mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
135 lines
4.5 KiB
Python
135 lines
4.5 KiB
Python
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
|
|
|
|
pretrained_net = torch.load(filename)
|
|
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)
|