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import subprocess
from pathlib import Path
import einops
import gradio as gr
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
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
model = Generator()
weights_path = hf_hub_download('huggingnft/dooggies', 'pytorch_model.bin')
model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))
@torch.no_grad()
def interpolate(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(choice, seed):
torch.manual_seed(seed)
if choice == 'interpolation':
interpolate()
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'
gr.Interface(
predict,
inputs=[
gr.inputs.Dropdown(['image', 'interpolation'], label='Output Type'),
gr.inputs.Slider(label='Seed', minimum=0, maximum=1000, default=42),
],
outputs="image",
title="Cryptopunks GAN",
description="These CryptoPunks do not exist. You have the choice of either generating random punks, or a gif showing the interpolation between two random punk grids.",
article="<p style='text-align: center'><a href='https://arxiv.org/pdf/1511.06434.pdf'>Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a> | <a href='https://github.com/teddykoker/cryptopunks-gan'>Github Repo</a></p>",
examples=[["interpolation", 123], ["interpolation", 42], ["image", 456], ["image", 42]],
).launch(cache_examples=True)
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