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Running
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Zero
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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import argparse
import binascii
import logging
import os
import os.path as osp
import imageio
import torch
import torchvision
__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):
# cache file
cache_file = osp.join('/tmp', rand_name(
suffix=suffix)) if save_file is None else save_file
# save to cache
error = None
for _ in range(retry):
try:
# preprocess
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()
# write video
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 e:
error = e
continue
else:
logging.info(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):
# cache file
suffix = osp.splitext(save_file)[1]
if suffix.lower() not in [
'.jpg', '.jpeg', '.png', '.tiff', '.gif', '.webp'
]:
suffix = '.png'
# save to cache
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 e:
error = e
continue
def str2bool(v):
"""
Convert a string to a boolean.
Supported true values: 'yes', 'true', 't', 'y', '1'
Supported false values: 'no', 'false', 'f', 'n', '0'
Args:
v (str): String to convert.
Returns:
bool: Converted boolean value.
Raises:
argparse.ArgumentTypeError: If the value cannot be converted to boolean.
"""
if isinstance(v, bool):
return v
v_lower = v.lower()
if v_lower in ('yes', 'true', 't', 'y', '1'):
return True
elif v_lower in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected (True/False)')
def masks_like(tensor, zero=False, generator=None, p=0.2):
assert isinstance(tensor, list)
out1 = [torch.ones(u.shape, dtype=u.dtype, device=u.device) for u in tensor]
out2 = [torch.ones(u.shape, dtype=u.dtype, device=u.device) for u in tensor]
if zero:
if generator is not None:
for u, v in zip(out1, out2):
random_num = torch.rand(
1, generator=generator, device=generator.device).item()
if random_num < p:
u[:, 0] = torch.normal(
mean=-3.5,
std=0.5,
size=(1,),
device=u.device,
generator=generator).expand_as(u[:, 0]).exp()
v[:, 0] = torch.zeros_like(v[:, 0])
else:
u[:, 0] = u[:, 0]
v[:, 0] = v[:, 0]
else:
for u, v in zip(out1, out2):
u[:, 0] = torch.zeros_like(u[:, 0])
v[:, 0] = torch.zeros_like(v[:, 0])
return out1, out2
def best_output_size(w, h, dw, dh, expected_area):
# float output size
ratio = w / h
ow = (expected_area * ratio)**0.5
oh = expected_area / ow
# process width first
ow1 = int(ow // dw * dw)
oh1 = int(expected_area / ow1 // dh * dh)
assert ow1 % dw == 0 and oh1 % dh == 0 and ow1 * oh1 <= expected_area
ratio1 = ow1 / oh1
# process height first
oh2 = int(oh // dh * dh)
ow2 = int(expected_area / oh2 // dw * dw)
assert oh2 % dh == 0 and ow2 % dw == 0 and ow2 * oh2 <= expected_area
ratio2 = ow2 / oh2
# compare ratios
if max(ratio / ratio1, ratio1 / ratio) < max(ratio / ratio2,
ratio2 / ratio):
return ow1, oh1
else:
return ow2, oh2
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