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import os
import sys
import traceback
import numpy as np
import torch
from PIL import Image
import modules.esrgam_model_arch as arch
from modules import shared
from modules.shared import opts
import modules.images
def load_model(filename):
# this code is adapted from https://github.com/xinntao/ESRGAN
if torch.has_mps:
map_l = 'cpu'
else:
map_l = None
pretrained_net = torch.load(filename, map_location=map_l)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
if 'conv_first.weight' in pretrained_net:
crt_model.load_state_dict(pretrained_net)
return crt_model
crt_net = crt_model.state_dict()
load_net_clean = {}
for k, v in pretrained_net.items():
if k.startswith('module.'):
load_net_clean[k[7:]] = v
else:
load_net_clean[k] = v
pretrained_net = load_net_clean
tbd = []
for k, v in crt_net.items():
tbd.append(k)
# directly copy
for k, v in crt_net.items():
if k in pretrained_net and pretrained_net[k].size() == v.size():
crt_net[k] = pretrained_net[k]
tbd.remove(k)
crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
for k in tbd.copy():
if 'RDB' in k:
ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
if '.weight' in k:
ori_k = ori_k.replace('.weight', '.0.weight')
elif '.bias' in k:
ori_k = ori_k.replace('.bias', '.0.bias')
crt_net[k] = pretrained_net[ori_k]
tbd.remove(k)
crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
crt_model.load_state_dict(crt_net)
crt_model.eval()
return crt_model
def upscale_without_tiling(model, img):
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(shared.device)
with torch.no_grad():
output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
output = 255. * np.moveaxis(output, 0, 2)
output = output.astype(np.uint8)
output = output[:, :, ::-1]
return Image.fromarray(output, 'RGB')
def esrgan_upscale(model, img):
if opts.ESRGAN_tile == 0:
return upscale_without_tiling(model, img)
grid = modules.images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
newtiles = []
scale_factor = 1
for y, h, row in grid.tiles:
newrow = []
for tiledata in row:
x, w, tile = tiledata
output = upscale_without_tiling(model, tile)
scale_factor = output.width // tile.width
newrow.append([x * scale_factor, w * scale_factor, output])
newtiles.append([y * scale_factor, h * scale_factor, newrow])
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)
output = modules.images.combine_grid(newgrid)
return output
class UpscalerESRGAN(modules.images.Upscaler):
def __init__(self, filename, title):
self.name = title
self.model = load_model(filename)
def do_upscale(self, img):
model = self.model.to(shared.device)
img = esrgan_upscale(model, img)
return img
def load_models(dirname):
for file in os.listdir(dirname):
path = os.path.join(dirname, file)
model_name, extension = os.path.splitext(file)
if extension != '.pt' and extension != '.pth':
continue
try:
modules.shared.sd_upscalers.append(UpscalerESRGAN(path, model_name))
except Exception:
print(f"Error loading ESRGAN model: {path}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
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