Pull in latest upscale model code from chainner.

comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py

@@ -0,0 +1,110 @@
+import math
+
+import torch.nn as nn
+
+
+class CA_layer(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(CA_layer, self).__init__()
+        # global average pooling
+        self.gap = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False),
+            nn.GELU(),
+            nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False),
+            # nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        y = self.fc(self.gap(x))
+        return x * y.expand_as(x)
+
+
+class Simple_CA_layer(nn.Module):
+    def __init__(self, channel):
+        super(Simple_CA_layer, self).__init__()
+        self.gap = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Conv2d(
+            in_channels=channel,
+            out_channels=channel,
+            kernel_size=1,
+            padding=0,
+            stride=1,
+            groups=1,
+            bias=True,
+        )
+
+    def forward(self, x):
+        return x * self.fc(self.gap(x))
+
+
+class ECA_layer(nn.Module):
+    """Constructs a ECA module.
+    Args:
+        channel: Number of channels of the input feature map
+        k_size: Adaptive selection of kernel size
+    """
+
+    def __init__(self, channel):
+        super(ECA_layer, self).__init__()
+
+        b = 1
+        gamma = 2
+        k_size = int(abs(math.log(channel, 2) + b) / gamma)
+        k_size = k_size if k_size % 2 else k_size + 1
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.conv = nn.Conv1d(
+            1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False
+        )
+        # self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        # x: input features with shape [b, c, h, w]
+        # b, c, h, w = x.size()
+
+        # feature descriptor on the global spatial information
+        y = self.avg_pool(x)
+
+        # Two different branches of ECA module
+        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
+
+        # Multi-scale information fusion
+        # y = self.sigmoid(y)
+
+        return x * y.expand_as(x)
+
+
+class ECA_MaxPool_layer(nn.Module):
+    """Constructs a ECA module.
+    Args:
+        channel: Number of channels of the input feature map
+        k_size: Adaptive selection of kernel size
+    """
+
+    def __init__(self, channel):
+        super(ECA_MaxPool_layer, self).__init__()
+
+        b = 1
+        gamma = 2
+        k_size = int(abs(math.log(channel, 2) + b) / gamma)
+        k_size = k_size if k_size % 2 else k_size + 1
+        self.max_pool = nn.AdaptiveMaxPool2d(1)
+        self.conv = nn.Conv1d(
+            1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False
+        )
+        # self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        # x: input features with shape [b, c, h, w]
+        # b, c, h, w = x.size()
+
+        # feature descriptor on the global spatial information
+        y = self.max_pool(x)
+
+        # Two different branches of ECA module
+        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
+
+        # Multi-scale information fusion
+        # y = self.sigmoid(y)
+
+        return x * y.expand_as(x)

comfy_extras/chainner_models/architecture/OmniSR/LICENSE

@@ -0,0 +1,201 @@
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comfy_extras/chainner_models/architecture/OmniSR/OSA.py

@@ -0,0 +1,577 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: OSA.py
+# Created Date: Tuesday April 28th 2022
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Sunday, 23rd April 2023 3:07:42 pm
+# Modified By: Chen Xuanhong
+# Copyright (c) 2020 Shanghai Jiao Tong University
+#############################################################
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange, Reduce
+from torch import einsum, nn
+
+from .layernorm import LayerNorm2d
+
+# helpers
+
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    return val if exists(val) else d
+
+
+def cast_tuple(val, length=1):
+    return val if isinstance(val, tuple) else ((val,) * length)
+
+
+# helper classes
+
+
+class PreNormResidual(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.fn = fn
+
+    def forward(self, x):
+        return self.fn(self.norm(x)) + x
+
+
+class Conv_PreNormResidual(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = LayerNorm2d(dim)
+        self.fn = fn
+
+    def forward(self, x):
+        return self.fn(self.norm(x)) + x
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, mult=2, dropout=0.0):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        self.net = nn.Sequential(
+            nn.Linear(dim, inner_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class Conv_FeedForward(nn.Module):
+    def __init__(self, dim, mult=2, dropout=0.0):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        self.net = nn.Sequential(
+            nn.Conv2d(dim, inner_dim, 1, 1, 0),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Conv2d(inner_dim, dim, 1, 1, 0),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class Gated_Conv_FeedForward(nn.Module):
+    def __init__(self, dim, mult=1, bias=False, dropout=0.0):
+        super().__init__()
+
+        hidden_features = int(dim * mult)
+
+        self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias)
+
+        self.dwconv = nn.Conv2d(
+            hidden_features * 2,
+            hidden_features * 2,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=hidden_features * 2,
+            bias=bias,
+        )
+
+        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)
+
+    def forward(self, x):
+        x = self.project_in(x)
+        x1, x2 = self.dwconv(x).chunk(2, dim=1)
+        x = F.gelu(x1) * x2
+        x = self.project_out(x)
+        return x
+
+
+# MBConv
+
+
+class SqueezeExcitation(nn.Module):
+    def __init__(self, dim, shrinkage_rate=0.25):
+        super().__init__()
+        hidden_dim = int(dim * shrinkage_rate)
+
+        self.gate = nn.Sequential(
+            Reduce("b c h w -> b c", "mean"),
+            nn.Linear(dim, hidden_dim, bias=False),
+            nn.SiLU(),
+            nn.Linear(hidden_dim, dim, bias=False),
+            nn.Sigmoid(),
+            Rearrange("b c -> b c 1 1"),
+        )
+
+    def forward(self, x):
+        return x * self.gate(x)
+
+
+class MBConvResidual(nn.Module):
+    def __init__(self, fn, dropout=0.0):
+        super().__init__()
+        self.fn = fn
+        self.dropsample = Dropsample(dropout)
+
+    def forward(self, x):
+        out = self.fn(x)
+        out = self.dropsample(out)
+        return out + x
+
+
+class Dropsample(nn.Module):
+    def __init__(self, prob=0):
+        super().__init__()
+        self.prob = prob
+
+    def forward(self, x):
+        device = x.device
+
+        if self.prob == 0.0 or (not self.training):
+            return x
+
+        keep_mask = (
+            torch.FloatTensor((x.shape[0], 1, 1, 1), device=device).uniform_()
+            > self.prob
+        )
+        return x * keep_mask / (1 - self.prob)
+
+
+def MBConv(
+    dim_in, dim_out, *, downsample, expansion_rate=4, shrinkage_rate=0.25, dropout=0.0
+):
+    hidden_dim = int(expansion_rate * dim_out)
+    stride = 2 if downsample else 1
+
+    net = nn.Sequential(
+        nn.Conv2d(dim_in, hidden_dim, 1),
+        # nn.BatchNorm2d(hidden_dim),
+        nn.GELU(),
+        nn.Conv2d(
+            hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim
+        ),
+        # nn.BatchNorm2d(hidden_dim),
+        nn.GELU(),
+        SqueezeExcitation(hidden_dim, shrinkage_rate=shrinkage_rate),
+        nn.Conv2d(hidden_dim, dim_out, 1),
+        # nn.BatchNorm2d(dim_out)
+    )
+
+    if dim_in == dim_out and not downsample:
+        net = MBConvResidual(net, dropout=dropout)
+
+    return net
+
+
+# attention related classes
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_head=32,
+        dropout=0.0,
+        window_size=7,
+        with_pe=True,
+    ):
+        super().__init__()
+        assert (
+            dim % dim_head
+        ) == 0, "dimension should be divisible by dimension per head"
+
+        self.heads = dim // dim_head
+        self.scale = dim_head**-0.5
+        self.with_pe = with_pe
+
+        self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
+
+        self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout))
+
+        self.to_out = nn.Sequential(
+            nn.Linear(dim, dim, bias=False), nn.Dropout(dropout)
+        )
+
+        # relative positional bias
+        if self.with_pe:
+            self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads)
+
+            pos = torch.arange(window_size)
+            grid = torch.stack(torch.meshgrid(pos, pos))
+            grid = rearrange(grid, "c i j -> (i j) c")
+            rel_pos = rearrange(grid, "i ... -> i 1 ...") - rearrange(
+                grid, "j ... -> 1 j ..."
+            )
+            rel_pos += window_size - 1
+            rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum(
+                dim=-1
+            )
+
+            self.register_buffer("rel_pos_indices", rel_pos_indices, persistent=False)
+
+    def forward(self, x):
+        batch, height, width, window_height, window_width, _, device, h = (
+            *x.shape,
+            x.device,
+            self.heads,
+        )
+
+        # flatten
+
+        x = rearrange(x, "b x y w1 w2 d -> (b x y) (w1 w2) d")
+
+        # project for queries, keys, values
+
+        q, k, v = self.to_qkv(x).chunk(3, dim=-1)
+
+        # split heads
+
+        q, k, v = map(lambda t: rearrange(t, "b n (h d ) -> b h n d", h=h), (q, k, v))
+
+        # scale
+
+        q = q * self.scale
+
+        # sim
+
+        sim = einsum("b h i d, b h j d -> b h i j", q, k)
+
+        # add positional bias
+        if self.with_pe:
+            bias = self.rel_pos_bias(self.rel_pos_indices)
+            sim = sim + rearrange(bias, "i j h -> h i j")
+
+        # attention
+
+        attn = self.attend(sim)
+
+        # aggregate
+
+        out = einsum("b h i j, b h j d -> b h i d", attn, v)
+
+        # merge heads
+
+        out = rearrange(
+            out, "b h (w1 w2) d -> b w1 w2 (h d)", w1=window_height, w2=window_width
+        )
+
+        # combine heads out
+
+        out = self.to_out(out)
+        return rearrange(out, "(b x y) ... -> b x y ...", x=height, y=width)
+
+
+class Block_Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_head=32,
+        bias=False,
+        dropout=0.0,
+        window_size=7,
+        with_pe=True,
+    ):
+        super().__init__()
+        assert (
+            dim % dim_head
+        ) == 0, "dimension should be divisible by dimension per head"
+
+        self.heads = dim // dim_head
+        self.ps = window_size
+        self.scale = dim_head**-0.5
+        self.with_pe = with_pe
+
+        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
+        self.qkv_dwconv = nn.Conv2d(
+            dim * 3,
+            dim * 3,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=dim * 3,
+            bias=bias,
+        )
+
+        self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout))
+
+        self.to_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
+
+    def forward(self, x):
+        # project for queries, keys, values
+        b, c, h, w = x.shape
+
+        qkv = self.qkv_dwconv(self.qkv(x))
+        q, k, v = qkv.chunk(3, dim=1)
+
+        # split heads
+
+        q, k, v = map(
+            lambda t: rearrange(
+                t,
+                "b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d",
+                h=self.heads,
+                w1=self.ps,
+                w2=self.ps,
+            ),
+            (q, k, v),
+        )
+
+        # scale
+
+        q = q * self.scale
+
+        # sim
+
+        sim = einsum("b h i d, b h j d -> b h i j", q, k)
+
+        # attention
+        attn = self.attend(sim)
+
+        # aggregate
+
+        out = einsum("b h i j, b h j d -> b h i d", attn, v)
+
+        # merge heads
+        out = rearrange(
+            out,
+            "(b x y) head (w1 w2) d -> b (head d) (x w1) (y w2)",
+            x=h // self.ps,
+            y=w // self.ps,
+            head=self.heads,
+            w1=self.ps,
+            w2=self.ps,
+        )
+
+        out = self.to_out(out)
+        return out
+
+
+class Channel_Attention(nn.Module):
+    def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7):
+        super(Channel_Attention, self).__init__()
+        self.heads = heads
+
+        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
+
+        self.ps = window_size
+
+        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
+        self.qkv_dwconv = nn.Conv2d(
+            dim * 3,
+            dim * 3,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=dim * 3,
+            bias=bias,
+        )
+        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+
+        qkv = self.qkv_dwconv(self.qkv(x))
+        qkv = qkv.chunk(3, dim=1)
+
+        q, k, v = map(
+            lambda t: rearrange(
+                t,
+                "b (head d) (h ph) (w pw) -> b (h w) head d (ph pw)",
+                ph=self.ps,
+                pw=self.ps,
+                head=self.heads,
+            ),
+            qkv,
+        )
+
+        q = F.normalize(q, dim=-1)
+        k = F.normalize(k, dim=-1)
+
+        attn = (q @ k.transpose(-2, -1)) * self.temperature
+        attn = attn.softmax(dim=-1)
+        out = attn @ v
+
+        out = rearrange(
+            out,
+            "b (h w) head d (ph pw) -> b (head d) (h ph) (w pw)",
+            h=h // self.ps,
+            w=w // self.ps,
+            ph=self.ps,
+            pw=self.ps,
+            head=self.heads,
+        )
+
+        out = self.project_out(out)
+
+        return out
+
+
+class Channel_Attention_grid(nn.Module):
+    def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7):
+        super(Channel_Attention_grid, self).__init__()
+        self.heads = heads
+
+        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
+
+        self.ps = window_size
+
+        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
+        self.qkv_dwconv = nn.Conv2d(
+            dim * 3,
+            dim * 3,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=dim * 3,
+            bias=bias,
+        )
+        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+
+        qkv = self.qkv_dwconv(self.qkv(x))
+        qkv = qkv.chunk(3, dim=1)
+
+        q, k, v = map(
+            lambda t: rearrange(
+                t,
+                "b (head d) (h ph) (w pw) -> b (ph pw) head d (h w)",
+                ph=self.ps,
+                pw=self.ps,
+                head=self.heads,
+            ),
+            qkv,
+        )
+
+        q = F.normalize(q, dim=-1)
+        k = F.normalize(k, dim=-1)
+
+        attn = (q @ k.transpose(-2, -1)) * self.temperature
+        attn = attn.softmax(dim=-1)
+        out = attn @ v
+
+        out = rearrange(
+            out,
+            "b (ph pw) head d (h w) -> b (head d) (h ph) (w pw)",
+            h=h // self.ps,
+            w=w // self.ps,
+            ph=self.ps,
+            pw=self.ps,
+            head=self.heads,
+        )
+
+        out = self.project_out(out)
+
+        return out
+
+
+class OSA_Block(nn.Module):
+    def __init__(
+        self,
+        channel_num=64,
+        bias=True,
+        ffn_bias=True,
+        window_size=8,
+        with_pe=False,
+        dropout=0.0,
+    ):
+        super(OSA_Block, self).__init__()
+
+        w = window_size
+
+        self.layer = nn.Sequential(
+            MBConv(
+                channel_num,
+                channel_num,
+                downsample=False,
+                expansion_rate=1,
+                shrinkage_rate=0.25,
+            ),
+            Rearrange(
+                "b d (x w1) (y w2) -> b x y w1 w2 d", w1=w, w2=w
+            ),  # block-like attention
+            PreNormResidual(
+                channel_num,
+                Attention(
+                    dim=channel_num,
+                    dim_head=channel_num // 4,
+                    dropout=dropout,
+                    window_size=window_size,
+                    with_pe=with_pe,
+                ),
+            ),
+            Rearrange("b x y w1 w2 d -> b d (x w1) (y w2)"),
+            Conv_PreNormResidual(
+                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
+            ),
+            # channel-like attention
+            Conv_PreNormResidual(
+                channel_num,
+                Channel_Attention(
+                    dim=channel_num, heads=4, dropout=dropout, window_size=window_size
+                ),
+            ),
+            Conv_PreNormResidual(
+                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
+            ),
+            Rearrange(
+                "b d (w1 x) (w2 y) -> b x y w1 w2 d", w1=w, w2=w
+            ),  # grid-like attention
+            PreNormResidual(
+                channel_num,
+                Attention(
+                    dim=channel_num,
+                    dim_head=channel_num // 4,
+                    dropout=dropout,
+                    window_size=window_size,
+                    with_pe=with_pe,
+                ),
+            ),
+            Rearrange("b x y w1 w2 d -> b d (w1 x) (w2 y)"),
+            Conv_PreNormResidual(
+                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
+            ),
+            # channel-like attention
+            Conv_PreNormResidual(
+                channel_num,
+                Channel_Attention_grid(
+                    dim=channel_num, heads=4, dropout=dropout, window_size=window_size
+                ),
+            ),
+            Conv_PreNormResidual(
+                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
+            ),
+        )
+
+    def forward(self, x):
+        out = self.layer(x)
+        return out

comfy_extras/chainner_models/architecture/OmniSR/OSAG.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: OSAG.py
+# Created Date: Tuesday April 28th 2022
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Sunday, 23rd April 2023 3:08:49 pm
+# Modified By: Chen Xuanhong
+# Copyright (c) 2020 Shanghai Jiao Tong University
+#############################################################
+
+
+import torch.nn as nn
+
+from .esa import ESA
+from .OSA import OSA_Block
+
+
+class OSAG(nn.Module):
+    def __init__(
+        self,
+        channel_num=64,
+        bias=True,
+        block_num=4,
+        ffn_bias=False,
+        window_size=0,
+        pe=False,
+    ):
+        super(OSAG, self).__init__()
+
+        # print("window_size: %d" % (window_size))
+        # print("with_pe", pe)
+        # print("ffn_bias: %d" % (ffn_bias))
+
+        # block_script_name = kwargs.get("block_script_name", "OSA")
+        # block_class_name = kwargs.get("block_class_name", "OSA_Block")
+
+        # script_name = "." + block_script_name
+        # package = __import__(script_name, fromlist=True)
+        block_class = OSA_Block  # getattr(package, block_class_name)
+        group_list = []
+        for _ in range(block_num):
+            temp_res = block_class(
+                channel_num,
+                bias,
+                ffn_bias=ffn_bias,
+                window_size=window_size,
+                with_pe=pe,
+            )
+            group_list.append(temp_res)
+        group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias))
+        self.residual_layer = nn.Sequential(*group_list)
+        esa_channel = max(channel_num // 4, 16)
+        self.esa = ESA(esa_channel, channel_num)
+
+    def forward(self, x):
+        out = self.residual_layer(x)
+        out = out + x
+        return self.esa(out)

comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: OmniSR.py
+# Created Date: Tuesday April 28th 2022
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Sunday, 23rd April 2023 3:06:36 pm
+# Modified By: Chen Xuanhong
+# Copyright (c) 2020 Shanghai Jiao Tong University
+#############################################################
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .OSAG import OSAG
+from .pixelshuffle import pixelshuffle_block
+
+
+class OmniSR(nn.Module):
+    def __init__(
+        self,
+        state_dict,
+        **kwargs,
+    ):
+        super(OmniSR, self).__init__()
+        self.state = state_dict
+
+        bias = True  # Fine to assume this for now
+        block_num = 1  # Fine to assume this for now
+        ffn_bias = True
+        pe = True
+
+        num_feat = state_dict["input.weight"].shape[0] or 64
+        num_in_ch = state_dict["input.weight"].shape[1] or 3
+        num_out_ch = num_in_ch  # we can just assume this for now. pixelshuffle smh
+
+        pixelshuffle_shape = state_dict["up.0.weight"].shape[0]
+        up_scale = math.sqrt(pixelshuffle_shape / num_out_ch)
+        if up_scale - int(up_scale) > 0:
+            print(
+                "out_nc is probably different than in_nc, scale calculation might be wrong"
+            )
+        up_scale = int(up_scale)
+        res_num = 0
+        for key in state_dict.keys():
+            if "residual_layer" in key:
+                temp_res_num = int(key.split(".")[1])
+                if temp_res_num > res_num:
+                    res_num = temp_res_num
+        res_num = res_num + 1  # zero-indexed
+
+        residual_layer = []
+        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)
+        self.up_scale = up_scale
+
+        for _ in range(res_num):
+            temp_res = OSAG(
+                channel_num=num_feat,
+                bias=bias,
+                block_num=block_num,
+                ffn_bias=ffn_bias,
+                window_size=self.window_size,
+                pe=pe,
+            )
+            residual_layer.append(temp_res)
+        self.residual_layer = nn.Sequential(*residual_layer)
+        self.input = nn.Conv2d(
+            in_channels=num_in_ch,
+            out_channels=num_feat,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=bias,
+        )
+        self.output = nn.Conv2d(
+            in_channels=num_feat,
+            out_channels=num_feat,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=bias,
+        )
+        self.up = pixelshuffle_block(num_feat, num_out_ch, up_scale, bias=bias)
+
+        # self.tail   = pixelshuffle_block(num_feat,num_out_ch,up_scale,bias=bias)
+
+        # for m in self.modules():
+        #     if isinstance(m, nn.Conv2d):
+        #         n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+        #         m.weight.data.normal_(0, sqrt(2. / n))
+
+        # chaiNNer specific stuff
+        self.model_arch = "OmniSR"
+        self.sub_type = "SR"
+        self.in_nc = num_in_ch
+        self.out_nc = num_out_ch
+        self.num_feat = num_feat
+        self.scale = up_scale
+
+        self.supports_fp16 = True  # TODO: Test this
+        self.supports_bfp16 = True
+        self.min_size_restriction = 16
+
+        self.load_state_dict(state_dict, strict=False)
+
+    def check_image_size(self, x):
+        _, _, h, w = x.size()
+        # import pdb; pdb.set_trace()
+        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')
+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "constant", 0)
+        return x
+
+    def forward(self, x):
+        H, W = x.shape[2:]
+        x = self.check_image_size(x)
+
+        residual = self.input(x)
+        out = self.residual_layer(residual)
+
+        # origin
+        out = torch.add(self.output(out), residual)
+        out = self.up(out)
+
+        out = out[:, :, : H * self.up_scale, : W * self.up_scale]
+        return out

comfy_extras/chainner_models/architecture/OmniSR/esa.py

@@ -0,0 +1,294 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: esa.py
+# Created Date: Tuesday April 28th 2022
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Thursday, 20th April 2023 9:28:06 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2020 Shanghai Jiao Tong University
+#############################################################
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .layernorm import LayerNorm2d
+
+
+def moment(x, dim=(2, 3), k=2):
+    assert len(x.size()) == 4
+    mean = torch.mean(x, dim=dim).unsqueeze(-1).unsqueeze(-1)
+    mk = (1 / (x.size(2) * x.size(3))) * torch.sum(torch.pow(x - mean, k), dim=dim)
+    return mk
+
+
+class ESA(nn.Module):
+    """
+    Modification of Enhanced Spatial Attention (ESA), which is proposed by
+    `Residual Feature Aggregation Network for Image Super-Resolution`
+    Note: `conv_max` and `conv3_` are NOT used here, so the corresponding codes
+    are deleted.
+    """
+
+    def __init__(self, esa_channels, n_feats, conv=nn.Conv2d):
+        super(ESA, self).__init__()
+        f = esa_channels
+        self.conv1 = conv(n_feats, f, kernel_size=1)
+        self.conv_f = conv(f, f, kernel_size=1)
+        self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0)
+        self.conv3 = conv(f, f, kernel_size=3, padding=1)
+        self.conv4 = conv(f, n_feats, kernel_size=1)
+        self.sigmoid = nn.Sigmoid()
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        c1_ = self.conv1(x)
+        c1 = self.conv2(c1_)
+        v_max = F.max_pool2d(c1, kernel_size=7, stride=3)
+        c3 = self.conv3(v_max)
+        c3 = F.interpolate(
+            c3, (x.size(2), x.size(3)), mode="bilinear", align_corners=False
+        )
+        cf = self.conv_f(c1_)
+        c4 = self.conv4(c3 + cf)
+        m = self.sigmoid(c4)
+        return x * m
+
+
+class LK_ESA(nn.Module):
+    def __init__(
+        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
+    ):
+        super(LK_ESA, self).__init__()
+        f = esa_channels
+        self.conv1 = conv(n_feats, f, kernel_size=1)
+        self.conv_f = conv(f, f, kernel_size=1)
+
+        kernel_size = 17
+        kernel_expand = kernel_expand
+        padding = kernel_size // 2
+
+        self.vec_conv = nn.Conv2d(
+            in_channels=f * kernel_expand,
+            out_channels=f * kernel_expand,
+            kernel_size=(1, kernel_size),
+            padding=(0, padding),
+            groups=2,
+            bias=bias,
+        )
+        self.vec_conv3x1 = nn.Conv2d(
+            in_channels=f * kernel_expand,
+            out_channels=f * kernel_expand,
+            kernel_size=(1, 3),
+            padding=(0, 1),
+            groups=2,
+            bias=bias,
+        )
+
+        self.hor_conv = nn.Conv2d(
+            in_channels=f * kernel_expand,
+            out_channels=f * kernel_expand,
+            kernel_size=(kernel_size, 1),
+            padding=(padding, 0),
+            groups=2,
+            bias=bias,
+        )
+        self.hor_conv1x3 = nn.Conv2d(
+            in_channels=f * kernel_expand,
+            out_channels=f * kernel_expand,
+            kernel_size=(3, 1),
+            padding=(1, 0),
+            groups=2,
+            bias=bias,
+        )
+
+        self.conv4 = conv(f, n_feats, kernel_size=1)
+        self.sigmoid = nn.Sigmoid()
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        c1_ = self.conv1(x)
+
+        res = self.vec_conv(c1_) + self.vec_conv3x1(c1_)
+        res = self.hor_conv(res) + self.hor_conv1x3(res)
+
+        cf = self.conv_f(c1_)
+        c4 = self.conv4(res + cf)
+        m = self.sigmoid(c4)
+        return x * m
+
+
+class LK_ESA_LN(nn.Module):
+    def __init__(
+        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
+    ):
+        super(LK_ESA_LN, self).__init__()
+        f = esa_channels
+        self.conv1 = conv(n_feats, f, kernel_size=1)
+        self.conv_f = conv(f, f, kernel_size=1)
+
+        kernel_size = 17
+        kernel_expand = kernel_expand
+        padding = kernel_size // 2
+
+        self.norm = LayerNorm2d(n_feats)
+
+        self.vec_conv = nn.Conv2d(
+            in_channels=f * kernel_expand,
+            out_channels=f * kernel_expand,
+            kernel_size=(1, kernel_size),
+            padding=(0, padding),
+            groups=2,
+            bias=bias,
+        )
+        self.vec_conv3x1 = nn.Conv2d(
+            in_channels=f * kernel_expand,
+            out_channels=f * kernel_expand,
+            kernel_size=(1, 3),
+            padding=(0, 1),
+            groups=2,
+            bias=bias,
+        )
+
+        self.hor_conv = nn.Conv2d(
+            in_channels=f * kernel_expand,
+            out_channels=f * kernel_expand,
+            kernel_size=(kernel_size, 1),
+            padding=(padding, 0),
+            groups=2,
+            bias=bias,
+        )
+        self.hor_conv1x3 = nn.Conv2d(
+            in_channels=f * kernel_expand,
+            out_channels=f * kernel_expand,
+            kernel_size=(3, 1),
+            padding=(1, 0),
+            groups=2,
+            bias=bias,
+        )
+
+        self.conv4 = conv(f, n_feats, kernel_size=1)
+        self.sigmoid = nn.Sigmoid()
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        c1_ = self.norm(x)
+        c1_ = self.conv1(c1_)
+
+        res = self.vec_conv(c1_) + self.vec_conv3x1(c1_)
+        res = self.hor_conv(res) + self.hor_conv1x3(res)
+
+        cf = self.conv_f(c1_)
+        c4 = self.conv4(res + cf)
+        m = self.sigmoid(c4)
+        return x * m
+
+
+class AdaGuidedFilter(nn.Module):
+    def __init__(
+        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
+    ):
+        super(AdaGuidedFilter, self).__init__()
+
+        self.gap = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Conv2d(
+            in_channels=n_feats,
+            out_channels=1,
+            kernel_size=1,
+            padding=0,
+            stride=1,
+            groups=1,
+            bias=True,
+        )
+
+        self.r = 5
+
+    def box_filter(self, x, r):
+        channel = x.shape[1]
+        kernel_size = 2 * r + 1
+        weight = 1.0 / (kernel_size**2)
+        box_kernel = weight * torch.ones(
+            (channel, 1, kernel_size, kernel_size), dtype=torch.float32, device=x.device
+        )
+        output = F.conv2d(x, weight=box_kernel, stride=1, padding=r, groups=channel)
+        return output
+
+    def forward(self, x):
+        _, _, H, W = x.shape
+        N = self.box_filter(
+            torch.ones((1, 1, H, W), dtype=x.dtype, device=x.device), self.r
+        )
+
+        # epsilon = self.fc(self.gap(x))
+        # epsilon = torch.pow(epsilon, 2)
+        epsilon = 1e-2
+
+        mean_x = self.box_filter(x, self.r) / N
+        var_x = self.box_filter(x * x, self.r) / N - mean_x * mean_x
+
+        A = var_x / (var_x + epsilon)
+        b = (1 - A) * mean_x
+        m = A * x + b
+
+        # mean_A = self.box_filter(A, self.r) / N
+        # mean_b = self.box_filter(b, self.r) / N
+        # m = mean_A * x + mean_b
+        return x * m
+
+
+class AdaConvGuidedFilter(nn.Module):
+    def __init__(
+        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
+    ):
+        super(AdaConvGuidedFilter, self).__init__()
+        f = esa_channels
+
+        self.conv_f = conv(f, f, kernel_size=1)
+
+        kernel_size = 17
+        kernel_expand = kernel_expand
+        padding = kernel_size // 2
+
+        self.vec_conv = nn.Conv2d(
+            in_channels=f,
+            out_channels=f,
+            kernel_size=(1, kernel_size),
+            padding=(0, padding),
+            groups=f,
+            bias=bias,
+        )
+
+        self.hor_conv = nn.Conv2d(
+            in_channels=f,
+            out_channels=f,
+            kernel_size=(kernel_size, 1),
+            padding=(padding, 0),
+            groups=f,
+            bias=bias,
+        )
+
+        self.gap = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Conv2d(
+            in_channels=f,
+            out_channels=f,
+            kernel_size=1,
+            padding=0,
+            stride=1,
+            groups=1,
+            bias=True,
+        )
+
+    def forward(self, x):
+        y = self.vec_conv(x)
+        y = self.hor_conv(y)
+
+        sigma = torch.pow(y, 2)
+        epsilon = self.fc(self.gap(y))
+
+        weight = sigma / (sigma + epsilon)
+
+        m = weight * x + (1 - weight)
+
+        return x * m

comfy_extras/chainner_models/architecture/OmniSR/layernorm.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: layernorm.py
+# Created Date: Tuesday April 28th 2022
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Thursday, 20th April 2023 9:28:20 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2020 Shanghai Jiao Tong University
+#############################################################
+
+import torch
+import torch.nn as nn
+
+
+class LayerNormFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, weight, bias, eps):
+        ctx.eps = eps
+        N, C, H, W = x.size()
+        mu = x.mean(1, keepdim=True)
+        var = (x - mu).pow(2).mean(1, keepdim=True)
+        y = (x - mu) / (var + eps).sqrt()
+        ctx.save_for_backward(y, var, weight)
+        y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)
+        return y
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        eps = ctx.eps
+
+        N, C, H, W = grad_output.size()
+        y, var, weight = ctx.saved_variables
+        g = grad_output * weight.view(1, C, 1, 1)
+        mean_g = g.mean(dim=1, keepdim=True)
+
+        mean_gy = (g * y).mean(dim=1, keepdim=True)
+        gx = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)
+        return (
+            gx,
+            (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0),
+            grad_output.sum(dim=3).sum(dim=2).sum(dim=0),
+            None,
+        )
+
+
+class LayerNorm2d(nn.Module):
+    def __init__(self, channels, eps=1e-6):
+        super(LayerNorm2d, self).__init__()
+        self.register_parameter("weight", nn.Parameter(torch.ones(channels)))
+        self.register_parameter("bias", nn.Parameter(torch.zeros(channels)))
+        self.eps = eps
+
+    def forward(self, x):
+        return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)
+
+
+class GRN(nn.Module):
+    """GRN (Global Response Normalization) layer"""
+
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1))
+        self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True)
+        Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x

comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py

@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: pixelshuffle.py
+# Created Date: Friday July 1st 2022
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Friday, 1st July 2022 10:18:39 am
+# Modified By: Chen Xuanhong
+# Copyright (c) 2022 Shanghai Jiao Tong University
+#############################################################
+
+import torch.nn as nn
+
+
+def pixelshuffle_block(
+    in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False
+):
+    """
+    Upsample features according to `upscale_factor`.
+    """
+    padding = kernel_size // 2
+    conv = nn.Conv2d(
+        in_channels,
+        out_channels * (upscale_factor**2),
+        kernel_size,
+        padding=1,
+        bias=bias,
+    )
+    pixel_shuffle = nn.PixelShuffle(upscale_factor)
+    return nn.Sequential(*[conv, pixel_shuffle])

comfy_extras/chainner_models/architecture/RRDB.py

@@ -79,6 +79,12 @@ class RRDBNet(nn.Module):
         self.scale: int = self.get_scale()
         self.num_filters: int = self.state[self.key_arr[0]].shape[0]
 
+        c2x2 = False
+        if self.state["model.0.weight"].shape[-2] == 2:
+            c2x2 = True
+            self.scale = round(math.sqrt(self.scale / 4))
+            self.model_arch = "ESRGAN-2c2"
+
         self.supports_fp16 = True
         self.supports_bfp16 = True
         self.min_size_restriction = None
@@ -105,11 +111,15 @@ class RRDBNet(nn.Module):
                 out_nc=self.num_filters,
                 upscale_factor=3,
                 act_type=self.act,
+                c2x2=c2x2,
             )
         else:
             upsample_blocks = [
                 upsample_block(
-                    in_nc=self.num_filters, out_nc=self.num_filters, act_type=self.act
+                    in_nc=self.num_filters,
+                    out_nc=self.num_filters,
+                    act_type=self.act,
+                    c2x2=c2x2,
                 )
                 for _ in range(int(math.log(self.scale, 2)))
             ]
@@ -122,6 +132,7 @@ class RRDBNet(nn.Module):
                 kernel_size=3,
                 norm_type=None,
                 act_type=None,
+                c2x2=c2x2,
             ),
             B.ShortcutBlock(
                 B.sequential(
@@ -138,6 +149,7 @@ class RRDBNet(nn.Module):
                             act_type=self.act,
                             mode="CNA",
                             plus=self.plus,
+                            c2x2=c2x2,
                         )
                         for _ in range(self.num_blocks)
                     ],
@@ -149,6 +161,7 @@ class RRDBNet(nn.Module):
                         norm_type=self.norm,
                         act_type=None,
                         mode=self.mode,
+                        c2x2=c2x2,
                     ),
                 )
             ),
@@ -160,6 +173,7 @@ class RRDBNet(nn.Module):
                 kernel_size=3,
                 norm_type=None,
                 act_type=self.act,
+                c2x2=c2x2,
             ),
             # hr_conv1
             B.conv_block(
@@ -168,6 +182,7 @@ class RRDBNet(nn.Module):
                 kernel_size=3,
                 norm_type=None,
                 act_type=None,
+                c2x2=c2x2,
             ),
         )
 

comfy_extras/chainner_models/architecture/block.py

@@ -141,6 +141,19 @@ def sequential(*args):
 ConvMode = Literal["CNA", "NAC", "CNAC"]
 
 
+# 2x2x2 Conv Block
+def conv_block_2c2(
+    in_nc,
+    out_nc,
+    act_type="relu",
+):
+    return sequential(
+        nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1),
+        nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0),
+        act(act_type) if act_type else None,
+    )
+
+
 def conv_block(
     in_nc: int,
     out_nc: int,
@@ -153,12 +166,17 @@ def conv_block(
     norm_type: str | None = None,
     act_type: str | None = "relu",
     mode: ConvMode = "CNA",
+    c2x2=False,
 ):
     """
     Conv layer with padding, normalization, activation
     mode: CNA --> Conv -> Norm -> Act
         NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16)
     """
+
+    if c2x2:
+        return conv_block_2c2(in_nc, out_nc, act_type=act_type)
+
     assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode)
     padding = get_valid_padding(kernel_size, dilation)
     p = pad(pad_type, padding) if pad_type and pad_type != "zero" else None
@@ -285,6 +303,7 @@ class RRDB(nn.Module):
         _convtype="Conv2D",
         _spectral_norm=False,
         plus=False,
+        c2x2=False,
     ):
         super(RRDB, self).__init__()
         self.RDB1 = ResidualDenseBlock_5C(
@@ -298,6 +317,7 @@ class RRDB(nn.Module):
             act_type,
             mode,
             plus=plus,
+            c2x2=c2x2,
         )
         self.RDB2 = ResidualDenseBlock_5C(
             nf,
@@ -310,6 +330,7 @@ class RRDB(nn.Module):
             act_type,
             mode,
             plus=plus,
+            c2x2=c2x2,
         )
         self.RDB3 = ResidualDenseBlock_5C(
             nf,
@@ -322,6 +343,7 @@ class RRDB(nn.Module):
             act_type,
             mode,
             plus=plus,
+            c2x2=c2x2,
         )
 
     def forward(self, x):
@@ -365,6 +387,7 @@ class ResidualDenseBlock_5C(nn.Module):
         act_type="leakyrelu",
         mode: ConvMode = "CNA",
         plus=False,
+        c2x2=False,
     ):
         super(ResidualDenseBlock_5C, self).__init__()
 
@@ -382,6 +405,7 @@ class ResidualDenseBlock_5C(nn.Module):
             norm_type=norm_type,
             act_type=act_type,
             mode=mode,
+            c2x2=c2x2,
         )
         self.conv2 = conv_block(
             nf + gc,
@@ -393,6 +417,7 @@ class ResidualDenseBlock_5C(nn.Module):
             norm_type=norm_type,
             act_type=act_type,
             mode=mode,
+            c2x2=c2x2,
         )
         self.conv3 = conv_block(
             nf + 2 * gc,
@@ -404,6 +429,7 @@ class ResidualDenseBlock_5C(nn.Module):
             norm_type=norm_type,
             act_type=act_type,
             mode=mode,
+            c2x2=c2x2,
         )
         self.conv4 = conv_block(
             nf + 3 * gc,
@@ -415,6 +441,7 @@ class ResidualDenseBlock_5C(nn.Module):
             norm_type=norm_type,
             act_type=act_type,
             mode=mode,
+            c2x2=c2x2,
         )
         if mode == "CNA":
             last_act = None
@@ -430,6 +457,7 @@ class ResidualDenseBlock_5C(nn.Module):
             norm_type=norm_type,
             act_type=last_act,
             mode=mode,
+            c2x2=c2x2,
         )
 
     def forward(self, x):
@@ -499,6 +527,7 @@ def upconv_block(
     norm_type: str | None = None,
     act_type="relu",
     mode="nearest",
+    c2x2=False,
 ):
     # Up conv
     # described in https://distill.pub/2016/deconv-checkerboard/
@@ -512,5 +541,6 @@ def upconv_block(
         pad_type=pad_type,
         norm_type=norm_type,
         act_type=act_type,
+        c2x2=c2x2,
     )
     return sequential(upsample, conv)

comfy_extras/chainner_models/model_loading.py

@@ -6,6 +6,7 @@ from .architecture.face.restoreformer_arch import RestoreFormer
 from .architecture.HAT import HAT
 from .architecture.LaMa import LaMa
 from .architecture.MAT import MAT
+from .architecture.OmniSR.OmniSR import OmniSR
 from .architecture.RRDB import RRDBNet as ESRGAN
 from .architecture.SPSR import SPSRNet as SPSR
 from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
@@ -32,6 +33,7 @@ def load_state_dict(state_dict) -> PyTorchModel:
         state_dict = state_dict["params"]
 
     state_dict_keys = list(state_dict.keys())
+
     # SRVGGNet Real-ESRGAN (v2)
     if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys:
         model = RealESRGANv2(state_dict)
@@ -79,6 +81,9 @@ def load_state_dict(state_dict) -> PyTorchModel:
     # MAT
     elif "synthesis.first_stage.conv_first.conv.resample_filter" in state_dict_keys:
         model = MAT(state_dict)
+    # Omni-SR
+    elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys:
+        model = OmniSR(state_dict)
     # Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1
     else:
         try:

comfy_extras/chainner_models/types.py

@@ -6,6 +6,7 @@ from .architecture.face.restoreformer_arch import RestoreFormer
 from .architecture.HAT import HAT
 from .architecture.LaMa import LaMa
 from .architecture.MAT import MAT
+from .architecture.OmniSR.OmniSR import OmniSR
 from .architecture.RRDB import RRDBNet as ESRGAN
 from .architecture.SPSR import SPSRNet as SPSR
 from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
@@ -13,7 +14,7 @@ from .architecture.SwiftSRGAN import Generator as SwiftSRGAN
 from .architecture.Swin2SR import Swin2SR
 from .architecture.SwinIR import SwinIR
 
-PyTorchSRModels = (RealESRGANv2, SPSR, SwiftSRGAN, ESRGAN, SwinIR, Swin2SR, HAT)
+PyTorchSRModels = (RealESRGANv2, SPSR, SwiftSRGAN, ESRGAN, SwinIR, Swin2SR, HAT, OmniSR)
 PyTorchSRModel = Union[
     RealESRGANv2,
     SPSR,
@@ -22,6 +23,7 @@ PyTorchSRModel = Union[
     SwinIR,
     Swin2SR,
     HAT,
+    OmniSR,
 ]