Update app.py

app.py

@@ -1,60 +1,90 @@
 import gradio as gr
 import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+from huggingface_hub import list_models, ModelFilter
+from diffusers import DiffusionPipeline
 
+# Проверка доступности модели на Hugging Face Hub
+def is_model_available(model_id):
+    try:
+        models = list_models(filter=ModelFilter(model_name=model_id))
+        return len(models) > 0
+    except Exception:
+        return False
+
+def validate_model(model_id):
+    if not model_id:
+        raise ValueError("Необходимо указать модель")
+    
+    if not is_model_available(model_id):
+        raise ValueError(f"Модель '{model_id}' не найдена на Hugging Face Hub")
+    
+    # Дополнительные проверки модели (можно настроить)
+    if not ("stable-diffusion" in model_id.lower() or "sdxl" in model_id.lower()):
+        raise ValueError("Поддерживаются только Stable Diffusion и SDXL модели")
+
+# Инициализация устройства
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+# Глобальные переменные
+current_model = None
+pipe = None
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+def load_pipeline(model_id):
+    global pipe, current_model
+    if model_id != current_model:
+        validate_model(model_id)
+        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
+        pipe = pipe.to(device)
+        current_model = model_id
+    return pipe
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
+    model_id,
     prompt,
     negative_prompt,
     seed,
-    randomize_seed,
     width,
     height,
     guidance_scale,
     num_inference_steps,
     progress=gr.Progress(track_tqdm=True),
 ):
-    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
-
+    try:
+        # Загрузка и проверка модели
+        pipeline = load_pipeline(model_id)
+        
+        # Генерация изображения
+        generator = torch.Generator(device=device).manual_seed(seed)
+        
+        result = pipeline(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
+        ).images[0]
+        
+        return result, seed
+    
+    except Exception as e:
+        raise gr.Error(f"Ошибка генерации: {str(e)}")
+
+# Список доступных моделей по умолчанию
+available_models = [
+    "stabilityai/stable-diffusion-2-1",
+    "stabilityai/sdxl-turbo",
+    "runwayml/stable-diffusion-v1-5",
+    "prompthero/openjourney-v4"
+]
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    ["stabilityai/sdxl-turbo", "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"],
+    ["runwayml/stable-diffusion-v1-5", "An astronaut riding a green horse"],
+    ["prompthero/openjourney-v4", "A cyberpunk cityscape at night, neon lights, rain"],
 ]
 
 css = """
@@ -66,89 +96,68 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+        gr.Markdown("# 🎨 Text-to-Image Generator")
+        
         with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+            model_id = gr.Dropdown(
+                label="Выберите или введите модель",
+                choices=available_models,
+                value="stabilityai/sdxl-turbo",
+                allow_custom_value=True,
+                scale=3
             )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        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)
-
+            
+        prompt = gr.Textbox(
+            label="Промпт",
+            placeholder="Введите описание изображения...",
+            lines=2
+        )
+        
+        negative_prompt = gr.Textbox(
+            label="Негативный промпт",
+            placeholder="Что исключить из изображения...",
+            lines=2
+        )
+        
+        with gr.Accordion("Настройки генерации", open=False):
             with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
+                seed = gr.Slider(0, 2147483647, value=42, label="Сид")
+                guidance_scale = gr.Slider(0.0, 20.0, value=7.5, label="Guidance Scale")
+            
             with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
+                width = gr.Slider(256, 1024, value=512, step=64, label="Ширина")
+                height = gr.Slider(256, 1024, value=512, step=64, label="Высота")
+            
+            num_inference_steps = gr.Slider(1, 100, value=20, step=1, label="Шаги генерации")
+
+        generate_btn = gr.Button("Сгенерировать", variant="primary")
+        
+        output_image = gr.Image(label="Результат", show_label=False)
+        used_seed = gr.Number(label="Использованный сид", visible=True)
+
+        gr.Examples(
+            examples=examples,
+            inputs=[model_id, prompt],
+            outputs=[output_image, used_seed],
+            fn=infer,
+            cache_examples=True,
+            label="Примеры"
+        )
+
+    generate_btn.click(
         fn=infer,
         inputs=[
+            model_id,
             prompt,
             negative_prompt,
             seed,
-            randomize_seed,
             width,
             height,
             guidance_scale,
             num_inference_steps,
         ],
-        outputs=[result, seed],
+        outputs=[output_image, used_seed]
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()