Merge pull request #1403 from d8ahazard/ScuNET
Add ScuNET Denoiser/Upscaler, Fix some Model Loader issues
This commit is contained in:
AUTOMATIC1111
GitHub
commit
6e063124ac
|
|
@ -5,7 +5,6 @@ import importlib
|
|||
from urllib.parse import urlparse
|
||||
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
|
||||
from modules import shared
|
||||
from modules.upscaler import Upscaler
|
||||
from modules.paths import script_path, models_path
|
||||
|
|
@ -121,16 +120,30 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
|
|||
|
||||
|
||||
def load_upscalers():
|
||||
sd = shared.script_path
|
||||
# We can only do this 'magic' method to dynamically load upscalers if they are referenced,
|
||||
# so we'll try to import any _model.py files before looking in __subclasses__
|
||||
modules_dir = os.path.join(sd, "modules")
|
||||
for file in os.listdir(modules_dir):
|
||||
if "_model.py" in file:
|
||||
model_name = file.replace("_model.py", "")
|
||||
full_model = f"modules.{model_name}_model"
|
||||
try:
|
||||
importlib.import_module(full_model)
|
||||
except:
|
||||
pass
|
||||
datas = []
|
||||
c_o = vars(shared.cmd_opts)
|
||||
for cls in Upscaler.__subclasses__():
|
||||
name = cls.__name__
|
||||
module_name = cls.__module__
|
||||
module = importlib.import_module(module_name)
|
||||
class_ = getattr(module, name)
|
||||
cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
|
||||
cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
|
||||
opt_string = None
|
||||
try:
|
||||
opt_string = shared.opts.__getattr__(cmd_name)
|
||||
if cmd_name in c_o:
|
||||
opt_string = c_o[cmd_name]
|
||||
except:
|
||||
pass
|
||||
scaler = class_(opt_string)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,90 @@
|
|||
import os.path
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import PIL.Image
|
||||
import numpy as np
|
||||
import torch
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
|
||||
import modules.upscaler
|
||||
from modules import shared, modelloader
|
||||
from modules.paths import models_path
|
||||
from modules.scunet_model_arch import SCUNet as net
|
||||
|
||||
|
||||
class UpscalerScuNET(modules.upscaler.Upscaler):
|
||||
def __init__(self, dirname):
|
||||
self.name = "ScuNET"
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.model_name = "ScuNET GAN"
|
||||
self.model_name2 = "ScuNET PSNR"
|
||||
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
|
||||
self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
|
||||
self.user_path = dirname
|
||||
super().__init__()
|
||||
model_paths = self.find_models(ext_filter=[".pth"])
|
||||
scalers = []
|
||||
add_model2 = True
|
||||
for file in model_paths:
|
||||
if "http" in file:
|
||||
name = self.model_name
|
||||
else:
|
||||
name = modelloader.friendly_name(file)
|
||||
if name == self.model_name2 or file == self.model_url2:
|
||||
add_model2 = False
|
||||
try:
|
||||
scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
|
||||
scalers.append(scaler_data)
|
||||
except Exception:
|
||||
print(f"Error loading ScuNET model: {file}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
if add_model2:
|
||||
scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
|
||||
scalers.append(scaler_data2)
|
||||
self.scalers = scalers
|
||||
|
||||
def do_upscale(self, img: PIL.Image, selected_file):
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
model = self.load_model(selected_file)
|
||||
if model is None:
|
||||
return img
|
||||
|
||||
device = shared.device
|
||||
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)
|
||||
|
||||
img = img.to(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]
|
||||
torch.cuda.empty_cache()
|
||||
return PIL.Image.fromarray(output, 'RGB')
|
||||
|
||||
def load_model(self, path: str):
|
||||
device = shared.device
|
||||
if "http" in path:
|
||||
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
|
||||
progress=True)
|
||||
else:
|
||||
filename = path
|
||||
if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
|
||||
print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
|
||||
return None
|
||||
|
||||
model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
|
||||
model.load_state_dict(torch.load(filename), strict=True)
|
||||
model.eval()
|
||||
for k, v in model.named_parameters():
|
||||
v.requires_grad = False
|
||||
model = model.to(device)
|
||||
|
||||
return model
|
||||
|
||||
|
|
@ -0,0 +1,265 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from einops import rearrange
|
||||
from einops.layers.torch import Rearrange
|
||||
from timm.models.layers import trunc_normal_, DropPath
|
||||
|
||||
|
||||
class WMSA(nn.Module):
|
||||
""" Self-attention module in Swin Transformer
|
||||
"""
|
||||
|
||||
def __init__(self, input_dim, output_dim, head_dim, window_size, type):
|
||||
super(WMSA, self).__init__()
|
||||
self.input_dim = input_dim
|
||||
self.output_dim = output_dim
|
||||
self.head_dim = head_dim
|
||||
self.scale = self.head_dim ** -0.5
|
||||
self.n_heads = input_dim // head_dim
|
||||
self.window_size = window_size
|
||||
self.type = type
|
||||
self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
|
||||
|
||||
self.relative_position_params = nn.Parameter(
|
||||
torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
|
||||
|
||||
self.linear = nn.Linear(self.input_dim, self.output_dim)
|
||||
|
||||
trunc_normal_(self.relative_position_params, std=.02)
|
||||
self.relative_position_params = torch.nn.Parameter(
|
||||
self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
|
||||
2).transpose(
|
||||
0, 1))
|
||||
|
||||
def generate_mask(self, h, w, p, shift):
|
||||
""" generating the mask of SW-MSA
|
||||
Args:
|
||||
shift: shift parameters in CyclicShift.
|
||||
Returns:
|
||||
attn_mask: should be (1 1 w p p),
|
||||
"""
|
||||
# supporting sqaure.
|
||||
attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
|
||||
if self.type == 'W':
|
||||
return attn_mask
|
||||
|
||||
s = p - shift
|
||||
attn_mask[-1, :, :s, :, s:, :] = True
|
||||
attn_mask[-1, :, s:, :, :s, :] = True
|
||||
attn_mask[:, -1, :, :s, :, s:] = True
|
||||
attn_mask[:, -1, :, s:, :, :s] = True
|
||||
attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
|
||||
return attn_mask
|
||||
|
||||
def forward(self, x):
|
||||
""" Forward pass of Window Multi-head Self-attention module.
|
||||
Args:
|
||||
x: input tensor with shape of [b h w c];
|
||||
attn_mask: attention mask, fill -inf where the value is True;
|
||||
Returns:
|
||||
output: tensor shape [b h w c]
|
||||
"""
|
||||
if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
|
||||
x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
|
||||
h_windows = x.size(1)
|
||||
w_windows = x.size(2)
|
||||
# sqaure validation
|
||||
# assert h_windows == w_windows
|
||||
|
||||
x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
|
||||
qkv = self.embedding_layer(x)
|
||||
q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
|
||||
sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
|
||||
# Adding learnable relative embedding
|
||||
sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
|
||||
# Using Attn Mask to distinguish different subwindows.
|
||||
if self.type != 'W':
|
||||
attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
|
||||
sim = sim.masked_fill_(attn_mask, float("-inf"))
|
||||
|
||||
probs = nn.functional.softmax(sim, dim=-1)
|
||||
output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
|
||||
output = rearrange(output, 'h b w p c -> b w p (h c)')
|
||||
output = self.linear(output)
|
||||
output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
|
||||
|
||||
if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
|
||||
dims=(1, 2))
|
||||
return output
|
||||
|
||||
def relative_embedding(self):
|
||||
cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
|
||||
relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
|
||||
# negative is allowed
|
||||
return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
|
||||
""" SwinTransformer Block
|
||||
"""
|
||||
super(Block, self).__init__()
|
||||
self.input_dim = input_dim
|
||||
self.output_dim = output_dim
|
||||
assert type in ['W', 'SW']
|
||||
self.type = type
|
||||
if input_resolution <= window_size:
|
||||
self.type = 'W'
|
||||
|
||||
self.ln1 = nn.LayerNorm(input_dim)
|
||||
self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.ln2 = nn.LayerNorm(input_dim)
|
||||
self.mlp = nn.Sequential(
|
||||
nn.Linear(input_dim, 4 * input_dim),
|
||||
nn.GELU(),
|
||||
nn.Linear(4 * input_dim, output_dim),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = x + self.drop_path(self.msa(self.ln1(x)))
|
||||
x = x + self.drop_path(self.mlp(self.ln2(x)))
|
||||
return x
|
||||
|
||||
|
||||
class ConvTransBlock(nn.Module):
|
||||
def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
|
||||
""" SwinTransformer and Conv Block
|
||||
"""
|
||||
super(ConvTransBlock, self).__init__()
|
||||
self.conv_dim = conv_dim
|
||||
self.trans_dim = trans_dim
|
||||
self.head_dim = head_dim
|
||||
self.window_size = window_size
|
||||
self.drop_path = drop_path
|
||||
self.type = type
|
||||
self.input_resolution = input_resolution
|
||||
|
||||
assert self.type in ['W', 'SW']
|
||||
if self.input_resolution <= self.window_size:
|
||||
self.type = 'W'
|
||||
|
||||
self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
|
||||
self.type, self.input_resolution)
|
||||
self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
|
||||
self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
|
||||
|
||||
self.conv_block = nn.Sequential(
|
||||
nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
|
||||
nn.ReLU(True),
|
||||
nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
|
||||
conv_x = self.conv_block(conv_x) + conv_x
|
||||
trans_x = Rearrange('b c h w -> b h w c')(trans_x)
|
||||
trans_x = self.trans_block(trans_x)
|
||||
trans_x = Rearrange('b h w c -> b c h w')(trans_x)
|
||||
res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
|
||||
x = x + res
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SCUNet(nn.Module):
|
||||
# def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
|
||||
def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
|
||||
super(SCUNet, self).__init__()
|
||||
if config is None:
|
||||
config = [2, 2, 2, 2, 2, 2, 2]
|
||||
self.config = config
|
||||
self.dim = dim
|
||||
self.head_dim = 32
|
||||
self.window_size = 8
|
||||
|
||||
# drop path rate for each layer
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
|
||||
|
||||
self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
|
||||
|
||||
begin = 0
|
||||
self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution)
|
||||
for i in range(config[0])] + \
|
||||
[nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
|
||||
|
||||
begin += config[0]
|
||||
self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution // 2)
|
||||
for i in range(config[1])] + \
|
||||
[nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
|
||||
|
||||
begin += config[1]
|
||||
self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution // 4)
|
||||
for i in range(config[2])] + \
|
||||
[nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
|
||||
|
||||
begin += config[2]
|
||||
self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution // 8)
|
||||
for i in range(config[3])]
|
||||
|
||||
begin += config[3]
|
||||
self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
|
||||
[ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution // 4)
|
||||
for i in range(config[4])]
|
||||
|
||||
begin += config[4]
|
||||
self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
|
||||
[ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution // 2)
|
||||
for i in range(config[5])]
|
||||
|
||||
begin += config[5]
|
||||
self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
|
||||
[ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution)
|
||||
for i in range(config[6])]
|
||||
|
||||
self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
|
||||
|
||||
self.m_head = nn.Sequential(*self.m_head)
|
||||
self.m_down1 = nn.Sequential(*self.m_down1)
|
||||
self.m_down2 = nn.Sequential(*self.m_down2)
|
||||
self.m_down3 = nn.Sequential(*self.m_down3)
|
||||
self.m_body = nn.Sequential(*self.m_body)
|
||||
self.m_up3 = nn.Sequential(*self.m_up3)
|
||||
self.m_up2 = nn.Sequential(*self.m_up2)
|
||||
self.m_up1 = nn.Sequential(*self.m_up1)
|
||||
self.m_tail = nn.Sequential(*self.m_tail)
|
||||
# self.apply(self._init_weights)
|
||||
|
||||
def forward(self, x0):
|
||||
|
||||
h, w = x0.size()[-2:]
|
||||
paddingBottom = int(np.ceil(h / 64) * 64 - h)
|
||||
paddingRight = int(np.ceil(w / 64) * 64 - w)
|
||||
x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
|
||||
|
||||
x1 = self.m_head(x0)
|
||||
x2 = self.m_down1(x1)
|
||||
x3 = self.m_down2(x2)
|
||||
x4 = self.m_down3(x3)
|
||||
x = self.m_body(x4)
|
||||
x = self.m_up3(x + x4)
|
||||
x = self.m_up2(x + x3)
|
||||
x = self.m_up1(x + x2)
|
||||
x = self.m_tail(x + x1)
|
||||
|
||||
x = x[..., :h, :w]
|
||||
|
||||
return x
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
|
@ -40,6 +40,7 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi
|
|||
parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
|
||||
parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
|
||||
parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
|
||||
parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET'))
|
||||
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
|
||||
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
|
||||
|
|
|
|||
17
webui.py
17
webui.py
|
|
@ -1,26 +1,23 @@
|
|||
import os
|
||||
import threading
|
||||
|
||||
from modules import devices
|
||||
from modules.paths import script_path
|
||||
import signal
|
||||
import threading
|
||||
import modules.paths
|
||||
|
||||
import modules.codeformer_model as codeformer
|
||||
import modules.esrgan_model as esrgan
|
||||
import modules.bsrgan_model as bsrgan
|
||||
import modules.extras
|
||||
import modules.face_restoration
|
||||
import modules.gfpgan_model as gfpgan
|
||||
import modules.ldsr_model as ldsr
|
||||
import modules.img2img
|
||||
|
||||
import modules.lowvram
|
||||
import modules.realesrgan_model as realesrgan
|
||||
import modules.paths
|
||||
import modules.scripts
|
||||
import modules.sd_hijack
|
||||
import modules.sd_models
|
||||
import modules.shared as shared
|
||||
import modules.swinir_model as swinir
|
||||
import modules.txt2img
|
||||
|
||||
import modules.ui
|
||||
from modules import devices
|
||||
from modules import modelloader
|
||||
from modules.paths import script_path
|
||||
from modules.shared import cmd_opts
|
||||
|
|
|
|||
Loading…
Reference in New Issue