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import sys
import os
import gradio as gr
from PIL import Image
os.system("git clone https://github.com/autonomousvision/projected_gan.git")
sys.path.append("projected_gan")
"""Generate images using pretrained network pickle."""
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import numpy as np
import PIL.Image
import torch
import legacy
from huggingface_hub import hf_hub_url
#----------------------------------------------------------------------------
def parse_range(s: Union[str, List]) -> List[int]:
'''Parse a comma separated list of numbers or ranges and return a list of ints.
Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
'''
if isinstance(s, list): return s
ranges = []
range_re = re.compile(r'^(\d+)-(\d+)$')
for p in s.split(','):
m = range_re.match(p)
if m:
ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
else:
ranges.append(int(p))
return ranges
#----------------------------------------------------------------------------
def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]:
'''Parse a floating point 2-vector of syntax 'a,b'.
Example:
'0,1' returns (0,1)
'''
if isinstance(s, tuple): return s
parts = s.split(',')
if len(parts) == 2:
return (float(parts[0]), float(parts[1]))
raise ValueError(f'cannot parse 2-vector {s}')
#----------------------------------------------------------------------------
def make_transform(translate: Tuple[float,float], angle: float):
m = np.eye(3)
s = np.sin(angle/360.0*np.pi*2)
c = np.cos(angle/360.0*np.pi*2)
m[0][0] = c
m[0][1] = s
m[0][2] = translate[0]
m[1][0] = -s
m[1][1] = c
m[1][2] = translate[1]
return m
#----------------------------------------------------------------------------
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
config_file_url = hf_hub_url("autonomousvision/Projected_GAN_Pokemon", filename="pokemon.pkl")
with dnnlib.util.open_url(config_file_url) as f:
G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
def generate_images(seeds):
"""Generate images using pretrained network pickle.
Examples:
\b
# Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
python gen_images.py --outdir=out --trunc=1 --seeds=2 \\
--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
\b
# Generate uncurated images with truncation using the MetFaces-U dataset
python gen_images.py --outdir=out --trunc=0.7 --seeds=600-605 \\
--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl
"""
# Labels.
label = torch.zeros([1, G.c_dim], device=device)
# Generate images.
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device).float()
# Construct an inverse rotation/translation matrix and pass to the generator. The
# generator expects this matrix as an inverse to avoid potentially failing numerical
# operations in the network.
if hasattr(G.synthesis, 'input'):
m = make_transform('0,0', 0)
m = np.linalg.inv(m)
G.synthesis.input.transform.copy_(torch.from_numpy(m))
img = G(z, label, truncation_psi=1, noise_mode='const')
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
pilimg = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
return pilimg
def inference(seedin):
listseed = [int(seedin)]
output = generate_images(listseed)
return output
title = "Projected GAN"
description = "Gradio demo for Projected GANs Converge Faster, Pokemon. To use it, add seed, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='http://www.cvlibs.net/publications/Sauer2021NEURIPS.pdf' target='_blank'>Projected GANs Converge Faster</a> | <a href='https://github.com/autonomousvision/projected_gan' target='_blank'>Github Repo</p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_projected_gan' alt='visitor badge'></center>"
gr.Interface(inference,gr.inputs.Slider(label="Seed",minimum=0, maximum=5000, step=1, default=0),"pil",title=title,description=description,article=article, examples=[
[0],[1],[10],[20],[30],[42],[50],[60],[77],[102]
]).launch(enable_queue=True,cache_examples=True) |