Update app.py

app.py

@@ -1,62 +1,111 @@
 import gradio as gr
 import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+from diffusers import DiffusionPipeline
+from peft import PeftModel
+import re
 
+# Устройство и тип данных
 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
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+# Регулярное выражение для проверки корректности модели
+VALID_REPO_ID_REGEX = re.compile(r"^[a-zA-Z0-9._\-]+/[a-zA-Z0-9._\-]+$")
+def is_valid_repo_id(repo_id):
+    return bool(VALID_REPO_ID_REGEX.match(repo_id)) and not repo_id.endswith(('-', '.'))
 
+# Базовые константы
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
+# Изначально загружаем модель по умолчанию
+model_repo_id = "CompVis/stable-diffusion-v1-4"
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype, safety_checker=None).to(device)
+
+# Попробуем подгрузить LoRA-модификации
+try:
+    pipe.unet = PeftModel.from_pretrained(pipe.unet, "./unet")
+    pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, "./text_encoder")
+except Exception as e:
+    print(f"Не удалось подгрузить LoRA по умолчанию: {e}")
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
+    model,
     prompt,
     negative_prompt,
     seed,
-    randomize_seed,
     width,
     height,
     guidance_scale,
     num_inference_steps,
+    use_controlnet,
+    control_strength,
+    controlnet_mode,
+    controlnet_image,
+    use_ip_adapter,
+    ip_adapter_scale,
+    ip_adapter_image,
     progress=gr.Progress(track_tqdm=True),
 ):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+    """
+    Функция генерации изображения.
+    Параметры use_controlnet, control_strength, controlnet_mode, controlnet_image,
+    use_ip_adapter, ip_adapter_scale, ip_adapter_image — это заглушки для демонстрации UI.
+    """
 
-    generator = torch.Generator().manual_seed(seed)
+    global model_repo_id, pipe
 
-    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]
+    # Если пользователь ввёл другую модель, пробуем её загрузить с нуля
+    if model != model_repo_id:
+        if not is_valid_repo_id(model):
+            raise gr.Error(f"Некорректный идентификатор модели: '{model}'. Проверьте название.")
 
-    return image, seed
+        try:
+            new_pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch_dtype).to(device)
+            # Повторно подгружаем LoRA
+            try:
+                new_pipe.unet = PeftModel.from_pretrained(new_pipe.unet, "./unet")
+                new_pipe.text_encoder = PeftModel.from_pretrained(new_pipe.text_encoder, "./text_encoder")
+            except Exception as e:
+                raise gr.Error(f"Не удалось подгрузить LoRA: {e}")
+
+            # Обновляем глобальные переменные
+            pipe = new_pipe
+            model_repo_id = model
+
+        except Exception as e:
+            raise gr.Error(f"Не удалось загрузить модель '{model}'.\nОшибка: {e}")
+
+    # Создаём генератор случайных чисел для детерминированности
+    generator = torch.Generator(device=device).manual_seed(seed)
 
+    # --- Здесь должна быть интеграция ControlNet, IP-adapter и т.д. ---
+    # Для демонстрации интерфейса просто вызываем pipe как обычно.
+    # ------------------------------------------------------------------
 
+    try:
+        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]
+    except Exception as e:
+        raise gr.Error(f"Ошибка при генерации изображения: {e}")
+
+    return image, seed
+
+# Примеры для удобного тестирования
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
     "A delicious ceviche cheesecake slice",
 ]
 
+# Дополнительный CSS для оформления
 css = """
 #col-container {
     margin: 0 auto;
@@ -64,91 +113,165 @@ css = """
 }
 """
 
+# Создаём Gradio-приложение
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
+        gr.Markdown("# Text-to-Image App")
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+        # Поле для ввода/смены модели
+        model = gr.Textbox(
+            label="Model",
+            value="CompVis/stable-diffusion-v1-4",  # Значение по умолчанию
+            interactive=True
+        )
 
-        result = gr.Image(label="Result", show_label=False)
+        # Основные поля для Prompt и Negative Prompt
+        prompt = gr.Text(
+            label="Prompt",
+            show_label=False,
+            max_lines=1,
+            placeholder="Enter your prompt",
+            container=False,
+        )
+        negative_prompt = gr.Text(
+            label="Negative prompt",
+            max_lines=1,
+            placeholder="Enter a negative prompt",
+            visible=True,
+        )
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
+        # Слайдер для выбора seed
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=42,
+        )
 
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
+        # Слайдеры для guidance_scale и num_inference_steps
+        guidance_scale = gr.Slider(
+            label="Guidance scale",
+            minimum=0.0,
+            maximum=10.0,
+            step=0.1,
+            value=7.0,
+        )
+        num_inference_steps = gr.Slider(
+            label="Number of inference steps",
+            minimum=1,
+            maximum=50,
+            step=1,
+            value=20,
+        )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        # Кнопка запуска
+        run_button = gr.Button("Run", variant="primary")
 
+        # Поле для отображения результата
+        result = gr.Image(label="Result", show_label=False)
+
+        # Продвинутые настройки (Accordion)
+        with gr.Accordion("Advanced Settings", 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
+                    value=512,
                 )
-
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=512,
                 )
 
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
+            # --- Дополнительные элементы для ControlNet ---
+            use_controlnet = gr.Checkbox(label="Use ControlNet", value=False)
+            with gr.Group(visible=False) as controlnet_group:
+                control_strength = gr.Slider(
+                    label="ControlNet Strength",
                     minimum=0.0,
-                    maximum=10.0,
+                    maximum=2.0,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=1.0,
+                )
+                controlnet_mode = gr.Dropdown(
+                    label="ControlNet Mode",
+                    choices=["edge_detection", "pose_estimation", "depth_estimation"],
+                    value="edge_detection",
+                )
+                controlnet_image = gr.Image(
+                    label="ControlNet Image",
+                    source="upload",
+                    type="pil"
                 )
 
-                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
+            # Функция для управления видимостью группы ControlNet
+            def update_controlnet_group(use_controlnet):
+                return {"visible": use_controlnet}
+
+            use_controlnet.change(
+                update_controlnet_group,
+                inputs=[use_controlnet],
+                outputs=[controlnet_group]
+            )
+
+            # --- Дополнительные элементы для IP-adapter ---
+            use_ip_adapter = gr.Checkbox(label="Use IP-adapter", value=False)
+            with gr.Group(visible=False) as ip_adapter_group:
+                ip_adapter_scale = gr.Slider(
+                    label="IP-adapter Scale",
+                    minimum=0.0,
+                    maximum=2.0,
+                    step=0.1,
+                    value=1.0,
+                )
+                ip_adapter_image = gr.Image(
+                    label="IP-adapter Image",
+                    source="upload",
+                    type="pil"
                 )
 
+            # Функция для управления видимостью группы IP-adapter
+            def update_ip_adapter_group(use_ip_adapter):
+                return {"visible": use_ip_adapter}
+
+            use_ip_adapter.change(
+                update_ip_adapter_group,
+                inputs=[use_ip_adapter],
+                outputs=[ip_adapter_group]
+            )
+
+        # Примеры
         gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
 
+        # Связка кнопки "Run" с функцией "infer"
+        run_button.click(
+            infer,
+            inputs=[
+                model,
+                prompt,
+                negative_prompt,
+                seed,
+                width,
+                height,
+                guidance_scale,
+                num_inference_steps,
+                use_controlnet,
+                control_strength,
+                controlnet_mode,
+                controlnet_image,
+                use_ip_adapter,
+                ip_adapter_scale,
+                ip_adapter_image
+            ],
+            outputs=[result, seed],
+        )
+
+# Запуск
 if __name__ == "__main__":
     demo.launch()