88835d68/src/diffusers/loaders.
+    py#L358  # noqa.
+
+    Args:
+        from_pretrained (Union[str, os.PathLike], optional): The *model id*
+            of a pretrained model or a path to a *directory* containing
+            model weights and config. Defaults to None.
+        from_config (Union[str, os.PathLike], optional): The *model id*
+            of a pretrained model or a path to a *directory* containing
+            model weights and config. Defaults to None.
+
+        *args, **kwargs: If `from_pretrained` is passed, *args and **kwargs
+            will be passed to `from_pretrained` function. Otherwise, *args
+            and **kwargs will be used to initialize the model by
+            `self._module_cls(*args, **kwargs)`.
+    """
+
+    def __init__(self, tokenizer: CLIPTokenizer):
+        self.wrapped = tokenizer
+        self.token_map = {}
+
+    def __getattr__(self, name: str) -> Any:
+        if name in self.__dict__:
+            return getattr(self, name)
+        # if name == "wrapped":
+        #    return getattr(self, 'wrapped')#super().__getattr__("wrapped")
+
+        try:
+            return getattr(self.wrapped, name)
+        except AttributeError:
+            raise AttributeError(
+                "'name' cannot be found in both "
+                f"'{self.__class__.__name__}' and "
+                f"'{self.__class__.__name__}.tokenizer'."
+            )
+
+    def try_adding_tokens(self, tokens: Union[str, List[str]], *args, **kwargs):
+        """Attempt to add tokens to the tokenizer.
+
+        Args:
+            tokens (Union[str, List[str]]): The tokens to be added.
+        """
+        num_added_tokens = self.wrapped.add_tokens(tokens, *args, **kwargs)
+        assert num_added_tokens != 0, (
+            f"The tokenizer already contains the token {tokens}. Please pass "
+            "a different `placeholder_token` that is not already in the "
+            "tokenizer."
+        )
+
+    def get_token_info(self, token: str) -> dict:
+        """Get the information of a token, including its start and end index in
+        the current tokenizer.
+
+        Args:
+            token (str): The token to be queried.
+
+        Returns:
+            dict: The information of the token, including its start and end
+                index in current tokenizer.
+        """
+        token_ids = self.__call__(token).input_ids
+        start, end = token_ids[1], token_ids[-2] + 1
+        return {"name": token, "start": start, "end": end}
+
+    def add_placeholder_token(self, placeholder_token: str, *args, num_vec_per_token: int = 1, **kwargs):
+        """Add placeholder tokens to the tokenizer.
+
+        Args:
+            placeholder_token (str): The placeholder token to be added.
+            num_vec_per_token (int, optional): The number of vectors of
+                the added placeholder token.
+            *args, **kwargs: The arguments for `self.wrapped.add_tokens`.
+        """
+        output = []
+        if num_vec_per_token == 1:
+            self.try_adding_tokens(placeholder_token, *args, **kwargs)
+            output.append(placeholder_token)
+        else:
+            output = []
+            for i in range(num_vec_per_token):
+                ith_token = placeholder_token + f"_{i}"
+                self.try_adding_tokens(ith_token, *args, **kwargs)
+                output.append(ith_token)
+
+        for token in self.token_map:
+            if token in placeholder_token:
+                raise ValueError(
+                    f"The tokenizer already has placeholder token {token} "
+                    f"that can get confused with {placeholder_token} "
+                    "keep placeholder tokens independent"
+                )
+        self.token_map[placeholder_token] = output
+
+    def replace_placeholder_tokens_in_text(
+            self, text: Union[str, List[str]], vector_shuffle: bool = False, prop_tokens_to_load: float = 1.0
+    ) -> Union[str, List[str]]:
+        """Replace the keywords in text with placeholder tokens. This function
+        will be called in `self.__call__` and `self.encode`.
+
+        Args:
+            text (Union[str, List[str]]): The text to be processed.
+            vector_shuffle (bool, optional): Whether to shuffle the vectors.
+                Defaults to False.
+            prop_tokens_to_load (float, optional): The proportion of tokens to
+                be loaded. If 1.0, all tokens will be loaded. Defaults to 1.0.
+
+        Returns:
+            Union[str, List[str]]: The processed text.
+        """
+        if isinstance(text, list):
+            output = []
+            for i in range(len(text)):
+                output.append(self.replace_placeholder_tokens_in_text(text[i], vector_shuffle=vector_shuffle))
+            return output
+
+        for placeholder_token in self.token_map:
+            if placeholder_token in text:
+                tokens = self.token_map[placeholder_token]
+                tokens = tokens[: 1 + int(len(tokens) * prop_tokens_to_load)]
+                if vector_shuffle:
+                    tokens = copy.copy(tokens)
+                    random.shuffle(tokens)
+                text = text.replace(placeholder_token, " ".join(tokens))
+        return text
+
+    def replace_text_with_placeholder_tokens(self, text: Union[str, List[str]]) -> Union[str, List[str]]:
+        """Replace the placeholder tokens in text with the original keywords.
+        This function will be called in `self.decode`.
+
+        Args:
+            text (Union[str, List[str]]): The text to be processed.
+
+        Returns:
+            Union[str, List[str]]: The processed text.
+        """
+        if isinstance(text, list):
+            output = []
+            for i in range(len(text)):
+                output.append(self.replace_text_with_placeholder_tokens(text[i]))
+            return output
+
+        for placeholder_token, tokens in self.token_map.items():
+            merged_tokens = " ".join(tokens)
+            if merged_tokens in text:
+                text = text.replace(merged_tokens, placeholder_token)
+        return text
+
+    def __call__(
+            self,
+            text: Union[str, List[str]],
+            *args,
+            vector_shuffle: bool = False,
+            prop_tokens_to_load: float = 1.0,
+            **kwargs,
+    ):
+        """The call function of the wrapper.
+
+        Args:
+            text (Union[str, List[str]]): The text to be tokenized.
+            vector_shuffle (bool, optional): Whether to shuffle the vectors.
+                Defaults to False.
+            prop_tokens_to_load (float, optional): The proportion of tokens to
+                be loaded. If 1.0, all tokens will be loaded. Defaults to 1.0
+            *args, **kwargs: The arguments for `self.wrapped.__call__`.
+        """
+        replaced_text = self.replace_placeholder_tokens_in_text(
+            text, vector_shuffle=vector_shuffle, prop_tokens_to_load=prop_tokens_to_load
+        )
+
+        return self.wrapped.__call__(replaced_text, *args, **kwargs)
+
+    def encode(self, text: Union[str, List[str]], *args, **kwargs):
+        """Encode the passed text to token index.
+
+        Args:
+            text (Union[str, List[str]]): The text to be encode.
+            *args, **kwargs: The arguments for `self.wrapped.__call__`.
+        """
+        replaced_text = self.replace_placeholder_tokens_in_text(text)
+        return self.wrapped(replaced_text, *args, **kwargs)
+
+    def decode(self, token_ids, return_raw: bool = False, *args, **kwargs) -> Union[str, List[str]]:
+        """Decode the token index to text.
+
+        Args:
+            token_ids: The token index to be decoded.
+            return_raw: Whether keep the placeholder token in the text.
+                Defaults to False.
+            *args, **kwargs: The arguments for `self.wrapped.decode`.
+
+        Returns:
+            Union[str, List[str]]: The decoded text.
+        """
+        text = self.wrapped.decode(token_ids, *args, **kwargs)
+        if return_raw:
+            return text
+        replaced_text = self.replace_text_with_placeholder_tokens(text)
+        return replaced_text
+
+    def __repr__(self):
+        """The representation of the wrapper."""
+        s = super().__repr__()
+        prefix = f"Wrapped Module Class: {self._module_cls}\n"
+        prefix += f"Wrapped Module Name: {self._module_name}\n"
+        if self._from_pretrained:
+            prefix += f"From Pretrained: {self._from_pretrained}\n"
+        s = prefix + s
+        return s
+
+
+class EmbeddingLayerWithFixes(nn.Module):
+    """The revised embedding layer to support external embeddings. This design
+    of this class is inspired by https://github.com/AUTOMATIC1111/stable-
+    diffusion-webui/blob/22bcc7be428c94e9408f589966c2040187245d81/modules/sd_hi
+    jack.py#L224  # noqa.
+
+    Args:
+        wrapped (nn.Emebdding): The embedding layer to be wrapped.
+        external_embeddings (Union[dict, List[dict]], optional): The external
+            embeddings added to this layer. Defaults to None.
+    """
+
+    def __init__(self, wrapped: nn.Embedding, external_embeddings: Optional[Union[dict, List[dict]]] = None):
+        super().__init__()
+        self.wrapped = wrapped
+        self.num_embeddings = wrapped.weight.shape[0]
+
+        self.external_embeddings = []
+        if external_embeddings:
+            self.add_embeddings(external_embeddings)
+
+        self.trainable_embeddings = nn.ParameterDict()
+
+    @property
+    def weight(self):
+        """Get the weight of wrapped embedding layer."""
+        return self.wrapped.weight
+
+    def check_duplicate_names(self, embeddings: List[dict]):
+        """Check whether duplicate names exist in list of 'external
+        embeddings'.
+
+        Args:
+            embeddings (List[dict]): A list of embedding to be check.
+        """
+        names = [emb["name"] for emb in embeddings]
+        assert len(names) == len(set(names)), (
+            "Found duplicated names in 'external_embeddings'. Name list: " f"'{names}'"
+        )
+
+    def check_ids_overlap(self, embeddings):
+        """Check whether overlap exist in token ids of 'external_embeddings'.
+
+        Args:
+            embeddings (List[dict]): A list of embedding to be check.
+        """
+        ids_range = [[emb["start"], emb["end"], emb["name"]] for emb in embeddings]
+        ids_range.sort()  # sort by 'start'
+        # check if 'end' has overlapping
+        for idx in range(len(ids_range) - 1):
+            name1, name2 = ids_range[idx][-1], ids_range[idx + 1][-1]
+            assert ids_range[idx][1] <= ids_range[idx + 1][0], (
+                f"Found ids overlapping between embeddings '{name1}' " f"and '{name2}'."
+            )
+
+    def add_embeddings(self, embeddings: Optional[Union[dict, List[dict]]]):
+        """Add external embeddings to this layer.
+        Use case:
+        Args:
+            embeddings (Union[dict, list[dict]]): The external embeddings to
+                be added. Each dict must contain the following 4 fields: 'name'
+                (the name of this embedding), 'embedding' (the embedding
+                tensor), 'start' (the start token id of this embedding), 'end'
+                (the end token id of this embedding). For example:
+                `{name: NAME, start: START, end: END, embedding: torch.Tensor}`
+        """
+        if isinstance(embeddings, dict):
+            embeddings = [embeddings]
+
+        self.external_embeddings += embeddings
+        self.check_duplicate_names(self.external_embeddings)
+        self.check_ids_overlap(self.external_embeddings)
+
+        # set for trainable
+        added_trainable_emb_info = []
+        for embedding in embeddings:
+            trainable = embedding.get("trainable", False)
+            if trainable:
+                name = embedding["name"]
+                embedding["embedding"] = torch.nn.Parameter(embedding["embedding"])
+                self.trainable_embeddings[name] = embedding["embedding"]
+                added_trainable_emb_info.append(name)
+
+        added_emb_info = [emb["name"] for emb in embeddings]
+        added_emb_info = ", ".join(added_emb_info)
+        print(f"Successfully add external embeddings: {added_emb_info}.", "current")
+
+        if added_trainable_emb_info:
+            added_trainable_emb_info = ", ".join(added_trainable_emb_info)
+            print("Successfully add trainable external embeddings: " f"{added_trainable_emb_info}", "current")
+
+    def replace_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        """Replace external input ids to 0.
+
+        Args:
+            input_ids (torch.Tensor): The input ids to be replaced.
+
+        Returns:
+            torch.Tensor: The replaced input ids.
+        """
+        input_ids_fwd = input_ids.clone()
+        input_ids_fwd[input_ids_fwd >= self.num_embeddings] = 0
+        return input_ids_fwd
+
+    def replace_embeddings(
+            self, input_ids: torch.Tensor, embedding: torch.Tensor, external_embedding: dict
+    ) -> torch.Tensor:
+        """Replace external embedding to the embedding layer. Noted that, in
+        this function we use `torch.cat` to avoid inplace modification.
+
+        Args:
+            input_ids (torch.Tensor): The original token ids. Shape like
+                [LENGTH, ].
+            embedding (torch.Tensor): The embedding of token ids after
+                `replace_input_ids` function.
+            external_embedding (dict): The external embedding to be replaced.
+
+        Returns:
+            torch.Tensor: The replaced embedding.
+        """
+        new_embedding = []
+
+        name = external_embedding["name"]
+        start = external_embedding["start"]
+        end = external_embedding["end"]
+        target_ids_to_replace = [i for i in range(start, end)]
+        ext_emb = external_embedding["embedding"].to(embedding.device)
+
+        # do not need to replace
+        if not (input_ids == start).any():
+            return embedding
+
+        # start replace
+        s_idx, e_idx = 0, 0
+        while e_idx < len(input_ids):
+            if input_ids[e_idx] == start:
+                if e_idx != 0:
+                    # add embedding do not need to replace
+                    new_embedding.append(embedding[s_idx:e_idx])
+
+                # check if the next embedding need to replace is valid
+                actually_ids_to_replace = [int(i) for i in input_ids[e_idx: e_idx + end - start]]
+                assert actually_ids_to_replace == target_ids_to_replace, (
+                    f"Invalid 'input_ids' in position: {s_idx} to {e_idx}. "
+                    f"Expect '{target_ids_to_replace}' for embedding "
+                    f"'{name}' but found '{actually_ids_to_replace}'."
+                )
+
+                new_embedding.append(ext_emb)
+
+                s_idx = e_idx + end - start
+                e_idx = s_idx + 1
+            else:
+                e_idx += 1
+
+        if e_idx == len(input_ids):
+            new_embedding.append(embedding[s_idx:e_idx])
+
+        return torch.cat(new_embedding, dim=0)
+
+    def forward(self, input_ids: torch.Tensor, external_embeddings: Optional[List[dict]] = None):
+        """The forward function.
+
+        Args:
+            input_ids (torch.Tensor): The token ids shape like [bz, LENGTH] or
+                [LENGTH, ].
+            external_embeddings (Optional[List[dict]]): The external
+                embeddings. If not passed, only `self.external_embeddings`
+                will be used.  Defaults to None.
+
+        input_ids: shape like [bz, LENGTH] or [LENGTH].
+        """
+        assert input_ids.ndim in [1, 2]
+        if input_ids.ndim == 1:
+            input_ids = input_ids.unsqueeze(0)
+
+        if external_embeddings is None and not self.external_embeddings:
+            return self.wrapped(input_ids)
+
+        input_ids_fwd = self.replace_input_ids(input_ids)
+        inputs_embeds = self.wrapped(input_ids_fwd)
+
+        vecs = []
+
+        if external_embeddings is None:
+            external_embeddings = []
+        elif isinstance(external_embeddings, dict):
+            external_embeddings = [external_embeddings]
+        embeddings = self.external_embeddings + external_embeddings
+
+        for input_id, embedding in zip(input_ids, inputs_embeds):
+            new_embedding = embedding
+            for external_embedding in embeddings:
+                new_embedding = self.replace_embeddings(input_id, new_embedding, external_embedding)
+            vecs.append(new_embedding)
+
+        return torch.stack(vecs)
+
+
+def add_tokens(
+        tokenizer, text_encoder, placeholder_tokens: list, initialize_tokens: list = None,
+        num_vectors_per_token: int = 1
+):
+    """Add token for training.
+
+    # TODO: support add tokens as dict, then we can load pretrained tokens.
+    """
+    if initialize_tokens is not None:
+        assert len(initialize_tokens) == len(
+            placeholder_tokens
+        ), "placeholder_token should be the same length as initialize_token"
+    for ii in range(len(placeholder_tokens)):
+        tokenizer.add_placeholder_token(placeholder_tokens[ii], num_vec_per_token=num_vectors_per_token)
+
+    # text_encoder.set_embedding_layer()
+    embedding_layer = text_encoder.text_model.embeddings.token_embedding
+    text_encoder.text_model.embeddings.token_embedding = EmbeddingLayerWithFixes(embedding_layer)
+    embedding_layer = text_encoder.text_model.embeddings.token_embedding
+
+    assert embedding_layer is not None, (
+        "Do not support get embedding layer for current text encoder. " "Please check your configuration."
+    )
+    initialize_embedding = []
+    if initialize_tokens is not None:
+        for ii in range(len(placeholder_tokens)):
+            init_id = tokenizer(initialize_tokens[ii]).input_ids[1]
+            temp_embedding = embedding_layer.weight[init_id]
+            initialize_embedding.append(temp_embedding[None, ...].repeat(num_vectors_per_token, 1))
+    else:
+        for ii in range(len(placeholder_tokens)):
+            init_id = tokenizer("a").input_ids[1]
+            temp_embedding = embedding_layer.weight[init_id]
+            len_emb = temp_embedding.shape[0]
+            init_weight = (torch.rand(num_vectors_per_token, len_emb) - 0.5) / 2.0
+            initialize_embedding.append(init_weight)
+
+    # initialize_embedding  = torch.cat(initialize_embedding,dim=0)
+
+    token_info_all = []
+    for ii in range(len(placeholder_tokens)):
+        token_info = tokenizer.get_token_info(placeholder_tokens[ii])
+        token_info["embedding"] = initialize_embedding[ii]
+        token_info["trainable"] = True
+        token_info_all.append(token_info)
+    embedding_layer.add_embeddings(token_info_all)

py/config.py

@@ -3,7 +3,7 @@ import folder_paths
 from pathlib import Path
 
 BASE_RESOLUTIONS = [
-    ("自定义", "自定义"),
+    ("width", "height"),
     (512, 512),
     (512, 768),
     (576, 1024),
@@ -15,6 +15,7 @@ BASE_RESOLUTIONS = [
     (768, 1536),
     (816, 1920),
     (832, 1152),
+    (832, 1216),
     (896, 1152),
     (896, 1088),
     (1024, 1024),
@@ -23,6 +24,7 @@ BASE_RESOLUTIONS = [
     (1080, 1920),
     (1440, 2560),
     (1088, 896),
+    (1216, 832),
     (1152, 832),
     (1152, 896),
     (1280, 768),
@@ -76,6 +78,15 @@ BRUSHNET_MODELS = {
         }
     }
 }
+POWERPAINT_MODELS = {
+    "base_fp16": {
+        "model_url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/text_encoder/model.fp16.safetensors"
+    },
+    "v2.1": {
+        "model_url": "https://huggingface.co/JunhaoZhuang/PowerPaint-v2-1/resolve/main/PowerPaint_Brushnet/diffusion_pytorch_model.safetensors",
+        "clip_url": "https://huggingface.co/JunhaoZhuang/PowerPaint-v2-1/resolve/main/PowerPaint_Brushnet/pytorch_model.bin",
+    }
+}
 
 # layerDiffuse
 LAYER_DIFFUSION_DIR = os.path.join(folder_paths.models_dir, "layer_model")
@@ -217,6 +228,14 @@ IPADAPTER_MODELS = {
             "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.safetensors"
         }
     },
+    "PLUS (kolors genernal)":{
+        "sd15":{
+            "model_url":""
+        },
+        "sdxl":{
+            "model_url":"https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-Plus/resolve/main/ip_adapter_plus_general.bin"
+        }
+    },
     "PLUS FACE (portraits)": {
         "sd15": {
             "model_url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.safetensors"
@@ -263,6 +282,14 @@ IPADAPTER_MODELS = {
             "lora_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sdxl_lora.safetensors"
         }
     },
+    "FACEID PLUS KOLORS":{
+        "sd15":{
+
+        },
+        "sdxl":{
+            "model_url":"https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus/resolve/main/ipa-faceid-plus.bin"
+        }
+    },
     "FACEID PORTRAIT (style transfer)": {
         "sd15": {
             "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait-v11_sd15.bin",
@@ -271,6 +298,14 @@ IPADAPTER_MODELS = {
             "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sdxl.bin",
         }
     },
+    "FACEID PORTRAIT UNNORM - SDXL only (strong)": {
+        "sd15": {
+            "model_url":""
+        },
+        "sdxl": {
+            "model_url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sdxl_unnorm.bin",
+        }
+    },
     "COMPOSITION": {
         "sd15": {
             "model_url": "https://huggingface.co/ostris/ip-composition-adapter/resolve/main/ip_plus_composition_sd15.safetensors"
@@ -280,6 +315,14 @@ IPADAPTER_MODELS = {
         }
     }
 }
+IPADAPTER_CLIPVISION_MODELS = {
+    "clip-vit-large-patch14-336":{
+        "model_url": "https://huggingface.co/openai/clip-vit-large-patch14-336/resolve/main/pytorch_model.bin"
+    },
+    "clip-vit-h-14-laion2B-s32B-b79K":{
+        "model_url": "https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_pytorch_model.safetensors"
+    }
+}
 
 # dynamiCrafter
 DYNAMICRAFTER_DIR = os.path.join(folder_paths.models_dir, "dynamicrafter_models")

py/dit/__init__.py

@@ -0,0 +1,2 @@
+#credit to city96 for this module
+#from https://github.com/city96/ComfyUI_ExtraModels/

py/dit/config.py

@@ -0,0 +1,120 @@
+"""
+List of all DiT model types / settings
+"""
+sampling_settings = {
+	"beta_schedule" : "sqrt_linear",
+	"linear_start"  : 0.0001,
+	"linear_end"    : 0.02,
+	"timesteps"     : 1000,
+}
+
+dit_conf = {
+	"XL/2": { # DiT_XL_2
+		"unet_config": {
+			"depth"       :   28,
+			"num_heads"   :   16,
+			"patch_size"  :    2,
+			"hidden_size" : 1152,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"XL/4": { # DiT_XL_4
+		"unet_config": {
+			"depth"       :   28,
+			"num_heads"   :   16,
+			"patch_size"  :    4,
+			"hidden_size" : 1152,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"XL/8": { # DiT_XL_8
+		"unet_config": {
+			"depth"       :   28,
+			"num_heads"   :   16,
+			"patch_size"  :    8,
+			"hidden_size" : 1152,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"L/2": { # DiT_L_2
+		"unet_config": {
+			"depth"       :   24,
+			"num_heads"   :   16,
+			"patch_size"  :    2,
+			"hidden_size" : 1024,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"L/4": { # DiT_L_4
+		"unet_config": {
+			"depth"       :   24,
+			"num_heads"   :   16,
+			"patch_size"  :    4,
+			"hidden_size" : 1024,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"L/8": { # DiT_L_8
+		"unet_config": {
+			"depth"       :   24,
+			"num_heads"   :   16,
+			"patch_size"  :    8,
+			"hidden_size" : 1024,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"B/2": { # DiT_B_2
+		"unet_config": {
+			"depth"       :   12,
+			"num_heads"   :   12,
+			"patch_size"  :    2,
+			"hidden_size" :  768,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"B/4": { # DiT_B_4
+		"unet_config": {
+			"depth"       :   12,
+			"num_heads"   :   12,
+			"patch_size"  :    4,
+			"hidden_size" :  768,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"B/8": { # DiT_B_8
+		"unet_config": {
+			"depth"       :   12,
+			"num_heads"   :   12,
+			"patch_size"  :    8,
+			"hidden_size" :  768,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"S/2": { # DiT_S_2
+		"unet_config": {
+			"depth"       :   12,
+			"num_heads"   :    6,
+			"patch_size"  :    2,
+			"hidden_size" :  384,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"S/4": { # DiT_S_4
+		"unet_config": {
+			"depth"       :   12,
+			"num_heads"   :    6,
+			"patch_size"  :    4,
+			"hidden_size" :  384,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"S/8": { # DiT_S_8
+		"unet_config": {
+			"depth"       :   12,
+			"num_heads"   :    6,
+			"patch_size"  :    8,
+			"hidden_size" :  384,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+}

py/dit/pixArt/LICENSE-Pixart

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
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py/dit/pixArt/config.py

@@ -0,0 +1,139 @@
+"""
+List of all PixArt model types / settings
+"""
+sampling_settings = {
+	"beta_schedule" : "sqrt_linear",
+	"linear_start"  : 0.0001,
+	"linear_end"    : 0.02,
+	"timesteps"     : 1000,
+}
+
+pixart_conf = {
+	"PixArtMS_XL_2": { # models/PixArtMS
+		"target": "PixArtMS",
+		"unet_config": {
+			"input_size"  : 1024//8,
+			"depth"       :   28,
+			"num_heads"   :   16,
+			"patch_size"  :    2,
+			"hidden_size" : 1152,
+			"pe_interpolation": 2,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"PixArtMS_Sigma_XL_2": {
+		"target": "PixArtMSSigma",
+		"unet_config": {
+			"input_size"  : 1024//8,
+			"token_num"   :  300,
+			"depth"       :   28,
+			"num_heads"   :   16,
+			"patch_size"  :    2,
+			"hidden_size" : 1152,
+			"micro_condition": False,
+			"pe_interpolation": 2,
+			"model_max_length": 300,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"PixArtMS_Sigma_XL_2_900M": {
+		"target": "PixArtMSSigma",
+		"unet_config": {
+			"input_size": 1024 // 8,
+			"token_num": 300,
+			"depth": 42,
+			"num_heads": 16,
+			"patch_size": 2,
+			"hidden_size": 1152,
+			"micro_condition": False,
+			"pe_interpolation": 2,
+			"model_max_length": 300,
+		},
+		"sampling_settings": sampling_settings,
+	},
+	"PixArtMS_Sigma_XL_2_2K": {
+		"target": "PixArtMSSigma",
+		"unet_config": {
+			"input_size"  : 2048//8,
+			"token_num"   :  300,
+			"depth"       :   28,
+			"num_heads"   :   16,
+			"patch_size"  :    2,
+			"hidden_size" : 1152,
+			"micro_condition": False,
+			"pe_interpolation": 4,
+			"model_max_length": 300,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+	"PixArt_XL_2": { # models/PixArt
+		"target": "PixArt",
+		"unet_config": {
+			"input_size"  :  512//8,
+			"token_num"   :  120,
+			"depth"       :   28,
+			"num_heads"   :   16,
+			"patch_size"  :    2,
+			"hidden_size" : 1152,
+			"pe_interpolation": 1,
+		},
+		"sampling_settings" : sampling_settings,
+	},
+}
+
+pixart_conf.update({ # controlnet models
+	"ControlPixArtHalf": {
+		"target": "ControlPixArtHalf",
+		"unet_config": pixart_conf["PixArt_XL_2"]["unet_config"],
+		"sampling_settings": pixart_conf["PixArt_XL_2"]["sampling_settings"],
+	},
+	"ControlPixArtMSHalf": {
+		"target": "ControlPixArtMSHalf",
+		"unet_config": pixart_conf["PixArtMS_XL_2"]["unet_config"],
+		"sampling_settings": pixart_conf["PixArtMS_XL_2"]["sampling_settings"],
+	}
+})
+
+pixart_res = {
+	"PixArtMS_XL_2": { # models/PixArtMS 1024x1024
+		'0.25': [512, 2048], '0.26': [512, 1984], '0.27': [512, 1920], '0.28': [512, 1856],
+		'0.32': [576, 1792], '0.33': [576, 1728], '0.35': [576, 1664], '0.40': [640, 1600],
+		'0.42': [640, 1536], '0.48': [704, 1472], '0.50': [704, 1408], '0.52': [704, 1344],
+		'0.57': [768, 1344], '0.60': [768, 1280], '0.68': [832, 1216], '0.72': [832, 1152],
+		'0.78': [896, 1152], '0.82': [896, 1088], '0.88': [960, 1088], '0.94': [960, 1024],
+		'1.00': [1024,1024], '1.07': [1024, 960], '1.13': [1088, 960], '1.21': [1088, 896],
+		'1.29': [1152, 896], '1.38': [1152, 832], '1.46': [1216, 832], '1.67': [1280, 768],
+		'1.75': [1344, 768], '2.00': [1408, 704], '2.09': [1472, 704], '2.40': [1536, 640],
+		'2.50': [1600, 640], '2.89': [1664, 576], '3.00': [1728, 576], '3.11': [1792, 576],
+		'3.62': [1856, 512], '3.75': [1920, 512], '3.88': [1984, 512], '4.00': [2048, 512],
+	},
+	"PixArt_XL_2": { # models/PixArt 512x512
+		'0.25': [256,1024], '0.26': [256, 992], '0.27': [256, 960], '0.28': [256, 928],
+		'0.32': [288, 896], '0.33': [288, 864], '0.35': [288, 832], '0.40': [320, 800],
+		'0.42': [320, 768], '0.48': [352, 736], '0.50': [352, 704], '0.52': [352, 672],
+		'0.57': [384, 672], '0.60': [384, 640], '0.68': [416, 608], '0.72': [416, 576],
+		'0.78': [448, 576], '0.82': [448, 544], '0.88': [480, 544], '0.94': [480, 512],
+		'1.00': [512, 512], '1.07': [512, 480], '1.13': [544, 480], '1.21': [544, 448],
+		'1.29': [576, 448], '1.38': [576, 416], '1.46': [608, 416], '1.67': [640, 384],
+		'1.75': [672, 384], '2.00': [704, 352], '2.09': [736, 352], '2.40': [768, 320],
+		'2.50': [800, 320], '2.89': [832, 288], '3.00': [864, 288], '3.11': [896, 288],
+		'3.62': [928, 256], '3.75': [960, 256], '3.88': [992, 256], '4.00': [1024,256]
+	},
+	"PixArtMS_Sigma_XL_2_2K": {
+		'0.25': [1024, 4096], '0.26': [1024, 3968], '0.27': [1024, 3840], '0.28': [1024, 3712],
+		'0.32': [1152, 3584], '0.33': [1152, 3456], '0.35': [1152, 3328], '0.40': [1280, 3200],
+		'0.42': [1280, 3072], '0.48': [1408, 2944], '0.50': [1408, 2816], '0.52': [1408, 2688],
+		'0.57': [1536, 2688], '0.60': [1536, 2560], '0.68': [1664, 2432], '0.72': [1664, 2304],
+		'0.78': [1792, 2304], '0.82': [1792, 2176], '0.88': [1920, 2176], '0.94': [1920, 2048],
+		'1.00': [2048, 2048], '1.07': [2048, 1920], '1.13': [2176, 1920], '1.21': [2176, 1792],
+		'1.29': [2304, 1792], '1.38': [2304, 1664], '1.46': [2432, 1664], '1.67': [2560, 1536],
+		'1.75': [2688, 1536], '2.00': [2816, 1408], '2.09': [2944, 1408], '2.40': [3072, 1280],
+		'2.50': [3200, 1280], '2.89': [3328, 1152], '3.00': [3456, 1152], '3.11': [3584, 1152],
+		'3.62': [3712, 1024], '3.75': [3840, 1024], '3.88': [3968, 1024], '4.00': [4096, 1024]
+	}
+}
+# These should be the same
+pixart_res.update({
+	"PixArtMS_Sigma_XL_2": pixart_res["PixArtMS_XL_2"],
+	"PixArtMS_Sigma_XL_2_512": pixart_res["PixArt_XL_2"],
+})

py/dit/pixArt/diffusers_convert.py

@@ -0,0 +1,216 @@
+# For using the diffusers format weights
+#  Based on the original ComfyUI function +
+#  https://github.com/PixArt-alpha/PixArt-alpha/blob/master/tools/convert_pixart_alpha_to_diffusers.py
+import torch
+
+conversion_map_ms = [  # for multi_scale_train (MS)
+    # Resolution
+    ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
+    ("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"),
+    ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
+    ("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"),
+    # Aspect ratio
+    ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
+    ("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
+    ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
+    ("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
+]
+
+
+def get_depth(state_dict):
+    return sum(key.endswith('.attn1.to_k.bias') for key in state_dict.keys())
+
+
+def get_lora_depth(state_dict):
+    return sum(key.endswith('.attn1.to_k.lora_A.weight') for key in state_dict.keys())
+
+
+def get_conversion_map(state_dict):
+    conversion_map = [  # main SD conversion map (PixArt reference, HF Diffusers)
+        # Patch embeddings
+        ("x_embedder.proj.weight", "pos_embed.proj.weight"),
+        ("x_embedder.proj.bias", "pos_embed.proj.bias"),
+        # Caption projection
+        ("y_embedder.y_embedding", "caption_projection.y_embedding"),
+        ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"),
+        ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"),
+        ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"),
+        ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"),
+        # AdaLN-single LN
+        ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"),
+        ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"),
+        ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"),
+        ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"),
+        # Shared norm
+        ("t_block.1.weight", "adaln_single.linear.weight"),
+        ("t_block.1.bias", "adaln_single.linear.bias"),
+        # Final block
+        ("final_layer.linear.weight", "proj_out.weight"),
+        ("final_layer.linear.bias", "proj_out.bias"),
+        ("final_layer.scale_shift_table", "scale_shift_table"),
+    ]
+
+    # Add actual transformer blocks
+    for depth in range(get_depth(state_dict)):
+        # Transformer blocks
+        conversion_map += [
+            (f"blocks.{depth}.scale_shift_table", f"transformer_blocks.{depth}.scale_shift_table"),
+            # Projection
+            (f"blocks.{depth}.attn.proj.weight", f"transformer_blocks.{depth}.attn1.to_out.0.weight"),
+            (f"blocks.{depth}.attn.proj.bias", f"transformer_blocks.{depth}.attn1.to_out.0.bias"),
+            # Feed-forward
+            (f"blocks.{depth}.mlp.fc1.weight", f"transformer_blocks.{depth}.ff.net.0.proj.weight"),
+            (f"blocks.{depth}.mlp.fc1.bias", f"transformer_blocks.{depth}.ff.net.0.proj.bias"),
+            (f"blocks.{depth}.mlp.fc2.weight", f"transformer_blocks.{depth}.ff.net.2.weight"),
+            (f"blocks.{depth}.mlp.fc2.bias", f"transformer_blocks.{depth}.ff.net.2.bias"),
+            # Cross-attention (proj)
+            (f"blocks.{depth}.cross_attn.proj.weight", f"transformer_blocks.{depth}.attn2.to_out.0.weight"),
+            (f"blocks.{depth}.cross_attn.proj.bias", f"transformer_blocks.{depth}.attn2.to_out.0.bias"),
+        ]
+    return conversion_map
+
+
+def find_prefix(state_dict, target_key):
+    prefix = ""
+    for k in state_dict.keys():
+        if k.endswith(target_key):
+            prefix = k.split(target_key)[0]
+            break
+    return prefix
+
+
+def convert_state_dict(state_dict):
+    if "adaln_single.emb.resolution_embedder.linear_1.weight" in state_dict.keys():
+        cmap = get_conversion_map(state_dict) + conversion_map_ms
+    else:
+        cmap = get_conversion_map(state_dict)
+
+    missing = [k for k, v in cmap if v not in state_dict]
+    new_state_dict = {k: state_dict[v] for k, v in cmap if k not in missing}
+    matched = list(v for k, v in cmap if v in state_dict.keys())
+
+    for depth in range(get_depth(state_dict)):
+        for wb in ["weight", "bias"]:
+            # Self Attention
+            key = lambda a: f"transformer_blocks.{depth}.attn1.to_{a}.{wb}"
+            new_state_dict[f"blocks.{depth}.attn.qkv.{wb}"] = torch.cat((
+                state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
+            ), dim=0)
+            matched += [key('q'), key('k'), key('v')]
+
+            # Cross-attention (linear)
+            key = lambda a: f"transformer_blocks.{depth}.attn2.to_{a}.{wb}"
+            new_state_dict[f"blocks.{depth}.cross_attn.q_linear.{wb}"] = state_dict[key('q')]
+            new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.{wb}"] = torch.cat((
+                state_dict[key('k')], state_dict[key('v')]
+            ), dim=0)
+            matched += [key('q'), key('k'), key('v')]
+
+    if len(matched) < len(state_dict):
+        print(f"PixArt: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
+        print(list(set(state_dict.keys()) - set(matched)))
+
+    if len(missing) > 0:
+        print(f"PixArt: UNET conversion has missing keys!")
+        print(missing)
+
+    return new_state_dict
+
+
+# Same as above but for LoRA weights:
+def convert_lora_state_dict(state_dict, peft=True):
+    # koyha
+    rep_ak = lambda x: x.replace(".weight", ".lora_down.weight")
+    rep_bk = lambda x: x.replace(".weight", ".lora_up.weight")
+    rep_pk = lambda x: x.replace(".weight", ".alpha")
+    if peft:  # peft
+        rep_ap = lambda x: x.replace(".weight", ".lora_A.weight")
+        rep_bp = lambda x: x.replace(".weight", ".lora_B.weight")
+        rep_pp = lambda x: x.replace(".weight", ".alpha")
+
+        prefix = find_prefix(state_dict, "adaln_single.linear.lora_A.weight")
+        state_dict = {k[len(prefix):]: v for k, v in state_dict.items()}
+    else:  # OneTrainer
+        rep_ap = lambda x: x.replace(".", "_")[:-7] + ".lora_down.weight"
+        rep_bp = lambda x: x.replace(".", "_")[:-7] + ".lora_up.weight"
+        rep_pp = lambda x: x.replace(".", "_")[:-7] + ".alpha"
+
+        prefix = "lora_transformer_"
+        t5_marker = "lora_te_encoder"
+        t5_keys = []
+        for key in list(state_dict.keys()):
+            if key.startswith(prefix):
+                state_dict[key[len(prefix):]] = state_dict.pop(key)
+            elif t5_marker in key:
+                t5_keys.append(state_dict.pop(key))
+        if len(t5_keys) > 0:
+            print(f"Text Encoder not supported for PixArt LoRA, ignoring {len(t5_keys)} keys")
+
+    cmap = []
+    cmap_unet = get_conversion_map(state_dict) + conversion_map_ms  # todo: 512 model
+    for k, v in cmap_unet:
+        if v.endswith(".weight"):
+            cmap.append((rep_ak(k), rep_ap(v)))
+            cmap.append((rep_bk(k), rep_bp(v)))
+            if not peft:
+                cmap.append((rep_pk(k), rep_pp(v)))
+
+    missing = [k for k, v in cmap if v not in state_dict]
+    new_state_dict = {k: state_dict[v] for k, v in cmap if k not in missing}
+    matched = list(v for k, v in cmap if v in state_dict.keys())
+
+    lora_depth = get_lora_depth(state_dict)
+    for fp, fk in ((rep_ap, rep_ak), (rep_bp, rep_bk)):
+        for depth in range(lora_depth):
+            # Self Attention
+            key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
+            new_state_dict[fk(f"blocks.{depth}.attn.qkv.weight")] = torch.cat((
+                state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
+            ), dim=0)
+
+            matched += [key('q'), key('k'), key('v')]
+            if not peft:
+                akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
+                new_state_dict[rep_pk((f"blocks.{depth}.attn.qkv.weight"))] = state_dict[akey("q")]
+                matched += [akey('q'), akey('k'), akey('v')]
+
+            # Self Attention projection?
+            key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
+            new_state_dict[fk(f"blocks.{depth}.attn.proj.weight")] = state_dict[key('out.0')]
+            matched += [key('out.0')]
+
+            # Cross-attention (linear)
+            key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
+            new_state_dict[fk(f"blocks.{depth}.cross_attn.q_linear.weight")] = state_dict[key('q')]
+            new_state_dict[fk(f"blocks.{depth}.cross_attn.kv_linear.weight")] = torch.cat((
+                state_dict[key('k')], state_dict[key('v')]
+            ), dim=0)
+            matched += [key('q'), key('k'), key('v')]
+            if not peft:
+                akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
+                new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.q_linear.weight"))] = state_dict[akey("q")]
+                new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.kv_linear.weight"))] = state_dict[akey("k")]
+                matched += [akey('q'), akey('k'), akey('v')]
+
+            # Cross Attention projection?
+            key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
+            new_state_dict[fk(f"blocks.{depth}.cross_attn.proj.weight")] = state_dict[key('out.0')]
+            matched += [key('out.0')]
+
+            key = fp(f"transformer_blocks.{depth}.ff.net.0.proj.weight")
+            new_state_dict[fk(f"blocks.{depth}.mlp.fc1.weight")] = state_dict[key]
+            matched += [key]
+
+            key = fp(f"transformer_blocks.{depth}.ff.net.2.weight")
+            new_state_dict[fk(f"blocks.{depth}.mlp.fc2.weight")] = state_dict[key]
+            matched += [key]
+
+    if len(matched) < len(state_dict):
+        print(f"PixArt: LoRA conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
+        print(list(set(state_dict.keys()) - set(matched)))
+
+    if len(missing) > 0:
+        print(f"PixArt: LoRA conversion has missing keys! (probably)")
+        print(missing)
+
+    return new_state_dict

py/dit/pixArt/loader.py

@@ -0,0 +1,329 @@
+import torch
+import math
+import comfy.supported_models_base
+import comfy.latent_formats
+import comfy.model_patcher
+import comfy.model_base
+import comfy.utils
+import comfy.conds
+from comfy import model_management
+from .diffusers_convert import convert_state_dict
+
+# checkpointbf
+class EXM_PixArt(comfy.supported_models_base.BASE):
+    unet_config = {}
+    unet_extra_config = {}
+    latent_format = comfy.latent_formats.SD15
+
+    def __init__(self, model_conf):
+        self.model_target = model_conf.get("target")
+        self.unet_config = model_conf.get("unet_config", {})
+        self.sampling_settings = model_conf.get("sampling_settings", {})
+        self.latent_format = self.latent_format()
+        # UNET is handled by extension
+        self.unet_config["disable_unet_model_creation"] = True
+
+    def model_type(self, state_dict, prefix=""):
+        return comfy.model_base.ModelType.EPS
+
+
+class EXM_PixArt_Model(comfy.model_base.BaseModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+
+        img_hw = kwargs.get("img_hw", None)
+        if img_hw is not None:
+            out["img_hw"] = comfy.conds.CONDRegular(torch.tensor(img_hw))
+
+        aspect_ratio = kwargs.get("aspect_ratio", None)
+        if aspect_ratio is not None:
+            out["aspect_ratio"] = comfy.conds.CONDRegular(torch.tensor(aspect_ratio))
+
+        cn_hint = kwargs.get("cn_hint", None)
+        if cn_hint is not None:
+            out["cn_hint"] = comfy.conds.CONDRegular(cn_hint)
+
+        return out
+
+
+def load_pixart(model_path, model_conf=None):
+    state_dict = comfy.utils.load_torch_file(model_path)
+    state_dict = state_dict.get("model", state_dict)
+
+    # prefix
+    for prefix in ["model.diffusion_model.", ]:
+        if any(True for x in state_dict if x.startswith(prefix)):
+            state_dict = {k[len(prefix):]: v for k, v in state_dict.items()}
+
+    # diffusers
+    if "adaln_single.linear.weight" in state_dict:
+        state_dict = convert_state_dict(state_dict)  # Diffusers
+
+    # guess auto config
+    if model_conf is None:
+        model_conf = guess_pixart_config(state_dict)
+
+    parameters = comfy.utils.calculate_parameters(state_dict)
+    unet_dtype = model_management.unet_dtype(model_params=parameters)
+    load_device = comfy.model_management.get_torch_device()
+    offload_device = comfy.model_management.unet_offload_device()
+
+    # ignore fp8/etc and use directly for now
+    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
+    if manual_cast_dtype:
+        print(f"PixArt: falling back to {manual_cast_dtype}")
+        unet_dtype = manual_cast_dtype
+
+    model_conf = EXM_PixArt(model_conf)  # convert to object
+    model = EXM_PixArt_Model(  # same as comfy.model_base.BaseModel
+        model_conf,
+        model_type=comfy.model_base.ModelType.EPS,
+        device=model_management.get_torch_device()
+    )
+
+    if model_conf.model_target == "PixArtMS":
+        from .models.PixArtMS import PixArtMS
+        model.diffusion_model = PixArtMS(**model_conf.unet_config)
+    elif model_conf.model_target == "PixArt":
+        from .models.PixArt import PixArt
+        model.diffusion_model = PixArt(**model_conf.unet_config)
+    elif model_conf.model_target == "PixArtMSSigma":
+        from .models.PixArtMS import PixArtMS
+        model.diffusion_model = PixArtMS(**model_conf.unet_config)
+        model.latent_format = comfy.latent_formats.SDXL()
+    elif model_conf.model_target == "ControlPixArtMSHalf":
+        from .models.PixArtMS import PixArtMS
+        from .models.pixart_controlnet import ControlPixArtMSHalf
+        model.diffusion_model = PixArtMS(**model_conf.unet_config)
+        model.diffusion_model = ControlPixArtMSHalf(model.diffusion_model)
+    elif model_conf.model_target == "ControlPixArtHalf":
+        from .models.PixArt import PixArt
+        from .models.pixart_controlnet import ControlPixArtHalf
+        model.diffusion_model = PixArt(**model_conf.unet_config)
+        model.diffusion_model = ControlPixArtHalf(model.diffusion_model)
+    else:
+        raise NotImplementedError(f"Unknown model target '{model_conf.model_target}'")
+
+    m, u = model.diffusion_model.load_state_dict(state_dict, strict=False)
+    if len(m) > 0: print("Missing UNET keys", m)
+    if len(u) > 0: print("Leftover UNET keys", u)
+    model.diffusion_model.dtype = unet_dtype
+    model.diffusion_model.eval()
+    model.diffusion_model.to(unet_dtype)
+
+    model_patcher = comfy.model_patcher.ModelPatcher(
+        model,
+        load_device=load_device,
+        offload_device=offload_device,
+        current_device="cpu",
+    )
+    return model_patcher
+
+
+def guess_pixart_config(sd):
+    """
+    Guess config based on converted state dict.
+    """
+    # Shared settings based on DiT_XL_2 - could be enumerated
+    config = {
+        "num_heads": 16,  # get from attention
+        "patch_size": 2,  # final layer I guess?
+        "hidden_size": 1152,  # pos_embed.shape[2]
+    }
+    config["depth"] = sum([key.endswith(".attn.proj.weight") for key in sd.keys()]) or 28
+
+    try:
+        # this is not present in the diffusers version for sigma?
+        config["model_max_length"] = sd["y_embedder.y_embedding"].shape[0]
+    except KeyError:
+        # need better logic to guess this
+        config["model_max_length"] = 300
+
+    if "pos_embed" in sd:
+        config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"]
+        config["pe_interpolation"] = config["input_size"] // (512 // 8)  # dumb guess
+
+    target_arch = "PixArtMS"
+    if config["model_max_length"] == 300:
+        # Sigma
+        target_arch = "PixArtMSSigma"
+        config["micro_condition"] = False
+        if "input_size" not in config:
+            # The diffusers weights for 1K/2K are exactly the same...?
+            # replace patch embed logic with HyDiT?
+            print(f"PixArt: diffusers weights - 2K model will be broken, use manual loading!")
+            config["input_size"] = 1024 // 8
+    else:
+        # Alpha
+        if "csize_embedder.mlp.0.weight" in sd:
+            # MS (microconds)
+            target_arch = "PixArtMS"
+            config["micro_condition"] = True
+            if "input_size" not in config:
+                config["input_size"] = 1024 // 8
+                config["pe_interpolation"] = 2
+        else:
+            # PixArt
+            target_arch = "PixArt"
+            if "input_size" not in config:
+                config["input_size"] = 512 // 8
+                config["pe_interpolation"] = 1
+
+    print("PixArt guessed config:", target_arch, config)
+    return {
+        "target": target_arch,
+        "unet_config": config,
+        "sampling_settings": {
+            "beta_schedule": "sqrt_linear",
+            "linear_start": 0.0001,
+            "linear_end": 0.02,
+            "timesteps": 1000,
+        }
+    }
+
+# lora
+class EXM_PixArt_ModelPatcher(comfy.model_patcher.ModelPatcher):
+    def calculate_weight(self, patches, weight, key):
+        """
+        This is almost the same as the comfy function, but stripped down to just the LoRA patch code.
+        The problem with the original code is the q/k/v keys being combined into one for the attention.
+        In the diffusers code, they're treated as separate keys, but in the reference code they're recombined (q+kv|qkv).
+        This means, for example, that the [1152,1152] weights become [3456,1152] in the state dict.
+        The issue with this is that the LoRA weights are [128,1152],[1152,128] and become [384,1162],[3456,128] instead.
+
+        This is the best thing I could think of that would fix that, but it's very fragile.
+         - Check key shape to determine if it needs the fallback logic
+         - Cut the input into parts based on the shape (undoing the torch.cat)
+         - Do the matrix multiplication logic
+         - Recombine them to match the expected shape
+        """
+        for p in patches:
+            alpha = p[0]
+            v = p[1]
+            strength_model = p[2]
+            if strength_model != 1.0:
+                weight *= strength_model
+
+            if isinstance(v, list):
+                v = (self.calculate_weight(v[1:], v[0].clone(), key),)
+
+            if len(v) == 2:
+                patch_type = v[0]
+                v = v[1]
+
+            if patch_type == "lora":
+                mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
+                mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
+                if v[2] is not None:
+                    alpha *= v[2] / mat2.shape[0]
+                try:
+                    mat1 = mat1.flatten(start_dim=1)
+                    mat2 = mat2.flatten(start_dim=1)
+
+                    ch1 = mat1.shape[0] // mat2.shape[1]
+                    ch2 = mat2.shape[0] // mat1.shape[1]
+                    ### Fallback logic for shape mismatch ###
+                    if mat1.shape[0] != mat2.shape[1] and ch1 == ch2 and (mat1.shape[0] / mat2.shape[1]) % 1 == 0:
+                        mat1 = mat1.chunk(ch1, dim=0)
+                        mat2 = mat2.chunk(ch1, dim=0)
+                        weight += torch.cat(
+                            [alpha * torch.mm(mat1[x], mat2[x]) for x in range(ch1)],
+                            dim=0,
+                        ).reshape(weight.shape).type(weight.dtype)
+                    else:
+                        weight += (alpha * torch.mm(mat1, mat2)).reshape(weight.shape).type(weight.dtype)
+                except Exception as e:
+                    print("ERROR", key, e)
+        return weight
+
+    def clone(self):
+        n = EXM_PixArt_ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device,
+                                    weight_inplace_update=self.weight_inplace_update)
+        n.patches = {}
+        for k in self.patches:
+            n.patches[k] = self.patches[k][:]
+
+        n.object_patches = self.object_patches.copy()
+        n.model_options = copy.deepcopy(self.model_options)
+        n.model_keys = self.model_keys
+        return n
+
+
+def replace_model_patcher(model):
+    n = EXM_PixArt_ModelPatcher(
+        model=model.model,
+        size=model.size,
+        load_device=model.load_device,
+        offload_device=model.offload_device,
+        current_device=model.current_device,
+        weight_inplace_update=model.weight_inplace_update,
+    )
+    n.patches = {}
+    for k in model.patches:
+        n.patches[k] = model.patches[k][:]
+
+    n.object_patches = model.object_patches.copy()
+    n.model_options = copy.deepcopy(model.model_options)
+    return n
+
+
+def find_peft_alpha(path):
+    def load_json(json_path):
+        with open(json_path) as f:
+            data = json.load(f)
+        alpha = data.get("lora_alpha")
+        alpha = alpha or data.get("alpha")
+        if not alpha:
+            print(" Found config but `lora_alpha` is missing!")
+        else:
+            print(f" Found config at {json_path} [alpha:{alpha}]")
+        return alpha
+
+    # For some weird reason peft doesn't include the alpha in the actual model
+    print("PixArt: Warning! This is a PEFT LoRA. Trying to find config...")
+    files = [
+        f"{os.path.splitext(path)[0]}.json",
+        f"{os.path.splitext(path)[0]}.config.json",
+        os.path.join(os.path.dirname(path), "adapter_config.json"),
+    ]
+    for file in files:
+        if os.path.isfile(file):
+            return load_json(file)
+
+    print(" Missing config/alpha! assuming alpha of 8. Consider converting it/adding a config json to it.")
+    return 8.0
+
+
+def load_pixart_lora(model, lora, lora_path, strength):
+    k_back = lambda x: x.replace(".lora_up.weight", "")
+    # need to convert the actual weights for this to work.
+    if any(True for x in lora.keys() if x.endswith("adaln_single.linear.lora_A.weight")):
+        lora = convert_lora_state_dict(lora, peft=True)
+        alpha = find_peft_alpha(lora_path)
+        lora.update({f"{k_back(x)}.alpha": torch.tensor(alpha) for x in lora.keys() if "lora_up" in x})
+    else:  # OneTrainer
+        lora = convert_lora_state_dict(lora, peft=False)
+
+    key_map = {k_back(x): f"diffusion_model.{k_back(x)}.weight" for x in lora.keys() if "lora_up" in x}  # fake
+
+    loaded = comfy.lora.load_lora(lora, key_map)
+    if model is not None:
+        # switch to custom model patcher when using LoRAs
+        if isinstance(model, EXM_PixArt_ModelPatcher):
+            new_modelpatcher = model.clone()
+        else:
+            new_modelpatcher = replace_model_patcher(model)
+        k = new_modelpatcher.add_patches(loaded, strength)
+    else:
+        k = ()
+        new_modelpatcher = None
+
+    k = set(k)
+    for x in loaded:
+        if (x not in k):
+            print("NOT LOADED", x)
+
+    return new_modelpatcher

py/dit/pixArt/models/PixArt.py

@@ -0,0 +1,250 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# GLIDE: https://github.com/openai/glide-text2im
+# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
+# --------------------------------------------------------
+import math
+import torch
+import torch.nn as nn
+import os
+import numpy as np
+from timm.models.layers import DropPath
+from timm.models.vision_transformer import PatchEmbed, Mlp
+
+
+from .utils import auto_grad_checkpoint, to_2tuple
+from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, LabelEmbedder, FinalLayer
+
+
+class PixArtBlock(nn.Module):
+    """
+    A PixArt block with adaptive layer norm (adaLN-single) conditioning.
+    """
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0, input_size=None, sampling=None, sr_ratio=1, qk_norm=False, **block_kwargs):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.attn = AttentionKVCompress(
+            hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
+            qk_norm=qk_norm, **block_kwargs
+        )
+        self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs)
+        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        # to be compatible with lower version pytorch
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
+        self.sampling = sampling
+        self.sr_ratio = sr_ratio
+
+    def forward(self, x, y, t, mask=None, **kwargs):
+        B, N, C = x.shape
+
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+        x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)).reshape(B, N, C))
+        x = x + self.cross_attn(x, y, mask)
+        x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
+
+        return x
+
+
+### Core PixArt Model ###
+class PixArt(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+    def __init__(
+            self,
+            input_size=32,
+            patch_size=2,
+            in_channels=4,
+            hidden_size=1152,
+            depth=28,
+            num_heads=16,
+            mlp_ratio=4.0,
+            class_dropout_prob=0.1,
+            pred_sigma=True,
+            drop_path: float = 0.,
+            caption_channels=4096,
+            pe_interpolation=1.0,
+            pe_precision=None,
+            config=None,
+            model_max_length=120,
+            qk_norm=False,
+            kv_compress_config=None,
+            **kwargs,
+    ):
+        super().__init__()
+        self.pred_sigma = pred_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if pred_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.pe_interpolation = pe_interpolation
+        self.pe_precision = pe_precision
+        self.depth = depth
+
+        self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
+        self.t_embedder = TimestepEmbedder(hidden_size)
+        num_patches = self.x_embedder.num_patches
+        self.base_size = input_size // self.patch_size
+        # Will use fixed sin-cos embedding:
+        self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
+
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.t_block = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
+        )
+        self.y_embedder = CaptionEmbedder(
+            in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,
+            act_layer=approx_gelu, token_num=model_max_length
+		)
+        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
+        self.kv_compress_config = kv_compress_config
+        if kv_compress_config is None:
+            self.kv_compress_config = {
+                'sampling': None,
+                'scale_factor': 1,
+                'kv_compress_layer': [],
+            }
+        self.blocks = nn.ModuleList([
+            PixArtBlock(
+                hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
+                input_size=(input_size // patch_size, input_size // patch_size),
+                sampling=self.kv_compress_config['sampling'],
+                sr_ratio=int(
+                    self.kv_compress_config['scale_factor']
+                ) if i in self.kv_compress_config['kv_compress_layer'] else 1,
+                qk_norm=qk_norm,
+            )
+            for i in range(depth)
+        ])
+        self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
+
+    def forward_raw(self, x, t, y, mask=None, data_info=None):
+        """
+        Original forward pass of PixArt.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N, 1, 120, C) tensor of class labels
+        """
+        x = x.to(self.dtype)
+        timestep = t.to(self.dtype)
+        y = y.to(self.dtype)
+        pos_embed = self.pos_embed.to(self.dtype)
+        self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
+        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        t = self.t_embedder(timestep.to(x.dtype))  # (N, D)
+        t0 = self.t_block(t)
+        y = self.y_embedder(y, self.training)  # (N, 1, L, D)
+        if mask is not None:
+            if mask.shape[0] != y.shape[0]:
+                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
+            mask = mask.squeeze(1).squeeze(1)
+            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
+            y_lens = mask.sum(dim=1).tolist()
+        else:
+            y_lens = [y.shape[2]] * y.shape[0]
+            y = y.squeeze(1).view(1, -1, x.shape[-1])
+        for block in self.blocks:
+            x = auto_grad_checkpoint(block, x, y, t0, y_lens)  # (N, T, D) #support grad checkpoint
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+        return x
+
+    def forward(self, x, timesteps, context, y=None, **kwargs):
+        """
+        Forward pass that adapts comfy input to original forward function
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        timesteps: (N,) tensor of diffusion timesteps
+        context: (N, 1, 120, C) conditioning
+        y: extra conditioning.
+        """
+        ## Still accepts the input w/o that dim but returns garbage
+        if len(context.shape) == 3:
+            context = context.unsqueeze(1)
+
+        ## run original forward pass
+        out = self.forward_raw(
+            x = x.to(self.dtype),
+            t = timesteps.to(self.dtype),
+            y = context.to(self.dtype),
+        )
+
+        ## only return EPS
+        out = out.to(torch.float)
+        eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
+        return eps
+
+    def unpatchify(self, x):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0]
+        h = w = int(x.shape[1] ** 0.5)
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
+        return imgs
+
+
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, pe_interpolation=1.0, base_size=16):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    if isinstance(grid_size, int):
+        grid_size = to_2tuple(grid_size)
+    grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0]/base_size) / pe_interpolation
+    grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1]/base_size) / pe_interpolation
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+    grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
+
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
+    return pos_embed.astype(np.float32)
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000 ** omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb

py/dit/pixArt/models/PixArtMS.py

@@ -0,0 +1,273 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# GLIDE: https://github.com/openai/glide-text2im
+# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
+# --------------------------------------------------------
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+from timm.models.layers import DropPath
+from timm.models.vision_transformer import Mlp
+
+from .utils import auto_grad_checkpoint, to_2tuple
+from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder
+from .PixArt import PixArt, get_2d_sincos_pos_embed
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D Image to Patch Embedding
+    """
+    def __init__(
+            self,
+            patch_size=16,
+            in_chans=3,
+            embed_dim=768,
+            norm_layer=None,
+            flatten=True,
+            bias=True,
+    ):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+        self.flatten = flatten
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        x = self.proj(x)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
+        x = self.norm(x)
+        return x
+
+
+class PixArtMSBlock(nn.Module):
+    """
+    A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,
+                 sampling=None, sr_ratio=1, qk_norm=False, **block_kwargs):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.attn = AttentionKVCompress(
+            hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
+            qk_norm=qk_norm, **block_kwargs
+        )
+        self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs)
+        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        # to be compatible with lower version pytorch
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
+
+    def forward(self, x, y, t, mask=None, HW=None, **kwargs):
+        B, N, C = x.shape
+
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+        x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
+        x = x + self.cross_attn(x, y, mask)
+        x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
+
+        return x
+
+
+### Core PixArt Model ###
+class PixArtMS(PixArt):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+    def __init__(
+            self,
+            input_size=32,
+            patch_size=2,
+            in_channels=4,
+            hidden_size=1152,
+            depth=28,
+            num_heads=16,
+            mlp_ratio=4.0,
+            class_dropout_prob=0.1,
+            learn_sigma=True,
+            pred_sigma=True,
+            drop_path: float = 0.,
+            caption_channels=4096,
+            pe_interpolation=None,
+            pe_precision=None,
+            config=None,
+            model_max_length=120,
+            micro_condition=True,
+            qk_norm=False,
+            kv_compress_config=None,
+            **kwargs,
+    ):
+        super().__init__(
+            input_size=input_size,
+            patch_size=patch_size,
+            in_channels=in_channels,
+            hidden_size=hidden_size,
+            depth=depth,
+            num_heads=num_heads,
+            mlp_ratio=mlp_ratio,
+            class_dropout_prob=class_dropout_prob,
+            learn_sigma=learn_sigma,
+            pred_sigma=pred_sigma,
+            drop_path=drop_path,
+            pe_interpolation=pe_interpolation,
+            config=config,
+            model_max_length=model_max_length,
+            qk_norm=qk_norm,
+            kv_compress_config=kv_compress_config,
+            **kwargs,
+        )
+        self.dtype = torch.get_default_dtype()
+        self.h = self.w = 0
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.t_block = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
+        )
+        self.x_embedder = PatchEmbed(patch_size, in_channels, hidden_size, bias=True)
+        self.y_embedder = CaptionEmbedder(in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, act_layer=approx_gelu, token_num=model_max_length)
+        self.micro_conditioning = micro_condition
+        if self.micro_conditioning:
+            self.csize_embedder = SizeEmbedder(hidden_size//3)  # c_size embed
+            self.ar_embedder = SizeEmbedder(hidden_size//3)     # aspect ratio embed
+        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
+        if kv_compress_config is None:
+            kv_compress_config = {
+                'sampling': None,
+                'scale_factor': 1,
+                'kv_compress_layer': [],
+            }
+        self.blocks = nn.ModuleList([
+            PixArtMSBlock(
+                hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
+                input_size=(input_size // patch_size, input_size // patch_size),
+                sampling=kv_compress_config['sampling'],
+                sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,
+                qk_norm=qk_norm,
+            )
+            for i in range(depth)
+        ])
+        self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
+
+    def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs):
+        """
+        Original forward pass of PixArt.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N, 1, 120, C) tensor of class labels
+        """
+        bs = x.shape[0]
+        x = x.to(self.dtype)
+        timestep = t.to(self.dtype)
+        y = y.to(self.dtype)
+        
+        pe_interpolation = self.pe_interpolation
+        if pe_interpolation is None or self.pe_precision is not None:
+            # calculate pe_interpolation on-the-fly
+            pe_interpolation = round((x.shape[-1]+x.shape[-2])/2.0 / (512/8.0), self.pe_precision or 0)
+
+        self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
+        pos_embed = torch.from_numpy(
+            get_2d_sincos_pos_embed(
+                self.pos_embed.shape[-1], (self.h, self.w), pe_interpolation=pe_interpolation,
+                base_size=self.base_size
+            )
+        ).unsqueeze(0).to(device=x.device, dtype=self.dtype)
+
+        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        t = self.t_embedder(timestep)  # (N, D)
+
+        if self.micro_conditioning:
+            c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype)
+            csize = self.csize_embedder(c_size, bs)  # (N, D)
+            ar = self.ar_embedder(ar, bs)  # (N, D)
+            t = t + torch.cat([csize, ar], dim=1)
+
+        t0 = self.t_block(t)
+        y = self.y_embedder(y, self.training)  # (N, D)
+
+        if mask is not None:
+            if mask.shape[0] != y.shape[0]:
+                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
+            mask = mask.squeeze(1).squeeze(1)
+            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
+            y_lens = mask.sum(dim=1).tolist()
+        else:
+            y_lens = [y.shape[2]] * y.shape[0]
+            y = y.squeeze(1).view(1, -1, x.shape[-1])
+        for block in self.blocks:
+            x = auto_grad_checkpoint(block, x, y, t0, y_lens, (self.h, self.w), **kwargs)  # (N, T, D) #support grad checkpoint
+
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+        
+        return x
+
+    def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, **kwargs):
+        """
+        Forward pass that adapts comfy input to original forward function
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        timesteps: (N,) tensor of diffusion timesteps
+        context: (N, 1, 120, C) conditioning
+        img_hw: height|width conditioning
+        aspect_ratio: aspect ratio conditioning
+        """
+        ## size/ar from cond with fallback based on the latent image shape.
+        bs = x.shape[0]
+        data_info = {}
+        if img_hw is None:
+            data_info["img_hw"] = torch.tensor(
+                [[x.shape[2]*8, x.shape[3]*8]],
+                dtype=self.dtype,
+                device=x.device
+            ).repeat(bs, 1)
+        else:
+            data_info["img_hw"] = img_hw.to(dtype=x.dtype, device=x.device)
+        if aspect_ratio is None or True:
+            data_info["aspect_ratio"] = torch.tensor(
+                [[x.shape[2]/x.shape[3]]],
+                dtype=self.dtype,
+                device=x.device
+            ).repeat(bs, 1)
+        else:
+            data_info["aspect_ratio"] = aspect_ratio.to(dtype=x.dtype, device=x.device)
+
+        ## Still accepts the input w/o that dim but returns garbage
+        if len(context.shape) == 3:
+            context = context.unsqueeze(1)
+
+        ## run original forward pass
+        out = self.forward_raw(
+            x = x.to(self.dtype),
+            t = timesteps.to(self.dtype),
+            y = context.to(self.dtype),
+            data_info=data_info,
+        )
+
+        ## only return EPS
+        out = out.to(torch.float)
+        eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
+        return eps
+
+    def unpatchify(self, x):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0]
+        assert self.h * self.w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
+        return imgs

py/dit/pixArt/models/PixArt_blocks.py

@@ -0,0 +1,477 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# GLIDE: https://github.com/openai/glide-text2im
+# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
+# --------------------------------------------------------
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.models.vision_transformer import Mlp, Attention as Attention_
+from einops import rearrange
+
+from comfy import model_management
+if model_management.xformers_enabled():
+    import xformers
+    import xformers.ops
+else:
+    print("""
+########################################
+ PixArt: Not using xformers!
+  Expect images to be non-deterministic!
+  Batch sizes > 1 are most likely broken
+########################################
+""")
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+def t2i_modulate(x, shift, scale):
+    return x * (1 + scale) + shift
+
+class MultiHeadCrossAttention(nn.Module):
+    def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., **block_kwargs):
+        super(MultiHeadCrossAttention, self).__init__()
+        assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
+
+        self.d_model = d_model
+        self.num_heads = num_heads
+        self.head_dim = d_model // num_heads
+
+        self.q_linear = nn.Linear(d_model, d_model)
+        self.kv_linear = nn.Linear(d_model, d_model*2)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(d_model, d_model)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, cond, mask=None):
+        # query/value: img tokens; key: condition; mask: if padding tokens
+        B, N, C = x.shape
+
+        q = self.q_linear(x).view(1, -1, self.num_heads, self.head_dim)
+        kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim)
+        k, v = kv.unbind(2)
+
+        if model_management.xformers_enabled():
+            attn_bias = None
+            if mask is not None:
+                attn_bias = xformers.ops.fmha.BlockDiagonalMask.from_seqlens([N] * B, mask)
+            x = xformers.ops.memory_efficient_attention(
+                q, k, v,
+                p=self.attn_drop.p,
+                attn_bias=attn_bias
+            )
+        else:
+            q, k, v = map(lambda t: t.permute(0, 2, 1, 3),(q, k, v),)
+            attn_mask = None
+            if mask is not None and len(mask) > 1:
+
+                # Create equivalent of xformer diagonal block mask, still only correct for square masks
+                # But depth doesn't matter as tensors can expand in that dimension
+                attn_mask_template = torch.ones(
+                    [q.shape[2] // B, mask[0]],
+                    dtype=torch.bool,
+                    device=q.device
+                )
+                attn_mask = torch.block_diag(attn_mask_template)
+
+                # create a mask on the diagonal for each mask in the batch
+                for n in range(B - 1):
+                    attn_mask = torch.block_diag(attn_mask, attn_mask_template)
+
+            x = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v,
+                attn_mask=attn_mask,
+                dropout_p=self.attn_drop.p
+            ).permute(0, 2, 1, 3).contiguous()
+        x = x.view(B, -1, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class AttentionKVCompress(Attention_):
+    """Multi-head Attention block with KV token compression and qk norm."""
+
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=True,
+        sampling='conv',
+        sr_ratio=1,
+        qk_norm=False,
+        **block_kwargs,
+    ):
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool:  If True, add a learnable bias to query, key, value.
+        """
+        super().__init__(dim, num_heads=num_heads, qkv_bias=qkv_bias, **block_kwargs)
+
+        self.sampling=sampling    # ['conv', 'ave', 'uniform', 'uniform_every']
+        self.sr_ratio = sr_ratio
+        if sr_ratio > 1 and sampling == 'conv':
+            # Avg Conv Init.
+            self.sr = nn.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio)
+            self.sr.weight.data.fill_(1/sr_ratio**2)
+            self.sr.bias.data.zero_()
+            self.norm = nn.LayerNorm(dim)
+        if qk_norm:
+            self.q_norm = nn.LayerNorm(dim)
+            self.k_norm = nn.LayerNorm(dim)
+        else:
+            self.q_norm = nn.Identity()
+            self.k_norm = nn.Identity()
+
+    def downsample_2d(self, tensor, H, W, scale_factor, sampling=None):
+        if sampling is None or scale_factor == 1:
+            return tensor
+        B, N, C = tensor.shape
+
+        if sampling == 'uniform_every':
+            return tensor[:, ::scale_factor], int(N // scale_factor)
+
+        tensor = tensor.reshape(B, H, W, C).permute(0, 3, 1, 2)
+        new_H, new_W = int(H / scale_factor), int(W / scale_factor)
+        new_N = new_H * new_W
+
+        if sampling == 'ave':
+            tensor = F.interpolate(
+                tensor, scale_factor=1 / scale_factor, mode='nearest'
+            ).permute(0, 2, 3, 1)
+        elif sampling == 'uniform':
+            tensor = tensor[:, :, ::scale_factor, ::scale_factor].permute(0, 2, 3, 1)
+        elif sampling == 'conv':
+            tensor = self.sr(tensor).reshape(B, C, -1).permute(0, 2, 1)
+            tensor = self.norm(tensor)
+        else:
+            raise ValueError
+
+        return tensor.reshape(B, new_N, C).contiguous(), new_N
+
+    def forward(self, x, mask=None, HW=None, block_id=None):
+        B, N, C = x.shape # 2 4096 1152
+        new_N = N
+        if HW is None:
+            H = W = int(N ** 0.5)
+        else:
+            H, W = HW
+        qkv = self.qkv(x).reshape(B, N, 3, C)
+
+        q, k, v = qkv.unbind(2)
+        dtype = q.dtype
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        # KV compression
+        if self.sr_ratio > 1:
+            k, new_N = self.downsample_2d(k, H, W, self.sr_ratio, sampling=self.sampling)
+            v, new_N = self.downsample_2d(v, H, W, self.sr_ratio, sampling=self.sampling)
+
+        q = q.reshape(B, N, self.num_heads, C // self.num_heads).to(dtype)
+        k = k.reshape(B, new_N, self.num_heads, C // self.num_heads).to(dtype)
+        v = v.reshape(B, new_N, self.num_heads, C // self.num_heads).to(dtype)
+
+        attn_bias = None
+        if mask is not None:
+            attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device)
+            attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float('-inf'))
+        # Switch between torch / xformers attention
+        if model_management.xformers_enabled():
+            x = xformers.ops.memory_efficient_attention(
+                q, k, v,
+                p=self.attn_drop.p,
+                attn_bias=attn_bias
+            )
+        else:
+            q, k, v = map(lambda t: t.transpose(1, 2),(q, k, v),)
+            x = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v,
+                dropout_p=self.attn_drop.p,
+                attn_mask=attn_bias
+            ).transpose(1, 2).contiguous()
+        x = x.view(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+#################################################################################
+#   AMP attention with fp32 softmax to fix loss NaN problem during training     #
+#################################################################################
+class Attention(Attention_):
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+        use_fp32_attention = getattr(self, 'fp32_attention', False)
+        if use_fp32_attention:
+            q, k = q.float(), k.float()
+        with torch.cuda.amp.autocast(enabled=not use_fp32_attention):
+            attn = (q @ k.transpose(-2, -1)) * self.scale
+            attn = attn.softmax(dim=-1)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of PixArt.
+    """
+
+    def __init__(self, hidden_size, patch_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+        )
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class T2IFinalLayer(nn.Module):
+    """
+    The final layer of PixArt.
+    """
+
+    def __init__(self, hidden_size, patch_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5)
+        self.out_channels = out_channels
+
+    def forward(self, x, t):
+        shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1)
+        x = t2i_modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class MaskFinalLayer(nn.Module):
+    """
+    The final layer of PixArt.
+    """
+
+    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True)
+        )
+    def forward(self, x, t):
+        shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class DecoderLayer(nn.Module):
+    """
+    The final layer of PixArt.
+    """
+
+    def __init__(self, hidden_size, decoder_hidden_size):
+        super().__init__()
+        self.norm_decoder = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+        )
+    def forward(self, x, t):
+        shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
+        x = modulate(self.norm_decoder(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+#################################################################################
+#               Embedding Layers for Timesteps and Class Labels                 #
+#################################################################################
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_emb = self.mlp(t_freq.to(t.dtype))
+        return t_emb
+
+
+class SizeEmbedder(TimestepEmbedder):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size)
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+        self.outdim = hidden_size
+
+    def forward(self, s, bs):
+        if s.ndim == 1:
+            s = s[:, None]
+        assert s.ndim == 2
+        if s.shape[0] != bs:
+            s = s.repeat(bs//s.shape[0], 1)
+            assert s.shape[0] == bs
+        b, dims = s.shape[0], s.shape[1]
+        s = rearrange(s, "b d -> (b d)")
+        s_freq = self.timestep_embedding(s, self.frequency_embedding_size)
+        s_emb = self.mlp(s_freq.to(s.dtype))
+        s_emb = rearrange(s_emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim)
+        return s_emb
+
+
+class LabelEmbedder(nn.Module):
+    """
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+    """
+
+    def __init__(self, num_classes, hidden_size, dropout_prob):
+        super().__init__()
+        use_cfg_embedding = dropout_prob > 0
+        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
+        self.num_classes = num_classes
+        self.dropout_prob = dropout_prob
+
+    def token_drop(self, labels, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(labels.shape[0]).cuda() < self.dropout_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        labels = torch.where(drop_ids, self.num_classes, labels)
+        return labels
+
+    def forward(self, labels, train, force_drop_ids=None):
+        use_dropout = self.dropout_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            labels = self.token_drop(labels, force_drop_ids)
+        embeddings = self.embedding_table(labels)
+        return embeddings
+
+
+class CaptionEmbedder(nn.Module):
+    """
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+    """
+
+    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120):
+        super().__init__()
+        self.y_proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0)
+        self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5))
+        self.uncond_prob = uncond_prob
+
+    def token_drop(self, caption, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
+        return caption
+
+    def forward(self, caption, train, force_drop_ids=None):
+        if train:
+            assert caption.shape[2:] == self.y_embedding.shape
+        use_dropout = self.uncond_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            caption = self.token_drop(caption, force_drop_ids)
+        caption = self.y_proj(caption)
+        return caption
+
+
+class CaptionEmbedderDoubleBr(nn.Module):
+    """
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+    """
+
+    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120):
+        super().__init__()
+        self.proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0)
+        self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5)
+        self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5)
+        self.uncond_prob = uncond_prob
+
+    def token_drop(self, global_caption, caption, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption)
+        caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
+        return global_caption, caption
+
+    def forward(self, caption, train, force_drop_ids=None):
+        assert caption.shape[2: ] == self.y_embedding.shape
+        global_caption = caption.mean(dim=2).squeeze()
+        use_dropout = self.uncond_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            global_caption, caption = self.token_drop(global_caption, caption, force_drop_ids)
+        y_embed = self.proj(global_caption)
+        return y_embed, caption

py/dit/pixArt/models/pixart_controlnet.py

@@ -0,0 +1,312 @@
+import re
+import torch
+import torch.nn as nn
+
+from copy import deepcopy
+from torch import Tensor
+from torch.nn import Module, Linear, init
+from typing import Any, Mapping
+
+from .PixArt import PixArt, get_2d_sincos_pos_embed
+from .PixArtMS import PixArtMSBlock, PixArtMS
+from .utils import auto_grad_checkpoint
+
+# The implementation of ControlNet-Half architrecture
+# https://github.com/lllyasviel/ControlNet/discussions/188
+class ControlT2IDitBlockHalf(Module):
+    def __init__(self, base_block: PixArtMSBlock, block_index: 0) -> None:
+        super().__init__()
+        self.copied_block = deepcopy(base_block)
+        self.block_index = block_index
+
+        for p in self.copied_block.parameters():
+            p.requires_grad_(True)
+
+        self.copied_block.load_state_dict(base_block.state_dict())
+        self.copied_block.train()
+        
+        self.hidden_size = hidden_size = base_block.hidden_size
+        if self.block_index == 0:
+            self.before_proj = Linear(hidden_size, hidden_size)
+            init.zeros_(self.before_proj.weight)
+            init.zeros_(self.before_proj.bias)
+        self.after_proj = Linear(hidden_size, hidden_size) 
+        init.zeros_(self.after_proj.weight)
+        init.zeros_(self.after_proj.bias)
+
+    def forward(self, x, y, t, mask=None, c=None):
+        
+        if self.block_index == 0:
+            # the first block
+            c = self.before_proj(c)
+            c = self.copied_block(x + c, y, t, mask)
+            c_skip = self.after_proj(c)
+        else:
+            # load from previous c and produce the c for skip connection
+            c = self.copied_block(c, y, t, mask)
+            c_skip = self.after_proj(c)
+        
+        return c, c_skip
+        
+
+# The implementation of ControlPixArtHalf net
+class ControlPixArtHalf(Module):
+    # only support single res model
+    def __init__(self, base_model: PixArt, copy_blocks_num: int = 13) -> None:
+        super().__init__()
+        self.dtype = torch.get_default_dtype()
+        self.base_model = base_model.eval()
+        self.controlnet = []
+        self.copy_blocks_num = copy_blocks_num
+        self.total_blocks_num = len(base_model.blocks)
+        for p in self.base_model.parameters():
+            p.requires_grad_(False)
+
+        # Copy first copy_blocks_num block
+        for i in range(copy_blocks_num):
+            self.controlnet.append(ControlT2IDitBlockHalf(base_model.blocks[i], i))
+        self.controlnet = nn.ModuleList(self.controlnet)
+    
+    def __getattr__(self, name: str) -> Tensor or Module:
+        if name in ['forward', 'forward_with_dpmsolver', 'forward_with_cfg', 'forward_c', 'load_state_dict']:
+            return self.__dict__[name]
+        elif name in ['base_model', 'controlnet']:
+            return super().__getattr__(name)
+        else:
+            return getattr(self.base_model, name)
+
+    def forward_c(self, c):
+        self.h, self.w = c.shape[-2]//self.patch_size, c.shape[-1]//self.patch_size
+        pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(c.device).to(self.dtype)
+        return self.x_embedder(c) + pos_embed if c is not None else c
+
+    # def forward(self, x, t, c, **kwargs):
+    #     return self.base_model(x, t, c=self.forward_c(c), **kwargs)
+    def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs):
+        # modify the original PixArtMS forward function
+        if c is not None:
+            c = c.to(self.dtype)
+            c = self.forward_c(c)
+        """
+        Forward pass of PixArt.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N, 1, 120, C) tensor of class labels
+        """
+        x = x.to(self.dtype)
+        timestep = timestep.to(self.dtype)
+        y = y.to(self.dtype)
+        pos_embed = self.pos_embed.to(self.dtype)
+        self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
+        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        t = self.t_embedder(timestep.to(x.dtype))  # (N, D)
+        t0 = self.t_block(t)
+        y = self.y_embedder(y, self.training)  # (N, 1, L, D)
+        if mask is not None:
+            if mask.shape[0] != y.shape[0]:
+                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
+            mask = mask.squeeze(1).squeeze(1)
+            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
+            y_lens = mask.sum(dim=1).tolist()
+        else:
+            y_lens = [y.shape[2]] * y.shape[0]
+            y = y.squeeze(1).view(1, -1, x.shape[-1])
+
+        # define the first layer
+        x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs)  # (N, T, D) #support grad checkpoint
+
+        if c is not None:
+            # update c
+            for index in range(1, self.copy_blocks_num + 1):
+                c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs)
+                x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs)
+        
+            # update x
+            for index in range(self.copy_blocks_num + 1, self.total_blocks_num):
+                x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
+        else:
+            for index in range(1, self.total_blocks_num):
+                x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
+
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+        return x
+
+    def forward(self, x, timesteps, context, cn_hint=None, **kwargs):
+        """
+        Forward pass that adapts comfy input to original forward function
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        timesteps: (N,) tensor of diffusion timesteps
+        context: (N, 1, 120, C) conditioning
+        cn_hint: controlnet hint
+        """
+        ## Still accepts the input w/o that dim but returns garbage
+        if len(context.shape) == 3:
+            context = context.unsqueeze(1)
+
+        ## run original forward pass
+        out = self.forward_raw(
+            x = x.to(self.dtype),
+            timestep = timesteps.to(self.dtype),
+            y = context.to(self.dtype),
+            c = cn_hint,
+        )
+
+        ## only return EPS
+        out = out.to(torch.float)
+        eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
+        return eps
+
+    def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs):
+        model_out = self.forward_raw(x, t, y, data_info=data_info, c=c, **kwargs)
+        return model_out.chunk(2, dim=1)[0]
+
+    # def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs):
+    #     return self.base_model.forward_with_dpmsolver(x, t, y, data_info=data_info, c=self.forward_c(c), **kwargs)
+
+    def forward_with_cfg(self, x, t, y, cfg_scale, data_info, c, **kwargs):
+        return self.base_model.forward_with_cfg(x, t, y, cfg_scale, data_info, c=self.forward_c(c), **kwargs)
+
+    def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
+        if all((k.startswith('base_model') or k.startswith('controlnet')) for k in state_dict.keys()):
+            return super().load_state_dict(state_dict, strict)
+        else:
+            new_key = {}
+            for k in state_dict.keys():
+                new_key[k] = re.sub(r"(blocks\.\d+)(.*)", r"\1.base_block\2", k)
+            for k, v in new_key.items():
+                if k != v:
+                    print(f"replace {k} to {v}")
+                    state_dict[v] = state_dict.pop(k)
+
+            return self.base_model.load_state_dict(state_dict, strict)
+    
+    def unpatchify(self, x):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0]
+        assert self.h * self.w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
+        return imgs
+
+    # @property
+    # def dtype(self):
+        ## 返回模型参数的数据类型
+        # return next(self.parameters()).dtype
+
+
+# The implementation for PixArtMS_Half + 1024 resolution
+class ControlPixArtMSHalf(ControlPixArtHalf):
+    # support multi-scale res model (multi-scale model can also be applied to single reso training & inference)
+    def __init__(self, base_model: PixArtMS, copy_blocks_num: int = 13) -> None:
+        super().__init__(base_model=base_model, copy_blocks_num=copy_blocks_num)
+
+    def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs):
+        # modify the original PixArtMS forward function
+        """
+        Forward pass of PixArt.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N, 1, 120, C) tensor of class labels
+        """
+        if c is not None:
+            c = c.to(self.dtype)
+            c = self.forward_c(c)
+        bs = x.shape[0]
+        x = x.to(self.dtype)
+        timestep = timestep.to(self.dtype)
+        y = y.to(self.dtype)
+        c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype)
+        self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
+
+        pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(x.device).to(self.dtype)
+        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        t = self.t_embedder(timestep)  # (N, D)
+        csize = self.csize_embedder(c_size, bs)  # (N, D)
+        ar = self.ar_embedder(ar, bs)  # (N, D)
+        t = t + torch.cat([csize, ar], dim=1)
+        t0 = self.t_block(t)
+        y = self.y_embedder(y, self.training)  # (N, D)
+        if mask is not None:
+            if mask.shape[0] != y.shape[0]:
+                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
+            mask = mask.squeeze(1).squeeze(1)
+            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
+            y_lens = mask.sum(dim=1).tolist()
+        else:
+            y_lens = [y.shape[2]] * y.shape[0]
+            y = y.squeeze(1).view(1, -1, x.shape[-1])
+
+        # define the first layer
+        x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs)  # (N, T, D) #support grad checkpoint
+
+        if c is not None:
+            # update c
+            for index in range(1, self.copy_blocks_num + 1):
+                c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs)
+                x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs)
+        
+            # update x
+            for index in range(self.copy_blocks_num + 1, self.total_blocks_num):
+                x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
+        else:
+            for index in range(1, self.total_blocks_num):
+                x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
+
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+        x = self.unpatchify(x)  # (N, out_channels, H, W)
+        return x
+
+    def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, cn_hint=None, **kwargs):
+        """
+        Forward pass that adapts comfy input to original forward function
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        timesteps: (N,) tensor of diffusion timesteps
+        context: (N, 1, 120, C) conditioning
+        img_hw: height|width conditioning
+        aspect_ratio: aspect ratio conditioning
+        cn_hint: controlnet hint
+        """
+        ## size/ar from cond with fallback based on the latent image shape.
+        bs = x.shape[0]
+        data_info = {}
+        if img_hw is None:
+            data_info["img_hw"] = torch.tensor(
+                [[x.shape[2]*8, x.shape[3]*8]],
+                dtype=self.dtype,
+                device=x.device
+            ).repeat(bs, 1)
+        else:
+            data_info["img_hw"] = img_hw.to(x.dtype)
+        if aspect_ratio is None or True:
+            data_info["aspect_ratio"] = torch.tensor(
+                [[x.shape[2]/x.shape[3]]],
+                dtype=self.dtype,
+                device=x.device
+            ).repeat(bs, 1)
+        else:
+            data_info["aspect_ratio"] = aspect_ratio.to(x.dtype)
+
+        ## Still accepts the input w/o that dim but returns garbage
+        if len(context.shape) == 3:
+            context = context.unsqueeze(1)
+
+        ## run original forward pass
+        out = self.forward_raw(
+            x = x.to(self.dtype),
+            timestep = timesteps.to(self.dtype),
+            y = context.to(self.dtype),
+            c = cn_hint,
+            data_info=data_info,
+        )
+
+        ## only return EPS
+        out = out.to(torch.float)
+        eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
+        return eps

py/dit/pixArt/models/utils.py

@@ -0,0 +1,122 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.checkpoint import checkpoint, checkpoint_sequential
+from collections.abc import Iterable
+from itertools import repeat
+
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, Iterable) and not isinstance(x, str):
+            return x
+        return tuple(repeat(x, n))
+    return parse
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+
+def set_grad_checkpoint(model, use_fp32_attention=False, gc_step=1):
+    assert isinstance(model, nn.Module)
+
+    def set_attr(module):
+        module.grad_checkpointing = True
+        module.fp32_attention = use_fp32_attention
+        module.grad_checkpointing_step = gc_step
+    model.apply(set_attr)
+
+def auto_grad_checkpoint(module, *args, **kwargs):
+    if getattr(module, 'grad_checkpointing', False):
+        if isinstance(module, Iterable):
+            gc_step = module[0].grad_checkpointing_step
+            return checkpoint_sequential(module, gc_step, *args, **kwargs)
+        else:
+            return checkpoint(module, *args, **kwargs)
+    return module(*args, **kwargs)
+
+def checkpoint_sequential(functions, step, input, *args, **kwargs):
+
+    # Hack for keyword-only parameter in a python 2.7-compliant way
+    preserve = kwargs.pop('preserve_rng_state', True)
+    if kwargs:
+        raise ValueError("Unexpected keyword arguments: " + ",".join(arg for arg in kwargs))
+
+    def run_function(start, end, functions):
+        def forward(input):
+            for j in range(start, end + 1):
+                input = functions[j](input, *args)
+            return input
+        return forward
+
+    if isinstance(functions, torch.nn.Sequential):
+        functions = list(functions.children())
+
+    # the last chunk has to be non-volatile
+    end = -1
+    segment = len(functions) // step
+    for start in range(0, step * (segment - 1), step):
+        end = start + step - 1
+        input = checkpoint(run_function(start, end, functions), input, preserve_rng_state=preserve)
+    return run_function(end + 1, len(functions) - 1, functions)(input)
+
+def get_rel_pos(q_size, k_size, rel_pos):
+    """
+    Get relative positional embeddings according to the relative positions of
+        query and key sizes.
+    Args:
+        q_size (int): size of query q.
+        k_size (int): size of key k.
+        rel_pos (Tensor): relative position embeddings (L, C).
+
+    Returns:
+        Extracted positional embeddings according to relative positions.
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size):
+    """
+    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    Args:
+        attn (Tensor): attention map.
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+
+    attn = (
+        attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+    ).view(B, q_h * q_w, k_h * k_w)
+
+    return attn

py/dit/utils.py

@@ -0,0 +1,38 @@
+import torch
+from comfy import model_management
+
+def string_to_dtype(s="none", mode=None):
+	s = s.lower().strip()
+	if s in ["default", "as-is"]:
+		return None
+	elif s in ["auto", "auto (comfy)"]:
+		if mode == "vae":
+			return model_management.vae_device()
+		elif mode == "text_encoder":
+			return model_management.text_encoder_dtype()
+		elif mode == "unet":
+			return model_management.unet_dtype()
+		else:
+			raise NotImplementedError(f"Unknown dtype mode '{mode}'")
+	elif s in ["none", "auto (hf)", "auto (hf/bnb)"]:
+		return None
+	elif s in ["fp32", "float32", "float"]:
+		return torch.float32
+	elif s in ["bf16", "bfloat16"]:
+		return torch.bfloat16
+	elif s in ["fp16", "float16", "half"]:
+		return torch.float16
+	elif "fp8" in s or "float8" in s:
+		if "e5m2" in s:
+			return torch.float8_e5m2
+		elif "e4m3" in s:
+			return torch.float8_e4m3fn
+		else:
+			raise NotImplementedError(f"Unknown 8bit dtype '{s}'")
+	elif "bnb" in s:
+		assert s in ["bnb8bit", "bnb4bit"], f"Unknown bnb mode '{s}'"
+		return s
+	elif s is None:
+		return None
+	else:
+		raise NotImplementedError(f"Unknown dtype '{s}'")

py/dynamiCrafter/__init__.py

@@ -1,3 +1,5 @@
+#credit to ExponentialML for this module
+#from https://github.com/ExponentialML/ComfyUI_Native_DynamiCrafter
 import os
 import torch
 import comfy

py/dynamiCrafter/utils/model_utils.py

@@ -7,7 +7,10 @@ from comfy import model_base
 from comfy import utils
 from comfy import diffusers_convert
 
-from comfy import sd2_clip
+try:
+    import comfy.text_encoders.sd2_clip
+except ImportError:
+    from comfy import sd2_clip
 
 from comfy import supported_models_base
 from comfy import latent_formats

py/fooocus.py

@@ -1,104 +0,0 @@
-import torch
-import comfy
-from comfy.model_patcher import ModelPatcher
-from comfy.model_management import cast_to_device
-
-from .log import log_node_warn, log_node_error, log_node_info
-
-# Inpaint
-class InpaintHead(torch.nn.Module):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.head = torch.nn.Parameter(torch.empty(size=(320, 5, 3, 3), device='cpu'))
-
-    def __call__(self, x):
-        x = torch.nn.functional.pad(x, (1, 1, 1, 1), "replicate")
-        return torch.nn.functional.conv2d(input=x, weight=self.head)
-
-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):
-        patch_dict = {}
-        loaded_keys = set()
-        for key in to_load.values():
-            if value := lora.get(key, None):
-                patch_dict[key] = ("fooocus", value)
-                loaded_keys.add(key)
-
-        not_loaded = sum(1 for x in lora if x not in loaded_keys)
-        log_node_info(self.node_name,
-            f"{len(loaded_keys)} Lora keys loaded, {not_loaded} remaining keys not found in model."
-        )
-        return patch_dict
-
-    def calculate_weight_patched(self: ModelPatcher, patches, weight, key):
-        remaining = []
-
-        for p in patches:
-            alpha, v, strength_model = p
-
-            is_fooocus_patch = isinstance(v, tuple) and len(v) == 2 and v[0] == "fooocus"
-            if not is_fooocus_patch:
-                remaining.append(p)
-                continue
-
-            if alpha != 0.0:
-                v = v[1]
-                w1 = cast_to_device(v[0], weight.device, torch.float32)
-                if w1.shape == weight.shape:
-                    w_min = cast_to_device(v[1], weight.device, torch.float32)
-                    w_max = cast_to_device(v[2], weight.device, torch.float32)
-                    w1 = (w1 / 255.0) * (w_max - w_min) + w_min
-                    weight += alpha * cast_to_device(w1, weight.device, weight.dtype)
-                else:
-                    pass
-                    # log_node_warn(self.node_name,
-                    #     f"Shape mismatch {key}, weight not merged ({w1.shape} != {weight.shape})"
-                    # )
-
-        if len(remaining) > 0:
-            return self.original_calculate_weight(self, remaining, weight, key)
-        return weight
-
-    def inject_patched_calculate_weight(self):
-        if not self.injected_model_patcher_calculate_weight:
-            log_node_info(self.node_name,"Injecting patched comfy.model_patcher.ModelPatcher.calculate_weight")
-            ModelPatcher.calculate_weight = self.calculate_weight_patched
-            self.injected_model_patcher_calculate_weight = True
-
-    def patch(self, model, latent, patch):
-        base_model = model.model
-        latent_pixels = base_model.process_latent_in(latent["samples"])
-        noise_mask = latent["noise_mask"].round()
-        latent_mask = torch.nn.functional.max_pool2d(noise_mask, (8, 8)).round().to(latent_pixels)
-
-        inpaint_head_model, inpaint_lora = patch
-        feed = torch.cat([latent_mask, latent_pixels], dim=1)
-        inpaint_head_model.to(device=feed.device, dtype=feed.dtype)
-        inpaint_head_feature = inpaint_head_model(feed)
-
-        def input_block_patch(h, transformer_options):
-            if transformer_options["block"][1] == 0:
-                h = h + inpaint_head_feature.to(h)
-            return h
-
-        lora_keys = comfy.lora.model_lora_keys_unet(model.model, {})
-        lora_keys.update({x: x for x in base_model.state_dict().keys()})
-        loaded_lora = self.load_fooocus_patch(inpaint_lora, lora_keys)
-
-        m = model.clone()
-        m.set_model_input_block_patch(input_block_patch)
-        patched = m.add_patches(loaded_lora, 1.0)
-
-        not_patched_count = sum(1 for x in loaded_lora if x not in patched)
-        if not_patched_count > 0:
-            log_node_error(self.node_name, f"Failed to patch {not_patched_count} keys")
-
-        self.inject_patched_calculate_weight()
-        return (m,)

py/ic_light/__init__.py

@@ -1,3 +1,5 @@
+#credit to huchenlei for this module
+#from https://github.com/huchenlei/ComfyUI-IC-Light-Native
 import torch
 import numpy as np
 from typing import Tuple, TypedDict, Callable

py/image.py

@@ -1,17 +1,19 @@
-from PIL import Image, ImageDraw, ImageFilter
 import os
 import hashlib
 import folder_paths
 import torch
 import numpy as np
+import comfy.utils
 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 PIL import Image, ImageDraw, ImageFilter
+from torchvision.transforms import Resize, CenterCrop, GaussianBlur
 from torchvision.transforms.functional import to_pil_image
-from .log import log_node_info
-from .libs.image import pil2tensor, tensor2pil, ResizeMode, get_new_bounds, RGB2RGBA, image2mask, mask2image, blendImage
+from .libs.log import log_node_info
+from .libs.utils import AlwaysEqualProxy
+from .libs.image import pil2tensor, tensor2pil, ResizeMode, get_new_bounds, RGB2RGBA, image2mask
 from .libs.colorfix import adain_color_fix, wavelet_color_fix
 from .libs.chooser import ChooserMessage, ChooserCancelled
 from .config import REMBG_DIR, REMBG_MODELS, HUMANPARSING_MODELS, MEDIAPIPE_MODELS, MEDIAPIPE_DIR
@@ -505,6 +507,48 @@ class JoinImageBatch:
       image = torch.transpose(torch.transpose(images, 1, 2).reshape(1, n * w, h, c), 1, 2)
     return (image,)
 
+class imageListToImageBatch:
+  @classmethod
+  def INPUT_TYPES(s):
+    return {"required": {
+      "images": ("IMAGE",),
+    }}
+
+  INPUT_IS_LIST = True
+
+  RETURN_TYPES = ("IMAGE",)
+  FUNCTION = "doit"
+
+  CATEGORY = "EasyUse/Image"
+
+  def doit(self, images):
+    if len(images) <= 1:
+      return (images[0],)
+    else:
+      image1 = images[0]
+      for image2 in images[1:]:
+        if image1.shape[1:] != image2.shape[1:]:
+          image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "lanczos",
+                                              "center").movedim(1, -1)
+        image1 = torch.cat((image1, image2), dim=0)
+      return (image1,)
+
+
+class imageBatchToImageList:
+  @classmethod
+  def INPUT_TYPES(s):
+    return {"required": {"image": ("IMAGE",), }}
+
+  RETURN_TYPES = ("IMAGE",)
+  OUTPUT_IS_LIST = (True,)
+  FUNCTION = "doit"
+
+  CATEGORY = "EasyUse/Image"
+
+  def doit(self, image):
+    images = [image[i:i + 1, ...] for i in range(image.shape[0])]
+    return (images,)
+
 # 图像拆分
 class imageSplitList:
   @classmethod
@@ -630,9 +674,13 @@ class imageRemBg:
     return {
       "required": {
         "images": ("IMAGE",),
-        "rem_mode": (("RMBG-1.4",),),
+        "rem_mode": (("RMBG-1.4","Inspyrenet"),),
         "image_output": (["Hide", "Preview", "Save", "Hide/Save"], {"default": "Preview"}),
         "save_prefix": ("STRING", {"default": "ComfyUI"}),
+
+      },
+      "optional":{
+        "torchscript_jit": ("BOOLEAN", {"default": False}),
       },
       "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
     }
@@ -644,7 +692,9 @@ class imageRemBg:
 
   CATEGORY = "EasyUse/Image"
 
-  def remove(self, rem_mode, images, image_output, save_prefix, prompt=None, extra_pnginfo=None):
+  def remove(self, rem_mode, images, image_output, save_prefix, torchscript_jit=False, prompt=None, extra_pnginfo=None):
+    new_images = list()
+    masks = list()
     if rem_mode == "RMBG-1.4":
       # load model
       model_url = REMBG_MODELS[rem_mode]['model_url']
@@ -658,8 +708,6 @@ class imageRemBg:
       net.eval()
       # prepare input
       model_input_size = [1024, 1024]
-      new_images = list()
-      masks = list()
       for image in images:
         orig_im = tensor2pil(image)
         w, h = orig_im.size
@@ -677,18 +725,33 @@ class imageRemBg:
       new_images = torch.cat(new_images, dim=0)
       masks = torch.cat(masks, dim=0)
 
+    elif rem_mode == "Inspyrenet":
+      from tqdm import tqdm
+      try:
+        from transparent_background import Remover
+      except:
+          install_package("transparent_background")
+          from transparent_background import Remover
 
-      results = easySave(new_images, save_prefix, image_output, prompt, extra_pnginfo)
+      remover = Remover(jit=torchscript_jit)
 
-      if image_output in ("Hide", "Hide/Save"):
-        return {"ui": {},
-                "result": (new_images, masks)}
+      for img in tqdm(images, "Inspyrenet Rembg"):
+        mid = remover.process(tensor2pil(img), type='rgba')
+        out = pil2tensor(mid)
+        new_images.append(out)
+        mask = out[:, :, :, 3]
+        masks.append(mask)
+      new_images = torch.cat(new_images, dim=0)
+      masks = torch.cat(masks, dim=0)
 
-      return {"ui": {"images": results},
+    results = easySave(new_images, save_prefix, image_output, prompt, extra_pnginfo)
+
+    if image_output in ("Hide", "Hide/Save"):
+      return {"ui": {},
               "result": (new_images, masks)}
 
-    else:
-      return (None, None)
+    return {"ui": {"images": results},
+            "result": (new_images, masks)}
 
 # 图像选择器
 class imageChooser(PreviewImage):
@@ -751,7 +814,11 @@ class imageChooser(PreviewImage):
     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]:
+      if not extra_pnginfo:
+        print("Error: extra_pnginfo is empty")
+      elif (not isinstance(extra_pnginfo[0], dict) or "workflow" not in extra_pnginfo[0]):
+        print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key")
+      else:
         workflow = extra_pnginfo[0]["workflow"]
         node = next((x for x in workflow["nodes"] if str(x["id"]) == id), None)
         if node:
@@ -831,6 +898,94 @@ class imageColorMatch(PreviewImage):
     return {"ui": {"images": results},
             "result": (new_images,)}
 
+class imageDetailTransfer:
+  @classmethod
+  def INPUT_TYPES(s):
+    return {
+      "required": {
+        "target": ("IMAGE",),
+        "source": ("IMAGE",),
+        "mode": (["add", "multiply", "screen", "overlay", "soft_light", "hard_light", "color_dodge", "color_burn", "difference", "exclusion", "divide",],{"default": "add"}),
+        "blur_sigma": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 100.0, "step": 0.01}),
+        "blend_factor": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001, "round": 0.001}),
+        "image_output": (["Hide", "Preview", "Save", "Hide/Save"], {"default": "Preview"}),
+        "save_prefix": ("STRING", {"default": "ComfyUI"}),
+      },
+      "optional": {
+        "mask": ("MASK",),
+      },
+      "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+    }
+
+  RETURN_TYPES = ("IMAGE",)
+  RETURN_NAMES = ("image",)
+  OUTPUT_NODE = True
+  FUNCTION = "transfer"
+  CATEGORY = "EasyUse/Image"
+
+
+
+  def transfer(self, target, source, mode, blur_sigma, blend_factor, image_output, save_prefix, mask=None, prompt=None, extra_pnginfo=None):
+    batch_size, height, width, _ = target.shape
+    device = comfy.model_management.get_torch_device()
+    target_tensor = target.permute(0, 3, 1, 2).clone().to(device)
+    source_tensor = source.permute(0, 3, 1, 2).clone().to(device)
+
+    if target.shape[1:] != source.shape[1:]:
+      source_tensor = comfy.utils.common_upscale(source_tensor, width, height, "bilinear", "disabled")
+
+    if source.shape[0] < batch_size:
+      source = source[0].unsqueeze(0).repeat(batch_size, 1, 1, 1)
+
+    kernel_size = int(6 * int(blur_sigma) + 1)
+
+    gaussian_blur = GaussianBlur(kernel_size=(kernel_size, kernel_size), sigma=(blur_sigma, blur_sigma))
+
+    blurred_target = gaussian_blur(target_tensor)
+    blurred_source = gaussian_blur(source_tensor)
+
+    if mode == "add":
+      new_image = (source_tensor - blurred_source) + blurred_target
+    elif mode == "multiply":
+      new_image = source_tensor * blurred_target
+    elif mode == "screen":
+      new_image = 1 - (1 - source_tensor) * (1 - blurred_target)
+    elif mode == "overlay":
+      new_image = torch.where(blurred_target < 0.5, 2 * source_tensor * blurred_target,
+                               1 - 2 * (1 - source_tensor) * (1 - blurred_target))
+    elif mode == "soft_light":
+      new_image = (1 - 2 * blurred_target) * source_tensor ** 2 + 2 * blurred_target * source_tensor
+    elif mode == "hard_light":
+      new_image = torch.where(source_tensor < 0.5, 2 * source_tensor * blurred_target,
+                               1 - 2 * (1 - source_tensor) * (1 - blurred_target))
+    elif mode == "difference":
+      new_image = torch.abs(blurred_target - source_tensor)
+    elif mode == "exclusion":
+      new_image = 0.5 - 2 * (blurred_target - 0.5) * (source_tensor - 0.5)
+    elif mode == "color_dodge":
+      new_image = blurred_target / (1 - source_tensor)
+    elif mode == "color_burn":
+      new_image = 1 - (1 - blurred_target) / source_tensor
+    elif mode == "divide":
+      new_image = (source_tensor / blurred_source) * blurred_target
+    else:
+      new_image = source_tensor
+
+    new_image = torch.lerp(target_tensor, new_image, blend_factor)
+    if mask is not None:
+      mask = mask.to(device)
+      new_image = torch.lerp(target_tensor, new_image, mask)
+    new_image = torch.clamp(new_image, 0, 1)
+    new_image = new_image.permute(0, 2, 3, 1).cpu().float()
+
+    results = easySave(new_image, save_prefix, image_output, prompt, extra_pnginfo)
+
+    if image_output in ("Hide", "Hide/Save"):
+      return {"ui": {},
+              "result": (new_image,)}
+
+    return {"ui": {"images": results},
+            "result": (new_image,)}
 
 # 图像反推
 from .libs.image import ci
@@ -1322,10 +1477,15 @@ class imageToBase64:
       return {"result": (base64_str,)}
 
 class removeLocalImage:
+
+  def __init__(self):
+    self.hasFile = False
+
   @classmethod
   def INPUT_TYPES(s):
       return {
         "required": {
+          "any": (AlwaysEqualProxy("*"),),
           "file_name": ("STRING",{"default":""}),
         },
       }
@@ -1335,15 +1495,27 @@ class removeLocalImage:
   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:
+
+
+  def remove(self, any, file_name):
+    self.hasFile = False
+    def listdir(path, dir_name=''):
+      for file in os.listdir(path):
+        file_path = os.path.join(path, file)
+        if os.path.isdir(file_path):
+          dir_name = os.path.basename(file_path)
+          listdir(file_path, dir_name)
+        else:
+          file = os.path.join(dir_name, file)
+          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))
+            self.hasFile = True
+            break
+
+    listdir(folder_paths.input_directory, '')
+
+    if self.hasFile:
       PromptServer.instance.send_sync("easyuse-toast", {"content": "Removed SuccessFully", "type":'success'})
     else:
       PromptServer.instance.send_sync("easyuse-toast", {"content": "Removed Failed", "type": 'error'})
@@ -1405,6 +1577,8 @@ NODE_CLASS_MAPPINGS = {
   "easy imageRatio": imageRatio,
   "easy imageToMask": imageToMask,
   "easy imageConcat": imageConcat,
+  "easy imageListToImageBatch": imageListToImageBatch,
+  "easy imageBatchToImageList": imageBatchToImageList,
   "easy imageSplitList": imageSplitList,
   "easy imageSplitGrid": imageSplitGrid,
   "easy imagesSplitImage": imagesSplitImage,
@@ -1414,6 +1588,7 @@ NODE_CLASS_MAPPINGS = {
   "easy imageRemBg": imageRemBg,
   "easy imageChooser": imageChooser,
   "easy imageColorMatch": imageColorMatch,
+  "easy imageDetailTransfer": imageDetailTransfer,
   "easy imageInterrogator": imageInterrogator,
   "easy loadImageBase64": loadImageBase64,
   "easy imageToBase64": imageToBase64,
@@ -1437,6 +1612,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
   "easy imageToMask": "ImageToMask",
   "easy imageHSVMask": "ImageHSVMask",
   "easy imageConcat": "imageConcat",
+  "easy imageListToImageBatch": "Image List To Image Batch",
+  "easy imageBatchToImageList": "Image Batch To Image List",
   "easy imageSplitList": "imageSplitList",
   "easy imageSplitGrid": "imageSplitGrid",
   "easy imagesSplitImage": "imagesSplitImage",
@@ -1446,6 +1623,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
   "easy imageRemBg": "Image Remove Bg",
   "easy imageChooser": "Image Chooser",
   "easy imageColorMatch": "Image Color Match",
+  "easy imageDetailTransfer": "Image Detail Transfer",
   "easy imageInterrogator": "Image To Prompt",
   "easy joinImageBatch": "JoinImageBatch",
   "easy loadImageBase64": "Load Image (Base64)",

py/kolors/chatglm/config_chatglm.json

@@ -0,0 +1,42 @@
+{
+  "_name_or_path": "THUDM/chatglm3-6b-base",
+  "model_type": "chatglm",
+  "architectures": [
+    "ChatGLMModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_chatglm.ChatGLMConfig",
+    "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
+    "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
+    "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
+    "AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
+  },
+  "add_bias_linear": false,
+  "add_qkv_bias": true,
+  "apply_query_key_layer_scaling": true,
+  "apply_residual_connection_post_layernorm": false,
+  "attention_dropout": 0.0,
+  "attention_softmax_in_fp32": true,
+  "bias_dropout_fusion": true,
+  "ffn_hidden_size": 13696,
+  "fp32_residual_connection": false,
+  "hidden_dropout": 0.0,
+  "hidden_size": 4096,
+  "kv_channels": 128,
+  "layernorm_epsilon": 1e-05,
+  "multi_query_attention": true,
+  "multi_query_group_num": 2,
+  "num_attention_heads": 32,
+  "num_layers": 28,
+  "original_rope": true,
+  "padded_vocab_size": 65024,
+  "post_layer_norm": true,
+  "rmsnorm": true,
+  "seq_length": 32768,
+  "use_cache": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.30.2",
+  "tie_word_embeddings": false,
+  "eos_token_id": 2,
+  "pad_token_id": 0
+}

py/kolors/chatglm/configuration_chatglm.py

@@ -0,0 +1,60 @@
+from transformers import PretrainedConfig
+
+class ChatGLMConfig(PretrainedConfig):
+    model_type = "chatglm"
+    def __init__(
+        self,
+        num_layers=28,
+        padded_vocab_size=65024,
+        hidden_size=4096,
+        ffn_hidden_size=13696,
+        kv_channels=128,
+        num_attention_heads=32,
+        seq_length=2048,
+        hidden_dropout=0.0,
+        classifier_dropout=None,
+        attention_dropout=0.0,
+        layernorm_epsilon=1e-5,
+        rmsnorm=True,
+        apply_residual_connection_post_layernorm=False,
+        post_layer_norm=True,
+        add_bias_linear=False,
+        add_qkv_bias=False,
+        bias_dropout_fusion=True,
+        multi_query_attention=False,
+        multi_query_group_num=1,
+        apply_query_key_layer_scaling=True,
+        attention_softmax_in_fp32=True,
+        fp32_residual_connection=False,
+        quantization_bit=0,
+        pre_seq_len=None,
+        prefix_projection=False,
+        **kwargs
+    ):
+        self.num_layers = num_layers
+        self.vocab_size = padded_vocab_size
+        self.padded_vocab_size = padded_vocab_size
+        self.hidden_size = hidden_size
+        self.ffn_hidden_size = ffn_hidden_size
+        self.kv_channels = kv_channels
+        self.num_attention_heads = num_attention_heads
+        self.seq_length = seq_length
+        self.hidden_dropout = hidden_dropout
+        self.classifier_dropout = classifier_dropout
+        self.attention_dropout = attention_dropout
+        self.layernorm_epsilon = layernorm_epsilon
+        self.rmsnorm = rmsnorm
+        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
+        self.post_layer_norm = post_layer_norm
+        self.add_bias_linear = add_bias_linear
+        self.add_qkv_bias = add_qkv_bias
+        self.bias_dropout_fusion = bias_dropout_fusion
+        self.multi_query_attention = multi_query_attention
+        self.multi_query_group_num = multi_query_group_num
+        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
+        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
+        self.fp32_residual_connection = fp32_residual_connection
+        self.quantization_bit = quantization_bit
+        self.pre_seq_len = pre_seq_len
+        self.prefix_projection = prefix_projection
+        super().__init__(**kwargs)

py/kolors/chatglm/tokenization_chatglm.py

@@ -0,0 +1,299 @@
+import json
+import os
+import re
+from typing import List, Optional, Union, Dict
+from sentencepiece import SentencePieceProcessor
+from transformers import PreTrainedTokenizer
+from transformers.utils import logging, PaddingStrategy
+from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
+
+class SPTokenizer:
+    def __init__(self, model_path: str):
+        # reload tokenizer
+        assert os.path.isfile(model_path), model_path
+        self.sp_model = SentencePieceProcessor(model_file=model_path)
+
+        # BOS / EOS token IDs
+        self.n_words: int = self.sp_model.vocab_size()
+        self.bos_id: int = self.sp_model.bos_id()
+        self.eos_id: int = self.sp_model.eos_id()
+        self.pad_id: int = self.sp_model.unk_id()
+        assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
+
+        role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"]
+        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
+        self.special_tokens = {}
+        self.index_special_tokens = {}
+        for token in special_tokens:
+            self.special_tokens[token] = self.n_words
+            self.index_special_tokens[self.n_words] = token
+            self.n_words += 1
+        self.role_special_token_expression = "|".join([re.escape(token) for token in role_special_tokens])
+
+    def tokenize(self, s: str, encode_special_tokens=False):
+        if encode_special_tokens:
+            last_index = 0
+            t = []
+            for match in re.finditer(self.role_special_token_expression, s):
+                if last_index < match.start():
+                    t.extend(self.sp_model.EncodeAsPieces(s[last_index:match.start()]))
+                t.append(s[match.start():match.end()])
+                last_index = match.end()
+            if last_index < len(s):
+                t.extend(self.sp_model.EncodeAsPieces(s[last_index:]))
+            return t
+        else:
+            return self.sp_model.EncodeAsPieces(s)
+
+    def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]:
+        assert type(s) is str
+        t = self.sp_model.encode(s)
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+        return t
+
+    def decode(self, t: List[int]) -> str:
+        text, buffer = "", []
+        for token in t:
+            if token in self.index_special_tokens:
+                if buffer:
+                    text += self.sp_model.decode(buffer)
+                    buffer = []
+                text += self.index_special_tokens[token]
+            else:
+                buffer.append(token)
+        if buffer:
+            text += self.sp_model.decode(buffer)
+        return text
+
+    def decode_tokens(self, tokens: List[str]) -> str:
+        text = self.sp_model.DecodePieces(tokens)
+        return text
+
+    def convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        return self.sp_model.PieceToId(token)
+
+    def convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        if index in self.index_special_tokens:
+            return self.index_special_tokens[index]
+        if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0:
+            return ""
+        return self.sp_model.IdToPiece(index)
+
+
+class ChatGLMTokenizer(PreTrainedTokenizer):
+    vocab_files_names = {"vocab_file": "tokenizer.model"}
+
+    model_input_names = ["input_ids", "attention_mask", "position_ids"]
+
+    def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, encode_special_tokens=False,
+                 **kwargs):
+        self.name = "GLMTokenizer"
+
+        self.vocab_file = vocab_file
+        self.tokenizer = SPTokenizer(vocab_file)
+        self.special_tokens = {
+            "<bos>": self.tokenizer.bos_id,
+            "<eos>": self.tokenizer.eos_id,
+            "<pad>": self.tokenizer.pad_id
+        }
+        self.encode_special_tokens = encode_special_tokens
+        super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+                         encode_special_tokens=encode_special_tokens,
+                         **kwargs)
+
+    def get_command(self, token):
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}"
+        return self.tokenizer.special_tokens[token]
+
+    @property
+    def unk_token(self) -> str:
+        return "<unk>"
+
+    @property
+    def pad_token(self) -> str:
+        return "<unk>"
+
+    @property
+    def pad_token_id(self):
+        return self.get_command("<pad>")
+
+    @property
+    def eos_token(self) -> str:
+        return "</s>"
+
+    @property
+    def eos_token_id(self):
+        return self.get_command("<eos>")
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.n_words
+
+    def get_vocab(self):
+        """ Returns vocab as a dict """
+        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text, **kwargs):
+        return self.tokenizer.tokenize(text, encode_special_tokens=self.encode_special_tokens)
+
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        return self.tokenizer.convert_token_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.tokenizer.convert_id_to_token(index)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        return self.tokenizer.decode_tokens(tokens)
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+            filename_prefix (`str`, *optional*):
+                An optional prefix to add to the named of the saved files.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, self.vocab_files_names["vocab_file"]
+            )
+        else:
+            vocab_file = save_directory
+
+        with open(self.vocab_file, 'rb') as fin:
+            proto_str = fin.read()
+
+        with open(vocab_file, "wb") as writer:
+            writer.write(proto_str)
+
+        return (vocab_file,)
+
+    def get_prefix_tokens(self):
+        prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")]
+        return prefix_tokens
+
+    def build_single_message(self, role, metadata, message):
+        assert role in ["system", "user", "assistant", "observation"], role
+        role_tokens = [self.get_command(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n")
+        message_tokens = self.tokenizer.encode(message)
+        tokens = role_tokens + message_tokens
+        return tokens
+
+    def build_chat_input(self, query, history=None, role="user"):
+        if history is None:
+            history = []
+        input_ids = []
+        for item in history:
+            content = item["content"]
+            if item["role"] == "system" and "tools" in item:
+                content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False)
+            input_ids.extend(self.build_single_message(item["role"], item.get("metadata", ""), content))
+        input_ids.extend(self.build_single_message(role, "", query))
+        input_ids.extend([self.get_command("<|assistant|>")])
+        return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True)
+
+    def build_inputs_with_special_tokens(
+            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A BERT sequence has the following format:
+
+        - single sequence: `[CLS] X [SEP]`
+        - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        prefix_tokens = self.get_prefix_tokens()
+        token_ids_0 = prefix_tokens + token_ids_0
+        if token_ids_1 is not None:
+            token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("<eos>")]
+        return token_ids_0
+
+    def _pad(
+            self,
+            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
+            max_length: Optional[int] = None,
+            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+            pad_to_multiple_of: Optional[int] = None,
+            return_attention_mask: Optional[bool] = None,
+    ) -> dict:
+        """
+        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+        Args:
+            encoded_inputs:
+                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+            max_length: maximum length of the returned list and optionally padding length (see below).
+                Will truncate by taking into account the special tokens.
+            padding_strategy: PaddingStrategy to use for padding.
+
+                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+                - PaddingStrategy.DO_NOT_PAD: Do not pad
+                The tokenizer padding sides are defined in self.padding_side:
+
+                    - 'left': pads on the left of the sequences
+                    - 'right': pads on the right of the sequences
+            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta).
+            return_attention_mask:
+                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+        """
+        # Load from model defaults
+        assert self.padding_side == "left"
+
+        required_input = encoded_inputs[self.model_input_names[0]]
+        seq_length = len(required_input)
+
+        if padding_strategy == PaddingStrategy.LONGEST:
+            max_length = len(required_input)
+
+        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+        # Initialize attention mask if not present.
+        if "attention_mask" not in encoded_inputs:
+            encoded_inputs["attention_mask"] = [1] * seq_length
+
+        if "position_ids" not in encoded_inputs:
+            encoded_inputs["position_ids"] = list(range(seq_length))
+
+        if needs_to_be_padded:
+            difference = max_length - len(required_input)
+
+            if "attention_mask" in encoded_inputs:
+                encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+            if "position_ids" in encoded_inputs:
+                encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
+            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+
+        return encoded_inputs

py/kolors/chatglm/tokenizer/tokenizer_config.json

@@ -0,0 +1,12 @@
+{
+  "name_or_path": "THUDM/chatglm3-6b-base",
+  "remove_space": false,
+  "do_lower_case": false,
+  "tokenizer_class": "ChatGLMTokenizer",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_chatglm.ChatGLMTokenizer",
+      null
+      ]
+  }
+}

py/kolors/clip_vision_config_vitl_336.json

@@ -0,0 +1,18 @@
+{
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 1024,
+  "image_size": 336,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 24,
+  "patch_size": 14,
+  "projection_dim": 768,
+  "torch_dtype": "float32"
+}

py/kolors/loader.py

@@ -0,0 +1,302 @@
+import json
+import os
+import torch
+import comfy.supported_models
+import comfy.model_patcher
+import comfy.model_management
+import comfy.model_detection as model_detection
+import comfy.model_base as model_base
+from comfy.model_base import sdxl_pooled, CLIPEmbeddingNoiseAugmentation, Timestep, ModelType
+from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel
+from comfy.clip_vision import ClipVisionModel, Output
+from comfy.utils import load_torch_file
+from .chatglm.modeling_chatglm import ChatGLMModel, ChatGLMConfig
+from .chatglm.tokenization_chatglm import ChatGLMTokenizer
+
+class KolorsUNetModel(UNetModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.encoder_hid_proj = torch.nn.Linear(4096, 2048, bias=True)
+
+    def forward(self, *args, **kwargs):
+        with torch.cuda.amp.autocast(enabled=True):
+            if "context" in kwargs:
+                kwargs["context"] = self.encoder_hid_proj(kwargs["context"])
+            result = super().forward(*args, **kwargs)
+            return result
+
+class KolorsSDXL(model_base.SDXL):
+    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+        model_base.BaseModel.__init__(self, model_config, model_type, device=device, unet_model=KolorsUNetModel)
+        self.embedder = Timestep(256)
+        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})
+
+    def encode_adm(self, **kwargs):
+        clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
+        width = kwargs.get("width", 768)
+        height = kwargs.get("height", 768)
+        crop_w = kwargs.get("crop_w", 0)
+        crop_h = kwargs.get("crop_h", 0)
+        target_width = kwargs.get("target_width", width)
+        target_height = kwargs.get("target_height", height)
+
+        out = []
+        out.append(self.embedder(torch.Tensor([height])))
+        out.append(self.embedder(torch.Tensor([width])))
+        out.append(self.embedder(torch.Tensor([crop_h])))
+        out.append(self.embedder(torch.Tensor([crop_w])))
+        out.append(self.embedder(torch.Tensor([target_height])))
+        out.append(self.embedder(torch.Tensor([target_width])))
+        flat = torch.flatten(torch.cat(out)).unsqueeze(
+            dim=0).repeat(clip_pooled.shape[0], 1)
+        return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
+
+class Kolors(comfy.supported_models.SDXL):
+    unet_config = {
+        "model_channels": 320,
+        "use_linear_in_transformer": True,
+        "transformer_depth": [0, 0, 2, 2, 10, 10],
+        "context_dim": 2048,
+        "adm_in_channels": 5632,
+        "use_temporal_attention": False,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = KolorsSDXL(self, model_type=self.model_type(state_dict, prefix), device=device, )
+        out.__class__ = model_base.SDXL
+        if self.inpaint_model():
+            out.set_inpaint()
+        return out
+
+def kolors_unet_config_from_diffusers_unet(state_dict, dtype=None):
+    match = {}
+    transformer_depth = []
+
+    attn_res = 1
+    count_blocks = model_detection.count_blocks
+    down_blocks = count_blocks(state_dict, "down_blocks.{}")
+    for i in range(down_blocks):
+        attn_blocks = count_blocks(
+            state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
+        res_blocks = count_blocks(
+            state_dict, "down_blocks.{}.resnets.".format(i) + '{}')
+        for ab in range(attn_blocks):
+            transformer_count = count_blocks(
+                state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
+            transformer_depth.append(transformer_count)
+            if transformer_count > 0:
+                match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(
+                    i, ab)].shape[1]
+
+        attn_res *= 2
+        if attn_blocks == 0:
+            for i in range(res_blocks):
+                transformer_depth.append(0)
+
+    match["transformer_depth"] = transformer_depth
+
+    match["model_channels"] = state_dict["conv_in.weight"].shape[0]
+    match["in_channels"] = state_dict["conv_in.weight"].shape[1]
+    match["adm_in_channels"] = None
+    if "class_embedding.linear_1.weight" in state_dict:
+        match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
+    elif "add_embedding.linear_1.weight" in state_dict:
+        match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
+
+    Kolors = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+              'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+              'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
+              'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+              'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+    Kolors_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True,
+                      'legacy': False,
+                      'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 9,
+                      'model_channels': 320,
+                      'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4],
+                      'transformer_depth_middle': 10,
+                      'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+                      'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+                      'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+    Kolors_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True,
+                   'legacy': False,
+                   'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 8,
+                   'model_channels': 320,
+                   'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4],
+                   'transformer_depth_middle': 10,
+                   'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+                   'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+                   'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+    SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True,
+            'legacy': False,
+            'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4,
+            'model_channels': 320,
+            'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4],
+            'transformer_depth_middle': 10,
+            'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+            'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+            'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+    SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True,
+                     'legacy': False,
+                     'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4,
+                     'model_channels': 320,
+                     'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4],
+                     'transformer_depth_middle': 1,
+                     'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+                     'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],
+                     'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+    SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True,
+                       'legacy': False,
+                       'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4,
+                       'model_channels': 320,
+                       'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4],
+                       'transformer_depth_middle': 0,
+                       'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1,
+                       'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                       'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+    supported_models = [Kolors, Kolors_inpaint,
+                        Kolors_ip2p, SDXL, SDXL_mid_cnet, SDXL_small_cnet]
+
+
+    for unet_config in supported_models:
+        matches = True
+        for k in match:
+            if match[k] != unet_config[k]:
+                # print("key {} does not match".format(k), match[k], "||", unet_config[k])
+                matches = False
+                break
+        if matches:
+            return model_detection.convert_config(unet_config)
+    return None
+
+# chatglm3 model
+class chatGLM3Model(torch.nn.Module):
+    def __init__(self, textmodel_json_config=None, device='cpu', offload_device='cpu', model_path=None):
+        super().__init__()
+        if model_path is None:
+            raise ValueError("model_path is required")
+        self.device = device
+        if textmodel_json_config is None:
+            textmodel_json_config = os.path.join(
+                os.path.dirname(os.path.realpath(__file__)),
+                "chatglm",
+                "config_chatglm.json"
+            )
+        with open(textmodel_json_config, 'r') as file:
+            config = json.load(file)
+        textmodel_json_config = ChatGLMConfig(**config)
+        is_accelerate_available = False
+        try:
+            from accelerate import init_empty_weights
+            from accelerate.utils import set_module_tensor_to_device
+            is_accelerate_available = True
+        except:
+            pass
+
+        from contextlib import nullcontext
+        with (init_empty_weights() if is_accelerate_available else nullcontext()):
+            with torch.no_grad():
+                print('torch version:', torch.__version__)
+                self.text_encoder = ChatGLMModel(textmodel_json_config).eval()
+                if '4bit' in model_path:
+                    try:
+                        import cpm_kernels
+                    except ImportError:
+                        print("Installing cpm_kernels...")
+                        subprocess.run([sys.executable, "-m", "pip", "install", "cpm_kernels"], check=True)
+                        pass
+                    self.text_encoder.quantize(4)
+                elif '8bit' in model_path:
+                    self.text_encoder.quantize(8)
+
+        sd = load_torch_file(model_path)
+        if is_accelerate_available:
+            for key in sd:
+                set_module_tensor_to_device(self.text_encoder, key, device=offload_device, value=sd[key])
+        else:
+            print("WARNING: Accelerate not available, use load_state_dict load model")
+            self.text_encoder.load_state_dict()
+
+def load_chatglm3(model_path=None):
+    if model_path is None:
+        return
+
+    load_device = comfy.model_management.text_encoder_device()
+    offload_device = comfy.model_management.text_encoder_offload_device()
+
+    glm3model = chatGLM3Model(
+        device=load_device,
+        offload_device=offload_device,
+        model_path=model_path
+    )
+    tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'chatglm', "tokenizer")
+    tokenizer = ChatGLMTokenizer.from_pretrained(tokenizer_path)
+    text_encoder = glm3model.text_encoder
+    return {"text_encoder":text_encoder, "tokenizer":tokenizer}
+
+
+# clipvision model
+def load_clipvision_vitl_336(path):
+    sd = load_torch_file(path)
+    if "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
+        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
+    else:
+        raise Exception("Unsupported clip vision model")
+    clip = ClipVisionModel(json_config)
+    m, u = clip.load_sd(sd)
+    if len(m) > 0:
+        print("missing clip vision: {}".format(m))
+    u = set(u)
+    keys = list(sd.keys())
+    for k in keys:
+        if k not in u:
+            t = sd.pop(k)
+            del t
+    return clip
+
+class applyKolorsUnet:
+    def __enter__(self):
+        import comfy.ldm.modules.diffusionmodules.openaimodel
+        import comfy.utils
+        import comfy.clip_vision
+
+        self.original_UNET_MAP_BASIC = comfy.utils.UNET_MAP_BASIC.copy()
+        comfy.utils.UNET_MAP_BASIC.add(("encoder_hid_proj.weight", "encoder_hid_proj.weight"),)
+        comfy.utils.UNET_MAP_BASIC.add(("encoder_hid_proj.bias", "encoder_hid_proj.bias"),)
+
+        self.original_unet_config_from_diffusers_unet = model_detection.unet_config_from_diffusers_unet
+        model_detection.unet_config_from_diffusers_unet = kolors_unet_config_from_diffusers_unet
+
+        import comfy.supported_models
+        self.original_supported_models = comfy.supported_models.models
+        comfy.supported_models.models = [Kolors]
+
+        self.original_load_clipvision_from_sd = comfy.clip_vision.load_clipvision_from_sd
+        comfy.clip_vision.load_clipvision_from_sd = load_clipvision_vitl_336
+
+    def __exit__(self, type, value, traceback):
+        import comfy.ldm.modules.diffusionmodules.openaimodel
+        import comfy.utils
+        import comfy.supported_models
+        import comfy.clip_vision
+
+        comfy.utils.UNET_MAP_BASIC = self.original_UNET_MAP_BASIC
+
+        model_detection.unet_config_from_diffusers_unet = self.original_unet_config_from_diffusers_unet
+        comfy.supported_models.models = self.original_supported_models
+
+        comfy.clip_vision.load_clipvision_from_sd = self.original_load_clipvision_from_sd
+
+
+def is_kolors_model(model):
+    base: BaseModel = model.model
+    model_config: comfy.supported_models.supported_models_base.BASE = base.model_config
+    if isinstance(model_config, Kolors):
+        return True
+    else:
+        return False

py/kolors/model_patch.py

@@ -0,0 +1,66 @@
+import torch
+from torch.nn import Linear
+from types import MethodType
+import comfy.model_management
+import comfy.samplers
+from comfy.cldm.cldm import ControlNet
+from comfy.controlnet import ControlLora
+
+def patch_controlnet(model, control_net):
+    import comfy.controlnet
+    if isinstance(control_net, ControlLora):
+        del_keys = []
+        for k in control_net.control_weights:
+            if k.startswith("label_emb.0.0."):
+                del_keys.append(k)
+
+        for k in del_keys:
+            control_net.control_weights.pop(k)
+
+        super_pre_run = ControlLora.pre_run
+        super_copy = ControlLora.copy
+
+        super_forward = ControlNet.forward
+
+        def KolorsControlNet_forward(self, x, hint, timesteps, context, **kwargs):
+            with torch.cuda.amp.autocast(enabled=True):
+                context = model.model.diffusion_model.encoder_hid_proj(context)
+                return super_forward(self, x, hint, timesteps, context, **kwargs)
+
+        def KolorsControlLora_pre_run(self, *args, **kwargs):
+            result = super_pre_run(self, *args, **kwargs)
+
+            if hasattr(self, "control_model"):
+                self.control_model.forward = MethodType(
+                    KolorsControlNet_forward, self.control_model)
+            return result
+
+        control_net.pre_run = MethodType(
+            KolorsControlLora_pre_run, control_net)
+
+        def KolorsControlLora_copy(self, *args, **kwargs):
+            c = super_copy(self, *args, **kwargs)
+            c.pre_run = MethodType(
+                KolorsControlLora_pre_run, c)
+            return c
+
+        control_net.copy = MethodType(KolorsControlLora_copy, control_net)
+
+    elif isinstance(control_net, comfy.controlnet.ControlNet):
+        model_label_emb = model.model.diffusion_model.label_emb
+        control_net.control_model.label_emb = model_label_emb
+        control_net.control_model_wrapped.model.label_emb = model_label_emb
+        super_forward = ControlNet.forward
+
+        def KolorsControlNet_forward(self, x, hint, timesteps, context, **kwargs):
+            with torch.cuda.amp.autocast(enabled=True):
+                context = model.model.diffusion_model.encoder_hid_proj(context)
+                return super_forward(self, x, hint, timesteps, context, **kwargs)
+
+        control_net.control_model.forward = MethodType(
+            KolorsControlNet_forward, control_net.control_model)
+
+    else:
+        raise NotImplementedError(f"Type {control_net} not supported for KolorsControlNetPatch")
+
+    return control_net

py/kolors/text_encode.py

@@ -0,0 +1,105 @@
+import re
+import random
+import gc
+import comfy.model_management as mm
+from nodes import ConditioningConcat, ConditioningZeroOut, ConditioningSetTimestepRange, ConditioningCombine
+
+def chatglm3_text_encode(chatglm3_model, prompt, clean_gpu=False):
+    device = mm.get_torch_device()
+    offload_device = mm.unet_offload_device()
+    if clean_gpu:
+        mm.unload_all_models()
+        mm.soft_empty_cache()
+    # Function to randomly select an option from the brackets
+
+    def choose_random_option(match):
+        options = match.group(1).split('|')
+        return random.choice(options)
+
+    prompt = re.sub(r'\{([^{}]*)\}', choose_random_option, prompt)
+
+    if "|" in prompt:
+        prompt = prompt.split("|")
+
+    if prompt is not None and isinstance(prompt, str):
+        batch_size = 1
+    elif prompt is not None and isinstance(prompt, list):
+        batch_size = len(prompt)
+
+    # Define tokenizers and text encoders
+    tokenizer = chatglm3_model['tokenizer']
+    text_encoder = chatglm3_model['text_encoder']
+    text_encoder.to(device)
+    text_inputs = tokenizer(
+        prompt,
+        padding="max_length",
+        max_length=256,
+        truncation=True,
+        return_tensors="pt",
+    ).to(device)
+
+    output = text_encoder(
+        input_ids=text_inputs['input_ids'],
+        attention_mask=text_inputs['attention_mask'],
+        position_ids=text_inputs['position_ids'],
+        output_hidden_states=True)
+
+    # [batch_size, 77, 4096]
+    prompt_embeds = output.hidden_states[-2].permute(1, 0, 2).clone()
+    text_proj = output.hidden_states[-1][-1, :, :].clone()  # [batch_size, 4096]
+    bs_embed, seq_len, _ = prompt_embeds.shape
+    prompt_embeds = prompt_embeds.repeat(1, 1, 1)
+    prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
+
+    bs_embed = text_proj.shape[0]
+    text_proj = text_proj.repeat(1, 1).view(bs_embed, -1)
+    text_encoder.to(offload_device)
+    if clean_gpu:
+        mm.soft_empty_cache()
+        gc.collect()
+    return [[prompt_embeds, {"pooled_output": text_proj},]]
+
+def chatglm3_adv_text_encode(chatglm3_model, text, clean_gpu=False):
+    time_start = 0
+    time_end = 1
+    match = re.search(r'TIMESTEP.*$', text)
+    if match:
+        timestep = match.group()
+        timestep = timestep.split(' ')
+        timestep = timestep[0]
+        text = text.replace(timestep, '')
+        value = timestep.split(':')
+        if len(value) >= 3:
+            time_start = float(value[1])
+            time_end = float(value[2])
+        elif len(value) == 2:
+            time_start = float(value[1])
+            time_end = 1
+        elif len(value) == 1:
+            time_start = 0.1
+            time_end = 1
+
+
+    pass3 = [x.strip() for x in text.split("BREAK")]
+    pass3 = [x for x in pass3 if x != '']
+
+    if len(pass3) == 0:
+        pass3 = ['']
+
+    conditioning = None
+
+    for text in pass3:
+        cond = chatglm3_text_encode(chatglm3_model, text, clean_gpu)
+        if conditioning is not None:
+            conditioning = ConditioningConcat().concat(conditioning, cond)[0]
+        else:
+            conditioning = cond
+
+    # setTimeStepRange
+    if time_start > 0 or time_end < 1:
+        conditioning_2, = ConditioningSetTimestepRange().set_range(conditioning, 0, time_start)
+        conditioning_1, = ConditioningZeroOut().zero_out(conditioning)
+        conditioning_1, = ConditioningSetTimestepRange().set_range(conditioning_1, time_start, time_end)
+        conditioning, = ConditioningCombine().combine(conditioning_1, conditioning_2)
+
+    return conditioning

py/layer_diffuse/__init__.py

@@ -0,0 +1,209 @@
+#credit to huchenlei for this module
+#from https://github.com/huchenlei/ComfyUI-layerdiffuse
+import torch
+import comfy.model_management
+import copy
+from typing import Optional
+from enum import Enum
+from comfy.utils import load_torch_file
+from comfy.conds import CONDRegular
+from comfy_extras.nodes_compositing import JoinImageWithAlpha
+from .model import ModelPatcher, TransparentVAEDecoder, calculate_weight_adjust_channel
+from .attension_sharing import AttentionSharingPatcher
+from ..config import LAYER_DIFFUSION, LAYER_DIFFUSION_DIR, LAYER_DIFFUSION_VAE
+from ..libs.utils import to_lora_patch_dict, get_local_filepath, get_sd_version
+
+load_layer_model_state_dict = load_torch_file
+class LayerMethod(Enum):
+    FG_ONLY_ATTN = "Attention Injection"
+    FG_ONLY_CONV = "Conv Injection"
+    FG_TO_BLEND = "Foreground"
+    FG_BLEND_TO_BG = "Foreground to Background"
+    BG_TO_BLEND = "Background"
+    BG_BLEND_TO_FG = "Background to Foreground"
+    EVERYTHING = "Everything"
+
+class LayerDiffuse:
+
+    def __init__(self) -> None:
+        self.vae_transparent_decoder = None
+        self.frames = 1
+
+    def get_layer_diffusion_method(self, method, has_blend_latent):
+        method = LayerMethod(method)
+        if method == LayerMethod.BG_TO_BLEND and has_blend_latent:
+            method = LayerMethod.BG_BLEND_TO_FG
+        elif method == LayerMethod.FG_TO_BLEND and has_blend_latent:
+            method = LayerMethod.FG_BLEND_TO_BG
+        return method
+
+    def apply_layer_c_concat(self, cond, uncond, c_concat):
+        def write_c_concat(cond):
+            new_cond = []
+            for t in cond:
+                n = [t[0], t[1].copy()]
+                if "model_conds" not in n[1]:
+                    n[1]["model_conds"] = {}
+                n[1]["model_conds"]["c_concat"] = CONDRegular(c_concat)
+                new_cond.append(n)
+            return new_cond
+
+        return (write_c_concat(cond), write_c_concat(uncond))
+
+    def apply_layer_diffusion(self, model: ModelPatcher, method, weight, samples, blend_samples, positive, negative, image=None, additional_cond=(None, None, None)):
+        control_img: Optional[torch.TensorType] = None
+        sd_version = get_sd_version(model)
+        model_url = LAYER_DIFFUSION[method.value][sd_version]["model_url"]
+
+        if image is not None:
+            image = image.movedim(-1, 1)
+
+        try:
+            ModelPatcher.calculate_weight = calculate_weight_adjust_channel(ModelPatcher.calculate_weight)
+        except:
+            pass
+
+        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 == '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 == 'sd1':
+            batch_size, _, height, width = samples['samples'].shape
+            self.frames = 3
+            if batch_size % 3 != 0:
+                raise Exception(f"The batch size should be a multiple of 3. 批次大小需为3的倍数")
+        if model_url is None:
+            raise Exception(f"{method.value} is not supported for {sd_version} model")
+
+        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 == 'sd1':
+            patcher = AttentionSharingPatcher(
+                work_model, self.frames, use_control=control_img is not None
+            )
+            patcher.load_state_dict(layer_lora_state_dict, strict=True)
+            if control_img is not None:
+                patcher.set_control(control_img)
+        else:
+            layer_lora_patch_dict = to_lora_patch_dict(layer_lora_state_dict)
+            work_model.add_patches(layer_lora_patch_dict, weight)
+
+        # cond_contact
+        if method in [LayerMethod.FG_ONLY_ATTN, LayerMethod.FG_ONLY_CONV]:
+            samp_model = work_model
+        elif sd_version == 'sdxl':
+            if method in [LayerMethod.BG_TO_BLEND, LayerMethod.FG_TO_BLEND]:
+                c_concat = model.model.latent_format.process_in(samples["samples"])
+            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 == '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]:
+                additional_cond = (additional_cond[1], None)
+
+            work_model.model_options.setdefault("transformer_options", {})
+            work_model.model_options["transformer_options"]["cond_overwrite"] = [
+                cond[0][0] if cond is not None else None
+                for cond in additional_cond
+            ]
+            samp_model = work_model
+
+        return samp_model, positive, negative
+
+    def join_image_with_alpha(self, image, alpha):
+        out = image.movedim(-1, 1)
+        if out.shape[1] == 3:  # RGB
+            out = torch.cat([out, torch.ones_like(out[:, :1, :, :])], dim=1)
+        for i in range(out.shape[0]):
+            out[i, 3, :, :] = alpha
+        return out.movedim(1, -1)
+
+    def image_to_alpha(self, image, latent):
+        pixel = image.movedim(-1, 1)  # [B, H, W, C] => [B, C, H, W]
+        decoded = []
+        sub_batch_size = 16
+        for start_idx in range(0, latent.shape[0], sub_batch_size):
+            decoded.append(
+                self.vae_transparent_decoder.decode_pixel(
+                    pixel[start_idx: start_idx + sub_batch_size],
+                    latent[start_idx: start_idx + sub_batch_size],
+                )
+            )
+        pixel_with_alpha = torch.cat(decoded, dim=0)
+        # [B, C, H, W] => [B, H, W, C]
+        pixel_with_alpha = pixel_with_alpha.movedim(1, -1)
+        image = pixel_with_alpha[..., 1:]
+        alpha = pixel_with_alpha[..., 0]
+
+        alpha = 1.0 - alpha
+        new_images, = JoinImageWithAlpha().join_image_with_alpha(image, alpha)
+        return new_images, alpha
+
+    def make_3d_mask(self, mask):
+        if len(mask.shape) == 4:
+            return mask.squeeze(0)
+
+        elif len(mask.shape) == 2:
+            return mask.unsqueeze(0)
+
+        return mask
+
+    def masks_to_list(self, masks):
+        if masks is None:
+            empty_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            return ([empty_mask],)
+
+        res = []
+
+        for mask in masks:
+            res.append(mask)
+
+        return [self.make_3d_mask(x) for x in res]
+
+    def layer_diffusion_decode(self, layer_diffusion_method, latent, blend_samples, samp_images, model):
+        alpha = []
+        if layer_diffusion_method is not None:
+            sd_version = get_sd_version(model)
+            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 == '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:
+                    model_url = LAYER_DIFFUSION_VAE['decode'][sd_version]["model_url"]
+                    if model_url is None:
+                        raise Exception(f"{method.value} is not supported for {sd_version} model")
+                    decoder_file = get_local_filepath(model_url, LAYER_DIFFUSION_DIR)
+                    self.vae_transparent_decoder = TransparentVAEDecoder(
+                        load_torch_file(decoder_file),
+                        device=comfy.model_management.get_torch_device(),
+                        dtype=(torch.float16 if comfy.model_management.should_use_fp16() else torch.float32),
+                    )
+                if method in [LayerMethod.EVERYTHING, LayerMethod.BG_BLEND_TO_FG, LayerMethod.BG_TO_BLEND]:
+                    new_images = []
+                    sliced_samples = copy.copy({"samples": latent})
+                    for index in range(len(samp_images)):
+                        if index % self.frames == 0:
+                            img = samp_images[index::self.frames]
+                            alpha_images, _alpha = self.image_to_alpha(img, sliced_samples["samples"][index::self.frames])
+                            alpha.append(self.make_3d_mask(_alpha[0]))
+                            new_images.append(alpha_images[0])
+                        else:
+                            new_images.append(samp_images[index])
+                else:
+                    new_images, alpha = self.image_to_alpha(samp_images, latent)
+            else:
+                new_images = samp_images
+        else:
+            new_images = samp_images
+
+
+        return (new_images, samp_images, alpha)

py/layer_diffuse/func.py

@@ -1,204 +0,0 @@
-import torch
-import comfy.model_management
-import copy
-from typing import Optional
-from enum import Enum
-from comfy.utils import load_torch_file
-from comfy.conds import CONDRegular
-from comfy_extras.nodes_compositing import JoinImageWithAlpha
-from .model import ModelPatcher, TransparentVAEDecoder, calculate_weight_adjust_channel
-from .attension_sharing import AttentionSharingPatcher
-from ..config import LAYER_DIFFUSION, LAYER_DIFFUSION_DIR, LAYER_DIFFUSION_VAE
-from ..libs.utils import to_lora_patch_dict, get_local_filepath, get_sd_version
-
-load_layer_model_state_dict = load_torch_file
-class LayerMethod(Enum):
-    FG_ONLY_ATTN = "Attention Injection"
-    FG_ONLY_CONV = "Conv Injection"
-    FG_TO_BLEND = "Foreground"
-    FG_BLEND_TO_BG = "Foreground to Background"
-    BG_TO_BLEND = "Background"
-    BG_BLEND_TO_FG = "Background to Foreground"
-    EVERYTHING = "Everything"
-
-class LayerDiffuse:
-
-    def __init__(self) -> None:
-        self.vae_transparent_decoder = None
-        self.frames = 1
-
-    def get_layer_diffusion_method(self, method, has_blend_latent):
-        method = LayerMethod(method)
-        if method == LayerMethod.BG_TO_BLEND and has_blend_latent:
-            method = LayerMethod.BG_BLEND_TO_FG
-        elif method == LayerMethod.FG_TO_BLEND and has_blend_latent:
-            method = LayerMethod.FG_BLEND_TO_BG
-        return method
-
-    def apply_layer_c_concat(self, cond, uncond, c_concat):
-        def write_c_concat(cond):
-            new_cond = []
-            for t in cond:
-                n = [t[0], t[1].copy()]
-                if "model_conds" not in n[1]:
-                    n[1]["model_conds"] = {}
-                n[1]["model_conds"]["c_concat"] = CONDRegular(c_concat)
-                new_cond.append(n)
-            return new_cond
-
-        return (write_c_concat(cond), write_c_concat(uncond))
-
-    def apply_layer_diffusion(self, model: ModelPatcher, method, weight, samples, blend_samples, positive, negative, image=None, additional_cond=(None, None, None)):
-        control_img: Optional[torch.TensorType] = None
-        sd_version = get_sd_version(model)
-        model_url = LAYER_DIFFUSION[method.value][sd_version]["model_url"]
-
-        try:
-            ModelPatcher.calculate_weight = calculate_weight_adjust_channel(ModelPatcher.calculate_weight)
-        except:
-            pass
-
-        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 == '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 == 'sd1':
-            batch_size, _, height, width = samples['samples'].shape
-            self.frames = 3
-            if batch_size % 3 != 0:
-                raise Exception(f"The batch size should be a multiple of 3. 批次大小需为3的倍数")
-        if model_url is None:
-            raise Exception(f"{method.value} is not supported for {sd_version} model")
-
-        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 == 'sd1':
-            patcher = AttentionSharingPatcher(
-                work_model, self.frames, use_control=control_img is not None
-            )
-            patcher.load_state_dict(layer_lora_state_dict, strict=True)
-            if control_img is not None:
-                patcher.set_control(control_img)
-        else:
-            layer_lora_patch_dict = to_lora_patch_dict(layer_lora_state_dict)
-            work_model.add_patches(layer_lora_patch_dict, weight)
-
-        # cond_contact
-        if method in [LayerMethod.FG_ONLY_ATTN, LayerMethod.FG_ONLY_CONV]:
-            samp_model = work_model
-        elif sd_version == 'sdxl':
-            if method in [LayerMethod.BG_TO_BLEND, LayerMethod.FG_TO_BLEND]:
-                c_concat = model.model.latent_format.process_in(samples["samples"])
-            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 == '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]:
-                additional_cond = (additional_cond[1], None)
-
-            work_model.model_options.setdefault("transformer_options", {})
-            work_model.model_options["transformer_options"]["cond_overwrite"] = [
-                cond[0][0] if cond is not None else None
-                for cond in additional_cond
-            ]
-            samp_model = work_model
-
-        return samp_model, positive, negative
-
-    def join_image_with_alpha(self, image, alpha):
-        out = image.movedim(-1, 1)
-        if out.shape[1] == 3:  # RGB
-            out = torch.cat([out, torch.ones_like(out[:, :1, :, :])], dim=1)
-        for i in range(out.shape[0]):
-            out[i, 3, :, :] = alpha
-        return out.movedim(1, -1)
-
-    def image_to_alpha(self, image, latent):
-        pixel = image.movedim(-1, 1)  # [B, H, W, C] => [B, C, H, W]
-        decoded = []
-        sub_batch_size = 16
-        for start_idx in range(0, latent.shape[0], sub_batch_size):
-            decoded.append(
-                self.vae_transparent_decoder.decode_pixel(
-                    pixel[start_idx: start_idx + sub_batch_size],
-                    latent[start_idx: start_idx + sub_batch_size],
-                )
-            )
-        pixel_with_alpha = torch.cat(decoded, dim=0)
-        # [B, C, H, W] => [B, H, W, C]
-        pixel_with_alpha = pixel_with_alpha.movedim(1, -1)
-        image = pixel_with_alpha[..., 1:]
-        alpha = pixel_with_alpha[..., 0]
-
-        alpha = 1.0 - alpha
-        new_images, = JoinImageWithAlpha().join_image_with_alpha(image, alpha)
-        return new_images, alpha
-
-    def make_3d_mask(self, mask):
-        if len(mask.shape) == 4:
-            return mask.squeeze(0)
-
-        elif len(mask.shape) == 2:
-            return mask.unsqueeze(0)
-
-        return mask
-
-    def masks_to_list(self, masks):
-        if masks is None:
-            empty_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
-            return ([empty_mask],)
-
-        res = []
-
-        for mask in masks:
-            res.append(mask)
-
-        return [self.make_3d_mask(x) for x in res]
-
-    def layer_diffusion_decode(self, layer_diffusion_method, latent, blend_samples, samp_images, model):
-        alpha = []
-        if layer_diffusion_method is not None:
-            sd_version = get_sd_version(model)
-            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 == '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:
-                    model_url = LAYER_DIFFUSION_VAE['decode'][sd_version]["model_url"]
-                    if model_url is None:
-                        raise Exception(f"{method.value} is not supported for {sd_version} model")
-                    decoder_file = get_local_filepath(model_url, LAYER_DIFFUSION_DIR)
-                    self.vae_transparent_decoder = TransparentVAEDecoder(
-                        load_torch_file(decoder_file),
-                        device=comfy.model_management.get_torch_device(),
-                        dtype=(torch.float16 if comfy.model_management.should_use_fp16() else torch.float32),
-                    )
-                if method in [LayerMethod.EVERYTHING, LayerMethod.BG_BLEND_TO_FG, LayerMethod.BG_TO_BLEND]:
-                    new_images = []
-                    sliced_samples = copy.copy({"samples": latent})
-                    for index in range(len(samp_images)):
-                        if index % self.frames == 0:
-                            img = samp_images[index::self.frames]
-                            alpha_images, _alpha = self.image_to_alpha(img, sliced_samples["samples"][index::self.frames])
-                            alpha.append(self.make_3d_mask(_alpha[0]))
-                            new_images.append(alpha_images[0])
-                        else:
-                            new_images.append(samp_images[index])
-                else:
-                    new_images, alpha = self.image_to_alpha(samp_images, latent)
-            else:
-                new_images = samp_images
-        else:
-            new_images = samp_images
-
-
-        return (new_images, samp_images, alpha)

py/libs/adv_encode.py

@@ -1,12 +1,16 @@
 import torch
 import numpy as np
+import re
 import itertools
 
 from comfy import model_management
 from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG
-from nodes import NODE_CLASS_MAPPINGS, ConditioningConcat, CLIPTextEncode
+try:
+    from comfy.text_encoders.sd3_clip import SD3ClipModel, T5XXLModel
+except ImportError:
+    from comfy.sd3_clip import SD3ClipModel, T5XXLModel
 
-from .libs.utils import compare_revision
+from nodes import NODE_CLASS_MAPPINGS, ConditioningConcat, ConditioningZeroOut, ConditioningSetTimestepRange, ConditioningCombine
 
 def _grouper(n, iterable):
     it = iter(iterable)
@@ -240,6 +244,9 @@ def encode_token_weights_l(model, token_weight_pairs):
     l_out, pooled = model.clip_l.encode_token_weights(token_weight_pairs)
     return l_out, pooled
 
+def encode_token_weights_t5(model, token_weight_pairs):
+    return model.t5xxl.encode_token_weights(token_weight_pairs)
+
 
 def encode_token_weights(model, token_weight_pairs, encode_func):
     if model.layer_idx is not None:
@@ -260,6 +267,14 @@ def prepareXL(embs_l, embs_g, pooled, clip_balance):
     else:
         return embs_g, pooled
 
+def prepareSD3(out, pooled, clip_balance):
+    lg_w = 1 - max(0, clip_balance - .5) * 2
+    t5_w = 1 - max(0, .5 - clip_balance) * 2
+    if out.shape[0] > 1:
+        return torch.cat([out[0] * lg_w, out[1] * t5_w], dim=-1), pooled
+    else:
+        return out, pooled
+
 def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5,
                     apply_to_pooled=True, width=1024, height=1024, crop_w=0, crop_h=0, target_width=1024, target_height=1024, a1111_prompt_style=False, steps=1):
 
@@ -272,6 +287,25 @@ def advanced_encode(clip, text, token_normalization, weight_interpretation, w_ma
         else:
             raise Exception(f"[smzNodes Not Found] you need to install 'ComfyUI-smzNodes'")
 
+    time_start = 0
+    time_end = 1
+    match = re.search(r'TIMESTEP.*$', text)
+    if match:
+        timestep = match.group()
+        timestep = timestep.split(' ')
+        timestep = timestep[0]
+        text = text.replace(timestep, '')
+        value = timestep.split(':')
+        if len(value) >= 3:
+            time_start = float(value[1])
+            time_end = float(value[2])
+        elif len(value) == 2:
+            time_start = float(value[1])
+            time_end = 1
+        elif len(value) == 1:
+            time_start = 0.1
+            time_end = 1
+
     pass3 = [x.strip() for x in text.split("BREAK")]
     pass3 = [x for x in pass3 if x != '']
 
@@ -285,7 +319,62 @@ def advanced_encode(clip, text, token_normalization, weight_interpretation, w_ma
 
     for text in pass3:
         tokenized = clip.tokenize(text, return_word_ids=True)
-        if isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)):
+        if SD3ClipModel and isinstance(clip.cond_stage_model, SD3ClipModel):
+            lg_out = None
+            pooled = None
+            out = None
+
+            if len(tokenized['l']) > 0 or len(tokenized['g']) > 0:
+                if 'l' in tokenized:
+                    lg_out, l_pooled = advanced_encode_from_tokens(tokenized['l'],
+                                                                           token_normalization,
+                                                                           weight_interpretation,
+                                                                           lambda x: encode_token_weights(clip, x, encode_token_weights_l),
+                                                                           w_max=w_max, return_pooled=True,)
+                else:
+                    l_pooled = torch.zeros((1, 768), device=model_management.intermediate_device())
+
+                if 'g' in tokenized:
+                    g_out, g_pooled = advanced_encode_from_tokens(tokenized['g'],
+                               token_normalization,
+                               weight_interpretation,
+                               lambda x: encode_token_weights(clip, x, encode_token_weights_g),
+                               w_max=w_max, return_pooled=True)
+                    if lg_out is not None:
+                        lg_out = torch.cat([lg_out, g_out], dim=-1)
+                    else:
+                        lg_out = torch.nn.functional.pad(g_out, (768, 0))
+                else:
+                    g_out = None
+                    g_pooled = torch.zeros((1, 1280), device=model_management.intermediate_device())
+
+                if lg_out is not None:
+                    lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
+                    out = lg_out
+                pooled = torch.cat((l_pooled, g_pooled), dim=-1)
+
+            # t5xxl
+            if 't5xxl' in tokenized and clip.cond_stage_model.t5xxl is not None:
+                t5_out, t5_pooled = advanced_encode_from_tokens(tokenized['t5xxl'],
+                               token_normalization,
+                               weight_interpretation,
+                               lambda x: encode_token_weights(clip, x, encode_token_weights_t5),
+                               w_max=w_max, return_pooled=True)
+                if lg_out is not None:
+                    out = torch.cat([lg_out, t5_out], dim=-2)
+                else:
+                    out = t5_out
+
+            if out is None:
+                out = torch.zeros((1, 77, 4096), device=model_management.intermediate_device())
+
+            if pooled is None:
+                pooled = torch.zeros((1, 768 + 1280), device=model_management.intermediate_device())
+
+            embeddings_final, pooled = prepareSD3(out, pooled, clip_balance)
+            cond = [[embeddings_final, {"pooled_output": pooled}]]
+
+        elif isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)):
             embs_l = None
             embs_g = None
             pooled = None
@@ -325,6 +414,13 @@ def advanced_encode(clip, text, token_normalization, weight_interpretation, w_ma
         else:
             conditioning = cond
 
+    # setTimeStepRange
+    if time_start > 0 or time_end < 1:
+        conditioning_2, = ConditioningSetTimestepRange().set_range(conditioning, 0, time_start)
+        conditioning_1, = ConditioningZeroOut().zero_out(conditioning)
+        conditioning_1, = ConditioningSetTimestepRange().set_range(conditioning_1, time_start, time_end)
+        conditioning, = ConditioningCombine().combine(conditioning_1, conditioning_2)
+
     return conditioning
 
 

py/libs/conditioning.py

@@ -1,14 +1,24 @@
 from .utils import find_wildcards_seed, find_nearest_steps, is_linked_styles_selector
-from ..log import log_node_warn
-from ..adv_encode import advanced_encode
-from ..wildcards import process_with_loras
+from .log import log_node_warn
+from .translate import zh_to_en, has_chinese
+from .wildcards import process_with_loras
+from .adv_encode import advanced_encode
 
-from nodes import ConditioningConcat, ConditioningCombine, ConditioningAverage, ConditioningSetTimestepRange
+from nodes import ConditioningConcat, ConditioningCombine, ConditioningAverage, ConditioningSetTimestepRange, CLIPTextEncode
 
-def prompt_to_cond(type, model, clip, clip_skip, lora_stack, text, prompt_token_normalization, prompt_weight_interpretation, a1111_prompt_style ,my_unique_id, prompt, easyCache, can_load_lora=True, steps=None):
+def prompt_to_cond(type, model, clip, clip_skip, lora_stack, text, prompt_token_normalization, prompt_weight_interpretation, a1111_prompt_style ,my_unique_id, prompt, easyCache, can_load_lora=True, steps=None, model_type=None):
     styles_selector = is_linked_styles_selector(prompt, my_unique_id, type)
     title = "正面提示词" if type == 'positive' else "负面提示词"
-    log_node_warn("正在处理" + title + "...")
+    log_node_warn("正在进行" + title + "...")
+
+    if model_type in ['hydit', 'flux']:
+        embeddings_final, = CLIPTextEncode().encode(clip, text)
+        return (embeddings_final, "", model, clip)
+
+    # Translate cn to en
+    if has_chinese(text):
+        text = zh_to_en([text])[0]
+
     positive_seed = find_wildcards_seed(my_unique_id, text, prompt)
     model, clip, text, cond_decode, show_prompt, pipe_lora_stack = process_with_loras(
         text, model, clip, type, positive_seed, can_load_lora, lora_stack, easyCache)
@@ -18,7 +28,7 @@ def prompt_to_cond(type, model, clip, clip_skip, lora_stack, text, prompt_token_
     if clip_skip != 0:
         clipped.clip_layer(clip_skip)
 
-    log_node_warn("正在处理" + title + "编码...")
+    log_node_warn("正在进行" + title + "编码...")
     steps = steps if steps is not None else find_nearest_steps(my_unique_id, prompt)
     return (advanced_encode(clipped, text, prompt_token_normalization,
                             prompt_weight_interpretation, w_max=1.0,

py/libs/controlnet.py

@@ -3,16 +3,36 @@ import comfy.controlnet
 import comfy.model_management
 from nodes import NODE_CLASS_MAPPINGS
 
+union_controlnet_types = {"auto": -1, "openpose": 0, "depth": 1, "hed/pidi/scribble/ted": 2, "canny/lineart/anime_lineart/mlsd": 3, "normal": 4, "segment": 5, "tile": 6, "repaint": 7}
+
 class easyControlnet:
     def __init__(self):
         pass
 
-    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):
+    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, union_type=None, easyCache=None, use_cache=True, model=None):
         if strength == 0:
             return (positive, negative)
 
-        if control_net is None:
-            control_net = easyCache.load_controlnet(control_net_name, scale_soft_weights)
+        # kolors controlnet patch
+        from ..kolors.loader import is_kolors_model, applyKolorsUnet
+        if is_kolors_model(model):
+            from ..kolors.model_patch import patch_controlnet
+            if control_net is None:
+                with applyKolorsUnet():
+                    control_net = easyCache.load_controlnet(control_net_name, scale_soft_weights, use_cache)
+                control_net = patch_controlnet(model, control_net)
+        else:
+            if control_net is None:
+                control_net = easyCache.load_controlnet(control_net_name, scale_soft_weights, use_cache)
+
+        # union controlnet
+        if union_type is not None:
+            control_net = control_net.copy()
+            type_number = union_controlnet_types[union_type]
+            if type_number >= 0:
+                control_net.set_extra_arg("control_type", [type_number])
+            else:
+                control_net.set_extra_arg("control_type", [])
 
         if mask is not None:
             mask = mask.to(self.device)

py/libs/fooocus.py

@@ -0,0 +1,113 @@
+#credit to Acly for this module
+#from https://github.com/Acly/comfyui-inpaint-nodes
+import torch
+import torch.nn.functional as F
+import comfy
+from comfy.model_base import BaseModel
+from comfy.model_patcher import ModelPatcher
+from comfy.model_management import cast_to_device
+
+from .log import log_node_warn, log_node_error, log_node_info
+
+# Inpaint
+original_calculate_weight = ModelPatcher.calculate_weight
+injected_model_patcher_calculate_weight = False
+
+class InpaintHead(torch.nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.head = torch.nn.Parameter(torch.empty(size=(320, 5, 3, 3), device="cpu"))
+
+    def __call__(self, x):
+        x = F.pad(x, (1, 1, 1, 1), "replicate")
+        return F.conv2d(x, weight=self.head)
+
+def calculate_weight_patched(self: ModelPatcher, patches, weight, key):
+    remaining = []
+
+    for p in patches:
+        alpha = p[0]
+        v = p[1]
+
+        is_fooocus_patch = isinstance(v, tuple) and len(v) == 2 and v[0] == "fooocus"
+        if not is_fooocus_patch:
+            remaining.append(p)
+            continue
+
+        if alpha != 0.0:
+            v = v[1]
+            w1 = cast_to_device(v[0], weight.device, torch.float32)
+            if w1.shape == weight.shape:
+                w_min = cast_to_device(v[1], weight.device, torch.float32)
+                w_max = cast_to_device(v[2], weight.device, torch.float32)
+                w1 = (w1 / 255.0) * (w_max - w_min) + w_min
+                weight += alpha * cast_to_device(w1, weight.device, weight.dtype)
+            else:
+                pass
+                # log_node_warn(self.node_name,
+                #     f"Shape mismatch {key}, weight not merged ({w1.shape} != {weight.shape})"
+                # )
+
+        if len(remaining) > 0:
+            return original_calculate_weight(self, remaining, weight, key)
+    return weight
+
+def inject_patched_calculate_weight():
+    global injected_model_patcher_calculate_weight
+    if not injected_model_patcher_calculate_weight:
+        print(
+            "[comfyui-inpaint-nodes] Injecting patched comfy.model_patcher.ModelPatcher.calculate_weight"
+        )
+        ModelPatcher.calculate_weight = calculate_weight_patched
+        injected_model_patcher_calculate_weight = True
+
+class InpaintWorker:
+    def __init__(self, node_name):
+        self.node_name = node_name if node_name is not None else ""
+
+    def load_fooocus_patch(self, lora: dict, to_load: dict):
+        patch_dict = {}
+        loaded_keys = set()
+        for key in to_load.values():
+            if value := lora.get(key, None):
+                patch_dict[key] = ("fooocus", value)
+                loaded_keys.add(key)
+
+        not_loaded = sum(1 for x in lora if x not in loaded_keys)
+        if not_loaded > 0:
+            log_node_info(self.node_name,
+                f"{len(loaded_keys)} Lora keys loaded, {not_loaded} remaining keys not found in model."
+            )
+        return patch_dict
+
+
+    def patch(self, model, latent, patch):
+        base_model: BaseModel = model.model
+        latent_pixels = base_model.process_latent_in(latent["samples"])
+        noise_mask = latent["noise_mask"].round()
+        latent_mask = F.max_pool2d(noise_mask, (8, 8)).round().to(latent_pixels)
+
+        inpaint_head_model, inpaint_lora = patch
+        feed = torch.cat([latent_mask, latent_pixels], dim=1)
+        inpaint_head_model.to(device=feed.device, dtype=feed.dtype)
+        inpaint_head_feature = inpaint_head_model(feed)
+
+        def input_block_patch(h, transformer_options):
+            if transformer_options["block"][1] == 0:
+                h = h + inpaint_head_feature.to(h)
+            return h
+
+        lora_keys = comfy.lora.model_lora_keys_unet(model.model, {})
+        lora_keys.update({x: x for x in base_model.state_dict().keys()})
+        loaded_lora = self.load_fooocus_patch(inpaint_lora, lora_keys)
+
+        m = model.clone()
+        m.set_model_input_block_patch(input_block_patch)
+        patched = m.add_patches(loaded_lora, 1.0)
+
+        not_patched_count = sum(1 for x in loaded_lora if x not in patched)
+        if not_patched_count > 0:
+            log_node_error(self.node_name, f"Failed to patch {not_patched_count} keys")
+
+        inject_patched_calculate_weight()
+        return (m,)

py/libs/loader.py

@@ -1,14 +1,18 @@
 import time, os, psutil
+import folder_paths
 import comfy.utils
 import comfy.sd
 import comfy.controlnet
-import folder_paths
+
+from comfy.model_patcher import ModelPatcher
 from nodes import NODE_CLASS_MAPPINGS
 from collections import defaultdict
-from ..log import log_node_info, log_node_error
+from .log import log_node_info, log_node_error
+from ..dit.pixArt.loader import load_pixart
 
-stable_diffusion_loaders = ["easy fullLoader", "easy a1111Loader", "easy comfyLoader", "easy zero123Loader", "easy svdLoader"]
+stable_diffusion_loaders = ["easy fullLoader", "easy a1111Loader", "easy comfyLoader", "easy hunyuanDiTLoader","easy zero123Loader", "easy svdLoader"]
 stable_cascade_loaders = ["easy cascadeLoader"]
+dit_loaders = ['easy pixArtLoader']
 controlnet_loaders = ["easy controlnetLoader", "easy controlnetLoaderADV"]
 instant_loaders = ["easy instantIDApply", "easy instantIDApplyADV"]
 cascade_vae_node = ["easy preSamplingCascade", "easy fullCascadeKSampler"]
@@ -26,8 +30,10 @@ class easyLoader:
             "vae": defaultdict(object),
             "lora": defaultdict(dict),  # {lora_name: {UID: (model_lora, clip_lora)}}
             "controlnet": defaultdict(dict),
+            "t5": defaultdict(tuple),
+            "chatglm3": defaultdict(tuple),
         }
-        self.memory_threshold = self.determine_memory_threshold(0.9)
+        self.memory_threshold = self.determine_memory_threshold(0.7)
         self.lora_name_cache = []
 
     def clean_values(self, values: str):
@@ -84,6 +90,8 @@ class easyLoader:
         desired_lora_names = set()
         desired_lora_settings = set()
         desired_controlnet_names = set()
+        desired_t5_names = set()
+        desired_glm3_names = set()
 
         for entry in prompt.values():
             class_type = entry["class_type"]
@@ -97,6 +105,22 @@ class easyLoader:
                 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 ['easy kolorsLoader']:
+                desired_unet_names.add(self.get_input_value(entry, "unet_name"))
+                desired_vae_names.add(self.get_input_value(entry, "vae_name"))
+                desired_glm3_names.add(self.get_input_value(entry, "chatglm3_name"))
+
+            elif class_type in dit_loaders:
+                t5_name = self.get_input_value(entry, "mt5_name") if "mt5_name" in entry["inputs"] else  None
+                clip_name = self.get_input_value(entry, "clip_name") if "clip_name" in entry["inputs"] else None
+                model_name = self.get_input_value(entry, "model_name")
+                ckpt_name = self.get_input_value(entry, "ckpt_name", prompt)
+                if t5_name:
+                    desired_t5_names.add(t5_name)
+                if clip_name:
+                    desired_clip_names.add(clip_name)
+                desired_ckpt_names.add(ckpt_name+'_'+model_name)
+
             elif class_type in stable_cascade_loaders:
                 desired_unet_names.add(self.get_input_value(entry, "stage_c"))
                 desired_unet_names.add(self.get_input_value(entry, "stage_b"))
@@ -128,7 +152,7 @@ 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", "controlnet"]
+        object_types = ["ckpt", "unet", "clip", "bvae", "vae", "lora", "controlnet", "t5"]
         for object_type in object_types:
             if object_type == 'unet':
                 desired_names = desired_unet_names
@@ -141,6 +165,10 @@ class easyLoader:
                 desired_names = desired_vae_names
             elif object_type == "controlnet":
                 desired_names = desired_controlnet_names
+            elif object_type == "t5":
+                desired_names = desired_t5_names
+            elif object_type == "chatglm3":
+                desired_names = desired_glm3_names
             else:
                 desired_names = desired_lora_names
             self.clear_unused_objects(desired_names, object_type)
@@ -179,7 +207,7 @@ class easyLoader:
         current_memory = self.get_memory_usage()
         if current_memory < self.memory_threshold:
             return
-        eviction_order = ["vae", "lora", "bvae", "clip", "ckpt", "controlnet"]
+        eviction_order = ["vae", "lora", "bvae", "clip", "ckpt", "controlnet", "unet", "t5", "chatglm3"]
         for obj_type in eviction_order:
             if current_memory < self.memory_threshold:
                 break
@@ -240,6 +268,7 @@ class easyLoader:
 
     def load_unet(self, unet_name):
         if unet_name in self.loaded_objects["unet"]:
+            log_node_info("Load UNet", f"{unet_name} cached")
             return self.loaded_objects["unet"][unet_name][0]
 
         unet_path = folder_paths.get_full_path("unet", unet_name)
@@ -249,9 +278,9 @@ class easyLoader:
 
         return model
 
-    def load_controlnet(self, control_net_name, scale_soft_weights=1):
+    def load_controlnet(self, control_net_name, scale_soft_weights=1, use_cache=True):
         unique_id = f'{control_net_name};{str(scale_soft_weights)}'
-        if unique_id in self.loaded_objects["controlnet"]:
+        if use_cache and 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:
@@ -260,19 +289,27 @@ class easyLoader:
                 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'")
+                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()
+        if use_cache:
+            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'):
+    def load_clip(self, clip_name, type='stable_diffusion', load_clip=None):
+        if clip_name in self.loaded_objects["clip"]:
+            return self.loaded_objects["clip"][clip_name][0]
+
         if type == 'stable_diffusion':
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
-        else:
+        elif type == 'stable_cascade':
             clip_type = comfy.sd.CLIPType.STABLE_CASCADE
+        elif type == 'sd3':
+            clip_type = comfy.sd.CLIPType.SD3
+        elif type == 'stable_audio':
+            clip_type = comfy.sd.CLIPType.STABLE_AUDIO
         clip_path = folder_paths.get_full_path("clip", clip_name)
         load_clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         self.add_to_cache("clip", clip_name, load_clip)
@@ -280,7 +317,7 @@ class easyLoader:
 
         return load_clip
 
-    def load_lora(self, lora, model=None, clip=None):
+    def load_lora(self, lora, model=None, clip=None, type=None):
         lora_name = lora["lora_name"]
         model = model if model is not None else lora["model"]
         clip = clip if clip is not None else lora["clip"]
@@ -291,11 +328,12 @@ class easyLoader:
         lbw_b = lora["lbw_b"] if "lbw_b" in lora else None
 
         model_hash = str(model)[44:-1]
-        clip_hash = str(clip)[25:-1]
+        clip_hash = str(clip)[25:-1] if clip else ''
 
         unique_id = f'{model_hash};{clip_hash};{lora_name};{model_strength};{clip_strength}'
 
-        if unique_id in self.loaded_objects["lora"] and unique_id in self.loaded_objects["lora"][lora_name]:
+        if unique_id in self.loaded_objects["lora"]:
+            log_node_info("Load LORA",f"{lora_name} cached")
             return self.loaded_objects["lora"][unique_id][0]
 
         orig_lora_name = lora_name
@@ -345,7 +383,12 @@ class easyLoader:
                     model.model.load_state_dict(mapping_norm, strict=False)
                     return (model, clip)
 
-                model, clip = comfy.sd.load_lora_for_models(model, clip, _lora, model_strength, clip_strength)
+                # PixArt
+                if type is not None and type == 'PixArt':
+                    from ..dit.pixArt.loader import load_pixart_lora
+                    model = load_pixart_lora(model, _lora, lora_path, model_strength)
+                else:
+                    model, clip = comfy.sd.load_lora_for_models(model, clip, _lora, model_strength, clip_strength)
 
             self.add_to_cache("lora", unique_id, (model, clip))
             self.eviction_based_on_memory()
@@ -373,4 +416,188 @@ class easyLoader:
                     self.lora_name_cache.append(x)
                     return x
 
-            return None
+            return None
+
+    def load_main(self, ckpt_name, config_name, vae_name, lora_name, lora_model_strength, lora_clip_strength, optional_lora_stack, model_override, clip_override, vae_override, prompt):
+        model: ModelPatcher | None = None
+        clip: comfy.sd.CLIP | None = None
+        vae: comfy.sd.VAE | None = None
+        clip_vision = None
+        lora_stack = []
+
+        can_load_lora = True
+        # 判断是否存在 模型或Lora叠加xyplot, 若存在优先缓存第一个模型
+        xy_model_id = next((x for x in prompt if str(prompt[x]["class_type"]) in ["easy XYInputs: ModelMergeBlocks",
+                                                                                  "easy XYInputs: Checkpoint"]), None)
+        xy_lora_id = next((x for x in prompt if str(prompt[x]["class_type"]) == "easy XYInputs: Lora"), None)
+        if xy_lora_id is not None:
+            can_load_lora = False
+        if xy_model_id is not None:
+            node = prompt[xy_model_id]
+            if "ckpt_name_1" in node["inputs"]:
+                ckpt_name_1 = node["inputs"]["ckpt_name_1"]
+                model, clip, vae, clip_vision = self.load_checkpoint(ckpt_name_1)
+                can_load_lora = False
+        # Load models
+        elif model_override is not None and clip_override is not None and vae_override is not None:
+            model = model_override
+            clip = clip_override
+            vae = vae_override
+        elif model_override is not None:
+            raise Exception(f"[ERROR] clip or vae is missing")
+        elif vae_override is not None:
+            raise Exception(f"[ERROR] model or clip is missing")
+        elif clip_override is not None:
+            raise Exception(f"[ERROR] model or vae is missing")
+        else:
+            model, clip, vae, clip_vision = self.load_checkpoint(ckpt_name, config_name)
+
+        if optional_lora_stack is not None and can_load_lora:
+            for lora in optional_lora_stack:
+                lora = {"lora_name": lora[0], "model": model, "clip": clip, "model_strength": lora[1],
+                        "clip_strength": lora[2]}
+                model, clip = self.load_lora(lora)
+                lora['model'] = model
+                lora['clip'] = clip
+                lora_stack.append(lora)
+
+        if lora_name != "None" and can_load_lora:
+            lora = {"lora_name": lora_name, "model": model, "clip": clip, "model_strength": lora_model_strength,
+                    "clip_strength": lora_clip_strength}
+            model, clip = self.load_lora(lora)
+            lora_stack.append(lora)
+
+        # Check for custom VAE
+        if vae_name not in ["Baked VAE", "Baked-VAE"]:
+            vae = self.load_vae(vae_name)
+        # CLIP skip
+        if not clip:
+            raise Exception("No CLIP found")
+
+        return model, clip, vae, clip_vision, lora_stack
+
+    # Kolors
+    def load_kolors_unet(self, unet_name):
+        if unet_name in self.loaded_objects["unet"]:
+            log_node_info("Load Kolors UNet", f"{unet_name} cached")
+            return self.loaded_objects["unet"][unet_name][0]
+        else:
+            from ..kolors.loader import applyKolorsUnet
+            with applyKolorsUnet():
+                unet_path = folder_paths.get_full_path("unet", unet_name)
+                sd = comfy.utils.load_torch_file(unet_path)
+                model = comfy.sd.load_unet_state_dict(sd)
+                if model is None:
+                    raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
+
+                self.add_to_cache("unet", unet_name, model)
+                self.eviction_based_on_memory()
+
+                return model
+
+    def load_chatglm3(self, chatglm3_name):
+        from ..kolors.loader import load_chatglm3
+        if chatglm3_name in self.loaded_objects["chatglm3"]:
+            log_node_info("Load ChatGLM3", f"{chatglm3_name} cached")
+            return self.loaded_objects["chatglm3"][chatglm3_name][0]
+
+        chatglm_model = load_chatglm3(model_path=folder_paths.get_full_path("llm", chatglm3_name))
+        self.add_to_cache("chatglm3", chatglm3_name, chatglm_model)
+        self.eviction_based_on_memory()
+
+        return chatglm_model
+
+
+    # DiT
+    def load_dit_ckpt(self, ckpt_name, model_name, **kwargs):
+        if (ckpt_name+'_'+model_name) in self.loaded_objects["ckpt"]:
+            return self.loaded_objects["ckpt"][ckpt_name+'_'+model_name][0]
+        model = None
+        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        model_type = kwargs['model_type'] if "model_type" in kwargs else 'PixArt'
+        if model_type == 'PixArt':
+            pixart_conf = kwargs['pixart_conf']
+            model_conf = pixart_conf[model_name]
+            model = load_pixart(ckpt_path, model_conf)
+        if model:
+            self.add_to_cache("ckpt", ckpt_name + '_' + model_name, model)
+            self.eviction_based_on_memory()
+        return model
+
+
+    def load_dit_clip(self, clip_name, **kwargs):
+        if clip_name in self.loaded_objects["clip"]:
+            return self.loaded_objects["clip"][clip_name][0]
+
+        clip_path = folder_paths.get_full_path("clip", clip_name)
+        sd = comfy.utils.load_torch_file(clip_path)
+
+        prefix = "bert."
+        state_dict = {}
+        for key in sd:
+            nkey = key
+            if key.startswith(prefix):
+                nkey = key[len(prefix):]
+            state_dict[nkey] = sd[key]
+
+        m, e = model.load_sd(state_dict)
+        if len(m) > 0 or len(e) > 0:
+            print(f"{clip_name}: clip missing {len(m)} keys ({len(e)} extra)")
+
+        self.add_to_cache("clip", clip_name, model)
+        self.eviction_based_on_memory()
+
+        return model
+
+    def load_dit_t5(self, t5_name, **kwargs):
+        if t5_name in self.loaded_objects["t5"]:
+            return self.loaded_objects["t5"][t5_name][0]
+
+        model_type = kwargs['model_type'] if "model_type" in kwargs else 'HyDiT'
+        if model_type == 'HyDiT':
+            del kwargs['model_type']
+            model = EXM_HyDiT_Tenc_Temp(model_class="mT5", **kwargs)
+        t5_path = folder_paths.get_full_path("t5", t5_name)
+        sd = comfy.utils.load_torch_file(t5_path)
+        m, e = model.load_sd(sd)
+        if len(m) > 0 or len(e) > 0:
+            print(f"{t5_name}: mT5 missing {len(m)} keys ({len(e)} extra)")
+
+        self.add_to_cache("t5", t5_name, model)
+        self.eviction_based_on_memory()
+
+        return model
+
+    def load_t5_from_sd3_clip(self, sd3_clip, padding):
+        try:
+            from comfy.text_encoders.sd3_clip import SD3Tokenizer, SD3ClipModel
+        except:
+            from comfy.sd3_clip import SD3Tokenizer, SD3ClipModel
+        import copy
+
+        clip = sd3_clip.clone()
+        assert clip.cond_stage_model.t5xxl is not None, "CLIP must have T5 loaded!"
+
+        # remove transformer
+        transformer = clip.cond_stage_model.t5xxl.transformer
+        clip.cond_stage_model.t5xxl.transformer = None
+
+        # clone object
+        tmp = SD3ClipModel(clip_l=False, clip_g=False, t5=False)
+        tmp.t5xxl = copy.deepcopy(clip.cond_stage_model.t5xxl)
+        # put transformer back
+        clip.cond_stage_model.t5xxl.transformer = transformer
+        tmp.t5xxl.transformer = transformer
+
+        # override special tokens
+        tmp.t5xxl.special_tokens = copy.deepcopy(clip.cond_stage_model.t5xxl.special_tokens)
+        tmp.t5xxl.special_tokens.pop("end")  # make sure empty tokens match
+
+        # tokenizer
+        tok = SD3Tokenizer()
+        tok.t5xxl.min_length = padding
+
+        clip.cond_stage_model = tmp
+        clip.tokenizer = tok
+
+        return clip

py/libs/sampler.py

@@ -1,17 +1,21 @@
 import comfy
+import comfy.model_management
+import comfy.samplers
 import torch
 import numpy as np
 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
+from ..brushnet.model_patch import add_model_patch
+
 class easySampler:
     def __init__(self):
         self.last_helds: dict[str, list] = {
             "results": [],
             "pipe_line": [],
         }
+        self.device = comfy.model_management.intermediate_device()
 
     @staticmethod
     def tensor2pil(image: torch.Tensor) -> Image.Image:
@@ -47,17 +51,31 @@ 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 emptyLatent(self, resolution, empty_latent_width, empty_latent_height, batch_size=1, compression=0, sd3=False):
+        if resolution not in ["自定义 x 自定义", 'width x height (custom)']:
+            try:
+                width, height = map(int, resolution.split(' x '))
+                empty_latent_width = width
+                empty_latent_height = height
+            except ValueError:
+                raise ValueError("Invalid base_resolution format.")
+        if sd3:
+            latent = torch.ones([batch_size, 16, empty_latent_height // 8, empty_latent_width // 8], device=self.device) * 0.0609
+            samples = {"samples": latent}
+        elif compression == 0:
+            latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8], device=self.device)
+            samples = {"samples": latent}
+        else:
+            latent_c = torch.zeros(
+                [batch_size, 16, empty_latent_height // compression, empty_latent_width // compression])
+            latent_b = torch.zeros([batch_size, 4, empty_latent_height // 4, empty_latent_width // 4])
+
+            samples = ({"samples": latent_c}, {"samples": latent_b})
+        return samples
 
     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):
+                        preview_latent=True, disable_pbar=False):
         device = comfy.model_management.get_torch_device()
         latent_image = latent["samples"]
 
@@ -82,50 +100,26 @@ class easySampler:
                 preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
             pbar.update_absolute(step + 1, total_steps, preview_bytes)
 
-        if custom is not None:
-            guider = custom['guider'] if 'guider' in custom else None
-            sampler = custom['sampler'] if 'sampler' in custom else None
-            sigmas = custom['sigmas'] if 'sigmas' in custom else None
-            noise = custom['noise'] if 'noise' in custom else None
-            samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas,
-                                    denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar,
-                                    seed=noise.seed)
-            samples = samples.to(comfy.model_management.intermediate_device())
+        if disable_noise:
+            noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout,
+                                device="cpu")
         else:
-            if disable_noise:
-                noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout,
-                                    device="cpu")
-            else:
-                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,
-                                          last_step=last_step,
-                                          force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback,
-                                          disable_pbar=disable_pbar, seed=seed)
+            batch_inds = latent["batch_index"] if "batch_index" in latent else None
+            noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
 
+        #######################################################################################
+        # add model patch
+        # brushnet
+        add_model_patch(model)
+        # kolors
+        #######################################################################################
+        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,
+                                      last_step=last_step,
+                                      force_full_denoise=force_full_denoise, noise_mask=noise_mask,
+                                      callback=callback,
+                                      disable_pbar=disable_pbar, seed=seed)
         out = latent.copy()
         out["samples"] = samples
         return out
@@ -157,43 +151,48 @@ 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)
 
-        samples = comfy.sample.sample_custom(model, noise, cfg, _sampler, sigmas, positive, negative, latent_image,
-                                             noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar,
-                                             seed=seed)
+        samples = comfy.samplers.sample(model, noise, positive, negative, cfg, device, _sampler, sigmas, latent_image=latent_image, model_options=model.model_options,
+               denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
 
         out = latent.copy()
         out["samples"] = samples
         return out
 
+    def custom_advanced_ksampler(self, noise, guider, sampler, sigmas, latent_image):
+        latent = latent_image
+        latent_image = latent["samples"]
+        latent = latent.copy()
+        latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
+        latent["samples"] = latent_image
+
+        noise_mask = None
+        if "noise_mask" in latent:
+            noise_mask = latent["noise_mask"]
+
+        x0_output = {}
+        callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
+
+        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+        samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask,
+                                callback=callback, disable_pbar=disable_pbar, seed=noise.seed)
+        samples = samples.to(comfy.model_management.intermediate_device())
+
+        out = latent.copy()
+        out["samples"] = samples
+        if "x0" in x0_output:
+            out_denoised = latent.copy()
+            out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
+        else:
+            out_denoised = out
+
+        return (out, out_denoised)
+
     def get_value_by_id(self, key: str, my_unique_id: Any) -> Optional[Any]:
         """Retrieve value by its associated ID."""
         try:
@@ -273,6 +272,19 @@ class easySampler:
             sdxl_pipe.get("seed")
         )
 
+def loglinear_interp(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
+
 class alignYourStepsScheduler:
 
     NOISE_LEVELS = {
@@ -282,20 +294,6 @@ class alignYourStepsScheduler:
                  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
@@ -306,8 +304,607 @@ class alignYourStepsScheduler:
 
         sigmas = self.NOISE_LEVELS[model_type][:]
         if (steps + 1) != len(sigmas):
-            sigmas = self.loglinear_interp(sigmas, steps + 1)
+            sigmas = loglinear_interp(sigmas, steps + 1)
 
         sigmas = sigmas[-(total_steps + 1):]
         sigmas[-1] = 0
-        return (torch.FloatTensor(sigmas),)
+        return (torch.FloatTensor(sigmas),)
+
+
+class gitsScheduler:
+
+    NOISE_LEVELS = {
+        0.80: [
+            [14.61464119, 7.49001646, 0.02916753],
+            [14.61464119, 11.54541874, 6.77309084, 0.02916753],
+            [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753],
+            [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
+            [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
+            [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+            [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636,
+             0.02916753],
+            [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966,
+             1.56271636, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077,
+             3.07277966, 1.56271636, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646,
+             5.85520077, 3.07277966, 1.56271636, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646,
+             6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278,
+             7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551,
+             8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551,
+             8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551,
+             8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547,
+             9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354,
+             0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547,
+             9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662,
+             0.83188516, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547,
+             9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966,
+             1.84880662, 0.83188516, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547,
+             9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.75677586,
+             2.84484982, 1.78698075, 0.803307, 0.02916753],
+        ],
+        0.85: [
+            [14.61464119, 7.49001646, 0.02916753],
+            [14.61464119, 7.49001646, 1.84880662, 0.02916753],
+            [14.61464119, 11.54541874, 6.77309084, 1.56271636, 0.02916753],
+            [14.61464119, 11.54541874, 7.11996698, 3.07277966, 1.24153244, 0.02916753],
+            [14.61464119, 11.54541874, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
+            [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
+            [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
+            [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307,
+             0.02916753],
+            [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966,
+             1.84880662, 0.803307, 0.02916753],
+            [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.65472794,
+             3.07277966, 1.84880662, 0.803307, 0.02916753],
+            [14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476,
+             4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
+            [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646,
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+            [14.61464119, 3.07277966, 1.61558151, 1.01931262, 0.72133851, 0.52423614, 0.41087446, 0.32104823,
+             0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 3.07277966, 1.61558151, 1.05362725, 0.74807048, 0.54755926, 0.43325692, 0.34370604,
+             0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.29807833,
+             0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.4783645, 0.38853383, 0.32104823,
+             0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 5.85520077, 2.45070267, 1.51179266, 1.01931262, 0.74807048, 0.57119018, 0.45573691,
+             0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532,
+             0.02916753],
+            [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446,
+             0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532,
+             0.02916753],
+            [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.43325692,
+             0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117,
+             0.09824532, 0.02916753],
+            [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691,
+             0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083,
+             0.13792117, 0.09824532, 0.02916753],
+        ],
+        1.40: [
+            [14.61464119, 0.59516323, 0.02916753],
+            [14.61464119, 0.95350921, 0.34370604, 0.02916753],
+            [14.61464119, 1.08895338, 0.43325692, 0.13792117, 0.02916753],
+            [14.61464119, 1.56271636, 0.64427125, 0.27464288, 0.09824532, 0.02916753],
+            [14.61464119, 1.61558151, 0.803307, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
+            [14.61464119, 2.05039096, 0.95350921, 0.54755926, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.24153244, 0.72133851, 0.43325692, 0.27464288, 0.17026083, 0.09824532,
+             0.02916753],
+            [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.17026083,
+             0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.52423614, 0.36617002, 0.27464288, 0.19894916, 0.13792117,
+             0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.38853383, 0.29807833, 0.22545385, 0.17026083,
+             0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.84484982, 1.41535246, 0.86115354, 0.59516323, 0.43325692, 0.32104823, 0.25053367,
+             0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.27464288,
+             0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.4783645, 0.36617002, 0.29807833, 0.25053367,
+             0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.69515091, 0.52423614, 0.41087446, 0.34370604,
+             0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.72133851, 0.54755926, 0.43325692, 0.36617002,
+             0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532,
+             0.02916753],
+            [14.61464119, 2.84484982, 1.61558151, 1.05362725, 0.74807048, 0.57119018, 0.45573691, 0.38853383,
+             0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532,
+             0.02916753],
+            [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.36617002,
+             0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532,
+             0.02916753],
+            [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.43325692, 0.38853383,
+             0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117,
+             0.09824532, 0.02916753],
+            [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.41087446,
+             0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083,
+             0.13792117, 0.09824532, 0.02916753],
+        ],
+        1.45: [
+            [14.61464119, 0.59516323, 0.02916753],
+            [14.61464119, 0.803307, 0.25053367, 0.02916753],
+            [14.61464119, 0.95350921, 0.34370604, 0.09824532, 0.02916753],
+            [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
+            [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
+            [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+            [14.61464119, 1.91321158, 0.95350921, 0.57119018, 0.36617002, 0.25053367, 0.17026083, 0.09824532,
+             0.02916753],
+            [14.61464119, 2.19988537, 1.08895338, 0.64427125, 0.41087446, 0.27464288, 0.19894916, 0.13792117,
+             0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.19894916,
+             0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.36617002, 0.27464288, 0.22545385,
+             0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.41087446, 0.32104823, 0.25053367, 0.19894916,
+             0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385,
+             0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.29807833,
+             0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.32104823,
+             0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.41087446, 0.34370604,
+             0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532,
+             0.02916753],
+            [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.43325692, 0.36617002,
+             0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532,
+             0.02916753],
+            [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.72133851, 0.54755926, 0.45573691, 0.38853383,
+             0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117,
+             0.09824532, 0.02916753],
+            [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 0.41087446, 0.36617002,
+             0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117,
+             0.09824532, 0.02916753],
+            [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 0.43325692,
+             0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916,
+             0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        ],
+        1.50: [
+            [14.61464119, 0.54755926, 0.02916753],
+            [14.61464119, 0.803307, 0.25053367, 0.02916753],
+            [14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753],
+            [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
+            [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
+            [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+            [14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532,
+             0.02916753],
+            [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117,
+             0.09824532, 0.02916753],
+            [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083,
+             0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916,
+             0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385,
+             0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367,
+             0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367,
+             0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288,
+             0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833,
+             0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823,
+             0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532,
+             0.02916753],
+            [14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002,
+             0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117,
+             0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446,
+             0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083,
+             0.13792117, 0.09824532, 0.02916753],
+            [14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383,
+             0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083,
+             0.13792117, 0.09824532, 0.02916753],
+        ],
+    }
+
+    def get_sigmas(self, coeff, steps, denoise):
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return (torch.FloatTensor([]),)
+            total_steps = round(steps * denoise)
+
+        if steps <= 20:
+            sigmas = self.NOISE_LEVELS[round(coeff, 2)][steps-2][:]
+        else:
+            sigmas = self.NOISE_LEVELS[round(coeff, 2)][-1][:]
+            sigmas = loglinear_interp(sigmas, steps + 1)
+
+        sigmas = sigmas[-(total_steps + 1):]
+        sigmas[-1] = 0
+        return (torch.FloatTensor(sigmas), )

py/libs/stability.py

@@ -6,7 +6,7 @@ import pathlib
 from aiohttp import web
 from server import PromptServer
 from .image import tensor2pil, pil2tensor, image2base64, pil2byte
-from ..log import log_node_error
+from .log import log_node_error
 
 
 root_path = pathlib.Path(__file__).parent.parent.parent

py/libs/translate.py

@@ -0,0 +1,248 @@
+#credit to shadowcz007 for this module
+#from https://github.com/shadowcz007/comfyui-mixlab-nodes/blob/main/nodes/TextGenerateNode.py
+import re
+import os
+import folder_paths
+
+import comfy.utils
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+from .utils import install_package
+try:
+    from lark import Lark, Transformer, v_args
+except:
+    print('install lark-parser...')
+    install_package('lark-parser')
+    from lark import Lark, Transformer, v_args
+
+model_path = os.path.join(folder_paths.models_dir, 'prompt_generator')
+zh_en_model_path = os.path.join(model_path, 'opus-mt-zh-en')
+zh_en_model, zh_en_tokenizer = None, None
+
+def correct_prompt_syntax(prompt=""):
+    # print("input prompt",prompt)
+    corrected_elements = []
+    # 处理成统一的英文标点
+    prompt = prompt.replace('（', '(').replace('）', ')').replace('，', ',').replace(';', ',').replace('。', '.').replace('：',':').replace('\\',',')
+    # 删除多余的空格
+    prompt = re.sub(r'\s+', ' ', prompt).strip()
+    prompt = prompt.replace("< ","<").replace(" >",">").replace("( ","(").replace(" )",")").replace("[ ","[").replace(' ]',']')
+
+    # 分词
+    prompt_elements = prompt.split(',')
+
+    def balance_brackets(element, open_bracket, close_bracket):
+        open_brackets_count = element.count(open_bracket)
+        close_brackets_count = element.count(close_bracket)
+        return element + close_bracket * (open_brackets_count - close_brackets_count)
+
+    for element in prompt_elements:
+        element = element.strip()
+
+        # 处理空元素
+        if not element:
+            continue
+
+        # 检查并处理圆括号、方括号、尖括号
+        if element[0] in '([':
+            corrected_element = balance_brackets(element, '(', ')') if element[0] == '(' else balance_brackets(element, '[', ']')
+        elif element[0] == '<':
+            corrected_element = balance_brackets(element, '<', '>')
+        else:
+            # 删除开头的右括号或右方括号
+            corrected_element = element.lstrip(')]')
+
+        corrected_elements.append(corrected_element)
+
+    # 重组修正后的prompt
+    return  ','.join(corrected_elements)
+
+def detect_language(input_str):
+    # 统计中文和英文字符的数量
+    count_cn = count_en = 0
+    for char in input_str:
+        if '\u4e00' <= char <= '\u9fff':
+            count_cn += 1
+        elif char.isalpha():
+            count_en += 1
+
+    # 根据统计的字符数量判断主要语言
+    if count_cn > count_en:
+        return "cn"
+    elif count_en > count_cn:
+        return "en"
+    else:
+        return "unknow"
+
+def has_chinese(text):
+    has_cn = False
+    _text = text
+    _text = re.sub(r'<.*?>', '', _text)
+    _text = re.sub(r'__.*?__', '', _text)
+    _text = re.sub(r'embedding:.*?(\d+)?', '', _text)
+    for char in _text:
+        if '\u4e00' <= char <= '\u9fff':
+            has_cn = True
+            break
+        elif char.isalpha():
+            continue
+    return has_cn
+
+def translate(text):
+    global zh_en_model_path, zh_en_model, zh_en_tokenizer
+
+    if not os.path.exists(zh_en_model_path):
+        zh_en_model_path = 'Helsinki-NLP/opus-mt-zh-en'
+
+    print(zh_en_model_path)
+    if zh_en_model is None:
+
+        zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval()
+        zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path, padding=True, truncation=True)
+
+    zh_en_model.to("cuda" if torch.cuda.is_available() else "cpu")
+    with torch.no_grad():
+        encoded = zh_en_tokenizer([text], return_tensors="pt")
+        encoded.to(zh_en_model.device)
+        sequences = zh_en_model.generate(**encoded)
+        return zh_en_tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
+
+@v_args(inline=True)  # Decorator to flatten the tree directly into the function arguments
+class ChinesePromptTranslate(Transformer):
+
+    def sentence(self, *args):
+        return ", ".join(args)
+
+    def phrase(self, *args):
+        return "".join(args)
+
+    def emphasis(self, *args):
+        # Reconstruct the emphasis with translated content
+        return "(" + "".join(args) + ")"
+
+    def weak_emphasis(self, *args):
+        print('weak_emphasis:', args)
+        return "[" + "".join(args) + "]"
+
+    def embedding(self, *args):
+        print('prompt embedding', args[0])
+        if len(args) == 1:
+            embedding_name = str(args[0])
+            return f"embedding:{embedding_name}"
+        elif len(args) > 1:
+            embedding_name, *numbers = args
+
+            if len(numbers) == 2:
+                return f"embedding:{embedding_name}:{numbers[0]}:{numbers[1]}"
+            elif len(numbers) == 1:
+                return f"embedding:{embedding_name}:{numbers[0]}"
+            else:
+                return f"embedding:{embedding_name}"
+
+    def lora(self, *args):
+        if len(args) == 1:
+            return f"<lora:{args[0]}>"
+        elif len(args) > 1:
+            # print('lora', args)
+            _, loar_name, *numbers = args
+            loar_name = str(loar_name).strip()
+            if len(numbers) == 2:
+                return f"<lora:{loar_name}:{numbers[0]}:{numbers[1]}>"
+            elif len(numbers) == 1:
+                return f"<lora:{loar_name}:{numbers[0]}>"
+            else:
+                return f"<lora:{loar_name}>"
+
+    def weight(self, word, number):
+        translated_word = translate(str(word)).rstrip('.')
+        return f"({translated_word}:{str(number).strip()})"
+
+    def schedule(self, *args):
+        print('prompt schedule', args)
+        data = [str(arg).strip() for arg in args]
+
+        return f"[{':'.join(data)}]"
+
+    def word(self, word):
+        # Translate each word using the dictionary
+        word = str(word)
+        match_cn = re.search(r'@.*?@', word)
+        if re.search(r'__.*?__', word):
+            return word.rstrip('.')
+        elif match_cn:
+            chinese = match_cn.group()
+            before = word.split('@', 1)
+            before = before[0] if len(before) > 0 else ''
+            before = translate(str(before)).rstrip('.') if before else ''
+            after = word.rsplit('@', 1)
+            after = after[len(after)-1] if len(after) > 1 else ''
+            after = translate(after).rstrip('.') if after else ''
+            return before + chinese.replace('@', '').rstrip('.') + after
+        elif detect_language(word) == "cn":
+            return translate(word).rstrip('.')
+        else:
+            return word.rstrip('.')
+
+
+#定义Prompt文法
+grammar = """
+start: sentence
+sentence: phrase ("," phrase)*
+phrase: emphasis | weight | word | lora | embedding | schedule 
+emphasis: "(" sentence ")" -> emphasis
+        | "[" sentence "]" -> weak_emphasis
+weight: "(" word ":" NUMBER ")"
+schedule: "[" word ":" word ":" NUMBER "]"
+lora: "<" WORD ":" WORD (":" NUMBER)? (":" NUMBER)? ">"
+embedding: "embedding" ":" WORD (":" NUMBER)? (":" NUMBER)?
+word: WORD
+
+NUMBER: /\s*-?\d+(\.\d+)?\s*/
+WORD: /[^,:\(\)\[\]<>]+/
+"""
+def zh_to_en(text):
+    global zh_en_model_path, zh_en_model, zh_en_tokenizer
+    # 进度条
+    pbar = comfy.utils.ProgressBar(len(text) + 1)
+    texts = [correct_prompt_syntax(t) for t in text]
+
+    install_package('sentencepiece', '0.2.0')
+
+    if not os.path.exists(zh_en_model_path):
+        zh_en_model_path = 'Helsinki-NLP/opus-mt-zh-en'
+
+    if zh_en_model is None:
+        zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval()
+        zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path, padding=True, truncation=True)
+
+    zh_en_model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+    prompt_result = []
+
+    en_texts = []
+
+    for t in texts:
+        if t:
+            # translated_text =  translated_word = translate(zh_en_tokenizer,zh_en_model,str(t))
+            parser = Lark(grammar, start="start", parser="lalr", transformer=ChinesePromptTranslate())
+            # print('t',t)
+            result = parser.parse(t).children
+            # print('en_result',result)
+            # en_text=translate(zh_en_tokenizer,zh_en_model,text_without_syntax)
+            en_texts.append(result[0])
+
+    zh_en_model.to('cpu')
+    # print("test en_text", en_texts)
+    # en_text.to("cuda" if torch.cuda.is_available() else "cpu")
+
+    pbar.update(1)
+    for t in en_texts:
+        prompt_result.append(t)
+        pbar.update(1)
+
+    # print('prompt_result', prompt_result, )
+    if len(prompt_result) == 0:
+        prompt_result = [""]
+
+    return prompt_result

py/libs/utils.py

@@ -5,6 +5,19 @@ class AlwaysEqualProxy(str):
     def __ne__(self, _):
         return False
 
+class TautologyStr(str):
+    def __ne__(self, other):
+        return False
+
+class ByPassTypeTuple(tuple):
+    def __getitem__(self, index):
+        if index>0:
+            index=0
+        item = super().__getitem__(index)
+        if isinstance(item, str):
+            return TautologyStr(item)
+        return item
+
 comfy_ui_revision = None
 def get_comfyui_revision():
     try:
@@ -92,6 +105,8 @@ def get_sd_version(model):
     model_config: comfy.supported_models.supported_models_base.BASE = base.model_config
     if isinstance(model_config, comfy.supported_models.SDXL):
         return 'sdxl'
+    elif isinstance(model_config, comfy.supported_models.SDXLRefiner):
+        return 'sdxl_refiner'
     elif isinstance(
             model_config, (comfy.supported_models.SD15, comfy.supported_models.SD20)
     ):
@@ -100,6 +115,12 @@ def get_sd_version(model):
             model_config, (comfy.supported_models.SVD_img2vid)
     ):
         return 'svd'
+    elif isinstance(model_config, comfy.supported_models.SD3):
+        return 'sd3'
+    elif isinstance(model_config, comfy.supported_models.HunyuanDiT):
+        return 'hydit'
+    elif isinstance(model_config, comfy.supported_models.Flux):
+        return 'flux'
     else:
         return 'unknown'
 

py/libs/xyplot.py

@@ -2,11 +2,12 @@ import os, torch
 from pathlib import Path
 from PIL import Image, ImageDraw, ImageFont
 from .utils import easySave
-from ..config import RESOURCES_DIR
-from ..log import log_node_warn
-from ..adv_encode import advanced_encode
+from .adv_encode import advanced_encode
 from .controlnet import easyControlnet
-from ..layer_diffuse.func import LayerDiffuse
+from .log import log_node_warn
+from ..layer_diffuse import LayerDiffuse
+from ..config import RESOURCES_DIR
+
 class easyXYPlot():
 
     def __init__(self, xyPlotData, save_prefix, image_output, prompt, extra_pnginfo, my_unique_id, sampler, easyCache):
@@ -185,7 +186,6 @@ class easyXYPlot():
 
         # 高级用法
         if plot_image_vars["x_node_type"] == "advanced" or plot_image_vars["y_node_type"] == "advanced":
-
             if self.x_type == "Seeds++ Batch" or self.y_type == "Seeds++ Batch":
                 seed = int(x_value) if self.x_type == "Seeds++ Batch" else int(y_value)
             if self.x_type == "Steps" or self.y_type == "Steps":
@@ -287,28 +287,12 @@ class easyXYPlot():
                 if plot_image_vars['clip_skip'] != 0:
                     clip.clip_layer(plot_image_vars['clip_skip'])
 
-            # Lora
-            if self.x_type == "Lora" or self.y_type == "Lora":
-                model = model if model is not None else plot_image_vars["model"]
-                clip = clip if clip is not None else plot_image_vars["clip"]
-
-                xy_values = x_value if self.x_type == "Lora" else y_value
-                lora_name, lora_model_strength, lora_clip_strength = xy_values.split(",")
-                lora_stack = [{"lora_name": lora_name, "model": model, "clip" :clip, "model_strength": float(lora_model_strength), "clip_strength": float(lora_clip_strength)}]
-                if 'lora_stack' in plot_image_vars:
-                    lora_stack = lora_stack + plot_image_vars['lora_stack']
-
-                if lora_stack is not None and lora_stack != []:
-                    for lora in lora_stack:
-                        model, clip = self.easyCache.load_lora(lora)
-
             # CheckPoint
             if self.x_type == "Checkpoint" or self.y_type == "Checkpoint":
                 xy_values = x_value if self.x_type == "Checkpoint" else y_value
                 ckpt_name, clip_skip, vae_name = xy_values.split(",")
                 ckpt_name = ckpt_name.replace('*', ',')
                 vae_name = vae_name.replace('*', ',')
-                print(ckpt_name)
                 model, clip, vae, clip_vision = self.easyCache.load_checkpoint(ckpt_name)
                 if vae_name != 'None':
                     vae = self.easyCache.load_vae(vae_name)
@@ -348,6 +332,21 @@ class easyXYPlot():
                     if "negative_cond" in plot_image_vars:
                         negative = negative + plot_image_vars["negative_cond"]
 
+            # Lora
+            if self.x_type == "Lora" or self.y_type == "Lora":
+                model = model if model is not None else plot_image_vars["model"]
+                clip = clip if clip is not None else plot_image_vars["clip"]
+
+                xy_values = x_value if self.x_type == "Lora" else y_value
+                lora_name, lora_model_strength, lora_clip_strength = xy_values.split(",")
+                lora_stack = [{"lora_name": lora_name, "model": model, "clip" :clip, "model_strength": float(lora_model_strength), "clip_strength": float(lora_clip_strength)}]
+                if 'lora_stack' in plot_image_vars:
+                    lora_stack = lora_stack + plot_image_vars['lora_stack']
+
+                if lora_stack is not None and lora_stack != []:
+                    for lora in lora_stack:
+                        model, clip = self.easyCache.load_lora(lora)
+
             # 提示词
             if "Positive" in self.x_type or "Positive" in self.y_type:
                 if self.x_type == 'Positive Prompt S/R' or self.y_type == 'Positive Prompt S/R':
@@ -395,7 +394,7 @@ class easyXYPlot():
             model, clip, vae, clip_vision = self.easyCache.load_checkpoint(plot_image_vars['ckpt_name'])
 
             if plot_image_vars['lora_name'] != "None":
-                lora = {"lora_name": plot_image_vars['lora_name'], "model": model, "clip": clip, "model_strength": plot_image_vars['model_strength'], "clip_strength": plot_image_vars['lora_clip_strength']}
+                lora = {"lora_name": plot_image_vars['lora_name'], "model": model, "clip": clip, "model_strength": plot_image_vars['lora_model_strength'], "clip_strength": plot_image_vars['lora_clip_strength']}
                 model, clip = self.easyCache.load_lora(lora)
 
             # Check for custom VAE
@@ -446,7 +445,6 @@ class easyXYPlot():
 
         samples = empty_samples if layer_diffusion_method is not None and empty_samples is not None else samples
         # Sample
-
         samples = self.sampler.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, samples,
                                           denoise=denoise, disable_noise=disable_noise, preview_latent=preview_latent,
                                           start_step=start_step, last_step=last_step,

py/logic.py

@@ -1,7 +1,8 @@
 from typing import Iterator, List, Tuple, Dict, Any, Union, Optional
 from _decimal import Context, getcontext
 from decimal import Decimal
-from .libs.utils import AlwaysEqualProxy, cleanGPUUsedForce
+from .libs.utils import AlwaysEqualProxy, ByPassTypeTuple, cleanGPUUsedForce
+from .libs.cache import remove_cache
 import numpy as np
 import json
 
@@ -74,7 +75,7 @@ class Int:
     @classmethod
     def INPUT_TYPES(s):
         return {
-            "required": {"value": ("INT", {"default": 0})},
+            "required": {"value": ("INT", {"default": 0, "min": -999999, "max": 999999,})},
         }
 
     RETURN_TYPES = ("INT",)
@@ -143,7 +144,7 @@ class Float:
     @classmethod
     def INPUT_TYPES(s):
         return {
-            "required": {"value": ("FLOAT", {"default": 0, "step": 0.01})},
+            "required": {"value": ("FLOAT", {"default": 0, "step": 0.01, "min": -999999, "max": 999999,})},
         }
 
     RETURN_TYPES = ("FLOAT",)
@@ -423,19 +424,18 @@ class ConvertAnything:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-            "anything": (AlwaysEqualProxy("*"),),
+            "*": (AlwaysEqualProxy("*"),),
             "output_type": (["string", "int", "float", "boolean"], {"default": "string"}),
         }}
 
-    RETURN_TYPES = (AlwaysEqualProxy("*"),),
-    RETURN_NAMES = ('*',)
+    RETURN_TYPES = ByPassTypeTuple((AlwaysEqualProxy("*"),))
     OUTPUT_NODE = True
     FUNCTION = "convert"
     CATEGORY = "EasyUse/Logic"
 
     def convert(self, *args, **kwargs):
         print(kwargs)
-        anything = kwargs['anything']
+        anything = kwargs['*']
         output_type = kwargs['output_type']
         params = None
         if output_type == 'string':
@@ -477,7 +477,11 @@ class showAnything:
                     values.append(str(val))
                     pass
 
-        if unique_id and extra_pnginfo and "workflow" in extra_pnginfo[0]:
+        if not extra_pnginfo:
+            print("Error: extra_pnginfo is empty")
+        elif (not isinstance(extra_pnginfo[0], dict) or "workflow" not in extra_pnginfo[0]):
+            print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key")
+        else:
             workflow = extra_pnginfo[0]["workflow"]
             node = next((x for x in workflow["nodes"] if str(x["id"]) == unique_id[0]), None)
             if node:
@@ -531,9 +535,9 @@ class cleanGPUUsed:
 
     def empty_cache(self, anything, unique_id=None, extra_pnginfo=None):
         cleanGPUUsedForce()
+        remove_cache('*')
         return ()
 
-from .libs.cache import remove_cache
 class clearCacheKey:
     @classmethod
     def INPUT_TYPES(s):

py/server.py

@@ -2,9 +2,6 @@ import random
 import server
 from enum import Enum
 
-
-seed_nodes = ["easy wildcards","easy preSampling","easy preSamplingAdvanced","easy preSamplingSdTurbo","easy preSamplingDynamicCFG","easy preSamplingLayerDiffusion","easy preSamplingCascade","easy fullCascadeKSampler","easy fullkSampler","easy seed","easy latentNoisy", "easy preSamplingNoiseIn"]
-
 class SGmode(Enum):
     FIX = 1
     INCR = 2
@@ -123,41 +120,6 @@ def prompt_seed_update(json_data):
         # control after generated
         if mode is not None and not mode:
             control_seed(node[1], action, seed_is_global)
-    # else:
-    #     prompts = json_data['prompt'].items()
-    #     for k, v in prompts:
-    #         if 'class_type' not in v:
-    #             continue
-    #         cls = v['class_type']
-    #         if cls in seed_nodes:
-    #             extra_data = next((x for x in workflow["nodes"] if str(x["id"]) == k), None)
-    #             if extra_data is not None:
-    #                 inputs = extra_data.get('inputs')
-    #                 widgets_value = extra_data.get('widgets_values')
-    #                 widgets_length = len(widgets_value)
-    #                 if "disable" in widgets_value:
-    #                     break
-    #                 if inputs is not None and inputs != []:
-    #                     seed_num_input = next((x for x in inputs if x['name'] == 'seed_num' and x['type'] == 'INT'), None)
-    #                     if seed_num_input is not None:
-    #                         action = 'fixed'
-    #                     else:
-    #                         action = widgets_value[widgets_length - 1]
-    #                 else:
-    #                     control_index = widgets_length - 2 if cls == 'easy seed' else widgets_length - 1
-    #                     action = widgets_value[control_index]
-    #
-    #                 # print(action)
-    #                 node = k, v
-    #                 value = control_seed(node[1], action, False)
-    #
-    #                 if k not in seed_widget_map:
-    #                     continue
-    #
-    #                 if 'seed_num' in v['inputs']:
-    #                     if isinstance(v['inputs']['seed_num'], int):
-    #                         v['inputs']['seed_num'] = value
-
 
     return value is not None
 

py/xyplot.py

@@ -0,0 +1,602 @@
+import os
+import comfy
+import folder_paths
+from .config import RESOURCES_DIR
+def load_preset(filename):
+    path = os.path.join(RESOURCES_DIR, filename)
+    path = os.path.abspath(path)
+    preset_list = []
+
+    if os.path.exists(path):
+        with open(path, 'r') as file:
+            for line in file:
+                preset_list.append(line.strip())
+
+        return preset_list
+    else:
+        return []
+def generate_floats(batch_count, first_float, last_float):
+    if batch_count > 1:
+        interval = (last_float - first_float) / (batch_count - 1)
+        values = [str(round(first_float + i * interval, 3)) for i in range(batch_count)]
+    else:
+        values = [str(first_float)] if batch_count == 1 else []
+    return "; ".join(values)
+
+def generate_ints(batch_count, first_int, last_int):
+    if batch_count > 1:
+        interval = (last_int - first_int) / (batch_count - 1)
+        values = [str(int(first_int + i * interval)) for i in range(batch_count)]
+    else:
+        values = [str(first_int)] if batch_count == 1 else []
+    # values = list(set(values))  # Remove duplicates
+    # values.sort()  # Sort in ascending order
+    return "; ".join(values)
+
+# Seed++ Batch
+class XYplot_SeedsBatch:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+            "batch_count": ("INT", {"default": 3, "min": 1, "max": 50}), },
+        }
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, batch_count):
+
+        axis = "advanced: Seeds++ Batch"
+        xy_values = {"axis": axis, "values": batch_count}
+        return (xy_values,)
+
+# Step Values
+class XYplot_Steps:
+    parameters = ["steps", "start_at_step", "end_at_step",]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "target_parameter": (cls.parameters,),
+                "batch_count": ("INT", {"default": 3, "min": 0, "max": 50}),
+                "first_step": ("INT", {"default": 10, "min": 1, "max": 10000}),
+                "last_step": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                "first_start_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
+                "last_start_step": ("INT", {"default": 10, "min": 0, "max": 10000}),
+                "first_end_step": ("INT", {"default": 10, "min": 0, "max": 10000}),
+                "last_end_step": ("INT", {"default": 20, "min": 0, "max": 10000}),
+            }
+        }
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, target_parameter, batch_count, first_step, last_step, first_start_step, last_start_step,
+                 first_end_step, last_end_step,):
+
+        axis, xy_first, xy_last = None, None, None
+
+        if target_parameter == "steps":
+            axis = "advanced: Steps"
+            xy_first = first_step
+            xy_last = last_step
+        elif target_parameter == "start_at_step":
+            axis = "advanced: StartStep"
+            xy_first = first_start_step
+            xy_last = last_start_step
+        elif target_parameter == "end_at_step":
+            axis = "advanced: EndStep"
+            xy_first = first_end_step
+            xy_last = last_end_step
+
+        values = generate_ints(batch_count, xy_first, xy_last)
+        return ({"axis": axis, "values": values},) if values is not None else (None,)
+
+class XYplot_CFG:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "batch_count": ("INT", {"default": 3, "min": 0, "max": 50}),
+                "first_cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
+                "last_cfg": ("FLOAT", {"default": 9.0, "min": 0.0, "max": 100.0}),
+            }
+        }
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, batch_count, first_cfg, last_cfg):
+        axis = "advanced: CFG Scale"
+        values = generate_floats(batch_count, first_cfg, last_cfg)
+        return ({"axis": axis, "values": values},) if values else (None,)
+
+# Step Values
+class XYplot_Sampler_Scheduler:
+    parameters = ["sampler", "scheduler", "sampler & scheduler"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        samplers = ["None"] + comfy.samplers.KSampler.SAMPLERS
+        schedulers = ["None"] + comfy.samplers.KSampler.SCHEDULERS
+        inputs = {
+            "required": {
+                "target_parameter": (cls.parameters,),
+                "input_count": ("INT", {"default": 1, "min": 1, "max": 30, "step": 1})
+            }
+        }
+        for i in range(1, 30 + 1):
+            inputs["required"][f"sampler_{i}"] = (samplers,)
+            inputs["required"][f"scheduler_{i}"] = (schedulers,)
+
+        return inputs
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, target_parameter, input_count, **kwargs):
+        axis, values, = None, None,
+        if target_parameter == "scheduler":
+            axis = "advanced: Scheduler"
+            schedulers = [kwargs.get(f"scheduler_{i}") for i in range(1, input_count + 1)]
+            values = [scheduler for scheduler in schedulers if scheduler != "None"]
+        elif target_parameter == "sampler":
+            axis = "advanced: Sampler"
+            samplers = [kwargs.get(f"sampler_{i}") for i in range(1, input_count + 1)]
+            values = [sampler for sampler in samplers if sampler != "None"]
+        else:
+            axis = "advanced: Sampler&Scheduler"
+            samplers = [kwargs.get(f"sampler_{i}") for i in range(1, input_count + 1)]
+            schedulers = [kwargs.get(f"scheduler_{i}") for i in range(1, input_count + 1)]
+            values = []
+            for sampler, scheduler in zip(samplers, schedulers):
+                sampler = sampler if sampler else 'None'
+                scheduler = scheduler if scheduler else 'None'
+                values.append(sampler +','+ scheduler)
+        values = "; ".join(values)
+        return ({"axis": axis, "values": values},) if values else (None,)
+
+class XYplot_Denoise:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "batch_count": ("INT", {"default": 3, "min": 0, "max": 50}),
+                "first_denoise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1}),
+                "last_denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1}),
+            }
+        }
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, batch_count, first_denoise, last_denoise):
+        axis = "advanced: Denoise"
+        values = generate_floats(batch_count, first_denoise, last_denoise)
+        return ({"axis": axis, "values": values},) if values else (None,)
+
+# PromptSR
+class XYplot_PromptSR:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        inputs = {
+            "required": {
+                "target_prompt": (["positive", "negative"],),
+                "search_txt": ("STRING", {"default": "", "multiline": False}),
+                "replace_all_text": ("BOOLEAN", {"default": False}),
+                "replace_count": ("INT", {"default": 3, "min": 1, "max": 30 - 1}),
+            }
+        }
+
+        # Dynamically add replace_X inputs
+        for i in range(1, 30):
+            replace_key = f"replace_{i}"
+            inputs["required"][replace_key] = ("STRING", {"default": "", "multiline": False, "placeholder": replace_key})
+
+        return inputs
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, target_prompt, search_txt, replace_all_text, replace_count, **kwargs):
+        axis = None
+
+        if target_prompt == "positive":
+            axis = "advanced: Positive Prompt S/R"
+        elif target_prompt == "negative":
+            axis = "advanced: Negative Prompt S/R"
+
+        # Create base entry
+        values = [(search_txt, None, replace_all_text)]
+
+        if replace_count > 0:
+            # Append additional entries based on replace_count
+            values.extend([(search_txt, kwargs.get(f"replace_{i+1}"), replace_all_text) for i in range(replace_count)])
+        return ({"axis": axis, "values": values},) if values is not None else (None,)
+
+# XYPlot Pos Condition
+class XYplot_Positive_Cond:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        inputs = {
+            "optional": {
+                "positive_1": ("CONDITIONING",),
+                "positive_2": ("CONDITIONING",),
+                "positive_3": ("CONDITIONING",),
+                "positive_4": ("CONDITIONING",),
+            }
+        }
+
+        return inputs
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, positive_1=None, positive_2=None, positive_3=None, positive_4=None):
+        axis = "advanced: Pos Condition"
+        values = []
+        cond = []
+        # Create base entry
+        if positive_1 is not None:
+            values.append("0")
+            cond.append(positive_1)
+        if positive_2 is not None:
+            values.append("1")
+            cond.append(positive_2)
+        if positive_3 is not None:
+            values.append("2")
+            cond.append(positive_3)
+        if positive_4 is not None:
+            values.append("3")
+            cond.append(positive_4)
+
+        return ({"axis": axis, "values": values, "cond": cond},) if values is not None else (None,)
+
+# XYPlot Neg Condition
+class XYplot_Negative_Cond:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        inputs = {
+            "optional": {
+                "negative_1": ("CONDITIONING",),
+                "negative_2": ("CONDITIONING",),
+                "negative_3": ("CONDITIONING",),
+                "negative_4": ("CONDITIONING",),
+            }
+        }
+
+        return inputs
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, negative_1=None, negative_2=None, negative_3=None, negative_4=None):
+        axis = "advanced: Neg Condition"
+        values = []
+        cond = []
+        # Create base entry
+        if negative_1 is not None:
+            values.append(0)
+            cond.append(negative_1)
+        if negative_2 is not None:
+            values.append(1)
+            cond.append(negative_2)
+        if negative_3 is not None:
+            values.append(2)
+            cond.append(negative_3)
+        if negative_4 is not None:
+            values.append(3)
+            cond.append(negative_4)
+
+        return ({"axis": axis, "values": values, "cond": cond},) if values is not None else (None,)
+
+# XYPlot Pos Condition List
+class XYplot_Positive_Cond_List:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "positive": ("CONDITIONING",),
+            }
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, positive):
+        axis = "advanced: Pos Condition"
+        values = []
+        cond = []
+        for index, c in enumerate(positive):
+            values.append(str(index))
+            cond.append(c)
+
+        return ({"axis": axis, "values": values, "cond": cond},) if values is not None else (None,)
+
+# XYPlot Neg Condition List
+class XYplot_Negative_Cond_List:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "negative": ("CONDITIONING",),
+            }
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, negative):
+        axis = "advanced: Neg Condition"
+        values = []
+        cond = []
+        for index, c in enumerate(negative):
+            values.append(index)
+            cond.append(c)
+
+        return ({"axis": axis, "values": values, "cond": cond},) if values is not None else (None,)
+
+# XY Plot: ControlNet
+class XYplot_Control_Net:
+    parameters = ["strength", "start_percent", "end_percent"]
+    @classmethod
+    def INPUT_TYPES(cls):
+        def get_file_list(filenames):
+            return [file for file in filenames if file != "put_models_here.txt" and "lllite" not in file]
+
+        return {
+            "required": {
+                "control_net_name": (get_file_list(folder_paths.get_filename_list("controlnet")),),
+                "image": ("IMAGE",),
+                "target_parameter": (cls.parameters,),
+                "batch_count": ("INT", {"default": 3, "min": 1, "max": 30}),
+                "first_strength": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+                "last_strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+                "first_start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+                "last_start_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+                "first_end_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+                "last_end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+            },
+        }
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, control_net_name, image, target_parameter, batch_count, first_strength, last_strength, first_start_percent,
+                 last_start_percent, first_end_percent, last_end_percent, strength, start_percent, end_percent):
+
+        axis, = None,
+
+        values = []
+
+        if target_parameter == "strength":
+            axis = "advanced: ControlNetStrength"
+
+            values.append([(control_net_name, image, first_strength, start_percent, end_percent)])
+            strength_increment = (last_strength - first_strength) / (batch_count - 1) if batch_count > 1 else 0
+            for i in range(1, batch_count - 1):
+                values.append([(control_net_name, image, first_strength + i * strength_increment, start_percent,
+                                end_percent)])
+            if batch_count > 1:
+                values.append([(control_net_name, image, last_strength, start_percent, end_percent)])
+
+        elif target_parameter == "start_percent":
+            axis = "advanced: ControlNetStart%"
+
+            percent_increment = (last_start_percent - first_start_percent) / (batch_count - 1) if batch_count > 1 else 0
+            values.append([(control_net_name, image, strength, first_start_percent, end_percent)])
+            for i in range(1, batch_count - 1):
+                values.append([(control_net_name, image, strength, first_start_percent + i * percent_increment,
+                                  end_percent)])
+
+            # Always add the last start_percent if batch_count is more than 1.
+            if batch_count > 1:
+                values.append((control_net_name, image, strength, last_start_percent, end_percent))
+
+        elif target_parameter == "end_percent":
+            axis = "advanced: ControlNetEnd%"
+
+            percent_increment = (last_end_percent - first_end_percent) / (batch_count - 1) if batch_count > 1 else 0
+            values.append([(control_net_name, image, image, strength, start_percent, first_end_percent)])
+            for i in range(1, batch_count - 1):
+                values.append([(control_net_name, image, strength, start_percent,
+                                  first_end_percent + i * percent_increment)])
+
+            if batch_count > 1:
+                values.append([(control_net_name, image, strength, start_percent, last_end_percent)])
+
+
+        return ({"axis": axis, "values": values},)
+
+
+#Checkpoints
+class XYplot_Checkpoint:
+
+    modes = ["Ckpt Names", "Ckpt Names+ClipSkip", "Ckpt Names+ClipSkip+VAE"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+
+        checkpoints = ["None"] + folder_paths.get_filename_list("checkpoints")
+        vaes = ["Baked VAE"] + folder_paths.get_filename_list("vae")
+
+        inputs = {
+            "required": {
+                "input_mode": (cls.modes,),
+                "ckpt_count": ("INT", {"default": 3, "min": 0, "max": 10, "step": 1}),
+            }
+        }
+
+        for i in range(1, 10 + 1):
+            inputs["required"][f"ckpt_name_{i}"] = (checkpoints,)
+            inputs["required"][f"clip_skip_{i}"] = ("INT", {"default": -1, "min": -24, "max": -1, "step": 1})
+            inputs["required"][f"vae_name_{i}"] = (vaes,)
+
+        inputs["optional"] = {
+            "optional_lora_stack": ("LORA_STACK",)
+        }
+        return inputs
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, input_mode, ckpt_count, **kwargs):
+
+        axis = "advanced: Checkpoint"
+
+        checkpoints = [kwargs.get(f"ckpt_name_{i}") for i in range(1, ckpt_count + 1)]
+        clip_skips = [kwargs.get(f"clip_skip_{i}") for i in range(1, ckpt_count + 1)]
+        vaes = [kwargs.get(f"vae_name_{i}") for i in range(1, ckpt_count + 1)]
+
+        # Set None for Clip Skip and/or VAE if not correct modes
+        for i in range(ckpt_count):
+            if "ClipSkip" not in input_mode:
+                clip_skips[i] = 'None'
+            if "VAE" not in input_mode:
+                vaes[i] = 'None'
+
+        # Extend each sub-array with lora_stack if it's not None
+        values = [checkpoint.replace(',', '*')+','+str(clip_skip)+','+vae.replace(',', '*') for checkpoint, clip_skip, vae in zip(checkpoints, clip_skips, vaes) if
+                        checkpoint != "None"]
+
+        optional_lora_stack = kwargs.get("optional_lora_stack") if "optional_lora_stack" in kwargs else []
+
+        xy_values = {"axis": axis, "values": values, "lora_stack": optional_lora_stack}
+        return (xy_values,)
+
+#Loras
+class XYplot_Lora:
+
+    modes = ["Lora Names", "Lora Names+Weights"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        loras = ["None"] + folder_paths.get_filename_list("loras")
+
+        inputs = {
+            "required": {
+                "input_mode": (cls.modes,),
+                "lora_count": ("INT", {"default": 3, "min": 0, "max": 10, "step": 1}),
+                "model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                "clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+            }
+        }
+
+        for i in range(1, 10 + 1):
+            inputs["required"][f"lora_name_{i}"] = (loras,)
+            inputs["required"][f"model_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+            inputs["required"][f"clip_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+
+        inputs["optional"] = {
+            "optional_lora_stack": ("LORA_STACK",)
+        }
+        return inputs
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, input_mode, lora_count, model_strength, clip_strength, **kwargs):
+
+        axis = "advanced: Lora"
+        # Extract values from kwargs
+        loras = [kwargs.get(f"lora_name_{i}") for i in range(1, lora_count + 1)]
+        model_strs = [kwargs.get(f"model_str_{i}", model_strength) for i in range(1, lora_count + 1)]
+        clip_strs = [kwargs.get(f"clip_str_{i}", clip_strength) for i in range(1, lora_count + 1)]
+
+        # Use model_strength and clip_strength for the loras where values are not provided
+        if "Weights" not in input_mode:
+            for i in range(lora_count):
+                model_strs[i] = model_strength
+                clip_strs[i] = clip_strength
+
+        # Extend each sub-array with lora_stack if it's not None
+        values = [lora.replace(',', '*')+','+str(model_str)+','+str(clip_str) for lora, model_str, clip_str
+                    in zip(loras, model_strs, clip_strs) if lora != "None"]
+
+        optional_lora_stack = kwargs.get("optional_lora_stack") if "optional_lora_stack" in kwargs else []
+
+        xy_values = {"axis": axis, "values": values, "lora_stack": optional_lora_stack}
+        return (xy_values,)
+
+# 模型叠加
+class XYplot_ModelMergeBlocks:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        checkpoints = folder_paths.get_filename_list("checkpoints")
+        vae = ["Use Model 1", "Use Model 2"] + folder_paths.get_filename_list("vae")
+
+        preset = ["Preset"]  # 20
+        preset += load_preset("mmb-preset.txt")
+        preset += load_preset("mmb-preset.custom.txt")
+
+        default_vectors = "1,0,0; \n0,1,0; \n0,0,1; \n1,1,0; \n1,0,1; \n0,1,1; "
+        return {
+            "required": {
+                "ckpt_name_1": (checkpoints,),
+                "ckpt_name_2": (checkpoints,),
+                "vae_use": (vae, {"default": "Use Model 1"}),
+                "preset": (preset, {"default": "preset"}),
+                "values": ("STRING", {"default": default_vectors, "multiline": True, "placeholder": 'Support 2 methods:\n\n1.input, middle, out in same line and insert values seperated by "; "\n\n2.model merge block number seperated by ", " in same line and insert values seperated by "; "'}),
+            },
+            "hidden": {"my_unique_id": "UNIQUE_ID"}
+        }
+
+    RETURN_TYPES = ("X_Y",)
+    RETURN_NAMES = ("X or Y",)
+    FUNCTION = "xy_value"
+
+    CATEGORY = "EasyUse/XY Inputs"
+
+    def xy_value(self, ckpt_name_1, ckpt_name_2, vae_use, preset, values, my_unique_id=None):
+
+        axis = "advanced: ModelMergeBlocks"
+        if ckpt_name_1 is None:
+            raise Exception("ckpt_name_1 is not found")
+        if ckpt_name_2 is None:
+            raise Exception("ckpt_name_2 is not found")
+
+        models = (ckpt_name_1, ckpt_name_2)
+
+        xy_values = {"axis":axis, "values":values, "models":models, "vae_use": vae_use}
+        return (xy_values,)

pyproject.toml

@@ -1,9 +1,9 @@
 [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"]
+version = "1.2.1"
+license = { file = "LICENSE" }
+dependencies = ["diffusers>=0.25.0", "accelerate>=0.25.0", "clip_interrogator>=0.6.0", "sentencepiece", "lark-parser", "onnxruntime", "spandrel", "opencv-python"]
 
 [project.urls]
 Repository = "https://github.com/yolain/ComfyUI-Easy-Use"

repair_dependency_list.txt

@@ -0,0 +1,4 @@
+numpy>=1.19.0
+huggingface_hub>=0.23.3
+transformers>=4.38.2
+protobuf>=4.25.3

requirements.txt

@@ -1,4 +1,8 @@
 diffusers>=0.25.0
+accelerate>=0.25.0
 clip_interrogator>=0.6.0
+lark-parser
 onnxruntime
-aiohttp
+opencv-python
+sentencepiece
+spandrel

web/css/easy.css

@@ -26,15 +26,14 @@ textarea{
     backdrop-filter: blur(8px) brightness(120%);
 }
 .comfy-menu{
-    top:38%;
     border-radius:16px;
     box-shadow:0 0 1px var(--descrip-text);
     backdrop-filter: blur(8px) brightness(120%);
 }
 .comfy-menu button,.comfy-modal button {
-    font-size: 16px;
-    padding:6px 0;
-    margin-bottom:8px;
+    font-size: 14px;
+    padding:4px 0;
+    margin-bottom:4px;
 }
 .comfy-menu button.comfy-settings-btn{
     font-size: 12px;
@@ -43,7 +42,7 @@ textarea{
     margin-bottom: 4px;
 }
 .comfy-menu-btns button,.comfy-list-actions button{
-    font-size: 12px;
+    font-size: 10px;
 }
 .comfy-menu > button,
 .comfy-menu-btns button,
@@ -52,6 +51,7 @@ textarea{
     border-width:1px;
 }
 
+
 dialog{
     border:1px solid var(--border-color);
     background:transparent;
@@ -71,6 +71,9 @@ dialog{
 hr{
    border:1px solid var(--border-color);
 }
+#comfy-dev-save-api-button{
+    justify-content: center;
+}
 #shareButton{
     background:linear-gradient(to left,var(--theme-color),var(--theme-color-light))!important;
     color:white!important;
@@ -78,10 +81,12 @@ hr{
 #queue-button{
     position:relative;
     overflow:hidden;
+    min-height:30px;
     z-index:1;
 }
 
 #queue-button:after{
+    clear: both;
     content:attr(data-attr);
     background:green;
     color:#FFF;
@@ -112,4 +117,11 @@ hr{
 }
 ::-webkit-scrollbar-thumb:hover {
     background-color: transparent;
+}
+
+[data-theme="dark"] .workspace_manager .chakra-card{
+    background-color:var(--comfy-menu-bg)!important;
+}
+.workspace_manager .chakra-card{
+    width: 400px;
 }

web/css/groupmap.css

@@ -9,7 +9,9 @@
   color: var(--descrip-text);
   background-color: var(--comfy-menu-bg);
   padding: 10px 4px;
-  border: 1px solid var(--border-color);z-index: 999999999;padding-top: 0;
+  border: 1px solid var(--border-color);
+  z-index: 399;
+  padding-top: 0;
 }
 #easyuse_groups_map .icon{
   width: 12px;

web/css/index.css

@@ -7,4 +7,5 @@
 @import "toast.css";
 @import "account.css";
 @import "chooser.css";
-@import "toolbar.css";
+@import "toolbar.css";
+@import "sliderControl.css";

web/css/sliderControl.css

@@ -0,0 +1,66 @@
+.easyuse-slider{
+    width:100%;
+    height:100%;
+    display: flex;
+    flex-direction: row;
+    justify-content: space-between;
+    position: relative;
+}
+.easyuse-slider-item{
+    height: inherit;
+    min-width: 25px;
+    justify-content: center;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+}
+.easyuse-slider-item.positive .easyuse-slider-item-label{
+    color: var(--success-color);
+}
+.easyuse-slider-item.negative .easyuse-slider-item-label{
+    color: var(--error-color);
+}
+.easyuse-slider-item-input{
+    height:15px;
+    font-size: 10px;
+    color: var(--input-text);
+}
+.easyuse-slider-item-label{
+    height:15px;
+    border: none;
+    color: var(--descrip-text);
+    font-size: 8px;
+}
+.easyuse-slider-item-scroll {
+    width: 5px;
+    height: calc(100% - 30px);
+    background: var(--comfy-input-bg);
+    border-radius: 10px;
+    position: relative;
+}
+.easyuse-slider-item-bar{
+    width: 10px;
+    height: 10px;
+    background: linear-gradient(to bottom, var(--input-text), var(--descrip-text));
+    border-radius:100%;
+    box-shadow: 0 2px 10px var(--bg-color);
+    position: absolute;
+    top: 0;
+    left:-2.5px;
+    cursor: pointer;
+    z-index:1;
+}
+.easyuse-slider-item-area{
+    width: 100%;
+    border-radius:20px;
+    position: absolute;
+    bottom: 0;
+    background: var(--input-text);
+    z-index:0;
+}
+.easyuse-slider-item.positive .easyuse-slider-item-area{
+    background: var(--success-color);
+}
+.easyuse-slider-item.negative .easyuse-slider-item-area{
+    background: var(--error-color);
+}

web/css/theme.css

@@ -1,8 +1,10 @@
 :root {
-   --theme-color:#3f3eed;
-   --theme-color-light: #008ecb;
+   /*--theme-color:#3f3eed;*/
+   /*--theme-color-light: #008ecb;*/
+   --theme-color:#236692;
+   --theme-color-light: #3485bb;
    --success-color: #52c41a;
    --error-color: #ff4d4f;
    --warning-color: #faad14;
    --font-family: Inter, -apple-system, BlinkMacSystemFont, Helvetica Neue, sans-serif;
-}
+}

web/css/toolbar.css

@@ -35,6 +35,10 @@
     color:white;
     transition: all 0.3s ease-in-out;
 }
+.easyuse-toolbar-icon svg{
+    width: 14px;
+    height: 14px;
+}
 .easyuse-toolbar-tips{
     visibility: hidden;
     opacity: 0;
@@ -210,4 +214,17 @@
 }
 .markdown-body .link{
     color:var(--theme-color-light)
+}
+
+#comfyui-menu-monitor{
+    width:120px;
+}
+#comfyui-menu-monitor #crystools-monitor-container{
+    margin:0 auto!important;
+}
+#comfyui-menu-monitor #crystools-monitor-container > div{
+    margin:2px 0!important;
+}
+#comfyui-menu-monitor #crystools-monitor-container > div > div > div{
+    padding:0 4px!important;
 }

web/js/common/icon.js

@@ -1,3 +1,23 @@
+export const logoIcon = `<svg width="347px" height="300px" viewBox="0 0 347 300" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">        <g id="icon" transform="translate(-17, -49)">
+    <g id="easy" transform="translate(17, 49)">
+        <rect id="矩形" fill="#3A3A3A" x="7" y="106" width="328" height="119"></rect>
+        <rect id="矩形" fill="#292929" x="7" y="69" width="328" height="39"></rect>
+        <path d="M328,57 C338.49341,57 347,65.5065898 347,76 L347,222 C347,232.49341 338.49341,241 328,241 L19,241 C8.50658975,241 7.74796342e-15,232.49341 0,222 L0,76 C-1.28507213e-15,65.5065898 8.50658975,57 19,57 L328,57 Z M318.532203,73.5128205 L30.820339,73.5128205 C24.192922,73.5128205 18.820339,78.8854035 18.820339,85.5128205 L18.820339,212.487179 C18.820339,219.114596 24.192922,224.487179 30.820339,224.487179 L318.532203,224.487179 C325.15962,224.487179 330.532203,219.114596 330.532203,212.487179 L330.532203,85.5128205 C330.532203,78.8854035 325.15962,73.5128205 318.532203,73.5128205 Z" id="矩形-2" fill="#000000"></path>
+        <path d="M236.357276,240.344534 C229.387569,244.108454 224.824659,250.430287 219.628567,256.080285 C212.724768,251.899851 209.926058,252.06928 204.312161,257.969893 C188.040097,275.069315 168.819854,284.472644 144.661264,281.434681 C143.99866,281.282901 143.336055,281.132297 142.674627,280.981693 L142.31096,280.918157 C135.57546,274.77752 128.299755,269.300481 121.68077,262.972765 C102.148644,244.300238 96.1628402,211.734505 108.227423,187.366093 C110.124615,183.533931 111.676969,179.508807 113.110455,175.473094 C118.554876,160.139738 116.691815,143.908641 118.645499,128.138769 C120.1496,115.999861 120.390868,103.703291 121.145272,91.4726095 C121.250018,89.7747862 121.798461,88.4475896 123.27667,87.5616153 C126.008296,95.5753884 126.567332,103.982143 127.811334,112.272416 C129.555526,123.897152 134.253781,128.17289 145.804051,128.989445 C145.43803,132.846316 147.270491,135.554833 150.727098,136.730249 C153.974214,137.835069 156.603448,136.356092 159.056145,133.892307 C189.877265,102.947918 220.789008,72.0929508 251.656029,41.1956258 C252.609332,40.2414091 253.343729,39.0706994 254.17934,38 C259.396616,43.3240821 259.109448,47.7339516 252.918861,53.9345953 C228.40955,78.485379 203.843747,102.979686 179.349736,127.544589 C174.94571,131.961518 174.769173,137.358549 178.597686,141.040108 C182.332046,144.632245 187.280985,144.378101 191.647349,140.241201 C194.491959,137.546804 197.127078,134.630031 199.99405,131.960342 C204.506352,127.755199 209.768351,127.501055 213.634526,131.137903 C217.616038,134.882998 217.557192,140.563588 213.322642,145.277018 C211.099445,147.751393 208.627918,150.002215 206.287029,152.371872 C201.132129,157.590061 200.424801,162.74942 204.299215,166.72748 C208.353696,170.890265 213.268504,170.338443 218.59288,165.120255 C220.272341,163.475378 221.841173,161.69637 223.650095,160.20798 C227.582177,156.97235 233.243151,156.980586 236.463198,160.338582 C240.098697,164.131917 239.948051,168.521784 237.31764,172.708101 C235.991254,174.818908 233.92694,176.474374 232.149795,178.291033 C227.054918,183.502163 226.346414,188.706232 230.231419,192.654877 C234.312969,196.80472 238.906479,196.2729 244.5192,191.001765 C250.03659,185.82005 255.291527,185.377651 259.431924,189.747517 C263.298098,193.82794 262.808501,199.286154 257.8984,204.298439 C251.179377,211.159151 244.326185,217.886908 237.556555,224.69938 C231.602528,230.69059 231.385975,233.755614 236.357276,240.344534" id="Fill-1" fill="#F7CABB"></path>
+        <path d="M142,281 C99.8323615,275.972516 72.8905321,229.508012 90.4429765,191.002025 C97.205854,176.166768 98.8592352,160.761783 99.9233733,144.957755 C101.420401,122.72429 103.161293,100.507306 104.785504,78.2820813 C105.081876,74.2292532 106.526397,70.816779 110.840123,70.1046207 C114.869146,69.4383701 117.276292,72.0374536 118.826993,75.5335034 C120.557384,79.4344829 122.314612,83.3248683 123.130218,87.5577959 C121.663528,88.442991 121.120957,89.7719608 121.017111,91.4705466 C120.268013,103.705544 120.028816,116.007637 118.537622,128.153173 C116.600704,143.930128 118.447777,160.167337 113.050077,175.508757 C111.630059,179.545105 110.091026,183.573214 108.210115,187.407097 C96.2478964,211.785276 102.182333,244.365636 121.546846,263.046548 C128.109031,269.377106 135.3223,274.857782 142,281" id="Fill-3" fill="#F2AB9B"></path>
+        <path d="M146.046133,128.926434 C150.920728,128.31052 154.622981,125.725088 158.028632,122.311802 C185.262118,95.0101969 212.557739,67.7694807 239.855703,40.5322774 C245.237922,35.1623528 248.464205,34.7232509 254,38.3742375 C253.166465,39.4397916 252.434924,40.6048756 251.4865,41.5545068 C220.738335,72.304526 189.947966,103.01005 159.246695,133.806906 C156.802363,136.257681 154.183353,137.729551 150.950036,136.630039 C147.505698,135.460272 145.680362,132.764771 146.046133,128.926434" id="Fill-5" fill="#F0A999"></path>
+        <path d="M151.112706,104.082785 C177.053192,78.2027637 202.891282,52.2205287 228.952997,26.4638686 C244.25247,11.3433004 268.927711,17.3551067 274.531279,37.5593344 C277.54433,48.4268757 274.452422,57.971514 266.398395,65.9606254 C252.071102,80.1741945 237.837967,94.4829279 223.515381,108.702371 C222.137146,110.069919 220.410526,111.087356 218.246073,112.71925 C229.562669,119.217452 234.902596,127.95613 234.371781,139.989141 C248.189445,143.544296 256.015139,152.051525 257.33688,166.30034 C257.462816,167.662013 259.216506,169.49011 260.624166,170.02585 C281.015224,177.772939 286.435185,200.143625 271.473503,216.024334 C264.220995,223.722078 256.614218,231.092033 249.026273,238.465513 C244.58673,242.779633 239.231503,242.893595 235.482844,239.124615 C231.700052,235.321563 231.975464,230.303696 236.490333,225.689984 C243.34267,218.688938 250.382146,211.871171 257.218005,204.856026 C258.813981,203.218258 261.014921,201.09292 261.001974,199.20608 C260.978434,195.842429 260.489991,191.269837 258.313767,189.474637 C256.110474,187.655939 251.585012,188.159958 248.164729,188.510069 C246.572284,188.673376 245.166978,190.850409 243.713416,192.155689 C238.92549,196.454536 234.024574,196.671887 230.19235,192.72785 C226.54491,188.976493 226.92978,183.714253 231.229263,179.166334 C232.842893,177.459249 234.637777,175.914295 236.173727,174.141418 C239.944749,169.788527 239.964757,163.749698 236.333795,160.393097 C232.76639,157.094064 227.265218,157.331387 223.065778,161.08627 C221.022552,162.912017 219.226492,165.012683 217.172674,166.825506 C212.995596,170.515771 208.141759,170.56629 204.515505,167.099251 C200.831579,163.574643 200.824518,158.196091 204.693228,153.987709 C207.475592,150.961246 210.60281,148.247298 213.346334,145.189113 C217.446908,140.617696 217.513996,135.200374 213.771221,131.512459 C210.002554,127.798697 204.702644,128.006649 200.152466,132.204457 C197.418357,134.728077 194.903166,137.485495 192.212606,140.058459 C187.331698,144.723865 182.149486,145.008183 178.271359,140.905539 C174.618035,137.040219 174.962888,131.914264 179.489527,127.381619 C203.306754,103.535296 227.179299,79.7453672 251.030658,55.9342906 C252.278249,54.6889291 253.59999,53.5023111 254.734592,52.1606104 C258.53033,47.6737844 258.68216,42.3786484 255.200674,38.9304069 C251.353149,35.1203057 245.99439,35.1109068 241.586626,39.3721578 C233.557316,47.1345194 225.738684,55.113057 217.832955,63.0023045 C198.000959,82.7965028 178.090106,102.514335 158.388753,122.437769 C152.951138,127.937332 146.992123,130.706499 139.410062,128.300366 C131.778569,125.877786 128.770226,120.109178 127.524988,112.464303 C125.807785,101.913976 123.226683,91.4999347 120.900984,81.0529967 C120.526706,79.3682341 119.960582,77.3874044 118.783609,76.2912513 C116.41907,74.0895462 113.255367,70.6072336 111.009702,71.0008148 C108.678118,71.4084945 105.634466,75.6239256 105.335515,78.4330381 C103.619488,94.562819 102.648485,110.771316 101.380885,126.949267 C100.28277,140.976032 99.7966796,155.096786 97.7334458,168.982566 C96.5811892,176.734354 93.2327008,184.294638 90.0642893,191.589402 C79.583344,215.716518 83.954622,239.009477 102.147095,258.025912 C117.64783,274.228535 135.84148,283.188089 158.615909,281.527998 C173.230384,280.463567 185.780448,274.550449 196.662741,264.951767 C200.186598,261.844238 203.329116,258.291433 206.960078,255.324888 C210.74287,252.234981 214.919947,252.206785 218.707446,255.52579 C222.194818,258.579276 222.709155,264.037719 219.548982,267.699786 C185.86519,306.748918 134.584473,309.856447 99.0551866,279.924302 C83.9922852,267.234538 71.9577355,252.377141 67.8442146,232.265728 C64.8076241,217.420079 65.1371765,202.689567 71.7458803,189.240838 C79.6009986,173.252042 81.3229102,156.469033 82.4116103,139.245449 C83.7674833,117.777062 85.484687,96.3286476 87.2701552,74.8896322 C88.1693626,64.0878837 96.7024174,54.8310883 107.046834,52.7539194 C117.546611,50.6462038 128.357108,55.6206005 133.80414,65.587017 C140.171564,77.238196 142.938628,90.0184245 144.667601,103.055949 C144.892403,104.747761 145.127798,106.439573 145.530322,109.399069 C148.033744,107.018784 149.607357,105.584268 151.112706,104.082785 Z" id="Fill-1" fill="#000000"></path>
+        <rect id="矩形" fill="#000000" x="154" y="0" width="19" height="36" rx="9"></rect>
+        <path d="M123.077967,7.43459974 L123.860245,7.03887616 C128.2773,4.80446126 133.669673,6.55651248 135.929845,10.9604444 L143.86657,26.4251086 C146.136108,30.8472904 144.391045,36.272001 139.968863,38.5415388 C139.953276,38.5495382 139.937666,38.557492 139.922033,38.5654003 L139.139755,38.9611238 C134.7227,41.1955387 129.330327,39.4434875 127.070155,35.0395556 L119.13343,19.5748914 C116.863892,15.1527096 118.608955,9.72799898 123.031137,7.45846116 C123.046724,7.45046179 123.062334,7.44250795 123.077967,7.43459974 Z" id="矩形备份" fill="#000000"></path>
+        <path d="M203.922033,7.43459974 L203.139755,7.03887616 C198.7227,4.80446126 193.330327,6.55651248 191.070155,10.9604444 L183.13343,26.4251086 C180.863892,30.8472904 182.608955,36.272001 187.031137,38.5415388 C187.046724,38.5495382 187.062334,38.557492 187.077967,38.5654003 L187.860245,38.9611238 C192.2773,41.1955387 197.669673,39.4434875 199.929845,35.0395556 L207.86657,19.5748914 C210.136108,15.1527096 208.391045,9.72799898 203.968863,7.45846116 C203.953276,7.45046179 203.937666,7.44250795 203.922033,7.43459974 Z" id="矩形备份" fill="#000000"></path>
+        <circle id="椭圆形" fill="#B014BD" cx="37" cy="127" r="10"></circle>
+        <circle id="椭圆形备份-2" fill="#357CFF" cx="37" cy="156" r="10"></circle>
+        <circle id="椭圆形备份" fill="#B014BD" cx="308" cy="127" r="10"></circle>
+        <circle id="椭圆形备份-3" fill="#357CFF" cx="308" cy="159" r="10"></circle>
+    </g>
+</g>
+</svg>`
+
 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>`

web/js/common/utils.js

@@ -20,6 +20,13 @@ export function addCss(href, base=true) {
     document.head.appendChild(link);
 }
 
+export function addMeta(name, content) {
+    const meta = document.createElement("meta");
+    meta.setAttribute("name", name);
+    meta.setAttribute('content', content);
+    document.head.appendChild(meta);
+}
+
 export function deepEqual(obj1, obj2) {
   if (typeof obj1 !== typeof obj2) {
     return false

web/js/easy/easy.js

@@ -1,7 +1,7 @@
 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 {deepEqual, addCss, addMeta, isLocalNetwork} from "../common/utils.js";
+import {logoIcon, 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";
@@ -172,7 +172,7 @@ function createGroupMap(){
             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.style = isGroupMute ? `background-color:var(--error-text);color:var(--input-text);` + defaultStyle : (isGroupShow ? `background-color:var(--theme-color);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
@@ -384,11 +384,20 @@ class GuideDialog {
     }
 }
 
-const getEnableToolBar = _ => app.ui.settings.getSettingValue(toolBarId, true)
-
+// toolbar
 const toolBarId = "Comfy.EasyUse.toolBar"
+const getEnableToolBar = _ => app.ui.settings.getSettingValue(toolBarId, true)
+const getNewMenuPosition = _ => {
+    try{
+        return app.ui.settings.getSettingValue('Comfy.UseNewMenu', 'Disabled')
+    }catch (e){
+        return 'Disabled'
+    }
+}
 
-let enableToolBar = getEnableToolBar()
+let note = null
+let toolbar = null
+let enableToolBar = getEnableToolBar() && getNewMenuPosition() == 'Disabled'
 let disableRenderInfo = localStorage['Comfy.Settings.Comfy.EasyUse.disableRenderInfo'] ? true : false
 export function addToolBar(app) {
 	app.ui.settings.addSetting({
@@ -404,15 +413,50 @@ export function addToolBar(app) {
 		},
 	});
 }
-
-let note = null
-let toolbar = null
 function showToolBar(){
-    toolbar.style.display = 'flex'
+    if(toolbar) toolbar.style.display = 'flex'
 }
 function hideToolBar(){
-    toolbar.style.display = 'none'
+    if(toolbar) toolbar.style.display = 'none'
 }
+let monitor = null
+function setCrystoolsUI(position){
+    const crystools = document.getElementById('crystools-root')?.children || null
+    if(crystools?.length>0){
+        if(!monitor){
+           for (let i = 0; i < crystools.length; i++) {
+                if (crystools[i].id === 'crystools-monitor-container') {
+                    monitor = crystools[i];
+                    break;
+                }
+           }
+        }
+        if(monitor){
+            if(position == 'Disabled'){
+                let replace = true
+                for (let i = 0; i < crystools.length; i++) {
+                    if (crystools[i].id === 'crystools-monitor-container') {
+                        replace = false
+                        break;
+                    }
+                }
+                document.getElementById('crystools-root').appendChild(monitor)
+            }
+            else {
+                let monitor_div = document.getElementById('comfyui-menu-monitor')
+                if(!monitor_div) app.menu.settingsGroup.element.before($el('div',{id:'comfyui-menu-monitor'},monitor))
+                else monitor_div.appendChild(monitor)
+            }
+        }
+    }
+}
+const changeNewMenuPosition = app.ui.settings.settingsLookup?.['Comfy.UseNewMenu']
+if(changeNewMenuPosition) changeNewMenuPosition.onChange = v => {
+    v == 'Disabled' ? showToolBar() : hideToolBar()
+    setCrystoolsUI(v)
+}
+
+
 
 app.registerExtension({
     name: "comfy.easyUse",
@@ -516,11 +560,44 @@ app.registerExtension({
                     note = null
                 }
             }
-            return loadGraphDataEvent.apply(this, [...arguments])
+            return await loadGraphDataEvent.apply(this, [...arguments])
         }
 
         addToolBar(app)
     },
+    async setup() {
+        // New style menu button
+        if(app.menu?.actionsGroup){
+            const groupMap = new (await import('../../../../scripts/ui/components/button.js')).ComfyButton({
+                icon:'list-box',
+                action:()=> createGroupMap(),
+                tooltip: "EasyUse Group Map",
+                // content: "EasyUse Group Map",
+                classList: "comfyui-button comfyui-menu-mobile-collapse"
+            });
+            app.menu.actionsGroup.element.after(groupMap.element);
+            const position = getNewMenuPosition()
+            setCrystoolsUI(position)
+            if(position == 'Disabled') showToolBar()
+            else hideToolBar()
+            // const easyNewMenu = $el('div.easyuse-new-menu',[
+            //    $el('div.easyuse-new-menu-intro',[
+            //      $el('div.easyuse-new-menu-logo',{innerHTML:logoIcon}),
+            //      $el('div.easyuse-new-menu-title',[
+            //          $el('div.title',{textContent:'ComfyUI-Easy-Use'}),
+            //          $el('div.desc',{textContent:'Version:'})
+            //      ])
+            //    ])
+            // ])
+            // app.menu?.actionsGroup.element.after(new (await import('../../../../scripts/ui/components/splitButton.js')).ComfySplitButton({
+            //     primary: groupMap,
+            //     mode:'click',
+            //     position:'absolute',
+            //     horizontal: 'right'
+            // },easyNewMenu).element);
+        }
+
+    },
     beforeRegisterNodeDef(nodeType, nodeData, app) {
         if (nodeData.name.startsWith("easy")) {
             const origOnConfigure = nodeType.prototype.onConfigure;

web/js/easy/easyContextMenu.js

@@ -109,19 +109,60 @@ app.registerExtension({
                     }
                 }
                 const newValues = [];
+                const add_sub_folder = (folder, folderName) => {
+                    let subs = []
+                    let less = []
+                    const b = folder.map(name=> {
+                        const _folders = {};
+                        const splitBy = name.indexOf('/') > -1 ? '/' : '\\';
+                        const valueSplit = name.split(splitBy);
+                        if(valueSplit.length > 1){
+                            const key = valueSplit.shift();
+                            _folders[key] = _folders[key] || [];
+                            _folders[key].push(valueSplit.join(splitBy));
+                        }
+                        const foldersCount = Object.values(folders).length;
+                        if(foldersCount > 0){
+                            let key = Object.keys(_folders)[0]
+                            if(key && _folders[key]) subs.push({key, value:_folders[key][0]})
+                            else{
+                                less.push(addContent(name,key))
+                            }
+                        }
+                        return addContent(name,folderName)
+                    })
+                    if(subs.length>0){
+                      let subs_obj = {}
+                      subs.forEach(item => {
+                        subs_obj[item.key] = subs_obj[item.key] || []
+                        subs_obj[item.key].push(item.value)
+                      })
+                      return [...Object.entries(subs_obj).map(f => {
+                          return {
+                              content: f[0],
+                              has_submenu: true,
+                              callback: () => {},
+                              submenu: {
+                                  options: add_sub_folder(f[1], f[0]),
+                              }
+                          }
+                      }),...less]
+                    }
+                    else return b
+                }
+
                 for(const [folderName,folder] of Object.entries(folders)){
                     newValues.push({
                         content:folderName,
                         has_submenu:true,
                         callback:() => {},
                         submenu:{
-                            options:folder.map(f => addContent(f,folderName)),
+                            options:add_sub_folder(folder,folderName),
                         }
                     });
                 }
                 newValues.push(...folderless.map(f => addContent(f, '')));
-                if(specialOps.length > 0)
-                    newValues.push(...specialOps.map(f => addContent(f, '')));
+                if(specialOps.length > 0) newValues.push(...specialOps.map(f => addContent(f, '')));
                 return existingContextMenu.call(this,newValues,options);
             }
             return existingContextMenu.apply(this,[...arguments]);

web/js/easy/easyDynamicWidgets.js

@@ -7,8 +7,7 @@ import { $t } from '../common/i18n.js';
 import { findWidgetByName, toggleWidget, updateNodeHeight} from "../common/utils.js";
 
 const seedNodes = ["easy seed", "easy latentNoisy", "easy wildcards", "easy preSampling", "easy preSamplingAdvanced", "easy preSamplingNoiseIn", "easy preSamplingSdTurbo", "easy preSamplingCascade", "easy preSamplingDynamicCFG", "easy preSamplingLayerDiffusion", "easy fullkSampler", "easy fullCascadeKSampler"]
-const loaderNodes = ["easy fullLoader", "easy a1111Loader", "easy comfyLoader"]
-
+const loaderNodes = ["easy fullLoader", "easy a1111Loader", "easy comfyLoader", "easy hunyuanDiTLoader", "easy pixArtLoader"]
 
 function widgetLogic(node, widget) {
 	if (widget.name === 'lora_name') {
@@ -77,14 +76,30 @@ function widgetLogic(node, widget) {
 		}
 	}
 	if (widget.name === 'add_noise') {
+		let control_before_widget = findWidgetByName(node, 'control_before_generate')
+		let control_after_widget = findWidgetByName(node, 'control_after_generate')
 		if (widget.value === "disable") {
 			toggleWidget(node, findWidgetByName(node, 'seed'))
-			toggleWidget(node, findWidgetByName(node, 'control_before_generate'))
-			toggleWidget(node, findWidgetByName(node, 'control_after_generate'))
+			if(control_before_widget){
+				control_before_widget.last_value = control_before_widget.value
+				control_before_widget.value = 'fixed'
+				toggleWidget(node, control_before_widget)
+			}
+			if(control_after_widget){
+				control_after_widget.last_value = control_after_widget.value
+				control_after_widget.value = 'fixed'
+				toggleWidget(node, control_after_widget)
+			}
 		} else {
 			toggleWidget(node, findWidgetByName(node, 'seed'), true)
-			toggleWidget(node, findWidgetByName(node, 'control_before_generate'), true)
-			toggleWidget(node, findWidgetByName(node, 'control_after_generate'), true)
+			if(control_before_widget){
+				if(control_before_widget?.last_value) control_before_widget.value = control_before_widget.last_value
+				toggleWidget(node, control_before_widget, true)
+			}
+			if(control_after_widget) {
+				if(control_after_widget?.last_value) control_after_widget.value = control_after_widget.last_value
+				toggleWidget(node, findWidgetByName(node, control_after_widget, true))
+			}
 		}
 		updateNodeHeight(node)
 	}
@@ -110,11 +125,34 @@ function widgetLogic(node, widget) {
 		}
 		updateNodeHeight(node)
 	}
+	if (widget.name === 'num_controlnet') {
+		let number_to_show = widget.value + 1
+		for (let i = 0; i < number_to_show; i++) {
+			toggleWidget(node, findWidgetByName(node, 'controlnet_'+i), true)
+			toggleWidget(node, findWidgetByName(node, 'controlnet_'+i+'_strength'), true)
+			toggleWidget(node, findWidgetByName(node, 'scale_soft_weight_'+i),true)
+			if (findWidgetByName(node, 'mode').value === "simple") {
+				toggleWidget(node, findWidgetByName(node, 'start_percent_'+i))
+				toggleWidget(node, findWidgetByName(node, 'end_percent_'+i))
+			} else {
+				toggleWidget(node, findWidgetByName(node, 'start_percent_'+i),true)
+				toggleWidget(node, findWidgetByName(node, 'end_percent_'+i), true)
+			}
+		}
+		for (let i = number_to_show; i < 10; i++) {
+			toggleWidget(node, findWidgetByName(node, 'controlnet_'+i))
+			toggleWidget(node, findWidgetByName(node, 'controlnet_'+i+'_strength'))
+			toggleWidget(node, findWidgetByName(node, 'start_percent_'+i))
+			toggleWidget(node, findWidgetByName(node, 'end_percent_'+i))
+			toggleWidget(node, findWidgetByName(node, 'scale_soft_weight_'+i))
+		}
+		updateNodeHeight(node)
+	}
+
 	if (widget.name === 'mode') {
 		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++) {
+				for (let i = 0; i < (findWidgetByName(node, 'num_loras').value + 1); i++) {
 					if (widget.value === "simple") {
 						toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'), true)
 						toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'))
@@ -124,6 +162,19 @@ function widgetLogic(node, widget) {
 						toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'), true)
 						toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'), true)}
 				}
+				updateNodeHeight(node)
+				break
+			case 'easy controlnetStack':
+				for (let i = 0; i < (findWidgetByName(node, 'num_controlnet').value + 1); i++) {
+					if (widget.value === "simple") {
+						toggleWidget(node, findWidgetByName(node, 'start_percent_'+i))
+						toggleWidget(node, findWidgetByName(node, 'end_percent_'+i))
+					} else {
+						toggleWidget(node, findWidgetByName(node, 'start_percent_' + i), true)
+						toggleWidget(node, findWidgetByName(node, 'end_percent_' + i), true)
+					}
+				}
+				updateNodeHeight(node)
 				break
 			case 'easy icLightApply':
 				if (widget.value === "Foreground") {
@@ -135,20 +186,31 @@ function widgetLogic(node, widget) {
 					toggleWidget(node, findWidgetByName(node, 'source'), true)
 					toggleWidget(node, findWidgetByName(node, 'remove_bg'))
 				}
+				updateNodeHeight(node)
 				break
 		}
-		updateNodeHeight(node)
 	}
 
 	if (widget.name === 'resolution') {
-		if (widget.value === "自定义 x 自定义") {
+		if(widget.value === "自定义 x 自定义"){
+			widget.value = 'width x height (custom)'
+		}
+		if (widget.value === "自定义 x 自定义" || widget.value === 'width x height (custom)') {
+			toggleWidget(node, findWidgetByName(node, 'empty_latent_width'), true)
+			toggleWidget(node, findWidgetByName(node, 'empty_latent_height'), true)
+		} else {
+			toggleWidget(node, findWidgetByName(node, 'empty_latent_width'), false)
+			toggleWidget(node, findWidgetByName(node, 'empty_latent_height'), false)
+		}
+	}
+	if (widget.name === 'ratio') {
+		if (widget.value === "custom") {
 			toggleWidget(node, findWidgetByName(node, 'empty_latent_width'), true)
 			toggleWidget(node, findWidgetByName(node, 'empty_latent_height'), true)
 		} else {
 			toggleWidget(node, findWidgetByName(node, 'empty_latent_width'), false)
 			toggleWidget(node, findWidgetByName(node, 'empty_latent_height'), false)
 		}
-		updateNodeHeight(node)
 	}
 	if (widget.name === 'downscale_mode') {
 		const widget_names = ['block_number', 'downscale_factor', 'start_percent', 'end_percent', 'downscale_after_skip', 'downscale_method', 'upscale_method']
@@ -216,13 +278,16 @@ function widgetLogic(node, widget) {
 		const faceid_presets = [
             'FACEID',
             'FACEID PLUS - SD1.5 only',
+			'FACEID PLUS KOLORS',
             'FACEID PLUS V2',
-			'FACEID PORTRAIT (style transfer)'
+			'FACEID PORTRAIT (style transfer)',
+			'FACEID PORTRAIT UNNORM - SDXL only (strong)'
         ]
 		if(normol_presets.includes(widget.value)){
 			toggleWidget(node, findWidgetByName(node, 'lora_strength'))
 			toggleWidget(node, findWidgetByName(node, 'provider'))
 			toggleWidget(node, findWidgetByName(node, 'weight_faceidv2'))
+			toggleWidget(node, findWidgetByName(node, 'weight_kolors'))
 			toggleWidget(node, findWidgetByName(node, 'use_tiled'), true)
 			let use_tiled = findWidgetByName(node, 'use_tiled')
 			if(use_tiled && use_tiled.value){
@@ -233,12 +298,9 @@ function widgetLogic(node, widget) {
 
 		}
 		else if(faceid_presets.includes(widget.value)){
-			if(widget.value == 'FACEID PLUS V2'){
-				toggleWidget(node, findWidgetByName(node, 'weight_faceidv2'), true)
-			}else{
-				toggleWidget(node, findWidgetByName(node, 'weight_faceidv2'))
-			}
-			if(widget.value == 'FACEID PORTRAIT (style transfer)'){
+			toggleWidget(node, findWidgetByName(node, 'weight_faceidv2'), ['FACEID PLUS V2','FACEID PLUS KOLORS'].includes(widget.value) ? true : false);
+			toggleWidget(node, findWidgetByName(node, 'weight_kolors'), ['FACEID PLUS KOLORS'].includes(widget.value) ? true : false);
+			if(['FACEID PLUS KOLORS','FACEID PORTRAIT (style transfer)','FACEID PORTRAIT UNNORM - SDXL only (strong)'].includes(widget.value)){
 				toggleWidget(node, findWidgetByName(node, 'lora_strength'), false)
 			}
 			else{
@@ -298,6 +360,7 @@ function widgetLogic(node, widget) {
 			toggleWidget(node, findWidgetByName(node, 'beta_d'))
 			toggleWidget(node, findWidgetByName(node, 'beta_min'))
 			toggleWidget(node, findWidgetByName(node, 'eps_s'))
+			toggleWidget(node, findWidgetByName(node, 'coeff'))
 			if(widget.value != 'exponentialADV'){
 				toggleWidget(node, findWidgetByName(node, 'rho'), true)
 			}else{
@@ -311,7 +374,9 @@ function widgetLogic(node, widget) {
 			toggleWidget(node, findWidgetByName(node, 'beta_d'),true)
 			toggleWidget(node, findWidgetByName(node, 'beta_min'),true)
 			toggleWidget(node, findWidgetByName(node, 'eps_s'),true)
-		}else{
+			toggleWidget(node, findWidgetByName(node, 'coeff'))
+		}
+		else{
 			toggleWidget(node, findWidgetByName(node, 'denoise'),true)
 			toggleWidget(node, findWidgetByName(node, 'sigma_max'))
 			toggleWidget(node, findWidgetByName(node, 'sigma_min'))
@@ -319,9 +384,73 @@ function widgetLogic(node, widget) {
 			toggleWidget(node, findWidgetByName(node, 'beta_min'))
 			toggleWidget(node, findWidgetByName(node, 'eps_s'))
 			toggleWidget(node, findWidgetByName(node, 'rho'))
+			if(widget.value == 'gits') 	toggleWidget(node, findWidgetByName(node, 'coeff'), true)
+			else toggleWidget(node, findWidgetByName(node, 'coeff'))
 		}
 		updateNodeHeight(node)
 	}
+
+	if(widget.name === 'inpaint_mode'){
+		switch (widget.value){
+			case 'normal':
+			case 'fooocus_inpaint':
+				toggleWidget(node, findWidgetByName(node, 'dtype'))
+				toggleWidget(node, findWidgetByName(node, 'fitting'))
+				toggleWidget(node, findWidgetByName(node, 'function'))
+				toggleWidget(node, findWidgetByName(node, 'scale'))
+				toggleWidget(node, findWidgetByName(node, 'start_at'))
+				toggleWidget(node, findWidgetByName(node, 'end_at'))
+				break
+			case 'brushnet_random':
+			case 'brushnet_segmentation':
+				toggleWidget(node, findWidgetByName(node, 'dtype'), true)
+				toggleWidget(node, findWidgetByName(node, 'fitting'))
+				toggleWidget(node, findWidgetByName(node, 'function'))
+				toggleWidget(node, findWidgetByName(node, 'scale'), true)
+				toggleWidget(node, findWidgetByName(node, 'start_at'), true)
+				toggleWidget(node, findWidgetByName(node, 'end_at'), true)
+				break
+			case 'powerpaint':
+				toggleWidget(node, findWidgetByName(node, 'dtype'), true)
+				toggleWidget(node, findWidgetByName(node, 'fitting'),true)
+				toggleWidget(node, findWidgetByName(node, 'function'),true)
+				toggleWidget(node, findWidgetByName(node, 'scale'), true)
+				toggleWidget(node, findWidgetByName(node, 'start_at'), true)
+				toggleWidget(node, findWidgetByName(node, 'end_at'), true)
+				break
+		}
+		updateNodeHeight(node)
+	}
+
+	if(widget.name == 't5_type'){
+		switch (widget.value){
+			case 'sd3':
+				toggleWidget(node, findWidgetByName(node, 'clip_name'), true)
+				toggleWidget(node, findWidgetByName(node, 'padding'), true)
+				toggleWidget(node, findWidgetByName(node, 't5_name'))
+				toggleWidget(node, findWidgetByName(node, 'device'))
+				toggleWidget(node, findWidgetByName(node, 'dtype'))
+				break
+			case 't5v11':
+				toggleWidget(node, findWidgetByName(node, 'clip_name'))
+				toggleWidget(node, findWidgetByName(node, 'padding'))
+				toggleWidget(node, findWidgetByName(node, 't5_name'),true)
+				toggleWidget(node, findWidgetByName(node, 'device'),true)
+				toggleWidget(node, findWidgetByName(node, 'dtype'),true)
+		}
+		updateNodeHeight(node)
+	}
+
+	if(widget.name == 'rem_mode'){
+		switch (widget.value){
+			case 'Inspyrenet':
+				toggleWidget(node, findWidgetByName(node, 'torchscript_jit'), true)
+				break
+			default:
+				toggleWidget(node, findWidgetByName(node, 'torchscript_jit'), false)
+				break
+		}
+	}
 }
 
 function widgetLogic2(node, widget) {
@@ -572,7 +701,11 @@ app.registerExtension({
 			case "easy cascadeLoader":
 			case "easy svdLoader":
 			case "easy dynamiCrafterLoader":
+			case "easy hunyuanDiTLoader":
+			case "easy pixArtLoader":
+			case "easy kolorsLoader":
 			case "easy loraStack":
+			case "easy controlnetStack":
 			case "easy latentNoisy":
 			case "easy preSampling":
 			case "easy preSamplingAdvanced":
@@ -594,6 +727,7 @@ app.registerExtension({
 			case "easy detailerFix":
 			case "easy imageRemBg":
 			case "easy imageColorMatch":
+			case "easy imageDetailTransfer":
 			case "easy loadImageBase64":
 			case "easy XYInputs: Steps":
 			case "easy XYInputs: Sampler/Scheduler":
@@ -608,7 +742,9 @@ app.registerExtension({
 			case 'easy icLightApply':
 			case 'easy ipadapterApply':
 			case 'easy ipadapterApplyADV':
+			case 'easy ipadapterApplyFaceIDKolors':
 			case 'easy ipadapterApplyEncoder':
+			case 'easy applyInpaint':
 				getSetters(node)
 				break
 			case "easy wildcards":
@@ -947,24 +1083,15 @@ app.registerExtension({
 			const onNodeCreated = nodeType.prototype.onNodeCreated;
 			nodeType.prototype.onNodeCreated = async function () {
 				onNodeCreated ? onNodeCreated.apply(this, []) : undefined;
-				// const values = ["randomize", "fixed", "increment", "decrement"]
-				// const seed_widget = this.widgets.find(w => w.name == 'seed_num')
-				// const seed_control = this.addWidget("combo", "control_before_generate", values[0], () => {
-				// }, {
-				// 	values,
-				// 	serialize: false
-				// })
-				// seed_widget.linkedWidgets = [seed_control]
 				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(nodeData.name == 'easy seed'){
-					this.addWidget("button", "🎲 Manual Random Seed", null, _=>{
-						if(seed_control.value != 'fixed'){
-							seed_control.value = 'fixed'
-						}
+					const randomSeedButton = this.addWidget("button", "🎲 Manual Random Seed", null, _=>{
+						if(seed_control.value != 'fixed') seed_control.value = 'fixed'
 						seed_widget.value = Math.floor(Math.random() * 1125899906842624)
 						app.queuePrompt(0, 1)
-					})
+					},{ serialize:false})
+					seed_widget.linkedWidgets = [randomSeedButton, seed_control];
 				}
 			}
 			const onAdded = nodeType.prototype.onAdded;
@@ -1059,22 +1186,15 @@ app.registerExtension({
 
 		if(nodeData.name == 'easy convertAnything'){
 			const onNodeCreated = nodeType.prototype.onNodeCreated;
-			const changeType = async function (type) {
-				const body = new FormData();
-				body.append("type", type);
-				const response = await api.fetchApi("/easyuse/convert", { method:'POST',body});
-			}
 			nodeType.prototype.onNodeCreated = async function () {
 				onNodeCreated ? onNodeCreated.apply(this, []) : undefined;
 				setTimeout(_=>{
 					const type_control = this.widgets[this.widgets.findIndex((w) => w.name === "output_type")]
 					let _this = this
-					changeType(type_control.value)
 					type_control.callback = async() => {
 						_this.outputs[0].type = (type_control.value).toUpperCase()
 						_this.outputs[0].name = type_control.value
 						_this.outputs[0].label = type_control.value
-						changeType(type_control.value)
 					}
 				},300)
 
@@ -1119,10 +1239,10 @@ const getSetWidgets = ['rescale_after_model', 'rescale',
 						'refiner_lora1_name', 'refiner_lora2_name', 'upscale_method', 
 						'image_output', 'add_noise', 'info', 'sampler_name',
 						'ckpt_B_name', 'ckpt_C_name', 'save_model', 'refiner_ckpt_name',
-						'num_loras', 'mode', 'toggle', 'resolution', 'target_parameter',
+						'num_loras', 'num_controlnet', 'mode', 'toggle', 'resolution', 'ratio', 'target_parameter',
 	'input_count', 'replace_count', 'downscale_mode', 'range_mode','text_combine_mode', 'input_mode',
 	'lora_count','ckpt_count', 'conditioning_mode', 'preset', 'use_tiled', 'use_batch', 'num_embeds',
-	"easing_mode", "guider", "scheduler"
+	"easing_mode", "guider", "scheduler", "inpaint_mode", 't5_type', 'rem_mode'
 ]
 
 function getSetters(node) {

web/js/easy/easyExtraMenu.js

@@ -2,13 +2,14 @@ import {app} from "../../../../scripts/app.js";
 import {$t} from '../common/i18n.js'
 import {CheckpointInfoDialog, LoraInfoDialog} from "../common/model.js";
 
-const loaders = ['easy fullLoader', 'easy a1111Loader', 'easy comfyLoader']
+const loaders = ['easy fullLoader', 'easy a1111Loader', 'easy comfyLoader', 'easy kolorsLoader', 'easy hunyuanDiTLoader', 'easy pixArtLoader']
 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', 'easy ipadapterApplyFromParams']
+const controlnet = ['easy controlnetLoader', 'easy controlnetLoaderADV', 'easy controlnetLoader++', 'easy instantIDApply', 'easy instantIDApplyADV']
+const ipadapter = ['easy ipadapterApply', 'easy ipadapterApplyADV', 'easy ipadapterApplyFaceIDKolors', 'easy ipadapterStyleComposition', 'easy ipadapterApplyFromParams', 'easy pulIDApply', 'easy pulIDApplyADV']
 const positive_prompt = ['easy positive', 'easy wildcards']
 const imageNode = ['easy loadImageBase64', 'LoadImage', 'LoadImageMask']
+const inpaint = ['easy applyBrushNet', 'easy applyPowerPaint', 'easy applyInpaint']
 const widgetMapping = {
     "positive_prompt":{
         "text": "positive",
@@ -59,11 +60,21 @@ const widgetMapping = {
         "end_at": "end_at",
         "cache_mode": "cache_mode",
         "use_tiled": "use_tiled",
+        "insightface": "insightface",
+        "pulid_file": "pulid_file"
     },
     "load_image":{
         "image":"image",
         "base64_data":"base64_data",
         "channel": "channel"
+    },
+    "inpaint":{
+        "dtype": "dtype",
+        "fitting": "fitting",
+        "function": "function",
+        "scale": "scale",
+        "start_at": "start_at",
+        "end_at": "end_at"
     }
 }
 const inputMapping = {
@@ -99,6 +110,11 @@ const inputMapping = {
         "image_style": "image",
         "attn_mask":"attn_mask",
         "optional_ipadapter":"optional_ipadapter"
+    },
+    "inpaint":{
+        "pipe": "pipe",
+        "image": "image",
+        "mask": "mask"
     }
 };
 
@@ -138,6 +154,9 @@ const outputMapping = {
         "masks":"masks",
         "ipadapter":"ipadapter"
     },
+    "inpaint":{
+        "pipe": "pipe",
+    }
 };
 
 // 替换节点
@@ -421,12 +440,14 @@ const reloadNode = function (node) {
 
     function handleLinks() {
         // re-convert inputs
-        for (let w of oldNode.widgets) {
-            if (w.type === 'converted-widget') {
-                const WidgetToConvert = newNode.widgets.find((nw) => nw.name === w.name);
-                for (let i of oldNode.inputs) {
-                    if (i.name === w.name) {
-                        convertToInput(newNode, WidgetToConvert, i.widget);
+        if(oldNode.widgets) {
+            for (let w of oldNode.widgets) {
+                if (w.type === 'converted-widget') {
+                    const WidgetToConvert = newNode.widgets.find((nw) => nw.name === w.name);
+                    for (let i of oldNode.inputs) {
+                        if (i.name === w.name) {
+                            convertToInput(newNode, WidgetToConvert, i.widget);
+                        }
                     }
                 }
             }
@@ -444,7 +465,7 @@ const reloadNode = function (node) {
 
     // fix widget values
     let values = oldNode.widgets_values;
-    if (!values) {
+    if (!values && newNode.widgets?.length>0) {
         newNode.widgets.forEach((newWidget, index) => {
             const oldWidget = oldNode.widgets[index];
             if (newWidget.name === oldWidget.name && newWidget.type === oldWidget.type) {
@@ -455,7 +476,7 @@ const reloadNode = function (node) {
         return;
     }
     let pass = false
-    const isIterateForwards = values.length <= newNode.widgets.length;
+    const isIterateForwards = values?.length <= newNode.widgets?.length;
     let vi = isIterateForwards ? 0 : values.length - 1;
     function evalWidgetValues(testValue, newWidg) {
         if (testValue === true || testValue === false) {
@@ -487,15 +508,15 @@ const reloadNode = function (node) {
             }
             vi++
             if (!isIterateForwards) {
-                vi = values.length - (newNode.widgets.length - 1 - wi);
+                vi = values.length - (newNode.widgets?.length - 1 - wi);
             }
         }
     };
-    if (isIterateForwards) {
+    if (isIterateForwards && newNode.widgets?.length>0) {
         for (let wi = 0; wi < newNode.widgets.length; wi++) {
             updateValue(wi);
         }
-    } else {
+    } else if(newNode.widgets?.length>0){
         for (let wi = newNode.widgets.length - 1; wi >= 0; wi--) {
             updateValue(wi);
         }
@@ -557,12 +578,16 @@ app.registerExtension({
         }
         // Swap IPAdapater
         if (ipadapter.includes(nodeData.name)) {
-            addMenu("↪️ Swap EasyIPAdapater", 'ipadapter', ipadapter, nodeType)
+            addMenu("↪️ Swap EasyAdapater", 'ipadapter', ipadapter, nodeType)
         }
         // Swap Image
         if (imageNode.includes(nodeData.name)) {
             addMenu("↪️ Swap LoadImage", 'load_image', imageNode, nodeType)
         }
+        // Swap inpaint
+        if (inpaint.includes(nodeData.name)) {
+            addMenu("↪️ Swap InpaintNode", 'inpaint', inpaint, nodeType)
+        }
     }
 });
 

web/js/easy/easyInterface.js

@@ -5,8 +5,8 @@ import {addPreconnect, addCss} from "../common/utils.js";
 
 const locale = localStorage['AGL.Locale'] || localStorage['Comfy.Settings.AGL.Locale'] || 'en-US'
 
-const customThemeColor = "#3f3eed"
-const customThemeColorLight = "#008ecb"
+const customThemeColor = "#236692"
+const customThemeColorLight = "#3485bb"
 // 增加Slot颜色
 const customPipeLineLink = "#7737AA"
 const customPipeLineSDXLLink = "#7737AA"
@@ -28,9 +28,9 @@ localStorage.setItem('Comfy.Settings.easyUse.customLinkColors', JSON.stringify(c
 
 // 增加自定义主题
 const ui = {
-  "version": 101,
+  "version": 102,
   "id": "obsidian",
-  "name": "黑曜石",
+  "name": "Obsidian",
   "colors": {
       "node_slot": {
           "CLIP": "#FFD500",
@@ -105,7 +105,7 @@ try{
         custom_theme.obsidian = ui
         let ui2 = JSON.parse(JSON.stringify(ui))
         ui2.id = 'obsidian_dark'
-        ui2.name = '黑曜石-深'
+        ui2.name = 'Obsidian Dark'
         ui2.colors.litegraph_base.BACKGROUND_IMAGE = dark_bg
         ui2.colors.litegraph_base.CLEAR_BACKGROUND_COLOR = '#000'
         custom_theme[ui2.id] = ui2
@@ -113,13 +113,13 @@ try{
     }
     let theme_name = localStorage.getItem('Comfy.Settings.Comfy.ColorPalette')
     control_mode = localStorage.getItem('Comfy.Settings.Comfy.WidgetControlMode')
-    if(control_mode) {
-        control_mode = JSON.parse(control_mode)
-        if(control_mode == 'before'){
-            localStorage['Comfy.Settings.AE.mouseover'] = false
-            localStorage['Comfy.Settings.AE.highlight'] = false
-        }
-    }
+    // if(control_mode) {
+    //     control_mode = JSON.parse(control_mode)
+    //     if(control_mode == 'before'){
+    //         localStorage['Comfy.Settings.AE.mouseover'] = false
+    //         localStorage['Comfy.Settings.AE.highlight'] = false
+    //     }
+    // }
     // 兼容 ComfyUI Revision: 1887 [235727fe] 以上版本
     if(api.storeSettings){
         const _settings = await api.getSettings()
@@ -128,11 +128,6 @@ try{
         if(!control_mode && _settings['Comfy.WidgetControlMode']) {
             control_mode = _settings['Comfy.WidgetControlMode']
         }else if(!control_mode) control_mode = 'after'
-        if(control_mode == 'before'){
-            if(!settings) settings = {}
-            settings["AE.mouseover"] = false
-            settings["AE.highlight"] = false
-        }
         // 主题设置
         if(!theme_name && _settings['Comfy.ColorPalette']) {
             theme_name = `"${_settings['Comfy.ColorPalette']}"`
@@ -559,7 +554,7 @@ try{
                         ctx.fill();
                         if(show_text && !w.disabled)
                             ctx.stroke();
-                        ctx.fillStyle = w.value ? customThemeColorLight : "#333";
+                        ctx.fillStyle = w.value ? customThemeColor : "#333";
                         ctx.beginPath();
                         ctx.arc( widget_width - margin * 2, y + H * 0.5, H * 0.25, 0, Math.PI * 2 );
                         ctx.fill();
@@ -593,7 +588,7 @@ try{
                         var nvalue = (w.value - w.options.min) / range;
                         if(nvalue < 0.0) nvalue = 0.0;
                         if(nvalue > 1.0) nvalue = 1.0;
-                        ctx.fillStyle = w.options.hasOwnProperty("slider_color") ? w.options.slider_color : (active_widget == w ? "#333" : customThemeColorLight);
+                        ctx.fillStyle = w.options.hasOwnProperty("slider_color") ? w.options.slider_color : (active_widget == w ? "#333" : customThemeColor);
                         ctx.beginPath();
                         ctx.roundRect(margin, y, nvalue * (widget_width - margin * 2), H, [H*0.25]);
                         ctx.fill();

web/js/easy/easySelector.js

@@ -182,13 +182,14 @@ app.registerExtension({
                                     let tags = styles_list_cache[styles_values]
                                     // 重新排序
                                     if(selector.value) tags = tags.sort((a,b)=> selector.value.includes(b.name) - selector.value.includes(a.name))
+                                    this.properties["values"] = []
                                     let list = getTagList(tags, value, language);
                                     selector.element.children[1].append(...list)
                                     selector.element.children[1].querySelectorAll(".easyuse-prompt-styles-tag").forEach(el => {
                                         if (this.properties["values"].includes(el.dataset.tag)) {
                                             el.classList.add("easyuse-prompt-styles-tag-selected");
                                         }
-                                        this.setSize([425, 500]);
+                                        if(this.size?.[0]<150 || this.size?.[1]<150) this.setSize([425, 500]);
                                     })
                                 }
                             })
@@ -284,7 +285,8 @@ app.registerExtension({
                             }
                         })
                     }
-                    this.setSize([425, 500]);
+                    if(this.size?.[0]<150 || this.size?.[1]<150) this.setSize([425, 500]);
+                    //
                 },100)
 
                 return onNodeCreated;

web/js/easy/easySliderControl.js

@@ -0,0 +1,173 @@
+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 { sleep } from "../common/utils.js";
+
+
+const calculatePercent = (value, min, max) => ((value-min)/(max-min)*100)
+
+const getLayerDefaultValue = (index) => {
+    switch (index){
+        case 3:
+            return 2.5
+        case 6:
+            return 1
+        default:
+            return 0
+    }
+}
+
+const addLayer = (_this, layer_total, arrays, sliders, i) => {
+    let scroll = $el('div.easyuse-slider-item-scroll')
+    let value = $el('div.easyuse-slider-item-input', {textContent: arrays[i]['value']})
+    let label = $el('div.easyuse-slider-item-label', {textContent: 'L'+i})
+    let girdTotal = (arrays[i]['max'] - arrays[i]['min']) / arrays[i]['step']
+    let area = $el('div.easyuse-slider-item-area', {style:{ height: calculatePercent(arrays[i]['default'],arrays[i]['min'],arrays[i]['max']) + '%'}})
+    let bar = $el('div.easyuse-slider-item-bar', {
+        style:{ top: (100-calculatePercent(arrays[i]['default'],arrays[i]['min'],arrays[i]['max'])) + '%'},
+        onmousedown: (e) => {
+            let event = e || window.event;
+            var y = event.clientY - bar.offsetTop;
+            document.onmousemove = (e) => {
+                let event = e || window.event;
+                let top = event.clientY - y;
+                if(top < 0){
+                    top = 0;
+                }
+                else if(top > scroll.offsetHeight - bar.offsetHeight){
+                    top = scroll.offsetHeight - bar.offsetHeight;
+                }
+                // top到最近的girdHeight值
+                let girlHeight = (scroll.offsetHeight - bar.offsetHeight)/ girdTotal
+                top = Math.round(top / girlHeight) * girlHeight;
+                bar.style.top = Math.floor(top/(scroll.offsetHeight - bar.offsetHeight)* 100) + '%';
+                area.style.height = Math.floor((scroll.offsetHeight - bar.offsetHeight - top)/(scroll.offsetHeight - bar.offsetHeight)* 100) + '%';
+                value.innerText = parseFloat(parseFloat(arrays[i]['max'] - (arrays[i]['max']-arrays[i]['min']) * (top/(scroll.offsetHeight - bar.offsetHeight))).toFixed(2))
+                arrays[i]['value'] = value.innerText
+                _this.properties['values'][i] = i+':'+value.innerText
+                window.getSelection ? window.getSelection().removeAllRanges() : document.selection.empty();
+            }
+        },
+        ondblclick:_=>{
+            bar.style.top = (100-calculatePercent(arrays[i]['default'],arrays[i]['min'],arrays[i]['max'])) + '%'
+            area.style.height = calculatePercent(arrays[i]['default'],arrays[i]['min'],arrays[i]['max']) + '%'
+            value.innerText = arrays[i]['default']
+            arrays[i]['value'] = arrays[i]['default']
+            _this.properties['values'][i] = i+':'+value.innerText
+        }
+    })
+    document.onmouseup = _=> document.onmousemove = null;
+
+    scroll.replaceChildren(bar,area)
+    let item_div = $el('div.easyuse-slider-item',[
+        value,
+        scroll,
+        label
+    ])
+    if(i == 3 ) layer_total == 12 ? item_div.classList.add('negative') : item_div.classList.remove('negative')
+    else if(i == 6) layer_total == 12 ?  item_div.classList.add('positive') : item_div.classList.remove('positive')
+    sliders.push(item_div)
+    return item_div
+}
+
+const setSliderValue = (_this, type, refresh=false, values_div, sliders_value) => {
+    let layer_total = type == 'sdxl' ? 12 : 16
+    let sliders = []
+    let arrays = Array.from({length: layer_total}, (v, i) => ({default: layer_total == 12 ? getLayerDefaultValue(i) : 0, min: -1, max: 3, step: 0.05, value:layer_total == 12 ? getLayerDefaultValue(i) : 0}))
+    _this.setProperty("values", Array.from({length: layer_total}, (v, i) => i+':'+arrays[i]['value']))
+    for (let i = 0; i < layer_total; i++) {
+        addLayer(_this, layer_total, arrays, sliders, i)
+    }
+    if(refresh) values_div.replaceChildren(...sliders)
+    else{
+        values_div = $el('div.easyuse-slider', sliders)
+        sliders_value = _this.addDOMWidget('values',"btn",values_div)
+    }
+
+    Object.defineProperty(sliders_value, 'value', {
+        set: function() {},
+        get: function() {
+            return _this.properties.values.join(',');
+        }
+    });
+    return {sliders, arrays, values_div, sliders_value}
+}
+
+
+app.registerExtension({
+    name: 'comfy.easyUse.sliderControl',
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if(nodeData.name == 'easy sliderControl'){
+            // 创建时
+            const onNodeCreated = nodeType.prototype.onNodeCreated;
+            nodeType.prototype.onNodeCreated = function() {
+                onNodeCreated && onNodeCreated.call(this);
+                const mode =  this.widgets[0];
+                const model_type = this.widgets[1];
+                let layer_total = model_type.value == 'sdxl' ? 12 : 16
+                let _this = this
+                let values_div = null
+                let sliders_value = null
+                mode.callback = async()=>{
+                    switch (mode.value) {
+                        case 'ipadapter layer weights':
+                            nodeData.output_name = ['layer_weights']
+                            _this.outputs[0]['name'] = 'layer_weights'
+                            _this.outputs[0]['label'] = 'layer_weights'
+                            break
+                    }
+                }
+
+                model_type.callback = async()=>{
+                    if(values_div) {
+                        let r2 = setSliderValue(_this, model_type.value, true, values_div, sliders_value)
+                        values_div = r2.values_div
+                        sliders_value = r2.sliders_value
+                    }
+                    _this.setSize(model_type.value == 'sdxl' ? [375,320] : [455,320])
+                }
+
+                let r1 =  setSliderValue(_this, model_type.value, false, values_div, sliders_value)
+                let sliders = r1.sliders
+                let arrays = r1.arrays
+                values_div = r1.values_div
+                sliders_value = r1.sliders_value
+                setTimeout(_=>{
+                    let values_widgets_index = this.widgets.findIndex((w) => w.name == 'values');
+                    if(values_widgets_index != -1){
+                        let old_values_widget = this.widgets[values_widgets_index];
+                        let old_value = old_values_widget.value.split(',')
+                        let layer_total = _this.widgets[1].value == 'sdxl' ? 12 : 16
+                        for (let i = 0; i < layer_total; i++) {
+                            let value = parseFloat(parseFloat(old_value[i].split(':')[1]).toFixed(2))
+                            let item_div = sliders[i] || null
+                             // 存在层即修改
+                            if(arrays[i]){
+                               arrays[i]['value'] = value
+                                _this.properties['values'][i] = old_value[i]
+                            }else{
+                                arrays.push({default: layer_total == 12 ? getLayerDefaultValue(i) : 0, min: -1, max: 3, step: 0.05, value:layer_total == 12 ? getLayerDefaultValue(i) : 0})
+                                _this.properties['values'].push(i+':'+arrays[i]['value'])
+                                // 添加缺失层
+                                item_div = addLayer(_this, layer_total, arrays, sliders, i)
+                                values_div.appendChild(item_div)
+                            }
+                            // todo: 修改bar位置等
+                            let input = item_div.getElementsByClassName('easyuse-slider-item-input')[0]
+                            let bar = item_div.getElementsByClassName('easyuse-slider-item-bar')[0]
+                            let area = item_div.getElementsByClassName('easyuse-slider-item-area')[0]
+                            if(i == 3 ) layer_total == 12 ? item_div.classList.add('negative') : item_div.classList.remove('negative')
+                            else if(i == 6) layer_total == 12 ?  item_div.classList.add('positive') : item_div.classList.remove('positive')
+                            input.textContent = value
+                            bar.style.top = (100-calculatePercent(value,arrays[i]['min'],arrays[i]['max'])) + '%'
+                            area.style.height = calculatePercent(value,arrays[i]['min'],arrays[i]['max']) + '%'
+                        }
+                    }
+                    _this.setSize(model_type.value == 'sdxl' ? [375,320] : [455,320])
+                },1)
+                return onNodeCreated;
+            }
+        }
+    }
+})

web/js/easy/easySuggestion.js

@@ -8,7 +8,7 @@ const preSamplingNodes = ["easy preSampling", "easy preSamplingAdvanced", "easy
 const kSampler = ["easy kSampler", "easy kSamplerTiled","easy kSamplerInpainting", "easy kSamplerDownscaleUnet", "easy kSamplerSDTurbo"]
 const controlNetNodes = ["easy controlnetLoader", "easy controlnetLoaderADV"]
 const instantIDNodes = ["easy instantIDApply", "easy instantIDApplyADV"]
-const ipadapterNodes = ["easy ipadapterApply", "easy ipadapterApplyADV" , "easy ipadapterStyleComposition"]
+const ipadapterNodes = ["easy ipadapterApply", "easy ipadapterApplyADV" ,"easy ipadapterApplyFaceIDKolors", "easy ipadapterStyleComposition"]
 const pipeNodes = ['easy pipeIn','easy pipeOut', 'easy pipeEdit']
 const xyNodes = ['easy XYPlot', 'easy XYPlotAdvanced']
 const extraNodes = ['easy setNode']
@@ -183,7 +183,8 @@ const suggestions = {
     },
     "easy ipadapterApplyADV":{
         "to":{
-            "COMBO": [...["Reroute", "easy promptLine"]]
+          "STRING": [...["Reroute", "easy sliderControl"], ...propmts],
+          "COMBO": [...["Reroute", "easy promptLine"]]
         }
     },
     "easy ipadapterStyleComposition":{

web/js/image_chooser/chooser.js

@@ -3,7 +3,7 @@ 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 { hud, 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 {$t} from "../common/i18n.js";
@@ -83,25 +83,24 @@ 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();}
-    const node = app.graph._nodes_by_id[this.node_id];
-    if (node) {
-        node.send_button_widget.name = "";
-        node.cancel_button_widget.name = "";
-    }
+}
+
+function enable_disabling(button) {
+    Object.defineProperty(button, 'clicked', {
+        get : function() { return this._clicked; },
+        set : function(v) { this._clicked = (v && this.name!=''); }
+    })
+}
+
+function disable_serialize(widget) {
+    if (!widget.options) widget.options = {  };
+    widget.options.serialize = false;
 }
 
 app.registerExtension({
@@ -110,6 +109,16 @@ app.registerExtension({
         window.addEventListener("beforeunload", send_cancel, true);
     },
     setup(app) {
+
+        const draw = LGraphCanvas.prototype.draw;
+        LGraphCanvas.prototype.draw = function() {
+            if (hud.update()) {
+                app.graph._nodes.forEach((node)=> { if (node.update) { node.update(); } })
+            }
+            draw.apply(this,arguments);
+        }
+
+
         function easyuseImageChooser(event) {
             const {node,image,isKSampler} = display_preview_images(event);
             if(isKSampler) {
@@ -148,13 +157,8 @@ 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;
-
             /* A property defining the top of the image when there is just one */
             if(node?.imageIndex === undefined){
               Object.defineProperty(node, 'imageIndex', {
@@ -169,6 +173,8 @@ app.registerExtension({
                 })
             }
 
+            /* Capture clicks */
+            const org_onMouseDown = node.onMouseDown;
             node.onMouseDown = function( e, pos, canvas ) {
                 if (e.isPrimary) {
                     const i = click_is_in_image(node, pos);
@@ -177,6 +183,13 @@ app.registerExtension({
                 return (org_onMouseDown && org_onMouseDown.apply(this, arguments));
             }
 
+            node.send_button_widget = node.addWidget("button", "", "", progressButtonPressed);
+            node.cancel_button_widget = node.addWidget("button", "", "", cancelButtonPressed);
+            enable_disabling(node.cancel_button_widget);
+            enable_disabling(node.send_button_widget);
+            disable_serialize(node.cancel_button_widget);
+            disable_serialize(node.send_button_widget);
+
         }
     },
 
@@ -190,9 +203,11 @@ app.registerExtension({
             }
 
             nodeType.prototype.imageClicked = function (imageIndex) {
-                if (this.selected.has(imageIndex)) this.selected.delete(imageIndex);
-                else this.selected.add(imageIndex);
-                this.update();
+                if (nodeType?.comfyClass==="easy imageChooser") {
+                    if (this.selected.has(imageIndex)) this.selected.delete(imageIndex);
+                    else this.selected.add(imageIndex);
+                    this.update();
+                }
             }
 
             const update = nodeType.prototype.update;
@@ -204,7 +219,7 @@ app.registerExtension({
                     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) {
+                    } else if (selection>0) {
                         this.send_button_widget.name = (selection>1) ? "Progress selected (" + selection + '/' + maxlength  +")" : "Progress selected image as restart";
                     }
                     else {

web/js/image_chooser/messaging.js

@@ -15,9 +15,9 @@ function send_message(id, message) {
 
 function send_cancel() {
     send_message(-1,'__cancel__');
-    //FlowState.cancelling = true;
-    //api.interrupt();
-    //FlowState.cancelling = false;
+    FlowState.cancelling = true;
+    api.interrupt();
+    FlowState.cancelling = false;
 }
 
 var skip_next = 0;

web/js/image_chooser/state.js

@@ -1,6 +1,33 @@
 import { app } from "../../../../scripts/app.js";
 
-export class FlowState {
+
+class HUD {
+    constructor() {
+        this.current_node_id = undefined;
+        this.class_of_current_node = null;
+        this.current_node_is_chooser = false;
+    }
+
+    update() {
+        if (app.runningNodeId==this.current_node_id) return false;
+
+        this.current_node_id = app.runningNodeId;
+
+        if (this.current_node_id) {
+            this.class_of_current_node = app.graph?._nodes_by_id[app.runningNodeId.toString()]?.comfyClass;
+            this.current_node_is_chooser = this.class_of_current_node === "easy imageChooser"
+        } else {
+            this.class_of_current_node = undefined;
+            this.current_node_is_chooser = false;
+        }
+        return true;
+    }
+}
+
+const hud = new HUD();
+
+
+class FlowState {
     constructor(){}
     static idle() {
         return (!app.runningNodeId);
@@ -23,4 +50,6 @@ export class FlowState {
         return "Idle";
     }
     static cancelling = false;
-}
+}
+
+export { hud, FlowState}