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from typing import List, Tuple | |
from einops import rearrange | |
import numpy as np, os, torch | |
from PIL import Image | |
from torchvision.utils import make_grid | |
import time | |
def get_output_folder(output_path, batch_folder): | |
out_path = os.path.join(output_path,time.strftime('%Y-%m')) | |
if batch_folder != "": | |
out_path = os.path.join(out_path, batch_folder) | |
os.makedirs(out_path, exist_ok=True) | |
return out_path | |
def save_samples( | |
args, x_samples: torch.Tensor, seed: int, n_rows: int | |
) -> Tuple[Image.Image, List[Image.Image]]: | |
"""Function to save samples to disk. | |
Args: | |
args: Stable deforum diffusion arguments. | |
x_samples: Samples to save. | |
seed: Seed for the experiment. | |
n_rows: Number of rows in the grid. | |
Returns: | |
A tuple of the grid image and a list of the generated images. | |
( grid_image, generated_images ) | |
""" | |
# save samples | |
images = [] | |
grid_image = None | |
if args.display_samples or args.save_samples: | |
for index, x_sample in enumerate(x_samples): | |
x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c") | |
images.append(Image.fromarray(x_sample.astype(np.uint8))) | |
if args.save_samples: | |
images[-1].save( | |
os.path.join( | |
args.outdir, f"{args.timestring}_{index:02}_{seed}.png" | |
) | |
) | |
# save grid | |
if args.display_grid or args.save_grid: | |
grid = torch.stack([x_samples], 0) | |
grid = rearrange(grid, "n b c h w -> (n b) c h w") | |
grid = make_grid(grid, nrow=n_rows, padding=0) | |
# to image | |
grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy() | |
grid_image = Image.fromarray(grid.astype(np.uint8)) | |
if args.save_grid: | |
grid_image.save( | |
os.path.join(args.outdir, f"{args.timestring}_{seed}_grid.png") | |
) | |
# return grid_image and individual sample images | |
return grid_image, images | |
def save_image(image, image_type, filename, args, video_args, root): | |
if video_args.store_frames_in_ram: | |
root.frames_cache.append({'path':os.path.join(args.outdir, filename), 'image':image, 'image_type':image_type}) | |
else: | |
image.save(os.path.join(args.outdir, filename)) | |
import cv2, gc | |
def reset_frames_cache(root): | |
root.frames_cache = [] | |
gc.collect() | |
def dump_frames_cache(root): | |
for image_cache in root.frames_cache: | |
if image_cache['image_type'] == 'cv2': | |
cv2.imwrite(image_cache['path'], image_cache['image']) | |
elif image_cache['image_type'] == 'PIL': | |
image_cache['image'].save(image_cache['path']) | |
# do not reset the cache since we're going to add frame erasing later function #TODO | |