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import os
import io
import base64
from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler, StableDiffusionXLPipeline
import torch
import peft
from flask import Flask, render_template, request, url_for
app = Flask(__name__)
model_id = "runwayml/stable-diffusion-v1-5"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#pipe = StableDiffusionPipeline.from_pretrained(model_id)
#pipe = pipe.to(device)
#pipe.enable_attention_slicing()
pipe = StableDiffusionPipeline.from_pretrained(model_id)
@app.route('/', methods=['GET', 'POST'])
def generate_image():
if request.method == 'POST':
prompt = request.form['prompt']
try:
image = pipe(prompt=prompt).images[0]
buffered = io.BytesIO()
image.save(buffered, format="PNG")
image_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
image_data_uri = f"data:image/png;base64,{image_base64}"
return render_template('index.html', image_url=image_data_uri)
except Exception as e:
return render_template('index.html', error=str(e))
return render_template('index.html')
if __name__ == '__main__':
app.run(debug=True)
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