mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
273 lines
10 KiB
Python
273 lines
10 KiB
Python
import math
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from enum import Enum
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from typing import Any, Optional
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import cv2
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import numpy as np
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import numpy.typing as npt
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import torch
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from cv2.typing import MatLike
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from tqdm import tqdm
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from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
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from invokeai.backend.model_manager.config import AnyModel
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from invokeai.backend.util.devices import TorchDevice
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"""
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Adapted from https://github.com/xinntao/Real-ESRGAN/blob/master/realesrgan/utils.py
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License is BSD3, copied to `LICENSE` in this directory.
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The adaptation here has a few changes:
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- Remove print statements, use `tqdm` to show progress
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- Remove unused "outscale" logic, which simply scales the final image to a given factor
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- Remove `dni_weight` logic, which was only used when multiple models were used
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- Remove logic to fetch models from network
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- Add types, rename a few things
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"""
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class ImageMode(str, Enum):
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L = "L"
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RGB = "RGB"
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RGBA = "RGBA"
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class RealESRGAN:
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"""A helper class for upsampling images with RealESRGAN.
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Args:
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scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
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model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
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model (nn.Module): The defined network. Default: None.
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tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
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input images into tiles, and then process each of them. Finally, they will be merged into one image.
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0 denotes for do not use tile. Default: 0.
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tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
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pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
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half (float): Whether to use half precision during inference. Default: False.
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"""
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output: torch.Tensor
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def __init__(
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self,
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scale: int,
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loadnet: AnyModel,
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model: RRDBNet,
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tile: int = 0,
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tile_pad: int = 10,
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pre_pad: int = 10,
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half: bool = False,
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) -> None:
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self.scale = scale
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self.tile_size = tile
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self.tile_pad = tile_pad
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self.pre_pad = pre_pad
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self.mod_scale: Optional[int] = None
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self.half = half
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self.device = TorchDevice.choose_torch_device()
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# prefer to use params_ema
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if "params_ema" in loadnet:
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keyname = "params_ema"
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else:
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keyname = "params"
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model.load_state_dict(loadnet[keyname], strict=True)
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model.eval()
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self.model = model.to(self.device)
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if self.half:
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self.model = self.model.half()
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def pre_process(self, img: MatLike) -> None:
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"""Pre-process, such as pre-pad and mod pad, so that the images can be divisible"""
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img_tensor: torch.Tensor = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
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self.img = img_tensor.unsqueeze(0).to(self.device)
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if self.half:
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self.img = self.img.half()
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# pre_pad
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if self.pre_pad != 0:
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self.img = torch.nn.functional.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), "reflect")
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# mod pad for divisible borders
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if self.scale == 2:
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self.mod_scale = 2
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elif self.scale == 1:
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self.mod_scale = 4
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if self.mod_scale is not None:
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self.mod_pad_h, self.mod_pad_w = 0, 0
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_, _, h, w = self.img.size()
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if h % self.mod_scale != 0:
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self.mod_pad_h = self.mod_scale - h % self.mod_scale
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if w % self.mod_scale != 0:
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self.mod_pad_w = self.mod_scale - w % self.mod_scale
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self.img = torch.nn.functional.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), "reflect")
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def process(self) -> None:
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# model inference
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self.output = self.model(self.img)
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def tile_process(self) -> None:
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"""It will first crop input images to tiles, and then process each tile.
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Finally, all the processed tiles are merged into one images.
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Modified from: https://github.com/ata4/esrgan-launcher
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"""
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batch, channel, height, width = self.img.shape
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output_height = height * self.scale
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output_width = width * self.scale
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output_shape = (batch, channel, output_height, output_width)
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# start with black image
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self.output = self.img.new_zeros(output_shape)
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tiles_x = math.ceil(width / self.tile_size)
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tiles_y = math.ceil(height / self.tile_size)
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# loop over all tiles
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total_steps = tiles_y * tiles_x
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for i in tqdm(range(total_steps), desc="Upscaling"):
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y = i // tiles_x
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x = i % tiles_x
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# extract tile from input image
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ofs_x = x * self.tile_size
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ofs_y = y * self.tile_size
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# input tile area on total image
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input_start_x = ofs_x
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input_end_x = min(ofs_x + self.tile_size, width)
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input_start_y = ofs_y
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input_end_y = min(ofs_y + self.tile_size, height)
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# input tile area on total image with padding
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input_start_x_pad = max(input_start_x - self.tile_pad, 0)
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input_end_x_pad = min(input_end_x + self.tile_pad, width)
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input_start_y_pad = max(input_start_y - self.tile_pad, 0)
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input_end_y_pad = min(input_end_y + self.tile_pad, height)
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# input tile dimensions
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input_tile_width = input_end_x - input_start_x
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input_tile_height = input_end_y - input_start_y
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input_tile = self.img[
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:,
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:,
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input_start_y_pad:input_end_y_pad,
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input_start_x_pad:input_end_x_pad,
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]
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# upscale tile
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with torch.no_grad():
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output_tile = self.model(input_tile)
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# output tile area on total image
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output_start_x = input_start_x * self.scale
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output_end_x = input_end_x * self.scale
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output_start_y = input_start_y * self.scale
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output_end_y = input_end_y * self.scale
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# output tile area without padding
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output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
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output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
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output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
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output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
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# put tile into output image
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self.output[:, :, output_start_y:output_end_y, output_start_x:output_end_x] = output_tile[
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:,
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:,
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output_start_y_tile:output_end_y_tile,
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output_start_x_tile:output_end_x_tile,
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]
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def post_process(self) -> torch.Tensor:
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# remove extra pad
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if self.mod_scale is not None:
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_, _, h, w = self.output.size()
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self.output = self.output[
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:,
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:,
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0 : h - self.mod_pad_h * self.scale,
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0 : w - self.mod_pad_w * self.scale,
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]
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# remove prepad
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if self.pre_pad != 0:
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_, _, h, w = self.output.size()
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self.output = self.output[
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:,
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:,
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0 : h - self.pre_pad * self.scale,
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0 : w - self.pre_pad * self.scale,
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]
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return self.output
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@torch.no_grad()
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def upscale(self, img: MatLike, esrgan_alpha_upscale: bool = True) -> npt.NDArray[Any]:
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np_img = img.astype(np.float32)
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alpha: Optional[np.ndarray] = None
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if np.max(np_img) > 256:
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# 16-bit image
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max_range = 65535
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else:
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max_range = 255
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np_img = np_img / max_range
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if len(np_img.shape) == 2:
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# grayscale image
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img_mode = ImageMode.L
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np_img = cv2.cvtColor(np_img, cv2.COLOR_GRAY2RGB)
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elif np_img.shape[2] == 4:
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# RGBA image with alpha channel
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img_mode = ImageMode.RGBA
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alpha = np_img[:, :, 3]
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np_img = np_img[:, :, 0:3]
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np_img = cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)
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if esrgan_alpha_upscale:
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alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
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else:
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img_mode = ImageMode.RGB
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np_img = cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)
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# ------------------- process image (without the alpha channel) ------------------- #
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self.pre_process(np_img)
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if self.tile_size > 0:
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self.tile_process()
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else:
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self.process()
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output_tensor = self.post_process()
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output_img: npt.NDArray[Any] = output_tensor.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
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if img_mode is ImageMode.L:
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output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
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# ------------------- process the alpha channel if necessary ------------------- #
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if img_mode is ImageMode.RGBA:
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if esrgan_alpha_upscale:
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assert alpha is not None
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self.pre_process(alpha)
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if self.tile_size > 0:
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self.tile_process()
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else:
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self.process()
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output_alpha_tensor = self.post_process()
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output_alpha: npt.NDArray[Any] = output_alpha_tensor.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
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output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
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else: # use the cv2 resize for alpha channel
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assert alpha is not None
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h, w = alpha.shape[0:2]
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output_alpha = cv2.resize(
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alpha,
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(w * self.scale, h * self.scale),
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interpolation=cv2.INTER_LINEAR,
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)
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# merge the alpha channel
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output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
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output_img[:, :, 3] = output_alpha
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# ------------------------------ return ------------------------------ #
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if max_range == 65535: # 16-bit image
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output = (output_img * 65535.0).round().astype(np.uint16)
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else:
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output = (output_img * 255.0).round().astype(np.uint8)
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return output
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