Replace chainner_models with Spandrel package (#2146)

* Replace chainner_models with Spandrel

* Update to latest spandrel

* Use spandrel_foss instead

* update spandrel to new FOSS-compliant version

comfy_extras/chainner_models/architecture/OmniSR/layernorm.py

-#!/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