fix vitmatte load models from local

README.MD

@@ -80,6 +80,7 @@ When this error has occurred, please check the network environment.
 ## Update
 <font size="4">**If the dependency package error after updating, please reinstall the relevant dependency packages. </font><br />    
 
+* Change the VitMatte model of the [Ultra](#Ultra) node to a local call. Please download [all files of vitmatte model](https://huggingface.co/hustvl/vitmatte-small-composition-1k/tree/main) to the ```ComfyUI/models/vitmatte``` folder.
 * [GetColorToneV2](#GetColorToneV2) node add the ```mask``` method to the color selection option, which can accurately obtain the main color and average color within the mask.
 * [ImageScaleByAspectRatioV2](#ImageScaleByAspectRatioV2) node add the "background_color" option.
 * [LUT Apply](#LUT) Add the "strength" option.
@@ -1390,6 +1391,8 @@ Note: When running for the first time, you need to download the vitmate model fi
 After successfully downloading the model, you can use ```VITMatte(local)``` without accessing the network.
 * VitMatte's options: ```device``` set whether to use CUDA for vitimate operations, which is about 5 times faster than CPU. ```max_megapixels``` set the maximum image size for vitmate operation, and oversized images will be reduced in size. For 16G VRAM, it is recommended to set it to 3.
 
+Download the vitmatte model files [all files of vitmatte model](https://huggingface.co/hustvl/vitmatte-small-composition-1k/tree/main) to the ```ComfyUI/models/vitmatte``` folder.
+
 The following figure is an example of the difference in output between three methods.
 ![image](image/mask_edge_ultra_detail_v2_example.jpg)   
 

README_CN.MD

@@ -80,6 +80,7 @@ git clone https://github.com/chflame163/ComfyUI_LayerStyle.git
 ## 更新说明
 <font size="4">**如果本插件更新后出现依赖包错误，请重新安装相关依赖包。
 
+* [Ultra](#Ultra) 节点的VitMatte模型改为本地调用，请下载[所有的vitmatte模型文件](https://huggingface.co/hustvl/vitmatte-small-composition-1k/tree/main)到```ComfyUI/models/vitmatte```文件夹。
 * [GetColorToneV2](#GetColorToneV2) 节点的取色选项增加```mask```方法，可精确获取遮罩内的主色和平均色。
 * [ImageScaleByAspectRatioV2](#ImageScaleByAspectRatioV2) 节点增加background_color选项。 
 * [LUT Apply](#LUT) 节点增加strenght选项。
@@ -1370,10 +1371,10 @@ mask为可选输入项，如果这里输入遮罩，将作用于输出结果。
 * ```GuideFilter``` 使用 opencv guidedfilter 根据颜色相似度对边缘进行羽化，对于边缘具有很强的颜色分离时效果最佳。    
 以上两种方法的代码来着spacepxl的[ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)的Alpha Matte节点，感谢原作者。
 * ```VitMatte``` 使用transfromer vit模型进行高质量的边缘处理，保留边缘细节，甚至可以生成半透明遮罩。
-注：首次运行时需要下载vitmatte模型文件，等待自动下载完成即可。如果无法完成下载，可运行命令```huggingface-cli download hustvl/vitmatte-small-composition-1k```手动下载模型。
-模型成功下载之后可以使用VITMatte(local)无需访问网络。
 * VitMatte的选项:```device``` 设置是否使用cuda进行vitmatte运算，cuda运算速度比cpu快5倍左右。```max_megapixels```设置vitmatte运算的最大图片尺寸，超大的图片将缩小处理。对于16G显存建议设置为3。
 
+请下载[所有的 vitmatte 模型文件](https://huggingface.co/hustvl/vitmatte-small-composition-1k/tree/main)到```ComfyUI/models/vitmatte```文件夹。
+
 下图为三种方法输出区别的示例。
 ![image](image/mask_edge_ultra_detail_v2_example.jpg)   
 

py/imagefunc.py

@@ -22,6 +22,7 @@ import scipy.ndimage
 import cv2
 import random
 import time
+from pathlib import Path
 from tqdm import tqdm
 from functools import lru_cache
 from typing import Union, List
@@ -1490,6 +1491,7 @@ class VITMatteModel:
         self.processor = processor
 
 def load_VITMatte_model(model_name:str, local_files_only:bool=False) -> object:
+    model_name = Path(os.path.join(folder_paths.models_dir, "vitmatte"))
     from transformers import VitMatteImageProcessor, VitMatteForImageMatting
     model = VitMatteForImageMatting.from_pretrained(model_name, local_files_only=local_files_only)
     processor = VitMatteImageProcessor.from_pretrained(model_name, local_files_only=local_files_only)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui_layerstyle"
 description = "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress."
-version = "1.0.14"
+version = "1.0.15"
 license = "MIT"
 dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "opencv-contrib-python", "pymatting", "segment_anything", "timm", "addict", "yapf", "colour-science", "wget", "mediapipe", "loguru", "typer_config", "fastapi", "rich", "google-generativeai", "diffusers", "omegaconf", "tqdm", "transformers", "kornia", "image-reward", "ultralytics", "blend_modes", "blind-watermark", "qrcode", "pyzbar", "psd-tools"]