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Running
on
L40S
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import os
from pathlib import Path
import numpy as np
import torch
import torchvision
from einops import rearrange
from PIL import Image
import imageio
def seed_everything(seed):
import random
import numpy as np
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed % (2**32))
random.seed(seed)
def save_videos_from_pil(pil_images, path, fps=8):
save_fmt = Path(path).suffix
os.makedirs(os.path.dirname(path), exist_ok=True)
if save_fmt == ".mp4":
with imageio.get_writer(path, fps=fps) as writer:
for img in pil_images:
img_array = np.array(img) # Convert PIL Image to numpy array
writer.append_data(img_array)
elif save_fmt == ".gif":
pil_images[0].save(
fp=path,
format="GIF",
append_images=pil_images[1:],
save_all=True,
duration=(1 / fps * 1000),
loop=0,
optimize=False,
lossless=True
)
else:
raise ValueError("Unsupported file type. Use .mp4 or .gif.")
def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8):
videos = rearrange(videos, "b c t h w -> t b c h w")
height, width = videos.shape[-2:]
outputs = []
for i, x in enumerate(videos):
x = torchvision.utils.make_grid(x, nrow=n_rows) # (c h w)
x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) # (h w c)
if rescale:
x = (x + 1.0) / 2.0 # -1,1 -> 0,1
x = (x * 255).numpy().astype(np.uint8)
x = Image.fromarray(x)
outputs.append(x)
os.makedirs(os.path.dirname(path), exist_ok=True)
save_videos_from_pil(outputs, path, fps)
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