File size: 3,344 Bytes
9c75821 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
# 2022aug31
#import gradio as gr
#def greet(name):
# return "Hello " + name + "!!"
#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()
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, nc=4, nz=100, ngf=64):
super(Generator, self).__init__()
self.network = nn.Sequential(
nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
nn.Tanh(),
)
def forward(self, input):
output = self.network(input)
return output
model = Generator()
weights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')
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
for i, z in enumerate(zs):
imgs = model(z)
# normalize
imgs = (imgs + 1) / 2
imgs = (imgs.permute(0, 2, 3, 1).cpu().numpy() * 255).astype(np.uint8)
# create grid
imgs = einops.rearrange(imgs, "(b1 b2) h w c -> (b1 h) (b2 w) c", b1=rows, b2=cols)
Image.fromarray(imgs).save(save_dir / f"{i:03}.png")
subprocess.call(f"convert -dispose previous -delay 10 -loop 0 {save_dir}/*.png out.gif".split())
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, "punks.png", normalize=True)
return 'punks.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)
|