2024-08-20 21:17:33 +02:00
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import math
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import cv2
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import numpy as np
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import torch
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from torch import nn
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import torch.nn.functional as F
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from loguru import logger
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2024-08-20 22:09:16 +02:00
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from inpaint.helper import download_model
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from inpaint.plugins.base_plugin import BasePlugin
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from inpaint.schema import RunPluginRequest, RealESRGANModel
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2024-08-20 21:17:33 +02:00
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class RealESRGANer:
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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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def __init__(
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self,
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scale,
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model_path,
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dni_weight=None,
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model=None,
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tile=0,
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tile_pad=10,
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pre_pad=10,
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half=False,
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device=None,
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gpu_id=None,
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):
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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 = None
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self.half = half
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# initialize model
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if gpu_id:
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self.device = (
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torch.device(f"cuda:{gpu_id}" if torch.cuda.is_available() else "cpu")
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if device is None
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else device
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)
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else:
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self.device = (
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torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if device is None
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else device
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)
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if isinstance(model_path, list):
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# dni
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assert len(model_path) == len(
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dni_weight
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), "model_path and dni_weight should have the save length."
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loadnet = self.dni(model_path[0], model_path[1], dni_weight)
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else:
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# if the model_path starts with https, it will first download models to the folder: weights
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loadnet = torch.load(model_path, map_location=torch.device("cpu"))
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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 dni(self, net_a, net_b, dni_weight, key="params", loc="cpu"):
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"""Deep network interpolation.
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``Paper: Deep Network Interpolation for Continuous Imagery Effect Transition``
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"""
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net_a = torch.load(net_a, map_location=torch.device(loc))
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net_b = torch.load(net_b, map_location=torch.device(loc))
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for k, v_a in net_a[key].items():
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net_a[key][k] = dni_weight[0] * v_a + dni_weight[1] * net_b[key][k]
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return net_a
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def pre_process(self, img):
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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 = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
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self.img = img.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 = F.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 = F.pad(
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self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), "reflect"
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)
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def process(self):
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# model inference
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self.output = self.model(self.img)
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def tile_process(self):
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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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for y in range(tiles_y):
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for x in range(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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tile_idx = y * tiles_x + x + 1
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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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try:
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with torch.no_grad():
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output_tile = self.model(input_tile)
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except RuntimeError as error:
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print("Error", error)
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print(f"\tTile {tile_idx}/{tiles_x * tiles_y}")
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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[
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:, :, output_start_y:output_end_y, output_start_x:output_end_x
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] = 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):
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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 enhance(self, img, outscale=None, alpha_upsampler="realesrgan"):
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h_input, w_input = img.shape[0:2]
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# img: numpy
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img = img.astype(np.float32)
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if np.max(img) > 256: # 16-bit image
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max_range = 65535
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print("\tInput is a 16-bit image")
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else:
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max_range = 255
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img = img / max_range
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if len(img.shape) == 2: # gray image
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img_mode = "L"
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
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elif img.shape[2] == 4: # RGBA image with alpha channel
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img_mode = "RGBA"
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alpha = img[:, :, 3]
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img = img[:, :, 0:3]
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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if alpha_upsampler == "realesrgan":
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alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
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else:
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img_mode = "RGB"
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# ------------------- process image (without the alpha channel) ------------------- #
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self.pre_process(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_img = self.post_process()
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output_img = output_img.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 == "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 == "RGBA":
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if alpha_upsampler == "realesrgan":
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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 = self.post_process()
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output_alpha = (
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output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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)
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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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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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if outscale is not None and outscale != float(self.scale):
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output = cv2.resize(
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output,
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(
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int(w_input * outscale),
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int(h_input * outscale),
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),
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interpolation=cv2.INTER_LANCZOS4,
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)
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return output, img_mode
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class SRVGGNetCompact(nn.Module):
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"""A compact VGG-style network structure for super-resolution.
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It is a compact network structure, which performs upsampling in the last layer and no convolution is
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conducted on the HR feature space.
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Args:
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num_in_ch (int): Channel number of inputs. Default: 3.
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num_out_ch (int): Channel number of outputs. Default: 3.
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num_feat (int): Channel number of intermediate features. Default: 64.
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num_conv (int): Number of convolution layers in the body network. Default: 16.
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upscale (int): Upsampling factor. Default: 4.
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act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
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"""
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def __init__(
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self,
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_conv=16,
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upscale=4,
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act_type="prelu",
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):
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super(SRVGGNetCompact, self).__init__()
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self.num_in_ch = num_in_ch
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self.num_out_ch = num_out_ch
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self.num_feat = num_feat
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self.num_conv = num_conv
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self.upscale = upscale
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self.act_type = act_type
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self.body = nn.ModuleList()
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# the first conv
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self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
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# the first activation
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if act_type == "relu":
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activation = nn.ReLU(inplace=True)
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elif act_type == "prelu":
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activation = nn.PReLU(num_parameters=num_feat)
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elif act_type == "leakyrelu":
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activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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self.body.append(activation)
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# the body structure
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for _ in range(num_conv):
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self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
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# activation
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if act_type == "relu":
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activation = nn.ReLU(inplace=True)
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elif act_type == "prelu":
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activation = nn.PReLU(num_parameters=num_feat)
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elif act_type == "leakyrelu":
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activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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|
self.body.append(activation)
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|
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# the last conv
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self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
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# upsample
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self.upsampler = nn.PixelShuffle(upscale)
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|
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def forward(self, x):
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out = x
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for i in range(0, len(self.body)):
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out = self.body[i](out)
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out = self.upsampler(out)
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# add the nearest upsampled image, so that the network learns the residual
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|
base = F.interpolate(x, scale_factor=self.upscale, mode="nearest")
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|
out += base
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return out
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|
|
class RealESRGANUpscaler(BasePlugin):
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name = "RealESRGAN"
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|
support_gen_image = True
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|
|
|
|
|
|
def __init__(self, name, device, no_half=False):
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|
super().__init__()
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|
self.model_name = name
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|
self.device = device
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|
self.no_half = no_half
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|
self._init_model(name)
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|
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|
def _init_model(self, name):
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|
from .basicsr import RRDBNet
|
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|
|
|
|
|
|
REAL_ESRGAN_MODELS = {
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|
|
RealESRGANModel.realesr_general_x4v3: {
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|
|
"url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
|
|
|
|
"scale": 4,
|
|
|
|
"model": lambda: SRVGGNetCompact(
|
|
|
|
num_in_ch=3,
|
|
|
|
num_out_ch=3,
|
|
|
|
num_feat=64,
|
|
|
|
num_conv=32,
|
|
|
|
upscale=4,
|
|
|
|
act_type="prelu",
|
|
|
|
),
|
|
|
|
"model_md5": "91a7644643c884ee00737db24e478156",
|
|
|
|
},
|
|
|
|
RealESRGANModel.RealESRGAN_x4plus: {
|
|
|
|
"url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
|
|
|
|
"scale": 4,
|
|
|
|
"model": lambda: RRDBNet(
|
|
|
|
num_in_ch=3,
|
|
|
|
num_out_ch=3,
|
|
|
|
num_feat=64,
|
|
|
|
num_block=23,
|
|
|
|
num_grow_ch=32,
|
|
|
|
scale=4,
|
|
|
|
),
|
|
|
|
"model_md5": "99ec365d4afad750833258a1a24f44ca",
|
|
|
|
},
|
|
|
|
RealESRGANModel.RealESRGAN_x4plus_anime_6B: {
|
|
|
|
"url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
|
|
|
|
"scale": 4,
|
|
|
|
"model": lambda: RRDBNet(
|
|
|
|
num_in_ch=3,
|
|
|
|
num_out_ch=3,
|
|
|
|
num_feat=64,
|
|
|
|
num_block=6,
|
|
|
|
num_grow_ch=32,
|
|
|
|
scale=4,
|
|
|
|
),
|
|
|
|
"model_md5": "d58ce384064ec1591c2ea7b79dbf47ba",
|
|
|
|
},
|
|
|
|
}
|
|
|
|
if name not in REAL_ESRGAN_MODELS:
|
|
|
|
raise ValueError(f"Unknown RealESRGAN model name: {name}")
|
|
|
|
model_info = REAL_ESRGAN_MODELS[name]
|
|
|
|
|
|
|
|
model_path = download_model(model_info["url"], model_info["model_md5"])
|
|
|
|
logger.info(f"RealESRGAN model path: {model_path}")
|
|
|
|
|
|
|
|
self.model = RealESRGANer(
|
|
|
|
scale=model_info["scale"],
|
|
|
|
model_path=model_path,
|
|
|
|
model=model_info["model"](),
|
|
|
|
half=True if "cuda" in str(self.device) and not self.no_half else False,
|
|
|
|
tile=512,
|
|
|
|
tile_pad=10,
|
|
|
|
pre_pad=10,
|
|
|
|
device=self.device,
|
|
|
|
)
|
|
|
|
|
|
|
|
def switch_model(self, new_model_name: str):
|
|
|
|
if self.model_name == new_model_name:
|
|
|
|
return
|
|
|
|
self._init_model(new_model_name)
|
|
|
|
self.model_name = new_model_name
|
|
|
|
|
|
|
|
def gen_image(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
|
|
|
|
bgr_np_img = cv2.cvtColor(rgb_np_img, cv2.COLOR_RGB2BGR)
|
|
|
|
logger.info(f"RealESRGAN input shape: {bgr_np_img.shape}, scale: {req.scale}")
|
|
|
|
result = self.forward(bgr_np_img, req.scale)
|
|
|
|
logger.info(f"RealESRGAN output shape: {result.shape}")
|
|
|
|
return result
|
|
|
|
|
|
|
|
@torch.inference_mode()
|
|
|
|
def forward(self, bgr_np_img, scale: float):
|
|
|
|
# 输出是 BGR
|
|
|
|
upsampled = self.model.enhance(bgr_np_img, outscale=scale)[0]
|
|
|
|
return upsampled
|