stable-diffusion-webui/modules/swinir_model.py

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import contextlib
import os
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import numpy as np
import torch
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from PIL import Image
from basicsr.utils.download_util import load_file_from_url
from tqdm import tqdm
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from modules import modelloader, devices
from modules.shared import cmd_opts, opts
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from modules.swinir_model_arch import SwinIR as net
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from modules.swinir_model_arch_v2 import Swin2SR as net2
from modules.upscaler import Upscaler, UpscalerData
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class UpscalerSwinIR(Upscaler):
def __init__(self, dirname):
self.name = "SwinIR"
self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
"/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
"-L_x4_GAN.pth "
self.model_name = "SwinIR 4x"
self.user_path = dirname
super().__init__()
scalers = []
model_files = self.find_models(ext_filter=[".pt", ".pth"])
for model in model_files:
if "http" in model:
name = self.model_name
else:
name = modelloader.friendly_name(model)
model_data = UpscalerData(name, model, self)
scalers.append(model_data)
self.scalers = scalers
def do_upscale(self, img, model_file):
model = self.load_model(model_file)
if model is None:
return img
model = model.to(devices.device_swinir)
img = upscale(img, model)
try:
torch.cuda.empty_cache()
except:
pass
return img
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def load_model(self, path, scale=4):
if "http" in path:
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dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
else:
filename = path
if filename is None or not os.path.exists(filename):
return None
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if filename.endswith(".v2.pth"):
model = net2(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
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depths=[6, 6, 6, 6, 6, 6],
embed_dim=180,
num_heads=[6, 6, 6, 6, 6, 6],
mlp_ratio=2,
upsampler="nearest+conv",
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resi_connection="1conv",
)
params = None
else:
model = net(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
embed_dim=240,
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="3conv",
)
params = "params_ema"
pretrained_model = torch.load(filename)
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if params is not None:
model.load_state_dict(pretrained_model[params], strict=True)
else:
model.load_state_dict(pretrained_model, strict=True)
if not cmd_opts.no_half:
model = model.half()
return model
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def upscale(
img,
model,
tile=opts.SWIN_tile,
tile_overlap=opts.SWIN_tile_overlap,
window_size=8,
scale=4,
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):
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(devices.device_swinir)
with torch.no_grad(), devices.autocast():
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_, _, h_old, w_old = img.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
output = inference(img, model, tile, tile_overlap, window_size, scale)
output = output[..., : h_old * scale, : w_old * scale]
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(
output[[2, 1, 0], :, :], (1, 2, 0)
) # CHW-RGB to HCW-BGR
output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
return Image.fromarray(output, "RGB")
def inference(img, model, tile, tile_overlap, window_size, scale):
# test the image tile by tile
b, c, h, w = img.size()
tile = min(tile, h, w)
assert tile % window_size == 0, "tile size should be a multiple of window_size"
sf = scale
stride = tile - tile_overlap
h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=devices.device_swinir).type_as(img)
W = torch.zeros_like(E, dtype=torch.half, device=devices.device_swinir)
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with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
for h_idx in h_idx_list:
for w_idx in w_idx_list:
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
out_patch = model(in_patch)
out_patch_mask = torch.ones_like(out_patch)
E[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch)
W[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch_mask)
pbar.update(1)
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output = E.div_(W)
return output