mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-29 19:05:05 +08:00
Use Spandrel for upscaling and face restoration architectures (aside from GFPGAN and LDSR)
This commit is contained in:
Aarni Koskela
parent
e472383acb
commit
b0f5934234
@ -7,9 +7,7 @@ from tqdm import tqdm
|
||||
|
||||
import modules.upscaler
|
||||
from modules import devices, modelloader, script_callbacks, errors
|
||||
from scunet_model_arch import SCUNet
|
||||
|
||||
from modules.modelloader import load_file_from_url
|
||||
from modules.shared import opts
|
||||
|
||||
|
||||
@ -120,17 +118,10 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
|
||||
device = devices.get_device_for('scunet')
|
||||
if path.startswith("http"):
|
||||
# TODO: this doesn't use `path` at all?
|
||||
filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
|
||||
filename = modelloader.load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
|
||||
else:
|
||||
filename = path
|
||||
model = SCUNet(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 _, v in model.named_parameters():
|
||||
v.requires_grad = False
|
||||
model = model.to(device)
|
||||
|
||||
return model
|
||||
return modelloader.load_spandrel_model(filename, device=device)
|
||||
|
||||
|
||||
def on_ui_settings():
|
||||
|
||||
@ -1,268 +0,0 @@
|
||||
# -*- 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 square.
|
||||
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)
|
||||
# square 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)
|
||||
@ -1,5 +1,5 @@
|
||||
import logging
|
||||
import sys
|
||||
import platform
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
@ -8,13 +8,11 @@ from tqdm import tqdm
|
||||
|
||||
from modules import modelloader, devices, script_callbacks, shared
|
||||
from modules.shared import opts, state
|
||||
from swinir_model_arch import SwinIR
|
||||
from swinir_model_arch_v2 import Swin2SR
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
|
||||
SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
|
||||
|
||||
device_swinir = devices.get_device_for('swinir')
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class UpscalerSwinIR(Upscaler):
|
||||
@ -37,26 +35,29 @@ class UpscalerSwinIR(Upscaler):
|
||||
scalers.append(model_data)
|
||||
self.scalers = scalers
|
||||
|
||||
def do_upscale(self, img, model_file):
|
||||
use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \
|
||||
and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows"
|
||||
def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image:
|
||||
current_config = (model_file, opts.SWIN_tile)
|
||||
|
||||
if use_compile and self._cached_model_config == current_config:
|
||||
device = self._get_device()
|
||||
|
||||
if self._cached_model_config == current_config:
|
||||
model = self._cached_model
|
||||
else:
|
||||
self._cached_model = None
|
||||
try:
|
||||
model = self.load_model(model_file)
|
||||
except Exception as e:
|
||||
print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
|
||||
return img
|
||||
model = model.to(device_swinir, dtype=devices.dtype)
|
||||
if use_compile:
|
||||
model = torch.compile(model)
|
||||
self._cached_model = model
|
||||
self._cached_model_config = current_config
|
||||
img = upscale(img, model)
|
||||
self._cached_model = model
|
||||
self._cached_model_config = current_config
|
||||
|
||||
img = upscale(
|
||||
img,
|
||||
model,
|
||||
tile=opts.SWIN_tile,
|
||||
tile_overlap=opts.SWIN_tile_overlap,
|
||||
device=device,
|
||||
)
|
||||
devices.torch_gc()
|
||||
return img
|
||||
|
||||
@ -69,69 +70,54 @@ class UpscalerSwinIR(Upscaler):
|
||||
)
|
||||
else:
|
||||
filename = path
|
||||
if filename.endswith(".v2.pth"):
|
||||
model = Swin2SR(
|
||||
upscale=scale,
|
||||
in_chans=3,
|
||||
img_size=64,
|
||||
window_size=8,
|
||||
img_range=1.0,
|
||||
depths=[6, 6, 6, 6, 6, 6],
|
||||
embed_dim=180,
|
||||
num_heads=[6, 6, 6, 6, 6, 6],
|
||||
mlp_ratio=2,
|
||||
upsampler="nearest+conv",
|
||||
resi_connection="1conv",
|
||||
)
|
||||
params = None
|
||||
else:
|
||||
model = SwinIR(
|
||||
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)
|
||||
if params is not None:
|
||||
model.load_state_dict(pretrained_model[params], strict=True)
|
||||
else:
|
||||
model.load_state_dict(pretrained_model, strict=True)
|
||||
model = modelloader.load_spandrel_model(
|
||||
filename,
|
||||
device=self._get_device(),
|
||||
dtype=devices.dtype,
|
||||
)
|
||||
if getattr(opts, 'SWIN_torch_compile', False):
|
||||
try:
|
||||
model = torch.compile(model)
|
||||
except Exception:
|
||||
logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True)
|
||||
return model
|
||||
|
||||
def _get_device(self):
|
||||
return devices.get_device_for('swinir')
|
||||
|
||||
|
||||
def upscale(
|
||||
img,
|
||||
model,
|
||||
tile=None,
|
||||
tile_overlap=None,
|
||||
window_size=8,
|
||||
scale=4,
|
||||
img,
|
||||
model,
|
||||
*,
|
||||
tile: int,
|
||||
tile_overlap: int,
|
||||
window_size=8,
|
||||
scale=4,
|
||||
device,
|
||||
):
|
||||
tile = tile or opts.SWIN_tile
|
||||
tile_overlap = tile_overlap or opts.SWIN_tile_overlap
|
||||
|
||||
|
||||
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(device_swinir, dtype=devices.dtype)
|
||||
img = img.unsqueeze(0).to(device, dtype=devices.dtype)
|
||||
with torch.no_grad(), devices.autocast():
|
||||
_, _, 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 = inference(
|
||||
img,
|
||||
model,
|
||||
tile=tile,
|
||||
tile_overlap=tile_overlap,
|
||||
window_size=window_size,
|
||||
scale=scale,
|
||||
device=device,
|
||||
)
|
||||
output = output[..., : h_old * scale, : w_old * scale]
|
||||
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
|
||||
if output.ndim == 3:
|
||||
@ -142,7 +128,16 @@ def upscale(
|
||||
return Image.fromarray(output, "RGB")
|
||||
|
||||
|
||||
def inference(img, model, tile, tile_overlap, window_size, scale):
|
||||
def inference(
|
||||
img,
|
||||
model,
|
||||
*,
|
||||
tile: int,
|
||||
tile_overlap: int,
|
||||
window_size: int,
|
||||
scale: int,
|
||||
device,
|
||||
):
|
||||
# test the image tile by tile
|
||||
b, c, h, w = img.size()
|
||||
tile = min(tile, h, w)
|
||||
@ -152,8 +147,8 @@ def inference(img, model, tile, tile_overlap, window_size, 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=devices.dtype, device=device_swinir).type_as(img)
|
||||
W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
|
||||
E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device).type_as(img)
|
||||
W = torch.zeros_like(E, dtype=devices.dtype, device=device)
|
||||
|
||||
with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
|
||||
for h_idx in h_idx_list:
|
||||
@ -185,8 +180,7 @@ def on_ui_settings():
|
||||
|
||||
shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
|
||||
shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
|
||||
if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows
|
||||
shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
|
||||
shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
|
||||
|
||||
|
||||
script_callbacks.on_ui_settings(on_ui_settings)
|
||||
|
||||
@ -1,867 +0,0 @@
|
||||
# -----------------------------------------------------------------------------------
|
||||
# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
|
||||
# Originally Written by Ze Liu, Modified by Jingyun Liang.
|
||||
# -----------------------------------------------------------------------------------
|
||||
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
||||
|
||||
|
||||
class Mlp(nn.Module):
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
def window_partition(x, window_size):
|
||||
"""
|
||||
Args:
|
||||
x: (B, H, W, C)
|
||||
window_size (int): window size
|
||||
|
||||
Returns:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
"""
|
||||
B, H, W, C = x.shape
|
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows
|
||||
|
||||
|
||||
def window_reverse(windows, window_size, H, W):
|
||||
"""
|
||||
Args:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
window_size (int): Window size
|
||||
H (int): Height of image
|
||||
W (int): Width of image
|
||||
|
||||
Returns:
|
||||
x: (B, H, W, C)
|
||||
"""
|
||||
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||||
return x
|
||||
|
||||
|
||||
class WindowAttention(nn.Module):
|
||||
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
|
||||
It supports both of shifted and non-shifted window.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
window_size (tuple[int]): The height and width of the window.
|
||||
num_heads (int): Number of attention heads.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
|
||||
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
||||
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
||||
"""
|
||||
|
||||
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.window_size = window_size # Wh, Ww
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
# define a parameter table of relative position bias
|
||||
self.relative_position_bias_table = nn.Parameter(
|
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
||||
|
||||
# get pair-wise relative position index for each token inside the window
|
||||
coords_h = torch.arange(self.window_size[0])
|
||||
coords_w = torch.arange(self.window_size[1])
|
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
|
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
self.register_buffer("relative_position_index", relative_position_index)
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=.02)
|
||||
self.softmax = nn.Softmax(dim=-1)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
"""
|
||||
Args:
|
||||
x: input features with shape of (num_windows*B, N, C)
|
||||
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
||||
"""
|
||||
B_, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
q = q * self.scale
|
||||
attn = (q @ k.transpose(-2, -1))
|
||||
|
||||
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
|
||||
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
||||
attn = attn + relative_position_bias.unsqueeze(0)
|
||||
|
||||
if mask is not None:
|
||||
nW = mask.shape[0]
|
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||||
attn = attn.view(-1, self.num_heads, N, N)
|
||||
attn = self.softmax(attn)
|
||||
else:
|
||||
attn = self.softmax(attn)
|
||||
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
|
||||
|
||||
def flops(self, N):
|
||||
# calculate flops for 1 window with token length of N
|
||||
flops = 0
|
||||
# qkv = self.qkv(x)
|
||||
flops += N * self.dim * 3 * self.dim
|
||||
# attn = (q @ k.transpose(-2, -1))
|
||||
flops += self.num_heads * N * (self.dim // self.num_heads) * N
|
||||
# x = (attn @ v)
|
||||
flops += self.num_heads * N * N * (self.dim // self.num_heads)
|
||||
# x = self.proj(x)
|
||||
flops += N * self.dim * self.dim
|
||||
return flops
|
||||
|
||||
|
||||
class SwinTransformerBlock(nn.Module):
|
||||
r""" Swin Transformer Block.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
input_resolution (tuple[int]): Input resolution.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
shift_size (int): Shift size for SW-MSA.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
||||
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
|
||||
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.input_resolution = input_resolution
|
||||
self.num_heads = num_heads
|
||||
self.window_size = window_size
|
||||
self.shift_size = shift_size
|
||||
self.mlp_ratio = mlp_ratio
|
||||
if min(self.input_resolution) <= self.window_size:
|
||||
# if window size is larger than input resolution, we don't partition windows
|
||||
self.shift_size = 0
|
||||
self.window_size = min(self.input_resolution)
|
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||||
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = WindowAttention(
|
||||
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
if self.shift_size > 0:
|
||||
attn_mask = self.calculate_mask(self.input_resolution)
|
||||
else:
|
||||
attn_mask = None
|
||||
|
||||
self.register_buffer("attn_mask", attn_mask)
|
||||
|
||||
def calculate_mask(self, x_size):
|
||||
# calculate attention mask for SW-MSA
|
||||
H, W = x_size
|
||||
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
|
||||
h_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
w_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
cnt = 0
|
||||
for h in h_slices:
|
||||
for w in w_slices:
|
||||
img_mask[:, h, w, :] = cnt
|
||||
cnt += 1
|
||||
|
||||
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
|
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
||||
|
||||
return attn_mask
|
||||
|
||||
def forward(self, x, x_size):
|
||||
H, W = x_size
|
||||
B, L, C = x.shape
|
||||
# assert L == H * W, "input feature has wrong size"
|
||||
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# cyclic shift
|
||||
if self.shift_size > 0:
|
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
shifted_x = x
|
||||
|
||||
# partition windows
|
||||
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
|
||||
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
|
||||
|
||||
# W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
|
||||
if self.input_resolution == x_size:
|
||||
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
|
||||
else:
|
||||
attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
|
||||
|
||||
# merge windows
|
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
|
||||
|
||||
# reverse cyclic shift
|
||||
if self.shift_size > 0:
|
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
x = shifted_x
|
||||
x = x.view(B, H * W, C)
|
||||
|
||||
# FFN
|
||||
x = shortcut + self.drop_path(x)
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
|
||||
return x
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
|
||||
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
|
||||
|
||||
def flops(self):
|
||||
flops = 0
|
||||
H, W = self.input_resolution
|
||||
# norm1
|
||||
flops += self.dim * H * W
|
||||
# W-MSA/SW-MSA
|
||||
nW = H * W / self.window_size / self.window_size
|
||||
flops += nW * self.attn.flops(self.window_size * self.window_size)
|
||||
# mlp
|
||||
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
|
||||
# norm2
|
||||
flops += self.dim * H * W
|
||||
return flops
|
||||
|
||||
|
||||
class PatchMerging(nn.Module):
|
||||
r""" Patch Merging Layer.
|
||||
|
||||
Args:
|
||||
input_resolution (tuple[int]): Resolution of input feature.
|
||||
dim (int): Number of input channels.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.input_resolution = input_resolution
|
||||
self.dim = dim
|
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||||
self.norm = norm_layer(4 * dim)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
x: B, H*W, C
|
||||
"""
|
||||
H, W = self.input_resolution
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
|
||||
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||||
|
||||
x = self.norm(x)
|
||||
x = self.reduction(x)
|
||||
|
||||
return x
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return f"input_resolution={self.input_resolution}, dim={self.dim}"
|
||||
|
||||
def flops(self):
|
||||
H, W = self.input_resolution
|
||||
flops = H * W * self.dim
|
||||
flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
|
||||
return flops
|
||||
|
||||
|
||||
class BasicLayer(nn.Module):
|
||||
""" A basic Swin Transformer layer for one stage.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
input_resolution (tuple[int]): Input resolution.
|
||||
depth (int): Number of blocks.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Local window size.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.input_resolution = input_resolution
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
|
||||
num_heads=num_heads, window_size=window_size,
|
||||
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop, attn_drop=attn_drop,
|
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
norm_layer=norm_layer)
|
||||
for i in range(depth)])
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x, x_size):
|
||||
for blk in self.blocks:
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x, x_size)
|
||||
else:
|
||||
x = blk(x, x_size)
|
||||
if self.downsample is not None:
|
||||
x = self.downsample(x)
|
||||
return x
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
|
||||
|
||||
def flops(self):
|
||||
flops = 0
|
||||
for blk in self.blocks:
|
||||
flops += blk.flops()
|
||||
if self.downsample is not None:
|
||||
flops += self.downsample.flops()
|
||||
return flops
|
||||
|
||||
|
||||
class RSTB(nn.Module):
|
||||
"""Residual Swin Transformer Block (RSTB).
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
input_resolution (tuple[int]): Input resolution.
|
||||
depth (int): Number of blocks.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Local window size.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
img_size: Input image size.
|
||||
patch_size: Patch size.
|
||||
resi_connection: The convolutional block before residual connection.
|
||||
"""
|
||||
|
||||
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
|
||||
img_size=224, patch_size=4, resi_connection='1conv'):
|
||||
super(RSTB, self).__init__()
|
||||
|
||||
self.dim = dim
|
||||
self.input_resolution = input_resolution
|
||||
|
||||
self.residual_group = BasicLayer(dim=dim,
|
||||
input_resolution=input_resolution,
|
||||
depth=depth,
|
||||
num_heads=num_heads,
|
||||
window_size=window_size,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop, attn_drop=attn_drop,
|
||||
drop_path=drop_path,
|
||||
norm_layer=norm_layer,
|
||||
downsample=downsample,
|
||||
use_checkpoint=use_checkpoint)
|
||||
|
||||
if resi_connection == '1conv':
|
||||
self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
|
||||
elif resi_connection == '3conv':
|
||||
# to save parameters and memory
|
||||
self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
||||
nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
|
||||
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
||||
nn.Conv2d(dim // 4, dim, 3, 1, 1))
|
||||
|
||||
self.patch_embed = PatchEmbed(
|
||||
img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
|
||||
norm_layer=None)
|
||||
|
||||
self.patch_unembed = PatchUnEmbed(
|
||||
img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
|
||||
norm_layer=None)
|
||||
|
||||
def forward(self, x, x_size):
|
||||
return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
|
||||
|
||||
def flops(self):
|
||||
flops = 0
|
||||
flops += self.residual_group.flops()
|
||||
H, W = self.input_resolution
|
||||
flops += H * W * self.dim * self.dim * 9
|
||||
flops += self.patch_embed.flops()
|
||||
flops += self.patch_unembed.flops()
|
||||
|
||||
return flops
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
r""" Image to Patch Embedding
|
||||
|
||||
Args:
|
||||
img_size (int): Image size. Default: 224.
|
||||
patch_size (int): Patch token size. Default: 4.
|
||||
in_chans (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.patches_resolution = patches_resolution
|
||||
self.num_patches = patches_resolution[0] * patches_resolution[1]
|
||||
|
||||
self.in_chans = in_chans
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
if norm_layer is not None:
|
||||
self.norm = norm_layer(embed_dim)
|
||||
else:
|
||||
self.norm = None
|
||||
|
||||
def forward(self, x):
|
||||
x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
|
||||
if self.norm is not None:
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
def flops(self):
|
||||
flops = 0
|
||||
H, W = self.img_size
|
||||
if self.norm is not None:
|
||||
flops += H * W * self.embed_dim
|
||||
return flops
|
||||
|
||||
|
||||
class PatchUnEmbed(nn.Module):
|
||||
r""" Image to Patch Unembedding
|
||||
|
||||
Args:
|
||||
img_size (int): Image size. Default: 224.
|
||||
patch_size (int): Patch token size. Default: 4.
|
||||
in_chans (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.patches_resolution = patches_resolution
|
||||
self.num_patches = patches_resolution[0] * patches_resolution[1]
|
||||
|
||||
self.in_chans = in_chans
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
def forward(self, x, x_size):
|
||||
B, HW, C = x.shape
|
||||
x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
|
||||
return x
|
||||
|
||||
def flops(self):
|
||||
flops = 0
|
||||
return flops
|
||||
|
||||
|
||||
class Upsample(nn.Sequential):
|
||||
"""Upsample module.
|
||||
|
||||
Args:
|
||||
scale (int): Scale factor. Supported scales: 2^n and 3.
|
||||
num_feat (int): Channel number of intermediate features.
|
||||
"""
|
||||
|
||||
def __init__(self, scale, num_feat):
|
||||
m = []
|
||||
if (scale & (scale - 1)) == 0: # scale = 2^n
|
||||
for _ in range(int(math.log(scale, 2))):
|
||||
m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
|
||||
m.append(nn.PixelShuffle(2))
|
||||
elif scale == 3:
|
||||
m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
|
||||
m.append(nn.PixelShuffle(3))
|
||||
else:
|
||||
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
|
||||
super(Upsample, self).__init__(*m)
|
||||
|
||||
|
||||
class UpsampleOneStep(nn.Sequential):
|
||||
"""UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
|
||||
Used in lightweight SR to save parameters.
|
||||
|
||||
Args:
|
||||
scale (int): Scale factor. Supported scales: 2^n and 3.
|
||||
num_feat (int): Channel number of intermediate features.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
|
||||
self.num_feat = num_feat
|
||||
self.input_resolution = input_resolution
|
||||
m = []
|
||||
m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
|
||||
m.append(nn.PixelShuffle(scale))
|
||||
super(UpsampleOneStep, self).__init__(*m)
|
||||
|
||||
def flops(self):
|
||||
H, W = self.input_resolution
|
||||
flops = H * W * self.num_feat * 3 * 9
|
||||
return flops
|
||||
|
||||
|
||||
class SwinIR(nn.Module):
|
||||
r""" SwinIR
|
||||
A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
|
||||
|
||||
Args:
|
||||
img_size (int | tuple(int)): Input image size. Default 64
|
||||
patch_size (int | tuple(int)): Patch size. Default: 1
|
||||
in_chans (int): Number of input image channels. Default: 3
|
||||
embed_dim (int): Patch embedding dimension. Default: 96
|
||||
depths (tuple(int)): Depth of each Swin Transformer layer.
|
||||
num_heads (tuple(int)): Number of attention heads in different layers.
|
||||
window_size (int): Window size. Default: 7
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
|
||||
drop_rate (float): Dropout rate. Default: 0
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.1
|
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
|
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
|
||||
upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
|
||||
img_range: Image range. 1. or 255.
|
||||
upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
|
||||
resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=64, patch_size=1, in_chans=3,
|
||||
embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
|
||||
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
|
||||
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
||||
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
|
||||
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
|
||||
**kwargs):
|
||||
super(SwinIR, self).__init__()
|
||||
num_in_ch = in_chans
|
||||
num_out_ch = in_chans
|
||||
num_feat = 64
|
||||
self.img_range = img_range
|
||||
if in_chans == 3:
|
||||
rgb_mean = (0.4488, 0.4371, 0.4040)
|
||||
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
|
||||
else:
|
||||
self.mean = torch.zeros(1, 1, 1, 1)
|
||||
self.upscale = upscale
|
||||
self.upsampler = upsampler
|
||||
self.window_size = window_size
|
||||
|
||||
#####################################################################################################
|
||||
################################### 1, shallow feature extraction ###################################
|
||||
self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
|
||||
|
||||
#####################################################################################################
|
||||
################################### 2, deep feature extraction ######################################
|
||||
self.num_layers = len(depths)
|
||||
self.embed_dim = embed_dim
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.num_features = embed_dim
|
||||
self.mlp_ratio = mlp_ratio
|
||||
|
||||
# split image into non-overlapping patches
|
||||
self.patch_embed = PatchEmbed(
|
||||
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
|
||||
norm_layer=norm_layer if self.patch_norm else None)
|
||||
num_patches = self.patch_embed.num_patches
|
||||
patches_resolution = self.patch_embed.patches_resolution
|
||||
self.patches_resolution = patches_resolution
|
||||
|
||||
# merge non-overlapping patches into image
|
||||
self.patch_unembed = PatchUnEmbed(
|
||||
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
|
||||
norm_layer=norm_layer if self.patch_norm else None)
|
||||
|
||||
# absolute position embedding
|
||||
if self.ape:
|
||||
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
|
||||
trunc_normal_(self.absolute_pos_embed, std=.02)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
|
||||
# build Residual Swin Transformer blocks (RSTB)
|
||||
self.layers = nn.ModuleList()
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = RSTB(dim=embed_dim,
|
||||
input_resolution=(patches_resolution[0],
|
||||
patches_resolution[1]),
|
||||
depth=depths[i_layer],
|
||||
num_heads=num_heads[i_layer],
|
||||
window_size=window_size,
|
||||
mlp_ratio=self.mlp_ratio,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
|
||||
norm_layer=norm_layer,
|
||||
downsample=None,
|
||||
use_checkpoint=use_checkpoint,
|
||||
img_size=img_size,
|
||||
patch_size=patch_size,
|
||||
resi_connection=resi_connection
|
||||
|
||||
)
|
||||
self.layers.append(layer)
|
||||
self.norm = norm_layer(self.num_features)
|
||||
|
||||
# build the last conv layer in deep feature extraction
|
||||
if resi_connection == '1conv':
|
||||
self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
|
||||
elif resi_connection == '3conv':
|
||||
# to save parameters and memory
|
||||
self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
|
||||
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
||||
nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
|
||||
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
||||
nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
|
||||
|
||||
#####################################################################################################
|
||||
################################ 3, high quality image reconstruction ################################
|
||||
if self.upsampler == 'pixelshuffle':
|
||||
# for classical SR
|
||||
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
|
||||
nn.LeakyReLU(inplace=True))
|
||||
self.upsample = Upsample(upscale, num_feat)
|
||||
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
|
||||
elif self.upsampler == 'pixelshuffledirect':
|
||||
# for lightweight SR (to save parameters)
|
||||
self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
|
||||
(patches_resolution[0], patches_resolution[1]))
|
||||
elif self.upsampler == 'nearest+conv':
|
||||
# for real-world SR (less artifacts)
|
||||
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
|
||||
nn.LeakyReLU(inplace=True))
|
||||
self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
|
||||
if self.upscale == 4:
|
||||
self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
|
||||
self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
|
||||
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
|
||||
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
||||
else:
|
||||
# for image denoising and JPEG compression artifact reduction
|
||||
self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and 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)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'absolute_pos_embed'}
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay_keywords(self):
|
||||
return {'relative_position_bias_table'}
|
||||
|
||||
def check_image_size(self, x):
|
||||
_, _, h, w = x.size()
|
||||
mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
|
||||
mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
|
||||
x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
|
||||
return x
|
||||
|
||||
def forward_features(self, x):
|
||||
x_size = (x.shape[2], x.shape[3])
|
||||
x = self.patch_embed(x)
|
||||
if self.ape:
|
||||
x = x + self.absolute_pos_embed
|
||||
x = self.pos_drop(x)
|
||||
|
||||
for layer in self.layers:
|
||||
x = layer(x, x_size)
|
||||
|
||||
x = self.norm(x) # B L C
|
||||
x = self.patch_unembed(x, x_size)
|
||||
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
H, W = x.shape[2:]
|
||||
x = self.check_image_size(x)
|
||||
|
||||
self.mean = self.mean.type_as(x)
|
||||
x = (x - self.mean) * self.img_range
|
||||
|
||||
if self.upsampler == 'pixelshuffle':
|
||||
# for classical SR
|
||||
x = self.conv_first(x)
|
||||
x = self.conv_after_body(self.forward_features(x)) + x
|
||||
x = self.conv_before_upsample(x)
|
||||
x = self.conv_last(self.upsample(x))
|
||||
elif self.upsampler == 'pixelshuffledirect':
|
||||
# for lightweight SR
|
||||
x = self.conv_first(x)
|
||||
x = self.conv_after_body(self.forward_features(x)) + x
|
||||
x = self.upsample(x)
|
||||
elif self.upsampler == 'nearest+conv':
|
||||
# for real-world SR
|
||||
x = self.conv_first(x)
|
||||
x = self.conv_after_body(self.forward_features(x)) + x
|
||||
x = self.conv_before_upsample(x)
|
||||
x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
|
||||
if self.upscale == 4:
|
||||
x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
|
||||
x = self.conv_last(self.lrelu(self.conv_hr(x)))
|
||||
else:
|
||||
# for image denoising and JPEG compression artifact reduction
|
||||
x_first = self.conv_first(x)
|
||||
res = self.conv_after_body(self.forward_features(x_first)) + x_first
|
||||
x = x + self.conv_last(res)
|
||||
|
||||
x = x / self.img_range + self.mean
|
||||
|
||||
return x[:, :, :H*self.upscale, :W*self.upscale]
|
||||
|
||||
def flops(self):
|
||||
flops = 0
|
||||
H, W = self.patches_resolution
|
||||
flops += H * W * 3 * self.embed_dim * 9
|
||||
flops += self.patch_embed.flops()
|
||||
for layer in self.layers:
|
||||
flops += layer.flops()
|
||||
flops += H * W * 3 * self.embed_dim * self.embed_dim
|
||||
flops += self.upsample.flops()
|
||||
return flops
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
upscale = 4
|
||||
window_size = 8
|
||||
height = (1024 // upscale // window_size + 1) * window_size
|
||||
width = (720 // upscale // window_size + 1) * window_size
|
||||
model = SwinIR(upscale=2, img_size=(height, width),
|
||||
window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
|
||||
embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
|
||||
print(model)
|
||||
print(height, width, model.flops() / 1e9)
|
||||
|
||||
x = torch.randn((1, 3, height, width))
|
||||
x = model(x)
|
||||
print(x.shape)
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,276 +0,0 @@
|
||||
# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
|
||||
|
||||
import math
|
||||
import torch
|
||||
from torch import nn, Tensor
|
||||
import torch.nn.functional as F
|
||||
from typing import Optional
|
||||
|
||||
from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
|
||||
from basicsr.utils.registry import ARCH_REGISTRY
|
||||
|
||||
def calc_mean_std(feat, eps=1e-5):
|
||||
"""Calculate mean and std for adaptive_instance_normalization.
|
||||
|
||||
Args:
|
||||
feat (Tensor): 4D tensor.
|
||||
eps (float): A small value added to the variance to avoid
|
||||
divide-by-zero. Default: 1e-5.
|
||||
"""
|
||||
size = feat.size()
|
||||
assert len(size) == 4, 'The input feature should be 4D tensor.'
|
||||
b, c = size[:2]
|
||||
feat_var = feat.view(b, c, -1).var(dim=2) + eps
|
||||
feat_std = feat_var.sqrt().view(b, c, 1, 1)
|
||||
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
|
||||
return feat_mean, feat_std
|
||||
|
||||
|
||||
def adaptive_instance_normalization(content_feat, style_feat):
|
||||
"""Adaptive instance normalization.
|
||||
|
||||
Adjust the reference features to have the similar color and illuminations
|
||||
as those in the degradate features.
|
||||
|
||||
Args:
|
||||
content_feat (Tensor): The reference feature.
|
||||
style_feat (Tensor): The degradate features.
|
||||
"""
|
||||
size = content_feat.size()
|
||||
style_mean, style_std = calc_mean_std(style_feat)
|
||||
content_mean, content_std = calc_mean_std(content_feat)
|
||||
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
|
||||
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
|
||||
|
||||
|
||||
class PositionEmbeddingSine(nn.Module):
|
||||
"""
|
||||
This is a more standard version of the position embedding, very similar to the one
|
||||
used by the Attention is all you need paper, generalized to work on images.
|
||||
"""
|
||||
|
||||
def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
|
||||
super().__init__()
|
||||
self.num_pos_feats = num_pos_feats
|
||||
self.temperature = temperature
|
||||
self.normalize = normalize
|
||||
if scale is not None and normalize is False:
|
||||
raise ValueError("normalize should be True if scale is passed")
|
||||
if scale is None:
|
||||
scale = 2 * math.pi
|
||||
self.scale = scale
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
if mask is None:
|
||||
mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
|
||||
not_mask = ~mask
|
||||
y_embed = not_mask.cumsum(1, dtype=torch.float32)
|
||||
x_embed = not_mask.cumsum(2, dtype=torch.float32)
|
||||
if self.normalize:
|
||||
eps = 1e-6
|
||||
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
|
||||
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
|
||||
|
||||
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
||||
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
|
||||
|
||||
pos_x = x_embed[:, :, :, None] / dim_t
|
||||
pos_y = y_embed[:, :, :, None] / dim_t
|
||||
pos_x = torch.stack(
|
||||
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos_y = torch.stack(
|
||||
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
|
||||
return pos
|
||||
|
||||
def _get_activation_fn(activation):
|
||||
"""Return an activation function given a string"""
|
||||
if activation == "relu":
|
||||
return F.relu
|
||||
if activation == "gelu":
|
||||
return F.gelu
|
||||
if activation == "glu":
|
||||
return F.glu
|
||||
raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
|
||||
|
||||
|
||||
class TransformerSALayer(nn.Module):
|
||||
def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
|
||||
# Implementation of Feedforward model - MLP
|
||||
self.linear1 = nn.Linear(embed_dim, dim_mlp)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_mlp, embed_dim)
|
||||
|
||||
self.norm1 = nn.LayerNorm(embed_dim)
|
||||
self.norm2 = nn.LayerNorm(embed_dim)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
|
||||
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward(self, tgt,
|
||||
tgt_mask: Optional[Tensor] = None,
|
||||
tgt_key_padding_mask: Optional[Tensor] = None,
|
||||
query_pos: Optional[Tensor] = None):
|
||||
|
||||
# self attention
|
||||
tgt2 = self.norm1(tgt)
|
||||
q = k = self.with_pos_embed(tgt2, query_pos)
|
||||
tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
|
||||
key_padding_mask=tgt_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout1(tgt2)
|
||||
|
||||
# ffn
|
||||
tgt2 = self.norm2(tgt)
|
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
|
||||
tgt = tgt + self.dropout2(tgt2)
|
||||
return tgt
|
||||
|
||||
class Fuse_sft_block(nn.Module):
|
||||
def __init__(self, in_ch, out_ch):
|
||||
super().__init__()
|
||||
self.encode_enc = ResBlock(2*in_ch, out_ch)
|
||||
|
||||
self.scale = nn.Sequential(
|
||||
nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
|
||||
|
||||
self.shift = nn.Sequential(
|
||||
nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
|
||||
|
||||
def forward(self, enc_feat, dec_feat, w=1):
|
||||
enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
|
||||
scale = self.scale(enc_feat)
|
||||
shift = self.shift(enc_feat)
|
||||
residual = w * (dec_feat * scale + shift)
|
||||
out = dec_feat + residual
|
||||
return out
|
||||
|
||||
|
||||
@ARCH_REGISTRY.register()
|
||||
class CodeFormer(VQAutoEncoder):
|
||||
def __init__(self, dim_embd=512, n_head=8, n_layers=9,
|
||||
codebook_size=1024, latent_size=256,
|
||||
connect_list=('32', '64', '128', '256'),
|
||||
fix_modules=('quantize', 'generator')):
|
||||
super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
|
||||
|
||||
if fix_modules is not None:
|
||||
for module in fix_modules:
|
||||
for param in getattr(self, module).parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
self.connect_list = connect_list
|
||||
self.n_layers = n_layers
|
||||
self.dim_embd = dim_embd
|
||||
self.dim_mlp = dim_embd*2
|
||||
|
||||
self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
|
||||
self.feat_emb = nn.Linear(256, self.dim_embd)
|
||||
|
||||
# transformer
|
||||
self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
|
||||
for _ in range(self.n_layers)])
|
||||
|
||||
# logits_predict head
|
||||
self.idx_pred_layer = nn.Sequential(
|
||||
nn.LayerNorm(dim_embd),
|
||||
nn.Linear(dim_embd, codebook_size, bias=False))
|
||||
|
||||
self.channels = {
|
||||
'16': 512,
|
||||
'32': 256,
|
||||
'64': 256,
|
||||
'128': 128,
|
||||
'256': 128,
|
||||
'512': 64,
|
||||
}
|
||||
|
||||
# after second residual block for > 16, before attn layer for ==16
|
||||
self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
|
||||
# after first residual block for > 16, before attn layer for ==16
|
||||
self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}
|
||||
|
||||
# fuse_convs_dict
|
||||
self.fuse_convs_dict = nn.ModuleDict()
|
||||
for f_size in self.connect_list:
|
||||
in_ch = self.channels[f_size]
|
||||
self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
|
||||
|
||||
def _init_weights(self, module):
|
||||
if isinstance(module, (nn.Linear, nn.Embedding)):
|
||||
module.weight.data.normal_(mean=0.0, std=0.02)
|
||||
if isinstance(module, nn.Linear) and module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.LayerNorm):
|
||||
module.bias.data.zero_()
|
||||
module.weight.data.fill_(1.0)
|
||||
|
||||
def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
|
||||
# ################### Encoder #####################
|
||||
enc_feat_dict = {}
|
||||
out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
|
||||
for i, block in enumerate(self.encoder.blocks):
|
||||
x = block(x)
|
||||
if i in out_list:
|
||||
enc_feat_dict[str(x.shape[-1])] = x.clone()
|
||||
|
||||
lq_feat = x
|
||||
# ################# Transformer ###################
|
||||
# quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
|
||||
pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
|
||||
# BCHW -> BC(HW) -> (HW)BC
|
||||
feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
|
||||
query_emb = feat_emb
|
||||
# Transformer encoder
|
||||
for layer in self.ft_layers:
|
||||
query_emb = layer(query_emb, query_pos=pos_emb)
|
||||
|
||||
# output logits
|
||||
logits = self.idx_pred_layer(query_emb) # (hw)bn
|
||||
logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n
|
||||
|
||||
if code_only: # for training stage II
|
||||
# logits doesn't need softmax before cross_entropy loss
|
||||
return logits, lq_feat
|
||||
|
||||
# ################# Quantization ###################
|
||||
# if self.training:
|
||||
# quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
|
||||
# # b(hw)c -> bc(hw) -> bchw
|
||||
# quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
|
||||
# ------------
|
||||
soft_one_hot = F.softmax(logits, dim=2)
|
||||
_, top_idx = torch.topk(soft_one_hot, 1, dim=2)
|
||||
quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
|
||||
# preserve gradients
|
||||
# quant_feat = lq_feat + (quant_feat - lq_feat).detach()
|
||||
|
||||
if detach_16:
|
||||
quant_feat = quant_feat.detach() # for training stage III
|
||||
if adain:
|
||||
quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
|
||||
|
||||
# ################## Generator ####################
|
||||
x = quant_feat
|
||||
fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
|
||||
|
||||
for i, block in enumerate(self.generator.blocks):
|
||||
x = block(x)
|
||||
if i in fuse_list: # fuse after i-th block
|
||||
f_size = str(x.shape[-1])
|
||||
if w>0:
|
||||
x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
|
||||
out = x
|
||||
# logits doesn't need softmax before cross_entropy loss
|
||||
return out, logits, lq_feat
|
||||
@ -1,435 +0,0 @@
|
||||
# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
|
||||
|
||||
'''
|
||||
VQGAN code, adapted from the original created by the Unleashing Transformers authors:
|
||||
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
|
||||
|
||||
'''
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from basicsr.utils import get_root_logger
|
||||
from basicsr.utils.registry import ARCH_REGISTRY
|
||||
|
||||
def normalize(in_channels):
|
||||
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
|
||||
|
||||
|
||||
@torch.jit.script
|
||||
def swish(x):
|
||||
return x*torch.sigmoid(x)
|
||||
|
||||
|
||||
# Define VQVAE classes
|
||||
class VectorQuantizer(nn.Module):
|
||||
def __init__(self, codebook_size, emb_dim, beta):
|
||||
super(VectorQuantizer, self).__init__()
|
||||
self.codebook_size = codebook_size # number of embeddings
|
||||
self.emb_dim = emb_dim # dimension of embedding
|
||||
self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
|
||||
self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
|
||||
self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
|
||||
|
||||
def forward(self, z):
|
||||
# reshape z -> (batch, height, width, channel) and flatten
|
||||
z = z.permute(0, 2, 3, 1).contiguous()
|
||||
z_flattened = z.view(-1, self.emb_dim)
|
||||
|
||||
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
|
||||
d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
|
||||
2 * torch.matmul(z_flattened, self.embedding.weight.t())
|
||||
|
||||
mean_distance = torch.mean(d)
|
||||
# find closest encodings
|
||||
# min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
|
||||
min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
|
||||
# [0-1], higher score, higher confidence
|
||||
min_encoding_scores = torch.exp(-min_encoding_scores/10)
|
||||
|
||||
min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
|
||||
min_encodings.scatter_(1, min_encoding_indices, 1)
|
||||
|
||||
# get quantized latent vectors
|
||||
z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
|
||||
# compute loss for embedding
|
||||
loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
|
||||
# preserve gradients
|
||||
z_q = z + (z_q - z).detach()
|
||||
|
||||
# perplexity
|
||||
e_mean = torch.mean(min_encodings, dim=0)
|
||||
perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
|
||||
# reshape back to match original input shape
|
||||
z_q = z_q.permute(0, 3, 1, 2).contiguous()
|
||||
|
||||
return z_q, loss, {
|
||||
"perplexity": perplexity,
|
||||
"min_encodings": min_encodings,
|
||||
"min_encoding_indices": min_encoding_indices,
|
||||
"min_encoding_scores": min_encoding_scores,
|
||||
"mean_distance": mean_distance
|
||||
}
|
||||
|
||||
def get_codebook_feat(self, indices, shape):
|
||||
# input indices: batch*token_num -> (batch*token_num)*1
|
||||
# shape: batch, height, width, channel
|
||||
indices = indices.view(-1,1)
|
||||
min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
|
||||
min_encodings.scatter_(1, indices, 1)
|
||||
# get quantized latent vectors
|
||||
z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
|
||||
|
||||
if shape is not None: # reshape back to match original input shape
|
||||
z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
|
||||
|
||||
return z_q
|
||||
|
||||
|
||||
class GumbelQuantizer(nn.Module):
|
||||
def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
|
||||
super().__init__()
|
||||
self.codebook_size = codebook_size # number of embeddings
|
||||
self.emb_dim = emb_dim # dimension of embedding
|
||||
self.straight_through = straight_through
|
||||
self.temperature = temp_init
|
||||
self.kl_weight = kl_weight
|
||||
self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits
|
||||
self.embed = nn.Embedding(codebook_size, emb_dim)
|
||||
|
||||
def forward(self, z):
|
||||
hard = self.straight_through if self.training else True
|
||||
|
||||
logits = self.proj(z)
|
||||
|
||||
soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
|
||||
|
||||
z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
|
||||
|
||||
# + kl divergence to the prior loss
|
||||
qy = F.softmax(logits, dim=1)
|
||||
diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
|
||||
min_encoding_indices = soft_one_hot.argmax(dim=1)
|
||||
|
||||
return z_q, diff, {
|
||||
"min_encoding_indices": min_encoding_indices
|
||||
}
|
||||
|
||||
|
||||
class Downsample(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
|
||||
|
||||
def forward(self, x):
|
||||
pad = (0, 1, 0, 1)
|
||||
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
|
||||
class Upsample(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.interpolate(x, scale_factor=2.0, mode="nearest")
|
||||
x = self.conv(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class ResBlock(nn.Module):
|
||||
def __init__(self, in_channels, out_channels=None):
|
||||
super(ResBlock, self).__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = in_channels if out_channels is None else out_channels
|
||||
self.norm1 = normalize(in_channels)
|
||||
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
||||
self.norm2 = normalize(out_channels)
|
||||
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
||||
if self.in_channels != self.out_channels:
|
||||
self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
|
||||
|
||||
def forward(self, x_in):
|
||||
x = x_in
|
||||
x = self.norm1(x)
|
||||
x = swish(x)
|
||||
x = self.conv1(x)
|
||||
x = self.norm2(x)
|
||||
x = swish(x)
|
||||
x = self.conv2(x)
|
||||
if self.in_channels != self.out_channels:
|
||||
x_in = self.conv_out(x_in)
|
||||
|
||||
return x + x_in
|
||||
|
||||
|
||||
class AttnBlock(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = normalize(in_channels)
|
||||
self.q = torch.nn.Conv2d(
|
||||
in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0
|
||||
)
|
||||
self.k = torch.nn.Conv2d(
|
||||
in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0
|
||||
)
|
||||
self.v = torch.nn.Conv2d(
|
||||
in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0
|
||||
)
|
||||
self.proj_out = torch.nn.Conv2d(
|
||||
in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q = self.q(h_)
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
b, c, h, w = q.shape
|
||||
q = q.reshape(b, c, h*w)
|
||||
q = q.permute(0, 2, 1)
|
||||
k = k.reshape(b, c, h*w)
|
||||
w_ = torch.bmm(q, k)
|
||||
w_ = w_ * (int(c)**(-0.5))
|
||||
w_ = F.softmax(w_, dim=2)
|
||||
|
||||
# attend to values
|
||||
v = v.reshape(b, c, h*w)
|
||||
w_ = w_.permute(0, 2, 1)
|
||||
h_ = torch.bmm(v, w_)
|
||||
h_ = h_.reshape(b, c, h, w)
|
||||
|
||||
h_ = self.proj_out(h_)
|
||||
|
||||
return x+h_
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
|
||||
super().__init__()
|
||||
self.nf = nf
|
||||
self.num_resolutions = len(ch_mult)
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.resolution = resolution
|
||||
self.attn_resolutions = attn_resolutions
|
||||
|
||||
curr_res = self.resolution
|
||||
in_ch_mult = (1,)+tuple(ch_mult)
|
||||
|
||||
blocks = []
|
||||
# initial convultion
|
||||
blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
|
||||
|
||||
# residual and downsampling blocks, with attention on smaller res (16x16)
|
||||
for i in range(self.num_resolutions):
|
||||
block_in_ch = nf * in_ch_mult[i]
|
||||
block_out_ch = nf * ch_mult[i]
|
||||
for _ in range(self.num_res_blocks):
|
||||
blocks.append(ResBlock(block_in_ch, block_out_ch))
|
||||
block_in_ch = block_out_ch
|
||||
if curr_res in attn_resolutions:
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
|
||||
if i != self.num_resolutions - 1:
|
||||
blocks.append(Downsample(block_in_ch))
|
||||
curr_res = curr_res // 2
|
||||
|
||||
# non-local attention block
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
|
||||
# normalise and convert to latent size
|
||||
blocks.append(normalize(block_in_ch))
|
||||
blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
|
||||
self.blocks = nn.ModuleList(blocks)
|
||||
|
||||
def forward(self, x):
|
||||
for block in self.blocks:
|
||||
x = block(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Generator(nn.Module):
|
||||
def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
|
||||
super().__init__()
|
||||
self.nf = nf
|
||||
self.ch_mult = ch_mult
|
||||
self.num_resolutions = len(self.ch_mult)
|
||||
self.num_res_blocks = res_blocks
|
||||
self.resolution = img_size
|
||||
self.attn_resolutions = attn_resolutions
|
||||
self.in_channels = emb_dim
|
||||
self.out_channels = 3
|
||||
block_in_ch = self.nf * self.ch_mult[-1]
|
||||
curr_res = self.resolution // 2 ** (self.num_resolutions-1)
|
||||
|
||||
blocks = []
|
||||
# initial conv
|
||||
blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
|
||||
|
||||
# non-local attention block
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
|
||||
for i in reversed(range(self.num_resolutions)):
|
||||
block_out_ch = self.nf * self.ch_mult[i]
|
||||
|
||||
for _ in range(self.num_res_blocks):
|
||||
blocks.append(ResBlock(block_in_ch, block_out_ch))
|
||||
block_in_ch = block_out_ch
|
||||
|
||||
if curr_res in self.attn_resolutions:
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
|
||||
if i != 0:
|
||||
blocks.append(Upsample(block_in_ch))
|
||||
curr_res = curr_res * 2
|
||||
|
||||
blocks.append(normalize(block_in_ch))
|
||||
blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
|
||||
|
||||
self.blocks = nn.ModuleList(blocks)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
for block in self.blocks:
|
||||
x = block(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
@ARCH_REGISTRY.register()
|
||||
class VQAutoEncoder(nn.Module):
|
||||
def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256,
|
||||
beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
|
||||
super().__init__()
|
||||
logger = get_root_logger()
|
||||
self.in_channels = 3
|
||||
self.nf = nf
|
||||
self.n_blocks = res_blocks
|
||||
self.codebook_size = codebook_size
|
||||
self.embed_dim = emb_dim
|
||||
self.ch_mult = ch_mult
|
||||
self.resolution = img_size
|
||||
self.attn_resolutions = attn_resolutions or [16]
|
||||
self.quantizer_type = quantizer
|
||||
self.encoder = Encoder(
|
||||
self.in_channels,
|
||||
self.nf,
|
||||
self.embed_dim,
|
||||
self.ch_mult,
|
||||
self.n_blocks,
|
||||
self.resolution,
|
||||
self.attn_resolutions
|
||||
)
|
||||
if self.quantizer_type == "nearest":
|
||||
self.beta = beta #0.25
|
||||
self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
|
||||
elif self.quantizer_type == "gumbel":
|
||||
self.gumbel_num_hiddens = emb_dim
|
||||
self.straight_through = gumbel_straight_through
|
||||
self.kl_weight = gumbel_kl_weight
|
||||
self.quantize = GumbelQuantizer(
|
||||
self.codebook_size,
|
||||
self.embed_dim,
|
||||
self.gumbel_num_hiddens,
|
||||
self.straight_through,
|
||||
self.kl_weight
|
||||
)
|
||||
self.generator = Generator(
|
||||
self.nf,
|
||||
self.embed_dim,
|
||||
self.ch_mult,
|
||||
self.n_blocks,
|
||||
self.resolution,
|
||||
self.attn_resolutions
|
||||
)
|
||||
|
||||
if model_path is not None:
|
||||
chkpt = torch.load(model_path, map_location='cpu')
|
||||
if 'params_ema' in chkpt:
|
||||
self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
|
||||
logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
|
||||
elif 'params' in chkpt:
|
||||
self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
|
||||
logger.info(f'vqgan is loaded from: {model_path} [params]')
|
||||
else:
|
||||
raise ValueError('Wrong params!')
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
x = self.encoder(x)
|
||||
quant, codebook_loss, quant_stats = self.quantize(x)
|
||||
x = self.generator(quant)
|
||||
return x, codebook_loss, quant_stats
|
||||
|
||||
|
||||
|
||||
# patch based discriminator
|
||||
@ARCH_REGISTRY.register()
|
||||
class VQGANDiscriminator(nn.Module):
|
||||
def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
|
||||
super().__init__()
|
||||
|
||||
layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
|
||||
ndf_mult = 1
|
||||
ndf_mult_prev = 1
|
||||
for n in range(1, n_layers): # gradually increase the number of filters
|
||||
ndf_mult_prev = ndf_mult
|
||||
ndf_mult = min(2 ** n, 8)
|
||||
layers += [
|
||||
nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
|
||||
nn.BatchNorm2d(ndf * ndf_mult),
|
||||
nn.LeakyReLU(0.2, True)
|
||||
]
|
||||
|
||||
ndf_mult_prev = ndf_mult
|
||||
ndf_mult = min(2 ** n_layers, 8)
|
||||
|
||||
layers += [
|
||||
nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
|
||||
nn.BatchNorm2d(ndf * ndf_mult),
|
||||
nn.LeakyReLU(0.2, True)
|
||||
]
|
||||
|
||||
layers += [
|
||||
nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map
|
||||
self.main = nn.Sequential(*layers)
|
||||
|
||||
if model_path is not None:
|
||||
chkpt = torch.load(model_path, map_location='cpu')
|
||||
if 'params_d' in chkpt:
|
||||
self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
|
||||
elif 'params' in chkpt:
|
||||
self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
|
||||
else:
|
||||
raise ValueError('Wrong params!')
|
||||
|
||||
def forward(self, x):
|
||||
return self.main(x)
|
||||
@ -8,9 +8,6 @@ import modules.shared
|
||||
from modules import shared, devices, modelloader, errors
|
||||
from modules.paths import models_path
|
||||
|
||||
# codeformer people made a choice to include modified basicsr library to their project which makes
|
||||
# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
|
||||
# I am making a choice to include some files from codeformer to work around this issue.
|
||||
model_dir = "Codeformer"
|
||||
model_path = os.path.join(models_path, model_dir)
|
||||
model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
|
||||
@ -18,115 +15,127 @@ model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codef
|
||||
codeformer = None
|
||||
|
||||
|
||||
class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
|
||||
def name(self):
|
||||
return "CodeFormer"
|
||||
|
||||
def __init__(self, dirname):
|
||||
self.net = None
|
||||
self.face_helper = None
|
||||
self.cmd_dir = dirname
|
||||
|
||||
def create_models(self):
|
||||
from facexlib.detection import retinaface
|
||||
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
|
||||
|
||||
if self.net is not None and self.face_helper is not None:
|
||||
self.net.to(devices.device_codeformer)
|
||||
return self.net, self.face_helper
|
||||
model_paths = modelloader.load_models(
|
||||
model_path,
|
||||
model_url,
|
||||
self.cmd_dir,
|
||||
download_name='codeformer-v0.1.0.pth',
|
||||
ext_filter=['.pth'],
|
||||
)
|
||||
|
||||
if len(model_paths) != 0:
|
||||
ckpt_path = model_paths[0]
|
||||
else:
|
||||
print("Unable to load codeformer model.")
|
||||
return None, None
|
||||
net = modelloader.load_spandrel_model(ckpt_path, device=devices.device_codeformer)
|
||||
|
||||
if hasattr(retinaface, 'device'):
|
||||
retinaface.device = devices.device_codeformer
|
||||
|
||||
face_helper = FaceRestoreHelper(
|
||||
upscale_factor=1,
|
||||
face_size=512,
|
||||
crop_ratio=(1, 1),
|
||||
det_model='retinaface_resnet50',
|
||||
save_ext='png',
|
||||
use_parse=True,
|
||||
device=devices.device_codeformer,
|
||||
)
|
||||
|
||||
self.net = net
|
||||
self.face_helper = face_helper
|
||||
|
||||
def send_model_to(self, device):
|
||||
self.net.to(device)
|
||||
self.face_helper.face_det.to(device)
|
||||
self.face_helper.face_parse.to(device)
|
||||
|
||||
def restore(self, np_image, w=None):
|
||||
from torchvision.transforms.functional import normalize
|
||||
from basicsr.utils import img2tensor, tensor2img
|
||||
np_image = np_image[:, :, ::-1]
|
||||
|
||||
original_resolution = np_image.shape[0:2]
|
||||
|
||||
self.create_models()
|
||||
if self.net is None or self.face_helper is None:
|
||||
return np_image
|
||||
|
||||
self.send_model_to(devices.device_codeformer)
|
||||
|
||||
self.face_helper.clean_all()
|
||||
self.face_helper.read_image(np_image)
|
||||
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
|
||||
self.face_helper.align_warp_face()
|
||||
|
||||
for cropped_face in self.face_helper.cropped_faces:
|
||||
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
|
||||
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
|
||||
cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
|
||||
|
||||
try:
|
||||
with torch.no_grad():
|
||||
res = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)
|
||||
if isinstance(res, tuple):
|
||||
output = res[0]
|
||||
else:
|
||||
output = res
|
||||
if not isinstance(res, torch.Tensor):
|
||||
raise TypeError(f"Expected torch.Tensor, got {type(res)}")
|
||||
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
|
||||
del output
|
||||
devices.torch_gc()
|
||||
except Exception:
|
||||
errors.report('Failed inference for CodeFormer', exc_info=True)
|
||||
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
|
||||
|
||||
restored_face = restored_face.astype('uint8')
|
||||
self.face_helper.add_restored_face(restored_face)
|
||||
|
||||
self.face_helper.get_inverse_affine(None)
|
||||
|
||||
restored_img = self.face_helper.paste_faces_to_input_image()
|
||||
restored_img = restored_img[:, :, ::-1]
|
||||
|
||||
if original_resolution != restored_img.shape[0:2]:
|
||||
restored_img = cv2.resize(
|
||||
restored_img,
|
||||
(0, 0),
|
||||
fx=original_resolution[1]/restored_img.shape[1],
|
||||
fy=original_resolution[0]/restored_img.shape[0],
|
||||
interpolation=cv2.INTER_LINEAR,
|
||||
)
|
||||
|
||||
self.face_helper.clean_all()
|
||||
|
||||
if shared.opts.face_restoration_unload:
|
||||
self.send_model_to(devices.cpu)
|
||||
|
||||
return restored_img
|
||||
|
||||
|
||||
def setup_model(dirname):
|
||||
os.makedirs(model_path, exist_ok=True)
|
||||
|
||||
path = modules.paths.paths.get("CodeFormer", None)
|
||||
if path is None:
|
||||
return
|
||||
|
||||
try:
|
||||
from torchvision.transforms.functional import normalize
|
||||
from modules.codeformer.codeformer_arch import CodeFormer
|
||||
from basicsr.utils import img2tensor, tensor2img
|
||||
from facelib.utils.face_restoration_helper import FaceRestoreHelper
|
||||
from facelib.detection.retinaface import retinaface
|
||||
|
||||
net_class = CodeFormer
|
||||
|
||||
class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
|
||||
def name(self):
|
||||
return "CodeFormer"
|
||||
|
||||
def __init__(self, dirname):
|
||||
self.net = None
|
||||
self.face_helper = None
|
||||
self.cmd_dir = dirname
|
||||
|
||||
def create_models(self):
|
||||
|
||||
if self.net is not None and self.face_helper is not None:
|
||||
self.net.to(devices.device_codeformer)
|
||||
return self.net, self.face_helper
|
||||
model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
|
||||
if len(model_paths) != 0:
|
||||
ckpt_path = model_paths[0]
|
||||
else:
|
||||
print("Unable to load codeformer model.")
|
||||
return None, None
|
||||
net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
|
||||
checkpoint = torch.load(ckpt_path)['params_ema']
|
||||
net.load_state_dict(checkpoint)
|
||||
net.eval()
|
||||
|
||||
if hasattr(retinaface, 'device'):
|
||||
retinaface.device = devices.device_codeformer
|
||||
face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
|
||||
|
||||
self.net = net
|
||||
self.face_helper = face_helper
|
||||
|
||||
return net, face_helper
|
||||
|
||||
def send_model_to(self, device):
|
||||
self.net.to(device)
|
||||
self.face_helper.face_det.to(device)
|
||||
self.face_helper.face_parse.to(device)
|
||||
|
||||
def restore(self, np_image, w=None):
|
||||
np_image = np_image[:, :, ::-1]
|
||||
|
||||
original_resolution = np_image.shape[0:2]
|
||||
|
||||
self.create_models()
|
||||
if self.net is None or self.face_helper is None:
|
||||
return np_image
|
||||
|
||||
self.send_model_to(devices.device_codeformer)
|
||||
|
||||
self.face_helper.clean_all()
|
||||
self.face_helper.read_image(np_image)
|
||||
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
|
||||
self.face_helper.align_warp_face()
|
||||
|
||||
for cropped_face in self.face_helper.cropped_faces:
|
||||
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
|
||||
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
|
||||
cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
|
||||
|
||||
try:
|
||||
with torch.no_grad():
|
||||
output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
|
||||
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
|
||||
del output
|
||||
devices.torch_gc()
|
||||
except Exception:
|
||||
errors.report('Failed inference for CodeFormer', exc_info=True)
|
||||
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
|
||||
|
||||
restored_face = restored_face.astype('uint8')
|
||||
self.face_helper.add_restored_face(restored_face)
|
||||
|
||||
self.face_helper.get_inverse_affine(None)
|
||||
|
||||
restored_img = self.face_helper.paste_faces_to_input_image()
|
||||
restored_img = restored_img[:, :, ::-1]
|
||||
|
||||
if original_resolution != restored_img.shape[0:2]:
|
||||
restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
self.face_helper.clean_all()
|
||||
|
||||
if shared.opts.face_restoration_unload:
|
||||
self.send_model_to(devices.cpu)
|
||||
|
||||
return restored_img
|
||||
|
||||
global codeformer
|
||||
codeformer = FaceRestorerCodeFormer(dirname)
|
||||
shared.face_restorers.append(codeformer)
|
||||
|
||||
except Exception:
|
||||
errors.report("Error setting up CodeFormer", exc_info=True)
|
||||
|
||||
# sys.path = stored_sys_path
|
||||
|
||||
@ -1,122 +1,9 @@
|
||||
import sys
|
||||
|
||||
import torch
|
||||
|
||||
import modules.esrgan_model_arch as arch
|
||||
from modules import modelloader, devices
|
||||
from modules import modelloader, devices, errors
|
||||
from modules.shared import opts
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
from modules.upscaler_utils import upscale_with_model
|
||||
|
||||
|
||||
def mod2normal(state_dict):
|
||||
# this code is copied from https://github.com/victorca25/iNNfer
|
||||
if 'conv_first.weight' in state_dict:
|
||||
crt_net = {}
|
||||
items = list(state_dict)
|
||||
|
||||
crt_net['model.0.weight'] = state_dict['conv_first.weight']
|
||||
crt_net['model.0.bias'] = state_dict['conv_first.bias']
|
||||
|
||||
for k in items.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[ori_k] = state_dict[k]
|
||||
items.remove(k)
|
||||
|
||||
crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
|
||||
crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
|
||||
crt_net['model.3.weight'] = state_dict['upconv1.weight']
|
||||
crt_net['model.3.bias'] = state_dict['upconv1.bias']
|
||||
crt_net['model.6.weight'] = state_dict['upconv2.weight']
|
||||
crt_net['model.6.bias'] = state_dict['upconv2.bias']
|
||||
crt_net['model.8.weight'] = state_dict['HRconv.weight']
|
||||
crt_net['model.8.bias'] = state_dict['HRconv.bias']
|
||||
crt_net['model.10.weight'] = state_dict['conv_last.weight']
|
||||
crt_net['model.10.bias'] = state_dict['conv_last.bias']
|
||||
state_dict = crt_net
|
||||
return state_dict
|
||||
|
||||
|
||||
def resrgan2normal(state_dict, nb=23):
|
||||
# this code is copied from https://github.com/victorca25/iNNfer
|
||||
if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
|
||||
re8x = 0
|
||||
crt_net = {}
|
||||
items = list(state_dict)
|
||||
|
||||
crt_net['model.0.weight'] = state_dict['conv_first.weight']
|
||||
crt_net['model.0.bias'] = state_dict['conv_first.bias']
|
||||
|
||||
for k in items.copy():
|
||||
if "rdb" in k:
|
||||
ori_k = k.replace('body.', 'model.1.sub.')
|
||||
ori_k = ori_k.replace('.rdb', '.RDB')
|
||||
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[ori_k] = state_dict[k]
|
||||
items.remove(k)
|
||||
|
||||
crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
|
||||
crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
|
||||
crt_net['model.3.weight'] = state_dict['conv_up1.weight']
|
||||
crt_net['model.3.bias'] = state_dict['conv_up1.bias']
|
||||
crt_net['model.6.weight'] = state_dict['conv_up2.weight']
|
||||
crt_net['model.6.bias'] = state_dict['conv_up2.bias']
|
||||
|
||||
if 'conv_up3.weight' in state_dict:
|
||||
# modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
|
||||
re8x = 3
|
||||
crt_net['model.9.weight'] = state_dict['conv_up3.weight']
|
||||
crt_net['model.9.bias'] = state_dict['conv_up3.bias']
|
||||
|
||||
crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
|
||||
crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
|
||||
crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
|
||||
crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
|
||||
|
||||
state_dict = crt_net
|
||||
return state_dict
|
||||
|
||||
|
||||
def infer_params(state_dict):
|
||||
# this code is copied from https://github.com/victorca25/iNNfer
|
||||
scale2x = 0
|
||||
scalemin = 6
|
||||
n_uplayer = 0
|
||||
plus = False
|
||||
|
||||
for block in list(state_dict):
|
||||
parts = block.split(".")
|
||||
n_parts = len(parts)
|
||||
if n_parts == 5 and parts[2] == "sub":
|
||||
nb = int(parts[3])
|
||||
elif n_parts == 3:
|
||||
part_num = int(parts[1])
|
||||
if (part_num > scalemin
|
||||
and parts[0] == "model"
|
||||
and parts[2] == "weight"):
|
||||
scale2x += 1
|
||||
if part_num > n_uplayer:
|
||||
n_uplayer = part_num
|
||||
out_nc = state_dict[block].shape[0]
|
||||
if not plus and "conv1x1" in block:
|
||||
plus = True
|
||||
|
||||
nf = state_dict["model.0.weight"].shape[0]
|
||||
in_nc = state_dict["model.0.weight"].shape[1]
|
||||
out_nc = out_nc
|
||||
scale = 2 ** scale2x
|
||||
|
||||
return in_nc, out_nc, nf, nb, plus, scale
|
||||
|
||||
|
||||
class UpscalerESRGAN(Upscaler):
|
||||
def __init__(self, dirname):
|
||||
self.name = "ESRGAN"
|
||||
@ -142,12 +29,11 @@ class UpscalerESRGAN(Upscaler):
|
||||
def do_upscale(self, img, selected_model):
|
||||
try:
|
||||
model = self.load_model(selected_model)
|
||||
except Exception as e:
|
||||
print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
|
||||
except Exception:
|
||||
errors.report(f"Unable to load ESRGAN model {selected_model}", exc_info=True)
|
||||
return img
|
||||
model.to(devices.device_esrgan)
|
||||
img = esrgan_upscale(model, img)
|
||||
return img
|
||||
return esrgan_upscale(model, img)
|
||||
|
||||
def load_model(self, path: str):
|
||||
if path.startswith("http"):
|
||||
@ -160,33 +46,10 @@ class UpscalerESRGAN(Upscaler):
|
||||
else:
|
||||
filename = path
|
||||
|
||||
state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
|
||||
|
||||
if "params_ema" in state_dict:
|
||||
state_dict = state_dict["params_ema"]
|
||||
elif "params" in state_dict:
|
||||
state_dict = state_dict["params"]
|
||||
num_conv = 16 if "realesr-animevideov3" in filename else 32
|
||||
model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
|
||||
model.load_state_dict(state_dict)
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
|
||||
nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
|
||||
state_dict = resrgan2normal(state_dict, nb)
|
||||
elif "conv_first.weight" in state_dict:
|
||||
state_dict = mod2normal(state_dict)
|
||||
elif "model.0.weight" not in state_dict:
|
||||
raise Exception("The file is not a recognized ESRGAN model.")
|
||||
|
||||
in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
|
||||
|
||||
model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
|
||||
model.load_state_dict(state_dict)
|
||||
model.eval()
|
||||
|
||||
return model
|
||||
return modelloader.load_spandrel_model(
|
||||
filename,
|
||||
device=('cpu' if devices.device_esrgan.type == 'mps' else None),
|
||||
)
|
||||
|
||||
|
||||
def esrgan_upscale(model, img):
|
||||
|
||||
@ -1,465 +0,0 @@
|
||||
# this file is adapted from https://github.com/victorca25/iNNfer
|
||||
|
||||
from collections import OrderedDict
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
####################
|
||||
# RRDBNet Generator
|
||||
####################
|
||||
|
||||
class RRDBNet(nn.Module):
|
||||
def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
|
||||
act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
|
||||
finalact=None, gaussian_noise=False, plus=False):
|
||||
super(RRDBNet, self).__init__()
|
||||
n_upscale = int(math.log(upscale, 2))
|
||||
if upscale == 3:
|
||||
n_upscale = 1
|
||||
|
||||
self.resrgan_scale = 0
|
||||
if in_nc % 16 == 0:
|
||||
self.resrgan_scale = 1
|
||||
elif in_nc != 4 and in_nc % 4 == 0:
|
||||
self.resrgan_scale = 2
|
||||
|
||||
fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
|
||||
rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
|
||||
norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
|
||||
gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
|
||||
LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
|
||||
|
||||
if upsample_mode == 'upconv':
|
||||
upsample_block = upconv_block
|
||||
elif upsample_mode == 'pixelshuffle':
|
||||
upsample_block = pixelshuffle_block
|
||||
else:
|
||||
raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
|
||||
if upscale == 3:
|
||||
upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
|
||||
else:
|
||||
upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
|
||||
HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
|
||||
HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
|
||||
|
||||
outact = act(finalact) if finalact else None
|
||||
|
||||
self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
|
||||
*upsampler, HR_conv0, HR_conv1, outact)
|
||||
|
||||
def forward(self, x, outm=None):
|
||||
if self.resrgan_scale == 1:
|
||||
feat = pixel_unshuffle(x, scale=4)
|
||||
elif self.resrgan_scale == 2:
|
||||
feat = pixel_unshuffle(x, scale=2)
|
||||
else:
|
||||
feat = x
|
||||
|
||||
return self.model(feat)
|
||||
|
||||
|
||||
class RRDB(nn.Module):
|
||||
"""
|
||||
Residual in Residual Dense Block
|
||||
(ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
|
||||
"""
|
||||
|
||||
def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
|
||||
norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
|
||||
spectral_norm=False, gaussian_noise=False, plus=False):
|
||||
super(RRDB, self).__init__()
|
||||
# This is for backwards compatibility with existing models
|
||||
if nr == 3:
|
||||
self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
|
||||
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
|
||||
gaussian_noise=gaussian_noise, plus=plus)
|
||||
self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
|
||||
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
|
||||
gaussian_noise=gaussian_noise, plus=plus)
|
||||
self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
|
||||
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
|
||||
gaussian_noise=gaussian_noise, plus=plus)
|
||||
else:
|
||||
RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
|
||||
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
|
||||
gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
|
||||
self.RDBs = nn.Sequential(*RDB_list)
|
||||
|
||||
def forward(self, x):
|
||||
if hasattr(self, 'RDB1'):
|
||||
out = self.RDB1(x)
|
||||
out = self.RDB2(out)
|
||||
out = self.RDB3(out)
|
||||
else:
|
||||
out = self.RDBs(x)
|
||||
return out * 0.2 + x
|
||||
|
||||
|
||||
class ResidualDenseBlock_5C(nn.Module):
|
||||
"""
|
||||
Residual Dense Block
|
||||
The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
|
||||
Modified options that can be used:
|
||||
- "Partial Convolution based Padding" arXiv:1811.11718
|
||||
- "Spectral normalization" arXiv:1802.05957
|
||||
- "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
|
||||
{Rakotonirina} and A. {Rasoanaivo}
|
||||
"""
|
||||
|
||||
def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
|
||||
norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
|
||||
spectral_norm=False, gaussian_noise=False, plus=False):
|
||||
super(ResidualDenseBlock_5C, self).__init__()
|
||||
|
||||
self.noise = GaussianNoise() if gaussian_noise else None
|
||||
self.conv1x1 = conv1x1(nf, gc) if plus else None
|
||||
|
||||
self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
|
||||
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
|
||||
spectral_norm=spectral_norm)
|
||||
self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
|
||||
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
|
||||
spectral_norm=spectral_norm)
|
||||
self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
|
||||
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
|
||||
spectral_norm=spectral_norm)
|
||||
self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
|
||||
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
|
||||
spectral_norm=spectral_norm)
|
||||
if mode == 'CNA':
|
||||
last_act = None
|
||||
else:
|
||||
last_act = act_type
|
||||
self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
|
||||
norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
|
||||
spectral_norm=spectral_norm)
|
||||
|
||||
def forward(self, x):
|
||||
x1 = self.conv1(x)
|
||||
x2 = self.conv2(torch.cat((x, x1), 1))
|
||||
if self.conv1x1:
|
||||
x2 = x2 + self.conv1x1(x)
|
||||
x3 = self.conv3(torch.cat((x, x1, x2), 1))
|
||||
x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
|
||||
if self.conv1x1:
|
||||
x4 = x4 + x2
|
||||
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
|
||||
if self.noise:
|
||||
return self.noise(x5.mul(0.2) + x)
|
||||
else:
|
||||
return x5 * 0.2 + x
|
||||
|
||||
|
||||
####################
|
||||
# ESRGANplus
|
||||
####################
|
||||
|
||||
class GaussianNoise(nn.Module):
|
||||
def __init__(self, sigma=0.1, is_relative_detach=False):
|
||||
super().__init__()
|
||||
self.sigma = sigma
|
||||
self.is_relative_detach = is_relative_detach
|
||||
self.noise = torch.tensor(0, dtype=torch.float)
|
||||
|
||||
def forward(self, x):
|
||||
if self.training and self.sigma != 0:
|
||||
self.noise = self.noise.to(x.device)
|
||||
scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
|
||||
sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
|
||||
x = x + sampled_noise
|
||||
return x
|
||||
|
||||
def conv1x1(in_planes, out_planes, stride=1):
|
||||
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
||||
|
||||
|
||||
####################
|
||||
# SRVGGNetCompact
|
||||
####################
|
||||
|
||||
class SRVGGNetCompact(nn.Module):
|
||||
"""A compact VGG-style network structure for super-resolution.
|
||||
This class is copied from https://github.com/xinntao/Real-ESRGAN
|
||||
"""
|
||||
|
||||
def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
|
||||
super(SRVGGNetCompact, self).__init__()
|
||||
self.num_in_ch = num_in_ch
|
||||
self.num_out_ch = num_out_ch
|
||||
self.num_feat = num_feat
|
||||
self.num_conv = num_conv
|
||||
self.upscale = upscale
|
||||
self.act_type = act_type
|
||||
|
||||
self.body = nn.ModuleList()
|
||||
# the first conv
|
||||
self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
|
||||
# the first activation
|
||||
if act_type == 'relu':
|
||||
activation = nn.ReLU(inplace=True)
|
||||
elif act_type == 'prelu':
|
||||
activation = nn.PReLU(num_parameters=num_feat)
|
||||
elif act_type == 'leakyrelu':
|
||||
activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
|
||||
self.body.append(activation)
|
||||
|
||||
# the body structure
|
||||
for _ in range(num_conv):
|
||||
self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
|
||||
# activation
|
||||
if act_type == 'relu':
|
||||
activation = nn.ReLU(inplace=True)
|
||||
elif act_type == 'prelu':
|
||||
activation = nn.PReLU(num_parameters=num_feat)
|
||||
elif act_type == 'leakyrelu':
|
||||
activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
|
||||
self.body.append(activation)
|
||||
|
||||
# the last conv
|
||||
self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
|
||||
# upsample
|
||||
self.upsampler = nn.PixelShuffle(upscale)
|
||||
|
||||
def forward(self, x):
|
||||
out = x
|
||||
for i in range(0, len(self.body)):
|
||||
out = self.body[i](out)
|
||||
|
||||
out = self.upsampler(out)
|
||||
# add the nearest upsampled image, so that the network learns the residual
|
||||
base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
|
||||
out += base
|
||||
return out
|
||||
|
||||
|
||||
####################
|
||||
# Upsampler
|
||||
####################
|
||||
|
||||
class Upsample(nn.Module):
|
||||
r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
|
||||
The input data is assumed to be of the form
|
||||
`minibatch x channels x [optional depth] x [optional height] x width`.
|
||||
"""
|
||||
|
||||
def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
|
||||
super(Upsample, self).__init__()
|
||||
if isinstance(scale_factor, tuple):
|
||||
self.scale_factor = tuple(float(factor) for factor in scale_factor)
|
||||
else:
|
||||
self.scale_factor = float(scale_factor) if scale_factor else None
|
||||
self.mode = mode
|
||||
self.size = size
|
||||
self.align_corners = align_corners
|
||||
|
||||
def forward(self, x):
|
||||
return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
|
||||
|
||||
def extra_repr(self):
|
||||
if self.scale_factor is not None:
|
||||
info = f'scale_factor={self.scale_factor}'
|
||||
else:
|
||||
info = f'size={self.size}'
|
||||
info += f', mode={self.mode}'
|
||||
return info
|
||||
|
||||
|
||||
def pixel_unshuffle(x, scale):
|
||||
""" Pixel unshuffle.
|
||||
Args:
|
||||
x (Tensor): Input feature with shape (b, c, hh, hw).
|
||||
scale (int): Downsample ratio.
|
||||
Returns:
|
||||
Tensor: the pixel unshuffled feature.
|
||||
"""
|
||||
b, c, hh, hw = x.size()
|
||||
out_channel = c * (scale**2)
|
||||
assert hh % scale == 0 and hw % scale == 0
|
||||
h = hh // scale
|
||||
w = hw // scale
|
||||
x_view = x.view(b, c, h, scale, w, scale)
|
||||
return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
|
||||
|
||||
|
||||
def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
|
||||
pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
|
||||
"""
|
||||
Pixel shuffle layer
|
||||
(Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
|
||||
Neural Network, CVPR17)
|
||||
"""
|
||||
conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
|
||||
pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
|
||||
pixel_shuffle = nn.PixelShuffle(upscale_factor)
|
||||
|
||||
n = norm(norm_type, out_nc) if norm_type else None
|
||||
a = act(act_type) if act_type else None
|
||||
return sequential(conv, pixel_shuffle, n, a)
|
||||
|
||||
|
||||
def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
|
||||
pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
|
||||
""" Upconv layer """
|
||||
upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
|
||||
upsample = Upsample(scale_factor=upscale_factor, mode=mode)
|
||||
conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
|
||||
pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
|
||||
return sequential(upsample, conv)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
####################
|
||||
# Basic blocks
|
||||
####################
|
||||
|
||||
|
||||
def make_layer(basic_block, num_basic_block, **kwarg):
|
||||
"""Make layers by stacking the same blocks.
|
||||
Args:
|
||||
basic_block (nn.module): nn.module class for basic block. (block)
|
||||
num_basic_block (int): number of blocks. (n_layers)
|
||||
Returns:
|
||||
nn.Sequential: Stacked blocks in nn.Sequential.
|
||||
"""
|
||||
layers = []
|
||||
for _ in range(num_basic_block):
|
||||
layers.append(basic_block(**kwarg))
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
|
||||
def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
|
||||
""" activation helper """
|
||||
act_type = act_type.lower()
|
||||
if act_type == 'relu':
|
||||
layer = nn.ReLU(inplace)
|
||||
elif act_type in ('leakyrelu', 'lrelu'):
|
||||
layer = nn.LeakyReLU(neg_slope, inplace)
|
||||
elif act_type == 'prelu':
|
||||
layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
|
||||
elif act_type == 'tanh': # [-1, 1] range output
|
||||
layer = nn.Tanh()
|
||||
elif act_type == 'sigmoid': # [0, 1] range output
|
||||
layer = nn.Sigmoid()
|
||||
else:
|
||||
raise NotImplementedError(f'activation layer [{act_type}] is not found')
|
||||
return layer
|
||||
|
||||
|
||||
class Identity(nn.Module):
|
||||
def __init__(self, *kwargs):
|
||||
super(Identity, self).__init__()
|
||||
|
||||
def forward(self, x, *kwargs):
|
||||
return x
|
||||
|
||||
|
||||
def norm(norm_type, nc):
|
||||
""" Return a normalization layer """
|
||||
norm_type = norm_type.lower()
|
||||
if norm_type == 'batch':
|
||||
layer = nn.BatchNorm2d(nc, affine=True)
|
||||
elif norm_type == 'instance':
|
||||
layer = nn.InstanceNorm2d(nc, affine=False)
|
||||
elif norm_type == 'none':
|
||||
def norm_layer(x): return Identity()
|
||||
else:
|
||||
raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
|
||||
return layer
|
||||
|
||||
|
||||
def pad(pad_type, padding):
|
||||
""" padding layer helper """
|
||||
pad_type = pad_type.lower()
|
||||
if padding == 0:
|
||||
return None
|
||||
if pad_type == 'reflect':
|
||||
layer = nn.ReflectionPad2d(padding)
|
||||
elif pad_type == 'replicate':
|
||||
layer = nn.ReplicationPad2d(padding)
|
||||
elif pad_type == 'zero':
|
||||
layer = nn.ZeroPad2d(padding)
|
||||
else:
|
||||
raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
|
||||
return layer
|
||||
|
||||
|
||||
def get_valid_padding(kernel_size, dilation):
|
||||
kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
|
||||
padding = (kernel_size - 1) // 2
|
||||
return padding
|
||||
|
||||
|
||||
class ShortcutBlock(nn.Module):
|
||||
""" Elementwise sum the output of a submodule to its input """
|
||||
def __init__(self, submodule):
|
||||
super(ShortcutBlock, self).__init__()
|
||||
self.sub = submodule
|
||||
|
||||
def forward(self, x):
|
||||
output = x + self.sub(x)
|
||||
return output
|
||||
|
||||
def __repr__(self):
|
||||
return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
|
||||
|
||||
|
||||
def sequential(*args):
|
||||
""" Flatten Sequential. It unwraps nn.Sequential. """
|
||||
if len(args) == 1:
|
||||
if isinstance(args[0], OrderedDict):
|
||||
raise NotImplementedError('sequential does not support OrderedDict input.')
|
||||
return args[0] # No sequential is needed.
|
||||
modules = []
|
||||
for module in args:
|
||||
if isinstance(module, nn.Sequential):
|
||||
for submodule in module.children():
|
||||
modules.append(submodule)
|
||||
elif isinstance(module, nn.Module):
|
||||
modules.append(module)
|
||||
return nn.Sequential(*modules)
|
||||
|
||||
|
||||
def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
|
||||
pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
|
||||
spectral_norm=False):
|
||||
""" Conv layer with padding, normalization, activation """
|
||||
assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
|
||||
padding = get_valid_padding(kernel_size, dilation)
|
||||
p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
|
||||
padding = padding if pad_type == 'zero' else 0
|
||||
|
||||
if convtype=='PartialConv2D':
|
||||
from torchvision.ops import PartialConv2d # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
|
||||
c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
||||
dilation=dilation, bias=bias, groups=groups)
|
||||
elif convtype=='DeformConv2D':
|
||||
from torchvision.ops import DeformConv2d # not tested
|
||||
c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
||||
dilation=dilation, bias=bias, groups=groups)
|
||||
elif convtype=='Conv3D':
|
||||
c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
||||
dilation=dilation, bias=bias, groups=groups)
|
||||
else:
|
||||
c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
||||
dilation=dilation, bias=bias, groups=groups)
|
||||
|
||||
if spectral_norm:
|
||||
c = nn.utils.spectral_norm(c)
|
||||
|
||||
a = act(act_type) if act_type else None
|
||||
if 'CNA' in mode:
|
||||
n = norm(norm_type, out_nc) if norm_type else None
|
||||
return sequential(p, c, n, a)
|
||||
elif mode == 'NAC':
|
||||
if norm_type is None and act_type is not None:
|
||||
a = act(act_type, inplace=False)
|
||||
n = norm(norm_type, in_nc) if norm_type else None
|
||||
return sequential(n, a, p, c)
|
||||
@ -1,8 +1,5 @@
|
||||
import os
|
||||
|
||||
import facexlib
|
||||
import gfpgan
|
||||
|
||||
import modules.face_restoration
|
||||
from modules import paths, shared, devices, modelloader, errors
|
||||
|
||||
@ -41,6 +38,8 @@ def gfpgann():
|
||||
print("Unable to load gfpgan model!")
|
||||
return None
|
||||
|
||||
import facexlib.detection.retinaface
|
||||
|
||||
if hasattr(facexlib.detection.retinaface, 'device'):
|
||||
facexlib.detection.retinaface.device = devices.device_gfpgan
|
||||
model_file_path = model_file
|
||||
@ -81,8 +80,10 @@ gfpgan_constructor = None
|
||||
def setup_model(dirname):
|
||||
try:
|
||||
os.makedirs(model_path, exist_ok=True)
|
||||
from gfpgan import GFPGANer
|
||||
from facexlib import detection, parsing # noqa: F401
|
||||
import gfpgan
|
||||
import facexlib.detection
|
||||
import facexlib.parsing
|
||||
|
||||
global user_path
|
||||
global have_gfpgan
|
||||
global gfpgan_constructor
|
||||
@ -111,7 +112,7 @@ def setup_model(dirname):
|
||||
facexlib.parsing.load_file_from_url = facex_load_file_from_url2
|
||||
user_path = dirname
|
||||
have_gfpgan = True
|
||||
gfpgan_constructor = GFPGANer
|
||||
gfpgan_constructor = gfpgan.GFPGANer
|
||||
|
||||
class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
|
||||
def name(self):
|
||||
|
||||
@ -345,13 +345,11 @@ def prepare_environment():
|
||||
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
|
||||
stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
|
||||
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
|
||||
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
|
||||
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
|
||||
|
||||
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
|
||||
stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "45c443b316737a4ab6e40413d7794a7f5657c19f")
|
||||
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
|
||||
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
|
||||
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
|
||||
|
||||
try:
|
||||
@ -408,15 +406,10 @@ def prepare_environment():
|
||||
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
|
||||
git_clone(stable_diffusion_xl_repo, repo_dir('generative-models'), "Stable Diffusion XL", stable_diffusion_xl_commit_hash)
|
||||
git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
|
||||
git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
|
||||
git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
|
||||
|
||||
startup_timer.record("clone repositores")
|
||||
|
||||
if not is_installed("lpips"):
|
||||
run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
|
||||
startup_timer.record("install CodeFormer requirements")
|
||||
|
||||
if not os.path.isfile(requirements_file):
|
||||
requirements_file = os.path.join(script_path, requirements_file)
|
||||
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
import shutil
|
||||
import importlib
|
||||
@ -10,6 +11,9 @@ from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, Upscale
|
||||
from modules.paths import script_path, models_path
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def load_file_from_url(
|
||||
url: str,
|
||||
*,
|
||||
@ -177,3 +181,15 @@ def load_upscalers():
|
||||
# Special case for UpscalerNone keeps it at the beginning of the list.
|
||||
key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
|
||||
)
|
||||
|
||||
|
||||
def load_spandrel_model(path, *, device, half: bool = False, dtype=None):
|
||||
import spandrel
|
||||
model = spandrel.ModelLoader(device=device).load_from_file(path)
|
||||
if half:
|
||||
model = model.model.half()
|
||||
if dtype:
|
||||
model = model.model.to(dtype=dtype)
|
||||
model.eval()
|
||||
logger.debug("Loaded %s from %s (device=%s, half=%s, dtype=%s)", model, path, device, half, dtype)
|
||||
return model
|
||||
|
||||
@ -38,7 +38,6 @@ mute_sdxl_imports()
|
||||
path_dirs = [
|
||||
(sd_path, 'ldm', 'Stable Diffusion', []),
|
||||
(os.path.join(sd_path, '../generative-models'), 'sgm', 'Stable Diffusion XL', ["sgm"]),
|
||||
(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
|
||||
(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
|
||||
(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
|
||||
]
|
||||
|
||||
@ -1,9 +1,6 @@
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from realesrgan import RealESRGANer
|
||||
|
||||
from modules.upscaler_utils import upscale_with_model
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
from modules.shared import cmd_opts, opts
|
||||
from modules import modelloader, errors
|
||||
@ -14,29 +11,20 @@ class UpscalerRealESRGAN(Upscaler):
|
||||
self.name = "RealESRGAN"
|
||||
self.user_path = path
|
||||
super().__init__()
|
||||
try:
|
||||
from basicsr.archs.rrdbnet_arch import RRDBNet # noqa: F401
|
||||
from realesrgan import RealESRGANer # noqa: F401
|
||||
from realesrgan.archs.srvgg_arch import SRVGGNetCompact # noqa: F401
|
||||
self.enable = True
|
||||
self.scalers = []
|
||||
scalers = self.load_models(path)
|
||||
self.enable = True
|
||||
self.scalers = []
|
||||
scalers = get_realesrgan_models(self)
|
||||
|
||||
local_model_paths = self.find_models(ext_filter=[".pth"])
|
||||
for scaler in scalers:
|
||||
if scaler.local_data_path.startswith("http"):
|
||||
filename = modelloader.friendly_name(scaler.local_data_path)
|
||||
local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
|
||||
if local_model_candidates:
|
||||
scaler.local_data_path = local_model_candidates[0]
|
||||
local_model_paths = self.find_models(ext_filter=[".pth"])
|
||||
for scaler in scalers:
|
||||
if scaler.local_data_path.startswith("http"):
|
||||
filename = modelloader.friendly_name(scaler.local_data_path)
|
||||
local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
|
||||
if local_model_candidates:
|
||||
scaler.local_data_path = local_model_candidates[0]
|
||||
|
||||
if scaler.name in opts.realesrgan_enabled_models:
|
||||
self.scalers.append(scaler)
|
||||
|
||||
except Exception:
|
||||
errors.report("Error importing Real-ESRGAN", exc_info=True)
|
||||
self.enable = False
|
||||
self.scalers = []
|
||||
if scaler.name in opts.realesrgan_enabled_models:
|
||||
self.scalers.append(scaler)
|
||||
|
||||
def do_upscale(self, img, path):
|
||||
if not self.enable:
|
||||
@ -48,20 +36,18 @@ class UpscalerRealESRGAN(Upscaler):
|
||||
errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
|
||||
return img
|
||||
|
||||
upsampler = RealESRGANer(
|
||||
scale=info.scale,
|
||||
model_path=info.local_data_path,
|
||||
model=info.model(),
|
||||
half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
|
||||
tile=opts.ESRGAN_tile,
|
||||
tile_pad=opts.ESRGAN_tile_overlap,
|
||||
mod = modelloader.load_spandrel_model(
|
||||
info.local_data_path,
|
||||
device=self.device,
|
||||
half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
|
||||
)
|
||||
return upscale_with_model(
|
||||
mod,
|
||||
img,
|
||||
tile_size=opts.ESRGAN_tile,
|
||||
tile_overlap=opts.ESRGAN_tile_overlap,
|
||||
# TODO: `outscale`?
|
||||
)
|
||||
|
||||
upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
|
||||
|
||||
image = Image.fromarray(upsampled)
|
||||
return image
|
||||
|
||||
def load_model(self, path):
|
||||
for scaler in self.scalers:
|
||||
@ -76,58 +62,43 @@ class UpscalerRealESRGAN(Upscaler):
|
||||
return scaler
|
||||
raise ValueError(f"Unable to find model info: {path}")
|
||||
|
||||
def load_models(self, _):
|
||||
return get_realesrgan_models(self)
|
||||
|
||||
|
||||
def get_realesrgan_models(scaler):
|
||||
try:
|
||||
from basicsr.archs.rrdbnet_arch import RRDBNet
|
||||
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
|
||||
models = [
|
||||
UpscalerData(
|
||||
name="R-ESRGAN General 4xV3",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
|
||||
scale=4,
|
||||
upscaler=scaler,
|
||||
model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
|
||||
),
|
||||
UpscalerData(
|
||||
name="R-ESRGAN General WDN 4xV3",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
|
||||
scale=4,
|
||||
upscaler=scaler,
|
||||
model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
|
||||
),
|
||||
UpscalerData(
|
||||
name="R-ESRGAN AnimeVideo",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
|
||||
scale=4,
|
||||
upscaler=scaler,
|
||||
model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
|
||||
),
|
||||
UpscalerData(
|
||||
name="R-ESRGAN 4x+",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
|
||||
scale=4,
|
||||
upscaler=scaler,
|
||||
model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
|
||||
),
|
||||
UpscalerData(
|
||||
name="R-ESRGAN 4x+ Anime6B",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
|
||||
scale=4,
|
||||
upscaler=scaler,
|
||||
model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
|
||||
),
|
||||
UpscalerData(
|
||||
name="R-ESRGAN 2x+",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
|
||||
scale=2,
|
||||
upscaler=scaler,
|
||||
model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
|
||||
),
|
||||
]
|
||||
return models
|
||||
except Exception:
|
||||
errors.report("Error making Real-ESRGAN models list", exc_info=True)
|
||||
def get_realesrgan_models(scaler: UpscalerRealESRGAN):
|
||||
return [
|
||||
UpscalerData(
|
||||
name="R-ESRGAN General 4xV3",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
|
||||
scale=4,
|
||||
upscaler=scaler,
|
||||
),
|
||||
UpscalerData(
|
||||
name="R-ESRGAN General WDN 4xV3",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
|
||||
scale=4,
|
||||
upscaler=scaler,
|
||||
),
|
||||
UpscalerData(
|
||||
name="R-ESRGAN AnimeVideo",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
|
||||
scale=4,
|
||||
upscaler=scaler,
|
||||
),
|
||||
UpscalerData(
|
||||
name="R-ESRGAN 4x+",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
|
||||
scale=4,
|
||||
upscaler=scaler,
|
||||
),
|
||||
UpscalerData(
|
||||
name="R-ESRGAN 4x+ Anime6B",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
|
||||
scale=4,
|
||||
upscaler=scaler,
|
||||
),
|
||||
UpscalerData(
|
||||
name="R-ESRGAN 2x+",
|
||||
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
|
||||
scale=2,
|
||||
upscaler=scaler,
|
||||
),
|
||||
]
|
||||
|
||||
@ -26,11 +26,9 @@ environment_whitelist = {
|
||||
"OPENCLIP_PACKAGE",
|
||||
"STABLE_DIFFUSION_REPO",
|
||||
"K_DIFFUSION_REPO",
|
||||
"CODEFORMER_REPO",
|
||||
"BLIP_REPO",
|
||||
"STABLE_DIFFUSION_COMMIT_HASH",
|
||||
"K_DIFFUSION_COMMIT_HASH",
|
||||
"CODEFORMER_COMMIT_HASH",
|
||||
"BLIP_COMMIT_HASH",
|
||||
"COMMANDLINE_ARGS",
|
||||
"IGNORE_CMD_ARGS_ERRORS",
|
||||
|
||||
@ -98,6 +98,9 @@ class UpscalerData:
|
||||
self.scale = scale
|
||||
self.model = model
|
||||
|
||||
def __repr__(self):
|
||||
return f"<UpscalerData name={self.name} path={self.data_path} scale={self.scale}>"
|
||||
|
||||
|
||||
class UpscalerNone(Upscaler):
|
||||
name = "None"
|
||||
|
||||
@ -6,6 +6,7 @@ basicsr
|
||||
blendmodes
|
||||
clean-fid
|
||||
einops
|
||||
facexlib
|
||||
fastapi>=0.90.1
|
||||
gfpgan
|
||||
gradio==3.41.2
|
||||
@ -20,13 +21,11 @@ open-clip-torch
|
||||
piexif
|
||||
psutil
|
||||
pytorch_lightning
|
||||
realesrgan
|
||||
requests
|
||||
resize-right
|
||||
|
||||
safetensors
|
||||
scikit-image>=0.19
|
||||
timm
|
||||
tomesd
|
||||
torch
|
||||
torchdiffeq
|
||||
|
||||
@ -5,6 +5,7 @@ basicsr==1.4.2
|
||||
blendmodes==2022
|
||||
clean-fid==0.1.35
|
||||
einops==0.4.1
|
||||
facexlib==0.3.0
|
||||
fastapi==0.94.0
|
||||
gfpgan==1.3.8
|
||||
gradio==3.41.2
|
||||
@ -19,11 +20,10 @@ open-clip-torch==2.20.0
|
||||
piexif==1.1.3
|
||||
psutil==5.9.5
|
||||
pytorch_lightning==1.9.4
|
||||
realesrgan==0.3.0
|
||||
resize-right==0.0.2
|
||||
safetensors==0.3.1
|
||||
scikit-image==0.21.0
|
||||
timm==0.9.2
|
||||
spandrel==0.1.6
|
||||
tomesd==0.1.3
|
||||
torch
|
||||
torchdiffeq==0.2.3
|
||||
|
||||
Loading…
Reference in New Issue
Block a user