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main.py
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import os
import argparse
import scipy
import torch
import numpy as np
from PIL import Image
from konlpy.tag import Okt
from models import (
ControlNet,
Summarizer,
Text2Audio
)
def parse_args():
parser = argparse.ArgumentParser()
# diary text
parser.add_argument("--text", type=str)
# model setting
parser.add_argument("--summarizer", default="psyche/KoT5-summarization", type=str)
parser.add_argument("--controlnet", default="lllyasviel/sd-controlnet-scribble", type=str)
parser.add_argument("--diffusion", default="runwayml/stable-diffusion-v1-5", type=str)
parser.add_argument("--audio_ldm", default="cvssp/audioldm2-large", type=str)
# summarize task hyperparameter setting
# img2img task hyperparameter setting
# text2audio hyperparameter setting
# directory setting
parser.add_argument("--output_dir", default="./outputs", type=str)
args = parser.parse_args()
return args
def main():
# load arguments
args = parse_args()
content = args.text
# load models
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
okt = Okt()
summarizer = Summarizer(
args.summarizer,
device=device
)
img2img = ControlNet(
args.controlnet,
args.diffusion,
device=device)
text2audio = Text2Audio(
args.audio_ldm,
device=device
)
# sketch drawings
keywords = list(set(okt.nouns(content)))
sketch = Image.fromarray(np.zeros((512,512,3), dtype=np.uint8))
img = img2img.generate(sketch)
# summarize text
summarized_content = summarizer.summarize(content)
# generate audio
audio = text2audio.generate_audio(summarized_content)
# save files
os.makedirs(args.output_dir, exist_ok=True)
img.save(os.path.join(args.output_dir, "image.png"))
scipy.io.wavfile.write(os.path.join(args.output_dir, "audio.wav"), rate=16000, data=audio)
print("Saved Files!")
if __name__ == "__main__":
main()