Update app.py

app.py

@@ -5,7 +5,6 @@ import spaces
 from diffusers import DiffusionPipeline
 import torch
 
-# Подключение к устройству
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_repo_id = "stabilityai/stable-diffusion-3.5-large"
 
@@ -18,58 +17,50 @@ pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 512  # Базовый размер изображения
+MAX_IMAGE_SIZE = 1024
 
-# Проверка доступного времени GPU
-def get_available_gpu_quota():
-    # Симуляция запроса (в реальном случае можно подключить API Hugging Face)
-    # Здесь вы можете вручную ввести оставшееся время
-    return 20  # Пример: 20 секунд доступно
-
-# Динамическая настройка
-@spaces.GPU(duration=min(get_available_gpu_quota(), 30))  # Устанавливаем время в пределах доступного
+@spaces.GPU(duration=65)
 def infer(
     prompt,
     negative_prompt="",
     seed=42,
     randomize_seed=False,
-    width=None,  # Размер будет определён динамически
-    height=None,  # Размер будет определён динамически
+    width=1024,
+    height=1024,
     guidance_scale=4.5,
-    num_inference_steps=10,  # Базовое значение
+    num_inference_steps=40,
     progress=gr.Progress(track_tqdm=True),
 ):
-    available_time = get_available_gpu_quota()  # Получаем доступное время
-    # Настраиваем параметры под оставшееся время
-    if available_time < 20:
-        num_inference_steps = max(5, int(available_time / 2))  # Уменьшаем шаги
-        width = height = min(MAX_IMAGE_SIZE, 384)  # Уменьшаем размер
-    else:
-        num_inference_steps = 15
-        width = height = 512  # Стандартный размер
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image
-
-# Примеры для интерфейса
+    try:
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
+
+        generator = torch.Generator().manual_seed(seed)
+
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+        ).images[0]
+
+        return image, seed
+
+    except Exception as e:
+        # Log the exception if needed
+        print(f"Error during image generation: {e}")
+
+        # Raise a Gradio-specific error with a user-friendly message
+        raise gr.Error(
+            "You have exceeded your GPU quota or encountered an unexpected error. "
+            "Please wait a moment and try again."
+        )
+
 examples = [
-    "A full-body depiction of a futuristic cyberpunk warrior wearing neon armor with intricate details, holding a sleek glowing katana in a ready stance. The character stands confidently with glowing eyes and a dynamic pose that exudes strength and mystery. Isolated on a clean, transparent, or neutral background to emphasize the character's design and details, with subtle reflections and lighting to enhance the neon glow and textures.",
-    "A lush fantasy forest with glowing mushrooms, magical fireflies, and a small elf standing by an ancient tree, surrounded by mystical mist.",
-    "A dystopian cityscape at night with flying cars, neon holographic advertisements, and a lone figure standing on a rooftop, looking at the skyline."
+    "A border collie lying in some Fall leaves as the forest trees change colors",
 ]
 
 # Define the custom theme with input styles
@@ -96,21 +87,26 @@ footer {
     padding: 0;
     overflow: hidden;
 }
+
 /* Import Google Fonts */
 @import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&family=Montserrat:wght@500;700&display=swap');
+
 /* Apply fonts to different elements */
 body, input, button, textarea, select, .gr-button {
     font-family: 'Roboto', sans-serif;
 }
+
 /* Make button text normal weight */
 .generate-button, .generate-button .gr-button {
     font-weight: normal !important;
 }
+
 /* Ensure headings use Montserrat */
 h1, h2, h3, h4, h5, h6 {
     font-family: 'Montserrat', sans-serif;
     font-weight: 700;
 }
+
 /* Additional styling for sliders and checkboxes if needed */
 input[type="range"]::-webkit-slider-thumb {
     background: #5271FF;
@@ -124,13 +120,13 @@ input[type="range"]::-ms-thumb {
 input[type="checkbox"]:checked {
     background-color: #5271FF;
 }
+
 /* Make Prompt text bold */
 .prompt-text {
     font-weight: bold;
 }
 """
 
-# Создание интерфейса Gradio
 with gr.Blocks(theme=CustomTheme(), css=css) as demo:
     with gr.Column(elem_id="col-container"):
         # Make "Prompt" bold using Markdown syntax and assign a class
@@ -154,26 +150,96 @@ with gr.Blocks(theme=CustomTheme(), css=css) as demo:
 
         result = gr.Image(label="Result", show_label=False)
 
-        # Добавляем примеры
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Text(
+                label="Negative Prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=False,
+            )
+
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+
+            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+
+            with gr.Row():
+                width = gr.Slider(
+                    label="Width",
+                    minimum=512,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024, 
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=512,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=0.0,
+                    maximum=7.5,
+                    step=0.1,
+                    value=4.5,
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of Inference Steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=40, 
+                )
+
         gr.Examples(
             examples=examples,
             inputs=[prompt],
-            outputs=[result],
+            outputs=[result, seed],
             fn=infer,
             cache_examples=True,
             cache_mode="lazy"
         )
 
-        run_button.click(
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result],
-        )
-        prompt.submit(
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result],
-        )
+    # Use click method for the button and submit for the text field
+    run_button.click(
+        fn=infer,
+        inputs=[
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, seed],
+    )
+    prompt.submit(
+        fn=infer,
+        inputs=[
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, seed],
+    )
 
 if __name__ == "__main__":
     demo.launch(