support inference with LyCORIS GLora networks

import network

class ModuleTypeGLora(network.ModuleType):

    def create_module(self, net: network.Network, weights: network.NetworkWeights):

        if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]):

            return NetworkModuleGLora(net, weights)

        return None

# adapted from https://github.com/KohakuBlueleaf/LyCORIS

class NetworkModuleGLora(network.NetworkModule):

    def __init__(self,  net: network.Network, weights: network.NetworkWeights):

        super().__init__(net, weights)

        if hasattr(self.sd_module, 'weight'):

            self.shape = self.sd_module.weight.shape

        self.w1a = weights.w["a1.weight"]

        self.w1b = weights.w["b1.weight"]

        self.w2a = weights.w["a2.weight"]

        self.w2b = weights.w["b2.weight"]

    def calc_updown(self, orig_weight):

        w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)

        w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)

        w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)

        w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)

        output_shape = [w1a.size(0), w1b.size(1)]

        updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a))

        return self.finalize_updown(updown, orig_weight, output_shape)

import lora_patches

import network

import network_lora

import network_glora

import network_hada

import network_ia3

import network_lokr

    network_lokr.ModuleTypeLokr(),

    network_full.ModuleTypeFull(),

    network_norm.ModuleTypeNorm(),

    network_glora.ModuleTypeGLora(),

]