diff --git a/modules/masking.py b/modules/masking.py
index 4fc54adaa..4c436a288 100644
--- a/modules/masking.py
+++ b/modules/masking.py
@@ -421,7 +421,7 @@ def run_mask(input_image: Image.Image, input_mask: Image.Image = None, return_ty
         mask = run_segment(input_image, input_mask)
     mask = cv2.resize(mask, (input_image.width, input_image.height), interpolation=cv2.INTER_LANCZOS4)
 
-    shared.log.trace(f'Mask shape={mask.shape} opts={opts} fn={fn}')
+    # shared.log.trace(f'Mask shape={mask.shape} opts={opts} fn={fn}')
     if opts.mask_erode > 0:
         try:
             kernel = np.ones((int(opts.mask_erode * size / 4) + 1, int(opts.mask_erode * size / 4) + 1), np.uint8)
diff --git a/modules/processing_class.py b/modules/processing_class.py
index 2ebfae677..660000c7c 100644
--- a/modules/processing_class.py
+++ b/modules/processing_class.py
@@ -483,9 +483,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                 self.width = int(vae_scale_factor * (self.init_images[0].width * self.scale_by // vae_scale_factor))
             if self.height is None or self.height == 0:
                 self.height = int(vae_scale_factor * (self.init_images[0].height * self.scale_by // vae_scale_factor))
-        if (getattr(self, 'image_mask', None) is not None) and (len(getattr(self, 'image_mask', [])) > 0):
+        if (getattr(self, 'image_mask', None) is not None) and ((len(self.image_mask) > 0) if isinstance(self.image_mask, list) else True):
             shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.INPAINTING)
-        elif (getattr(self, 'init_images', None) is not None) and (len(getattr(self, 'init_images', [])) > 0):
+        elif (getattr(self, 'init_images', None) is not None) and ((len(self.init_images) > 0) if isinstance(self.init_images, list) else True):
             shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
 
         if all_prompts is not None: