Pull in latest upscale model code from chainner.

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)