fix import timm.models._registry

py/BiRefNet_legacy/backbones/pvt_v2.py

@@ -3,7 +3,7 @@
 from functools import partial
 
 from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from timm.models.registry import register_model
+from timm.models import register_model
 
 import math
 

py/BiRefNet_legacy/modules/mlp.py

@@ -3,7 +3,7 @@
 from functools import partial
 
 from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from timm.models.registry import register_model
+from timm.models import register_model
 
 import math
 

py/BiRefNet_v2/models/backbones/pvt_v2.py

@@ -3,7 +3,7 @@
 from functools import partial
 
 from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from timm.models.registry import register_model
+from timm.models import register_model
 
 import math
 

py/BiRefNet_v2/models/modules/mlp.py

@@ -3,7 +3,7 @@
 from functools import partial
 
 from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from timm.models.registry import register_model
+from timm.models import register_model
 
 import math
 

py/evf_sam/model/unilm/beit3/modeling_finetune.py

@@ -8,7 +8,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from timm.models.registry import register_model
+from timm.models import register_model
 import numpy as np
 
 import utils

py/imagefunc.py

@@ -950,25 +950,30 @@ def get_image_color_average(image:Image, mask:Image=None) -> str:
 def get_gray_average(image:Image, mask:Image=None) -> int:
     # image.mode = 'HSV', mask.mode = 'L'
     image = image.convert('HSV')
-    _, _, _v = image.convert('HSV').split()
+
     if mask is not None:
         if mask.mode != 'L':
             mask = mask.convert('L')
-    width, height = image.size
-    total_gray = 0
-    valid_pixels = 0
-    for y in range(height):
-        for x in range(width):
-            if mask is not None:
-                if mask.getpixel((x, y)) > 16:  #mask亮度低于16的忽略不计
-                    gray = _v.getpixel((x, y))
-                    total_gray += gray
-                    valid_pixels += 1
-            else:
-                gray = _v.getpixel((x, y))
-                total_gray += gray
-                valid_pixels += 1
-    average_gray = total_gray // valid_pixels
+    else:
+        mask = Image.new('L', size=image.size, color='white')
+    _, _, _v = image.convert('HSV').split()
+    _v = np.array(_v)
+    average_gray = _v[np.array(mask) > 16].mean()
+    # width, height = image.size
+    # total_gray = 0
+    # valid_pixels = 0
+    # for y in range(height):
+    #     for x in range(width):
+    #         if mask is not None:
+    #             if mask.getpixel((x, y)) > 16:  #mask亮度低于16的忽略不计
+    #                 gray = _v.getpixel((x, y))
+    #                 total_gray += gray
+    #                 valid_pixels += 1
+    #         else:
+    #             gray = _v.getpixel((x, y))
+    #             total_gray += gray
+    #             valid_pixels += 1
+    # average_gray = total_gray // valid_pixels
     return average_gray
 
 def calculate_shadow_highlight_level(gray:int) -> float:

py/sam_hq/modeling/tiny_vit.py

@@ -14,7 +14,7 @@
 import torch.utils.checkpoint as checkpoint
 from timm.models.layers import DropPath as TimmDropPath,\
     to_2tuple, trunc_normal_
-from timm.models.registry import register_model
+from timm.models import register_model
 from typing import Tuple
 
 

py/shadow_highlight_mask.py

@@ -66,7 +66,6 @@ def shadow_and_highlight_mask(self, image,
             _image = tensor2pil(_image).convert('RGB')
             _mask = input_masks[i] if i < len(input_masks) else input_masks[-1]
 
-
             avg_gray = get_gray_average(_image, _mask)
             shadow_level, highlight_level = calculate_shadow_highlight_level(avg_gray)
             shadow_low_threshold = (shadow_level + shadow_level_offset) / 100 + shadow_range / 2

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 = "1.0.77"
+version = "1.0.78"
 license = "MIT"
 dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "scikit_learn", "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", "transparent-background", "huggingface_hub", "accelerate", "bitsandbytes", "torchscale", "wandb", "hydra-core", "psd-tools", "inference-cli[yolo-world]", "inference-gpu[yolo-world]", "onnxruntime", "peft"]