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| import subprocess | |
| from pathlib import Path | |
| import einops | |
| import numpy as np | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| from torch import nn | |
| from torchvision.utils import save_image | |
| from huggingface_hub.hf_api import HfApi | |
| import streamlit as st | |
| hfapi = HfApi() | |
| class Generator(nn.Module): | |
| def __init__(self, num_channels=4, latent_dim=100, hidden_size=64): | |
| super(Generator, self).__init__() | |
| self.model = nn.Sequential( | |
| # input is Z, going into a convolution | |
| nn.ConvTranspose2d(latent_dim, hidden_size * 8, 4, 1, 0, bias=False), | |
| nn.BatchNorm2d(hidden_size * 8), | |
| nn.ReLU(True), | |
| # state size. (hidden_size*8) x 4 x 4 | |
| nn.ConvTranspose2d(hidden_size * 8, hidden_size * 4, 4, 2, 1, bias=False), | |
| nn.BatchNorm2d(hidden_size * 4), | |
| nn.ReLU(True), | |
| # state size. (hidden_size*4) x 8 x 8 | |
| nn.ConvTranspose2d(hidden_size * 4, hidden_size * 2, 4, 2, 1, bias=False), | |
| nn.BatchNorm2d(hidden_size * 2), | |
| nn.ReLU(True), | |
| # state size. (hidden_size*2) x 16 x 16 | |
| nn.ConvTranspose2d(hidden_size * 2, hidden_size, 4, 2, 1, bias=False), | |
| nn.BatchNorm2d(hidden_size), | |
| nn.ReLU(True), | |
| # state size. (hidden_size) x 32 x 32 | |
| nn.ConvTranspose2d(hidden_size, num_channels, 4, 2, 1, bias=False), | |
| nn.Tanh() | |
| # state size. (num_channels) x 64 x 64 | |
| ) | |
| def forward(self, noise): | |
| pixel_values = self.model(noise) | |
| return pixel_values | |
| def interpolate(model, save_dir='./lerp/', frames=100, rows=8, cols=8): | |
| save_dir = Path(save_dir) | |
| save_dir.mkdir(exist_ok=True, parents=True) | |
| z1 = torch.randn(rows * cols, 100, 1, 1) | |
| z2 = torch.randn(rows * cols, 100, 1, 1) | |
| zs = [] | |
| for i in range(frames): | |
| alpha = i / frames | |
| z = (1 - alpha) * z1 + alpha * z2 | |
| zs.append(z) | |
| zs += zs[::-1] # also go in reverse order to complete loop | |
| frames = [] | |
| for i, z in enumerate(zs): | |
| imgs = model(z) | |
| save_image(imgs, save_dir / f"{i:03}.png", normalize=True) | |
| img = Image.open(save_dir / f"{i:03}.png").convert('RGBA') | |
| img.putalpha(255) | |
| frames.append(img) | |
| img.save(save_dir / f"{i:03}.png") | |
| frames[0].save("out.gif", format="GIF", append_images=frames, | |
| save_all=True, duration=100, loop=1) | |
| def predict(model_name, choice, seed): | |
| model = Generator() | |
| weights_path = hf_hub_download(f'huggingnft/{model_name}', 'pytorch_model.bin') | |
| model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) | |
| torch.manual_seed(seed) | |
| if choice == 'interpolation': | |
| interpolate(model) | |
| return 'out.gif' | |
| else: | |
| z = torch.randn(64, 100, 1, 1) | |
| punks = model(z) | |
| save_image(punks, "image.png", normalize=True) | |
| img = Image.open(f"image.png").convert('RGBA') | |
| img.putalpha(255) | |
| img.save("image.png") | |
| return 'image.png' | |
| model_names = [model.modelId[model.modelId.index("/") + 1:] for model in hfapi.list_models(author="huggingnft")] | |
| st.set_page_config(page_title="Hugging NFT") | |
| st.title("Hugging NFT") | |
| st.sidebar.markdown( | |
| """ | |
| <style> | |
| .aligncenter { | |
| text-align: center; | |
| } | |
| </style> | |
| <p class="aligncenter"> | |
| <img src="https://raw.githubusercontent.com/AlekseyKorshuk/optimum-transformers/master/data/social_preview.png" width="300" /> | |
| </p> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.sidebar.markdown( | |
| """ | |
| <style> | |
| .aligncenter { | |
| text-align: center; | |
| } | |
| </style> | |
| <p style='text-align: center'> | |
| <a href="https://github.com/AlekseyKorshuk/huggingnft" target="_blank">GitHub</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, | |
| ) | |
| st.markdown( | |
| "🤗 [Hugging NFT](https://github.com/AlekseyKorshuk/huggingnft) - Generate NFT by OpenSea collection name.") | |
| st.markdown( | |
| "🚀️ SN-GAN used to train all models.") | |
| st.markdown( | |
| "⁉️ Want to train your model? Check [project repository](https://github.com/AlekseyKorshuk/huggingnft) and make in in few clicks!") | |
| # | |
| # st.markdown("🚀 Up to 1ms on Bert-based transformers") | |
| # | |
| # st.markdown( | |
| # "‼️ NOTE: This Space **does not show** the real power of this project because: low recources, not possbile to optimize models. Check [project repository](https://github.com/AlekseyKorshuk/optimum-transformers) with real bechmarks!") | |
| # st.sidebar.header("Settings:") | |
| model_name = st.selectbox( | |
| 'Choose model:', | |
| model_names) | |
| output_type = st.selectbox( | |
| 'Output type:', | |
| ['image', 'interpolation']) | |
| seed_value = st.slider("Seed:", | |
| min_value=1, | |
| max_value=1000, | |
| step=1, | |
| value=100, | |
| ) | |
| model_html = """ | |
| <div class="inline-flex flex-col" style="line-height: 1.5;"> | |
| <div class="flex"> | |
| <div | |
| \t\t\tstyle="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('USER_PROFILE')"> | |
| </div> | |
| </div> | |
| <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> | |
| <div style="text-align: center; font-size: 16px; font-weight: 800">USER_NAME</div> | |
| <a href="https://genius.com/artists/USER_HANDLE"> | |
| \t<div style="text-align: center; font-size: 14px;">@USER_HANDLE</div> | |
| </a> | |
| </div> | |
| """ | |
| if st.button("Run"): | |
| with st.spinner(text=f"Generating..."): | |
| st.image(predict(model_name, output_type, seed_value)) | |
| st.subheader("Please star project repository, this space and follow my Twitter:") | |
| st.markdown( | |
| """ | |
| <style> | |
| .aligncenter { | |
| text-align: center; | |
| } | |
| </style> | |
| <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, | |
| ) | |