Pull in latest upscale model code from chainner.

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