Function generate_images

gen_videos_proj.py:231–268

Render a latent vector interpolation video.

(
    network_pkl: str,
    latent: str,
    output: str,
    truncation_psi: float,
    truncation_cutoff: int,
    grid: Tuple[int,int],
    num_keyframes: Optional[int],
    w_frames: int,
    reload_modules: bool,
    cfg: str,
    image_mode: str,
    sampling_multiplier: float,
    nrr: Optional[int],
    shapes: bool,
    interpolate: bool,
)

@click.option('--interpolate', type=bool, help='Interpolate between seeds', default=True, show_default=True)

def generate_images(
    network_pkl: str,
    latent: str,
    output: str,
    truncation_psi: float,
    truncation_cutoff: int,
    grid: Tuple[int,int],
    num_keyframes: Optional[int],
    w_frames: int,
    reload_modules: bool,
    cfg: str,
    image_mode: str,
    sampling_multiplier: float,
    nrr: Optional[int],
    shapes: bool,
    interpolate: bool,
):
    """Render a latent vector interpolation video.

    """

    print('Loading networks from "%s"...' % network_pkl)
    device = torch.device('cuda')
    with dnnlib.util.open_url(network_pkl) as f:
        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore


    G.rendering_kwargs['depth_resolution'] = int(G.rendering_kwargs['depth_resolution'] * sampling_multiplier)
    G.rendering_kwargs['depth_resolution_importance'] = int(G.rendering_kwargs['depth_resolution_importance'] * sampling_multiplier)
    if nrr is not None: G.neural_rendering_resolution = nrr

    if truncation_cutoff == 0:
        truncation_psi = 1.0 # truncation cutoff of 0 means no truncation anyways
    if truncation_psi == 1.0:
        truncation_cutoff = 14 # no truncation so doesn't matter where we cutoff

    ws = torch.tensor(np.load(latent)['w']).to(device)
    gen_interp_video(G=G, mp4=output, ws=ws, bitrate='10M', grid_dims=grid, num_keyframes=num_keyframes, w_frames=w_frames, psi=truncation_psi, truncation_cutoff=truncation_cutoff, cfg=cfg, image_mode=image_mode, gen_shapes=shapes)

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