| |
| """Manipulates synthesized or real images with existing boundary. |
| |
| Support StyleGAN2 and StyleGAN3. |
| """ |
|
|
| import os.path |
| import argparse |
| import numpy as np |
| from tqdm import tqdm |
| import torch |
|
|
| from models import build_model |
| from utils.visualizers.html_visualizer import HtmlVisualizer |
| from utils.image_utils import save_image |
| from utils.parsing_utils import parse_index |
| from utils.image_utils import postprocess_image |
| from utils.custom_utils import to_numpy, linear_interpolate |
| from utils.custom_utils import make_transform |
|
|
|
|
| def parse_args(): |
| """Parses arguments.""" |
| parser = argparse.ArgumentParser() |
| group = parser.add_argument_group('General options.') |
| group.add_argument('weight_path', type=str, |
| help='Weight path to the pre-trained model.') |
| group.add_argument('boundary_path', type=str, |
| help='Path to the attribute vectors.') |
| group.add_argument('--save_dir', type=str, default=None, |
| help='Directory to save the results. If not specified, ' |
| 'the results will be saved to ' |
| '`work_dirs/{TASK_SPECIFIC}/` by default.') |
| group.add_argument('--job', type=str, default='manipulations', |
| help='Name for the job. (default: manipulations)') |
| group.add_argument('--seed', type=int, default=4, |
| help='Seed for sampling. (default: 4)') |
| group.add_argument('--nums', type=int, default=10, |
| help='Number of samples to synthesized. (default: 10)') |
| group.add_argument('--img_size', type=int, default=1024, |
| help='Size of the synthesized images. (default: 1024)') |
| group.add_argument('--vis_size', type=int, default=256, |
| help='Size of the visualize images. (default: 256)') |
| group.add_argument('--w_dim', type=int, default=512, |
| help='Dimension of the latent w. (default: 512)') |
| group.add_argument('--batch_size', type=int, default=4, |
| help='Batch size. (default: 4)') |
| group.add_argument('--save_jpg', action='store_true', default=False, |
| help='Whether to save raw image. (default: False)') |
| group.add_argument('-d', '--data_name', type=str, default='ffhq', |
| help='Name of the datasets. (default: ffhq)') |
| group.add_argument('--latent_path', type=str, default='', |
| help='Path to the given latent codes. (default: None)') |
| group.add_argument('--trunc_psi', type=float, default=0.7, |
| help='Psi factor used for truncation. (default: 0.7)') |
| group.add_argument('--trunc_layers', type=int, default=8, |
| help='Number of layers to perform truncation.' |
| ' (default: 8)') |
| group.add_argument('--name', type=str, default='resefa', |
| help='Name of help save the results.') |
|
|
| group = parser.add_argument_group('StyleGAN2') |
| group.add_argument('--stylegan2', action='store_true', |
| help='Whether or not using StyleGAN2. (default: False)') |
| group.add_argument('--scale_stylegan2', type=float, default=1.0, |
| help='Scale for the number of channel fro stylegan2.') |
| group.add_argument('--randomize_noise', type=str, default='const', |
| help='Noise type when editing. (const or random)') |
|
|
| group = parser.add_argument_group('StyleGAN3') |
| group.add_argument('--stylegan3', action='store_true', |
| help='Whether or not using StyleGAN3. (default: False)') |
| group.add_argument('--cfg', type=str, default='T', |
| help='Config of the stylegan3 (T/R)') |
| group.add_argument('--scale_stylegan3r', type=float, default=2.0, |
| help='Scale for the number of channel for stylegan3 R.') |
| group.add_argument('--scale_stylegan3t', type=float, default=1.0, |
| help='Scale for the number of channel for stylegan3 T.') |
| group.add_argument('--tx', type=float, default=0, |
| help='Translate X-coordinate. (default: 0.0)') |
| group.add_argument('--ty', type=float, default=0, |
| help='Translate Y-coordinate. (default: 0.0)') |
| group.add_argument('--rotate', type=float, default=0, |
| help='Rotation angle in degrees. (default: 0)') |
|
|
| group = parser.add_argument_group('Manipulation') |
| group.add_argument('--mani_layers', type=str, default='4,5,6,7', |
| help='The layers will be manipulated.' |
| '(default: 4,5,6,7). For the eyebrow and lipstick,' |
| 'using [8-11] layers instead.') |
| group.add_argument('--step', type=int, default=7, |
| help='Number of manipulation steps. (default: 7)') |
| group.add_argument('--start', type=int, default=0, |
| help='The start index of the manipulation directions.') |
| group.add_argument('--end', type=int, default=1, |
| help='The end index of the manipulation directions.') |
| group.add_argument('--start_distance', type=float, default=-10.0, |
| help='Start distance for manipulation. (default: -10.0)') |
| group.add_argument('--end_distance', type=float, default=10.0, |
| help='End distance for manipulation. (default: 10.0)') |
|
|
| return parser.parse_args() |
|
|
|
|
| def main(): |
| """Main function.""" |
| args = parse_args() |
| |
| assert (args.stylegan2 and not args.stylegan3) or \ |
| (not args.stylegan2 and args.stylegan3) |
| checkpoint_path = args.weight_path |
| boundary_path = args.boundary_path |
| assert os.path.exists(checkpoint_path) |
| assert os.path.exists(boundary_path) |
| boundary_name = os.path.splitext(os.path.basename(boundary_path))[0] |
| job_disc = '' |
| if args.stylegan2: |
| config = dict(model_type='StyleGAN2Generator', |
| resolution=args.img_size, |
| w_dim=args.w_dim, |
| fmaps_base=int(args.scale_stylegan2 * (32 << 10)), |
| fmaps_max=512,) |
| job_disc += 'stylegan2' |
| else: |
| if args.stylegan3 and args.cfg == 'R': |
| config = dict(model_type='StyleGAN3Generator', |
| resolution=args.img_size, |
| w_dim=args.w_dim, |
| fmaps_base=int(args.scale_stylegan3r * (32 << 10)), |
| fmaps_max=1024, |
| use_radial_filter=True,) |
| job_disc += 'stylegan3r' |
| elif args.stylegan3 and args.cfg == 'T': |
| config = dict(model_type='StyleGAN3Generator', |
| resolution=args.img_size, |
| w_dim=args.w_dim, |
| fmaps_base=int(args.scale_stylegan3t * (32 << 10)), |
| fmaps_max=512, |
| use_radial_filter=False, |
| kernel_size=3,) |
| job_disc += 'stylegan3t' |
| else: |
| raise TypeError(f'StyleGAN3 config type error, need `R/T`,' |
| f' but got {args.cfg} instead.') |
|
|
| |
| save_dir = args.save_dir or f'work_dirs/{args.job}/{args.data_name}' |
| os.makedirs(save_dir, exist_ok=True) |
| job_name = f'seed_{args.seed}_num_{args.nums}_{job_disc}_{boundary_name}' |
| os.makedirs(f'{save_dir}/{job_name}', exist_ok=True) |
|
|
| print('Building generator...') |
| generator = build_model(**config) |
| print(f'Loading checkpoint from `{checkpoint_path}` ...') |
| checkpoint = torch.load(checkpoint_path, map_location='cpu')['models'] |
| if 'generator_smooth' in checkpoint: |
| generator.load_state_dict(checkpoint['generator_smooth']) |
| else: |
| generator.load_state_dict(checkpoint['generator']) |
| generator = generator.eval().cuda() |
| print('Finish loading checkpoint.') |
| if args.stylegan3 and hasattr(generator.synthesis, 'early_layer'): |
| m = make_transform(args.tx, args.ty, args.rotate) |
| m = np.linalg.inv(m) |
| generator.synthesis.early_layer.transform.copy_(torch.from_numpy(m)) |
|
|
| np.random.seed(args.seed) |
| torch.manual_seed(args.seed) |
| if os.path.exists(args.latent_path): |
| print(f'Load latent codes from {args.latent_path}') |
| latent_zs = np.load(args.latent_path) |
| latent_zs = latent_zs[:args.nums] |
| else: |
| print('Sampling latent code randomly') |
| latent_zs = np.random.randn(args.nums, generator.z_dim) |
| latent_zs = torch.from_numpy(latent_zs.astype(np.float32)) |
| latent_zs = latent_zs.cuda() |
| num_images = latent_zs.shape[0] |
| wp = [] |
| for idx in range(0, num_images, args.batch_size): |
| latent_z = latent_zs[idx:idx+args.batch_size] |
| latent_w_ = generator.mapping(latent_z, None)['wp'] |
| wp.append(latent_w_) |
| wp = torch.cat(wp, dim=0) |
| trunc_psi = args.trunc_psi |
| trunc_layers = args.trunc_layers |
| if trunc_psi < 1.0 and trunc_layers > 0: |
| w_avg = generator.w_avg |
| w_avg = w_avg.reshape(1, -1, generator.w_dim)[:, :trunc_layers] |
| wp[:, :trunc_layers] = w_avg.lerp(wp[:, :trunc_layers], trunc_psi) |
| print(f'Shape of the latent ws: {wp.shape}') |
| image_list = [] |
| for i in range(num_images): |
| image_list.append(f'{i:06d}') |
|
|
| print('Loading boundary.') |
| directions = np.load(boundary_path) |
| layer_index = parse_index(args.mani_layers) |
| if not layer_index: |
| layer_index = list(range(generator.num_layers - 1)) |
| print(f'Manipulating on layers `{layer_index}`.') |
|
|
| vis_size = None if args.vis_size == 0 else args.vis_size |
| delta_num = args.end - args.start |
| visualizer = HtmlVisualizer(num_rows=num_images * delta_num, |
| num_cols=args.step + 2, |
| image_size=vis_size) |
| visualizer.set_headers( |
| ['Name', 'Origin'] + |
| [f'Step {i:02d}' for i in range(1, args.step + 1)] |
| ) |
| |
| print('Start manipulation.') |
| for row in tqdm(range(num_images)): |
| latent_w = wp[row:row+1] |
| images_ori = generator.synthesis(latent_w)['image'] |
| images_ori = postprocess_image(to_numpy(images_ori)) |
| if args.save_jpg: |
| save_image(f'{save_dir}/{job_name}/{row:06d}_orin.jpg', |
| images_ori[0]) |
| for num_direc in range(args.start, args.end): |
| html_row = num_direc - args.start |
| direction = directions[num_direc:num_direc+1] |
| direction = np.tile(direction, [1, generator.num_layers, 1]) |
| visualizer.set_cell(row * delta_num + html_row, 0, |
| text=f'{image_list[row]}_{num_direc:03d}') |
| visualizer.set_cell(row * delta_num + html_row, 1, |
| image=images_ori[0]) |
| mani_codes = linear_interpolate(latent_code=to_numpy(latent_w), |
| boundary=direction, |
| layer_index=layer_index, |
| start_distance=args.start_distance, |
| end_distance=args.end_distance, |
| steps=args.step) |
| mani_codes = torch.from_numpy(mani_codes.astype(np.float32)).cuda() |
| for idx in range(0, mani_codes.shape[0], args.batch_size): |
| codes_ = mani_codes[idx:idx+args.batch_size] |
| images_ = generator.synthesis(codes_)['image'] |
| images_ = postprocess_image(to_numpy(images_)) |
| for i in range(images_.shape[0]): |
| visualizer.set_cell(row * delta_num + html_row, idx+i+2, |
| image=images_[i]) |
| if args.save_jpg: |
| save_image(f'{save_dir}/{job_name}/{row:06d}_ind_' |
| f'{num_direc:06d}_mani_{idx+i:06d}.jpg', |
| images_[i]) |
| |
| np.save(f'{save_dir}/{job_name}/latent_codes.npy', to_numpy(wp)) |
| visualizer.save(f'{save_dir}/{job_name}_{args.name}.html') |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
