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| import torch | |
| import lyco_helpers | |
| import network | |
| from modules import devices | |
| class ModuleTypeLora(network.ModuleType): | |
| def create_module(self, net: network.Network, weights: network.NetworkWeights): | |
| if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]): | |
| return NetworkModuleLora(net, weights) | |
| return None | |
| class NetworkModuleLora(network.NetworkModule): | |
| def __init__(self, net: network.Network, weights: network.NetworkWeights): | |
| super().__init__(net, weights) | |
| self.up_model = self.create_module(weights.w, "lora_up.weight") | |
| self.down_model = self.create_module(weights.w, "lora_down.weight") | |
| self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True) | |
| self.dim = weights.w["lora_down.weight"].shape[0] | |
| def create_module(self, weights, key, none_ok=False): | |
| weight = weights.get(key) | |
| if weight is None and none_ok: | |
| return None | |
| is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention] | |
| is_conv = type(self.sd_module) in [torch.nn.Conv2d] | |
| if is_linear: | |
| weight = weight.reshape(weight.shape[0], -1) | |
| module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) | |
| elif is_conv and key == "lora_down.weight" or key == "dyn_up": | |
| if len(weight.shape) == 2: | |
| weight = weight.reshape(weight.shape[0], -1, 1, 1) | |
| if weight.shape[2] != 1 or weight.shape[3] != 1: | |
| module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) | |
| else: | |
| module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) | |
| elif is_conv and key == "lora_mid.weight": | |
| module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) | |
| elif is_conv and key == "lora_up.weight" or key == "dyn_down": | |
| module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) | |
| else: | |
| raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}') | |
| with torch.no_grad(): | |
| if weight.shape != module.weight.shape: | |
| weight = weight.reshape(module.weight.shape) | |
| module.weight.copy_(weight) | |
| module.to(device=devices.cpu, dtype=devices.dtype) | |
| module.weight.requires_grad_(False) | |
| return module | |
| def calc_updown(self, orig_weight): | |
| up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) | |
| down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) | |
| output_shape = [up.size(0), down.size(1)] | |
| if self.mid_model is not None: | |
| # cp-decomposition | |
| mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) | |
| updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid) | |
| output_shape += mid.shape[2:] | |
| else: | |
| if len(down.shape) == 4: | |
| output_shape += down.shape[2:] | |
| updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim) | |
| return self.finalize_updown(updown, orig_weight, output_shape) | |
| def forward(self, x, y): | |
| self.up_model.to(device=devices.device) | |
| self.down_model.to(device=devices.device) | |
| return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale() | |