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