import pandas as pd
import numpy as np
import streamlit as st
from models import Generator, Discriminrator
from StyleMix import style_mix
import torch
import torchvision.transforms as T
from torchvision.utils import make_grid
from PIL import Image
from streamlit_lottie import st_lottie
from streamlit_option_menu import option_menu
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',
"universe": 'huggan/fastgan-few-shot-universe',
"grumpy cat": 'huggan/fastgan-few-shot-grumpy-cat',
"anime": 'huggan/fastgan-few-shot-anime-face',
"moon gate": 'huggan/fastgan-few-shot-moongate',
}
#@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 show_model_summary(expanded):
st.subheader("Model gallery")
with st.expander('', expanded=expanded):
col1, col2, col3, col4 = st.columns(4)
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=200)
st.markdown('Painting GAN [model](https://huggingface.co/huggan/fastgan-few-shot-painting)', unsafe_allow_html=True)
st.image('painting.png', width=200)
with col2:
st.markdown('Aurora GAN [model](https://huggingface.co/huggan/fastgan-few-shot-aurora)', unsafe_allow_html=True)
st.image('aurora.png', width=200)
st.markdown('Universe GAN [model](https://huggingface.co/huggan/fastgan-few-shot-universe)', unsafe_allow_html=True)
st.image('universe.png', width=200)
with col3:
st.markdown('Anime GAN [model](https://huggingface.co/huggan/fastgan-few-shot-anime-face)', unsafe_allow_html=True)
st.image('anime.png', width=200)
st.markdown('Shell GAN [model](https://huggingface.co/huggan/fastgan-few-shot-shells)', unsafe_allow_html=True)
st.image('shell.png', width=200)
with col4:
st.markdown('Grumpy cat GAN [model](https://huggingface.co/huggan/fastgan-few-shot-grumpy-cat)', unsafe_allow_html=True)
st.image('grumpy_cat.png', width=200)
st.markdown('Moon gate GAN [model](https://huggingface.co/huggan/fastgan-few-shot-moongate)', unsafe_allow_html=True)
st.image('moon_gate.png', width=200)
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)
choose = option_menu("FastGAN", ["Model Gallery", "Generate images", "Mix style"],
icons=['collection', 'file-plus', 'intersect'],
menu_icon="infinity", default_index=0,
styles={
"container": {"padding": ".0rem", "font-size": "14px"},
"nav-link-selected": {"color": "#000000", "font-size": "16px"},
}
)
st.sidebar.markdown(
"""
___
FastGAN is a few-shot GAN model trained on high-fidelity images which requires less computation resource and samples for training.
Article
Model training and Spaces creating by
Chien Vu | Nhu Hoang
""",
unsafe_allow_html=True,
)
if choose == 'Model Gallery':
st.header("Welcome to FastGAN")
show_model_summary(True)
elif choose == 'Generate images':
st.header("Generate images")
col11, col12, col13 = st.columns([3,3.5,3.5])
with col11:
img_type = st.selectbox("Choose type of image to generate", index=0,
options=["aurora", "anime", "painting", "fauvism", "shell", "universe", "grumpy cat", "moon gate"])
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("""
Predictions may take up to 1 minute under high load. Please stand by.
""",
unsafe_allow_html=True,)
if generate_button:
with col11:
with st.spinner(text=f"Loading selected model..."):
generator = load_generator(model_name[img_type])
with st.spinner(text=f"Generating images..."):
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)
elif choose == 'Mix style':
st.header("Mix style")
st.markdown(
"""
Get the style representations of 2 images generated from the model to create a new one that mixes the style of two.
""",
unsafe_allow_html=True,
)
st.markdown("""___""")
col21, col22 = st.columns([3, 6])
with col21:
img_type = st.selectbox("Choose type of image to mix", index=0,
options=["aurora", "anime", "painting", "fauvism", "shell", "universe", "grumpy cat", "moon gate"])
number_imgs = st.slider('How many images you want to generate ?', min_value=1, max_value=3)
generate_button = st.button('Mix style')
if generate_button:
with col21:
with st.spinner(text=f"Mixing styles..."):
mix_imgs = style_mix(model_name[img_type], number_imgs, device)
mix_imgs = make_grid(mix_imgs, nrow=number_imgs+1, normalize=True)
mix_imgs = mix_imgs.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
mix_imgs = Image.fromarray(mix_imgs)
with col22:
st.image(mix_imgs, width=600)
if __name__ == '__main__':
main()