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('Image gallery', 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('assets/image/fauvism.png', width=200) st.markdown('Painting GAN [model](https://huggingface.co/huggan/fastgan-few-shot-painting)', unsafe_allow_html=True) st.image('assets/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('assets/image/aurora.png', width=200) st.markdown('Universe GAN [model](https://huggingface.co/huggan/fastgan-few-shot-universe)', unsafe_allow_html=True) st.image('assets/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('assets/image/anime.png', width=200) st.markdown('Shell GAN [model](https://huggingface.co/huggan/fastgan-few-shot-shells)', unsafe_allow_html=True) st.image('assets/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('assets/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('assets/image/moon_gate.png', width=200) with st.expander('Video gallery', expanded=False): cols=st.columns(4) # cols[0].write("Universe GAN") # cols[0].image('assets/video/universe.gif') # cols[0].write("Fauvism still life GAN") # cols[0].image('assets/video/fauvism.gif') # # cols[1].write("Aurora GAN") # cols[1].image('assets/video/aurora.gif') # cols[1].write("Moon gate GAN") # cols[1].image('assets/video/moon_gate.gif') # # cols[2].write("Anime GAN") # cols[2].image('assets/video/anime.gif') # cols[2].write("Painting GAN") # cols[2].image('assets/video/painting.gif') # # cols[3].write("Grumpy cat GAN") # cols[3].image('assets/video/grumpy_cat.gif') cols[0].write("Universe GAN") cols[0].video('assets/video/universe.mp4') cols[0].write("Fauvism still life GAN") cols[0].video('assets/video/fauvism.mp4') cols[1].write("Aurora GAN") cols[1].video('assets/video/aurora.mp4') cols[1].write("Moon gate GAN") cols[1].video('assets/video/moongate.mp4') cols[2].write("Anime GAN") cols[2].video('assets/video/anime.mp4') cols[2].write("Painting GAN") cols[2].video('assets/video/painting.mp4') cols[3].write("Grumpy cat GAN") cols[3].video('assets/video/grumpy.mp4') 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()