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import logging
from typing import Callable
import numpy as np
import torch
import tqdm
from PIL import Image
from modules import devices, images
logger = logging.getLogger(__name__)
def upscale_without_tiling(model, img: Image.Image):
img = np.array(img)
img = img[:, :, ::-1]
img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
img = torch.from_numpy(img).float()
model_weight = next(iter(model.parameters()))
img = img.unsqueeze(0).to(device=model_weight.device, dtype=model_weight.dtype)
with torch.no_grad():
output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
output = 255. * np.moveaxis(output, 0, 2)
output = output.astype(np.uint8)
output = output[:, :, ::-1]
return Image.fromarray(output, 'RGB')
def upscale_with_model(
model: Callable[[torch.Tensor], torch.Tensor],
img: Image.Image,
*,
tile_size: int,
tile_overlap: int = 0,
desc="tiled upscale",
) -> Image.Image:
if tile_size <= 0:
logger.debug("Upscaling %s without tiling", img)
output = upscale_without_tiling(model, img)
logger.debug("=> %s", output)
return output
grid = images.split_grid(img, tile_size, tile_size, tile_overlap)
newtiles = []
with tqdm.tqdm(total=grid.tile_count, desc=desc) as p:
for y, h, row in grid.tiles:
newrow = []
for x, w, tile in row:
logger.debug("Tile (%d, %d) %s...", x, y, tile)
output = upscale_without_tiling(model, tile)
scale_factor = output.width // tile.width
logger.debug("=> %s (scale factor %s)", output, scale_factor)
newrow.append([x * scale_factor, w * scale_factor, output])
p.update(1)
newtiles.append([y * scale_factor, h * scale_factor, newrow])
newgrid = images.Grid(
newtiles,
tile_w=grid.tile_w * scale_factor,
tile_h=grid.tile_h * scale_factor,
image_w=grid.image_w * scale_factor,
image_h=grid.image_h * scale_factor,
overlap=grid.overlap * scale_factor,
)
return images.combine_grid(newgrid)
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