Simplify space

.gitignore

@@ -0,0 +1,3 @@
+flagged
+gradio_cached_examples
+punks.png

app.py

@@ -1,12 +1,6 @@
-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
 
@@ -38,60 +32,22 @@ 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):
+def predict(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'
+    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]],
+    examples=[[123], [42], [456], [1337]],
 ).launch(cache_examples=True)