Update app.py

app.py

@@ -198,15 +198,12 @@ def restore_in_Xmin(
     output_format,
     allocation
 ):
-    # 省略可能な詳細なログを出力
     print("Starting restoration process...")
-    # 以下、省略: パラメータの表示や前処理
-
     input_format = re.sub(r"^.*\.([^\.]+)$", r"\1", noisy_image)
 
     if input_format not in ['png', 'webp', 'jpg', 'jpeg', 'gif', 'bmp', 'heic']:
         gr.Warning('Invalid image format. Please first convert into *.png, *.webp, *.jpg, *.jpeg, *.gif, *.bmp or *.heic.')
-        return None, None, None
+        return None, gr.update(value="Invalid image format.", visible=True), gr.update(visible=True)
 
     if output_format == "input":
         if noisy_image is None:
@@ -233,11 +230,12 @@ def restore_in_Xmin(
     denoise_image = np.array(Image.open(noisy_image if denoise_image is None else denoise_image))
 
     if rotation == 90:
-        denoise_image = np.rot90(denoise_image, k=-1)
+        denoise_image = np.array(list(zip(*denoise_image[::-1])))
     elif rotation == 180:
-        denoise_image = np.rot90(denoise_image, k=2)
+        denoise_image = np.array(list(zip(*denoise_image[::-1])))
+        denoise_image = np.array(list(zip(*denoise_image[::-1])))
     elif rotation == -90:
-        denoise_image = np.rot90(denoise_image, k=1)
+        denoise_image = np.array(list(zip(*denoise_image))[::-1])
 
     if 1 < downscale:
         input_height, input_width, input_channel = denoise_image.shape
@@ -267,7 +265,7 @@ def restore_in_Xmin(
             s_stage2, s_cfg, seed, s_churn, s_noise, color_fix_type, linear_CFG, linear_s_stage2, spt_linear_CFG,
             spt_linear_s_stage2, output_format
         )
-    # 省略: 他のallocation条件
+    # 他のallocation条件は省略
 
     # デフォルトの処理
     return restore_in_3min(
@@ -282,8 +280,6 @@ def restore_in_1min(*args, **kwargs):
     return restore_on_gpu(*args, **kwargs)
 
 
-# 他のrestore_in_Xmin関数も同様に定義
-
 def restore_on_gpu(
     noisy_image,
     input_image,
@@ -314,7 +310,8 @@ def restore_on_gpu(
     with torch.no_grad():
         input_image = upscale_image(input_image, upscale, unit_resolution=32, min_size=min_size)
         LQ = np.array(input_image) / 255.0
-        LQ = (LQ ** gamma_correction) * 255.0
+        LQ = np.power(LQ, gamma_correction)
+        LQ *= 255.0
         LQ = LQ.round().clip(0, 255).astype(np.uint8)
         LQ = LQ / 255 * 2 - 1
         LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
@@ -401,6 +398,7 @@ title_html = """
     <p><center><a style="display:inline-block" href='https://github.com/Fanghua-Yu/SUPIR'><img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/Fanghua-Yu/SUPIR?style=social"></a></center></p>
     """
 
+
 claim_md = """
 ## **Piracy**
 The images are not stored but the logs are saved during a month.
@@ -493,7 +491,7 @@ with gr.Blocks() as interface:
         reset_btn = gr.Button(value="🧹 Reinit page", variant="stop", elem_id="reset_button", visible=False)
 
         restore_information = gr.HTML(value="Restart the process to get another result.", visible=False)
-        result_image = gr.Image(label='Result Image', show_label=True, interactive=False, elem_id="result_image")
+        result_image = gr.Image(label='Result Image', show_label=True, interactive=False, elem_id='result_image')
 
     gr.Examples(
         examples=[
@@ -655,4 +653,4 @@ with gr.Blocks() as interface:
         allocation
     ], queue=False, show_progress=False)
 
-    interface.queue(max_size=10).launch(show_error=True)
+    interface.queue(10).launch(show_error=True)