Generated Image

from diffusers import StableDiffusionPipeline, DiffusionPipeline
import torch
import random
from datetime import datetime
from flask import Flask, render_template_string, send_file
import io
from PIL import Image

resolution = (768, 1024)  # Risoluzione dell'immagine (width, height)
num_steps = 20
guidance_scale = 7.5
neg_prompt = "blurry"

model_id = "stablediffusionapi/duchaiten-real3d-nsfw-xl"
pipe = DiffusionPipeline.from_pretrained(model_id)

# Imposta il dispositivo su GPU se disponibile
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = pipe.to(device)

app = Flask(__name__)

# Funzione per generare un'immagine
def generate_image(prompt, seed, steps, neg_prompt):
    generator = torch.manual_seed(seed)
    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]
    return image

@app.route('/')
def home():
    # Genera un'immagine
    prompt = "A beautiful landscape"
    seed = random.randint(1, 1000000)
    image = generate_image(prompt, seed, num_steps, neg_prompt)

    # Salva l'immagine in un buffer
    img_io = io.BytesIO()
    image.save(img_io, 'JPEG', quality=70)
    img_io.seek(0)

    # Genera la pagina HTML
    html = """
    <!doctype html>
    <title>Generated Image</title>
    <h1>Generated Image</h1>
    <img src="/image" alt="Generated Image">
    """
    return render_template_string(html)

@app.route('/image')
def image():
    # Genera un'immagine
    prompt = "A beautiful landscape"
    seed = random.randint(1, 1000000)
    image = generate_image(prompt, seed, num_steps, neg_prompt)

    # Salva l'immagine in un buffer
    img_io = io.BytesIO()
    image.save(img_io, 'JPEG', quality=70)
    img_io.seek(0)
    
    return send_file(img_io, mimetype='image/jpeg')

if __name__ == '__main__':
    app.run(debug=True)