Spaces:
Running
Running
| import json | |
| import torch | |
| from huggingnft.lightweight_gan.train import timestamped_filename | |
| from streamlit_option_menu import option_menu | |
| from huggingface_hub import hf_hub_download, file_download | |
| from PIL import Image | |
| from huggingface_hub.hf_api import HfApi | |
| import streamlit as st | |
| from huggingnft.lightweight_gan.lightweight_gan import Generator, LightweightGAN, evaluate_in_chunks, Trainer | |
| from accelerate import Accelerator | |
| from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet | |
| from torchvision import transforms as T | |
| from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip | |
| from torchvision.utils import make_grid | |
| import requests | |
| hfapi = HfApi() | |
| model_names = [model.modelId[model.modelId.index("/") + 1:] for model in hfapi.list_models(author="huggingnft")] | |
| # streamlit-option-menu | |
| # st.set_page_config(page_title="Streamlit App Gallery", page_icon="", layout="wide") | |
| # sysmenu = ''' | |
| # <style> | |
| # #MainMenu {visibility:hidden;} | |
| # footer {visibility:hidden;} | |
| # ''' | |
| # st.markdown(sysmenu,unsafe_allow_html=True) | |
| # # Add a logo (optional) in the sidebar | |
| # logo = Image.open(r'C:\Users\13525\Desktop\Insights_Bees_logo.png') | |
| # profile = Image.open(r'C:\Users\13525\Desktop\medium_profile.png') | |
| ABOUT_TEXT = "🤗 Hugging NFT - Generate NFT by OpenSea collection name." | |
| CONTACT_TEXT = "Here is some contact info" | |
| GENERATE_IMAGE_TEXT = "Generate NFT by selecting existing model based on OpenSea collection. You can create new model or imporve existing in few clicks — check [project repository](https://github.com/AlekseyKorshuk/huggingnft)." | |
| INTERPOLATION_TEXT = "Generate interpolation between two NFTs by selecting existing model based on OpenSea collection. You can create new model or imporve existing in few clicks — check [project repository](https://github.com/AlekseyKorshuk/huggingnft)." | |
| COLLECTION2COLLECTION_TEXT = "Generate first NFT with existing model and transform it to another collection by selecting existing model based on OpenSea collections. You can create new model or imporve existing in few clicks — check [project repository](https://github.com/AlekseyKorshuk/huggingnft)." | |
| STOPWORDS = ["-old"] | |
| COLLECTION2COLLECTION_KEYS = ["__2__"] | |
| def load_lightweight_model(model_name): | |
| file_path = file_download.hf_hub_download( | |
| repo_id=model_name, | |
| filename="config.json" | |
| ) | |
| config = json.loads(open(file_path).read()) | |
| organization_name, name = model_name.split("/") | |
| model = Trainer(**config, organization_name=organization_name, name=name) | |
| model.load(use_cpu=True) | |
| model.accelerator = Accelerator() | |
| return model | |
| def clean_models(model_names, stopwords): | |
| cleaned_model_names = [] | |
| for model_name in model_names: | |
| clear = True | |
| for stopword in stopwords: | |
| if stopword in model_name: | |
| clear = False | |
| break | |
| if clear: | |
| cleaned_model_names.append(model_name) | |
| return cleaned_model_names | |
| def get_concat_h(im1, im2): | |
| dst = Image.new('RGB', (im1.width + im2.width, im1.height)) | |
| dst.paste(im1, (0, 0)) | |
| dst.paste(im2, (im1.width, 0)) | |
| return dst | |
| model_names = clean_models(model_names, STOPWORDS) | |
| with st.sidebar: | |
| choose = option_menu("Hugging NFT", | |
| ["About", "Generate image", "Interpolation", "Collection2Collection", "Contact"], | |
| icons=['house', 'camera fill', 'bi bi-youtube', 'book', 'person lines fill'], | |
| menu_icon="app-indicator", default_index=0, | |
| styles={ | |
| # "container": {"padding": "5!important", "background-color": "#fafafa", }, | |
| "container": {"border-radius": ".0rem"}, | |
| # "icon": {"color": "orange", "font-size": "25px"}, | |
| # "nav-link": {"font-size": "16px", "text-align": "left", "margin": "0px", | |
| # "--hover-color": "#eee"}, | |
| # "nav-link-selected": {"background-color": "#02ab21"}, | |
| } | |
| ) | |
| st.sidebar.markdown( | |
| """ | |
| <style> | |
| .aligncenter { | |
| text-align: center; | |
| } | |
| </style> | |
| <p style='text-align: center'> | |
| <a href="https://github.com/AlekseyKorshuk/huggingnft" target="_blank">Project Repository</a> | |
| </p> | |
| <p class="aligncenter"> | |
| <a href="https://github.com/AlekseyKorshuk/huggingnft" target="_blank"> | |
| <img src="https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social"/> | |
| </a> | |
| </p> | |
| <p class="aligncenter"> | |
| <a href="https://twitter.com/alekseykorshuk" target="_blank"> | |
| <img src="https://img.shields.io/twitter/follow/alekseykorshuk?style=social"/> | |
| </a> | |
| </p> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| if choose == "About": | |
| README = requests.get("https://raw.githubusercontent.com/AlekseyKorshuk/huggingnft/main/README.md").text | |
| README = str(README).replace('width="1200"','width="700"') | |
| # st.title(choose) | |
| st.markdown(README, unsafe_allow_html=True) | |
| if choose == "Contact": | |
| st.title(choose) | |
| st.markdown(CONTACT_TEXT) | |
| if choose == "Generate image": | |
| st.title(choose) | |
| st.markdown(GENERATE_IMAGE_TEXT) | |
| model_name = st.selectbox( | |
| 'Choose model:', | |
| clean_models(model_names, COLLECTION2COLLECTION_KEYS) | |
| ) | |
| generation_type = st.selectbox( | |
| 'Select generation type:', | |
| ["default", "ema"] | |
| ) | |
| nrows = st.number_input("Number of rows:", | |
| min_value=1, | |
| max_value=10, | |
| step=1, | |
| value=8, | |
| ) | |
| generate_image_button = st.button("Generate") | |
| if generate_image_button: | |
| with st.spinner(text=f"Downloading selected model..."): | |
| model = load_lightweight_model(f"huggingnft/{model_name}") | |
| with st.spinner(text=f"Generating..."): | |
| st.image( | |
| model.generate_app( | |
| num=timestamped_filename(), | |
| nrow=nrows, | |
| checkpoint=-1, | |
| types=generation_type | |
| )[0] | |
| ) | |
| if choose == "Interpolation": | |
| st.title(choose) | |
| st.markdown(INTERPOLATION_TEXT) | |
| model_name = st.selectbox( | |
| 'Choose model:', | |
| clean_models(model_names, COLLECTION2COLLECTION_KEYS) | |
| ) | |
| nrows = st.number_input("Number of rows:", | |
| min_value=1, | |
| max_value=10, | |
| step=1, | |
| value=1, | |
| ) | |
| num_steps = st.number_input("Number of steps:", | |
| min_value=1, | |
| max_value=1000, | |
| step=1, | |
| value=100, | |
| ) | |
| generate_image_button = st.button("Generate") | |
| if generate_image_button: | |
| with st.spinner(text=f"Downloading selected model..."): | |
| model = load_lightweight_model(f"huggingnft/{model_name}") | |
| my_bar = st.progress(0) | |
| result = model.generate_interpolation( | |
| num=timestamped_filename(), | |
| num_image_tiles=nrows, | |
| num_steps=num_steps, | |
| save_frames=False, | |
| progress_bar=my_bar | |
| ) | |
| my_bar.empty() | |
| with st.spinner(text=f"Uploading result..."): | |
| st.image(result) | |
| if choose == "Collection2Collection": | |
| st.title(choose) | |
| st.markdown(COLLECTION2COLLECTION_TEXT) | |
| model_name = st.selectbox( | |
| 'Choose model:', | |
| set(model_names) - set(clean_models(model_names, COLLECTION2COLLECTION_KEYS)) | |
| ) | |
| nrows = st.number_input("Number of images to generate:", | |
| min_value=1, | |
| max_value=10, | |
| step=1, | |
| value=1, | |
| ) | |
| generate_image_button = st.button("Generate") | |
| if generate_image_button: | |
| n_channels = 3 | |
| image_size = 256 | |
| input_shape = (image_size, image_size) | |
| transform = Compose([ | |
| T.ToPILImage(), | |
| T.Resize(input_shape), | |
| ToTensor(), | |
| Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), | |
| ]) | |
| with st.spinner(text=f"Downloading selected model..."): | |
| translator = GeneratorResNet.from_pretrained(f'huggingnft/{model_name}', | |
| input_shape=(n_channels, image_size, image_size), | |
| num_residual_blocks=9) | |
| z = torch.randn(nrows, 100, 1, 1) | |
| with st.spinner(text=f"Downloading selected model..."): | |
| model = load_lightweight_model(f"huggingnft/{model_name.split('__2__')[0]}") | |
| with st.spinner(text=f"Generating input images..."): | |
| punks = model.generate_app( | |
| num=timestamped_filename(), | |
| nrow=4, | |
| checkpoint=-1, | |
| types="default" | |
| )[1] | |
| pipe_transform = T.Resize((256, 256)) | |
| input = pipe_transform(punks) | |
| with st.spinner(text=f"Generating output images..."): | |
| output = translator(input) | |
| out_img = make_grid(output, | |
| nrow=4, normalize=True) | |
| # out_img = make_grid(punks, | |
| # nrow=8, normalize=True) | |
| out_transform = Compose([ | |
| T.ToPILImage() | |
| ]) | |
| results = [] | |
| for out_punk, out_ape in zip(input, output): | |
| results.append( | |
| get_concat_h(out_transform(make_grid(out_punk, nrow=1, normalize=True)), out_transform(make_grid(out_ape, nrow=1, normalize=True))) | |
| ) | |
| for result in results: | |
| st.image(result) | |