import os
import random
import io
from PIL import Image
from deep_translator import GoogleTranslator
import aiohttp
from quart import Quart, request, jsonify, send_file, render_template_string
from flask_caching import Cache # flask-cachingを使用
# アプリケーションの設定
app = Quart(__name__)
# キャッシュの設定
cache_config = {
"CACHE_TYPE": "SimpleCache", # メモリベースのシンプルなキャッシュ
"CACHE_DEFAULT_TIMEOUT": 60 * 60 * 24 # 24時間
}
app.config.from_mapping(cache_config)
cache = Cache(app)
API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 50000 # タイムアウトを300秒に設定
# 非同期APIリクエストの実行関数
async def query_async(prompt, negative_prompt="", steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024, num_inference_steps=30, guidance_scale=7.5, top_k=50, top_p=0.9, eta=0.1):
if not prompt:
return None, "Prompt is required"
key = random.randint(0, 999)
# Translate the prompt from Russian to English if necessary
prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
print(f'Generation {key} translation: {prompt}')
prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
print(f'Generation {key}: {prompt}')
payload = {
"inputs": prompt,
"is_negative": False,
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed != -1 else random.randint(1, 1000000000),
"strength": strength,
"negative_prompt": negative_prompt,
"top_k": top_k,
"top_p": top_p,
"eta": eta,
"parameters": {
"width": width,
"height": height,
"num_inference_steps": num_inference_steps,
"guidance_scale": guidance_scale
}
}
async with aiohttp.ClientSession() as session:
try:
async with session.post(API_URL, json=payload, headers=headers, timeout=timeout) as response:
if response.status != 200:
return None, f"Error: Failed to get image. Status code: {response.status}, Details: {await response.text()}"
image_bytes = await response.read()
image = Image.open(io.BytesIO(image_bytes))
return image, None
except asyncio.TimeoutError:
return None, "Error: The request timed out. Please try again."
except Exception as e:
return None, f"Request Exception: {str(e)}"
# Content-Security-Policyヘッダーを設定するための関数
@app.after_request
async def add_security_headers(response):
response.headers['Content-Security-Policy'] = (
"default-src 'self'; "
"connect-src 'self' ^https?:\/\/[\w.-]+\.[\w.-]+(\/[\w.-]*)*(\?[^\s]*)?$"
"img-src 'self' data:; "
"style-src 'self' 'unsafe-inline'; "
"script-src 'self' 'unsafe-inline'; "
)
return response
# HTMLテンプレート
index_html = """
Image Generator
Welcome to the Image Generator
"""
@app.route('/')
async def index():
return await render_template_string(index_html)
@app.route('/generate', methods=['GET'])
async def generate_image():
prompt = request.args.get("prompt", "")
negative_prompt = request.args.get("negative_prompt", "")
steps = int(request.args.get("steps", 35))
cfg_scale = float(request.args.get("cfgs", 7))
sampler = request.args.get("sampler", "DPM++ 2M Karras")
strength = float(request.args.get("strength", 0.7))
seed = int(request.args.get("seed", -1))
width = int(request.args.get("width", 1024))
height = int(request.args.get("height", 1024))
num_inference_steps = int(request.args.get("num_inference_steps", 30))
guidance_scale = float(request.args.get("guidance_scale", 7.5))
top_k = int(request.args.get("top_k", 50))
top_p = float(request.args.get("top_p", 0.9))
eta = float(request.args.get("eta", 0.1))
# キャッシュを確認
cached_image = cache.get(prompt)
if cached_image:
return await send_file(io.BytesIO(cached_image), mimetype='image/png')
image, error = await query_async(prompt, negative_prompt, steps, cfg_scale, sampler, seed, strength, width, height, num_inference_steps, guidance_scale, top_k, top_p, eta)
if error:
return jsonify({"error": error}), 400
img_bytes = io.BytesIO()
image.save(img_bytes, format='PNG')
img_bytes.seek(0)
# 画像をキャッシュに保存
cache.set(prompt, img_bytes.getvalue())
return await send_file(img_bytes, mimetype='image/png')
if __name__ == "__main__":
app.run(host='0.0.0.0', port=7860)