diff --git a/modules/processing.py b/modules/processing.py
index bc7e5311..accb31d1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -740,7 +740,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.denoising_strength: float = denoising_strength
         self.init_latent = None
         self.image_mask = mask
-        #self.image_unblurred_mask = None
         self.latent_mask = None
         self.mask_for_overlay = None
         self.mask_blur = mask_blur
@@ -756,36 +755,36 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
         crop_region = None
 
-        if self.image_mask is not None:
-            self.image_mask = self.image_mask.convert('L')
+        image_mask = self.image_mask
+
+        if image_mask is not None:
+            image_mask = image_mask.convert('L')
 
             if self.inpainting_mask_invert:
-                self.image_mask = ImageOps.invert(self.image_mask)
-
-            #self.image_unblurred_mask = self.image_mask
+                image_mask = ImageOps.invert(image_mask)
 
             if self.mask_blur > 0:
-                self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
+                image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
 
             if self.inpaint_full_res:
-                self.mask_for_overlay = self.image_mask
-                mask = self.image_mask.convert('L')
+                self.mask_for_overlay = image_mask
+                mask = image_mask.convert('L')
                 crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
                 crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
                 x1, y1, x2, y2 = crop_region
 
                 mask = mask.crop(crop_region)
-                self.image_mask = images.resize_image(2, mask, self.width, self.height)
+                image_mask = images.resize_image(2, mask, self.width, self.height)
                 self.paste_to = (x1, y1, x2-x1, y2-y1)
             else:
-                self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
-                np_mask = np.array(self.image_mask)
+                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
+                np_mask = np.array(image_mask)
                 np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
                 self.mask_for_overlay = Image.fromarray(np_mask)
 
             self.overlay_images = []
 
-        latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
+        latent_mask = self.latent_mask if self.latent_mask is not None else image_mask
 
         add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
         if add_color_corrections:
@@ -797,7 +796,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if crop_region is None:
                 image = images.resize_image(self.resize_mode, image, self.width, self.height)
 
-            if self.image_mask is not None:
+            if image_mask is not None:
                 image_masked = Image.new('RGBa', (image.width, image.height))
                 image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
 
@@ -807,7 +806,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                 image = image.crop(crop_region)
                 image = images.resize_image(2, image, self.width, self.height)
 
-            if self.image_mask is not None:
+            if image_mask is not None:
                 if self.inpainting_fill != 1:
                     image = masking.fill(image, latent_mask)
 
@@ -839,7 +838,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
 
-        if self.image_mask is not None:
+        if image_mask is not None:
             init_mask = latent_mask
             latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
             latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
@@ -856,7 +855,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
+        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)