Spaces:
Running
Running
File size: 2,340 Bytes
5c824be e42078a 5c824be e42078a 5c824be f29d636 5c824be 8d90090 5c824be |
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 |
#!/usr/bin/env python3
import gradio as gr
import numpy as np
import torch
import pickle
import types
from huggingface_hub import hf_hub_url, cached_download
# with open('../models/gamma500/network-snapshot-010000.pkl', 'rb') as f:
with open(cached_download(hf_hub_url('ykilcher/apes', 'gamma500/network-snapshot-010000.pkl')), 'rb') as f:
G = pickle.load(f)['G_ema']# torch.nn.Module
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda")
G = G.to(device)
else:
_old_forward = G.forward
def _new_forward(self, *args, **kwargs):
kwargs["force_fp32"] = True
return _old_forward(*args, **kwargs)
G.forward = types.MethodType(_new_forward, G)
_old_synthesis_forward = G.synthesis.forward
def _new_synthesis_forward(self, *args, **kwargs):
kwargs["force_fp32"] = True
return _old_synthesis_forward(*args, **kwargs)
G.synthesis.forward = types.MethodType(_new_synthesis_forward, G.synthesis)
def generate(num_images, interpolate):
if interpolate:
z1 = torch.randn([1, G.z_dim])# latent codes
z2 = torch.randn([1, G.z_dim])# latent codes
zs = torch.cat([z1 + (z2 - z1) * i / (num_images-1) for i in range(num_images)], 0)
else:
zs = torch.randn([num_images, G.z_dim])# latent codes
with torch.no_grad():
zs = zs.to(device)
img = G(zs, None, force_fp32=True, noise_mode='const')
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
return img.cpu().numpy()
def greet(num_images, interpolate):
img = generate(round(num_images), interpolate)
imgs = list(img)
if len(imgs) == 1:
return imgs[0]
grid_len = int(np.ceil(np.sqrt(len(imgs)))) * 2
grid_height = int(np.ceil(len(imgs) / grid_len))
grid = np.zeros((grid_height * imgs[0].shape[0], grid_len * imgs[0].shape[1], 3), dtype=np.uint8)
for i, img in enumerate(imgs):
y = (i // grid_len) * img.shape[0]
x = (i % grid_len) * img.shape[1]
grid[y:y+img.shape[0], x:x+img.shape[1], :] = img
return grid
iface = gr.Interface(fn=greet, inputs=[
gr.inputs.Slider(default=1, label="Num Images", minimum=1, maximum=16, step=1),
gr.inputs.Checkbox(default=False, label="Interpolate")
], outputs="image")
iface.launch()
|