diff --git a/README.md b/README.md
index 4a53c154..e635e5ae 100644
--- a/README.md
+++ b/README.md
@@ -249,7 +249,7 @@ ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [docum
 
 ## Change History
 
-### Working in progress
+### Feb 24, 2024 / 2024/2/24: v0.8.4
 
 - The log output has been improved. PR [#905](https://github.com/kohya-ss/sd-scripts/pull/905) Thanks to shirayu!
   - The log is formatted by default. The `rich` library is required. Please see [Upgrade](#upgrade) and update the library.
@@ -260,10 +260,12 @@ ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [docum
   - `--console_log_file` option can be used to output the log to a file. The default is `None` (output to the console).
 - The sample image generation during multi-GPU training is now done with multiple GPUs. PR [#1061](https://github.com/kohya-ss/sd-scripts/pull/1061) Thanks to DKnight54!
 - The support for mps devices is improved. PR [#1054](https://github.com/kohya-ss/sd-scripts/pull/1054) Thanks to akx! If mps device exists instead of CUDA, the mps device is used automatically.
+- The `--new_conv_rank` option to specify the new rank of Conv2d is added to `networks/resize_lora.py`. PR [#1102](https://github.com/kohya-ss/sd-scripts/pull/1102) Thanks to mgz-dev!
 - An option `--highvram` to disable the optimization for environments with little VRAM is added to the training scripts. If you specify it when there is enough VRAM, the operation will be faster.
   - Currently, only the cache part of latents is optimized.
 - The IPEX support is improved. PR [#1086](https://github.com/kohya-ss/sd-scripts/pull/1086) Thanks to Disty0!
 - Fixed a bug that `svd_merge_lora.py` crashes in some cases. PR [#1087](https://github.com/kohya-ss/sd-scripts/pull/1087) Thanks to mgz-dev!
+- DyLoRA is fixed to work with SDXL. PR [#1126](https://github.com/kohya-ss/sd-scripts/pull/1126) Thanks to tamlog06!
 - The common image generation script `gen_img.py` for SD 1/2 and SDXL is added. The basic functions are the same as the scripts for SD 1/2 and SDXL, but some new features are added.
   - External scripts to generate prompts can be supported. It can be called with `--from_module` option. (The documentation will be added later)
   - The normalization method after prompt weighting can be specified with `--emb_normalize_mode` option. `original` is the original method, `abs` is the normalization with the average of the absolute values, `none` is no normalization.
@@ -278,10 +280,12 @@ ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [docum
   - `--console_log_file` でログファイルを出力できます。デフォルトは `None`（コンソールに出力） です。
 - 複数 GPU 学習時に学習中のサンプル画像生成を複数 GPU で行うようになりました。 PR [#1061](https://github.com/kohya-ss/sd-scripts/pull/1061) DKnight54 氏に感謝します。
 - mps デバイスのサポートが改善されました。 PR [#1054](https://github.com/kohya-ss/sd-scripts/pull/1054) akx 氏に感謝します。CUDA ではなく mps が存在する場合には自動的に mps デバイスを使用します。
+- `networks/resize_lora.py` に Conv2d の新しいランクを指定するオプション `--new_conv_rank` が追加されました。 PR [#1102](https://github.com/kohya-ss/sd-scripts/pull/1102) mgz-dev 氏に感謝します。
 - 学習スクリプトに VRAMが少ない環境向け最適化を無効にするオプション `--highvram` を追加しました。VRAM に余裕がある場合に指定すると動作が高速化されます。
   - 現在は latents のキャッシュ部分のみ高速化されます。
 - IPEX サポートが改善されました。 PR [#1086](https://github.com/kohya-ss/sd-scripts/pull/1086) Disty0 氏に感謝します。
 - `svd_merge_lora.py` が場合によってエラーになる不具合が修正されました。 PR [#1087](https://github.com/kohya-ss/sd-scripts/pull/1087) mgz-dev 氏に感謝します。
+- DyLoRA が SDXL で動くよう修正されました。PR [#1126](https://github.com/kohya-ss/sd-scripts/pull/1126) tamlog06 氏に感謝します。
 - SD 1/2 および SDXL 共通の生成スクリプト `gen_img.py` を追加しました。基本的な機能は SD 1/2、SDXL 向けスクリプトと同じですが、いくつかの新機能が追加されています。
   - プロンプトを動的に生成する外部スクリプトをサポートしました。 `--from_module` で呼び出せます。（ドキュメントはのちほど追加します）
   - プロンプト重みづけ後の正規化方法を `--emb_normalize_mode` で指定できます。`original` は元の方法、`abs` は絶対値の平均値で正規化、`none` は正規化を行いません。
@@ -358,45 +362,6 @@ network_multiplier = -1.0
 ```
 
 
-### Jan 17, 2024 / 2024/1/17: v0.8.1
-
-- Fixed a bug that the VRAM usage without Text Encoder training is larger than before in training scripts for LoRA etc (`train_network.py`, `sdxl_train_network.py`).
-  - Text Encoders were not moved to CPU.
-- Fixed typos. Thanks to akx! [PR #1053](https://github.com/kohya-ss/sd-scripts/pull/1053)
-
-- LoRA 等の学習スクリプト（`train_network.py`、`sdxl_train_network.py`）で、Text Encoder を学習しない場合の VRAM 使用量が以前に比べて大きくなっていた不具合を修正しました。 
-  - Text Encoder が GPU に保持されたままになっていました。
-- 誤字が修正されました。 [PR #1053](https://github.com/kohya-ss/sd-scripts/pull/1053) akx 氏に感謝します。
-
-### Jan 15, 2024 / 2024/1/15: v0.8.0
-
-- Diffusers, Accelerate, Transformers and other related libraries have been updated. Please update the libraries with [Upgrade](#upgrade).
-  - Some model files (Text Encoder without position_id) based on the latest Transformers can be loaded.
-- `torch.compile` is supported (experimental). PR [#1024](https://github.com/kohya-ss/sd-scripts/pull/1024) Thanks to p1atdev!
-  - This feature works only on Linux or WSL.
-  - Please specify `--torch_compile` option in each training script.
-  - You can select the backend with `--dynamo_backend` option. The default is `"inductor"`. `inductor` or `eager` seems to work.
-  - Please use `--sdpa` option instead of `--xformers` option.
-  - PyTorch 2.1 or later is recommended.
-  - Please see [PR](https://github.com/kohya-ss/sd-scripts/pull/1024) for details.
-- The session name for wandb can be specified with `--wandb_run_name` option. PR [#1032](https://github.com/kohya-ss/sd-scripts/pull/1032) Thanks to hopl1t!
-- IPEX library is updated. PR [#1030](https://github.com/kohya-ss/sd-scripts/pull/1030) Thanks to Disty0!
-- Fixed a bug that Diffusers format model cannot be saved.
-
-- Diffusers、Accelerate、Transformers 等の関連ライブラリを更新しました。[Upgrade](#upgrade) を参照し更新をお願いします。
-  - 最新の Transformers を前提とした一部のモデルファイル（Text Encoder が position_id を持たないもの）が読み込めるようになりました。
-- `torch.compile` がサポートされしました（実験的）。 PR [#1024](https://github.com/kohya-ss/sd-scripts/pull/1024) p1atdev 氏に感謝します。
-  - Linux または WSL でのみ動作します。
-  - 各学習スクリプトで `--torch_compile` オプションを指定してください。
-  - `--dynamo_backend` オプションで使用される backend を選択できます。デフォルトは `"inductor"` です。 `inductor` または `eager` が動作するようです。
-  - `--xformers` オプションとは互換性がありません。 代わりに `--sdpa` オプションを使用してください。
-  - PyTorch 2.1以降を推奨します。
-  - 詳細は [PR](https://github.com/kohya-ss/sd-scripts/pull/1024) をご覧ください。
-- wandb 保存時のセッション名が各学習スクリプトの `--wandb_run_name` オプションで指定できるようになりました。 PR [#1032](https://github.com/kohya-ss/sd-scripts/pull/1032) hopl1t 氏に感謝します。
-- IPEX ライブラリが更新されました。[PR #1030](https://github.com/kohya-ss/sd-scripts/pull/1030) Disty0 氏に感謝します。
-- Diffusers 形式でのモデル保存ができなくなっていた不具合を修正しました。
-
-
 Please read [Releases](https://github.com/kohya-ss/sd-scripts/releases) for recent updates.
 最近の更新情報は [Release](https://github.com/kohya-ss/sd-scripts/releases) をご覧ください。
 
diff --git a/fine_tune.py b/fine_tune.py
index 8df896b4..875a9195 100644
--- a/fine_tune.py
+++ b/fine_tune.py
@@ -211,8 +211,8 @@ def train(args):
     _, _, optimizer = train_util.get_optimizer(args, trainable_params=trainable_params)
 
     # dataloaderを準備する
-    # DataLoaderのプロセス数：0はメインプロセスになる
-    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
         batch_size=1,
diff --git a/networks/check_lora_weights.py b/networks/check_lora_weights.py
index 6ec60a89..794659c9 100644
--- a/networks/check_lora_weights.py
+++ b/networks/check_lora_weights.py
@@ -20,16 +20,16 @@ def main(file):
     for key in keys:
         if "lora_up" in key or "lora_down" in key:
             values.append((key, sd[key]))
-    logger.info(f"number of LoRA modules: {len(values)}")
+    print(f"number of LoRA modules: {len(values)}")
 
     if args.show_all_keys:
         for key in [k for k in keys if k not in values]:
             values.append((key, sd[key]))
-        logger.info(f"number of all modules: {len(values)}")
+        print(f"number of all modules: {len(values)}")
 
     for key, value in values:
         value = value.to(torch.float32)
-        logger.info(f"{key},{str(tuple(value.size())).replace(', ', '-')},{torch.mean(torch.abs(value))},{torch.min(torch.abs(value))}")
+        print(f"{key},{str(tuple(value.size())).replace(', ', '-')},{torch.mean(torch.abs(value))},{torch.min(torch.abs(value))}")
 
 
 def setup_parser() -> argparse.ArgumentParser:
diff --git a/networks/dylora.py b/networks/dylora.py
index 26269901..d71279c5 100644
--- a/networks/dylora.py
+++ b/networks/dylora.py
@@ -12,7 +12,9 @@
 import math
 import os
 import random
-from typing import List, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Type, Union
+from diffusers import AutoencoderKL
+from transformers import CLIPTextModel
 import torch
 from torch import nn
 from library.utils import setup_logging
@@ -168,7 +170,15 @@ class DyLoRAModule(torch.nn.Module):
         super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
 
 
-def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **kwargs):
+def create_network(
+    multiplier: float,
+    network_dim: Optional[int],
+    network_alpha: Optional[float],
+    vae: AutoencoderKL,
+    text_encoder: Union[CLIPTextModel, List[CLIPTextModel]],
+    unet,
+    **kwargs,
+):
     if network_dim is None:
         network_dim = 4  # default
     if network_alpha is None:
@@ -185,6 +195,7 @@ def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, un
             conv_alpha = 1.0
         else:
             conv_alpha = float(conv_alpha)
+            
     if unit is not None:
         unit = int(unit)
     else:
@@ -309,8 +320,22 @@ class DyLoRANetwork(torch.nn.Module):
                             lora = module_class(lora_name, child_module, self.multiplier, dim, alpha, unit)
                             loras.append(lora)
             return loras
+        
+        text_encoders = text_encoder if type(text_encoder) == list else [text_encoder]
+        
+        self.text_encoder_loras = []
+        for i, text_encoder in enumerate(text_encoders):
+            if len(text_encoders) > 1:
+                index = i + 1
+                print(f"create LoRA for Text Encoder {index}")
+            else:
+                index = None
+                print(f"create LoRA for Text Encoder")
+            
+            text_encoder_loras = create_modules(False, text_encoder, DyLoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
+            self.text_encoder_loras.extend(text_encoder_loras)
 
-        self.text_encoder_loras = create_modules(False, text_encoder, DyLoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
+        # self.text_encoder_loras = create_modules(False, text_encoder, DyLoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
         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
diff --git a/networks/resize_lora.py b/networks/resize_lora.py
index c5932a89..d697baa4 100644
--- a/networks/resize_lora.py
+++ b/networks/resize_lora.py
@@ -2,84 +2,91 @@
 # 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 os
 import argparse
 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
+
+from library import train_util
+from library import model_util
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
 MIN_SV = 1e-6
 
 # Model save and load functions
 
+
 def load_state_dict(file_name, dtype):
-  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
+    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
 
-  for key in list(sd.keys()):
-    if type(sd[key]) == torch.Tensor:
-      sd[key] = sd[key].to(dtype)
+    for key in list(sd.keys()):
+        if type(sd[key]) == torch.Tensor:
+            sd[key] = sd[key].to(dtype)
 
-  return sd, metadata
+    return sd, metadata
 
 
-def save_to_file(file_name, model, state_dict, dtype, metadata):
-  if dtype is not None:
-    for key in list(state_dict.keys()):
-      if type(state_dict[key]) == torch.Tensor:
-        state_dict[key] = state_dict[key].to(dtype)
+def save_to_file(file_name, state_dict, dtype, metadata):
+    if dtype is not None:
+        for key in list(state_dict.keys()):
+            if type(state_dict[key]) == torch.Tensor:
+                state_dict[key] = state_dict[key].to(dtype)
 
-  if model_util.is_safetensors(file_name):
-    save_file(model, file_name, metadata)
-  else:
-    torch.save(model, file_name)
+    if model_util.is_safetensors(file_name):
+        save_file(state_dict, file_name, metadata)
+    else:
+        torch.save(state_dict, 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_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))
+    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
+    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))
+    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
+    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"]
 
@@ -96,17 +103,17 @@ def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale
 
 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
@@ -117,7 +124,7 @@ 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)
 
@@ -131,236 +138,274 @@ 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":
+    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)
+        new_alpha = float(scale * new_rank)
 
-    elif dynamic_method=="sv_cumulative":
+    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)
+        new_alpha = float(scale * new_rank)
 
-    elif dynamic_method=="sv_fro":
+    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)
+        new_alpha = float(scale * new_rank)
     else:
         new_rank = rank
-        new_alpha = float(scale*new_rank)
+        new_alpha = float(scale * new_rank)
 
-    
-    if S[0] <= MIN_SV: # Zero matrix, set dim to 1
+    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_alpha = float(scale * new_rank)
+    elif new_rank > rank:  # cap max rank at rank
         new_rank = rank
-        new_alpha = float(scale*new_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)
+    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["sum_retained"] = (s_rank) / s_sum
     param_dict["fro_retained"] = fro_percent
-    param_dict["max_ratio"] = S[0]/S[new_rank - 1]
+    param_dict["max_ratio"] = S[0] / S[new_rank - 1]
 
     return param_dict
 
 
-def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
-  network_alpha = None
-  network_dim = None
-  verbose_str = "\n"
-  fro_list = []
+def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
+    network_alpha = None
+    network_dim = None
+    verbose_str = "\n"
+    fro_list = []
 
-  # Extract loaded lora dim and alpha
-  for key, value in lora_sd.items():
-    if network_alpha is None and 'alpha' in key:
-      network_alpha = value
-    if network_dim is None and 'lora_down' in key and len(value.size()) == 2:
-      network_dim = value.size()[0]
-    if network_alpha is not None and network_dim is not None:
-      break
-    if network_alpha is None:
-      network_alpha = network_dim
+    # Extract loaded lora dim and alpha
+    for key, value in lora_sd.items():
+        if network_alpha is None and "alpha" in key:
+            network_alpha = value
+        if network_dim is None and "lora_down" in key and len(value.size()) == 2:
+            network_dim = value.size()[0]
+        if network_alpha is not None and network_dim is not None:
+            break
+        if network_alpha is None:
+            network_alpha = network_dim
 
-  scale = network_alpha/network_dim
+    scale = network_alpha / network_dim
 
-  if dynamic_method:
-    logger.info(f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}, max rank is {new_rank}")
+    if dynamic_method:
+        logger.info(
+            f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}, max rank is {new_rank}"
+        )
 
-  lora_down_weight = None
-  lora_up_weight = None
+    lora_down_weight = None
+    lora_up_weight = None
 
-  o_lora_sd = lora_sd.copy()
-  block_down_name = None
-  block_up_name = None
+    o_lora_sd = lora_sd.copy()
+    block_down_name = None
+    block_up_name = None
 
-  with torch.no_grad():
-    for key, value in tqdm(lora_sd.items()):
-      weight_name = None
-      if 'lora_down' in key:
-        block_down_name = key.rsplit('.lora_down', 1)[0]
-        weight_name = key.rsplit(".", 1)[-1]
-        lora_down_weight = value
-      else:
-        continue
+    with torch.no_grad():
+        for key, value in tqdm(lora_sd.items()):
+            weight_name = None
+            if "lora_down" in key:
+                block_down_name = key.rsplit(".lora_down", 1)[0]
+                weight_name = key.rsplit(".", 1)[-1]
+                lora_down_weight = value
+            else:
+                continue
 
-      # find corresponding lora_up and alpha
-      block_up_name = block_down_name
-      lora_up_weight = lora_sd.get(block_up_name + '.lora_up.' + weight_name, None)
-      lora_alpha = lora_sd.get(block_down_name + '.alpha', None)
+            # find corresponding lora_up and alpha
+            block_up_name = block_down_name
+            lora_up_weight = lora_sd.get(block_up_name + ".lora_up." + weight_name, None)
+            lora_alpha = lora_sd.get(block_down_name + ".alpha", None)
 
-      weights_loaded = (lora_down_weight is not None and lora_up_weight is not None)
+            weights_loaded = lora_down_weight is not None and lora_up_weight is not None
 
-      if weights_loaded:
+            if weights_loaded:
 
-        conv2d = (len(lora_down_weight.size()) == 4)
-        if lora_alpha is None:
-          scale = 1.0
-        else:
-          scale = lora_alpha/lora_down_weight.size()[0]
+                conv2d = len(lora_down_weight.size()) == 4
+                if lora_alpha is None:
+                    scale = 1.0
+                else:
+                    scale = lora_alpha / lora_down_weight.size()[0]
 
-        if conv2d:
-          full_weight_matrix = merge_conv(lora_down_weight, lora_up_weight, device)
-          param_dict = extract_conv(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
-        else:
-          full_weight_matrix = merge_linear(lora_down_weight, lora_up_weight, device)
-          param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
+                if conv2d:
+                    full_weight_matrix = merge_conv(lora_down_weight, lora_up_weight, device)
+                    param_dict = extract_conv(full_weight_matrix, new_conv_rank, dynamic_method, dynamic_param, device, scale)
+                else:
+                    full_weight_matrix = merge_linear(lora_down_weight, lora_up_weight, device)
+                    param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
 
-        if verbose:
-          max_ratio = param_dict['max_ratio']
-          sum_retained = param_dict['sum_retained']
-          fro_retained = param_dict['fro_retained']
-          if not np.isnan(fro_retained):
-            fro_list.append(float(fro_retained))
+                if verbose:
+                    max_ratio = param_dict["max_ratio"]
+                    sum_retained = param_dict["sum_retained"]
+                    fro_retained = param_dict["fro_retained"]
+                    if not np.isnan(fro_retained):
+                        fro_list.append(float(fro_retained))
 
-          verbose_str+=f"{block_down_name:75} | "
-          verbose_str+=f"sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}"
+                    verbose_str += f"{block_down_name:75} | "
+                    verbose_str += (
+                        f"sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}"
+                    )
 
-        if verbose and dynamic_method:
-          verbose_str+=f", dynamic | dim: {param_dict['new_rank']}, alpha: {param_dict['new_alpha']}\n"
-        else:
-          verbose_str+=f"\n"
+                if verbose and dynamic_method:
+                    verbose_str += f", dynamic | dim: {param_dict['new_rank']}, alpha: {param_dict['new_alpha']}\n"
+                else:
+                    verbose_str += "\n"
 
-        new_alpha = param_dict['new_alpha']
-        o_lora_sd[block_down_name + "." + "lora_down.weight"] = param_dict["lora_down"].to(save_dtype).contiguous()
-        o_lora_sd[block_up_name + "." + "lora_up.weight"] = param_dict["lora_up"].to(save_dtype).contiguous()
-        o_lora_sd[block_up_name + "." "alpha"] = torch.tensor(param_dict['new_alpha']).to(save_dtype)
+                new_alpha = param_dict["new_alpha"]
+                o_lora_sd[block_down_name + "." + "lora_down.weight"] = param_dict["lora_down"].to(save_dtype).contiguous()
+                o_lora_sd[block_up_name + "." + "lora_up.weight"] = param_dict["lora_up"].to(save_dtype).contiguous()
+                o_lora_sd[block_up_name + "." "alpha"] = torch.tensor(param_dict["new_alpha"]).to(save_dtype)
 
-        block_down_name = None
-        block_up_name = None
-        lora_down_weight = None
-        lora_up_weight = None
-        weights_loaded = False
-        del param_dict
+                block_down_name = None
+                block_up_name = None
+                lora_down_weight = None
+                lora_up_weight = None
+                weights_loaded = False
+                del param_dict
 
-  if verbose:
-    logger.info(verbose_str)
-
-    logger.info(f"Average Frobenius norm retention: {np.mean(fro_list):.2%} | std: {np.std(fro_list):0.3f}")
-  logger.info("resizing complete")
-  return o_lora_sd, network_dim, new_alpha
+    if verbose:
+        print(verbose_str)
+        print(f"Average Frobenius norm retention: {np.mean(fro_list):.2%} | std: {np.std(fro_list):0.3f}")
+    logger.info("resizing complete")
+    return o_lora_sd, network_dim, new_alpha
 
 
 def resize(args):
-  if args.save_to is None or not (args.save_to.endswith('.ckpt') or args.save_to.endswith('.pt') or args.save_to.endswith('.pth') or args.save_to.endswith('.safetensors')):
-    raise Exception("The --save_to argument must be specified and must be a .ckpt , .pt, .pth or .safetensors file.")
+    if args.save_to is None or not (
+        args.save_to.endswith(".ckpt")
+        or args.save_to.endswith(".pt")
+        or args.save_to.endswith(".pth")
+        or args.save_to.endswith(".safetensors")
+    ):
+        raise Exception("The --save_to argument must be specified and must be a .ckpt , .pt, .pth or .safetensors file.")
 
-    
-  def str_to_dtype(p):
-    if p == 'float':
-      return torch.float
-    if p == 'fp16':
-      return torch.float16
-    if p == 'bf16':
-      return torch.bfloat16
-    return None
+    args.new_conv_rank = args.new_conv_rank if args.new_conv_rank is not None else args.new_rank
 
-  if args.dynamic_method and not args.dynamic_param:
-    raise Exception("If using dynamic_method, then dynamic_param is required")
+    def str_to_dtype(p):
+        if p == "float":
+            return torch.float
+        if p == "fp16":
+            return torch.float16
+        if p == "bf16":
+            return torch.bfloat16
+        return None
 
-  merge_dtype = str_to_dtype('float')  # matmul method above only seems to work in float32
-  save_dtype = str_to_dtype(args.save_precision)
-  if save_dtype is None:
-    save_dtype = merge_dtype
+    if args.dynamic_method and not args.dynamic_param:
+        raise Exception("If using dynamic_method, then dynamic_param is required")
 
-  logger.info("loading Model...")
-  lora_sd, metadata = load_state_dict(args.model, merge_dtype)
+    merge_dtype = str_to_dtype("float")  # matmul method above only seems to work in float32
+    save_dtype = str_to_dtype(args.save_precision)
+    if save_dtype is None:
+        save_dtype = merge_dtype
 
-  logger.info("Resizing Lora...")
-  state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.dynamic_method, args.dynamic_param, args.verbose)
+    logger.info("loading Model...")
+    lora_sd, metadata = load_state_dict(args.model, merge_dtype)
 
-  # update metadata
-  if metadata is None:
-    metadata = {}
+    logger.info("Resizing Lora...")
+    state_dict, old_dim, new_alpha = resize_lora_model(
+        lora_sd, args.new_rank, args.new_conv_rank, save_dtype, args.device, args.dynamic_method, args.dynamic_param, args.verbose
+    )
 
-  comment = metadata.get("ss_training_comment", "")
+    # update metadata
+    if metadata is None:
+        metadata = {}
 
-  if not args.dynamic_method:
-    metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {args.new_rank}; {comment}"
-    metadata["ss_network_dim"] = str(args.new_rank)
-    metadata["ss_network_alpha"] = str(new_alpha)
-  else:
-    metadata["ss_training_comment"] = f"Dynamic resize with {args.dynamic_method}: {args.dynamic_param} from {old_dim}; {comment}"
-    metadata["ss_network_dim"] = 'Dynamic'
-    metadata["ss_network_alpha"] = 'Dynamic'
+    comment = metadata.get("ss_training_comment", "")
 
-  model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
-  metadata["sshs_model_hash"] = model_hash
-  metadata["sshs_legacy_hash"] = legacy_hash
+    if not args.dynamic_method:
+        conv_desc = "" if args.new_rank == args.new_conv_rank else f" (conv: {args.new_conv_rank})"
+        metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {args.new_rank}{conv_desc}; {comment}"
+        metadata["ss_network_dim"] = str(args.new_rank)
+        metadata["ss_network_alpha"] = str(new_alpha)
+    else:
+        metadata["ss_training_comment"] = (
+            f"Dynamic resize with {args.dynamic_method}: {args.dynamic_param} from {old_dim}; {comment}"
+        )
+        metadata["ss_network_dim"] = "Dynamic"
+        metadata["ss_network_alpha"] = "Dynamic"
 
-  logger.info(f"saving model to: {args.save_to}")
-  save_to_file(args.save_to, state_dict, state_dict, save_dtype, 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
+
+    logger.info(f"saving model to: {args.save_to}")
+    save_to_file(args.save_to, state_dict, save_dtype, metadata)
 
 
 def setup_parser() -> argparse.ArgumentParser:
-  parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser()
 
-  parser.add_argument("--save_precision", type=str, default=None,
-                      choices=[None, "float", "fp16", "bf16"], help="precision in saving, float if omitted / 保存時の精度、未指定時はfloat")
-  parser.add_argument("--new_rank", type=int, default=4,
-                      help="Specify rank of output LoRA / 出力するLoRAのrank (dim)")
-  parser.add_argument("--save_to", type=str, default=None,
-                      help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors")
-  parser.add_argument("--model", type=str, default=None,
-                      help="LoRA model to resize at to new rank: ckpt or safetensors file / 読み込むLoRAモデル、ckptまたはsafetensors")
-  parser.add_argument("--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う")
-  parser.add_argument("--verbose", action="store_true", 
-                      help="Display verbose resizing information / rank変更時の詳細情報を出力する")
-  parser.add_argument("--dynamic_method", type=str, default=None, choices=[None, "sv_ratio", "sv_fro", "sv_cumulative"],
-                      help="Specify dynamic resizing method, --new_rank is used as a hard limit for max rank")
-  parser.add_argument("--dynamic_param", type=float, default=None,
-                      help="Specify target for dynamic reduction")
-       
-  return parser
+    parser.add_argument(
+        "--save_precision",
+        type=str,
+        default=None,
+        choices=[None, "float", "fp16", "bf16"],
+        help="precision in saving, float if omitted / 保存時の精度、未指定時はfloat",
+    )
+    parser.add_argument("--new_rank", type=int, default=4, help="Specify rank of output LoRA / 出力するLoRAのrank (dim)")
+    parser.add_argument(
+        "--new_conv_rank",
+        type=int,
+        default=None,
+        help="Specify rank of output LoRA for Conv2d 3x3, None for same as new_rank / 出力するConv2D 3x3 LoRAのrank (dim)、Noneでnew_rankと同じ",
+    )
+    parser.add_argument(
+        "--save_to",
+        type=str,
+        default=None,
+        help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        help="LoRA model to resize at to new rank: ckpt or safetensors file / 読み込むLoRAモデル、ckptまたはsafetensors",
+    )
+    parser.add_argument(
+        "--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う"
+    )
+    parser.add_argument(
+        "--verbose", action="store_true", help="Display verbose resizing information / rank変更時の詳細情報を出力する"
+    )
+    parser.add_argument(
+        "--dynamic_method",
+        type=str,
+        default=None,
+        choices=[None, "sv_ratio", "sv_fro", "sv_cumulative"],
+        help="Specify dynamic resizing method, --new_rank is used as a hard limit for max rank",
+    )
+    parser.add_argument("--dynamic_param", type=float, default=None, help="Specify target for dynamic reduction")
+
+    return parser
 
 
-if __name__ == '__main__':
-  parser = setup_parser()
+if __name__ == "__main__":
+    parser = setup_parser()
 
-  args = parser.parse_args()
-  resize(args)
+    args = parser.parse_args()
+    resize(args)
diff --git a/requirements.txt b/requirements.txt
index a399d8bd..279de350 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,7 +4,7 @@ diffusers[torch]==0.25.0
 ftfy==6.1.1
 # albumentations==1.3.0
 opencv-python==4.7.0.68
-einops==0.6.1
+einops==0.7.0
 pytorch-lightning==1.9.0
 # bitsandbytes==0.39.1
 tensorboard==2.10.1
diff --git a/sdxl_train.py b/sdxl_train.py
index aa161e8a..e0df263d 100644
--- a/sdxl_train.py
+++ b/sdxl_train.py
@@ -354,8 +354,8 @@ def train(args):
     _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
 
     # dataloaderを準備する
-    # DataLoaderのプロセス数：0はメインプロセスになる
-    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
         batch_size=1,
diff --git a/sdxl_train_control_net_lllite.py b/sdxl_train_control_net_lllite.py
index b11999bd..1e5f9234 100644
--- a/sdxl_train_control_net_lllite.py
+++ b/sdxl_train_control_net_lllite.py
@@ -242,8 +242,8 @@ def train(args):
     _, _, optimizer = train_util.get_optimizer(args, trainable_params)
 
     # dataloaderを準備する
-    # DataLoaderのプロセス数：0はメインプロセスになる
-    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
 
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
diff --git a/sdxl_train_control_net_lllite_old.py b/sdxl_train_control_net_lllite_old.py
index 89a1bc8e..dac56eed 100644
--- a/sdxl_train_control_net_lllite_old.py
+++ b/sdxl_train_control_net_lllite_old.py
@@ -210,8 +210,8 @@ def train(args):
     _, _, optimizer = train_util.get_optimizer(args, trainable_params)
 
     # dataloaderを準備する
-    # DataLoaderのプロセス数：0はメインプロセスになる
-    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
 
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
diff --git a/tools/cache_latents.py b/tools/cache_latents.py
index e25506e4..347db27f 100644
--- a/tools/cache_latents.py
+++ b/tools/cache_latents.py
@@ -116,8 +116,8 @@ def cache_to_disk(args: argparse.Namespace) -> None:
     # dataloaderを準備する
     train_dataset_group.set_caching_mode("latents")
 
-    # DataLoaderのプロセス数：0はメインプロセスになる
-    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
 
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
diff --git a/tools/cache_text_encoder_outputs.py b/tools/cache_text_encoder_outputs.py
index 46bffc4e..5f1d6d20 100644
--- a/tools/cache_text_encoder_outputs.py
+++ b/tools/cache_text_encoder_outputs.py
@@ -121,8 +121,8 @@ def cache_to_disk(args: argparse.Namespace) -> None:
     # dataloaderを準備する
     train_dataset_group.set_caching_mode("text")
 
-    # DataLoaderのプロセス数：0はメインプロセスになる
-    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
 
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
diff --git a/train_controlnet.py b/train_controlnet.py
index 8963a5d6..dc73a91c 100644
--- a/train_controlnet.py
+++ b/train_controlnet.py
@@ -239,8 +239,8 @@ def train(args):
     _, _, optimizer = train_util.get_optimizer(args, trainable_params)
 
     # dataloaderを準備する
-    # DataLoaderのプロセス数：0はメインプロセスになる
-    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
 
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
diff --git a/train_db.py b/train_db.py
index c89caaf2..8d36097a 100644
--- a/train_db.py
+++ b/train_db.py
@@ -180,8 +180,8 @@ def train(args):
     _, _, optimizer = train_util.get_optimizer(args, trainable_params)
 
     # dataloaderを準備する
-    # DataLoaderのプロセス数：0はメインプロセスになる
-    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
         batch_size=1,
diff --git a/train_network.py b/train_network.py
index af15560c..e0fa6945 100644
--- a/train_network.py
+++ b/train_network.py
@@ -349,8 +349,8 @@ class NetworkTrainer:
         optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)
 
         # dataloaderを準備する
-        # DataLoaderのプロセス数：0はメインプロセスになる
-        n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+        # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+        n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
 
         train_dataloader = torch.utils.data.DataLoader(
             train_dataset_group,
diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index a78a37b2..df1d8485 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -385,8 +385,8 @@ class TextualInversionTrainer:
         _, _, optimizer = train_util.get_optimizer(args, trainable_params)
 
         # dataloaderを準備する
-        # DataLoaderのプロセス数：0はメインプロセスになる
-        n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+        # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+        n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
         train_dataloader = torch.utils.data.DataLoader(
             train_dataset_group,
             batch_size=1,
diff --git a/train_textual_inversion_XTI.py b/train_textual_inversion_XTI.py
index 3f915597..695fad2a 100644
--- a/train_textual_inversion_XTI.py
+++ b/train_textual_inversion_XTI.py
@@ -305,8 +305,8 @@ def train(args):
     _, _, optimizer = train_util.get_optimizer(args, trainable_params)
 
     # dataloaderを準備する
-    # DataLoaderのプロセス数：0はメインプロセスになる
-    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
         batch_size=1,