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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', | |
| #"painting":"geninhu/fastgan-few-shot-art", | |
| "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([3,3.5,3.5]) | |
| 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() | |