X commited on
Update app.py
Browse files
app.py
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# Шаг 2. Импорт
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import torch
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import numpy as np
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import gradio as gr
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# Шаг 3.
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print("Загрузка модели...")
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model_id = "runwayml/stable-diffusion-v1-5"
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print("Модель загружена!")
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# Шаг
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def generate_image(prompt, negative_prompt):
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if len(prompt.split()) < 10:
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prompt = prompt + ", detailed, sharp focus, masterpiece"
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generator = torch.Generator(device="cuda").manual_seed(2021)
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with torch.
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt if negative_prompt else "",
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return img_final
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# Шаг
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("#")
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outputs=output
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)
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# Шаг
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demo.launch(share=True, debug=False)
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# Шаг 1. Установка
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!pip install diffusers transformers accelerate safetensors gradio --quiet
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# Шаг 2. Импорт
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import torch
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import numpy as np
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import gradio as gr
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# Шаг 3. Проверка GPU
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"Используется: {device}")
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# Шаг 4. Загрузка модели (без float16 для CPU)
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print("Загрузка модели...")
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model_id = "runwayml/stable-diffusion-v1-5"
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if device == "cuda":
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pipe = StableDiffusionPipeline.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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safety_checker=None,
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requires_safety_checker=False
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)
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else:
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pipe = StableDiffusionPipeline.from_pretrained(
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model_id,
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safety_checker=None,
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requires_safety_checker=False
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)
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pipe = pipe.to(device)
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# Экономия памяти
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try:
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pipe.enable_attention_slicing()
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except:
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pass
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try:
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pipe.vae.enable_slicing()
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except:
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pass
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print("Модель загружена!")
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# Шаг 5. Функция генерации
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def generate_image(prompt, negative_prompt):
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generator = torch.Generator(device=device).manual_seed(2021)
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with torch.no_grad():
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt if negative_prompt else "",
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return img_final
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# Шаг 6. Интерфейс
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("#")
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outputs=output
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)
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# Шаг 7. Запуск
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demo.launch(share=True, debug=False)
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