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#!/usr/bin/env python
from __future__ import annotations
import argparse
import functools
import os
import subprocess
import sys
import gradio as gr
import numpy as np
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
if os.environ.get('SYSTEM') == 'spaces':
subprocess.call('git apply ../patch'.split(), cwd='stylegan2-pytorch')
sys.path.insert(0, 'stylegan2-pytorch')
from model import Generator
TITLE = 'TADNE (This Anime Does Not Exist) Interpolation'
DESCRIPTION = 'The original TADNE site is https://thisanimedoesnotexist.ai/.'
ARTICLE = None
TOKEN = os.environ['TOKEN']
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--theme', type=str)
parser.add_argument('--live', action='store_true')
parser.add_argument('--share', action='store_true')
parser.add_argument('--port', type=int)
parser.add_argument('--disable-queue',
dest='enable_queue',
action='store_false')
parser.add_argument('--allow-flagging', type=str, default='never')
parser.add_argument('--allow-screenshot', action='store_true')
return parser.parse_args()
def load_model(device: torch.device) -> nn.Module:
model = Generator(512, 1024, 4, channel_multiplier=2)
path = hf_hub_download('hysts/TADNE',
'models/aydao-anime-danbooru2019s-512-5268480.pt',
use_auth_token=TOKEN)
checkpoint = torch.load(path)
model.load_state_dict(checkpoint['g_ema'])
model.eval()
model.to(device)
model.latent_avg = checkpoint['latent_avg'].to(device)
with torch.inference_mode():
z = torch.zeros((1, model.style_dim)).to(device)
model([z], truncation=0.7, truncation_latent=model.latent_avg)
return model
def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor:
return torch.from_numpy(np.random.RandomState(seed).randn(
1, z_dim)).to(device).float()
@torch.inference_mode()
def generate_image(model: nn.Module, z: torch.Tensor, truncation_psi: float,
randomize_noise: bool) -> np.ndarray:
out, _ = model([z],
truncation=truncation_psi,
truncation_latent=model.latent_avg,
randomize_noise=randomize_noise)
out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
return out[0].cpu().numpy()
@torch.inference_mode()
def generate_interpolated_images(seed0: int, seed1: int, num_intermediate: int,
psi0: float, psi1: float,
randomize_noise: bool, model: nn.Module,
device: torch.device) -> np.ndarray:
seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max))
seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max))
z0 = generate_z(model.style_dim, seed0, device)
if num_intermediate == -1:
out = generate_image(model, z0, psi0, randomize_noise)
return out
z1 = generate_z(model.style_dim, seed1, device)
vec = z1 - z0
dvec = vec / (num_intermediate + 1)
zs = [z0 + dvec * i for i in range(num_intermediate + 2)]
dpsi = (psi1 - psi0) / (num_intermediate + 1)
psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)]
res = []
for z, psi in zip(zs, psis):
out = generate_image(model, z, psi, randomize_noise)
res.append(out)
res = np.hstack(res)
return res
def main():
gr.close_all()
args = parse_args()
device = torch.device(args.device)
model = load_model(device)
func = functools.partial(generate_interpolated_images,
model=model,
device=device)
func = functools.update_wrapper(func, generate_interpolated_images)
examples = [
[29703, 55376, 3, 0.7, 0.7, False],
[34141, 36864, 5, 0.7, 0.7, False],
[74650, 88322, 7, 0.7, 0.7, False],
[84314, 70317410, 9, 0.7, 0.7, False],
[55376, 55376, 5, 0.3, 1.3, False],
]
gr.Interface(
func,
[
gr.inputs.Number(default=29703, label='Seed 1'),
gr.inputs.Number(default=55376, label='Seed 2'),
gr.inputs.Slider(-1,
11,
step=1,
default=3,
label='Number of Intermediate Frames'),
gr.inputs.Slider(
0, 2, step=0.05, default=0.7, label='Truncation psi 1'),
gr.inputs.Slider(
0, 2, step=0.05, default=0.7, label='Truncation psi 2'),
gr.inputs.Checkbox(default=False, label='Randomize Noise'),
],
gr.outputs.Image(type='numpy', label='Output'),
examples=examples,
title=TITLE,
description=DESCRIPTION,
article=ARTICLE,
theme=args.theme,
allow_screenshot=args.allow_screenshot,
allow_flagging=args.allow_flagging,
live=args.live,
).launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
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