add DyLoRA (experimental)

networks/dylora.py

@@ -0,0 +1,448 @@
+# some codes are copied from:
+# https://github.com/huawei-noah/KD-NLP/blob/main/DyLoRA/
+
+# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
+# Changes made to the original code:
+# 2022.08.20 - Integrate the DyLoRA layer for the LoRA Linear layer
+#  ------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
+#  ------------------------------------------------------------------------------------------
+
+import math
+import os
+import random
+from typing import List, Tuple, Union
+import torch
+from torch import nn
+
+
+class DyLoRAModule(torch.nn.Module):
+    """
+    replaces forward method of the original Linear, instead of replacing the original Linear module.
+    """
+
+    # NOTE: support dropout in future
+    def __init__(self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1, unit=1):
+        super().__init__()
+        self.lora_name = lora_name
+        self.lora_dim = lora_dim
+        self.unit = unit
+        assert self.lora_dim % self.unit == 0, "rank must be a multiple of unit"
+
+        if org_module.__class__.__name__ == "Conv2d":
+            in_dim = org_module.in_channels
+            out_dim = org_module.out_channels
+        else:
+            in_dim = org_module.in_features
+            out_dim = org_module.out_features
+
+        if type(alpha) == torch.Tensor:
+            alpha = alpha.detach().float().numpy()  # without casting, bf16 causes error
+        alpha = self.lora_dim if alpha is None or alpha == 0 else alpha
+        self.scale = alpha / self.lora_dim
+        self.register_buffer("alpha", torch.tensor(alpha))  # 定数として扱える
+
+        self.is_conv2d = org_module.__class__.__name__ == "Conv2d"
+        self.is_conv2d_3x3 = self.is_conv2d and org_module.kernel_size == (3, 3)
+
+        if self.is_conv2d and self.is_conv2d_3x3:
+            kernel_size = org_module.kernel_size
+            self.stride = org_module.stride
+            self.padding = org_module.padding
+            self.lora_A = nn.Parameter(org_module.weight.new_zeros((self.lora_dim, in_dim, *kernel_size)))
+            self.lora_B = nn.Parameter(org_module.weight.new_zeros((out_dim, self.lora_dim, 1, 1)))
+        else:
+            self.lora_A = nn.Parameter(org_module.weight.new_zeros((self.lora_dim, in_dim)))
+            self.lora_B = nn.Parameter(org_module.weight.new_zeros((out_dim, self.lora_dim)))
+
+        # same as microsoft's
+        torch.nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
+        torch.nn.init.zeros_(self.lora_B)
+
+        self.multiplier = multiplier
+        self.org_module = org_module  # remove in applying
+
+    def apply_to(self):
+        self.org_forward = self.org_module.forward
+        self.org_module.forward = self.forward
+        del self.org_module
+
+    def forward(self, x):
+        result = self.org_forward(x)
+
+        # specify the dynamic rank
+        trainable_rank = random.randint(0, self.lora_dim - 1)
+        trainable_rank = trainable_rank - trainable_rank % self.unit  # make sure the rank is a multiple of unit
+
+        # 一部のパラメータを固定して、残りのパラメータを学習する
+
+        # make lora_A
+        if trainable_rank > 0:
+            lora_A_nt1 = [self.lora_A[:trainable_rank].detach()]
+        else:
+            lora_A_nt1 = []
+
+        lora_A_t = self.lora_A[trainable_rank : trainable_rank + self.unit]
+
+        if trainable_rank < self.lora_dim - self.unit:
+            lora_A_nt2 = [self.lora_A[trainable_rank + self.unit :].detach()]
+        else:
+            lora_A_nt2 = []
+
+        lora_A = torch.cat(lora_A_nt1 + [lora_A_t] + lora_A_nt2, dim=0)
+
+        # make lora_B
+        if trainable_rank > 0:
+            lora_B_nt1 = [self.lora_B[:, :trainable_rank].detach()]
+        else:
+            lora_B_nt1 = []
+
+        lora_B_t = self.lora_B[:, trainable_rank : trainable_rank + self.unit]
+
+        if trainable_rank < self.lora_dim - self.unit:
+            lora_B_nt2 = [self.lora_B[:, trainable_rank + self.unit :].detach()]
+        else:
+            lora_B_nt2 = []
+
+        lora_B = torch.cat(lora_B_nt1 + [lora_B_t] + lora_B_nt2, dim=1)
+
+        # print("lora_A", lora_A.size(), "lora_B", lora_B.size(), "x", x.size(), "result", result.size())
+
+        # calculate with lora_A and lora_B
+        if self.is_conv2d_3x3:
+            ab = torch.nn.functional.conv2d(x, lora_A, stride=self.stride, padding=self.padding)
+            ab = torch.nn.functional.conv2d(ab, lora_B)
+        else:
+            ab = x
+            if self.is_conv2d:
+                ab = ab.reshape(ab.size(0), ab.size(1), -1).transpose(1, 2)
+
+            ab = torch.nn.functional.linear(ab, lora_A)
+            ab = torch.nn.functional.linear(ab, lora_B)
+
+            if self.is_conv2d:
+                ab = ab.transpose(1, 2).reshape(ab.size(0), -1, *x.size()[2:])
+
+        # 最後の項は、低rankをより大きくするためのスケーリング（じゃないかな）
+        result = result + ab * self.scale * math.sqrt(self.lora_dim / (trainable_rank + self.unit))
+
+        # NOTE weightに加算してからlinear/conv2dを呼んだほうが速いかも
+        return result
+
+    def state_dict(self, destination=None, prefix="", keep_vars=False):
+        # state dictを通常のLoRAと同じにする
+        state_dict = super().state_dict(destination, prefix, keep_vars)
+
+        lora_A_weight = state_dict.pop(self.lora_name + ".lora_A")
+        if self.is_conv2d and not self.is_conv2d_3x3:
+            lora_A_weight = lora_A_weight.unsqueeze(-1).unsqueeze(-1)
+        state_dict[self.lora_name + ".lora_down.weight"] = lora_A_weight
+
+        lora_B_weight = state_dict.pop(self.lora_name + ".lora_B")
+        if self.is_conv2d and not self.is_conv2d_3x3:
+            lora_B_weight = lora_B_weight.unsqueeze(-1).unsqueeze(-1)
+        state_dict[self.lora_name + ".lora_up.weight"] = lora_B_weight
+
+        return state_dict
+
+    def load_state_dict(self, state_dict, strict=True):
+        # 通常のLoRAと同じstate dictを読み込めるようにする
+        state_dict = state_dict.copy()
+
+        lora_A_weight = state_dict.pop(self.lora_name + ".lora_down.weight")
+        if self.is_conv2d and not self.is_conv2d_3x3:
+            lora_A_weight = lora_A_weight.squeeze(-1).squeeze(-1)
+        state_dict[self.lora_name + ".lora_A"] = lora_A_weight
+
+        lora_B_weight = state_dict.pop(self.lora_name + ".lora_up.weight")
+        if self.is_conv2d and not self.is_conv2d_3x3:
+            lora_B_weight = lora_B_weight.squeeze(-1).squeeze(-1)
+        state_dict[self.lora_name + ".lora_B"] = lora_B_weight
+
+        super().load_state_dict(state_dict, strict=strict)
+
+
+def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **kwargs):
+    if network_dim is None:
+        network_dim = 4  # default
+    if network_alpha is None:
+        network_alpha = 1.0
+
+    # extract dim/alpha for conv2d, and block dim
+    conv_dim = kwargs.get("conv_dim", None)
+    conv_alpha = kwargs.get("conv_alpha", None)
+    unit = kwargs.get("unit", None)
+    if conv_dim is not None:
+        conv_dim = int(conv_dim)
+        assert conv_dim == network_dim, "conv_dim must be same as network_dim"
+        if conv_alpha is None:
+            conv_alpha = 1.0
+        else:
+            conv_alpha = float(conv_alpha)
+    if unit is not None:
+        unit = int(unit)
+    else:
+        unit = 1
+
+    network = DyLoRANetwork(
+        text_encoder,
+        unet,
+        multiplier=multiplier,
+        lora_dim=network_dim,
+        alpha=network_alpha,
+        apply_to_conv=conv_dim is not None,
+        unit=unit,
+        varbose=True,
+    )
+    return network
+
+
+# Create network from weights for inference, weights are not loaded here (because can be merged)
+def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, for_inference=False, **kwargs):
+    if weights_sd is None:
+        if os.path.splitext(file)[1] == ".safetensors":
+            from safetensors.torch import load_file, safe_open
+
+            weights_sd = load_file(file)
+        else:
+            weights_sd = torch.load(file, map_location="cpu")
+
+    # get dim/alpha mapping
+    modules_dim = {}
+    modules_alpha = {}
+    for key, value in weights_sd.items():
+        if "." not in key:
+            continue
+
+        lora_name = key.split(".")[0]
+        if "alpha" in key:
+            modules_alpha[lora_name] = value
+        elif "lora_down" in key:
+            dim = value.size()[0]
+            modules_dim[lora_name] = dim
+            # print(lora_name, value.size(), dim)
+
+    # support old LoRA without alpha
+    for key in modules_dim.keys():
+        if key not in modules_alpha:
+            modules_alpha = modules_dim[key]
+
+    module_class = DyLoRAModule
+
+    network = DyLoRANetwork(
+        text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha, module_class=module_class
+    )
+    return network, weights_sd
+
+
+class DyLoRANetwork(torch.nn.Module):
+    UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
+    UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
+    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
+    LORA_PREFIX_UNET = "lora_unet"
+    LORA_PREFIX_TEXT_ENCODER = "lora_te"
+
+    def __init__(
+        self,
+        text_encoder,
+        unet,
+        multiplier=1.0,
+        lora_dim=4,
+        alpha=1,
+        apply_to_conv=False,
+        modules_dim=None,
+        modules_alpha=None,
+        unit=1,
+        module_class=DyLoRAModule,
+        varbose=False,
+    ) -> None:
+        super().__init__()
+        self.multiplier = multiplier
+
+        self.lora_dim = lora_dim
+        self.alpha = alpha
+        self.apply_to_conv = apply_to_conv
+
+        if modules_dim is not None:
+            print(f"create LoRA network from weights")
+        else:
+            print(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}, unit: {unit}")
+            if self.apply_to_conv:
+                print(f"apply LoRA to Conv2d with kernel size (3,3).")
+
+        # create module instances
+        def create_modules(is_unet, root_module: torch.nn.Module, target_replace_modules) -> List[DyLoRAModule]:
+            prefix = DyLoRANetwork.LORA_PREFIX_UNET if is_unet else DyLoRANetwork.LORA_PREFIX_TEXT_ENCODER
+            loras = []
+            for name, module in root_module.named_modules():
+                if module.__class__.__name__ in target_replace_modules:
+                    for child_name, child_module in module.named_modules():
+                        is_linear = child_module.__class__.__name__ == "Linear"
+                        is_conv2d = child_module.__class__.__name__ == "Conv2d"
+                        is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1)
+
+                        if is_linear or is_conv2d:
+                            lora_name = prefix + "." + name + "." + child_name
+                            lora_name = lora_name.replace(".", "_")
+
+                            dim = None
+                            alpha = None
+                            if modules_dim is not None:
+                                if lora_name in modules_dim:
+                                    dim = modules_dim[lora_name]
+                                    alpha = modules_alpha[lora_name]
+                            else:
+                                if is_linear or is_conv2d_1x1 or apply_to_conv:
+                                    dim = self.lora_dim
+                                    alpha = self.alpha
+
+                            if dim is None or dim == 0:
+                                continue
+
+                            # dropout and fan_in_fan_out is default
+                            lora = module_class(lora_name, child_module, self.multiplier, dim, alpha, unit)
+                            loras.append(lora)
+            return loras
+
+        self.text_encoder_loras = create_modules(False, text_encoder, DyLoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
+        print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
+
+        # extend U-Net target modules if conv2d 3x3 is enabled, or load from weights
+        target_modules = DyLoRANetwork.UNET_TARGET_REPLACE_MODULE
+        if modules_dim is not None or self.apply_to_conv:
+            target_modules += DyLoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
+
+        self.unet_loras = create_modules(True, unet, target_modules)
+        print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
+
+    def set_multiplier(self, multiplier):
+        self.multiplier = multiplier
+        for lora in self.text_encoder_loras + self.unet_loras:
+            lora.multiplier = self.multiplier
+
+    def load_weights(self, file):
+        if os.path.splitext(file)[1] == ".safetensors":
+            from safetensors.torch import load_file
+
+            weights_sd = load_file(file)
+        else:
+            weights_sd = torch.load(file, map_location="cpu")
+
+        info = self.load_state_dict(weights_sd, False)
+        return info
+
+    def apply_to(self, text_encoder, unet, apply_text_encoder=True, apply_unet=True):
+        if apply_text_encoder:
+            print("enable LoRA for text encoder")
+        else:
+            self.text_encoder_loras = []
+
+        if apply_unet:
+            print("enable LoRA for U-Net")
+        else:
+            self.unet_loras = []
+
+        for lora in self.text_encoder_loras + self.unet_loras:
+            lora.apply_to()
+            self.add_module(lora.lora_name, lora)
+
+    """
+    def merge_to(self, text_encoder, unet, weights_sd, dtype, device):
+        apply_text_encoder = apply_unet = False
+        for key in weights_sd.keys():
+            if key.startswith(DyLoRANetwork.LORA_PREFIX_TEXT_ENCODER):
+                apply_text_encoder = True
+            elif key.startswith(DyLoRANetwork.LORA_PREFIX_UNET):
+                apply_unet = True
+
+        if apply_text_encoder:
+            print("enable LoRA for text encoder")
+        else:
+            self.text_encoder_loras = []
+
+        if apply_unet:
+            print("enable LoRA for U-Net")
+        else:
+            self.unet_loras = []
+
+        for lora in self.text_encoder_loras + self.unet_loras:
+            sd_for_lora = {}
+            for key in weights_sd.keys():
+                if key.startswith(lora.lora_name):
+                    sd_for_lora[key[len(lora.lora_name) + 1 :]] = weights_sd[key]
+            lora.merge_to(sd_for_lora, dtype, device)
+
+        print(f"weights are merged")
+    """
+
+    def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
+        self.requires_grad_(True)
+        all_params = []
+
+        def enumerate_params(loras):
+            params = []
+            for lora in loras:
+                params.extend(lora.parameters())
+            return params
+
+        if self.text_encoder_loras:
+            param_data = {"params": enumerate_params(self.text_encoder_loras)}
+            if text_encoder_lr is not None:
+                param_data["lr"] = text_encoder_lr
+            all_params.append(param_data)
+
+        if self.unet_loras:
+            param_data = {"params": enumerate_params(self.unet_loras)}
+            if unet_lr is not None:
+                param_data["lr"] = unet_lr
+            all_params.append(param_data)
+
+        return all_params
+
+    def enable_gradient_checkpointing(self):
+        # not supported
+        pass
+
+    def prepare_grad_etc(self, text_encoder, unet):
+        self.requires_grad_(True)
+
+    def on_epoch_start(self, text_encoder, unet):
+        self.train()
+
+    def get_trainable_params(self):
+        return self.parameters()
+
+    def save_weights(self, file, dtype, metadata):
+        if metadata is not None and len(metadata) == 0:
+            metadata = None
+
+        state_dict = self.state_dict()
+
+        if dtype is not None:
+            for key in list(state_dict.keys()):
+                v = state_dict[key]
+                v = v.detach().clone().to("cpu").to(dtype)
+                state_dict[key] = v
+
+        if os.path.splitext(file)[1] == ".safetensors":
+            from safetensors.torch import save_file
+            from library import train_util
+
+            # Precalculate model hashes to save time on indexing
+            if metadata is None:
+                metadata = {}
+            model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
+            metadata["sshs_model_hash"] = model_hash
+            metadata["sshs_legacy_hash"] = legacy_hash
+
+            save_file(state_dict, file, metadata)
+        else:
+            torch.save(state_dict, file)
+
+    # mask is a tensor with values from 0 to 1
+    def set_region(self, sub_prompt_index, is_last_network, mask):
+        pass
+
+    def set_current_generation(self, batch_size, num_sub_prompts, width, height, shared):
+        pass

networks/extract_lora_from_dylora.py

@@ -0,0 +1,261 @@
+# Convert LoRA to different rank approximation (should only be used to go to lower rank)
+# This code is based off the extract_lora_from_models.py file which is based on https://github.com/cloneofsimo/lora/blob/develop/lora_diffusion/cli_svd.py
+# Thanks to cloneofsimo
+
+import argparse
+import os
+import torch
+from safetensors.torch import load_file, save_file, safe_open
+from tqdm import tqdm
+from library import train_util, model_util
+import numpy as np
+
+
+def load_state_dict(file_name):
+    if model_util.is_safetensors(file_name):
+        sd = load_file(file_name)
+        with safe_open(file_name, framework="pt") as f:
+            metadata = f.metadata()
+    else:
+        sd = torch.load(file_name, map_location="cpu")
+        metadata = None
+
+    return sd, metadata
+
+
+def save_to_file(file_name, model, metadata):
+    if model_util.is_safetensors(file_name):
+        save_file(model, file_name, metadata)
+    else:
+        torch.save(model, file_name)
+
+
+# Indexing functions
+
+
+def index_sv_cumulative(S, target):
+    original_sum = float(torch.sum(S))
+    cumulative_sums = torch.cumsum(S, dim=0) / original_sum
+    index = int(torch.searchsorted(cumulative_sums, target)) + 1
+    index = max(1, min(index, len(S) - 1))
+
+    return index
+
+
+def index_sv_fro(S, target):
+    S_squared = S.pow(2)
+    s_fro_sq = float(torch.sum(S_squared))
+    sum_S_squared = torch.cumsum(S_squared, dim=0) / s_fro_sq
+    index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
+    index = max(1, min(index, len(S) - 1))
+
+    return index
+
+
+def index_sv_ratio(S, target):
+    max_sv = S[0]
+    min_sv = max_sv / target
+    index = int(torch.sum(S > min_sv).item())
+    index = max(1, min(index, len(S) - 1))
+
+    return index
+
+
+# Modified from Kohaku-blueleaf's extract/merge functions
+def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+    out_size, in_size, kernel_size, _ = weight.size()
+    U, S, Vh = torch.linalg.svd(weight.reshape(out_size, -1).to(device))
+
+    param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
+    lora_rank = param_dict["new_rank"]
+
+    U = U[:, :lora_rank]
+    S = S[:lora_rank]
+    U = U @ torch.diag(S)
+    Vh = Vh[:lora_rank, :]
+
+    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size, kernel_size, kernel_size).cpu()
+    param_dict["lora_up"] = U.reshape(out_size, lora_rank, 1, 1).cpu()
+    del U, S, Vh, weight
+    return param_dict
+
+
+def extract_linear(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+    out_size, in_size = weight.size()
+
+    U, S, Vh = torch.linalg.svd(weight.to(device))
+
+    param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
+    lora_rank = param_dict["new_rank"]
+
+    U = U[:, :lora_rank]
+    S = S[:lora_rank]
+    U = U @ torch.diag(S)
+    Vh = Vh[:lora_rank, :]
+
+    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size).cpu()
+    param_dict["lora_up"] = U.reshape(out_size, lora_rank).cpu()
+    del U, S, Vh, weight
+    return param_dict
+
+
+def merge_conv(lora_down, lora_up, device):
+    in_rank, in_size, kernel_size, k_ = lora_down.shape
+    out_size, out_rank, _, _ = lora_up.shape
+    assert in_rank == out_rank and kernel_size == k_, f"rank {in_rank} {out_rank} or kernel {kernel_size} {k_} mismatch"
+
+    lora_down = lora_down.to(device)
+    lora_up = lora_up.to(device)
+
+    merged = lora_up.reshape(out_size, -1) @ lora_down.reshape(in_rank, -1)
+    weight = merged.reshape(out_size, in_size, kernel_size, kernel_size)
+    del lora_up, lora_down
+    return weight
+
+
+def merge_linear(lora_down, lora_up, device):
+    in_rank, in_size = lora_down.shape
+    out_size, out_rank = lora_up.shape
+    assert in_rank == out_rank, f"rank {in_rank} {out_rank} mismatch"
+
+    lora_down = lora_down.to(device)
+    lora_up = lora_up.to(device)
+
+    weight = lora_up @ lora_down
+    del lora_up, lora_down
+    return weight
+
+
+# Calculate new rank
+
+
+def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
+    param_dict = {}
+
+    if dynamic_method == "sv_ratio":
+        # Calculate new dim and alpha based off ratio
+        new_rank = index_sv_ratio(S, dynamic_param) + 1
+        new_alpha = float(scale * new_rank)
+
+    elif dynamic_method == "sv_cumulative":
+        # Calculate new dim and alpha based off cumulative sum
+        new_rank = index_sv_cumulative(S, dynamic_param) + 1
+        new_alpha = float(scale * new_rank)
+
+    elif dynamic_method == "sv_fro":
+        # Calculate new dim and alpha based off sqrt sum of squares
+        new_rank = index_sv_fro(S, dynamic_param) + 1
+        new_alpha = float(scale * new_rank)
+    else:
+        new_rank = rank
+        new_alpha = float(scale * new_rank)
+
+    if S[0] <= MIN_SV:  # Zero matrix, set dim to 1
+        new_rank = 1
+        new_alpha = float(scale * new_rank)
+    elif new_rank > rank:  # cap max rank at rank
+        new_rank = rank
+        new_alpha = float(scale * new_rank)
+
+    # Calculate resize info
+    s_sum = torch.sum(torch.abs(S))
+    s_rank = torch.sum(torch.abs(S[:new_rank]))
+
+    S_squared = S.pow(2)
+    s_fro = torch.sqrt(torch.sum(S_squared))
+    s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
+    fro_percent = float(s_red_fro / s_fro)
+
+    param_dict["new_rank"] = new_rank
+    param_dict["new_alpha"] = new_alpha
+    param_dict["sum_retained"] = (s_rank) / s_sum
+    param_dict["fro_retained"] = fro_percent
+    param_dict["max_ratio"] = S[0] / S[new_rank - 1]
+
+    return param_dict
+
+
+def split_lora_model(lora_sd, unit):
+    max_rank = 0
+
+    # Extract loaded lora dim and alpha
+    for key, value in lora_sd.items():
+        if "lora_down" in key:
+            rank = value.size()[0]
+            if rank > max_rank:
+                max_rank = rank
+    print(f"Max rank: {max_rank}")
+
+    rank = unit
+    splitted_models = []
+    while rank < max_rank:
+        print(f"Splitting rank {rank}")
+        new_sd = {}
+        for key, value in lora_sd.items():
+            if "lora_down" in key:
+                new_sd[key] = value[:rank].contiguous()
+            elif "lora_up" in key:
+                new_sd[key] = value[:, :rank].contiguous()
+            else:
+                new_sd[key] = value  # alpha and other parameters
+
+        splitted_models.append((new_sd, rank))
+        rank += unit
+
+    return max_rank, splitted_models
+
+
+def split(args):
+    print("loading Model...")
+    lora_sd, metadata = load_state_dict(args.model)
+
+    print("Splitting Model...")
+    original_rank, splitted_models = split_lora_model(lora_sd, args.unit)
+
+    comment = metadata.get("ss_training_comment", "")
+    for state_dict, new_rank in splitted_models:
+        # update metadata
+        if metadata is None:
+            new_metadata = {}
+        else:
+            new_metadata = metadata.copy()
+
+        new_metadata["ss_training_comment"] = f"split from DyLoRA from {original_rank} to {new_rank}; {comment}"
+        new_metadata["ss_network_dim"] = str(new_rank)
+
+        model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
+        metadata["sshs_model_hash"] = model_hash
+        metadata["sshs_legacy_hash"] = legacy_hash
+
+        filename, ext = os.path.splitext(args.save_to)
+        model_file_name = filename + f"-{new_rank:04d}{ext}"
+
+        print(f"saving model to: {model_file_name}")
+        save_to_file(model_file_name, state_dict, new_metadata)
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--unit", type=int, default=None, help="size of rank to split into / rankを分割するサイズ")
+    parser.add_argument(
+        "--save_to",
+        type=str,
+        default=None,
+        help="destination base file name: ckpt or safetensors file / 保存先のファイル名のbase、ckptまたはsafetensors",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        help="DyLoRA model to resize at to new rank: ckpt or safetensors file / 読み込むDyLoRAモデル、ckptまたはsafetensors",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
+    args = parser.parse_args()
+    split(args)

train_network.py

@@ -197,7 +197,7 @@ def train(args):
     network = network_module.create_network(1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, **net_kwargs)
     if network is None:
         return
-    
+
     if hasattr(network, "prepare_network"):
         network.prepare_network(args)
 
@@ -221,7 +221,9 @@ def train(args):
     try:
         trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate)
     except TypeError:
-        print("Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)")
+        print(
+            "Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)"
+        )
         trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
 
     optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)
@@ -541,6 +543,12 @@ def train(args):
     loss_list = []
     loss_total = 0.0
     del train_dataset_group
+
+    # if hasattr(network, "on_step_start"):
+    #     on_step_start = network.on_step_start
+    # else:
+    #     on_step_start = lambda *args, **kwargs: None
+
     for epoch in range(num_train_epochs):
         if is_main_process:
             print(f"epoch {epoch+1}/{num_train_epochs}")
@@ -553,6 +561,8 @@ def train(args):
         for step, batch in enumerate(train_dataloader):
             current_step.value = global_step
             with accelerator.accumulate(network):
+                # on_step_start(text_encoder, unet)
+
                 with torch.no_grad():
                     if "latents" in batch and batch["latents"] is not None:
                         latents = batch["latents"].to(accelerator.device)
@@ -565,16 +575,17 @@ def train(args):
                 with torch.set_grad_enabled(train_text_encoder):
                     # Get the text embedding for conditioning
                     if args.weighted_captions:
-                      encoder_hidden_states = get_weighted_text_embeddings(tokenizer,
+                        encoder_hidden_states = get_weighted_text_embeddings(
-                        text_encoder,
+                            tokenizer,
-                        batch["captions"],
+                            text_encoder,
-                        accelerator.device,
+                            batch["captions"],
-                        args.max_token_length // 75 if args.max_token_length else 1,
+                            accelerator.device,
-                        clip_skip=args.clip_skip,
+                            args.max_token_length // 75 if args.max_token_length else 1,
+                            clip_skip=args.clip_skip,
                         )
                     else:
-                      input_ids = batch["input_ids"].to(accelerator.device)
+                        input_ids = batch["input_ids"].to(accelerator.device)
-                      encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, weight_dtype)
+                        encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, weight_dtype)
                 # Sample noise that we'll add to the latents
                 noise = torch.randn_like(latents, device=latents.device)
                 if args.noise_offset:
@@ -759,4 +770,4 @@ if __name__ == "__main__":
     args = parser.parse_args()
     args = train_util.read_config_from_file(args, parser)
 
-    train(args)
+    train(args)