Update app.py

app.py

@@ -14,10 +14,15 @@ DESCRIPTIONx = """## STABLE HAMSTER 🐹
 """
 
 css = '''
-.gradio-container{max-width: 560px !important}
-h1{text-align:center}
+.gradio-container {
+    max-width: 560px !important;
+    margin: 0 auto !important;
+}
+h1 {
+    text-align: center;
+}
 footer {
-    visibility: hidden
+    visibility: hidden;
 }
 '''
 
@@ -27,17 +32,15 @@ examples = [
     "Vector illustration of a horse, vector graphic design with flat colors on an brown background in the style of vector art, using simple shapes and graphics with simple details, professionally designed as a tshirt logo ready for print on a white background. --ar 89:82 --v 6.0 --style raw",
     "Man in brown leather jacket posing for camera, in the style of sleek and stylized, clockpunk, subtle shades, exacting precision, ferrania p30  --ar 67:101 --v 5",
     "Commercial photography, giant burger, white lighting, studio light, 8k octane rendering, high resolution photography, insanely detailed, fine details, on white isolated plain, 8k, commercial photography, stock photo, professional color grading, --v 4 --ar 9:16 "
-    
 ]
 
-
-MODEL_ID = os.getenv("MODEL_VAL_PATH") #uses SG161222/RealVisXL_V5.0_Lightning or SG161222/RealVisXL_V4.0_Lightning
+MODEL_ID = os.getenv("MODEL_VAL_PATH")  # uses SG161222/RealVisXL_V5.0_Lightning or SG161222/RealVisXL_V4.0_Lightning
 MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
 USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
 ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
 BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))  # Allow generating multiple images at once
 
-#Load model outside of function
+# Load model outside of function
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 pipe = StableDiffusionXLPipeline.from_pretrained(
     MODEL_ID,
@@ -78,14 +81,14 @@ def generate(
     guidance_scale: float = 3,
     num_inference_steps: int = 25,
     randomize_seed: bool = False,
-    use_resolution_binning: bool = True, 
+    use_resolution_binning: bool = True,
     num_images: int = 4,  # Number of images to generate
     progress=gr.Progress(track_tqdm=True),
 ):
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator(device=device).manual_seed(seed)
 
-    #Options 
+    # Options
     options = {
         "prompt": [prompt] * num_images,
         "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
@@ -100,7 +103,7 @@ def generate(
     if use_resolution_binning:
         options["use_resolution_binning"] = True
 
-    #Images potential batches
+    # Images potential batches
     images = []
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
@@ -113,7 +116,7 @@ def generate(
     return image_paths, seed
 
 with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
-    gr.Markdown(DESCRIPTIONx)  
+    gr.Markdown(DESCRIPTIONx)
     with gr.Group():
         with gr.Row():
             prompt = gr.Text(
@@ -124,7 +127,7 @@ with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
                 container=False,
             )
             run_button = gr.Button("Run", scale=0)
-        result = gr.Gallery(label="Result", columns=2, show_label=False) 
+        result = gr.Gallery(label="Result", columns=2, show_label=False)
     with gr.Accordion("Advanced options", open=False, visible=True):
         num_images = gr.Slider(
             label="Number of Images",
@@ -216,7 +219,7 @@ with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
         ],
         outputs=[result, seed],
         api_name="run",
-    )   
+    )
 
 if __name__ == "__main__":
     demo.queue(max_size=40).launch(ssr_mode=False)