NimaBoscarino commited on
Commit
1e67ab5
•
1 Parent(s): ad7f9c9

Simplify space

Browse files
Files changed (2) hide show
  1. .gitignore +3 -0
  2. app.py +6 -50
.gitignore ADDED
@@ -0,0 +1,3 @@
 
 
 
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+ flagged
2
+ gradio_cached_examples
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+ punks.png
app.py CHANGED
@@ -1,12 +1,6 @@
1
- import subprocess
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- from pathlib import Path
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-
4
- import einops
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  import gradio as gr
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- import numpy as np
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  import torch
8
  from huggingface_hub import hf_hub_download
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- from PIL import Image
10
  from torch import nn
11
  from torchvision.utils import save_image
12
 
@@ -38,60 +32,22 @@ weights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')
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  model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))
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40
 
41
- @torch.no_grad()
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- def interpolate(save_dir='./lerp/', frames=100, rows=8, cols=8):
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- save_dir = Path(save_dir)
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- save_dir.mkdir(exist_ok=True, parents=True)
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-
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- z1 = torch.randn(rows * cols, 100, 1, 1)
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- z2 = torch.randn(rows * cols, 100, 1, 1)
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-
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- zs = []
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- for i in range(frames):
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- alpha = i / frames
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- z = (1 - alpha) * z1 + alpha * z2
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- zs.append(z)
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-
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- zs += zs[::-1] # also go in reverse order to complete loop
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-
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- for i, z in enumerate(zs):
58
- imgs = model(z)
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-
60
- # normalize
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- imgs = (imgs + 1) / 2
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-
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- imgs = (imgs.permute(0, 2, 3, 1).cpu().numpy() * 255).astype(np.uint8)
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-
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- # create grid
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- imgs = einops.rearrange(imgs, "(b1 b2) h w c -> (b1 h) (b2 w) c", b1=rows, b2=cols)
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-
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- Image.fromarray(imgs).save(save_dir / f"{i:03}.png")
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-
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- subprocess.call(f"convert -dispose previous -delay 10 -loop 0 {save_dir}/*.png out.gif".split())
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-
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-
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- def predict(choice, seed):
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  torch.manual_seed(seed)
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-
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- if choice == 'interpolation':
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- interpolate()
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- return 'out.gif'
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- else:
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- z = torch.randn(64, 100, 1, 1)
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- punks = model(z)
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- save_image(punks, "punks.png", normalize=True)
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- return 'punks.png'
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85
 
86
  gr.Interface(
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  predict,
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  inputs=[
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- gr.inputs.Dropdown(['image', 'interpolation'], label='Output Type'),
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  gr.inputs.Slider(label='Seed', minimum=0, maximum=1000, default=42),
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  ],
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  outputs="image",
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  title="Cryptopunks GAN",
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  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.",
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  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>",
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- examples=[["interpolation", 123], ["interpolation", 42], ["image", 456], ["image", 42]],
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  ).launch(cache_examples=True)
 
 
 
 
1
  import gradio as gr
 
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  import torch
3
  from huggingface_hub import hf_hub_download
 
4
  from torch import nn
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  from torchvision.utils import save_image
6
 
32
  model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))
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34
 
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+ def predict(seed):
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  torch.manual_seed(seed)
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+ z = torch.randn(64, 100, 1, 1)
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+ punks = model(z)
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+ save_image(punks, "punks.png", normalize=True)
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+ return 'punks.png'
 
 
 
 
 
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42
 
43
  gr.Interface(
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  predict,
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  inputs=[
 
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  gr.inputs.Slider(label='Seed', minimum=0, maximum=1000, default=42),
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  ],
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  outputs="image",
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  title="Cryptopunks GAN",
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  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.",
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  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>",
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+ examples=[[123], [42], [456], [1337]],
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  ).launch(cache_examples=True)