Update app.py

app.py

@@ -58,30 +58,24 @@ pipe.load_lora_weights("purotan_1750.safetensors")
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-# Define the image generation function with progress
+# Define the image generation function
 @spaces.GPU  # [uncomment to use ZeroGPU]
-def generate_image(place, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress: gr.Progress):
-    with progress:
-        progress(10, desc="シードの設定中...")
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
+def generate_image(place, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
         
-        progress(30, desc="プロンプトの生成中...")
-        prompt = make_prompt(place)
-        
-        progress(50, desc="画像生成中...")
-        generator = torch.Generator().manual_seed(seed)
-        
-        image = pipe(
-            prompt=prompt, 
-            guidance_scale=guidance_scale, 
-            num_inference_steps=num_inference_steps, 
-            width=width, 
-            height=height,
-            generator=generator
-        ).images[0]
-        
-        progress(100, desc="画像生成完了!")
+    prompt = make_prompt(place)
+    
+    generator = torch.Generator().manual_seed(seed)
+    
+    image = pipe(
+        prompt=prompt, 
+        guidance_scale=guidance_scale, 
+        num_inference_steps=num_inference_steps, 
+        width=width, 
+        height=height,
+        generator=generator
+    ).images[0] 
     
     return image, seed
 
@@ -98,7 +92,7 @@ with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown("""
-        # ぷろたんガチャガチャ画像生成！
+        # Text-to-Image Gradio Template
         ボタンを押して場所を選択し、画像を生成してください！
         """)
         
@@ -196,9 +190,9 @@ with gr.Blocks(css=css) as demo:
             height, 
             guidance_scale, 
             num_inference_steps
-            # progress parameter is handled automatically by Gradio
         ],
         outputs=[result, seed]
     )
 
 demo.queue().launch()
+