lora-scripts/sd-scripts/networks/lora_fa.py

# LoRA network module
# reference:
# https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
# https://github.com/cloneofsimo/lora/blob/master/lora_diffusion/lora.py

# temporary implementation of LoRA-FA: https://arxiv.org/abs/2308.03303
# need to be refactored and merged to lora.py

import math
import os
from typing import Dict, List, Optional, Tuple, Type, Union
from diffusers import AutoencoderKL
from transformers import CLIPTextModel
import numpy as np
import torch
import re
from library.utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)

RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_")


class LoRAModule(torch.nn.Module):
    """
    replaces forward method of the original Linear, instead of replacing the original Linear module.
    """

    def __init__(
        self,
        lora_name,
        org_module: torch.nn.Module,
        multiplier=1.0,
        lora_dim=4,
        alpha=1,
        dropout=None,
        rank_dropout=None,
        module_dropout=None,
    ):
        """if alpha == 0 or None, alpha is rank (no scaling)."""
        super().__init__()
        self.lora_name = lora_name

        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 limit_rank:
        #   self.lora_dim = min(lora_dim, in_dim, out_dim)
        #   if self.lora_dim != lora_dim:
        #     logger.info(f"{lora_name} dim (rank) is changed to: {self.lora_dim}")
        # else:
        self.lora_dim = lora_dim

        if org_module.__class__.__name__ == "Conv2d":
            kernel_size = org_module.kernel_size
            stride = org_module.stride
            padding = org_module.padding
            self.lora_down = torch.nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False)
            self.lora_up = torch.nn.Conv2d(self.lora_dim, out_dim, (1, 1), (1, 1), bias=False)
        else:
            self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False)
            self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False)

        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

        # # same as microsoft's
        # torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))

        # according to the paper, initialize LoRA-A (down) as normal distribution
        torch.nn.init.normal_(self.lora_down.weight, std=math.sqrt(2.0 / (in_dim + self.lora_dim)))

        torch.nn.init.zeros_(self.lora_up.weight)

        self.multiplier = multiplier
        self.org_module = org_module  # remove in applying
        self.dropout = dropout
        self.rank_dropout = rank_dropout
        self.module_dropout = module_dropout

    def get_trainable_params(self):
        params = self.named_parameters()
        trainable_params = []
        for param in params:
            if param[0] == "lora_up.weight":  # up only
                trainable_params.append(param[1])
        return trainable_params

    def requires_grad_(self, requires_grad: bool = True):
        self.lora_up.requires_grad_(requires_grad)
        self.lora_down.requires_grad_(False)
        return self

    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):
        org_forwarded = self.org_forward(x)

        # module dropout
        if self.module_dropout is not None and self.training:
            if torch.rand(1) < self.module_dropout:
                return org_forwarded

        lx = self.lora_down(x)

        # normal dropout
        if self.dropout is not None and self.training:
            lx = torch.nn.functional.dropout(lx, p=self.dropout)

        # rank dropout
        if self.rank_dropout is not None and self.training:
            mask = torch.rand((lx.size(0), self.lora_dim), device=lx.device) > self.rank_dropout
            if len(lx.size()) == 3:
                mask = mask.unsqueeze(1)  # for Text Encoder
            elif len(lx.size()) == 4:
                mask = mask.unsqueeze(-1).unsqueeze(-1)  # for Conv2d
            lx = lx * mask

            # scaling for rank dropout: treat as if the rank is changed
            # maskから計算することも考えられるが、augmentation的な効果を期待してrank_dropoutを用いる
            scale = self.scale * (1.0 / (1.0 - self.rank_dropout))  # redundant for readability
        else:
            scale = self.scale

        lx = self.lora_up(lx)

        return org_forwarded + lx * self.multiplier * scale


class LoRAInfModule(LoRAModule):
    def __init__(
        self,
        lora_name,
        org_module: torch.nn.Module,
        multiplier=1.0,
        lora_dim=4,
        alpha=1,
        **kwargs,
    ):
        # no dropout for inference
        super().__init__(lora_name, org_module, multiplier, lora_dim, alpha)

        self.org_module_ref = [org_module]  # 後から参照できるように
        self.enabled = True

        # check regional or not by lora_name
        self.text_encoder = False
        if lora_name.startswith("lora_te_"):
            self.regional = False
            self.use_sub_prompt = True
            self.text_encoder = True
        elif "attn2_to_k" in lora_name or "attn2_to_v" in lora_name:
            self.regional = False
            self.use_sub_prompt = True
        elif "time_emb" in lora_name:
            self.regional = False
            self.use_sub_prompt = False
        else:
            self.regional = True
            self.use_sub_prompt = False

        self.network: LoRANetwork = None

    def set_network(self, network):
        self.network = network

    # freezeしてマージする
    def merge_to(self, sd, dtype, device):
        # get up/down weight
        up_weight = sd["lora_up.weight"].to(torch.float).to(device)
        down_weight = sd["lora_down.weight"].to(torch.float).to(device)

        # extract weight from org_module
        org_sd = self.org_module.state_dict()
        weight = org_sd["weight"].to(torch.float)

        # merge weight
        if len(weight.size()) == 2:
            # linear
            weight = weight + self.multiplier * (up_weight @ down_weight) * self.scale
        elif down_weight.size()[2:4] == (1, 1):
            # conv2d 1x1
            weight = (
                weight
                + self.multiplier
                * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
                * self.scale
            )
        else:
            # conv2d 3x3
            conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
            # logger.info(conved.size(), weight.size(), module.stride, module.padding)
            weight = weight + self.multiplier * conved * self.scale

        # set weight to org_module
        org_sd["weight"] = weight.to(dtype)
        self.org_module.load_state_dict(org_sd)

    # 復元できるマージのため、このモジュールのweightを返す
    def get_weight(self, multiplier=None):
        if multiplier is None:
            multiplier = self.multiplier

        # get up/down weight from module
        up_weight = self.lora_up.weight.to(torch.float)
        down_weight = self.lora_down.weight.to(torch.float)

        # pre-calculated weight
        if len(down_weight.size()) == 2:
            # linear
            weight = self.multiplier * (up_weight @ down_weight) * self.scale
        elif down_weight.size()[2:4] == (1, 1):
            # conv2d 1x1
            weight = (
                self.multiplier
                * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
                * self.scale
            )
        else:
            # conv2d 3x3
            conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
            weight = self.multiplier * conved * self.scale

        return weight

    def set_region(self, region):
        self.region = region
        self.region_mask = None

    def default_forward(self, x):
        # logger.info("default_forward", self.lora_name, x.size())
        return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale

    def forward(self, x):
        if not self.enabled:
            return self.org_forward(x)

        if self.network is None or self.network.sub_prompt_index is None:
            return self.default_forward(x)
        if not self.regional and not self.use_sub_prompt:
            return self.default_forward(x)

        if self.regional:
            return self.regional_forward(x)
        else:
            return self.sub_prompt_forward(x)

    def get_mask_for_x(self, x):
        # calculate size from shape of x
        if len(x.size()) == 4:
            h, w = x.size()[2:4]
            area = h * w
        else:
            area = x.size()[1]

        mask = self.network.mask_dic[area]
        if mask is None:
            raise ValueError(f"mask is None for resolution {area}")
        if len(x.size()) != 4:
            mask = torch.reshape(mask, (1, -1, 1))
        return mask

    def regional_forward(self, x):
        if "attn2_to_out" in self.lora_name:
            return self.to_out_forward(x)

        if self.network.mask_dic is None:  # sub_prompt_index >= 3
            return self.default_forward(x)

        # apply mask for LoRA result
        lx = self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
        mask = self.get_mask_for_x(lx)
        # logger.info("regional", self.lora_name, self.network.sub_prompt_index, lx.size(), mask.size())
        lx = lx * mask

        x = self.org_forward(x)
        x = x + lx

        if "attn2_to_q" in self.lora_name and self.network.is_last_network:
            x = self.postp_to_q(x)

        return x

    def postp_to_q(self, x):
        # repeat x to num_sub_prompts
        has_real_uncond = x.size()[0] // self.network.batch_size == 3
        qc = self.network.batch_size  # uncond
        qc += self.network.batch_size * self.network.num_sub_prompts  # cond
        if has_real_uncond:
            qc += self.network.batch_size  # real_uncond

        query = torch.zeros((qc, x.size()[1], x.size()[2]), device=x.device, dtype=x.dtype)
        query[: self.network.batch_size] = x[: self.network.batch_size]

        for i in range(self.network.batch_size):
            qi = self.network.batch_size + i * self.network.num_sub_prompts
            query[qi : qi + self.network.num_sub_prompts] = x[self.network.batch_size + i]

        if has_real_uncond:
            query[-self.network.batch_size :] = x[-self.network.batch_size :]

        # logger.info("postp_to_q", self.lora_name, x.size(), query.size(), self.network.num_sub_prompts)
        return query

    def sub_prompt_forward(self, x):
        if x.size()[0] == self.network.batch_size:  # if uncond in text_encoder, do not apply LoRA
            return self.org_forward(x)

        emb_idx = self.network.sub_prompt_index
        if not self.text_encoder:
            emb_idx += self.network.batch_size

        # apply sub prompt of X
        lx = x[emb_idx :: self.network.num_sub_prompts]
        lx = self.lora_up(self.lora_down(lx)) * self.multiplier * self.scale

        # logger.info("sub_prompt_forward", self.lora_name, x.size(), lx.size(), emb_idx)

        x = self.org_forward(x)
        x[emb_idx :: self.network.num_sub_prompts] += lx

        return x

    def to_out_forward(self, x):
        # logger.info("to_out_forward", self.lora_name, x.size(), self.network.is_last_network)

        if self.network.is_last_network:
            masks = [None] * self.network.num_sub_prompts
            self.network.shared[self.lora_name] = (None, masks)
        else:
            lx, masks = self.network.shared[self.lora_name]

        # call own LoRA
        x1 = x[self.network.batch_size + self.network.sub_prompt_index :: self.network.num_sub_prompts]
        lx1 = self.lora_up(self.lora_down(x1)) * self.multiplier * self.scale

        if self.network.is_last_network:
            lx = torch.zeros(
                (self.network.num_sub_prompts * self.network.batch_size, *lx1.size()[1:]), device=lx1.device, dtype=lx1.dtype
            )
            self.network.shared[self.lora_name] = (lx, masks)

        # logger.info("to_out_forward", lx.size(), lx1.size(), self.network.sub_prompt_index, self.network.num_sub_prompts)
        lx[self.network.sub_prompt_index :: self.network.num_sub_prompts] += lx1
        masks[self.network.sub_prompt_index] = self.get_mask_for_x(lx1)

        # if not last network, return x and masks
        x = self.org_forward(x)
        if not self.network.is_last_network:
            return x

        lx, masks = self.network.shared.pop(self.lora_name)

        # if last network, combine separated x with mask weighted sum
        has_real_uncond = x.size()[0] // self.network.batch_size == self.network.num_sub_prompts + 2

        out = torch.zeros((self.network.batch_size * (3 if has_real_uncond else 2), *x.size()[1:]), device=x.device, dtype=x.dtype)
        out[: self.network.batch_size] = x[: self.network.batch_size]  # uncond
        if has_real_uncond:
            out[-self.network.batch_size :] = x[-self.network.batch_size :]  # real_uncond

        # logger.info("to_out_forward", self.lora_name, self.network.sub_prompt_index, self.network.num_sub_prompts)
        # for i in range(len(masks)):
        #     if masks[i] is None:
        #         masks[i] = torch.zeros_like(masks[-1])

        mask = torch.cat(masks)
        mask_sum = torch.sum(mask, dim=0) + 1e-4
        for i in range(self.network.batch_size):
            # 1枚の画像ごとに処理する
            lx1 = lx[i * self.network.num_sub_prompts : (i + 1) * self.network.num_sub_prompts]
            lx1 = lx1 * mask
            lx1 = torch.sum(lx1, dim=0)

            xi = self.network.batch_size + i * self.network.num_sub_prompts
            x1 = x[xi : xi + self.network.num_sub_prompts]
            x1 = x1 * mask
            x1 = torch.sum(x1, dim=0)
            x1 = x1 / mask_sum

            x1 = x1 + lx1
            out[self.network.batch_size + i] = x1

        # logger.info("to_out_forward", x.size(), out.size(), has_real_uncond)
        return out


def parse_block_lr_kwargs(nw_kwargs):
    down_lr_weight = nw_kwargs.get("down_lr_weight", None)
    mid_lr_weight = nw_kwargs.get("mid_lr_weight", None)
    up_lr_weight = nw_kwargs.get("up_lr_weight", None)

    # 以上のいずれにも設定がない場合は無効としてNoneを返す
    if down_lr_weight is None and mid_lr_weight is None and up_lr_weight is None:
        return None, None, None

    # extract learning rate weight for each block
    if down_lr_weight is not None:
        # if some parameters are not set, use zero
        if "," in down_lr_weight:
            down_lr_weight = [(float(s) if s else 0.0) for s in down_lr_weight.split(",")]

    if mid_lr_weight is not None:
        mid_lr_weight = float(mid_lr_weight)

    if up_lr_weight is not None:
        if "," in up_lr_weight:
            up_lr_weight = [(float(s) if s else 0.0) for s in up_lr_weight.split(",")]

    down_lr_weight, mid_lr_weight, up_lr_weight = get_block_lr_weight(
        down_lr_weight, mid_lr_weight, up_lr_weight, float(nw_kwargs.get("block_lr_zero_threshold", 0.0))
    )

    return down_lr_weight, mid_lr_weight, up_lr_weight


def create_network(
    multiplier: float,
    network_dim: Optional[int],
    network_alpha: Optional[float],
    vae: AutoencoderKL,
    text_encoder: Union[CLIPTextModel, List[CLIPTextModel]],
    unet,
    neuron_dropout: Optional[float] = None,
    **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)
    if conv_dim is not None:
        conv_dim = int(conv_dim)
        if conv_alpha is None:
            conv_alpha = 1.0
        else:
            conv_alpha = float(conv_alpha)

    # block dim/alpha/lr
    block_dims = kwargs.get("block_dims", None)
    down_lr_weight, mid_lr_weight, up_lr_weight = parse_block_lr_kwargs(kwargs)

    # 以上のいずれかに指定があればblockごとのdim(rank)を有効にする
    if block_dims is not None or down_lr_weight is not None or mid_lr_weight is not None or up_lr_weight is not None:
        block_alphas = kwargs.get("block_alphas", None)
        conv_block_dims = kwargs.get("conv_block_dims", None)
        conv_block_alphas = kwargs.get("conv_block_alphas", None)

        block_dims, block_alphas, conv_block_dims, conv_block_alphas = get_block_dims_and_alphas(
            block_dims, block_alphas, network_dim, network_alpha, conv_block_dims, conv_block_alphas, conv_dim, conv_alpha
        )

        # remove block dim/alpha without learning rate
        block_dims, block_alphas, conv_block_dims, conv_block_alphas = remove_block_dims_and_alphas(
            block_dims, block_alphas, conv_block_dims, conv_block_alphas, down_lr_weight, mid_lr_weight, up_lr_weight
        )

    else:
        block_alphas = None
        conv_block_dims = None
        conv_block_alphas = None

    # rank/module dropout
    rank_dropout = kwargs.get("rank_dropout", None)
    if rank_dropout is not None:
        rank_dropout = float(rank_dropout)
    module_dropout = kwargs.get("module_dropout", None)
    if module_dropout is not None:
        module_dropout = float(module_dropout)

    # すごく引数が多いな ( ^ω^)･･･
    network = LoRANetwork(
        text_encoder,
        unet,
        multiplier=multiplier,
        lora_dim=network_dim,
        alpha=network_alpha,
        dropout=neuron_dropout,
        rank_dropout=rank_dropout,
        module_dropout=module_dropout,
        conv_lora_dim=conv_dim,
        conv_alpha=conv_alpha,
        block_dims=block_dims,
        block_alphas=block_alphas,
        conv_block_dims=conv_block_dims,
        conv_block_alphas=conv_block_alphas,
        varbose=True,
    )

    if up_lr_weight is not None or mid_lr_weight is not None or down_lr_weight is not None:
        network.set_block_lr_weight(up_lr_weight, mid_lr_weight, down_lr_weight)

    return network


# このメソッドは外部から呼び出される可能性を考慮しておく
# network_dim, network_alpha にはデフォルト値が入っている。
# block_dims, block_alphas は両方ともNoneまたは両方とも値が入っている
# conv_dim, conv_alpha は両方ともNoneまたは両方とも値が入っている
def get_block_dims_and_alphas(
    block_dims, block_alphas, network_dim, network_alpha, conv_block_dims, conv_block_alphas, conv_dim, conv_alpha
):
    num_total_blocks = LoRANetwork.NUM_OF_BLOCKS * 2 + 1

    def parse_ints(s):
        return [int(i) for i in s.split(",")]

    def parse_floats(s):
        return [float(i) for i in s.split(",")]

    # block_dimsとblock_alphasをパースする。必ず値が入る
    if block_dims is not None:
        block_dims = parse_ints(block_dims)
        assert (
            len(block_dims) == num_total_blocks
        ), f"block_dims must have {num_total_blocks} elements / block_dimsは{num_total_blocks}個指定してください"
    else:
        logger.warning(f"block_dims is not specified. all dims are set to {network_dim} / block_dimsが指定されていません。すべてのdimは{network_dim}になります")
        block_dims = [network_dim] * num_total_blocks

    if block_alphas is not None:
        block_alphas = parse_floats(block_alphas)
        assert (
            len(block_alphas) == num_total_blocks
        ), f"block_alphas must have {num_total_blocks} elements / block_alphasは{num_total_blocks}個指定してください"
    else:
        logger.warning(
            f"block_alphas is not specified. all alphas are set to {network_alpha} / block_alphasが指定されていません。すべてのalphaは{network_alpha}になります"
        )
        block_alphas = [network_alpha] * num_total_blocks

    # conv_block_dimsとconv_block_alphasを、指定がある場合のみパースする。指定がなければconv_dimとconv_alphaを使う
    if conv_block_dims is not None:
        conv_block_dims = parse_ints(conv_block_dims)
        assert (
            len(conv_block_dims) == num_total_blocks
        ), f"conv_block_dims must have {num_total_blocks} elements / conv_block_dimsは{num_total_blocks}個指定してください"

        if conv_block_alphas is not None:
            conv_block_alphas = parse_floats(conv_block_alphas)
            assert (
                len(conv_block_alphas) == num_total_blocks
            ), f"conv_block_alphas must have {num_total_blocks} elements / conv_block_alphasは{num_total_blocks}個指定してください"
        else:
            if conv_alpha is None:
                conv_alpha = 1.0
            logger.warning(
                f"conv_block_alphas is not specified. all alphas are set to {conv_alpha} / conv_block_alphasが指定されていません。すべてのalphaは{conv_alpha}になります"
            )
            conv_block_alphas = [conv_alpha] * num_total_blocks
    else:
        if conv_dim is not None:
            logger.warning(
                f"conv_dim/alpha for all blocks are set to {conv_dim} and {conv_alpha} / すべてのブロックのconv_dimとalphaは{conv_dim}および{conv_alpha}になります"
            )
            conv_block_dims = [conv_dim] * num_total_blocks
            conv_block_alphas = [conv_alpha] * num_total_blocks
        else:
            conv_block_dims = None
            conv_block_alphas = None

    return block_dims, block_alphas, conv_block_dims, conv_block_alphas


# 層別学習率用に層ごとの学習率に対する倍率を定義する、外部から呼び出される可能性を考慮しておく
def get_block_lr_weight(
    down_lr_weight, mid_lr_weight, up_lr_weight, zero_threshold
) -> Tuple[List[float], List[float], List[float]]:
    # パラメータ未指定時は何もせず、今までと同じ動作とする
    if up_lr_weight is None and mid_lr_weight is None and down_lr_weight is None:
        return None, None, None

    max_len = LoRANetwork.NUM_OF_BLOCKS  # フルモデル相当でのup,downの層の数

    def get_list(name_with_suffix) -> List[float]:
        import math

        tokens = name_with_suffix.split("+")
        name = tokens[0]
        base_lr = float(tokens[1]) if len(tokens) > 1 else 0.0

        if name == "cosine":
            return [math.sin(math.pi * (i / (max_len - 1)) / 2) + base_lr for i in reversed(range(max_len))]
        elif name == "sine":
            return [math.sin(math.pi * (i / (max_len - 1)) / 2) + base_lr for i in range(max_len)]
        elif name == "linear":
            return [i / (max_len - 1) + base_lr for i in range(max_len)]
        elif name == "reverse_linear":
            return [i / (max_len - 1) + base_lr for i in reversed(range(max_len))]
        elif name == "zeros":
            return [0.0 + base_lr] * max_len
        else:
            logger.error(
                "Unknown lr_weight argument %s is used. Valid arguments:  / 不明なlr_weightの引数 %s が使われました。有効な引数:\n\tcosine, sine, linear, reverse_linear, zeros"
                % (name)
            )
            return None

    if type(down_lr_weight) == str:
        down_lr_weight = get_list(down_lr_weight)
    if type(up_lr_weight) == str:
        up_lr_weight = get_list(up_lr_weight)

    if (up_lr_weight != None and len(up_lr_weight) > max_len) or (down_lr_weight != None and len(down_lr_weight) > max_len):
        logger.warning("down_weight or up_weight is too long. Parameters after %d-th are ignored." % max_len)
        logger.warning("down_weightもしくはup_weightが長すぎます。%d個目以降のパラメータは無視されます。" % max_len)
        up_lr_weight = up_lr_weight[:max_len]
        down_lr_weight = down_lr_weight[:max_len]

    if (up_lr_weight != None and len(up_lr_weight) < max_len) or (down_lr_weight != None and len(down_lr_weight) < max_len):
        logger.warning("down_weight or up_weight is too short. Parameters after %d-th are filled with 1." % max_len)
        logger.warning("down_weightもしくはup_weightが短すぎます。%d個目までの不足したパラメータは1で補われます。" % max_len)

        if down_lr_weight != None and len(down_lr_weight) < max_len:
            down_lr_weight = down_lr_weight + [1.0] * (max_len - len(down_lr_weight))
        if up_lr_weight != None and len(up_lr_weight) < max_len:
            up_lr_weight = up_lr_weight + [1.0] * (max_len - len(up_lr_weight))

    if (up_lr_weight != None) or (mid_lr_weight != None) or (down_lr_weight != None):
        logger.info("apply block learning rate / 階層別学習率を適用します。")
        if down_lr_weight != None:
            down_lr_weight = [w if w > zero_threshold else 0 for w in down_lr_weight]
            logger.info(f"down_lr_weight (shallower -> deeper, 浅い層->深い層): {down_lr_weight}")
        else:
            logger.info("down_lr_weight: all 1.0, すべて1.0")

        if mid_lr_weight != None:
            mid_lr_weight = mid_lr_weight if mid_lr_weight > zero_threshold else 0
            logger.info(f"mid_lr_weight: {mid_lr_weight}")
        else:
            logger.info("mid_lr_weight: 1.0")

        if up_lr_weight != None:
            up_lr_weight = [w if w > zero_threshold else 0 for w in up_lr_weight]
            logger.info(f"up_lr_weight (deeper -> shallower, 深い層->浅い層): {up_lr_weight}")
        else:
            logger.info("up_lr_weight: all 1.0, すべて1.0")

    return down_lr_weight, mid_lr_weight, up_lr_weight


# lr_weightが0のblockをblock_dimsから除外する、外部から呼び出す可能性を考慮しておく
def remove_block_dims_and_alphas(
    block_dims, block_alphas, conv_block_dims, conv_block_alphas, down_lr_weight, mid_lr_weight, up_lr_weight
):
    # set 0 to block dim without learning rate to remove the block
    if down_lr_weight != None:
        for i, lr in enumerate(down_lr_weight):
            if lr == 0:
                block_dims[i] = 0
                if conv_block_dims is not None:
                    conv_block_dims[i] = 0
    if mid_lr_weight != None:
        if mid_lr_weight == 0:
            block_dims[LoRANetwork.NUM_OF_BLOCKS] = 0
            if conv_block_dims is not None:
                conv_block_dims[LoRANetwork.NUM_OF_BLOCKS] = 0
    if up_lr_weight != None:
        for i, lr in enumerate(up_lr_weight):
            if lr == 0:
                block_dims[LoRANetwork.NUM_OF_BLOCKS + 1 + i] = 0
                if conv_block_dims is not None:
                    conv_block_dims[LoRANetwork.NUM_OF_BLOCKS + 1 + i] = 0

    return block_dims, block_alphas, conv_block_dims, conv_block_alphas


# 外部から呼び出す可能性を考慮しておく
def get_block_index(lora_name: str) -> int:
    block_idx = -1  # invalid lora name

    m = RE_UPDOWN.search(lora_name)
    if m:
        g = m.groups()
        i = int(g[1])
        j = int(g[3])
        if g[2] == "resnets":
            idx = 3 * i + j
        elif g[2] == "attentions":
            idx = 3 * i + j
        elif g[2] == "upsamplers" or g[2] == "downsamplers":
            idx = 3 * i + 2

        if g[0] == "down":
            block_idx = 1 + idx  # 0に該当するLoRAは存在しない
        elif g[0] == "up":
            block_idx = LoRANetwork.NUM_OF_BLOCKS + 1 + idx

    elif "mid_block_" in lora_name:
        block_idx = LoRANetwork.NUM_OF_BLOCKS  # idx=12

    return block_idx


# 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
            # logger.info(lora_name, value.size(), dim)

    # support old LoRA without alpha
    for key in modules_dim.keys():
        if key not in modules_alpha:
            modules_alpha[key] = modules_dim[key]

    module_class = LoRAInfModule if for_inference else LoRAModule

    network = LoRANetwork(
        text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha, module_class=module_class
    )

    # block lr
    down_lr_weight, mid_lr_weight, up_lr_weight = parse_block_lr_kwargs(kwargs)
    if up_lr_weight is not None or mid_lr_weight is not None or down_lr_weight is not None:
        network.set_block_lr_weight(up_lr_weight, mid_lr_weight, down_lr_weight)

    return network, weights_sd


class LoRANetwork(torch.nn.Module):
    NUM_OF_BLOCKS = 12  # フルモデル相当でのup,downの層の数

    UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
    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"

    # SDXL: must starts with LORA_PREFIX_TEXT_ENCODER
    LORA_PREFIX_TEXT_ENCODER1 = "lora_te1"
    LORA_PREFIX_TEXT_ENCODER2 = "lora_te2"

    def __init__(
        self,
        text_encoder: Union[List[CLIPTextModel], CLIPTextModel],
        unet,
        multiplier: float = 1.0,
        lora_dim: int = 4,
        alpha: float = 1,
        dropout: Optional[float] = None,
        rank_dropout: Optional[float] = None,
        module_dropout: Optional[float] = None,
        conv_lora_dim: Optional[int] = None,
        conv_alpha: Optional[float] = None,
        block_dims: Optional[List[int]] = None,
        block_alphas: Optional[List[float]] = None,
        conv_block_dims: Optional[List[int]] = None,
        conv_block_alphas: Optional[List[float]] = None,
        modules_dim: Optional[Dict[str, int]] = None,
        modules_alpha: Optional[Dict[str, int]] = None,
        module_class: Type[object] = LoRAModule,
        varbose: Optional[bool] = False,
    ) -> None:
        """
        LoRA network: すごく引数が多いが、パターンは以下の通り
        1. lora_dimとalphaを指定
        2. lora_dim、alpha、conv_lora_dim、conv_alphaを指定
        3. block_dimsとblock_alphasを指定 :  Conv2d3x3には適用しない
        4. block_dims、block_alphas、conv_block_dims、conv_block_alphasを指定 : Conv2d3x3にも適用する
        5. modules_dimとmodules_alphaを指定 (推論用)
        """
        super().__init__()
        self.multiplier = multiplier

        self.lora_dim = lora_dim
        self.alpha = alpha
        self.conv_lora_dim = conv_lora_dim
        self.conv_alpha = conv_alpha
        self.dropout = dropout
        self.rank_dropout = rank_dropout
        self.module_dropout = module_dropout

        if modules_dim is not None:
            logger.info(f"create LoRA network from weights")
        elif block_dims is not None:
            logger.info(f"create LoRA network from block_dims")
            logger.info(f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}")
            logger.info(f"block_dims: {block_dims}")
            logger.info(f"block_alphas: {block_alphas}")
            if conv_block_dims is not None:
                logger.info(f"conv_block_dims: {conv_block_dims}")
                logger.info(f"conv_block_alphas: {conv_block_alphas}")
        else:
            logger.info(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
            logger.info(f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}")
            if self.conv_lora_dim is not None:
                logger.info(f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}")

        # create module instances
        def create_modules(
            is_unet: bool,
            text_encoder_idx: Optional[int],  # None, 1, 2
            root_module: torch.nn.Module,
            target_replace_modules: List[torch.nn.Module],
        ) -> List[LoRAModule]:
            prefix = (
                self.LORA_PREFIX_UNET
                if is_unet
                else (
                    self.LORA_PREFIX_TEXT_ENCODER
                    if text_encoder_idx is None
                    else (self.LORA_PREFIX_TEXT_ENCODER1 if text_encoder_idx == 1 else self.LORA_PREFIX_TEXT_ENCODER2)
                )
            )
            loras = []
            skipped = []
            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:
                                # モジュール指定あり
                                if lora_name in modules_dim:
                                    dim = modules_dim[lora_name]
                                    alpha = modules_alpha[lora_name]
                            elif is_unet and block_dims is not None:
                                # U-Netでblock_dims指定あり
                                block_idx = get_block_index(lora_name)
                                if is_linear or is_conv2d_1x1:
                                    dim = block_dims[block_idx]
                                    alpha = block_alphas[block_idx]
                                elif conv_block_dims is not None:
                                    dim = conv_block_dims[block_idx]
                                    alpha = conv_block_alphas[block_idx]
                            else:
                                # 通常、すべて対象とする
                                if is_linear or is_conv2d_1x1:
                                    dim = self.lora_dim
                                    alpha = self.alpha
                                elif self.conv_lora_dim is not None:
                                    dim = self.conv_lora_dim
                                    alpha = self.conv_alpha

                            if dim is None or dim == 0:
                                # skipした情報を出力
                                if is_linear or is_conv2d_1x1 or (self.conv_lora_dim is not None or conv_block_dims is not None):
                                    skipped.append(lora_name)
                                continue

                            lora = module_class(
                                lora_name,
                                child_module,
                                self.multiplier,
                                dim,
                                alpha,
                                dropout=dropout,
                                rank_dropout=rank_dropout,
                                module_dropout=module_dropout,
                            )
                            loras.append(lora)
            return loras, skipped

        text_encoders = text_encoder if type(text_encoder) == list else [text_encoder]

        # create LoRA for text encoder
        # 毎回すべてのモジュールを作るのは無駄なので要検討
        self.text_encoder_loras = []
        skipped_te = []
        for i, text_encoder in enumerate(text_encoders):
            if len(text_encoders) > 1:
                index = i + 1
                logger.info(f"create LoRA for Text Encoder {index}:")
            else:
                index = None
                logger.info(f"create LoRA for Text Encoder:")

            text_encoder_loras, skipped = create_modules(False, index, text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
            self.text_encoder_loras.extend(text_encoder_loras)
            skipped_te += skipped
        logger.info(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 = LoRANetwork.UNET_TARGET_REPLACE_MODULE
        if modules_dim is not None or self.conv_lora_dim is not None or conv_block_dims is not None:
            target_modules += LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3

        self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules)
        logger.info(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")

        skipped = skipped_te + skipped_un
        if varbose and len(skipped) > 0:
            logger.warning(
                f"because block_lr_weight is 0 or dim (rank) is 0, {len(skipped)} LoRA modules are skipped / block_lr_weightまたはdim (rank)が0の為、次の{len(skipped)}個のLoRAモジュールはスキップされます:"
            )
            for name in skipped:
                logger.info(f"\t{name}")

        self.up_lr_weight: List[float] = None
        self.down_lr_weight: List[float] = None
        self.mid_lr_weight: float = None
        self.block_lr = False

        # assertion
        names = set()
        for lora in self.text_encoder_loras + self.unet_loras:
            assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
            names.add(lora.lora_name)

    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:
            logger.info("enable LoRA for text encoder")
        else:
            self.text_encoder_loras = []

        if apply_unet:
            logger.info("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)

    # マージできるかどうかを返す
    def is_mergeable(self):
        return True

    # TODO refactor to common function with apply_to
    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(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
                apply_text_encoder = True
            elif key.startswith(LoRANetwork.LORA_PREFIX_UNET):
                apply_unet = True

        if apply_text_encoder:
            logger.info("enable LoRA for text encoder")
        else:
            self.text_encoder_loras = []

        if apply_unet:
            logger.info("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)

        logger.info(f"weights are merged")

    # 層別学習率用に層ごとの学習率に対する倍率を定義する　引数の順番が逆だがとりあえず気にしない
    def set_block_lr_weight(
        self,
        up_lr_weight: List[float] = None,
        mid_lr_weight: float = None,
        down_lr_weight: List[float] = None,
    ):
        self.block_lr = True
        self.down_lr_weight = down_lr_weight
        self.mid_lr_weight = mid_lr_weight
        self.up_lr_weight = up_lr_weight

    def get_lr_weight(self, lora: LoRAModule) -> float:
        lr_weight = 1.0
        block_idx = get_block_index(lora.lora_name)
        if block_idx < 0:
            return lr_weight

        if block_idx < LoRANetwork.NUM_OF_BLOCKS:
            if self.down_lr_weight != None:
                lr_weight = self.down_lr_weight[block_idx]
        elif block_idx == LoRANetwork.NUM_OF_BLOCKS:
            if self.mid_lr_weight != None:
                lr_weight = self.mid_lr_weight
        elif block_idx > LoRANetwork.NUM_OF_BLOCKS:
            if self.up_lr_weight != None:
                lr_weight = self.up_lr_weight[block_idx - LoRANetwork.NUM_OF_BLOCKS - 1]

        return lr_weight

    # 二つのText Encoderに別々の学習率を設定できるようにするといいかも
    def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
        self.requires_grad_(True)
        all_params = []

        def enumerate_params(loras: List[LoRAModule]):
            params = []
            for lora in loras:
                # params.extend(lora.parameters())
                params.extend(lora.get_trainable_params())
            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:
            if self.block_lr:
                # 学習率のグラフをblockごとにしたいので、blockごとにloraを分類
                block_idx_to_lora = {}
                for lora in self.unet_loras:
                    idx = get_block_index(lora.lora_name)
                    if idx not in block_idx_to_lora:
                        block_idx_to_lora[idx] = []
                    block_idx_to_lora[idx].append(lora)

                # blockごとにパラメータを設定する
                for idx, block_loras in block_idx_to_lora.items():
                    param_data = {"params": enumerate_params(block_loras)}

                    if unet_lr is not None:
                        param_data["lr"] = unet_lr * self.get_lr_weight(block_loras[0])
                    elif default_lr is not None:
                        param_data["lr"] = default_lr * self.get_lr_weight(block_loras[0])
                    if ("lr" in param_data) and (param_data["lr"] == 0):
                        continue
                    all_params.append(param_data)

            else:
                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):
        if mask.max() == 0:
            mask = torch.ones_like(mask)

        self.mask = mask
        self.sub_prompt_index = sub_prompt_index
        self.is_last_network = is_last_network

        for lora in self.text_encoder_loras + self.unet_loras:
            lora.set_network(self)

    def set_current_generation(self, batch_size, num_sub_prompts, width, height, shared):
        self.batch_size = batch_size
        self.num_sub_prompts = num_sub_prompts
        self.current_size = (height, width)
        self.shared = shared

        # create masks
        mask = self.mask
        mask_dic = {}
        mask = mask.unsqueeze(0).unsqueeze(1)  # b(1),c(1),h,w
        ref_weight = self.text_encoder_loras[0].lora_down.weight if self.text_encoder_loras else self.unet_loras[0].lora_down.weight
        dtype = ref_weight.dtype
        device = ref_weight.device

        def resize_add(mh, mw):
            # logger.info(mh, mw, mh * mw)
            m = torch.nn.functional.interpolate(mask, (mh, mw), mode="bilinear")  # doesn't work in bf16
            m = m.to(device, dtype=dtype)
            mask_dic[mh * mw] = m

        h = height // 8
        w = width // 8
        for _ in range(4):
            resize_add(h, w)
            if h % 2 == 1 or w % 2 == 1:  # add extra shape if h/w is not divisible by 2
                resize_add(h + h % 2, w + w % 2)
            h = (h + 1) // 2
            w = (w + 1) // 2

        self.mask_dic = mask_dic

    def backup_weights(self):
        # 重みのバックアップを行う
        loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
        for lora in loras:
            org_module = lora.org_module_ref[0]
            if not hasattr(org_module, "_lora_org_weight"):
                sd = org_module.state_dict()
                org_module._lora_org_weight = sd["weight"].detach().clone()
                org_module._lora_restored = True

    def restore_weights(self):
        # 重みのリストアを行う
        loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
        for lora in loras:
            org_module = lora.org_module_ref[0]
            if not org_module._lora_restored:
                sd = org_module.state_dict()
                sd["weight"] = org_module._lora_org_weight
                org_module.load_state_dict(sd)
                org_module._lora_restored = True

    def pre_calculation(self):
        # 事前計算を行う
        loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
        for lora in loras:
            org_module = lora.org_module_ref[0]
            sd = org_module.state_dict()

            org_weight = sd["weight"]
            lora_weight = lora.get_weight().to(org_weight.device, dtype=org_weight.dtype)
            sd["weight"] = org_weight + lora_weight
            assert sd["weight"].shape == org_weight.shape
            org_module.load_state_dict(sd)

            org_module._lora_restored = False
            lora.enabled = False

    def apply_max_norm_regularization(self, max_norm_value, device):
        downkeys = []
        upkeys = []
        alphakeys = []
        norms = []
        keys_scaled = 0

        state_dict = self.state_dict()
        for key in state_dict.keys():
            if "lora_down" in key and "weight" in key:
                downkeys.append(key)
                upkeys.append(key.replace("lora_down", "lora_up"))
                alphakeys.append(key.replace("lora_down.weight", "alpha"))

        for i in range(len(downkeys)):
            down = state_dict[downkeys[i]].to(device)
            up = state_dict[upkeys[i]].to(device)
            alpha = state_dict[alphakeys[i]].to(device)
            dim = down.shape[0]
            scale = alpha / dim

            if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
                updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
            elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
                updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
            else:
                updown = up @ down

            updown *= scale

            norm = updown.norm().clamp(min=max_norm_value / 2)
            desired = torch.clamp(norm, max=max_norm_value)
            ratio = desired.cpu() / norm.cpu()
            sqrt_ratio = ratio**0.5
            if ratio != 1:
                keys_scaled += 1
                state_dict[upkeys[i]] *= sqrt_ratio
                state_dict[downkeys[i]] *= sqrt_ratio
            scalednorm = updown.norm() * ratio
            norms.append(scalednorm.item())

        return keys_scaled, sum(norms) / len(norms), max(norms)