compat/wan/utils/utils.py

import argparse
import binascii
import os
import os.path as osp

__all__ = ["cache_video", "cache_image", "str2bool"]


def rand_name(length=8, suffix=""):
    name = binascii.b2a_hex(os.urandom(length)).decode("utf-8")
    if suffix:
        if not suffix.startswith("."):
            suffix = "." + suffix
        name += suffix
    return name


def cache_video(
    tensor,
    save_file=None,
    fps=30,
    suffix=".mp4",
    nrow=8,
    normalize=True,
    value_range=(-1, 1),
    retry=5,
):
    import imageio
    import torch
    import torchvision

    cache_file = osp.join("/tmp", rand_name(suffix=suffix)) if save_file is None else save_file
    error = None
    for _ in range(retry):
        try:
            tensor = tensor.clamp(min(value_range), max(value_range))
            tensor = torch.stack(
                [
                    torchvision.utils.make_grid(
                        u,
                        nrow=nrow,
                        normalize=normalize,
                        value_range=value_range,
                    )
                    for u in tensor.unbind(2)
                ],
                dim=1,
            ).permute(1, 2, 3, 0)
            tensor = (tensor * 255).type(torch.uint8).cpu()

            writer = imageio.get_writer(cache_file, fps=fps, codec="libx264", quality=8)
            for frame in tensor.numpy():
                writer.append_data(frame)
            writer.close()
            return cache_file
        except Exception as exc:
            error = exc
            continue
    print(f"cache_video failed, error: {error}", flush=True)
    return None


def cache_image(
    tensor,
    save_file,
    nrow=8,
    normalize=True,
    value_range=(-1, 1),
    retry=5,
):
    import torchvision

    suffix = osp.splitext(save_file)[1]
    if suffix.lower() not in [".jpg", ".jpeg", ".png", ".tiff", ".gif", ".webp"]:
        suffix = ".png"

    error = None
    for _ in range(retry):
        try:
            tensor = tensor.clamp(min(value_range), max(value_range))
            torchvision.utils.save_image(
                tensor,
                save_file,
                nrow=nrow,
                normalize=normalize,
                value_range=value_range,
            )
            return save_file
        except Exception as exc:
            error = exc
            continue
    print(f"cache_image failed, error: {error}", flush=True)
    return None


def str2bool(value):
    if isinstance(value, bool):
        return value
    value_lower = value.lower()
    if value_lower in ("yes", "true", "t", "y", "1"):
        return True
    if value_lower in ("no", "false", "f", "n", "0"):
        return False
    raise argparse.ArgumentTypeError("Boolean value expected (True/False)")