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import pandas as pd
import numpy as np
import streamlit as st
from models import Generator, Discriminrator
from utils import image_to_base64
import torch
import torchvision.transforms as T
from torchvision.utils import make_grid
from PIL import Image
from streamlit_lottie import st_lottie
import requests
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_name = {
"aurora": 'huggan/fastgan-few-shot-aurora',
"painting": 'huggan/fastgan-few-shot-painting',
"shell": 'huggan/fastgan-few-shot-shells',
"fauvism": 'huggan/fastgan-few-shot-fauvism-still-life',
}
#@st.cache(allow_output_mutation=True)
def load_generator(model_name_or_path):
generator = Generator(in_channels=256, out_channels=3)
generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3)
_ = generator.to(device)
_ = generator.eval()
return generator
def _denormalize(input: torch.Tensor) -> torch.Tensor:
return (input * 127.5) + 127.5
def generate_images(generator, number_imgs):
noise = torch.zeros(number_imgs, 256, 1, 1, device=device).normal_(0.0, 1.0)
with torch.no_grad():
gan_images, _ = generator(noise)
gan_images = _denormalize(gan_images.detach()).cpu()
gan_images = [i for i in gan_images]
gan_images = [make_grid(i, nrow=1, normalize=True) for i in gan_images]
gan_images = [i.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy() for i in gan_images]
gan_images = [Image.fromarray(i) for i in gan_images]
return gan_images
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
def main():
st.set_page_config(
page_title="FastGAN Generator",
page_icon="🖥️",
layout="wide",
initial_sidebar_state="expanded"
)
lottie_penguin = load_lottieurl('https://assets7.lottiefiles.com/packages/lf20_mm4bsl3l.json')
with st.sidebar:
st_lottie(lottie_penguin, height=200)
st.sidebar.markdown(
"""
___
<p style='text-align: center'>
FastGAN is an few-shot GAN model that generates images of several types!
</p>
<p style='text-align: center'>
Model training and Space creation by
<br/>
<a href="https://huggingface.co/vumichien" target="_blank">Chien Vu</a> | <a href="https://huggingface.co/geninhu" target="_blank">Nhu Hoang</a>
<br/>
</p>
<p style='text-align: center'>
based on
<br/>
<a href="https://github.com/silentz/Towards-Faster-And-Stabilized-GAN-Training-For-High-Fidelity-Few-Shot-Image-Synthesis" target="_blank">FastGAN model</a> | <a href="https://arxiv.org/abs/2101.04775" target="_blank">Article</a>
</p>
""",
unsafe_allow_html=True,
)
st.header("Welcome to FastGAN")
col1, col2, col3, col4 = st.columns([3,3,3,3])
with col1:
st.markdown('Fauvism GAN [model](https://huggingface.co/huggan/fastgan-few-shot-fauvism-still-life)', unsafe_allow_html=True)
st.image('fauvism.png', width=220)
with col2:
st.markdown('Aurora GAN [model](https://huggingface.co/huggan/fastgan-few-shot-aurora-bs8)', unsafe_allow_html=True)
st.image('aurora.png', width=220)
with col3:
st.markdown('Painting GAN [model](https://huggingface.co/huggan/fastgan-few-shot-painting-bs8)', unsafe_allow_html=True)
st.image('painting.png', width=220)
with col4:
st.markdown('Shell GAN [model](https://huggingface.co/huggan/fastgan-few-shot-shells)', unsafe_allow_html=True)
st.image('shell.png', width=220)
st.markdown('___')
if st.checkbox('Click if you want to create one of your own !'):
col11, col12, col13 = st.columns([3,3,3])
with col11:
img_type = st.selectbox("Choose type of image to generate", index=0, options=["aurora", "painting", "fauvism", "shell"])
# with col12:
number_imgs = st.slider('How many images you want to generate ?', min_value=1, max_value=5)
if number_imgs is None:
st.write('Invalid number ! Please insert number of images to generate !')
raise ValueError('Invalid number ! Please insert number of images to generate !')
generate_button = st.button('Get Image!')
if generate_button:
st.markdown("""
<small><i>Predictions may take up to 1 minute under high load. Please stand by.</i></small>
""",
unsafe_allow_html=True,)
if generate_button:
generator = load_generator(model_name[img_type])
gan_images = generate_images(generator, number_imgs)
with col12:
st.image(gan_images[0], width=300)
if len(gan_images) > 1:
with col13:
if len(gan_images) <= 2:
st.image(gan_images[1], width=300)
else:
st.image(gan_images[1:], width=150)
if __name__ == '__main__':
main()
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