Update app.py

app.py

@@ -124,7 +124,7 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
         seed = random.randint(0, MAX_SEED)
     return seed
 
-@spaces.GPU(duration=70, enable_queue=True)
+@spaces.GPU(duration=40, enable_queue=True)
 def generate(
     model_choice: str,
     prompt: str,
@@ -143,56 +143,7 @@ def generate(
 ):
     global models
     pipe = models[model_choice]
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator(device=device).manual_seed(seed)
-
-    prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
-
-    options = {
-        "prompt": [prompt] * num_images,
-        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
-        "width": width,
-        "height": height,
-        "guidance_scale": guidance_scale,
-        "num_inference_steps": num_inference_steps,
-        "generator": generator,
-        "output_type": "pil",
-    }
-
-    if use_resolution_binning:
-        options["use_resolution_binning"] = True
-
-    images = []
-    for i in range(0, num_images, BATCH_SIZE):
-        batch_options = options.copy()
-        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
-        if "negative_prompt" in batch_options:
-            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
-        images.extend(pipe(**batch_options).images)
-
-    image_paths = [save_image(img) for img in images]
-    return image_paths, seed
-
-def generate_cpu(
-    model_choice: str,
-    prompt: str,
-    negative_prompt: str = "",
-    use_negative_prompt: bool = False,
-    style_selection: str = DEFAULT_STYLE_NAME,
-    seed: int = 1,
-    width: int = 768,
-    height: int = 768,
-    guidance_scale: float = 4,
-    num_inference_steps: int = 150,
-    randomize_seed: bool = False,
-    use_resolution_binning: bool = True, 
-    num_images: int = 1,  
-    progress=gr.Progress(track_tqdm=True),
-):
-    global models
-    pipe = models[model_choice]
-    pipe.to("cpu")
-
+    
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator(device=device).manual_seed(seed)
 
@@ -264,7 +215,6 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
             container=False,
         )
         run_button = gr.Button("Run", scale=0)
-        cpu_run_button = gr.Button("CPU Run", scale=0)
     result = gr.Gallery(label="Result", columns=1, show_label=False) 
 
     with gr.Row():
@@ -378,29 +328,6 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
         outputs=[result, seed],
     )
 
-    gr.on(
-        triggers=[
-            cpu_run_button.click,
-        ],
-        api_name="generate",  # Add this line
-        fn=generate_cpu,
-        inputs=[
-            model_choice,
-            prompt,
-            negative_prompt,
-            use_negative_prompt,
-            style_selection,
-            seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-            randomize_seed, 
-            num_images, 
-        ],
-        outputs=[result, seed],
-    )
-
     gr.Markdown("### REALVISXL V5.0")
     predefined_gallery = gr.Gallery(label="REALVISXL V5.0", columns=3, show_label=False, value=load_predefined_images1())