:sparkles: add interpolation option

app.py

@@ -1,6 +1,12 @@
+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
 
@@ -32,18 +38,60 @@ weights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')
 model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))
 
 
-def predict(text):
-    z = torch.randn(64, 100, 1, 1)
-    punks = model(z)
-    save_image(punks, "punks.png", normalize=True)
-    return 'punks.png'
+@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="text",
+    inputs=[
+        gr.inputs.Dropdown(['image', 'interpolation'], label='Output Type'),
+        gr.inputs.Slider(label='Seed', minimum=0, maximum=1000, default=42),
+    ],
     outputs="image",
-    title="InfiniPunks",
-    description="These CryptoPunks do not exist.",
+    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"], ["image", "456"]],
 ).launch()

packages.txt

@@ -0,0 +1 @@
+imagemagick

requirements.txt

@@ -1,3 +1,4 @@
 gradio
 torch
-torchvision
+torchvision
+einops