135 lines
4.5 KiB
Python
135 lines
4.5 KiB
Python
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import os
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import sys
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import traceback
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import numpy as np
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import torch
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from PIL import Image
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import modules.esrgam_model_arch as arch
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from modules import shared
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from modules.shared import opts
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import modules.images
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def load_model(filename):
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# this code is adapted from https://github.com/xinntao/ESRGAN
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pretrained_net = torch.load(filename)
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crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
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if 'conv_first.weight' in pretrained_net:
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crt_model.load_state_dict(pretrained_net)
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return crt_model
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crt_net = crt_model.state_dict()
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load_net_clean = {}
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for k, v in pretrained_net.items():
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if k.startswith('module.'):
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load_net_clean[k[7:]] = v
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else:
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load_net_clean[k] = v
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pretrained_net = load_net_clean
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tbd = []
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for k, v in crt_net.items():
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tbd.append(k)
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# directly copy
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for k, v in crt_net.items():
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if k in pretrained_net and pretrained_net[k].size() == v.size():
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crt_net[k] = pretrained_net[k]
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tbd.remove(k)
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crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
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crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
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for k in tbd.copy():
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if 'RDB' in k:
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ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
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if '.weight' in k:
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ori_k = ori_k.replace('.weight', '.0.weight')
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elif '.bias' in k:
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ori_k = ori_k.replace('.bias', '.0.bias')
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crt_net[k] = pretrained_net[ori_k]
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tbd.remove(k)
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crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
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crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
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crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
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crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
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crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
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crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
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crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
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crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
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crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
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crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
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crt_model.load_state_dict(crt_net)
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crt_model.eval()
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return crt_model
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def upscale_without_tiling(model, img):
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img = np.array(img)
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(shared.device)
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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 esrgan_upscale(model, img):
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if opts.ESRGAN_tile == 0:
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return upscale_without_tiling(model, img)
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grid = modules.images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
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newtiles = []
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scale_factor = 1
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for y, h, row in grid.tiles:
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newrow = []
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for tiledata in row:
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x, w, tile = tiledata
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output = upscale_without_tiling(model, tile)
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scale_factor = output.width // tile.width
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newrow.append([x * scale_factor, w * scale_factor, output])
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newtiles.append([y * scale_factor, h * scale_factor, newrow])
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newgrid = modules.images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
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output = modules.images.combine_grid(newgrid)
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return output
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class UpscalerESRGAN(modules.images.Upscaler):
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def __init__(self, filename, title):
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self.name = title
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self.model = load_model(filename)
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def do_upscale(self, img):
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model = self.model.to(shared.device)
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img = esrgan_upscale(model, img)
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return img
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def load_models(dirname):
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for file in os.listdir(dirname):
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path = os.path.join(dirname, file)
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model_name, extension = os.path.splitext(file)
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if extension != '.pt' and extension != '.pth':
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continue
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try:
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modules.shared.sd_upscalers.append(UpscalerESRGAN(path, model_name))
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except Exception:
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print(f"Error loading ESRGAN model: {path}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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