diff --git a/sdxl_gen_img.py b/sdxl_gen_img.py
index bab164f53..9e94d4f6c 100755
--- a/sdxl_gen_img.py
+++ b/sdxl_gen_img.py
@@ -802,6 +802,10 @@ def __call__(
                     latents, scale_factor=current_ratio, mode="bicubic", align_corners=False
                 ).to(org_dtype)
 
+                # apply unsharp mask / アンシャープマスクを適用する
+                blurred = torchvision.transforms.transforms.GaussianBlur(3, sigma=(0.5, 0.5))(latents)
+                latents = latents + (latents - blurred) * 0.5
+
         for i, t in enumerate(tqdm(timesteps)):
             resized_size = None
             if enable_gradual_latent:
@@ -1434,8 +1438,9 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.resized_size = None
 
-    def set_resized_size(self, size):
+    def set_resized_size(self, size, s_noise=0.5):
         self.resized_size = size
+        self.s_noise = s_noise
 
     def step(
         self,
@@ -1518,6 +1523,7 @@ def step(
             noise = diffusers.schedulers.scheduling_euler_ancestral_discrete.randn_tensor(
                 model_output.shape, dtype=model_output.dtype, device=device, generator=generator
             )
+            s_noise = 1.0
         else:
             print(
                 "resized_size", self.resized_size, "model_output.shape", model_output.shape, "prev_sample.shape", prev_sample.shape
@@ -1530,14 +1536,19 @@ def step(
                 prev_sample.float(), size=self.resized_size, mode="bicubic", align_corners=False
             ).to(dtype=org_dtype)
 
+            # apply unsharp mask / アンシャープマスクを適用する
+            blurred = torchvision.transforms.transforms.GaussianBlur(3, sigma=(0.5, 0.5))(prev_sample)
+            prev_sample = prev_sample + (prev_sample - blurred) * 0.5
+
             noise = diffusers.schedulers.scheduling_euler_ancestral_discrete.randn_tensor(
                 (model_output.shape[0], model_output.shape[1], self.resized_size[0], self.resized_size[1]),
                 dtype=model_output.dtype,
                 device=device,
                 generator=generator,
             )
+            s_noise = self.s_noise
 
-        prev_sample = prev_sample + noise * sigma_up
+        prev_sample = prev_sample + noise * sigma_up * s_noise
 
         # upon completion increase step index by one
         self._step_index += 1