diff --git a/modules/consistory/utils/general_utils.py b/modules/consistory/utils/general_utils.py
index 4493fa96e..bdf0686ca 100644
--- a/modules/consistory/utils/general_utils.py
+++ b/modules/consistory/utils/general_utils.py
@@ -6,15 +6,16 @@
 import torch
 import torch.nn.functional as F
 import numpy as np
-from skimage import filters
 
 
 ## Attention Utils
 def get_dynamic_threshold(tensor):
+    from skimage import filters
     return filters.threshold_otsu(tensor.float().cpu().numpy())
 
 
 def attn_map_to_binary(attention_map, scaler=1.):
+    from skimage import filters
     attention_map_np = attention_map.float().cpu().numpy()
     threshold_value = filters.threshold_otsu(attention_map_np) * scaler
     binary_mask = (attention_map_np > threshold_value).astype(np.uint8)
diff --git a/modules/face/reswapper_utils.py b/modules/face/reswapper_utils.py
index f5dbf0c93..ae260054d 100644
--- a/modules/face/reswapper_utils.py
+++ b/modules/face/reswapper_utils.py
@@ -1,6 +1,5 @@
 import cv2
 import numpy as np
-from skimage import transform as trans
 
 
 ### https://github.com/somanchiu/ReSwapper/blob/GAN/Image.py
@@ -74,6 +73,7 @@ arcface_dst = np.array(
 
 
 def estimate_norm(lmk, image_size=112,mode='arcface'): # pylint: disable=unused-argument
+    from skimage import transform as trans
     if image_size%112==0:
         ratio = float(image_size)/112.0
         diff_x = 0
@@ -126,6 +126,7 @@ def square_crop(im, S):
 
 
 def transform(data, center, output_size, scale, rotation):
+    from skimage import transform as trans
     scale_ratio = scale
     rot = float(rotation) * np.pi / 180.0
     t1 = trans.SimilarityTransform(scale=scale_ratio)
diff --git a/modules/processing_helpers.py b/modules/processing_helpers.py
index 2535127ff..0f2d7bc6c 100644
--- a/modules/processing_helpers.py
+++ b/modules/processing_helpers.py
@@ -8,7 +8,6 @@ import torch
 import numpy as np
 import cv2
 from PIL import Image
-from skimage import exposure
 from blendmodes.blend import blendLayers, BlendType
 from modules import shared, devices, images, sd_models, sd_samplers, sd_hijack_hypertile, processing_vae, timer
 
@@ -33,6 +32,7 @@ def setup_color_correction(image):
 
 
 def apply_color_correction(correction, original_image):
+    from skimage import exposure
     shared.log.debug(f"Applying color correction: correction={correction.shape} image={original_image}")
     np_image = np.asarray(original_image)
     np_recolor = cv2.cvtColor(np_image, cv2.COLOR_RGB2LAB)
@@ -545,11 +545,15 @@ def apply_circular(enable: bool, model):
     if getattr(model, 'texture_tiling', False) == enable:
         return
     try:
+        i = 0
         for layer in [layer for layer in model.unet.modules() if type(layer) is torch.nn.Conv2d]:
+            i += 1
             layer.padding_mode = 'circular' if enable else 'zeros'
         for layer in [layer for layer in model.vae.modules() if type(layer) is torch.nn.Conv2d]:
+            i += 1
             layer.padding_mode = 'circular' if enable else 'zeros'
         model.texture_tiling = enable
+        shared.log.debug(f'Apply texture tiling: enabled={enable} layers={i} cls={model.__class__.__name__} ')
     except Exception as e:
         debug(f"Diffusers tiling failed: {e}")
 
diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py
index 383587cc6..7fd56e353 100644
--- a/scripts/outpainting_mk_2.py
+++ b/scripts/outpainting_mk_2.py
@@ -1,6 +1,5 @@
 import math
 import numpy as np
-import skimage
 import gradio as gr
 from PIL import Image, ImageDraw
 import modules.scripts as scripts
@@ -60,6 +59,8 @@ def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.0
         for c in range(3):
             np_mask_rgb[:, :, c] = hardened[:]
         return np_mask_rgb
+
+    import skimage
     width = _np_src_image.shape[0]
     height = _np_src_image.shape[1]
     num_channels = _np_src_image.shape[2]