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Commit 766c7b38 authored by comfyanonymous's avatar comfyanonymous
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Update upscale model code to latest Chainner model code. 

Don't add SRFormer because the code license is incompatible with the GPL.

Remove MAT because it's unused and the license is incompatible with GPL.
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comfy_extras/chainner_models/architecture/LICENSE-SCUNet 0 → 100644
View file @ 766c7b38
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comfy_extras/chainner_models/architecture/MAT.py deleted 100644 → 0
View file @ 62efc78a
This diff is collapsed.
comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py
View file @ 766c7b38
...@@ -56,7 +56,17 @@ class OmniSR(nn.Module): ...@@ -56,7 +56,17 @@ class OmniSR(nn.Module):
residual_layer = [] residual_layer = []
self.res_num = res_num self.res_num = res_num
self.window_size = 8 # we can just assume this for now, but there's probably a way to calculate it (just need to get the sqrt of the right layer) if (
"residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight"
in state_dict.keys()
):
rel_pos_bias_weight = state_dict[
"residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight"
].shape[0]
self.window_size = int((math.sqrt(rel_pos_bias_weight) + 1) / 2)
else:
self.window_size = 8
self.up_scale = up_scale self.up_scale = up_scale
for _ in range(res_num): for _ in range(res_num):
......
comfy_extras/chainner_models/architecture/SCUNet.py 0 → 100644
View file @ 766c7b38
# pylint: skip-file
# -----------------------------------------------------------------------------------
# SCUNet: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, https://arxiv.org/abs/2203.13278
# Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Timofte, Radu and Van Gool, Luc
# -----------------------------------------------------------------------------------
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from einops.layers.torch import Rearrange
from .timm.drop import DropPath
from .timm.weight_init import trunc_normal_
# Borrowed from https://github.com/cszn/SCUNet/blob/main/models/network_scunet.py
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)
)
# TODO recover
# self.relative_position_params = nn.Parameter(torch.zeros(self.n_heads, 2 * window_size - 1, 2 * window_size -1))
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=0.02)
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 > 0.0 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,
state_dict,
in_nc=3,
config=[4, 4, 4, 4, 4, 4, 4],
dim=64,
drop_path_rate=0.0,
input_resolution=256,
):
super(SCUNet, self).__init__()
self.model_arch = "SCUNet"
self.sub_type = "SR"
self.num_filters: int = 0
self.state = state_dict
self.config = config
self.dim = dim
self.head_dim = 32
self.window_size = 8
self.in_nc = in_nc
self.out_nc = self.in_nc
self.scale = 1
self.supports_fp16 = True
# 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)
self.load_state_dict(state_dict, strict=True)
def check_image_size(self, x):
_, _, h, w = x.size()
mod_pad_h = (64 - h % 64) % 64
mod_pad_w = (64 - w % 64) % 64
x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
return x
def forward(self, x0):
h, w = x0.size()[-2:]
x0 = self.check_image_size(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=0.02)
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)
comfy_extras/chainner_models/architecture/SPSR.py
View file @ 766c7b38
...@@ -60,7 +60,6 @@ class SPSRNet(nn.Module): ...@@ -60,7 +60,6 @@ class SPSRNet(nn.Module):
self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0] self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0]
self.scale = self.get_scale(4) self.scale = self.get_scale(4)
print(self.scale)
self.num_filters: int = self.state["model.0.weight"].shape[0] self.num_filters: int = self.state["model.0.weight"].shape[0]
self.supports_fp16 = True self.supports_fp16 = True
......
comfy_extras/chainner_models/architecture/SwinIR.py
View file @ 766c7b38
...@@ -972,6 +972,7 @@ class SwinIR(nn.Module): ...@@ -972,6 +972,7 @@ class SwinIR(nn.Module):
self.upsampler = upsampler self.upsampler = upsampler
self.img_size = img_size self.img_size = img_size
self.img_range = img_range self.img_range = img_range
self.resi_connection = resi_connection
self.supports_fp16 = False # Too much weirdness to support this at the moment self.supports_fp16 = False # Too much weirdness to support this at the moment
self.supports_bfp16 = True self.supports_bfp16 = True
......
comfy_extras/chainner_models/architecture/mat/utils.py deleted 100644 → 0
View file @ 62efc78a
This diff is collapsed.
comfy_extras/chainner_models/model_loading.py
View file @ 766c7b38
import logging as logger import logging as logger
from .architecture.DAT import DAT
from .architecture.face.codeformer import CodeFormer from .architecture.face.codeformer import CodeFormer
from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean
from .architecture.face.restoreformer_arch import RestoreFormer from .architecture.face.restoreformer_arch import RestoreFormer
from .architecture.HAT import HAT from .architecture.HAT import HAT
from .architecture.LaMa import LaMa from .architecture.LaMa import LaMa
from .architecture.MAT import MAT
from .architecture.OmniSR.OmniSR import OmniSR from .architecture.OmniSR.OmniSR import OmniSR
from .architecture.RRDB import RRDBNet as ESRGAN from .architecture.RRDB import RRDBNet as ESRGAN
from .architecture.SCUNet import SCUNet
from .architecture.SPSR import SPSRNet as SPSR from .architecture.SPSR import SPSRNet as SPSR
from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
from .architecture.SwiftSRGAN import Generator as SwiftSRGAN from .architecture.SwiftSRGAN import Generator as SwiftSRGAN
...@@ -33,7 +34,6 @@ def load_state_dict(state_dict) -> PyTorchModel: ...@@ -33,7 +34,6 @@ def load_state_dict(state_dict) -> PyTorchModel:
state_dict = state_dict["params"] state_dict = state_dict["params"]
state_dict_keys = list(state_dict.keys()) state_dict_keys = list(state_dict.keys())
# SRVGGNet Real-ESRGAN (v2) # SRVGGNet Real-ESRGAN (v2)
if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys: if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys:
model = RealESRGANv2(state_dict) model = RealESRGANv2(state_dict)
...@@ -46,12 +46,14 @@ def load_state_dict(state_dict) -> PyTorchModel: ...@@ -46,12 +46,14 @@ def load_state_dict(state_dict) -> PyTorchModel:
and "initial.cnn.depthwise.weight" in state_dict["model"].keys() and "initial.cnn.depthwise.weight" in state_dict["model"].keys()
): ):
model = SwiftSRGAN(state_dict) model = SwiftSRGAN(state_dict)
# HAT -- be sure it is above swinir # SwinIR, Swin2SR, HAT
elif "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" in state_dict_keys:
model = HAT(state_dict)
# SwinIR
elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys: elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys:
if "patch_embed.proj.weight" in state_dict_keys: if (
"layers.0.residual_group.blocks.0.conv_block.cab.0.weight"
in state_dict_keys
):
model = HAT(state_dict)
elif "patch_embed.proj.weight" in state_dict_keys:
model = Swin2SR(state_dict) model = Swin2SR(state_dict)
else: else:
model = SwinIR(state_dict) model = SwinIR(state_dict)
...@@ -78,12 +80,15 @@ def load_state_dict(state_dict) -> PyTorchModel: ...@@ -78,12 +80,15 @@ def load_state_dict(state_dict) -> PyTorchModel:
or "generator.model.1.bn_l.running_mean" in state_dict_keys or "generator.model.1.bn_l.running_mean" in state_dict_keys
): ):
model = LaMa(state_dict) model = LaMa(state_dict)
# MAT
elif "synthesis.first_stage.conv_first.conv.resample_filter" in state_dict_keys:
model = MAT(state_dict)
# Omni-SR # Omni-SR
elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys: elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys:
model = OmniSR(state_dict) model = OmniSR(state_dict)
# SCUNet
elif "m_head.0.weight" in state_dict_keys and "m_tail.0.weight" in state_dict_keys:
model = SCUNet(state_dict)
# DAT
elif "layers.0.blocks.2.attn.attn_mask_0" in state_dict_keys:
model = DAT(state_dict)
# Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1 # Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1
else: else:
try: try:
......
comfy_extras/chainner_models/types.py
View file @ 766c7b38
from typing import Union from typing import Union
from .architecture.DAT import DAT
from .architecture.face.codeformer import CodeFormer from .architecture.face.codeformer import CodeFormer
from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean
from .architecture.face.restoreformer_arch import RestoreFormer from .architecture.face.restoreformer_arch import RestoreFormer
from .architecture.HAT import HAT from .architecture.HAT import HAT
from .architecture.LaMa import LaMa from .architecture.LaMa import LaMa
from .architecture.MAT import MAT
from .architecture.OmniSR.OmniSR import OmniSR from .architecture.OmniSR.OmniSR import OmniSR
from .architecture.RRDB import RRDBNet as ESRGAN from .architecture.RRDB import RRDBNet as ESRGAN
from .architecture.SCUNet import SCUNet
from .architecture.SPSR import SPSRNet as SPSR from .architecture.SPSR import SPSRNet as SPSR
from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
from .architecture.SwiftSRGAN import Generator as SwiftSRGAN from .architecture.SwiftSRGAN import Generator as SwiftSRGAN
from .architecture.Swin2SR import Swin2SR from .architecture.Swin2SR import Swin2SR
from .architecture.SwinIR import SwinIR from .architecture.SwinIR import SwinIR
PyTorchSRModels = (RealESRGANv2, SPSR, SwiftSRGAN, ESRGAN, SwinIR, Swin2SR, HAT, OmniSR) PyTorchSRModels = (
RealESRGANv2,
SPSR,
SwiftSRGAN,
ESRGAN,
SwinIR,
Swin2SR,
HAT,
OmniSR,
SCUNet,
DAT,
)
PyTorchSRModel = Union[ PyTorchSRModel = Union[
RealESRGANv2, RealESRGANv2,
SPSR, SPSR,
...@@ -24,6 +36,8 @@ PyTorchSRModel = Union[ ...@@ -24,6 +36,8 @@ PyTorchSRModel = Union[
Swin2SR, Swin2SR,
HAT, HAT,
OmniSR, OmniSR,
SCUNet,
DAT,
] ]
...@@ -39,8 +53,8 @@ def is_pytorch_face_model(model: object): ...@@ -39,8 +53,8 @@ def is_pytorch_face_model(model: object):
return isinstance(model, PyTorchFaceModels) return isinstance(model, PyTorchFaceModels)
PyTorchInpaintModels = (LaMa, MAT) PyTorchInpaintModels = (LaMa,)
PyTorchInpaintModel = Union[LaMa, MAT] PyTorchInpaintModel = Union[LaMa]
def is_pytorch_inpaint_model(model: object): def is_pytorch_inpaint_model(model: object):
......
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