Update app.py
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app.py
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from diffusers import StableDiffusionPipeline
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import
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from diffusers import StableDiffusionPipeline, DiffusionPipeline
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import torch
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import random
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from datetime import datetime
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from flask import Flask, render_template_string, send_file
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import io
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from PIL import Image
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resolution = (768, 1024) # Risoluzione dell'immagine (width, height)
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num_steps = 20
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guidance_scale = 7.5
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neg_prompt = "blurry"
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model_id = "stablediffusionapi/duchaiten-real3d-nsfw-xl"
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pipe = DiffusionPipeline.from_pretrained(model_id)
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# Imposta il dispositivo su GPU se disponibile
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = pipe.to(device)
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app = Flask(__name__)
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# Funzione per generare un'immagine
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def generate_image(prompt, seed, steps, neg_prompt):
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generator = torch.manual_seed(seed)
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image = pipe(prompt, height=resolution[1], width=resolution[0], num_inference_steps=steps, guidance_scale=guidance_scale, generator=generator, negative_prompt=neg_prompt).images[0]
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return image
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@app.route('/')
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def home():
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# Genera un'immagine
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prompt = "A beautiful landscape"
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seed = random.randint(1, 1000000)
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image = generate_image(prompt, seed, num_steps, neg_prompt)
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# Salva l'immagine in un buffer
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img_io = io.BytesIO()
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image.save(img_io, 'JPEG', quality=70)
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img_io.seek(0)
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# Genera la pagina HTML
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html = """
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<!doctype html>
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<title>Generated Image</title>
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<h1>Generated Image</h1>
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<img src="/image" alt="Generated Image">
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"""
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return render_template_string(html)
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@app.route('/image')
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def image():
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# Genera un'immagine
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prompt = "A beautiful landscape"
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seed = random.randint(1, 1000000)
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image = generate_image(prompt, seed, num_steps, neg_prompt)
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# Salva l'immagine in un buffer
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img_io = io.BytesIO()
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image.save(img_io, 'JPEG', quality=70)
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img_io.seek(0)
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return send_file(img_io, mimetype='image/jpeg')
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if __name__ == '__main__':
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app.run(debug=True)
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