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import argparse
import math
import os
import pickle
import torch
import torchvision
from torch import optim
from tqdm import tqdm
from StyleCLIP.criteria.clip_loss import CLIPLoss
from StyleCLIP.models.stylegan2.model import Generator
import clip
from StyleCLIP.utils import ensure_checkpoint_exists
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, use_old_G):
ensure_checkpoint_exists(args.ckpt)
text_inputs = torch.cat([clip.tokenize(args.description)]).cuda()
os.makedirs(args.results_dir, exist_ok=True)
new_generator_path = f'/disk2/danielroich/Sandbox/stylegan2_ada_pytorch/checkpoints/model_{args.run_id}_{args.image_name}.pt'
old_generator_path = '/disk2/danielroich/Sandbox/pretrained_models/ffhq.pkl'
if not use_old_G:
with open(new_generator_path, 'rb') as f:
G = torch.load(f).cuda().eval()
else:
with open(old_generator_path, 'rb') as f:
G = pickle.load(f)['G_ema'].cuda().eval()
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.mapping(latent_code_init_not_trunc, None)
latent = latent_code_init.detach().clone()
latent.requires_grad = True
clip_loss = CLIPLoss(args)
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.synthesis(latent, noise_mode='const')
c_loss = clip_loss(img_gen, text_inputs)
if args.mode == "edit":
l2_loss = ((latent_code_init - latent) ** 2).sum()
loss = c_loss + args.l2_lambda * l2_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.synthesis(latent, noise_mode='const')
torchvision.utils.save_image(img_gen,
f"/disk2/danielroich/Sandbox/StyleCLIP/results/inference_results/{str(i).zfill(5)}.png",
normalize=True, range=(-1, 1))
if args.mode == "edit":
with torch.no_grad():
img_orig = G.synthesis(latent_code_init, noise_mode='const')
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("--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("--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")
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))
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