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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, shared, torch_utils
logger = logging.getLogger(__name__)
def pil_image_to_torch_bgr(img: Image.Image) -> torch.Tensor:
img = np.array(img.convert("RGB"))
img = img[:, :, ::-1] # flip RGB to BGR
img = np.transpose(img, (2, 0, 1)) # HWC to CHW
img = np.ascontiguousarray(img) / 255 # Rescale to [0, 1]
return torch.from_numpy(img)
def torch_bgr_to_pil_image(tensor: torch.Tensor) -> Image.Image:
if tensor.ndim == 4:
# If we're given a tensor with a batch dimension, squeeze it out
# (but only if it's a batch of size 1).
if tensor.shape[0] != 1:
raise ValueError(f"{tensor.shape} does not describe a BCHW tensor")
tensor = tensor.squeeze(0)
assert tensor.ndim == 3, f"{tensor.shape} does not describe a CHW tensor"
# TODO: is `tensor.float().cpu()...numpy()` the most efficient idiom?
arr = tensor.float().cpu().clamp_(0, 1).numpy() # clamp
arr = 255.0 * np.moveaxis(arr, 0, 2) # CHW to HWC, rescale
arr = arr.round().astype(np.uint8)
arr = arr[:, :, ::-1] # flip BGR to RGB
return Image.fromarray(arr, "RGB")
def upscale_pil_patch(model, img: Image.Image) -> Image.Image:
"""
Upscale a given PIL image using the given model.
"""
param = torch_utils.get_param(model)
with torch.no_grad():
tensor = pil_image_to_torch_bgr(img).unsqueeze(0) # add batch dimension
tensor = tensor.to(device=param.device, dtype=param.dtype)
with devices.without_autocast():
return torch_bgr_to_pil_image(model(tensor))
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_pil_patch(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, disable=not shared.opts.enable_upscale_progressbar) 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_pil_patch(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)
def tiled_upscale_2(
img: torch.Tensor,
model,
*,
tile_size: int,
tile_overlap: int,
scale: int,
device: torch.device,
desc="Tiled upscale",
):
# Alternative implementation of `upscale_with_model` originally used by
# SwinIR and ScuNET. It differs from `upscale_with_model` in that tiling and
# weighting is done in PyTorch space, as opposed to `images.Grid` doing it in
# Pillow space without weighting.
b, c, h, w = img.size()
tile_size = min(tile_size, h, w)
if tile_size <= 0:
logger.debug("Upscaling %s without tiling", img.shape)
return model(img)
stride = tile_size - tile_overlap
h_idx_list = list(range(0, h - tile_size, stride)) + [h - tile_size]
w_idx_list = list(range(0, w - tile_size, stride)) + [w - tile_size]
result = torch.zeros(
b,
c,
h * scale,
w * scale,
device=device,
dtype=img.dtype,
)
weights = torch.zeros_like(result)
logger.debug("Upscaling %s to %s with tiles", img.shape, result.shape)
with tqdm.tqdm(total=len(h_idx_list) * len(w_idx_list), desc=desc, disable=not shared.opts.enable_upscale_progressbar) as pbar:
for h_idx in h_idx_list:
if shared.state.interrupted or shared.state.skipped:
break
for w_idx in w_idx_list:
if shared.state.interrupted or shared.state.skipped:
break
# Only move this patch to the device if it's not already there.
in_patch = img[
...,
h_idx : h_idx + tile_size,
w_idx : w_idx + tile_size,
].to(device=device)
out_patch = model(in_patch)
result[
...,
h_idx * scale : (h_idx + tile_size) * scale,
w_idx * scale : (w_idx + tile_size) * scale,
].add_(out_patch)
out_patch_mask = torch.ones_like(out_patch)
weights[
...,
h_idx * scale : (h_idx + tile_size) * scale,
w_idx * scale : (w_idx + tile_size) * scale,
].add_(out_patch_mask)
pbar.update(1)
output = result.div_(weights)
return output
def upscale_2(
img: Image.Image,
model,
*,
tile_size: int,
tile_overlap: int,
scale: int,
desc: str,
):
"""
Convenience wrapper around `tiled_upscale_2` that handles PIL images.
"""
param = torch_utils.get_param(model)
tensor = pil_image_to_torch_bgr(img).to(dtype=param.dtype).unsqueeze(0) # add batch dimension
with torch.no_grad():
output = tiled_upscale_2(
tensor,
model,
tile_size=tile_size,
tile_overlap=tile_overlap,
scale=scale,
desc=desc,
device=param.device,
)
return torch_bgr_to_pil_image(output)
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