diff --git a/lama_cleaner/model/base.py b/lama_cleaner/model/base.py
index 45f468f..882aa42 100644
--- a/lama_cleaner/model/base.py
+++ b/lama_cleaner/model/base.py
@@ -56,17 +56,15 @@ class InpaintModel:
         result = self.forward(pad_image, pad_mask, config)
         result = result[0:origin_height, 0:origin_width, :]
 
-        if config.sd_match_histograms:
-            result = self._match_histograms(result, image[:, :, ::-1], mask)
-
-        if config.sd_mask_blur != 0:
-            k = 2 * config.sd_mask_blur + 1
-            mask = cv2.GaussianBlur(mask, (k, k), 0)
+        result, image, mask = self.forward_post_process(result, image, mask, config)
 
         mask = mask[:, :, np.newaxis]
         result = result * (mask / 255) + image[:, :, ::-1] * (1 - (mask / 255))
         return result
 
+    def forward_post_process(self, result, image, mask, config):
+        return result, image, mask
+
     @torch.no_grad()
     def __call__(self, image, mask, config: Config):
         """
@@ -179,7 +177,7 @@ class InpaintModel:
         cdf = histogram.cumsum()
         normalized_cdf = cdf / float(cdf.max())
         return normalized_cdf
-    
+
     def _calculate_lookup(self, source_cdf, reference_cdf):
         lookup_table = np.zeros(256)
         lookup_val = 0
@@ -190,27 +188,27 @@ class InpaintModel:
                     break
             lookup_table[source_index] = lookup_val
         return lookup_table
-    
+
     def _match_histograms(self, source, reference, mask):
         transformed_channels = []
         for channel in range(source.shape[-1]):
             source_channel = source[:, :, channel]
             reference_channel = reference[:, :, channel]
-    
+
             # only calculate histograms for non-masked parts
-            source_histogram, _ = np.histogram(source_channel[mask == 0], 256, [0,256])
-            reference_histogram, _ = np.histogram(reference_channel[mask == 0], 256, [0,256])    
-        
+            source_histogram, _ = np.histogram(source_channel[mask == 0], 256, [0, 256])
+            reference_histogram, _ = np.histogram(reference_channel[mask == 0], 256, [0, 256])
+
             source_cdf = self._calculate_cdf(source_histogram)
             reference_cdf = self._calculate_cdf(reference_histogram)
-        
+
             lookup = self._calculate_lookup(source_cdf, reference_cdf)
-        
+
             transformed_channels.append(cv2.LUT(source_channel, lookup))
-    
+
         result = cv2.merge(transformed_channels)
         result = cv2.convertScaleAbs(result)
-    
+
         return result
 
     def _run_box(self, image, mask, box, config: Config):
diff --git a/lama_cleaner/model/sd.py b/lama_cleaner/model/sd.py
index 8eaf2c5..1b0abba 100644
--- a/lama_cleaner/model/sd.py
+++ b/lama_cleaner/model/sd.py
@@ -200,17 +200,21 @@ class SD(InpaintModel):
 
         return inpaint_result
 
+    def forward_post_process(self, result, image, mask, config):
+        if config.sd_match_histograms:
+            result = self._match_histograms(result, image[:, :, ::-1], mask)
+
+        if config.sd_mask_blur != 0:
+            k = 2 * config.sd_mask_blur + 1
+            mask = cv2.GaussianBlur(mask, (k, k), 0)
+        return result, image, mask
+
     @staticmethod
     def is_downloaded() -> bool:
         # model will be downloaded when app start, and can't switch in frontend settings
         return True
 
 
-class SD14(SD):
-    model_id_or_path = "CompVis/stable-diffusion-v1-4"
-    image_key = "init_image"
-
-
 class SD15(SD):
     model_id_or_path = "runwayml/stable-diffusion-inpainting"
     image_key = "image"
diff --git a/lama_cleaner/tests/test_sd_model.py b/lama_cleaner/tests/test_sd_model.py
index 267a60d..6abc261 100644
--- a/lama_cleaner/tests/test_sd_model.py
+++ b/lama_cleaner/tests/test_sd_model.py
@@ -109,7 +109,8 @@ def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler):
         sd_steps=sd_steps,
         prompt='Face of a fox, high resolution, sitting on a park bench',
         negative_prompt='orange, yellow, small',
-        sd_sampler=sampler
+        sd_sampler=sampler,
+        sd_match_histograms=True
     )
 
     name = f"{sampler}_negative_prompt"