fix for #3086 failing to load any previous hypernet

    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):

        super().__init__()

        if layer_structure is not None:

        assert layer_structure is not None, "layer_structure mut not be None"

        assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"

        assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"

        else:

            layer_structure = parse_layer_structure(dim, state_dict)

        linears = []

        for i in range(len(layer_structure) - 1):

        self.linear = torch.nn.Sequential(*linears)

        if state_dict is not None:

            try:

            self.fix_old_state_dict(state_dict)

            self.load_state_dict(state_dict)

            except RuntimeError:

                self.try_load_previous(state_dict)

        else:

            for layer in self.linear:

                layer.weight.data.normal_(mean=0.0, std=0.01)

        self.to(devices.device)

    def try_load_previous(self, state_dict):

        states = self.state_dict()

        states['linear.0.bias'].copy_(state_dict['linear1.bias'])

        states['linear.0.weight'].copy_(state_dict['linear1.weight'])

        states['linear.1.bias'].copy_(state_dict['linear2.bias'])

        states['linear.1.weight'].copy_(state_dict['linear2.weight'])

    def fix_old_state_dict(self, state_dict):

        changes = {

            'linear1.bias': 'linear.0.bias',

            'linear1.weight': 'linear.0.weight',

            'linear2.bias': 'linear.1.bias',

            'linear2.weight': 'linear.1.weight',

        }

        for fr, to in changes.items():

            x = state_dict.get(fr, None)

            if x is None:

                continue

            del state_dict[fr]

            state_dict[to] = x

    def forward(self, x):

        return x + self.linear(x) * self.multiplier

    HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength

def parse_layer_structure(dim, state_dict):

    i = 0

    layer_structure = [1]

    while (key := "linear.{}.weight".format(i)) in state_dict:

        weight = state_dict[key]

        layer_structure.append(len(weight) // dim)

        i += 1

    return layer_structure

class Hypernetwork:

    filename = None

    name = None

        state_dict = torch.load(filename, map_location='cpu')

        self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])

        self.add_layer_norm = state_dict.get('is_layer_norm', False)

        for size, sd in state_dict.items():

            if type(size) == int:

                self.layers[size] = (

                    HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]),

                    HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]),

                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm),

                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm),

                )

        self.name = state_dict.get('name', self.name)

        self.step = state_dict.get('step', 0)

        self.layer_structure = state_dict.get('layer_structure', None)

        self.add_layer_norm = state_dict.get('is_layer_norm', False)

        self.sd_checkpoint = state_dict.get('sd_checkpoint', None)

        self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)

    return torch.stack(conds)

def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

    assert hypernetwork_name, 'hypernetwork not selected'