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run_opt.py
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import argparse
import math
import os
import librosa
import torch
import torchvision
from torch import optim
from tqdm import tqdm
from criteria.clip_loss import CLIPLoss
from criteria.id_loss import IDLoss
from criteria.aclip_loss import CLIPLoss1D
from mapper.training.train_utils import STYLESPACE_DIMENSIONS
from models.stylegan2.model import Generator
import clip
from utils import ensure_checkpoint_exists
STYLESPACE_INDICES_WITHOUT_TORGB = [i for i in range(len(STYLESPACE_DIMENSIONS)) if i not in list(range(1, len(STYLESPACE_DIMENSIONS), 3))]
def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05):
lr_ramp = min(1, (1 - t) / rampdown)
lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)
lr_ramp = lr_ramp * min(1, t / rampup)
return initial_lr * lr_ramp
def main(args):
ensure_checkpoint_exists(args.ckpt)
if args.audio:
text_inputs, _ = librosa.load(args.audio, sr=16000)
else: text_inputs = torch.cat([clip.tokenize(args.description)]).cuda()
os.makedirs(args.results_dir, exist_ok=True)
g_ema = Generator(args.stylegan_size, 512, 8)
g_ema.load_state_dict(torch.load(args.ckpt)["g_ema"], strict=False)
g_ema.eval()
g_ema = g_ema.cuda()
mean_latent = g_ema.mean_latent(4096)
if args.latent_path:
latent_code_init = torch.load(args.latent_path).cuda()
elif args.mode == "edit":
latent_code_init_not_trunc = torch.randn(1, 512).cuda()
with torch.no_grad():
_, latent_code_init, _ = g_ema([latent_code_init_not_trunc], return_latents=True,
truncation=args.truncation, truncation_latent=mean_latent)
else:
latent_code_init = mean_latent.detach().clone().repeat(1, 18, 1)
with torch.no_grad():
img_orig, _ = g_ema([latent_code_init], input_is_latent=True, randomize_noise=False)
if args.work_in_stylespace:
with torch.no_grad():
_, _, latent_code_init = g_ema([latent_code_init], input_is_latent=True, return_latents=True)
latent = [s.detach().clone() for s in latent_code_init]
for c, s in enumerate(latent):
if c in STYLESPACE_INDICES_WITHOUT_TORGB:
s.requires_grad = True
else:
latent = latent_code_init.detach().clone()
latent.requires_grad = True
clip_loss = CLIPLoss(args)
if args.audio:
clip_loss = CLIPLoss1D(args)
id_loss = IDLoss(args)
if args.work_in_stylespace:
optimizer = optim.Adam(latent, lr=args.lr)
else:
optimizer = optim.Adam([latent], lr=args.lr)
pbar = tqdm(range(args.step))
for i in pbar:
t = i / args.step
lr = get_lr(t, args.lr)
optimizer.param_groups[0]["lr"] = lr
img_gen, _ = g_ema([latent], input_is_latent=True, randomize_noise=False, input_is_stylespace=args.work_in_stylespace)
c_loss = clip_loss(img_gen, text_inputs)
if args.id_lambda > 0:
i_loss = id_loss(img_gen, img_orig)[0]
else:
i_loss = 0
if args.mode == "edit":
if args.work_in_stylespace:
l2_loss = sum([((latent_code_init[c] - latent[c]) ** 2).sum() for c in range(len(latent_code_init))])
else:
l2_loss = ((latent_code_init - latent) ** 2).sum()
loss = c_loss + args.l2_lambda * l2_loss + args.id_lambda * i_loss
else:
loss = c_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
pbar.set_description(
(
f"loss: {loss.item():.4f};"
)
)
if args.save_intermediate_image_every > 0 and i % args.save_intermediate_image_every == 0:
with torch.no_grad():
img_gen, _ = g_ema([latent], input_is_latent=True, randomize_noise=False, input_is_stylespace=args.work_in_stylespace)
torchvision.utils.save_image(img_gen, f"results/{str(i).zfill(5)}.jpg", normalize=True, range=(-1, 1))
if args.mode == "edit":
final_result = torch.cat([img_orig, img_gen])
else:
final_result = img_gen
return final_result
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--description", type=str, default="a person with purple hair", help="the text that guides the editing/generation")
parser.add_argument("--ckpt", type=str, default="../pretrained_models/stylegan2-ffhq-config-f.pt", help="pretrained StyleGAN2 weights")
parser.add_argument("--stylegan_size", type=int, default=1024, help="StyleGAN resolution")
parser.add_argument("--lr_rampup", type=float, default=0.05)
parser.add_argument("--lr", type=float, default=0.1)
parser.add_argument("--step", type=int, default=300, help="number of optimization steps")
parser.add_argument("--mode", type=str, default="edit", choices=["edit", "free_generation"], help="choose between edit an image an generate a free one")
parser.add_argument("--l2_lambda", type=float, default=0.008, help="weight of the latent distance (used for editing only)")
parser.add_argument("--id_lambda", type=float, default=0.000, help="weight of id loss (used for editing only)")
parser.add_argument("--latent_path", type=str, default=None, help="starts the optimization from the given latent code if provided. Otherwose, starts from"
"the mean latent in a free generation, and from a random one in editing. "
"Expects a .pt format")
parser.add_argument("--truncation", type=float, default=0.7, help="used only for the initial latent vector, and only when a latent code path is"
"not provided")
parser.add_argument('--work_in_stylespace', default=False, action='store_true')
parser.add_argument("--save_intermediate_image_every", type=int, default=20, help="if > 0 then saves intermidate results during the optimization")
parser.add_argument("--results_dir", type=str, default="results")
parser.add_argument('--ir_se50_weights', default='../pretrained_models/model_ir_se50.pth', type=str,
help="Path to facial recognition network used in ID loss")
parser.add_argument("--audio", type=str, default=None)
args = parser.parse_args()
result_image = main(args)
torchvision.utils.save_image(result_image.detach().cpu(), os.path.join(args.results_dir, "final_result.jpg"), normalize=True, scale_each=True, range=(-1, 1))