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import io
import os
import random
import sys
import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
import torch
from diffusers import StableDiffusionPipeline
# from dotenv import load_dotenv
from huggingface_hub import notebook_login
from PIL import Image
# ローカル実行用 .envファイルから環境変数読み込み
# load_dotenv(".env")
# ACCESS_TOKEN = os.environ.get("ACCESS_TOKEN")
# Hugging SpaceのSecret Repoから環境変数読み取り
ACCESS_TOKEN = st.secrets["ACCESS_TOKEN"]
sys.path.append("./")
from simulation import *
# シード値の固定
SEED = 42
np.random.seed(seed=SEED)
random.seed(SEED)
def main():
# 生息地を表すワード
HABITAT_WORDS = " Alien from Mars"
# パラメーター
GENOMS_SIZE = 4 # 遺伝配列 0, 1 のどちらかを要素とした配列のサイズ
TOUNAMENT_NUM = 10 # トーナメント方式で競わせる数
CROSSOVER_PB = 0.8 # cross over(交差) する確率
MUTATION_PB = 0.5 # mutation(突然変異)する確率
# グローバル変数
global best
POPURATIONS = st.slider(
label="人口数",
min_value=3,
max_value=3000,
value=500,
)
NUM_GENERATION = st.slider(
label="世代数",
min_value=10,
max_value=10000,
value=1000,
)
# キーワード候補
word_dict = {
"body_size": ["Fingertip sized", "Palm sized", "", "Tall", "Giant"],
"body_hair": ["Bald", "Smooth", "", "Furry", "Very Furry"],
"herd_num": ["Lone", "Pair", "", "Herd of", "Swarm of"],
"eating": ["No teeth", "Herbivorous", "Omnivorous", "Carnivorous", "Fang"],
"body_color": [
"Lightest skin",
"Lighter skin",
"",
"Darker skin",
"Darkest skin",
],
"ferocity": ["Peaceful", "Gentle", "", "Ferocious", "Tyrannical"],
}
if st.button("実行", key="ga"):
st.write("遺伝アルゴリズムの実行")
progress_bar_ga = st.progress(0)
# create first genetarion
generation = create_generation(POPURATIONS, GENOMS_SIZE)
progress_bar_ga.progress(50)
# アルゴリズムの実行
best, worst = ga_solve(
generation,
NUM_GENERATION,
POPURATIONS,
TOUNAMENT_NUM,
CROSSOVER_PB,
MUTATION_PB,
)
progress_bar_ga.progress(100)
st.write("遺伝アルゴリズム処理の終了")
st.write("画像生成の実行")
progress_bar_image = st.progress(0)
progress_bar_image.progress(0)
pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", use_auth_token=ACCESS_TOKEN
)
pipe.enable_attention_slicing()
progress_bar_image.progress(7)
device = "gpu" if torch.cuda.is_available() else "cpu"
print("used device is", device)
pipe.to(device)
# NSFWフィルターの回避
def null_safety(images, **kwargs):
return images, False
pipe.safety_checker = null_safety
last_generation = NUM_GENERATION - 1
plt.figure(figsize=(8, 8))
plt.rcParams["font.size"] = 9
words = (
get_word_for_image_generate(word_dict, best, last_generation)
+ HABITAT_WORDS
)
image = pipe(words)["sample"][0]
plt.title(f"{last_generation + 1}th\n{words}.")
plt.xticks([])
plt.yticks([])
plt.imshow(image)
progress_bar_image.progress(100)
plt.tight_layout()
buf = io.BytesIO()
plt.savefig(buf, format="png")
buf.seek(0)
im = Image.open(buf)
numpy_image = np.array(im)
st.image(numpy_image)
if __name__ == "__main__":
main()
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