Update README.md

support ControlNet

.github/workflows/typos.yml

@@ -0,0 +1,21 @@
+---
+# yamllint disable rule:line-length
+name: Typos
+
+on:  # yamllint disable-line rule:truthy
+  push:
+  pull_request:
+    types:
+      - opened
+      - synchronize
+      - reopened
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: typos-action
+        uses: crate-ci/typos@v1.13.10

.gitignore

@@ -3,4 +3,5 @@ __pycache__
 wd14_tagger_model
 venv
 *.egg-info
-build
+build
+.vscode

README-ja.md

@@ -1,7 +1,7 @@
 ## リポジトリについて
 Stable Diffusionの学習、画像生成、その他のスクリプトを入れたリポジトリです。
 
-[README in English](./README.md)
+[README in English](./README.md) ←更新情報はこちらにあります
 
 GUIやPowerShellスクリプトなど、より使いやすくする機能が[bmaltais氏のリポジトリ](https://github.com/bmaltais/kohya_ss)で提供されています（英語です）のであわせてご覧ください。bmaltais氏に感謝します。
 
@@ -16,9 +16,11 @@ GUIやPowerShellスクリプトなど、より使いやすくする機能が[bma
 
 当リポジトリ内およびnote.comに記事がありますのでそちらをご覧ください（将来的にはすべてこちらへ移すかもしれません）。
 
-* note.com [環境整備とDreamBooth学習スクリプトについて](https://note.com/kohya_ss/n/nba4eceaa4594)
+* [DreamBoothの学習について](./train_db_README-ja.md)
 * [fine-tuningのガイド](./fine_tune_README_ja.md):
 BLIPによるキャプショニングと、DeepDanbooruまたはWD14 taggerによるタグ付けを含みます
+* [LoRAの学習について](./train_network_README-ja.md)
+* [Textual Inversionの学習について](./train_ti_README-ja.md)
 * note.com [画像生成スクリプト](https://note.com/kohya_ss/n/n2693183a798e)
 * note.com [モデル変換スクリプト](https://note.com/kohya_ss/n/n374f316fe4ad)
 
@@ -44,12 +46,11 @@ PowerShellを使う場合、venvを使えるようにするためには以下の
 
 通常の（管理者ではない）PowerShellを開き以下を順に実行します。
 
-
 ```powershell
 git clone https://github.com/kohya-ss/sd-scripts.git
 cd sd-scripts
 
-python -m venv --system-site-packages venv
+python -m venv venv
 .\venv\Scripts\activate
 
 pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
@@ -63,6 +64,12 @@ cp .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_set
 accelerate config
 ```
 
+<!-- 
+pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
+pip install --use-pep517 --upgrade -r requirements.txt
+pip install -U -I --no-deps xformers==0.0.16
+-->
+
 コマンドプロンプトでは以下になります。
 
 
@@ -70,7 +77,7 @@ accelerate config
 git clone https://github.com/kohya-ss/sd-scripts.git
 cd sd-scripts
 
-python -m venv --system-site-packages venv
+python -m venv venv
 .\venv\Scripts\activate
 
 pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
@@ -84,6 +91,8 @@ copy /y .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cud
 accelerate config
 ```
 
+（注:``python -m venv venv`` のほうが ``python -m venv --system-site-packages venv`` より安全そうなため書き換えました。globalなpythonにパッケージがインストールしてあると、後者だといろいろと問題が起きます。）
+
 accelerate configの質問には以下のように答えてください。（bf16で学習する場合、最後の質問にはbf16と答えてください。）
 
 ※0.15.0から日本語環境では選択のためにカーソルキーを押すと落ちます（……）。数字キーの0、1、2……で選択できますので、そちらを使ってください。
@@ -99,7 +108,11 @@ accelerate configの質問には以下のように答えてください。（bf1
 ```
 
 ※場合によって ``ValueError: fp16 mixed precision requires a GPU`` というエラーが出ることがあるようです。この場合、6番目の質問（
-``What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]:``）に「0」と答えてください。（id `0`のGPUが使われます。）
+``What GPU(s) (by id) should be used for training on this machine as a comma-separated list? [all]:``）に「0」と答えてください。（id `0`のGPUが使われます。）
+
+### PyTorchとxformersのバージョンについて
+
+他のバージョンでは学習がうまくいかない場合があるようです。特に他の理由がなければ指定のバージョンをお使いください。
 
 ## アップグレード
 
@@ -109,7 +122,7 @@ accelerate configの質問には以下のように答えてください。（bf1
 cd sd-scripts
 git pull
 .\venv\Scripts\activate
-pip install --upgrade -r <requirement file name>
+pip install --use-pep517 --upgrade -r requirements.txt
 ```
 
 コマンドが成功すれば新しいバージョンが使用できます。

README.md

@@ -1,33 +1,22 @@
 This repository contains training, generation and utility scripts for Stable Diffusion.
 
-## Updates
-
-- January 14, 2023, 2023/1/14
-  - Fix loading some VAE or .safetensors as VAE is failed for ``--vae`` option. Thanks to Fannovel16!
-  - Add negative prompt scaling for ``gen_img_diffusers.py`` You can set another conditioning scale to the negative prompt with ``--negative_scale`` option, and ``--nl`` option for the prompt. Thanks to laksjdjf!
-  - ``--vae`` オプションに一部のVAEや .safetensors 形式のモデルを指定するとエラーになる不具合を修正しました。Fannovel16氏に感謝します。
-  - ``gen_img_diffusers.py`` に、ネガティブプロンプトに異なる guidance scale を設定できる ``--negative_scale`` オプションを追加しました。プロンプトからは ``--nl`` で指定できます。laksjdjf氏に感謝します。
-- January 12, 2023, 2023/1/12
-  - Metadata is saved on the model (.safetensors only) (model name, VAE name, training steps, learning rate etc.) The metadata will be able to inspect by sd-webui-additional-networks extension in near future. If you do not want to save it, specify ``no_metadata`` option.
-  - メタデータが保存されるようになりました（ .safetensors 形式の場合のみ）（モデル名、VAE 名、ステップ数、学習率など）。近日中に拡張から確認できるようになる予定です。メタデータを保存したくない場合は ``no_metadata`` オプションをしてしてください。
-  
-**January 9, 2023: Important information about the update can be found at [the end of the page](#updates-jan-9-2023).**
-
-**20231/1/9: 更新情報が[ページ末尾](#更新情報-202319)にありますのでご覧ください。**
+[__Change History__](#change-history) is moved to the bottom of the page.
+更新履歴は[ページ末尾](#change-history)に移しました。
 
 [日本語版README](./README-ja.md)
 
-##
-
 For easier use (GUI and PowerShell scripts etc...), please visit [the repository maintained by bmaltais](https://github.com/bmaltais/kohya_ss). Thanks to @bmaltais!
 
 This repository contains the scripts for:
 
 * DreamBooth training, including U-Net and Text Encoder
-* fine-tuning (native training), including U-Net and Text Encoder
+* Fine-tuning (native training), including U-Net and Text Encoder
 * LoRA training
-* image generation
-* model conversion (supports 1.x and 2.x, Stable Diffision ckpt/safetensors and Diffusers)
+* Texutl Inversion training
+* Image generation
+* Model conversion (supports 1.x and 2.x, Stable Diffision ckpt/safetensors and Diffusers)
+
+__Stable Diffusion web UI now seems to support LoRA trained by ``sd-scripts``.__ (SD 1.x based only) Thank you for great work!!! 
 
 ## About requirements.txt
 
@@ -43,6 +32,7 @@ All documents are in Japanese currently, and CUI based.
 * [Step by Step fine-tuning guide](./fine_tune_README_ja.md):
 Including BLIP captioning and tagging by DeepDanbooru or WD14 tagger
 * [training LoRA](./train_network_README-ja.md)
+* [training Textual Inversion](./train_ti_README-ja.md)
 * note.com [Image generation](https://note.com/kohya_ss/n/n2693183a798e)
 * note.com [Model conversion](https://note.com/kohya_ss/n/n374f316fe4ad)
 
@@ -67,7 +57,7 @@ Open a regular Powershell terminal and type the following inside:
 git clone https://github.com/kohya-ss/sd-scripts.git
 cd sd-scripts
 
-python -m venv --system-site-packages venv
+python -m venv venv
 .\venv\Scripts\activate
 
 pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
@@ -79,9 +69,10 @@ cp .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\ce
 cp .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py
 
 accelerate config
-
 ```
 
+update: ``python -m venv venv`` is seemed to be safer than ``python -m venv --system-site-packages venv`` (some user have packages in global python).
+
 Answers to accelerate config:
 
 ```txt
@@ -94,11 +85,16 @@ Answers to accelerate config:
 - fp16
 ```
 
-note: Some user reports ``ValueError: fp16 mixed precision requires a GPU`` is occured in training. In this case, answer `0` for the 6th question: 
-``What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]:`` 
+note: Some user reports ``ValueError: fp16 mixed precision requires a GPU`` is occurred in training. In this case, answer `0` for the 6th question: 
+``What GPU(s) (by id) should be used for training on this machine as a comma-separated list? [all]:`` 
 
 (Single GPU with id `0` will be used.)
 
+### about PyTorch and xformers
+
+Other versions of PyTorch and xformers seem to have problems with training.
+If there is no other reason, please install the specified version.
+
 ## Upgrade
 
 When a new release comes out you can upgrade your repo with the following command:
@@ -107,7 +103,7 @@ When a new release comes out you can upgrade your repo with the following comman
 cd sd-scripts
 git pull
 .\venv\Scripts\activate
-pip install --upgrade -r requirements.txt
+pip install --use-pep517 --upgrade -r requirements.txt
 ```
 
 Once the commands have completed successfully you should be ready to use the new version.
@@ -126,78 +122,47 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
 
 [BLIP](https://github.com/salesforce/BLIP): BSD-3-Clause
 
+## Change History
 
-# Updates: Jan 9. 2023 
+- 23 Feb. 2023, 2023/2/23:
+  - Fix instability training issue in ``train_network.py``.
+    - ``fp16`` training is probably not affected by this issue.
+    - Training with ``float`` for SD2.x models will work now. Also training with ``bf16`` might be improved. 
+    - This issue seems to have occurred in [PR#190](https://github.com/kohya-ss/sd-scripts/pull/190).
+  - Add some metadata to LoRA model. Thanks to space-nuko!
+  - Raise an error if optimizer options conflict (e.g. ``--optimizer_type`` and ``--use_8bit_adam``.) 
+  - Support ControlNet in ``gen_img_diffusers.py`` (no documentation yet.)
+  - ``train_network.py`` で学習が不安定になる不具合を修正しました。
+    - ``fp16`` 精度での学習には恐らくこの問題は影響しません。
+    - ``float`` 精度での SD2.x モデルの学習が正しく動作するようになりました。また ``bf16`` 精度の学習も改善する可能性があります。
+    - この問題は [PR#190](https://github.com/kohya-ss/sd-scripts/pull/190) から起きていたようです。
+  - いくつかのメタデータを LoRA モデルに追加しました。 space-nuko 氏に感謝します。
+  - オプティマイザ関係のオプションが矛盾していた場合、エラーとするように修正しました（例: ``--optimizer_type`` と ``--use_8bit_adam``）。
+  - ``gen_img_diffusers.py`` で ControlNet をサポートしました（ドキュメントはのちほど追加します）。
 
-All training scripts are updated. 
+- 22 Feb. 2023, 2023/2/22:
+  - Refactor optmizer options. Thanks to mgz-dev!
+    - Add ``--optimizer_type`` option for each training script. Please see help. Japanese documentation is [here](https://github.com/kohya-ss/sd-scripts/blob/main/train_network_README-ja.md#%E3%82%AA%E3%83%97%E3%83%86%E3%82%A3%E3%83%9E%E3%82%A4%E3%82%B6%E3%81%AE%E6%8C%87%E5%AE%9A%E3%81%AB%E3%81%A4%E3%81%84%E3%81%A6).
+    - ``--use_8bit_adam`` and ``--use_lion_optimizer`` options also work, but override above option.
+  - Add SGDNesterov and its 8bit.
+  - Add [D-Adaptation](https://github.com/facebookresearch/dadaptation) optimizer. Thanks to BootsofLagrangian and all! 
+    - Please install D-Adaptation optimizer with ``pip install dadaptation`` (it is not in requirements.txt currently.)
+    - Please see https://github.com/kohya-ss/sd-scripts/issues/181 for details.
+  - Add AdaFactor optimizer. Thanks to Toshiaki!
+  - Extra lr scheduler settings (num_cycles etc.) are working in training scripts other than ``train_network.py``.
+  - Add ``--max_grad_norm`` option for each training script for gradient clipping. ``0.0`` disables clipping. 
+  - Symbolic link can be loaded in each training script. Thanks to TkskKurumi!
+  - オプティマイザ関連のオプションを見直しました。mgz-dev氏に感謝します。
+    - ``--optimizer_type`` を各学習スクリプトに追加しました。ドキュメントは[こちら](https://github.com/kohya-ss/sd-scripts/blob/main/train_network_README-ja.md#%E3%82%AA%E3%83%97%E3%83%86%E3%82%A3%E3%83%9E%E3%82%A4%E3%82%B6%E3%81%AE%E6%8C%87%E5%AE%9A%E3%81%AB%E3%81%A4%E3%81%84%E3%81%A6)。
+    - ``--use_8bit_adam`` と ``--use_lion_optimizer`` のオプションは依然として動作しますがoptimizer_typeを上書きしますのでご注意ください。
+  - SGDNesterov オプティマイザおよびその8bit版を追加しました。
+  - [D-Adaptation](https://github.com/facebookresearch/dadaptation) オプティマイザを追加しました。BootsofLagrangian 氏および諸氏に感謝します。
+    - ``pip install dadaptation`` コマンドで別途インストールが必要です（現時点ではrequirements.txtに含まれておりません）。
+    - こちらのissueもあわせてご覧ください。 https://github.com/kohya-ss/sd-scripts/issues/181 
+  - AdaFactor オプティマイザを追加しました。Toshiaki氏に感謝します。 
+  - 追加のスケジューラ設定（num_cycles等）が ``train_network.py`` 以外の学習スクリプトでも使えるようになりました。
+  - 勾配クリップ時の最大normを指定する ``--max_grad_norm`` オプションを追加しました。``0.0``を指定するとクリップしなくなります。
+  - 各学習スクリプトでシンボリックリンクが読み込めるようになりました。TkskKurumi氏に感謝します。
 
-## Breaking Changes
-
-- The ``fine_tuning`` option in ``train_db.py`` is removed. Please use DreamBooth with captions or ``fine_tune.py``.
-- The Hypernet feature in ``fine_tune.py`` is removed, will be implemented in ``train_network.py`` in future.
-
-## Features, Improvements and Bug Fixes
-
-### for all script: train_db.py, fine_tune.py and train_network.py
-
-- Added ``output_name`` option. The name of output file can be specified.
-    - With ``--output_name style1``, the output file is like ``style1_000001.ckpt`` (or ``.safetensors``) for each epoch and ``style1.ckpt`` for last.
-    - If ommitted (default), same to previous. ``epoch-000001.ckpt`` and ``last.ckpt``.
-- Added ``save_last_n_epochs`` option. Keep only latest n files for the checkpoints and the states. Older files are removed. (Thanks to shirayu!)
-    - If the options are ``--save_every_n_epochs=2 --save_last_n_epochs=3``, in the end of epoch 8, ``epoch-000008.ckpt`` is created and ``epoch-000002.ckpt`` is removed.
-
-### train_db.py
-
-- Added ``max_token_length`` option. Captions can have more than 75 tokens.
-
-### fine_tune.py
-
-- The script now works without .npz files. If .npz is not found, the scripts get the latents with VAE.
-    - You can omit ``prepare_buckets_latents.py`` in preprocessing. However, it is recommended if you train more than 1 or 2 epochs.
-    - ``--resolution`` option is required to specify the training resolution.
-- Added ``cache_latents`` and ``color_aug`` options.
-
-### train_network.py
-
-- Now ``--gradient_checkpointing`` is effective for U-Net and Text Encoder.
-    - The memory usage is reduced. The larger batch size is avilable, but the training speed will be slow.
-    - The training might be possible with 6GB VRAM for dimension=4 with batch size=1.
-
-Documents are not updated now, I will update one by one.
-
-# 更新情報 (2023/1/9)
-
-学習スクリプトを更新しました。
-
-## 削除された機能
-- ``train_db.py`` の ``fine_tuning`` は削除されました。キャプション付きの DreamBooth または ``fine_tune.py`` を使ってください。
-- ``fine_tune.py`` の Hypernet学習の機能は削除されました。将来的に``train_network.py``に追加される予定です。
-
-## その他の機能追加、バグ修正など
-
-### 学習スクリプトに共通: train_db.py, fine_tune.py and train_network.py
-
-- ``output_name``オプションを追加しました。保存されるモデルファイルの名前を指定できます。
-    - ``--output_name style1``と指定すると、エポックごとに保存されるファイル名は``style1_000001.ckpt`` (または ``.safetensors``) に、最後に保存されるファイル名は``style1.ckpt``になります。
-    - 省略時は今までと同じです（``epoch-000001.ckpt``および``last.ckpt``）。
-- ``save_last_n_epochs``オプションを追加しました。最新の n ファイル、stateだけ保存し、古いものは削除します。（shirayu氏に感謝します。)
-    - たとえば``--save_every_n_epochs=2 --save_last_n_epochs=3``と指定した時、8エポック目の終了時には、``epoch-000008.ckpt``が保存され``epoch-000002.ckpt``が削除されます。
-
-### train_db.py
-
-- ``max_token_length``オプションを追加しました。75文字を超えるキャプションが使えるようになります。
-
-### fine_tune.py
-
-- .npzファイルがなくても動作するようになりました。.npzファイルがない場合、VAEからlatentsを取得して動作します。
-    -  ``prepare_buckets_latents.py``を前処理で実行しなくても良くなります。ただし事前取得をしておいたほうが、2エポック以上学習する場合にはトータルで高速です。
-    - この場合、解像度を指定するために``--resolution``オプションが必要です。
-- ``cache_latents``と``color_aug``オプションを追加しました。
-
-### train_network.py
-
-- ``--gradient_checkpointing``がU-NetとText Encoderにも有効になりました。
-    - メモリ消費が減ります。バッチサイズを大きくできますが、トータルでの学習時間は長くなるかもしれません。
-    - dimension=4のLoRAはバッチサイズ1で6GB VRAMで学習できるかもしれません。
-
-ドキュメントは未更新ですが少しずつ更新の予定です。
+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。

_typos.toml

@@ -0,0 +1,15 @@
+# Files for typos
+# Instruction:  https://github.com/marketplace/actions/typos-action#getting-started
+
+[default.extend-identifiers]
+
+[default.extend-words]
+NIN="NIN"
+parms="parms"
+nin="nin"
+extention="extention" # Intentionally left
+nd="nd"
+
+
+[files]
+extend-exclude = ["_typos.toml"]

fine_tune.py

@@ -33,8 +33,13 @@ def train(args):
   train_dataset = train_util.FineTuningDataset(args.in_json, args.train_batch_size, args.train_data_dir,
                                                tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens,
                                                args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
+                                               args.bucket_reso_steps, args.bucket_no_upscale,
                                                args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop,
                                                args.dataset_repeats, args.debug_dataset)
+
+  # 学習データのdropout率を設定する
+  train_dataset.set_caption_dropout(args.caption_dropout_rate, args.caption_dropout_every_n_epochs, args.caption_tag_dropout_rate)
+
   train_dataset.make_buckets()
 
   if args.debug_dataset:
@@ -144,30 +149,23 @@ def train(args):
 
   # 学習に必要なクラスを準備する
   print("prepare optimizer, data loader etc.")
-
-  # 8-bit Adamを使う
-  if args.use_8bit_adam:
-    try:
-      import bitsandbytes as bnb
-    except ImportError:
-      raise ImportError("No bitsand bytes / bitsandbytesがインストールされていないようです")
-    print("use 8-bit Adam optimizer")
-    optimizer_class = bnb.optim.AdamW8bit
-  else:
-    optimizer_class = torch.optim.AdamW
-
-  # betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略
-  optimizer = optimizer_class(params_to_optimize, lr=args.learning_rate)
+  _, _, 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(8, os.cpu_count() - 1)      # cpu_count-1 ただし最大8
+  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
   train_dataloader = torch.utils.data.DataLoader(
-      train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers)
+      train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
+
+  # 学習ステップ数を計算する
+  if args.max_train_epochs is not None:
+    args.max_train_steps = args.max_train_epochs * len(train_dataloader)
+    print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
   # lr schedulerを用意する
-  lr_scheduler = diffusers.optimization.get_scheduler(
-      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
+  lr_scheduler = train_util.get_scheduler_fix(args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
+                                              num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+                                              num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
 
   # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
   if args.full_fp16:
@@ -195,6 +193,8 @@ def train(args):
   # epoch数を計算する
   num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
   num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+  if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
+    args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
 
   # 学習する
   total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
@@ -218,6 +218,8 @@ def train(args):
 
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
+    train_dataset.set_current_epoch(epoch + 1)
+
     for m in training_models:
       m.train()
 
@@ -241,6 +243,9 @@ def train(args):
 
         # Sample noise that we'll add to the latents
         noise = torch.randn_like(latents, device=latents.device)
+        if args.noise_offset:
+          # https://www.crosslabs.org//blog/diffusion-with-offset-noise
+          noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
 
         # Sample a random timestep for each image
         timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
@@ -262,11 +267,11 @@ def train(args):
         loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean")
 
         accelerator.backward(loss)
-        if accelerator.sync_gradients:
+        if accelerator.sync_gradients and args.max_grad_norm != 0.0:
           params_to_clip = []
           for m in training_models:
             params_to_clip.extend(m.parameters())
-          accelerator.clip_grad_norm_(params_to_clip, 1.0)  # args.max_grad_norm)
+          accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
         optimizer.step()
         lr_scheduler.step()
@@ -279,9 +284,12 @@ def train(args):
 
       current_loss = loss.detach().item()        # 平均なのでbatch sizeは関係ないはず
       if args.logging_dir is not None:
-        logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
+        logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
+        if args.optimizer_type.lower() == "DAdaptation".lower():  # tracking d*lr value
+          logs["lr/d*lr"] = lr_scheduler.optimizers[0].param_groups[0]['d']*lr_scheduler.optimizers[0].param_groups[0]['lr']
         accelerator.log(logs, step=global_step)
 
+      # TODO moving averageにする
       loss_total += current_loss
       avr_loss = loss_total / (step+1)
       logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
@@ -291,7 +299,7 @@ def train(args):
         break
 
     if args.logging_dir is not None:
-      logs = {"epoch_loss": loss_total / len(train_dataloader)}
+      logs = {"loss/epoch": loss_total / len(train_dataloader)}
       accelerator.log(logs, step=epoch+1)
 
     accelerator.wait_for_everyone()
@@ -324,9 +332,10 @@ if __name__ == '__main__':
   parser = argparse.ArgumentParser()
 
   train_util.add_sd_models_arguments(parser)
-  train_util.add_dataset_arguments(parser, False, True)
+  train_util.add_dataset_arguments(parser, False, True, True)
   train_util.add_training_arguments(parser, False)
   train_util.add_sd_saving_arguments(parser)
+  train_util.add_optimizer_arguments(parser)
 
   parser.add_argument("--diffusers_xformers", action='store_true',
                       help='use xformers by diffusers / Diffusersでxformersを使用する')

fine_tune_README_ja.md

@@ -324,7 +324,7 @@ __※引数を都度書き換えて、別のメタデータファイルに書き
 ## 学習の実行
 たとえば以下のように実行します。以下は省メモリ化のための設定です。
 ```
-accelerate launch --num_cpu_threads_per_process 8 fine_tune.py 
+accelerate launch --num_cpu_threads_per_process 1 fine_tune.py 
     --pretrained_model_name_or_path=model.ckpt 
     --in_json meta_lat.json 
     --train_data_dir=train_data 
@@ -336,7 +336,7 @@ accelerate launch --num_cpu_threads_per_process 8 fine_tune.py
     --save_every_n_epochs=4
 ```
 
-accelerateのnum_cpu_threads_per_processにはCPUのコア数を指定するとよいようです。
+accelerateのnum_cpu_threads_per_processには通常は1を指定するとよいようです。
 
 pretrained_model_name_or_pathに学習対象のモデルを指定します（Stable DiffusionのcheckpointかDiffusersのモデル）。Stable Diffusionのcheckpointは.ckptと.safetensorsに対応しています（拡張子で自動判定）。
 

finetune/clean_captions_and_tags.py

@@ -5,13 +5,32 @@ import argparse
 import glob
 import os
 import json
+import re
 
 from tqdm import tqdm
 
+PATTERN_HAIR_LENGTH = re.compile(r', (long|short|medium) hair, ')
+PATTERN_HAIR_CUT = re.compile(r', (bob|hime) cut, ')
+PATTERN_HAIR = re.compile(r', ([\w\-]+) hair, ')
+PATTERN_WORD = re.compile(r', ([\w\-]+|hair ornament), ')
+
+# 複数人がいるとき、複数の髪色や目の色が定義されていれば削除する
+PATTERNS_REMOVE_IN_MULTI = [
+    PATTERN_HAIR_LENGTH,
+    PATTERN_HAIR_CUT,
+    re.compile(r', [\w\-]+ eyes, '),
+    re.compile(r', ([\w\-]+ sleeves|sleeveless), '),
+    # 複数の髪型定義がある場合は削除する
+    re.compile(
+        r', (ponytail|braid|ahoge|twintails|[\w\-]+ bun|single hair bun|single side bun|two side up|two tails|[\w\-]+ braid|sidelocks), '),
+]
+
 
 def clean_tags(image_key, tags):
   # replace '_' to ' '
+  tags = tags.replace('^_^', '^@@@^')
   tags = tags.replace('_', ' ')
+  tags = tags.replace('^@@@^', '^_^')
 
   # remove rating: deepdanbooruのみ
   tokens = tags.split(", rating")
@@ -26,6 +45,37 @@ def clean_tags(image_key, tags):
       print(f"{image_key} {tags}")
     tags = tokens[0]
 
+  tags = ", " + tags.replace(", ", ", , ") + ", "     # カンマ付きで検索をするための身も蓋もない対策
+  
+  # 複数の人物がいる場合は髪色等のタグを削除する
+  if 'girls' in tags or 'boys' in tags:
+    for pat in PATTERNS_REMOVE_IN_MULTI:
+      found = pat.findall(tags)
+      if len(found) > 1:                        # 二つ以上、タグがある
+        tags = pat.sub("", tags)
+
+    # 髪の特殊対応
+    srch_hair_len = PATTERN_HAIR_LENGTH.search(tags)   # 髪の長さタグは例外なので避けておく（全員が同じ髪の長さの場合）
+    if srch_hair_len:
+      org = srch_hair_len.group()
+      tags = PATTERN_HAIR_LENGTH.sub(", @@@, ", tags)
+
+    found = PATTERN_HAIR.findall(tags)
+    if len(found) > 1:
+      tags = PATTERN_HAIR.sub("", tags)
+
+    if srch_hair_len:
+      tags = tags.replace(", @@@, ", org)                   # 戻す
+
+  # white shirtとshirtみたいな重複タグの削除
+  found = PATTERN_WORD.findall(tags)
+  for word in found:
+    if re.search(f", ((\w+) )+{word}, ", tags):
+      tags = tags.replace(f", {word}, ", "")
+
+  tags = tags.replace(", , ", ", ")
+  assert tags.startswith(", ") and tags.endswith(", ")
+  tags = tags[2:-2]
   return tags
 
 
@@ -88,13 +138,23 @@ def main(args):
     if tags is None:
       print(f"image does not have tags / メタデータにタグがありません: {image_key}")
     else:
-      metadata[image_key]['tags'] = clean_tags(image_key, tags)
+      org = tags
+      tags = clean_tags(image_key, tags)
+      metadata[image_key]['tags'] = tags
+      if args.debug and org != tags:
+        print("FROM: " + org)
+        print("TO:   " + tags)
 
     caption = metadata[image_key].get('caption')
     if caption is None:
       print(f"image does not have caption / メタデータにキャプションがありません: {image_key}")
     else:
-      metadata[image_key]['caption'] = clean_caption(caption)
+      org = caption
+      caption = clean_caption(caption)
+      metadata[image_key]['caption'] = caption
+      if args.debug and org != caption:
+        print("FROM: " + org)
+        print("TO:   " + caption)
 
   # metadataを書き出して終わり
   print(f"writing metadata: {args.out_json}")
@@ -108,6 +168,7 @@ if __name__ == '__main__':
   # parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ")
   parser.add_argument("in_json", type=str, help="metadata file to input / 読み込むメタデータファイル")
   parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先")
+  parser.add_argument("--debug", action="store_true", help="debug mode")
 
   args, unknown = parser.parse_known_args()
   if len(unknown) == 1:

finetune/make_captions.py

@@ -11,18 +11,59 @@ import torch
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 from blip.blip import blip_decoder
-# from Salesforce_BLIP.models.blip import blip_decoder
+import library.train_util as train_util
 
 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 
+IMAGE_SIZE = 384
+
+# 正方形でいいのか？　という気がするがソースがそうなので
+IMAGE_TRANSFORM = transforms.Compose([
+    transforms.Resize((IMAGE_SIZE, IMAGE_SIZE), interpolation=InterpolationMode.BICUBIC),
+    transforms.ToTensor(),
+    transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+])
+
+# 共通化したいが微妙に処理が異なる……
+class ImageLoadingTransformDataset(torch.utils.data.Dataset):
+  def __init__(self, image_paths):
+    self.images = image_paths
+
+  def __len__(self):
+    return len(self.images)
+
+  def __getitem__(self, idx):
+    img_path = self.images[idx]
+
+    try:
+      image = Image.open(img_path).convert("RGB")
+      # convert to tensor temporarily so dataloader will accept it
+      tensor = IMAGE_TRANSFORM(image)
+    except Exception as e:
+      print(f"Could not load image path / 画像を読み込めません: {img_path}, error: {e}")
+      return None
+
+    return (tensor, img_path)
+
+
+def collate_fn_remove_corrupted(batch):
+  """Collate function that allows to remove corrupted examples in the
+  dataloader. It expects that the dataloader returns 'None' when that occurs.
+  The 'None's in the batch are removed.
+  """
+  # Filter out all the Nones (corrupted examples)
+  batch = list(filter(lambda x: x is not None, batch))
+  return batch
+
+
 def main(args):
   # fix the seed for reproducibility
-  seed = args.seed # + utils.get_rank()
+  seed = args.seed  # + utils.get_rank()
   torch.manual_seed(seed)
   np.random.seed(seed)
   random.seed(seed)
-    
+
   if not os.path.exists("blip"):
     args.train_data_dir = os.path.abspath(args.train_data_dir)        # convert to absolute path
 
@@ -31,24 +72,15 @@ def main(args):
     os.chdir('finetune')
 
   print(f"load images from {args.train_data_dir}")
-  image_paths = glob.glob(os.path.join(args.train_data_dir, "*.jpg")) + \
-      glob.glob(os.path.join(args.train_data_dir, "*.png")) + glob.glob(os.path.join(args.train_data_dir, "*.webp"))
+  image_paths = train_util.glob_images(args.train_data_dir)
   print(f"found {len(image_paths)} images.")
 
   print(f"loading BLIP caption: {args.caption_weights}")
-  image_size = 384
-  model = blip_decoder(pretrained=args.caption_weights, image_size=image_size, vit='large', med_config="./blip/med_config.json")
+  model = blip_decoder(pretrained=args.caption_weights, image_size=IMAGE_SIZE, vit='large', med_config="./blip/med_config.json")
   model.eval()
   model = model.to(DEVICE)
   print("BLIP loaded")
 
-  # 正方形でいいのか？　という気がするがソースがそうなので
-  transform = transforms.Compose([
-      transforms.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC),
-      transforms.ToTensor(),
-      transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-  ])
-
   # captioningする
   def run_batch(path_imgs):
     imgs = torch.stack([im for _, im in path_imgs]).to(DEVICE)
@@ -66,18 +98,35 @@ def main(args):
         if args.debug:
           print(image_path, caption)
 
-  b_imgs = []
-  for image_path in tqdm(image_paths, smoothing=0.0):
-    raw_image = Image.open(image_path)
-    if raw_image.mode != "RGB":
-      print(f"convert image mode {raw_image.mode} to RGB: {image_path}")
-      raw_image = raw_image.convert("RGB")
+  # 読み込みの高速化のためにDataLoaderを使うオプション
+  if args.max_data_loader_n_workers is not None:
+    dataset = ImageLoadingTransformDataset(image_paths)
+    data = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=False,
+                                      num_workers=args.max_data_loader_n_workers, collate_fn=collate_fn_remove_corrupted, drop_last=False)
+  else:
+    data = [[(None, ip)] for ip in image_paths]
 
-    image = transform(raw_image)
-    b_imgs.append((image_path, image))
-    if len(b_imgs) >= args.batch_size:
-      run_batch(b_imgs)
-      b_imgs.clear()
+  b_imgs = []
+  for data_entry in tqdm(data, smoothing=0.0):
+    for data in data_entry:
+      if data is None:
+        continue
+
+      img_tensor, image_path = data
+      if img_tensor is None:
+        try:
+          raw_image = Image.open(image_path)
+          if raw_image.mode != 'RGB':
+            raw_image = raw_image.convert("RGB")
+          img_tensor = IMAGE_TRANSFORM(raw_image)
+        except Exception as e:
+          print(f"Could not load image path / 画像を読み込めません: {image_path}, error: {e}")
+          continue
+
+      b_imgs.append((image_path, img_tensor))
+      if len(b_imgs) >= args.batch_size:
+        run_batch(b_imgs)
+        b_imgs.clear()
   if len(b_imgs) > 0:
     run_batch(b_imgs)
 
@@ -95,6 +144,8 @@ if __name__ == '__main__':
   parser.add_argument("--beam_search", action="store_true",
                       help="use beam search (default Nucleus sampling) / beam searchを使う（このオプション未指定時はNucleus sampling）")
   parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
+  parser.add_argument("--max_data_loader_n_workers", type=int, default=None,
+                      help="enable image reading by DataLoader with this number of workers (faster) / DataLoaderによる画像読み込みを有効にしてこのワーカー数を適用する（読み込みを高速化）")
   parser.add_argument("--num_beams", type=int, default=1, help="num of beams in beam search /beam search時のビーム数（多いと精度が上がるが時間がかかる）")
   parser.add_argument("--top_p", type=float, default=0.9, help="top_p in Nucleus sampling / Nucleus sampling時のtop_p")
   parser.add_argument("--max_length", type=int, default=75, help="max length of caption / captionの最大長")

finetune/make_captions_by_git.py

@@ -0,0 +1,145 @@
+import argparse
+import os
+import re
+
+from PIL import Image
+from tqdm import tqdm
+import torch
+from transformers import AutoProcessor, AutoModelForCausalLM
+from transformers.generation.utils import GenerationMixin
+
+import library.train_util as train_util
+
+
+DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+PATTERN_REPLACE = [
+    re.compile(r'(has|with|and) the (words?|letters?|name) (" ?[^"]*"|\w+)( ?(is )?(on|in) (the |her |their |him )?\w+)?'),
+    re.compile(r'(with a sign )?that says ?(" ?[^"]*"|\w+)( ?on it)?'),
+    re.compile(r"(with a sign )?that says ?(' ?(i'm)?[^']*'|\w+)( ?on it)?"),
+    re.compile(r'with the number \d+ on (it|\w+ \w+)'),
+    re.compile(r'with the words "'),
+    re.compile(r'word \w+ on it'),
+    re.compile(r'that says the word \w+ on it'),
+    re.compile('that says\'the word "( on it)?'),
+]
+
+# 誤検知しまくりの with the word xxxx を消す
+
+
+def remove_words(captions, debug):
+  removed_caps = []
+  for caption in captions:
+    cap = caption
+    for pat in PATTERN_REPLACE:
+      cap = pat.sub("", cap)
+    if debug and cap != caption:
+      print(caption)
+      print(cap)
+    removed_caps.append(cap)
+  return removed_caps
+
+
+def collate_fn_remove_corrupted(batch):
+  """Collate function that allows to remove corrupted examples in the
+  dataloader. It expects that the dataloader returns 'None' when that occurs.
+  The 'None's in the batch are removed.
+  """
+  # Filter out all the Nones (corrupted examples)
+  batch = list(filter(lambda x: x is not None, batch))
+  return batch
+
+
+def main(args):
+  # GITにバッチサイズが1より大きくても動くようにパッチを当てる: transformers 4.26.0用
+  org_prepare_input_ids_for_generation = GenerationMixin._prepare_input_ids_for_generation
+  curr_batch_size = [args.batch_size]         # ループの最後で件数がbatch_size未満になるので入れ替えられるように
+
+  # input_idsがバッチサイズと同じ件数である必要がある：バッチサイズはこの関数から参照できないので外から渡す
+  # ここより上で置き換えようとするとすごく大変
+  def _prepare_input_ids_for_generation_patch(self, bos_token_id, encoder_outputs):
+    input_ids = org_prepare_input_ids_for_generation(self, bos_token_id, encoder_outputs)
+    if input_ids.size()[0] != curr_batch_size[0]:
+      input_ids = input_ids.repeat(curr_batch_size[0], 1)
+    return input_ids
+  GenerationMixin._prepare_input_ids_for_generation = _prepare_input_ids_for_generation_patch
+
+  print(f"load images from {args.train_data_dir}")
+  image_paths = train_util.glob_images(args.train_data_dir)
+  print(f"found {len(image_paths)} images.")
+
+  # できればcacheに依存せず明示的にダウンロードしたい
+  print(f"loading GIT: {args.model_id}")
+  git_processor = AutoProcessor.from_pretrained(args.model_id)
+  git_model = AutoModelForCausalLM.from_pretrained(args.model_id).to(DEVICE)
+  print("GIT loaded")
+
+  # captioningする
+  def run_batch(path_imgs):
+    imgs = [im for _, im in path_imgs]
+
+    curr_batch_size[0] = len(path_imgs)
+    inputs = git_processor(images=imgs, return_tensors="pt").to(DEVICE)           # 画像はpil形式
+    generated_ids = git_model.generate(pixel_values=inputs.pixel_values, max_length=args.max_length)
+    captions = git_processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+    if args.remove_words:
+      captions = remove_words(captions, args.debug)
+
+    for (image_path, _), caption in zip(path_imgs, captions):
+      with open(os.path.splitext(image_path)[0] + args.caption_extension, "wt", encoding='utf-8') as f:
+        f.write(caption + "\n")
+        if args.debug:
+          print(image_path, caption)
+
+  # 読み込みの高速化のためにDataLoaderを使うオプション
+  if args.max_data_loader_n_workers is not None:
+    dataset = train_util.ImageLoadingDataset(image_paths)
+    data = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=False,
+                                       num_workers=args.max_data_loader_n_workers, collate_fn=collate_fn_remove_corrupted, drop_last=False)
+  else:
+    data = [[(None, ip)] for ip in image_paths]
+
+  b_imgs = []
+  for data_entry in tqdm(data, smoothing=0.0):
+    for data in data_entry:
+      if data is None:
+        continue
+
+      image, image_path = data
+      if image is None:
+        try:
+          image = Image.open(image_path)
+          if image.mode != 'RGB':
+            image = image.convert("RGB")
+        except Exception as e:
+          print(f"Could not load image path / 画像を読み込めません: {image_path}, error: {e}")
+          continue
+
+      b_imgs.append((image_path, image))
+      if len(b_imgs) >= args.batch_size:
+        run_batch(b_imgs)
+        b_imgs.clear()
+
+  if len(b_imgs) > 0:
+    run_batch(b_imgs)
+
+  print("done!")
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ")
+  parser.add_argument("--caption_extension", type=str, default=".caption", help="extension of caption file / 出力されるキャプションファイルの拡張子")
+  parser.add_argument("--model_id", type=str, default="microsoft/git-large-textcaps",
+                      help="model id for GIT in Hugging Face / 使用するGITのHugging FaceのモデルID")
+  parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
+  parser.add_argument("--max_data_loader_n_workers", type=int, default=None,
+                      help="enable image reading by DataLoader with this number of workers (faster) / DataLoaderによる画像読み込みを有効にしてこのワーカー数を適用する（読み込みを高速化）")
+  parser.add_argument("--max_length", type=int, default=50, help="max length of caption / captionの最大長")
+  parser.add_argument("--remove_words", action="store_true",
+                      help="remove like `with the words xxx` from caption / `with the words xxx`のような部分をキャプションから削除する")
+  parser.add_argument("--debug", action="store_true", help="debug mode")
+
+  args = parser.parse_args()
+  main(args)

finetune/merge_captions_to_metadata.py

@@ -1,26 +1,24 @@
-# このスクリプトのライセンスは、Apache License 2.0とします
-# (c) 2022 Kohya S. @kohya_ss
-
 import argparse
-import glob
-import os
 import json
-
+from pathlib import Path
+from typing import List
 from tqdm import tqdm
+import library.train_util as train_util
 
 
 def main(args):
-  image_paths = glob.glob(os.path.join(args.train_data_dir, "*.jpg")) + \
-      glob.glob(os.path.join(args.train_data_dir, "*.png")) + glob.glob(os.path.join(args.train_data_dir, "*.webp"))
+  assert not args.recursive or (args.recursive and args.full_path), "recursive requires full_path / recursiveはfull_pathと同時に指定してください"
+
+  train_data_dir_path = Path(args.train_data_dir)
+  image_paths: List[Path] = train_util.glob_images_pathlib(train_data_dir_path, args.recursive)
   print(f"found {len(image_paths)} images.")
 
-  if args.in_json is None and os.path.isfile(args.out_json):
+  if args.in_json is None and Path(args.out_json).is_file():
     args.in_json = args.out_json
 
   if args.in_json is not None:
     print(f"loading existing metadata: {args.in_json}")
-    with open(args.in_json, "rt", encoding='utf-8') as f:
-      metadata = json.load(f)
+    metadata = json.loads(Path(args.in_json).read_text(encoding='utf-8'))
     print("captions for existing images will be overwritten / 既存の画像のキャプションは上書きされます")
   else:
     print("new metadata will be created / 新しいメタデータファイルが作成されます")
@@ -28,11 +26,10 @@ def main(args):
 
   print("merge caption texts to metadata json.")
   for image_path in tqdm(image_paths):
-    caption_path = os.path.splitext(image_path)[0] + args.caption_extension
-    with open(caption_path, "rt", encoding='utf-8') as f:
-      caption = f.readlines()[0].strip()
+    caption_path = image_path.with_suffix(args.caption_extension)
+    caption = caption_path.read_text(encoding='utf-8').strip()
 
-    image_key = image_path if args.full_path else os.path.splitext(os.path.basename(image_path))[0]
+    image_key = str(image_path) if args.full_path else image_path.stem
     if image_key not in metadata:
       metadata[image_key] = {}
 
@@ -42,8 +39,7 @@ def main(args):
 
   # metadataを書き出して終わり
   print(f"writing metadata: {args.out_json}")
-  with open(args.out_json, "wt", encoding='utf-8') as f:
-    json.dump(metadata, f, indent=2)
+  Path(args.out_json).write_text(json.dumps(metadata, indent=2), encoding='utf-8')
   print("done!")
 
 
@@ -51,12 +47,15 @@ if __name__ == '__main__':
   parser = argparse.ArgumentParser()
   parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ")
   parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先")
-  parser.add_argument("--in_json", type=str, help="metadata file to input (if omitted and out_json exists, existing out_json is read) / 読み込むメタデータファイル（省略時、out_jsonが存在すればそれを読み込む）")
+  parser.add_argument("--in_json", type=str,
+                      help="metadata file to input (if omitted and out_json exists, existing out_json is read) / 読み込むメタデータファイル（省略時、out_jsonが存在すればそれを読み込む）")
   parser.add_argument("--caption_extention", type=str, default=None,
                       help="extension of caption file (for backward compatibility) / 読み込むキャプションファイルの拡張子（スペルミスしていたのを残してあります）")
   parser.add_argument("--caption_extension", type=str, default=".caption", help="extension of caption file / 読み込むキャプションファイルの拡張子")
   parser.add_argument("--full_path", action="store_true",
                       help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）")
+  parser.add_argument("--recursive", action="store_true",
+                      help="recursively look for training tags in all child folders of train_data_dir / train_data_dirのすべての子フォルダにある学習タグを再帰的に探す")
   parser.add_argument("--debug", action="store_true", help="debug mode")
 
   args = parser.parse_args()

finetune/merge_dd_tags_to_metadata.py

@@ -1,26 +1,24 @@
-# このスクリプトのライセンスは、Apache License 2.0とします
-# (c) 2022 Kohya S. @kohya_ss
-
 import argparse
-import glob
-import os
 import json
-
+from pathlib import Path
+from typing import List
 from tqdm import tqdm
+import library.train_util as train_util
 
 
 def main(args):
-  image_paths = glob.glob(os.path.join(args.train_data_dir, "*.jpg")) + \
-      glob.glob(os.path.join(args.train_data_dir, "*.png")) + glob.glob(os.path.join(args.train_data_dir, "*.webp"))
+  assert not args.recursive or (args.recursive and args.full_path), "recursive requires full_path / recursiveはfull_pathと同時に指定してください"
+
+  train_data_dir_path = Path(args.train_data_dir)
+  image_paths: List[Path] = train_util.glob_images_pathlib(train_data_dir_path, args.recursive)
   print(f"found {len(image_paths)} images.")
 
-  if args.in_json is None and os.path.isfile(args.out_json):
+  if args.in_json is None and Path(args.out_json).is_file():
     args.in_json = args.out_json
 
   if args.in_json is not None:
     print(f"loading existing metadata: {args.in_json}")
-    with open(args.in_json, "rt", encoding='utf-8') as f:
-      metadata = json.load(f)
+    metadata = json.loads(Path(args.in_json).read_text(encoding='utf-8'))
     print("tags data for existing images will be overwritten / 既存の画像のタグは上書きされます")
   else:
     print("new metadata will be created / 新しいメタデータファイルが作成されます")
@@ -28,11 +26,10 @@ def main(args):
 
   print("merge tags to metadata json.")
   for image_path in tqdm(image_paths):
-    tags_path = os.path.splitext(image_path)[0] + '.txt'
-    with open(tags_path, "rt", encoding='utf-8') as f:
-      tags = f.readlines()[0].strip()
+    tags_path = image_path.with_suffix(args.caption_extension)
+    tags = tags_path.read_text(encoding='utf-8').strip()
 
-    image_key = image_path if args.full_path else os.path.splitext(os.path.basename(image_path))[0]
+    image_key = str(image_path) if args.full_path else image_path.stem
     if image_key not in metadata:
       metadata[image_key] = {}
 
@@ -42,8 +39,8 @@ def main(args):
 
   # metadataを書き出して終わり
   print(f"writing metadata: {args.out_json}")
-  with open(args.out_json, "wt", encoding='utf-8') as f:
-    json.dump(metadata, f, indent=2)
+  Path(args.out_json).write_text(json.dumps(metadata, indent=2), encoding='utf-8')
+
   print("done!")
 
 
@@ -51,9 +48,14 @@ if __name__ == '__main__':
   parser = argparse.ArgumentParser()
   parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ")
   parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先")
-  parser.add_argument("--in_json", type=str, help="metadata file to input (if omitted and out_json exists, existing out_json is read) / 読み込むメタデータファイル（省略時、out_jsonが存在すればそれを読み込む）")
+  parser.add_argument("--in_json", type=str,
+                      help="metadata file to input (if omitted and out_json exists, existing out_json is read) / 読み込むメタデータファイル（省略時、out_jsonが存在すればそれを読み込む）")
   parser.add_argument("--full_path", action="store_true",
                       help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）")
+  parser.add_argument("--recursive", action="store_true",
+                      help="recursively look for training tags in all child folders of train_data_dir / train_data_dirのすべての子フォルダにある学習タグを再帰的に探す")
+  parser.add_argument("--caption_extension", type=str, default=".txt",
+                      help="extension of caption (tag) file / 読み込むキャプション（タグ）ファイルの拡張子")
   parser.add_argument("--debug", action="store_true", help="debug mode, print tags")
 
   args = parser.parse_args()

finetune/prepare_buckets_latents.py

@@ -1,20 +1,16 @@
-# このスクリプトのライセンスは、Apache License 2.0とします
-# (c) 2022 Kohya S. @kohya_ss
-
 import argparse
-import glob
 import os
 import json
 
 from tqdm import tqdm
 import numpy as np
-from diffusers import AutoencoderKL
 from PIL import Image
 import cv2
 import torch
 from torchvision import transforms
 
 import library.model_util as model_util
+import library.train_util as train_util
 
 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
@@ -26,6 +22,16 @@ IMAGE_TRANSFORMS = transforms.Compose(
 )
 
 
+def collate_fn_remove_corrupted(batch):
+  """Collate function that allows to remove corrupted examples in the
+  dataloader. It expects that the dataloader returns 'None' when that occurs.
+  The 'None's in the batch are removed.
+  """
+  # Filter out all the Nones (corrupted examples)
+  batch = list(filter(lambda x: x is not None, batch))
+  return batch
+
+
 def get_latents(vae, images, weight_dtype):
   img_tensors = [IMAGE_TRANSFORMS(image) for image in images]
   img_tensors = torch.stack(img_tensors)
@@ -35,9 +41,22 @@ def get_latents(vae, images, weight_dtype):
   return latents
 
 
+def get_npz_filename_wo_ext(data_dir, image_key, is_full_path, flip):
+  if is_full_path:
+    base_name = os.path.splitext(os.path.basename(image_key))[0]
+  else:
+    base_name = image_key
+  if flip:
+    base_name += '_flip'
+  return os.path.join(data_dir, base_name)
+
+
 def main(args):
-  image_paths = glob.glob(os.path.join(args.train_data_dir, "*.jpg")) + \
-      glob.glob(os.path.join(args.train_data_dir, "*.png")) + glob.glob(os.path.join(args.train_data_dir, "*.webp"))
+  # assert args.bucket_reso_steps % 8 == 0, f"bucket_reso_steps must be divisible by 8 / bucket_reso_stepは8で割り切れる必要があります"
+  if args.bucket_reso_steps % 8 > 0:
+    print(f"resolution of buckets in training time is a multiple of 8 / 学習時の各bucketの解像度は8単位になります")
+
+  image_paths = train_util.glob_images(args.train_data_dir)
   print(f"found {len(image_paths)} images.")
 
   if os.path.exists(args.in_json):
@@ -62,89 +81,144 @@ def main(args):
   max_reso = tuple([int(t) for t in args.max_resolution.split(',')])
   assert len(max_reso) == 2, f"illegal resolution (not 'width,height') / 画像サイズに誤りがあります。'幅,高さ'で指定してください: {args.max_resolution}"
 
-  bucket_resos, bucket_aspect_ratios = model_util.make_bucket_resolutions(
-      max_reso, args.min_bucket_reso, args.max_bucket_reso)
+  bucket_manager = train_util.BucketManager(args.bucket_no_upscale, max_reso,
+                                            args.min_bucket_reso, args.max_bucket_reso, args.bucket_reso_steps)
+  if not args.bucket_no_upscale:
+    bucket_manager.make_buckets()
+  else:
+    print("min_bucket_reso and max_bucket_reso are ignored if bucket_no_upscale is set, because bucket reso is defined by image size automatically / bucket_no_upscaleが指定された場合は、bucketの解像度は画像サイズから自動計算されるため、min_bucket_resoとmax_bucket_resoは無視されます")
 
   # 画像をひとつずつ適切なbucketに割り当てながらlatentを計算する
-  bucket_aspect_ratios = np.array(bucket_aspect_ratios)
-  buckets_imgs = [[] for _ in range(len(bucket_resos))]
-  bucket_counts = [0 for _ in range(len(bucket_resos))]
   img_ar_errors = []
-  for i, image_path in enumerate(tqdm(image_paths, smoothing=0.0)):
+
+  def process_batch(is_last):
+    for bucket in bucket_manager.buckets:
+      if (is_last and len(bucket) > 0) or len(bucket) >= args.batch_size:
+        latents = get_latents(vae, [img for _, img in bucket], weight_dtype)
+        assert latents.shape[2] == bucket[0][1].shape[0] // 8 and latents.shape[3] == bucket[0][1].shape[1] // 8, \
+            f"latent shape {latents.shape}, {bucket[0][1].shape}"
+
+        for (image_key, _), latent in zip(bucket, latents):
+          npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False)
+          np.savez(npz_file_name, latent)
+
+        # flip
+        if args.flip_aug:
+          latents = get_latents(vae, [img[:, ::-1].copy() for _, img in bucket], weight_dtype)   # copyがないとTensor変換できない
+
+          for (image_key, _), latent in zip(bucket, latents):
+            npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True)
+            np.savez(npz_file_name, latent)
+        else:
+          # remove existing flipped npz
+          for image_key, _ in bucket:
+            npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz"
+            if os.path.isfile(npz_file_name):
+              print(f"remove existing flipped npz / 既存のflipされたnpzファイルを削除します: {npz_file_name}")
+              os.remove(npz_file_name)
+
+        bucket.clear()
+
+  # 読み込みの高速化のためにDataLoaderを使うオプション
+  if args.max_data_loader_n_workers is not None:
+    dataset = train_util.ImageLoadingDataset(image_paths)
+    data = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False,
+                                       num_workers=args.max_data_loader_n_workers, collate_fn=collate_fn_remove_corrupted, drop_last=False)
+  else:
+    data = [[(None, ip)] for ip in image_paths]
+
+  bucket_counts = {}
+  for data_entry in tqdm(data, smoothing=0.0):
+    if data_entry[0] is None:
+      continue
+
+    img_tensor, image_path = data_entry[0]
+    if img_tensor is not None:
+      image = transforms.functional.to_pil_image(img_tensor)
+    else:
+      try:
+        image = Image.open(image_path)
+        if image.mode != 'RGB':
+          image = image.convert("RGB")
+      except Exception as e:
+        print(f"Could not load image path / 画像を読み込めません: {image_path}, error: {e}")
+        continue
+
     image_key = image_path if args.full_path else os.path.splitext(os.path.basename(image_path))[0]
     if image_key not in metadata:
       metadata[image_key] = {}
 
-    image = Image.open(image_path)
-    if image.mode != 'RGB':
-      image = image.convert("RGB")
+    # 本当はこのあとの部分もDataSetに持っていけば高速化できるがいろいろ大変
 
-    aspect_ratio = image.width / image.height
-    ar_errors = bucket_aspect_ratios - aspect_ratio
-    bucket_id = np.abs(ar_errors).argmin()
-    reso = bucket_resos[bucket_id]
-    ar_error = ar_errors[bucket_id]
+    reso, resized_size, ar_error = bucket_manager.select_bucket(image.width, image.height)
     img_ar_errors.append(abs(ar_error))
+    bucket_counts[reso] = bucket_counts.get(reso, 0) + 1
 
-    # どのサイズにリサイズするか→トリミングする方向で
-    if ar_error <= 0:                   # 横が長い→縦を合わせる
-      scale = reso[1] / image.height
-    else:
-      scale = reso[0] / image.width
+    # メタデータに記録する解像度はlatent単位とするので、8単位で切り捨て
+    metadata[image_key]['train_resolution'] = (reso[0] - reso[0] % 8, reso[1] - reso[1] % 8)
 
-    resized_size = (int(image.width * scale + .5), int(image.height * scale + .5))
+    if not args.bucket_no_upscale:
+      # upscaleを行わないときには、resize後のサイズは、bucketのサイズと、縦横どちらかが同じであることを確認する
+      assert resized_size[0] == reso[0] or resized_size[1] == reso[
+          1], f"internal error, resized size not match: {reso}, {resized_size}, {image.width}, {image.height}"
+      assert resized_size[0] >= reso[0] and resized_size[1] >= reso[
+          1], f"internal error, resized size too small: {reso}, {resized_size}, {image.width}, {image.height}"
 
-    # print(image.width, image.height, bucket_id, bucket_resos[bucket_id], ar_errors[bucket_id], resized_size,
-    #       bucket_resos[bucket_id][0] - resized_size[0], bucket_resos[bucket_id][1] - resized_size[1])
-
-    assert resized_size[0] == reso[0] or resized_size[1] == reso[
-        1], f"internal error, resized size not match: {reso}, {resized_size}, {image.width}, {image.height}"
     assert resized_size[0] >= reso[0] and resized_size[1] >= reso[
-        1], f"internal error, resized size too small: {reso}, {resized_size}, {image.width}, {image.height}"
+        1], f"internal error resized size is small: {resized_size}, {reso}"
+
+    # 既に存在するファイルがあればshapeを確認して同じならskipする
+    if args.skip_existing:
+      npz_files = [get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False) + ".npz"]
+      if args.flip_aug:
+        npz_files.append(get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, True) + ".npz")
+
+      found = True
+      for npz_file in npz_files:
+        if not os.path.exists(npz_file):
+          found = False
+          break
+
+        dat = np.load(npz_file)['arr_0']
+        if dat.shape[1] != reso[1] // 8 or dat.shape[2] != reso[0] // 8:     # latentsのshapeを確認
+          found = False
+          break
+      if found:
+        continue
 
     # 画像をリサイズしてトリミングする
     # PILにinter_areaがないのでcv2で……
     image = np.array(image)
-    image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)
+    if resized_size[0] != image.shape[1] or resized_size[1] != image.shape[0]:            # リサイズ処理が必要？
+      image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)
+
     if resized_size[0] > reso[0]:
       trim_size = resized_size[0] - reso[0]
       image = image[:, trim_size//2:trim_size//2 + reso[0]]
-    elif resized_size[1] > reso[1]:
+
+    if resized_size[1] > reso[1]:
       trim_size = resized_size[1] - reso[1]
       image = image[trim_size//2:trim_size//2 + reso[1]]
+
     assert image.shape[0] == reso[1] and image.shape[1] == reso[0], f"internal error, illegal trimmed size: {image.shape}, {reso}"
 
     # # debug
-    # cv2.imwrite(f"r:\\test\\img_{i:05d}.jpg", image[:, :, ::-1])
+    # cv2.imwrite(f"r:\\test\\img_{len(img_ar_errors)}.jpg", image[:, :, ::-1])
 
     # バッチへ追加
-    buckets_imgs[bucket_id].append((image_key, reso, image))
-    bucket_counts[bucket_id] += 1
-    metadata[image_key]['train_resolution'] = reso
+    bucket_manager.add_image(reso, (image_key, image))
 
     # バッチを推論するか判定して推論する
-    is_last = i == len(image_paths) - 1
-    for j in range(len(buckets_imgs)):
-      bucket = buckets_imgs[j]
-      if (is_last and len(bucket) > 0) or len(bucket) >= args.batch_size:
-        latents = get_latents(vae, [img for _, _, img in bucket], weight_dtype)
+    process_batch(False)
 
-        for (image_key, reso, _), latent in zip(bucket, latents):
-          npz_file_name = os.path.splitext(os.path.basename(image_key))[0] if args.full_path else image_key
-          np.savez(os.path.join(args.train_data_dir, npz_file_name), latent)
+  # 残りを処理する
+  process_batch(True)
 
-        # flip
-        if args.flip_aug:
-          latents = get_latents(vae, [img[:, ::-1].copy() for _, _, img in bucket], weight_dtype)   # copyがないとTensor変換できない
-
-          for (image_key, reso, _), latent in zip(bucket, latents):
-            npz_file_name = os.path.splitext(os.path.basename(image_key))[0] if args.full_path else image_key
-            np.savez(os.path.join(args.train_data_dir, npz_file_name + '_flip'), latent)
-
-        bucket.clear()
-
-  for i, (reso, count) in enumerate(zip(bucket_resos, bucket_counts)):
-    print(f"bucket {i} {reso}: {count}")
+  bucket_manager.sort()
+  for i, reso in enumerate(bucket_manager.resos):
+    count = bucket_counts.get(reso, 0)
+    if count > 0:
+      print(f"bucket {i} {reso}: {count}")
   img_ar_errors = np.array(img_ar_errors)
   print(f"mean ar error: {np.mean(img_ar_errors)}")
 
@@ -162,18 +236,26 @@ if __name__ == '__main__':
   parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先")
   parser.add_argument("model_name_or_path", type=str, help="model name or path to encode latents / latentを取得するためのモデル")
   parser.add_argument("--v2", action='store_true',
-                      help='load Stable Diffusion v2.0 model / Stable Diffusion 2.0のモデルを読み込む')
+                      help='not used (for backward compatibility) / 使用されません（互換性のため残してあります）')
   parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
+  parser.add_argument("--max_data_loader_n_workers", type=int, default=None,
+                      help="enable image reading by DataLoader with this number of workers (faster) / DataLoaderによる画像読み込みを有効にしてこのワーカー数を適用する（読み込みを高速化）")
   parser.add_argument("--max_resolution", type=str, default="512,512",
                       help="max resolution in fine tuning (width,height) / fine tuning時の最大画像サイズ 「幅,高さ」（使用メモリ量に関係します）")
   parser.add_argument("--min_bucket_reso", type=int, default=256, help="minimum resolution for buckets / bucketの最小解像度")
   parser.add_argument("--max_bucket_reso", type=int, default=1024, help="maximum resolution for buckets / bucketの最小解像度")
+  parser.add_argument("--bucket_reso_steps", type=int, default=64,
+                      help="steps of resolution for buckets, divisible by 8 is recommended / bucketの解像度の単位、8で割り切れる値を推奨します")
+  parser.add_argument("--bucket_no_upscale", action="store_true",
+                      help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します")
   parser.add_argument("--mixed_precision", type=str, default="no",
                       choices=["no", "fp16", "bf16"], help="use mixed precision / 混合精度を使う場合、その精度")
   parser.add_argument("--full_path", action="store_true",
                       help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）")
   parser.add_argument("--flip_aug", action="store_true",
                       help="flip augmentation, save latents for flipped images / 左右反転した画像もlatentを取得、保存する")
+  parser.add_argument("--skip_existing", action="store_true",
+                      help="skip images if npz already exists (both normal and flipped exists if flip_aug is enabled) / npzが既に存在する画像をスキップする（flip_aug有効時は通常、反転の両方が存在する画像をスキップ）")
 
   args = parser.parse_args()
   main(args)

finetune/tag_images_by_wd14_tagger.py

@@ -1,6 +1,3 @@
-# このスクリプトのライセンスは、Apache License 2.0とします
-# (c) 2022 Kohya S. @kohya_ss
-
 import argparse
 import csv
 import glob
@@ -12,32 +9,87 @@ from tqdm import tqdm
 import numpy as np
 from tensorflow.keras.models import load_model
 from huggingface_hub import hf_hub_download
+import torch
+
+import library.train_util as train_util
 
 # from wd14 tagger
 IMAGE_SIZE = 448
 
-WD14_TAGGER_REPO = 'SmilingWolf/wd-v1-4-vit-tagger'
+# wd-v1-4-swinv2-tagger-v2 / wd-v1-4-vit-tagger / wd-v1-4-vit-tagger-v2/ wd-v1-4-convnext-tagger / wd-v1-4-convnext-tagger-v2
+DEFAULT_WD14_TAGGER_REPO = 'SmilingWolf/wd-v1-4-convnext-tagger-v2'
 FILES = ["keras_metadata.pb", "saved_model.pb", "selected_tags.csv"]
 SUB_DIR = "variables"
 SUB_DIR_FILES = ["variables.data-00000-of-00001", "variables.index"]
 CSV_FILE = FILES[-1]
 
 
+def preprocess_image(image):
+  image = np.array(image)
+  image = image[:, :, ::-1]                         # RGB->BGR
+
+  # pad to square
+  size = max(image.shape[0:2])
+  pad_x = size - image.shape[1]
+  pad_y = size - image.shape[0]
+  pad_l = pad_x // 2
+  pad_t = pad_y // 2
+  image = np.pad(image, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode='constant', constant_values=255)
+
+  interp = cv2.INTER_AREA if size > IMAGE_SIZE else cv2.INTER_LANCZOS4
+  image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), interpolation=interp)
+
+  image = image.astype(np.float32)
+  return image
+
+
+class ImageLoadingPrepDataset(torch.utils.data.Dataset):
+  def __init__(self, image_paths):
+    self.images = image_paths
+
+  def __len__(self):
+    return len(self.images)
+
+  def __getitem__(self, idx):
+    img_path = self.images[idx]
+
+    try:
+      image = Image.open(img_path).convert("RGB")
+      image = preprocess_image(image)
+      tensor = torch.tensor(image)
+    except Exception as e:
+      print(f"Could not load image path / 画像を読み込めません: {img_path}, error: {e}")
+      return None
+
+    return (tensor, img_path)
+
+
+def collate_fn_remove_corrupted(batch):
+  """Collate function that allows to remove corrupted examples in the
+  dataloader. It expects that the dataloader returns 'None' when that occurs.
+  The 'None's in the batch are removed.
+  """
+  # Filter out all the Nones (corrupted examples)
+  batch = list(filter(lambda x: x is not None, batch))
+  return batch
+
+
 def main(args):
   # hf_hub_downloadをそのまま使うとsymlink関係で問題があるらしいので、キャッシュディレクトリとforce_filenameを指定してなんとかする
   # depreacatedの警告が出るけどなくなったらその時
   # https://github.com/toriato/stable-diffusion-webui-wd14-tagger/issues/22
   if not os.path.exists(args.model_dir) or args.force_download:
-    print("downloading wd14 tagger model from hf_hub")
+    print(f"downloading wd14 tagger model from hf_hub. id: {args.repo_id}")
     for file in FILES:
       hf_hub_download(args.repo_id, file, cache_dir=args.model_dir, force_download=True, force_filename=file)
     for file in SUB_DIR_FILES:
       hf_hub_download(args.repo_id, file, subfolder=SUB_DIR, cache_dir=os.path.join(
           args.model_dir, SUB_DIR), force_download=True, force_filename=file)
+  else:
+    print("using existing wd14 tagger model")
 
   # 画像を読み込む
-  image_paths = glob.glob(os.path.join(args.train_data_dir, "*.jpg")) + \
-      glob.glob(os.path.join(args.train_data_dir, "*.png")) + glob.glob(os.path.join(args.train_data_dir, "*.webp"))
+  image_paths = train_util.glob_images(args.train_data_dir)
   print(f"found {len(image_paths)} images.")
 
   print("loading model and labels")
@@ -72,7 +124,7 @@ def main(args):
       # Everything else is tags: pick any where prediction confidence > threshold
       tag_text = ""
       for i, p in enumerate(prob[4:]):                # numpyとか使うのが良いけど、まあそれほど数も多くないのでループで
-        if p >= args.thresh:
+        if p >= args.thresh and i < len(tags):
           tag_text += ", " + tags[i]
 
       if len(tag_text) > 0:
@@ -83,34 +135,37 @@ def main(args):
         if args.debug:
           print(image_path, tag_text)
 
+  # 読み込みの高速化のためにDataLoaderを使うオプション
+  if args.max_data_loader_n_workers is not None:
+    dataset = ImageLoadingPrepDataset(image_paths)
+    data = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=False,
+                                       num_workers=args.max_data_loader_n_workers, collate_fn=collate_fn_remove_corrupted, drop_last=False)
+  else:
+    data = [[(None, ip)] for ip in image_paths]
+
   b_imgs = []
-  for image_path in tqdm(image_paths, smoothing=0.0):
-    img = Image.open(image_path)                  # cv2は日本語ファイル名で死ぬのとモード変換したいのでpillowで開く
-    if img.mode != 'RGB':
-      img = img.convert("RGB")
-    img = np.array(img)
-    img = img[:, :, ::-1]                         # RGB->BGR
+  for data_entry in tqdm(data, smoothing=0.0):
+    for data in data_entry:
+      if data is None:
+        continue
 
-    # pad to square
-    size = max(img.shape[0:2])
-    pad_x = size - img.shape[1]
-    pad_y = size - img.shape[0]
-    pad_l = pad_x // 2
-    pad_t = pad_y // 2
-    img = np.pad(img, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode='constant', constant_values=255)
+      image, image_path = data
+      if image is not None:
+        image = image.detach().numpy()
+      else:
+        try:
+          image = Image.open(image_path)
+          if image.mode != 'RGB':
+            image = image.convert("RGB")
+          image = preprocess_image(image)
+        except Exception as e:
+          print(f"Could not load image path / 画像を読み込めません: {image_path}, error: {e}")
+          continue
+      b_imgs.append((image_path, image))
 
-    interp = cv2.INTER_AREA if size > IMAGE_SIZE else cv2.INTER_LANCZOS4
-    img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE), interpolation=interp)
-    # cv2.imshow("img", img)
-    # cv2.waitKey()
-    # cv2.destroyAllWindows()
-
-    img = img.astype(np.float32)
-    b_imgs.append((image_path, img))
-
-    if len(b_imgs) >= args.batch_size:
-      run_batch(b_imgs)
-      b_imgs.clear()
+      if len(b_imgs) >= args.batch_size:
+        run_batch(b_imgs)
+        b_imgs.clear()
 
   if len(b_imgs) > 0:
     run_batch(b_imgs)
@@ -121,7 +176,7 @@ def main(args):
 if __name__ == '__main__':
   parser = argparse.ArgumentParser()
   parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ")
-  parser.add_argument("--repo_id", type=str, default=WD14_TAGGER_REPO,
+  parser.add_argument("--repo_id", type=str, default=DEFAULT_WD14_TAGGER_REPO,
                       help="repo id for wd14 tagger on Hugging Face / Hugging Faceのwd14 taggerのリポジトリID")
   parser.add_argument("--model_dir", type=str, default="wd14_tagger_model",
                       help="directory to store wd14 tagger model / wd14 taggerのモデルを格納するディレクトリ")
@@ -129,6 +184,8 @@ if __name__ == '__main__':
                       help="force downloading wd14 tagger models / wd14 taggerのモデルを再ダウンロードします")
   parser.add_argument("--thresh", type=float, default=0.35, help="threshold of confidence to add a tag / タグを追加するか判定する閾値")
   parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
+  parser.add_argument("--max_data_loader_n_workers", type=int, default=None,
+                      help="enable image reading by DataLoader with this number of workers (faster) / DataLoaderによる画像読み込みを有効にしてこのワーカー数を適用する（読み込みを高速化）")
   parser.add_argument("--caption_extention", type=str, default=None,
                       help="extension of caption file (for backward compatibility) / 出力されるキャプションファイルの拡張子（スペルミスしていたのを残してあります）")
   parser.add_argument("--caption_extension", type=str, default=".txt", help="extension of caption file / 出力されるキャプションファイルの拡張子")

gen_img_diffusers.py

@@ -47,7 +47,7 @@ VGG(
 """
 
 import json
-from typing import List, Optional, Union
+from typing import Any, List, NamedTuple, Optional, Tuple, Union, Callable
 import glob
 import importlib
 import inspect
@@ -60,7 +60,6 @@ import math
 import os
 import random
 import re
-from typing import Any, Callable, List, Optional, Union
 
 import diffusers
 import numpy as np
@@ -81,6 +80,8 @@ from PIL import Image
 from PIL.PngImagePlugin import PngInfo
 
 import library.model_util as model_util
+import tools.original_control_net as original_control_net
+from tools.original_control_net import ControlNetInfo
 
 # Tokenizer: checkpointから読み込むのではなくあらかじめ提供されているものを使う
 TOKENIZER_PATH = "openai/clip-vit-large-patch14"
@@ -470,6 +471,9 @@ class PipelineLike():
     self.scheduler = scheduler
     self.safety_checker = None
 
+    # Textual Inversion
+    self.token_replacements = {}
+
     # CLIP guidance
     self.clip_guidance_scale = clip_guidance_scale
     self.clip_image_guidance_scale = clip_image_guidance_scale
@@ -484,7 +488,27 @@ class PipelineLike():
       self.vgg16_feat_model = torchvision.models._utils.IntermediateLayerGetter(vgg16_model.features, return_layers=return_layers)
       self.vgg16_normalize = transforms.Normalize(mean=VGG16_IMAGE_MEAN, std=VGG16_IMAGE_STD)
 
+    # ControlNet
+    self.control_nets: List[ControlNetInfo] = []
+
+  # Textual Inversion
+  def add_token_replacement(self, target_token_id, rep_token_ids):
+    self.token_replacements[target_token_id] = rep_token_ids
+
+  def replace_token(self, tokens):
+    new_tokens = []
+    for token in tokens:
+      if token in self.token_replacements:
+        new_tokens.extend(self.token_replacements[token])
+      else:
+        new_tokens.append(token)
+    return new_tokens
+
+  def set_control_nets(self, ctrl_nets):
+    self.control_nets = ctrl_nets
+
   # region xformersとか使う部分：独自に書き換えるので関係なし
+
   def enable_xformers_memory_efficient_attention(self):
     r"""
     Enable memory efficient attention as implemented in xformers.
@@ -736,7 +760,7 @@ class PipelineLike():
       text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)
       text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)      # prompt複数件でもOK
 
-    if self.clip_image_guidance_scale > 0 or self.vgg16_guidance_scale > 0 and clip_guide_images is not None:
+    if self.clip_image_guidance_scale > 0 or self.vgg16_guidance_scale > 0 and clip_guide_images is not None or self.control_nets:
       if isinstance(clip_guide_images, PIL.Image.Image):
         clip_guide_images = [clip_guide_images]
 
@@ -749,7 +773,7 @@ class PipelineLike():
         image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
         if len(image_embeddings_clip) == 1:
           image_embeddings_clip = image_embeddings_clip.repeat((batch_size, 1, 1, 1))
-      else:
+      elif self.vgg16_guidance_scale > 0:
         size = (width // VGG16_INPUT_RESIZE_DIV, height // VGG16_INPUT_RESIZE_DIV)            # とりあえず1/4に（小さいか?）
         clip_guide_images = [preprocess_vgg16_guide_image(im, size) for im in clip_guide_images]
         clip_guide_images = torch.cat(clip_guide_images, dim=0)
@@ -758,6 +782,10 @@ class PipelineLike():
         image_embeddings_vgg16 = self.vgg16_feat_model(clip_guide_images)['feat']
         if len(image_embeddings_vgg16) == 1:
           image_embeddings_vgg16 = image_embeddings_vgg16.repeat((batch_size, 1, 1, 1))
+      else:
+        # ControlNetのhintにguide imageを流用する
+        # 前処理はControlNet側で行う
+        pass
 
     # set timesteps
     self.scheduler.set_timesteps(num_inference_steps, self.device)
@@ -848,12 +876,21 @@ class PipelineLike():
       extra_step_kwargs["eta"] = eta
 
     num_latent_input = (3 if negative_scale is not None else 2) if do_classifier_free_guidance else 1
+
+    if self.control_nets:
+      guided_hints = original_control_net.get_guided_hints(self.control_nets, num_latent_input, batch_size, clip_guide_images)
+
     for i, t in enumerate(tqdm(timesteps)):
       # expand the latents if we are doing classifier free guidance
       latent_model_input = latents.repeat((num_latent_input, 1, 1, 1))
       latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
       # predict the noise residual
-      noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+      if self.control_nets:
+        noise_pred = original_control_net.call_unet_and_control_net(
+            i, num_latent_input, self.unet, self.control_nets, guided_hints, i / len(timesteps), latent_model_input, t, text_embeddings).sample
+      else:
+        noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
 
       # perform guidance
       if do_classifier_free_guidance:
@@ -1507,6 +1544,9 @@ def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length:
     for word, weight in texts_and_weights:
       # tokenize and discard the starting and the ending token
       token = pipe.tokenizer(word).input_ids[1:-1]
+
+      token = pipe.replace_token(token)
+
       text_token += token
       # copy the weight by length of token
       text_weight += [weight] * len(token)
@@ -1798,6 +1838,34 @@ def preprocess_mask(mask):
 #   return text_encoder
 
 
+class BatchDataBase(NamedTuple):
+  # バッチ分割が必要ないデータ
+  step: int
+  prompt: str
+  negative_prompt: str
+  seed: int
+  init_image: Any
+  mask_image: Any
+  clip_prompt: str
+  guide_image: Any
+
+
+class BatchDataExt(NamedTuple):
+  # バッチ分割が必要なデータ
+  width: int
+  height: int
+  steps: int
+  scale:  float
+  negative_scale: float
+  strength: float
+  network_muls: Tuple[float]
+
+
+class BatchData(NamedTuple):
+  base: BatchDataBase
+  ext: BatchDataExt
+
+
 def main(args):
   if args.fp16:
     dtype = torch.float16
@@ -1826,12 +1894,12 @@ def main(args):
     text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.ckpt)
   else:
     print("load Diffusers pretrained models")
-    pipe = StableDiffusionPipeline.from_pretrained(args.ckpt, safety_checker=None, torch_dtype=dtype)
-    text_encoder = pipe.text_encoder
-    vae = pipe.vae
-    unet = pipe.unet
-    tokenizer = pipe.tokenizer
-    del pipe
+    loading_pipe = StableDiffusionPipeline.from_pretrained(args.ckpt, safety_checker=None, torch_dtype=dtype)
+    text_encoder = loading_pipe.text_encoder
+    vae = loading_pipe.vae
+    unet = loading_pipe.unet
+    tokenizer = loading_pipe.tokenizer
+    del loading_pipe
 
   # VAEを読み込む
   if args.vae is not None:
@@ -1976,12 +2044,13 @@ def main(args):
   # networkを組み込む
   if args.network_module:
     networks = []
+    network_default_muls = []
     for i, network_module in enumerate(args.network_module):
       print("import network module:", network_module)
       imported_module = importlib.import_module(network_module)
 
       network_mul = 1.0 if args.network_mul is None or len(args.network_mul) <= i else args.network_mul[i]
-      network_dim = None if args.network_dim is None or len(args.network_dim) <= i else args.network_dim[i]
+      network_default_muls.append(network_mul)
 
       net_kwargs = {}
       if args.network_args and i < len(args.network_args):
@@ -1992,22 +2061,22 @@ def main(args):
           key, value = net_arg.split("=")
           net_kwargs[key] = value
 
-      network = imported_module.create_network(network_mul, network_dim, vae, text_encoder, unet, **net_kwargs)
-      if network is None:
-        return
-
       if args.network_weights and i < len(args.network_weights):
         network_weight = args.network_weights[i]
         print("load network weights from:", network_weight)
 
-        if os.path.splitext(network_weight)[1] == '.safetensors':
+        if model_util.is_safetensors(network_weight) and args.network_show_meta:
           from safetensors.torch import safe_open
           with safe_open(network_weight, framework="pt") as f:
             metadata = f.metadata()
           if metadata is not None:
             print(f"metadata for: {network_weight}: {metadata}")
 
-        network.load_weights(network_weight)
+        network = imported_module.create_network_from_weights(network_mul, network_weight, vae, text_encoder, unet, **net_kwargs)
+      else:
+        raise ValueError("No weight. Weight is required.")
+      if network is None:
+        return
 
       network.apply_to(text_encoder, unet)
 
@@ -2019,6 +2088,18 @@ def main(args):
   else:
     networks = []
 
+  # ControlNetの処理
+  control_nets: List[ControlNetInfo] = []
+  if args.control_net_models:
+    for i, model in enumerate(args.control_net_models):
+      prep_type = None if not args.control_net_preps or len(args.control_net_preps) <= i else args.control_net_preps[i]
+      weight = 1.0 if not args.control_net_weights or len(args.control_net_weights) <= i else args.control_net_weights[i]
+      ratio = 1.0 if not args.control_net_ratios or len(args.control_net_ratios) <= i else args.control_net_ratios[i]
+
+      ctrl_unet, ctrl_net = original_control_net.load_control_net(args.v2, unet, model)
+      prep = original_control_net.load_preprocess(prep_type)
+      control_nets.append(ControlNetInfo(ctrl_unet, ctrl_net, prep, weight, ratio))
+
   if args.opt_channels_last:
     print(f"set optimizing: channels last")
     text_encoder.to(memory_format=torch.channels_last)
@@ -2032,14 +2113,57 @@ def main(args):
     if vgg16_model is not None:
       vgg16_model.to(memory_format=torch.channels_last)
 
+    for cn in control_nets:
+      cn.unet.to(memory_format=torch.channels_last)
+      cn.net.to(memory_format=torch.channels_last)
+
   pipe = PipelineLike(device, vae, text_encoder, tokenizer, unet, scheduler, args.clip_skip,
                       clip_model, args.clip_guidance_scale, args.clip_image_guidance_scale,
                       vgg16_model, args.vgg16_guidance_scale, args.vgg16_guidance_layer)
+  pipe.set_control_nets(control_nets)
   print("pipeline is ready.")
 
   if args.diffusers_xformers:
     pipe.enable_xformers_memory_efficient_attention()
 
+  # Textual Inversionを処理する
+  if args.textual_inversion_embeddings:
+    token_ids_embeds = []
+    for embeds_file in args.textual_inversion_embeddings:
+      if model_util.is_safetensors(embeds_file):
+        from safetensors.torch import load_file
+        data = load_file(embeds_file)
+      else:
+        data = torch.load(embeds_file, map_location="cpu")
+
+      embeds = next(iter(data.values()))
+      if type(embeds) != torch.Tensor:
+        raise ValueError(f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {embeds_file}")
+
+      num_vectors_per_token = embeds.size()[0]
+      token_string = os.path.splitext(os.path.basename(embeds_file))[0]
+      token_strings = [token_string] + [f"{token_string}{i+1}" for i in range(num_vectors_per_token - 1)]
+
+      # add new word to tokenizer, count is num_vectors_per_token
+      num_added_tokens = tokenizer.add_tokens(token_strings)
+      assert num_added_tokens == num_vectors_per_token, f"tokenizer has same word to token string (filename). please rename the file / 指定した名前（ファイル名）のトークンが既に存在します。ファイルをリネームしてください: {embeds_file}"
+
+      token_ids = tokenizer.convert_tokens_to_ids(token_strings)
+      print(f"Textual Inversion embeddings `{token_string}` loaded. Tokens are added: {token_ids}")
+      assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
+      assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
+
+      if num_vectors_per_token > 1:
+        pipe.add_token_replacement(token_ids[0], token_ids)
+
+      token_ids_embeds.append((token_ids, embeds))
+
+    text_encoder.resize_token_embeddings(len(tokenizer))
+    token_embeds = text_encoder.get_input_embeddings().weight.data
+    for token_ids, embeds in token_ids_embeds:
+      for token_id, embed in zip(token_ids, embeds):
+        token_embeds[token_id] = embed
+
   # promptを取得する
   if args.from_file is not None:
     print(f"reading prompts from {args.from_file}")
@@ -2130,9 +2254,12 @@ def main(args):
 
   prev_image = None               # for VGG16 guided
   if args.guide_image_path is not None:
-    print(f"load image for CLIP/VGG16 guidance: {args.guide_image_path}")
-    guide_images = load_images(args.guide_image_path)
-    print(f"loaded {len(guide_images)} guide images for CLIP/VGG16 guidance")
+    print(f"load image for CLIP/VGG16/ControlNet guidance: {args.guide_image_path}")
+    guide_images = []
+    for p in args.guide_image_path:
+      guide_images.extend(load_images(p))
+
+    print(f"loaded {len(guide_images)} guide images for guidance")
     if len(guide_images) == 0:
       print(f"No guide image, use previous generated image. / ガイド画像がありません。直前に生成した画像を使います: {args.image_path}")
       guide_images = None
@@ -2158,38 +2285,42 @@ def main(args):
   os.makedirs(args.outdir, exist_ok=True)
   max_embeddings_multiples = 1 if args.max_embeddings_multiples is None else args.max_embeddings_multiples
 
-  for iter in range(args.n_iter):
-    print(f"iteration {iter+1}/{args.n_iter}")
+  for gen_iter in range(args.n_iter):
+    print(f"iteration {gen_iter+1}/{args.n_iter}")
     iter_seed = random.randint(0, 0x7fffffff)
 
     # バッチ処理の関数
-    def process_batch(batch, highres_fix, highres_1st=False):
+    def process_batch(batch: List[BatchData], highres_fix, highres_1st=False):
       batch_size = len(batch)
 
       # highres_fixの処理
       if highres_fix and not highres_1st:
-        # 1st stageのバッチを作成して呼び出す
+        # 1st stageのバッチを作成して呼び出す：サイズを小さくして呼び出す
         print("process 1st stage1")
         batch_1st = []
-        for params1, (width, height, steps, scale, negative_scale, strength) in batch:
-          width_1st = int(width * args.highres_fix_scale + .5)
-          height_1st = int(height * args.highres_fix_scale + .5)
+        for base, ext in batch:
+          width_1st = int(ext.width * args.highres_fix_scale + .5)
+          height_1st = int(ext.height * args.highres_fix_scale + .5)
           width_1st = width_1st - width_1st % 32
           height_1st = height_1st - height_1st % 32
-          batch_1st.append((params1, (width_1st, height_1st, args.highres_fix_steps, scale, negative_scale, strength)))
+
+          ext_1st = BatchDataExt(width_1st, height_1st, args.highres_fix_steps, ext.scale,
+                                 ext.negative_scale, ext.strength, ext.network_muls)
+          batch_1st.append(BatchData(base, ext_1st))
         images_1st = process_batch(batch_1st, True, True)
 
         # 2nd stageのバッチを作成して以下処理する
         print("process 2nd stage1")
         batch_2nd = []
-        for i, (b1, image) in enumerate(zip(batch, images_1st)):
-          image = image.resize((width, height), resample=PIL.Image.LANCZOS)
-          (step, prompt, negative_prompt, seed, _, _, clip_prompt, guide_image), params2 = b1
-          batch_2nd.append(((step, prompt, negative_prompt, seed+1, image, None, clip_prompt, guide_image), params2))
+        for i, (bd, image) in enumerate(zip(batch, images_1st)):
+          image = image.resize((bd.ext.width, bd.ext.height), resample=PIL.Image.LANCZOS)      # img2imgとして設定
+          bd_2nd = BatchData(BatchDataBase(*bd.base[0:3], bd.base.seed+1, image, None, *bd.base[6:]), bd.ext)
+          batch_2nd.append(bd_2nd)
         batch = batch_2nd
 
-      (step_first, _, _, _, init_image, mask_image, _, guide_image), (width,
-                                                                      height, steps, scale, negative_scale, strength) = batch[0]
+      # このバッチの情報を取り出す
+      (step_first, _, _, _, init_image, mask_image, _, guide_image), \
+          (width, height, steps, scale, negative_scale, strength, network_muls) = batch[0]
       noise_shape = (LATENT_CHANNELS, height // DOWNSAMPLING_FACTOR, width // DOWNSAMPLING_FACTOR)
 
       prompts = []
@@ -2239,9 +2370,13 @@ def main(args):
             all_masks_are_same = mask_images[-2] is mask_image
 
         if guide_image is not None:
-          guide_images.append(guide_image)
-          if i > 0 and all_guide_images_are_same:
-            all_guide_images_are_same = guide_images[-2] is guide_image
+          if type(guide_image) is list:
+            guide_images.extend(guide_image)
+            all_guide_images_are_same = False
+          else:
+            guide_images.append(guide_image)
+            if i > 0 and all_guide_images_are_same:
+              all_guide_images_are_same = guide_images[-2] is guide_image
 
         # make start code
         torch.manual_seed(seed)
@@ -2264,7 +2399,19 @@ def main(args):
       if guide_images is not None and all_guide_images_are_same:
         guide_images = guide_images[0]
 
+      # ControlNet使用時はguide imageをリサイズする
+      if control_nets:
+        # TODO resampleのメソッド
+        guide_images = guide_images if type(guide_images) == list else [guide_images]
+        guide_images = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in guide_images]
+        if len(guide_images) == 1:
+          guide_images = guide_images[0]
+
       # generate
+      if networks:
+        for n, m in zip(networks, network_muls if network_muls else network_default_muls):
+          n.set_multiplier(m)
+
       images = pipe(prompts, negative_prompts, init_images, mask_images, height, width, steps, scale, negative_scale, strength, latents=start_code,
                     output_type='pil', max_embeddings_multiples=max_embeddings_multiples, img2img_noise=i2i_noises, clip_prompts=clip_prompts, clip_guide_images=guide_images)[0]
       if highres_1st and not args.highres_fix_save_1st:
@@ -2342,6 +2489,7 @@ def main(args):
       strength = 0.8 if args.strength is None else args.strength
       negative_prompt = ""
       clip_prompt = None
+      network_muls = None
 
       prompt_args = prompt.strip().split(' --')
       prompt = prompt_args[0]
@@ -2405,6 +2553,15 @@ def main(args):
             clip_prompt = m.group(1)
             print(f"clip prompt: {clip_prompt}")
             continue
+
+          m = re.match(r'am ([\d\.\-,]+)', parg, re.IGNORECASE)
+          if m:               # network multiplies
+            network_muls = [float(v) for v in m.group(1).split(",")]
+            while len(network_muls) < len(networks):
+              network_muls.append(network_muls[-1])
+            print(f"network mul: {network_muls}")
+            continue
+
         except ValueError as ex:
           print(f"Exception in parsing / 解析エラー: {parg}")
           print(ex)
@@ -2442,7 +2599,12 @@ def main(args):
           mask_image = mask_images[global_step % len(mask_images)]
 
         if guide_images is not None:
-          guide_image = guide_images[global_step % len(guide_images)]
+          if control_nets:                                                        # 複数件の場合あり
+            c = len(control_nets)
+            p = global_step % (len(guide_images) // c)
+            guide_image = guide_images[p * c:p * c + c]
+          else:
+            guide_image = guide_images[global_step % len(guide_images)]
         elif args.clip_image_guidance_scale > 0 or args.vgg16_guidance_scale > 0:
           if prev_image is None:
             print("Generate 1st image without guide image.")
@@ -2450,9 +2612,8 @@ def main(args):
             print("Use previous image as guide image.")
             guide_image = prev_image
 
-        # TODO named tupleか何かにする
-        b1 = ((global_step, prompt, negative_prompt, seed, init_image, mask_image, clip_prompt, guide_image),
-              (width, height, steps, scale, negative_scale, strength))
+        b1 = BatchData(BatchDataBase(global_step, prompt, negative_prompt, seed, init_image, mask_image, clip_prompt, guide_image),
+                       BatchDataExt(width, height, steps, scale, negative_scale, strength, tuple(network_muls) if network_muls else None))
         if len(batch_data) > 0 and batch_data[-1][1] != b1[1]:  # バッチ分割必要？
           process_batch(batch_data, highres_fix)
           batch_data.clear()
@@ -2518,18 +2679,21 @@ if __name__ == '__main__':
   parser.add_argument("--bf16", action='store_true', help='use bfloat16 / bfloat16を指定し省メモリ化する')
   parser.add_argument("--xformers", action='store_true', help='use xformers / xformersを使用し高速化する')
   parser.add_argument("--diffusers_xformers", action='store_true',
-                      help='use xformers by diffusers (Hypernetworks doen\'t work) / Diffusersでxformersを使用する（Hypernetwork利用不可）')
+                      help='use xformers by diffusers (Hypernetworks doesn\'t work) / Diffusersでxformersを使用する（Hypernetwork利用不可）')
   parser.add_argument("--opt_channels_last", action='store_true',
-                      help='set channels last option to model / モデルにchannles lastを指定し最適化する')
+                      help='set channels last option to model / モデルにchannels lastを指定し最適化する')
   parser.add_argument("--network_module", type=str, default=None, nargs='*',
-                      help='Hypernetwork module to use / Hypernetworkを使う時そのモジュール名')
+                      help='additional network module to use / 追加ネットワークを使う時そのモジュール名')
   parser.add_argument("--network_weights", type=str, default=None, nargs='*',
-                      help='Hypernetwork weights to load / Hypernetworkの重み')
-  parser.add_argument("--network_mul", type=float, default=None, nargs='*', help='Hypernetwork multiplier / Hypernetworkの効果の倍率')
-  parser.add_argument("--network_dim", type=int, default=None, nargs='*',
-                      help='network dimensions (depends on each network) / モジュールの次元数（ネットワークにより定義は異なります）')
+                      help='additional network weights to load / 追加ネットワークの重み')
+  parser.add_argument("--network_mul", type=float, default=None, nargs='*',
+                      help='additional network multiplier / 追加ネットワークの効果の倍率')
   parser.add_argument("--network_args", type=str, default=None, nargs='*',
                       help='additional argmuments for network (key=value) / ネットワークへの追加の引数')
+  parser.add_argument("--network_show_meta", action='store_true',
+                      help='show metadata of network model / ネットワークモデルのメタデータを表示する')
+  parser.add_argument("--textual_inversion_embeddings", type=str, default=None, nargs='*',
+                      help='Embeddings files of Textual Inversion / Textual Inversionのembeddings')
   parser.add_argument("--clip_skip", type=int, default=None, help='layer number from bottom to use in CLIP / CLIPの後ろからn層目の出力を使う')
   parser.add_argument("--max_embeddings_multiples", type=int, default=None,
                       help='max embeding multiples, max token length is 75 * multiples / トークン長をデフォルトの何倍とするか 75*この値 がトークン長となる')
@@ -2541,7 +2705,8 @@ if __name__ == '__main__':
                       help='enable VGG16 guided SD by image, scale for guidance / 画像によるVGG16 guided SDを有効にしてこのscaleを適用する')
   parser.add_argument("--vgg16_guidance_layer", type=int, default=20,
                       help='layer of VGG16 to calculate contents guide (1~30, 20 for conv4_2) / VGG16のcontents guideに使うレイヤー番号 (1~30、20はconv4_2)')
-  parser.add_argument("--guide_image_path", type=str, default=None, help="image to CLIP guidance / CLIP guided SDでガイドに使う画像")
+  parser.add_argument("--guide_image_path", type=str, default=None, nargs="*",
+                      help="image to CLIP guidance / CLIP guided SDでガイドに使う画像")
   parser.add_argument("--highres_fix_scale", type=float, default=None,
                       help="enable highres fix, reso scale for 1st stage / highres fixを有効にして最初の解像度をこのscaleにする")
   parser.add_argument("--highres_fix_steps", type=int, default=28,
@@ -2551,5 +2716,13 @@ if __name__ == '__main__':
   parser.add_argument("--negative_scale", type=float, default=None,
                       help="set another guidance scale for negative prompt / ネガティブプロンプトのscaleを指定する")
 
+  parser.add_argument("--control_net_models", type=str, default=None, nargs='*',
+                      help='ControlNet models to use / 使用するControlNetのモデル名')
+  parser.add_argument("--control_net_preps", type=str, default=None, nargs='*',
+                      help='ControlNet preprocess to use / 使用するControlNetのプリプロセス名')
+  parser.add_argument("--control_net_weights", type=float, default=None, nargs='*', help='ControlNet weights / ControlNetの重み')
+  parser.add_argument("--control_net_ratios", type=float, default=None, nargs='*',
+                      help='ControlNet guidance ratio for steps / ControlNetでガイドするステップ比率')
+
   args = parser.parse_args()
   main(args)

library/model_util.py

@@ -16,7 +16,7 @@ BETA_END = 0.0120
 UNET_PARAMS_MODEL_CHANNELS = 320
 UNET_PARAMS_CHANNEL_MULT = [1, 2, 4, 4]
 UNET_PARAMS_ATTENTION_RESOLUTIONS = [4, 2, 1]
-UNET_PARAMS_IMAGE_SIZE = 32  # unused
+UNET_PARAMS_IMAGE_SIZE = 64  # fixed from old invalid value `32`
 UNET_PARAMS_IN_CHANNELS = 4
 UNET_PARAMS_OUT_CHANNELS = 4
 UNET_PARAMS_NUM_RES_BLOCKS = 2
@@ -1163,15 +1163,14 @@ def make_bucket_resolutions(max_reso, min_size=256, max_size=1024, divisible=64)
 
   resos = list(resos)
   resos.sort()
-
-  aspect_ratios = [w / h for w, h in resos]
-  return resos, aspect_ratios
+  return resos
 
 
 if __name__ == '__main__':
-  resos, aspect_ratios = make_bucket_resolutions((512, 768))
+  resos = make_bucket_resolutions((512, 768))
   print(len(resos))
   print(resos)
+  aspect_ratios = [w / h for w, h in resos]
   print(aspect_ratios)
 
   ars = set()

networks/check_lora_weights.py

@@ -0,0 +1,32 @@
+import argparse
+import os
+import torch
+from safetensors.torch import load_file
+
+
+def main(file):
+  print(f"loading: {file}")
+  if os.path.splitext(file)[1] == '.safetensors':
+    sd = load_file(file)
+  else:
+    sd = torch.load(file, map_location='cpu')
+
+  values = []
+
+  keys = list(sd.keys())
+  for key in keys:
+    if 'lora_up' in key or 'lora_down' in key:
+      values.append((key, sd[key]))
+  print(f"number of LoRA modules: {len(values)}")
+
+  for key, value in values:
+    value = value.to(torch.float32)
+    print(f"{key},{torch.mean(torch.abs(value))},{torch.min(torch.abs(value))}")
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument("file", type=str, help="model file to check / 重みを確認するモデルファイル")
+  args = parser.parse_args()
+
+  main(args.file)

networks/extract_lora_from_models.py

@@ -44,9 +44,9 @@ def svd(args):
   print(f"loading SD model : {args.model_tuned}")
   text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_tuned)
 
-  # create LoRA network to extract weights
-  lora_network_o = lora.create_network(1.0, args.dim, None, text_encoder_o, unet_o)
-  lora_network_t = lora.create_network(1.0, args.dim, None, text_encoder_t, unet_t)
+  # create LoRA network to extract weights: Use dim (rank) as alpha
+  lora_network_o = lora.create_network(1.0, args.dim, args.dim, None, text_encoder_o, unet_o)
+  lora_network_t = lora.create_network(1.0, args.dim, args.dim, None, text_encoder_t, unet_t)
   assert len(lora_network_o.text_encoder_loras) == len(
       lora_network_t.text_encoder_loras), f"model version is different (SD1.x vs SD2.x) / それぞれのモデルのバージョンが違います（SD1.xベースとSD2.xベース） "
 
@@ -77,10 +77,10 @@ def svd(args):
     module_t = lora_t.org_module
     diff = module_t.weight - module_o.weight
     diff = diff.float()
-    
+
     if args.device:
       diff = diff.to(args.device)
-      
+
     diffs[lora_name] = diff
 
   # make LoRA with svd
@@ -116,6 +116,9 @@ def svd(args):
   print(f"LoRA has {len(lora_sd)} weights.")
 
   for key in list(lora_sd.keys()):
+    if "alpha" in key:
+      continue
+
     lora_name = key.split('.')[0]
     i = 0 if "lora_up" in key else 1
 
@@ -124,7 +127,7 @@ def svd(args):
     if len(lora_sd[key].size()) == 4:
       weights = weights.unsqueeze(2).unsqueeze(3)
 
-    assert weights.size() == lora_sd[key].size()
+    assert weights.size() == lora_sd[key].size(), f"size unmatch: {key}"
     lora_sd[key] = weights
 
   # load state dict to LoRA and save it
@@ -135,7 +138,10 @@ def svd(args):
   if dir_name and not os.path.exists(dir_name):
     os.makedirs(dir_name, exist_ok=True)
 
-  lora_network_o.save_weights(args.save_to, save_dtype, {})
+  # minimum metadata
+  metadata = {"ss_network_dim": str(args.dim), "ss_network_alpha": str(args.dim)}
+
+  lora_network_o.save_weights(args.save_to, save_dtype, metadata)
   print(f"LoRA weights are saved to: {args.save_to}")
 
 
@@ -151,8 +157,8 @@ if __name__ == '__main__':
                       help="Stable Diffusion tuned model, LoRA is difference of `original to tuned`: ckpt or safetensors file / 派生モデル（生成されるLoRAは元→派生の差分になります）、ckptまたはsafetensors")
   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("--dim", type=int, default=4, help="dimension of LoRA (default 4) / LoRAの次元数（デフォルト4）")
-  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("--dim", type=int, default=4, help="dimension (rank) of LoRA (default 4) / LoRAの次元数（rank）（デフォルト4）")
+  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")
 
   args = parser.parse_args()
   svd(args)

networks/lora.py

@@ -5,17 +5,22 @@
 
 import math
 import os
+from typing import List
 import torch
 
+from library import train_util
+
 
 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):
+  def __init__(self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1):
+    """ if alpha == 0 or None, alpha is rank (no scaling). """
     super().__init__()
     self.lora_name = lora_name
+    self.lora_dim = lora_dim
 
     if org_module.__class__.__name__ == 'Conv2d':
       in_dim = org_module.in_channels
@@ -28,6 +33,12 @@ class LoRAModule(torch.nn.Module):
       self.lora_down = torch.nn.Linear(in_dim, lora_dim, bias=False)
       self.lora_up = torch.nn.Linear(lora_dim, out_dim, bias=False)
 
+    if type(alpha) == torch.Tensor:
+      alpha = alpha.detach().float().numpy()                              # without casting, bf16 causes error
+    alpha = 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))
     torch.nn.init.zeros_(self.lora_up.weight)
@@ -41,13 +52,37 @@ class LoRAModule(torch.nn.Module):
     del self.org_module
 
   def forward(self, x):
-    return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier
+    return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
 
 
-def create_network(multiplier, network_dim, vae, text_encoder, unet, **kwargs):
+def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **kwargs):
   if network_dim is None:
     network_dim = 4                     # default
-  network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim)
+  network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim, alpha=network_alpha)
+  return network
+
+
+def create_network_from_weights(multiplier, file, vae, text_encoder, unet, **kwargs):
+  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 (rank)
+  network_alpha = None
+  network_dim = None
+  for key, value in weights_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 None:
+    network_alpha = network_dim
+
+  network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim, alpha=network_alpha)
+  network.weights_sd = weights_sd
   return network
 
 
@@ -57,13 +92,14 @@ class LoRANetwork(torch.nn.Module):
   LORA_PREFIX_UNET = 'lora_unet'
   LORA_PREFIX_TEXT_ENCODER = 'lora_te'
 
-  def __init__(self, text_encoder, unet, multiplier=1.0, lora_dim=4) -> None:
+  def __init__(self, text_encoder, unet, multiplier=1.0, lora_dim=4, alpha=1) -> None:
     super().__init__()
     self.multiplier = multiplier
     self.lora_dim = lora_dim
+    self.alpha = alpha
 
     # create module instances
-    def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> list[LoRAModule]:
+    def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> List[LoRAModule]:
       loras = []
       for name, module in root_module.named_modules():
         if module.__class__.__name__ in target_replace_modules:
@@ -71,7 +107,7 @@ class LoRANetwork(torch.nn.Module):
             if child_module.__class__.__name__ == "Linear" or (child_module.__class__.__name__ == "Conv2d" and child_module.kernel_size == (1, 1)):
               lora_name = prefix + '.' + name + '.' + child_name
               lora_name = lora_name.replace('.', '_')
-              lora = LoRAModule(lora_name, child_module, self.multiplier, self.lora_dim)
+              lora = LoRAModule(lora_name, child_module, self.multiplier, self.lora_dim, self.alpha)
               loras.append(lora)
       return loras
 
@@ -90,6 +126,11 @@ class LoRANetwork(torch.nn.Module):
       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, safe_open
@@ -149,21 +190,21 @@ class LoRANetwork(torch.nn.Module):
       return params
 
     self.requires_grad_(True)
-    params = []
+    all_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
-      params.append(param_data)
+      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
-      params.append(param_data)
+      all_params.append(param_data)
 
-    return params
+    return all_params
 
   def prepare_grad_etc(self, text_encoder, unet):
     self.requires_grad_(True)
@@ -188,6 +229,14 @@ class LoRANetwork(torch.nn.Module):
 
     if os.path.splitext(file)[1] == '.safetensors':
       from safetensors.torch import save_file
+
+      # 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)

networks/lora_interrogator.py

@@ -0,0 +1,122 @@
+
+
+from tqdm import tqdm
+from library import model_util
+import argparse
+from transformers import CLIPTokenizer
+import torch
+
+import library.model_util as model_util
+import lora
+
+TOKENIZER_PATH = "openai/clip-vit-large-patch14"
+V2_STABLE_DIFFUSION_PATH = "stabilityai/stable-diffusion-2"     # ここからtokenizerだけ使う
+
+DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+
+def interrogate(args):
+  # いろいろ準備する
+  print(f"loading SD model: {args.sd_model}")
+  text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
+
+  print(f"loading LoRA: {args.model}")
+  network = lora.create_network_from_weights(1.0, args.model, vae, text_encoder, unet)
+
+  # text encoder向けの重みがあるかチェックする：本当はlora側でやるのがいい
+  has_te_weight = False
+  for key in network.weights_sd.keys():
+    if 'lora_te' in key:
+      has_te_weight = True
+      break
+  if not has_te_weight:
+    print("This LoRA does not have modules for Text Encoder, cannot interrogate / このLoRAはText Encoder向けのモジュールがないため調査できません")
+    return
+  del vae
+
+  print("loading tokenizer")
+  if args.v2:
+    tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(V2_STABLE_DIFFUSION_PATH, subfolder="tokenizer")
+  else:
+    tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(TOKENIZER_PATH)  # , model_max_length=max_token_length + 2)
+
+  text_encoder.to(DEVICE)
+  text_encoder.eval()
+  unet.to(DEVICE)
+  unet.eval()               # U-Netは呼び出さないので不要だけど
+
+  # トークンをひとつひとつ当たっていく
+  token_id_start = 0
+  token_id_end = max(tokenizer.all_special_ids)
+  print(f"interrogate tokens are: {token_id_start} to {token_id_end}")
+
+  def get_all_embeddings(text_encoder):
+    embs = []
+    with torch.no_grad():
+      for token_id in tqdm(range(token_id_start, token_id_end + 1, args.batch_size)):
+        batch = []
+        for tid in range(token_id, min(token_id_end + 1, token_id + args.batch_size)):
+          tokens = [tokenizer.bos_token_id, tid, tokenizer.eos_token_id]
+          # tokens = [tid]                                                    # こちらは結果がいまひとつ
+          batch.append(tokens)
+
+        # batch_embs = text_encoder(torch.tensor(batch).to(DEVICE))[0].to("cpu")  # bos/eosも含めたほうが差が出るようだ [:, 1]
+        # clip skip対応
+        batch = torch.tensor(batch).to(DEVICE)
+        if args.clip_skip is None:
+          encoder_hidden_states = text_encoder(batch)[0]
+        else:
+          enc_out = text_encoder(batch, output_hidden_states=True, return_dict=True)
+          encoder_hidden_states = enc_out['hidden_states'][-args.clip_skip]
+          encoder_hidden_states = text_encoder.text_model.final_layer_norm(encoder_hidden_states)
+        encoder_hidden_states = encoder_hidden_states.to("cpu")
+
+        embs.extend(encoder_hidden_states)
+    return torch.stack(embs)
+
+  print("get original text encoder embeddings.")
+  orig_embs = get_all_embeddings(text_encoder)
+
+  network.apply_to(text_encoder, unet, True, len(network.unet_loras) > 0)
+  network.to(DEVICE)
+  network.eval()
+
+  print("You can ignore warning messages start with '_IncompatibleKeys' (LoRA model does not have alpha because trained by older script) / '_IncompatibleKeys'の警告は無視して構いません（以前のスクリプトで学習されたLoRAモデルのためalphaの定義がありません）")
+  print("get text encoder embeddings with lora.")
+  lora_embs = get_all_embeddings(text_encoder)
+
+  # 比べる：とりあえず単純に差分の絶対値で
+  print("comparing...")
+  diffs = {}
+  for i, (orig_emb, lora_emb) in enumerate(zip(orig_embs, tqdm(lora_embs))):
+    diff = torch.mean(torch.abs(orig_emb - lora_emb))
+    # diff = torch.mean(torch.cosine_similarity(orig_emb, lora_emb, dim=1))       # うまく検出できない
+    diff = float(diff.detach().to('cpu').numpy())
+    diffs[token_id_start + i] = diff
+
+  diffs_sorted = sorted(diffs.items(), key=lambda x: -x[1])
+
+  # 結果を表示する
+  print("top 100:")
+  for i, (token, diff) in enumerate(diffs_sorted[:100]):
+    # if diff < 1e-6:
+    #   break
+    string = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens([token]))
+    print(f"[{i:3d}]: {token:5d} {string:<20s}: {diff:.5f}")
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--v2", action='store_true',
+                      help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む')
+  parser.add_argument("--sd_model", type=str, default=None,
+                      help="Stable Diffusion model to load: ckpt or safetensors file / 読み込むSDのモデル、ckptまたはsafetensors")
+  parser.add_argument("--model", type=str, default=None,
+                      help="LoRA model to interrogate: ckpt or safetensors file / 調査するLoRAモデル、ckptまたはsafetensors")
+  parser.add_argument("--batch_size", type=int, default=16,
+                      help="batch size for processing with Text Encoder / Text Encoderで処理するときのバッチサイズ")
+  parser.add_argument("--clip_skip", type=int, default=None,
+                      help="use output of nth layer from back of text encoder (n>=1) / text encoderの後ろからn番目の層の出力を用いる（nは1以上）")
+
+  args = parser.parse_args()
+  interrogate(args)

networks/merge_lora.py

@@ -1,5 +1,5 @@
 
-
+import math
 import argparse
 import os
 import torch
@@ -61,6 +61,7 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
     for key in lora_sd.keys():
       if "lora_down" in key:
         up_key = key.replace("lora_down", "lora_up")
+        alpha_key = key[:key.index("lora_down")] + 'alpha'
 
         # find original module for this lora
         module_name = '.'.join(key.split('.')[:-2])               # remove trailing ".lora_down.weight"
@@ -73,33 +74,85 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
         down_weight = lora_sd[key]
         up_weight = lora_sd[up_key]
 
+        dim = down_weight.size()[0]
+        alpha = lora_sd.get(alpha_key, dim)
+        scale = alpha / dim
+
         # W <- W + U * D
         weight = module.weight
         if len(weight.size()) == 2:
           # linear
-          weight = weight + ratio * (up_weight @ down_weight)
+          weight = weight + ratio * (up_weight @ down_weight) * scale
         else:
           # conv2d
-          weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
+          weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)
+                                     ).unsqueeze(2).unsqueeze(3) * scale
 
         module.weight = torch.nn.Parameter(weight)
 
 
 def merge_lora_models(models, ratios, merge_dtype):
-  merged_sd = {}
+  base_alphas = {}                          # alpha for merged model
+  base_dims = {}
 
+  merged_sd = {}
   for model, ratio in zip(models, ratios):
     print(f"loading: {model}")
     lora_sd = load_state_dict(model, merge_dtype)
 
+    # get alpha and dim
+    alphas = {}                             # alpha for current model
+    dims = {}                               # dims for current model
+    for key in lora_sd.keys():
+      if 'alpha' in key:
+        lora_module_name = key[:key.rfind(".alpha")]
+        alpha = float(lora_sd[key].detach().numpy())
+        alphas[lora_module_name] = alpha
+        if lora_module_name not in base_alphas:
+          base_alphas[lora_module_name] = alpha
+      elif "lora_down" in key:
+        lora_module_name = key[:key.rfind(".lora_down")]
+        dim = lora_sd[key].size()[0]
+        dims[lora_module_name] = dim
+        if lora_module_name not in base_dims:
+          base_dims[lora_module_name] = dim
+
+    for lora_module_name in dims.keys():
+      if lora_module_name not in alphas:
+        alpha = dims[lora_module_name]
+        alphas[lora_module_name] = alpha
+        if lora_module_name not in base_alphas:
+          base_alphas[lora_module_name] = alpha
+    
+    print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
+
+    # merge
     print(f"merging...")
     for key in lora_sd.keys():
+      if 'alpha' in key:
+        continue
+
+      lora_module_name = key[:key.rfind(".lora_")]
+
+      base_alpha = base_alphas[lora_module_name]
+      alpha = alphas[lora_module_name]
+
+      scale = math.sqrt(alpha / base_alpha) * ratio
+
       if key in merged_sd:
         assert merged_sd[key].size() == lora_sd[key].size(
         ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
-        merged_sd[key] = merged_sd[key] + lora_sd[key] * ratio
+        merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
       else:
-        merged_sd[key] = lora_sd[key] * ratio
+        merged_sd[key] = lora_sd[key] * scale
+  
+  # set alpha to sd
+  for lora_module_name, alpha in base_alphas.items():
+    key = lora_module_name + ".alpha"
+    merged_sd[key] = torch.tensor(alpha)
+
+  print("merged model")
+  print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
 
   return merged_sd
 
@@ -145,7 +198,7 @@ if __name__ == '__main__':
   parser.add_argument("--save_precision", type=str, default=None,
                       choices=[None, "float", "fp16", "bf16"], help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ")
   parser.add_argument("--precision", type=str, default="float",
-                      choices=["float", "fp16", "bf16"], help="precision in merging / マージの計算時の精度")
+                      choices=["float", "fp16", "bf16"], help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）")
   parser.add_argument("--sd_model", type=str, default=None,
                       help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする")
   parser.add_argument("--save_to", type=str, default=None,

networks/merge_lora_old.py

@@ -0,0 +1,179 @@
+
+
+import argparse
+import os
+import torch
+from safetensors.torch import load_file, save_file
+import library.model_util as model_util
+import lora
+
+
+def load_state_dict(file_name, dtype):
+  if os.path.splitext(file_name)[1] == '.safetensors':
+    sd = load_file(file_name)
+  else:
+    sd = torch.load(file_name, map_location='cpu')
+  for key in list(sd.keys()):
+    if type(sd[key]) == torch.Tensor:
+      sd[key] = sd[key].to(dtype)
+  return sd
+
+
+def save_to_file(file_name, model, state_dict, dtype):
+  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 os.path.splitext(file_name)[1] == '.safetensors':
+    save_file(model, file_name)
+  else:
+    torch.save(model, file_name)
+
+
+def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
+  text_encoder.to(merge_dtype)
+  unet.to(merge_dtype)
+
+  # create module map
+  name_to_module = {}
+  for i, root_module in enumerate([text_encoder, unet]):
+    if i == 0:
+      prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
+      target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
+    else:
+      prefix = lora.LoRANetwork.LORA_PREFIX_UNET
+      target_replace_modules = lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE
+
+    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():
+          if child_module.__class__.__name__ == "Linear" or (child_module.__class__.__name__ == "Conv2d" and child_module.kernel_size == (1, 1)):
+            lora_name = prefix + '.' + name + '.' + child_name
+            lora_name = lora_name.replace('.', '_')
+            name_to_module[lora_name] = child_module
+
+  for model, ratio in zip(models, ratios):
+    print(f"loading: {model}")
+    lora_sd = load_state_dict(model, merge_dtype)
+
+    print(f"merging...")
+    for key in lora_sd.keys():
+      if "lora_down" in key:
+        up_key = key.replace("lora_down", "lora_up")
+        alpha_key = key[:key.index("lora_down")] + 'alpha'
+
+        # find original module for this lora
+        module_name = '.'.join(key.split('.')[:-2])               # remove trailing ".lora_down.weight"
+        if module_name not in name_to_module:
+          print(f"no module found for LoRA weight: {key}")
+          continue
+        module = name_to_module[module_name]
+        # print(f"apply {key} to {module}")
+
+        down_weight = lora_sd[key]
+        up_weight = lora_sd[up_key]
+
+        dim = down_weight.size()[0]
+        alpha = lora_sd.get(alpha_key, dim)
+        scale = alpha / dim
+
+        # W <- W + U * D
+        weight = module.weight
+        if len(weight.size()) == 2:
+          # linear
+          weight = weight + ratio * (up_weight @ down_weight) * scale
+        else:
+          # conv2d
+          weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3) * scale
+
+        module.weight = torch.nn.Parameter(weight)
+
+
+def merge_lora_models(models, ratios, merge_dtype):
+  merged_sd = {}
+
+  alpha = None
+  dim = None
+  for model, ratio in zip(models, ratios):
+    print(f"loading: {model}")
+    lora_sd = load_state_dict(model, merge_dtype)
+
+    print(f"merging...")
+    for key in lora_sd.keys():
+      if 'alpha' in key:
+        if key in merged_sd:
+          assert merged_sd[key] == lora_sd[key], f"alpha mismatch / alphaが異なる場合、現時点ではマージできません"
+        else:
+          alpha = lora_sd[key].detach().numpy()
+          merged_sd[key] = lora_sd[key]
+      else:
+        if key in merged_sd:
+          assert merged_sd[key].size() == lora_sd[key].size(
+          ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
+          merged_sd[key] = merged_sd[key] + lora_sd[key] * ratio
+        else:
+          if "lora_down" in key:
+            dim = lora_sd[key].size()[0]
+          merged_sd[key] = lora_sd[key] * ratio
+
+  print(f"dim (rank): {dim}, alpha: {alpha}")
+  if alpha is None:
+    alpha = dim
+
+  return merged_sd, dim, alpha
+
+
+def merge(args):
+  assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
+
+  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(args.precision)
+  save_dtype = str_to_dtype(args.save_precision)
+  if save_dtype is None:
+    save_dtype = merge_dtype
+
+  if args.sd_model is not None:
+    print(f"loading SD model: {args.sd_model}")
+
+    text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
+
+    merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
+
+    print(f"saving SD model to: {args.save_to}")
+    model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet,
+                                                args.sd_model, 0, 0, save_dtype, vae)
+  else:
+    state_dict, _, _ = merge_lora_models(args.models, args.ratios, merge_dtype)
+
+    print(f"saving model to: {args.save_to}")
+    save_to_file(args.save_to, state_dict, state_dict, save_dtype)
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--v2", action='store_true',
+                      help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む')
+  parser.add_argument("--save_precision", type=str, default=None,
+                      choices=[None, "float", "fp16", "bf16"], help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ")
+  parser.add_argument("--precision", type=str, default="float",
+                      choices=["float", "fp16", "bf16"], help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）")
+  parser.add_argument("--sd_model", type=str, default=None,
+                      help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする")
+  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("--models", type=str, nargs='*',
+                      help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors")
+  parser.add_argument("--ratios", type=float, nargs='*',
+                      help="ratios for each model / それぞれのLoRAモデルの比率")
+
+  args = parser.parse_args()
+  merge(args)

networks/resize_lora.py

@@ -0,0 +1,198 @@
+# 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 and kohya
+
+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
+
+
+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
+
+  for key in list(sd.keys()):
+    if type(sd[key]) == torch.Tensor:
+      sd[key] = sd[key].to(dtype)
+
+  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)
+
+  if model_util.is_safetensors(file_name):
+    save_file(model, file_name, metadata)
+  else:
+    torch.save(model, file_name)
+
+
+def resize_lora_model(lora_sd, new_rank, save_dtype, device, verbose):
+  network_alpha = None
+  network_dim = None
+  verbose_str = "\n"
+
+  CLAMP_QUANTILE = 0.99
+
+  # 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
+  new_alpha = float(scale*new_rank)  # calculate new alpha from scale
+
+  print(f"old dimension: {network_dim}, old alpha: {network_alpha}, new alpha: {new_alpha}")
+
+  lora_down_weight = None
+  lora_up_weight = None
+
+  o_lora_sd = lora_sd.copy()
+  block_down_name = None
+  block_up_name = None
+
+  print("resizing lora...")
+  with torch.no_grad():
+    for key, value in tqdm(lora_sd.items()):
+      if 'lora_down' in key:
+        block_down_name = key.split(".")[0]
+        lora_down_weight = value
+      if 'lora_up' in key:
+        block_up_name = key.split(".")[0]
+        lora_up_weight = value
+
+      weights_loaded = (lora_down_weight is not None and lora_up_weight is not None)
+
+      if (block_down_name == block_up_name) and weights_loaded:
+
+        conv2d = (len(lora_down_weight.size()) == 4)
+
+        if conv2d:
+          lora_down_weight = lora_down_weight.squeeze()
+          lora_up_weight = lora_up_weight.squeeze()
+
+        if device:
+          org_device = lora_up_weight.device
+          lora_up_weight = lora_up_weight.to(args.device)
+          lora_down_weight = lora_down_weight.to(args.device)
+
+        full_weight_matrix = torch.matmul(lora_up_weight, lora_down_weight)
+
+        U, S, Vh = torch.linalg.svd(full_weight_matrix)
+
+        if verbose:
+          s_sum = torch.sum(torch.abs(S))
+          s_rank = torch.sum(torch.abs(S[:new_rank]))
+          verbose_str+=f"{block_down_name:76} | "
+          verbose_str+=f"sum(S) retained: {(s_rank)/s_sum:.1%}, max(S) ratio: {S[0]/S[new_rank]:0.1f}\n"
+
+        U = U[:, :new_rank]
+        S = S[:new_rank]
+        U = U @ torch.diag(S)
+
+        Vh = Vh[:new_rank, :]
+
+        dist = torch.cat([U.flatten(), Vh.flatten()])
+        hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+        low_val = -hi_val
+
+        U = U.clamp(low_val, hi_val)
+        Vh = Vh.clamp(low_val, hi_val)
+
+        if conv2d:
+          U = U.unsqueeze(2).unsqueeze(3)
+          Vh = Vh.unsqueeze(2).unsqueeze(3)
+
+        if device:
+          U = U.to(org_device)
+          Vh = Vh.to(org_device)
+
+        o_lora_sd[block_down_name + "." + "lora_down.weight"] = Vh.to(save_dtype).contiguous()
+        o_lora_sd[block_up_name + "." + "lora_up.weight"] = U.to(save_dtype).contiguous()
+        o_lora_sd[block_up_name + "." "alpha"] = torch.tensor(new_alpha).to(save_dtype)
+
+        block_down_name = None
+        block_up_name = None
+        lora_down_weight = None
+        lora_up_weight = None
+        weights_loaded = False
+
+  if verbose:
+    print(verbose_str)
+  print("resizing complete")
+  return o_lora_sd, network_dim, new_alpha
+
+
+def resize(args):
+
+  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
+
+  print("loading Model...")
+  lora_sd, metadata = load_state_dict(args.model, merge_dtype)
+
+  print("resizing rank...")
+  state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.verbose)
+
+  # update metadata
+  if metadata is None:
+    metadata = {}
+
+  comment = metadata.get("ss_training_comment", "")
+  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)
+
+  model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
+  metadata["sshs_model_hash"] = model_hash
+  metadata["sshs_legacy_hash"] = legacy_hash
+
+  print(f"saving model to: {args.save_to}")
+  save_to_file(args.save_to, state_dict, state_dict, save_dtype, metadata)
+
+
+if __name__ == '__main__':
+  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")
+
+  args = parser.parse_args()
+  resize(args)

networks/svd_merge_lora.py

@@ -0,0 +1,164 @@
+
+import math
+import argparse
+import os
+import torch
+from safetensors.torch import load_file, save_file
+from tqdm import tqdm
+import library.model_util as model_util
+import lora
+
+
+CLAMP_QUANTILE = 0.99
+
+
+def load_state_dict(file_name, dtype):
+  if os.path.splitext(file_name)[1] == '.safetensors':
+    sd = load_file(file_name)
+  else:
+    sd = torch.load(file_name, map_location='cpu')
+  for key in list(sd.keys()):
+    if type(sd[key]) == torch.Tensor:
+      sd[key] = sd[key].to(dtype)
+  return sd
+
+
+def save_to_file(file_name, model, state_dict, dtype):
+  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 os.path.splitext(file_name)[1] == '.safetensors':
+    save_file(model, file_name)
+  else:
+    torch.save(model, file_name)
+
+
+def merge_lora_models(models, ratios, new_rank, device,  merge_dtype):
+  merged_sd = {}
+  for model, ratio in zip(models, ratios):
+    print(f"loading: {model}")
+    lora_sd = load_state_dict(model, merge_dtype)
+
+    # merge
+    print(f"merging...")
+    for key in tqdm(list(lora_sd.keys())):
+      if 'lora_down' not in key:
+        continue
+
+      lora_module_name = key[:key.rfind(".lora_down")]
+
+      down_weight = lora_sd[key]
+      network_dim = down_weight.size()[0]
+
+      up_weight = lora_sd[lora_module_name + '.lora_up.weight']
+      alpha = lora_sd.get(lora_module_name + '.alpha', network_dim)
+
+      in_dim = down_weight.size()[1]
+      out_dim = up_weight.size()[0]
+      conv2d = len(down_weight.size()) == 4
+      print(lora_module_name, network_dim, alpha, in_dim, out_dim)
+
+      # make original weight if not exist
+      if lora_module_name not in merged_sd:
+        weight = torch.zeros((out_dim, in_dim, 1, 1) if conv2d else (out_dim, in_dim), dtype=merge_dtype)
+        if device:
+          weight = weight.to(device)
+      else:
+        weight = merged_sd[lora_module_name]
+
+      # merge to weight
+      if device:
+        up_weight = up_weight.to(device)
+        down_weight = down_weight.to(device)
+
+      # W <- W + U * D
+      scale = (alpha / network_dim)
+      if not conv2d:        # linear
+        weight = weight + ratio * (up_weight @ down_weight) * scale
+      else:
+        weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)
+                                   ).unsqueeze(2).unsqueeze(3) * scale
+
+      merged_sd[lora_module_name] = weight
+
+  # extract from merged weights
+  print("extract new lora...")
+  merged_lora_sd = {}
+  with torch.no_grad():
+    for lora_module_name, mat in tqdm(list(merged_sd.items())):
+      conv2d = (len(mat.size()) == 4)
+      if conv2d:
+        mat = mat.squeeze()
+
+      U, S, Vh = torch.linalg.svd(mat)
+
+      U = U[:, :new_rank]
+      S = S[:new_rank]
+      U = U @ torch.diag(S)
+
+      Vh = Vh[:new_rank, :]
+
+      dist = torch.cat([U.flatten(), Vh.flatten()])
+      hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+      low_val = -hi_val
+
+      U = U.clamp(low_val, hi_val)
+      Vh = Vh.clamp(low_val, hi_val)
+
+      up_weight = U
+      down_weight = Vh
+
+      if conv2d:
+        up_weight = up_weight.unsqueeze(2).unsqueeze(3)
+        down_weight = down_weight.unsqueeze(2).unsqueeze(3)
+
+      merged_lora_sd[lora_module_name + '.lora_up.weight'] = up_weight.to("cpu").contiguous()
+      merged_lora_sd[lora_module_name + '.lora_down.weight'] = down_weight.to("cpu").contiguous()
+      merged_lora_sd[lora_module_name + '.alpha'] = torch.tensor(new_rank)
+
+  return merged_lora_sd
+
+
+def merge(args):
+  assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
+
+  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(args.precision)
+  save_dtype = str_to_dtype(args.save_precision)
+  if save_dtype is None:
+    save_dtype = merge_dtype
+
+  state_dict = merge_lora_models(args.models, args.ratios, args.new_rank, args.device, merge_dtype)
+
+  print(f"saving model to: {args.save_to}")
+  save_to_file(args.save_to, state_dict, state_dict, save_dtype)
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--save_precision", type=str, default=None,
+                      choices=[None, "float", "fp16", "bf16"], help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ")
+  parser.add_argument("--precision", type=str, default="float",
+                      choices=["float", "fp16", "bf16"], help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）")
+  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("--models", type=str, nargs='*',
+                      help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors")
+  parser.add_argument("--ratios", type=float, nargs='*',
+                      help="ratios for each model / それぞれのLoRAモデルの比率")
+  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("--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")
+
+  args = parser.parse_args()
+  merge(args)

requirements.txt

@@ -1,23 +1,24 @@
 accelerate==0.15.0
-transformers==4.25.1
-ftfy
-albumentations
-opencv-python
-einops
+transformers==4.26.0
+ftfy==6.1.1
+albumentations==1.3.0
+opencv-python==4.7.0.68
+einops==0.6.0
 diffusers[torch]==0.10.2
-pytorch_lightning
+pytorch-lightning==1.9.0
 bitsandbytes==0.35.0
-tensorboard
+tensorboard==2.10.1
 safetensors==0.2.6
-gradio
-altair
-easygui
+gradio==3.16.2
+altair==4.2.2
+easygui==0.98.3
 # for BLIP captioning
-requests
-timm==0.4.12
-fairscale==0.4.4
+requests==2.28.2
+timm==0.6.12
+fairscale==0.4.13
 # for WD14 captioning
-tensorflow<2.11
-huggingface-hub
+# tensorflow<2.11
+tensorflow==2.10.1
+huggingface-hub==0.12.0
 # for kohya_ss library
-.
+.

tools/canny.py

@@ -0,0 +1,24 @@
+import argparse
+import cv2
+
+
+def canny(args):
+  img = cv2.imread(args.input)
+  img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+  canny_img = cv2.Canny(img, args.thres1, args.thres2)
+  # canny_img = 255 - canny_img
+
+  cv2.imwrite(args.output, canny_img)
+  print("done!")
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--input", type=str, default=None, help="input path")
+  parser.add_argument("--output", type=str, default=None, help="output path")
+  parser.add_argument("--thres1", type=int, default=32, help="thres1")
+  parser.add_argument("--thres2", type=int, default=224, help="thres2")
+
+  args = parser.parse_args()
+  canny(args)

tools/convert_diffusers20_original_sd.py

@@ -1,8 +1,4 @@
 # convert Diffusers v1.x/v2.0 model to original Stable Diffusion
-# v1: initial version
-# v2: support safetensors
-# v3: fix to support another format
-# v4: support safetensors in Diffusers
 
 import argparse
 import os

tools/original_control_net.py

@@ -0,0 +1,320 @@
+from typing import List, NamedTuple, Any
+import numpy as np
+import cv2
+import torch
+from safetensors.torch import load_file
+
+from diffusers import UNet2DConditionModel
+from diffusers.models.unet_2d_condition import UNet2DConditionOutput
+
+import library.model_util as model_util
+
+
+class ControlNetInfo(NamedTuple):
+  unet: Any
+  net: Any
+  prep: Any
+  weight: float
+  ratio: float
+
+
+class ControlNet(torch.nn.Module):
+  def __init__(self) -> None:
+    super().__init__()
+
+    # make control model
+    self.control_model = torch.nn.Module()
+
+    dims = [320, 320, 320, 320, 640, 640, 640, 1280, 1280, 1280, 1280, 1280]
+    zero_convs = torch.nn.ModuleList()
+    for i, dim in enumerate(dims):
+      sub_list = torch.nn.ModuleList([torch.nn.Conv2d(dim, dim, 1)])
+      zero_convs.append(sub_list)
+    self.control_model.add_module("zero_convs", zero_convs)
+
+    middle_block_out = torch.nn.Conv2d(1280, 1280, 1)
+    self.control_model.add_module("middle_block_out", torch.nn.ModuleList([middle_block_out]))
+
+    dims = [16, 16, 32, 32, 96, 96, 256, 320]
+    strides = [1, 1, 2, 1, 2, 1, 2, 1]
+    prev_dim = 3
+    input_hint_block = torch.nn.Sequential()
+    for i, (dim, stride) in enumerate(zip(dims, strides)):
+      input_hint_block.append(torch.nn.Conv2d(prev_dim, dim, 3, stride, 1))
+      if i < len(dims) - 1:
+        input_hint_block.append(torch.nn.SiLU())
+      prev_dim = dim
+    self.control_model.add_module("input_hint_block", input_hint_block)
+
+
+def load_control_net(v2, unet, model):
+  device = unet.device
+
+  # control sdからキー変換しつつU-Netに対応する部分のみ取り出し、DiffusersのU-Netに読み込む
+  # state dictを読み込む
+  print(f"ControlNet: loading control SD model : {model}")
+
+  if model_util.is_safetensors(model):
+    ctrl_sd_sd = load_file(model)
+  else:
+    ctrl_sd_sd = torch.load(model, map_location='cpu')
+    ctrl_sd_sd = ctrl_sd_sd.pop("state_dict", ctrl_sd_sd)
+
+  # 重みをU-Netに読み込めるようにする。ControlNetはSD版のstate dictなので、それを読み込む
+  is_difference = "difference" in ctrl_sd_sd
+  print("ControlNet: loading difference")
+
+  # ControlNetには存在しないキーがあるので、まず現在のU-NetでSD版の全keyを作っておく
+  # またTransfer Controlの元weightとなる
+  ctrl_unet_sd_sd = model_util.convert_unet_state_dict_to_sd(v2, unet.state_dict())
+
+  # 元のU-Netに影響しないようにコピーする。またprefixが付いていないので付ける
+  for key in list(ctrl_unet_sd_sd.keys()):
+    ctrl_unet_sd_sd["model.diffusion_model." + key] = ctrl_unet_sd_sd.pop(key).clone()
+
+  zero_conv_sd = {}
+  for key in list(ctrl_sd_sd.keys()):
+    if key.startswith("control_"):
+      unet_key = "model.diffusion_" + key[len("control_"):]
+      if unet_key not in ctrl_unet_sd_sd:               # zero conv
+        zero_conv_sd[key] = ctrl_sd_sd[key]
+        continue
+      if is_difference:                                 # Transfer Control
+        ctrl_unet_sd_sd[unet_key] += ctrl_sd_sd[key].to(device, dtype=unet.dtype)
+      else:
+        ctrl_unet_sd_sd[unet_key] = ctrl_sd_sd[key].to(device, dtype=unet.dtype)
+
+  unet_config = model_util.create_unet_diffusers_config(v2)
+  ctrl_unet_du_sd = model_util.convert_ldm_unet_checkpoint(v2, ctrl_unet_sd_sd, unet_config)    # DiffUsers版ControlNetのstate dict
+
+  # ControlNetのU-Netを作成する
+  ctrl_unet = UNet2DConditionModel(**unet_config)
+  info = ctrl_unet.load_state_dict(ctrl_unet_du_sd)
+  print("ControlNet: loading Control U-Net:", info)
+
+  # U-Net以外のControlNetを作成する
+  # TODO support middle only
+  ctrl_net = ControlNet()
+  info = ctrl_net.load_state_dict(zero_conv_sd)
+  print("ControlNet: loading ControlNet:", info)
+
+  ctrl_unet.to(unet.device, dtype=unet.dtype)
+  ctrl_net.to(unet.device, dtype=unet.dtype)
+  return ctrl_unet, ctrl_net
+
+
+def load_preprocess(prep_type: str):
+  if prep_type is None or prep_type.lower() == "none":
+    return None
+
+  if prep_type.startswith("canny"):
+    args = prep_type.split("_")
+    th1 = int(args[1]) if len(args) >= 2 else 63
+    th2 = int(args[2]) if len(args) >= 3 else 191
+
+    def canny(img):
+      img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+      return cv2.Canny(img, th1, th2)
+    return canny
+
+  print("Unsupported prep type:", prep_type)
+  return None
+
+
+def preprocess_ctrl_net_hint_image(image):
+  image = np.array(image).astype(np.float32) / 255.0
+  image = image[:, :, ::-1].copy()                         # rgb to bgr
+  image = image[None].transpose(0, 3, 1, 2)       # nchw
+  image = torch.from_numpy(image)
+  return image                              # 0 to 1
+
+
+def get_guided_hints(control_nets: List[ControlNetInfo], num_latent_input, b_size, hints):
+  guided_hints = []
+  for i, cnet_info in enumerate(control_nets):
+    # hintは 1枚目の画像のcnet1, 1枚目の画像のcnet2, 1枚目の画像のcnet3, 2枚目の画像のcnet1, 2枚目の画像のcnet2 ... と並んでいること
+    b_hints = []
+    if len(hints) == 1:           # すべて同じ画像をhintとして使う
+      hint = hints[0]
+      if cnet_info.prep is not None:
+        hint = cnet_info.prep(hint)
+      hint = preprocess_ctrl_net_hint_image(hint)
+      b_hints = [hint for _ in range(b_size)]
+    else:
+      for bi in range(b_size):
+        hint = hints[(bi * len(control_nets) + i) % len(hints)]
+        if cnet_info.prep is not None:
+          hint = cnet_info.prep(hint)
+        hint = preprocess_ctrl_net_hint_image(hint)
+        b_hints.append(hint)
+    b_hints = torch.cat(b_hints, dim=0)
+    b_hints = b_hints.to(cnet_info.unet.device, dtype=cnet_info.unet.dtype)
+
+    guided_hint = cnet_info.net.control_model.input_hint_block(b_hints)
+    guided_hints.append(guided_hint)
+  return guided_hints
+
+
+def call_unet_and_control_net(step, num_latent_input, original_unet, control_nets: List[ControlNetInfo], guided_hints, current_ratio, sample, timestep, encoder_hidden_states):
+  # ControlNet
+  # 複数のControlNetの場合は、出力をマージするのではなく交互に適用する
+  cnet_cnt = len(control_nets)
+  cnet_idx = step % cnet_cnt
+  cnet_info = control_nets[cnet_idx]
+
+  # print(current_ratio, cnet_info.prep, cnet_info.weight, cnet_info.ratio)
+  if cnet_info.ratio < current_ratio:
+    return original_unet(sample, timestep, encoder_hidden_states)
+
+  guided_hint = guided_hints[cnet_idx]
+  guided_hint = guided_hint.repeat((num_latent_input, 1, 1, 1))
+  outs = unet_forward(True, cnet_info.net, cnet_info.unet, guided_hint, None, sample, timestep, encoder_hidden_states)
+  outs = [o * cnet_info.weight for o in outs]
+
+  # U-Net
+  return unet_forward(False, cnet_info.net, original_unet, None, outs, sample, timestep, encoder_hidden_states)
+
+
+"""
+  # これはmergeのバージョン
+  # ControlNet
+  cnet_outs_list = []
+  for i, cnet_info in enumerate(control_nets):
+    # print(current_ratio, cnet_info.prep, cnet_info.weight, cnet_info.ratio)
+    if cnet_info.ratio < current_ratio:
+      continue
+    guided_hint = guided_hints[i]
+    outs = unet_forward(True, cnet_info.net, cnet_info.unet, guided_hint, None, sample, timestep, encoder_hidden_states)
+    for i in range(len(outs)):
+      outs[i] *= cnet_info.weight
+
+    cnet_outs_list.append(outs)
+
+  count = len(cnet_outs_list)
+  if count == 0:
+    return original_unet(sample, timestep, encoder_hidden_states)
+
+  # sum of controlnets
+  for i in range(1, count):
+    cnet_outs_list[0] += cnet_outs_list[i]
+
+  # U-Net
+  return unet_forward(False, cnet_info.net, original_unet, None, cnet_outs_list[0], sample, timestep, encoder_hidden_states)
+"""
+
+
+def unet_forward(is_control_net, control_net: ControlNet, unet: UNet2DConditionModel, guided_hint, ctrl_outs, sample, timestep, encoder_hidden_states):
+  # copy from UNet2DConditionModel
+  default_overall_up_factor = 2**unet.num_upsamplers
+
+  forward_upsample_size = False
+  upsample_size = None
+
+  if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+    print("Forward upsample size to force interpolation output size.")
+    forward_upsample_size = True
+
+  # 0. center input if necessary
+  if unet.config.center_input_sample:
+    sample = 2 * sample - 1.0
+
+  # 1. time
+  timesteps = timestep
+  if not torch.is_tensor(timesteps):
+    # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+    # This would be a good case for the `match` statement (Python 3.10+)
+    is_mps = sample.device.type == "mps"
+    if isinstance(timestep, float):
+      dtype = torch.float32 if is_mps else torch.float64
+    else:
+      dtype = torch.int32 if is_mps else torch.int64
+    timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+  elif len(timesteps.shape) == 0:
+    timesteps = timesteps[None].to(sample.device)
+
+  # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+  timesteps = timesteps.expand(sample.shape[0])
+
+  t_emb = unet.time_proj(timesteps)
+
+  # timesteps does not contain any weights and will always return f32 tensors
+  # but time_embedding might actually be running in fp16. so we need to cast here.
+  # there might be better ways to encapsulate this.
+  t_emb = t_emb.to(dtype=unet.dtype)
+  emb = unet.time_embedding(t_emb)
+
+  outs = []                     # output of ControlNet
+  zc_idx = 0
+
+  # 2. pre-process
+  sample = unet.conv_in(sample)
+  if is_control_net:
+    sample += guided_hint
+    outs.append(control_net.control_model.zero_convs[zc_idx][0](sample))  # , emb, encoder_hidden_states))
+    zc_idx += 1
+
+  # 3. down
+  down_block_res_samples = (sample,)
+  for downsample_block in unet.down_blocks:
+    if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+      sample, res_samples = downsample_block(
+          hidden_states=sample,
+          temb=emb,
+          encoder_hidden_states=encoder_hidden_states,
+      )
+    else:
+      sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+    if is_control_net:
+      for rs in res_samples:
+        outs.append(control_net.control_model.zero_convs[zc_idx][0](rs))  # , emb, encoder_hidden_states))
+        zc_idx += 1
+
+    down_block_res_samples += res_samples
+
+  # 4. mid
+  sample = unet.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states)
+  if is_control_net:
+    outs.append(control_net.control_model.middle_block_out[0](sample))
+    return outs
+
+  if not is_control_net:
+    sample += ctrl_outs.pop()
+
+  # 5. up
+  for i, upsample_block in enumerate(unet.up_blocks):
+    is_final_block = i == len(unet.up_blocks) - 1
+
+    res_samples = down_block_res_samples[-len(upsample_block.resnets):]
+    down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+    if not is_control_net and len(ctrl_outs) > 0:
+      res_samples = list(res_samples)
+      apply_ctrl_outs = ctrl_outs[-len(res_samples):]
+      ctrl_outs = ctrl_outs[:-len(res_samples)]
+      for j in range(len(res_samples)):
+        res_samples[j] = res_samples[j] + apply_ctrl_outs[j]
+      res_samples = tuple(res_samples)
+
+    # if we have not reached the final block and need to forward the
+    # upsample size, we do it here
+    if not is_final_block and forward_upsample_size:
+      upsample_size = down_block_res_samples[-1].shape[2:]
+
+    if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+      sample = upsample_block(
+          hidden_states=sample,
+          temb=emb,
+          res_hidden_states_tuple=res_samples,
+          encoder_hidden_states=encoder_hidden_states,
+          upsample_size=upsample_size,
+      )
+    else:
+      sample = upsample_block(
+          hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
+      )
+  # 6. post-process
+  sample = unet.conv_norm_out(sample)
+  sample = unet.conv_act(sample)
+  sample = unet.conv_out(sample)
+
+  return UNet2DConditionOutput(sample=sample)

tools/resize_images_to_resolution.py

@@ -0,0 +1,122 @@
+import glob
+import os
+import cv2
+import argparse
+import shutil
+import math
+from PIL import Image
+import numpy as np
+
+
+def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divisible_by=2, interpolation=None, save_as_png=False, copy_associated_files=False):
+  # Split the max_resolution string by "," and strip any whitespaces
+  max_resolutions = [res.strip() for res in max_resolution.split(',')]
+
+  # # Calculate max_pixels from max_resolution string
+  # max_pixels = int(max_resolution.split("x")[0]) * int(max_resolution.split("x")[1])
+
+  # Create destination folder if it does not exist
+  if not os.path.exists(dst_img_folder):
+    os.makedirs(dst_img_folder)
+
+  # Select interpolation method
+  if interpolation == 'lanczos4':
+    cv2_interpolation = cv2.INTER_LANCZOS4
+  elif interpolation == 'cubic':
+    cv2_interpolation = cv2.INTER_CUBIC
+  else:
+    cv2_interpolation = cv2.INTER_AREA
+
+  # Iterate through all files in src_img_folder
+  img_exts = (".png", ".jpg", ".jpeg", ".webp", ".bmp")                   # copy from train_util.py
+  for filename in os.listdir(src_img_folder):
+    # Check if the image is png, jpg or webp etc...
+    if not filename.endswith(img_exts):
+      # Copy the file to the destination folder if not png, jpg or webp etc (.txt or .caption or etc.)
+      shutil.copy(os.path.join(src_img_folder, filename), os.path.join(dst_img_folder, filename))
+      continue
+
+    # Load image
+    # img = cv2.imread(os.path.join(src_img_folder, filename))
+    image = Image.open(os.path.join(src_img_folder, filename))
+    if not image.mode == "RGB":
+      image = image.convert("RGB")
+    img = np.array(image, np.uint8)
+
+    base, _ = os.path.splitext(filename)
+    for max_resolution in max_resolutions:
+      # Calculate max_pixels from max_resolution string
+      max_pixels = int(max_resolution.split("x")[0]) * int(max_resolution.split("x")[1])
+
+      # Calculate current number of pixels
+      current_pixels = img.shape[0] * img.shape[1]
+
+      # Check if the image needs resizing
+      if current_pixels > max_pixels:
+        # Calculate scaling factor
+        scale_factor = max_pixels / current_pixels
+
+        # Calculate new dimensions
+        new_height = int(img.shape[0] * math.sqrt(scale_factor))
+        new_width = int(img.shape[1] * math.sqrt(scale_factor))
+
+        # Resize image
+        img = cv2.resize(img, (new_width, new_height), interpolation=cv2_interpolation)
+      else:
+        new_height, new_width = img.shape[0:2]
+
+      # Calculate the new height and width that are divisible by divisible_by (with/without resizing)
+      new_height = new_height if new_height % divisible_by == 0 else new_height - new_height % divisible_by
+      new_width = new_width if new_width % divisible_by == 0 else new_width - new_width % divisible_by
+
+      # Center crop the image to the calculated dimensions
+      y = int((img.shape[0] - new_height) / 2)
+      x = int((img.shape[1] - new_width) / 2)
+      img = img[y:y + new_height, x:x + new_width]
+
+      # Split filename into base and extension
+      new_filename = base + '+' + max_resolution + ('.png' if save_as_png else '.jpg')
+
+      # Save resized image in dst_img_folder
+      # cv2.imwrite(os.path.join(dst_img_folder, new_filename), img, [cv2.IMWRITE_JPEG_QUALITY, 100])
+      image = Image.fromarray(img)
+      image.save(os.path.join(dst_img_folder, new_filename), quality=100)
+
+      proc = "Resized" if current_pixels > max_pixels else "Saved"
+      print(f"{proc} image: {filename} with size {img.shape[0]}x{img.shape[1]} as {new_filename}")
+
+    # If other files with same basename, copy them with resolution suffix
+    if copy_associated_files:
+      asoc_files = glob.glob(os.path.join(src_img_folder, base + ".*"))
+      for asoc_file in asoc_files:
+        ext = os.path.splitext(asoc_file)[1]
+        if ext in img_exts:
+          continue
+        for max_resolution in max_resolutions:
+          new_asoc_file = base + '+' + max_resolution + ext
+          print(f"Copy {asoc_file} as {new_asoc_file}")
+          shutil.copy(os.path.join(src_img_folder, asoc_file), os.path.join(dst_img_folder, new_asoc_file))
+
+
+def main():
+  parser = argparse.ArgumentParser(
+      description='Resize images in a folder to a specified max resolution(s) / 指定されたフォルダ内の画像を指定した最大画像サイズ（面積）以下にアスペクト比を維持したままリサイズします')
+  parser.add_argument('src_img_folder', type=str, help='Source folder containing the images / 元画像のフォルダ')
+  parser.add_argument('dst_img_folder', type=str, help='Destination folder to save the resized images / リサイズ後の画像を保存するフォルダ')
+  parser.add_argument('--max_resolution', type=str,
+                      help='Maximum resolution(s) in the format "512x512,384x384, etc, etc" / 最大画像サイズをカンマ区切りで指定 ("512x512,384x384, etc, etc" など)', default="512x512,384x384,256x256,128x128")
+  parser.add_argument('--divisible_by', type=int,
+                      help='Ensure new dimensions are divisible by this value / リサイズ後の画像のサイズをこの値で割り切れるようにします', default=1)
+  parser.add_argument('--interpolation', type=str, choices=['area', 'cubic', 'lanczos4'],
+                      default='area', help='Interpolation method for resizing / リサイズ時の補完方法')
+  parser.add_argument('--save_as_png', action='store_true', help='Save as png format / png形式で保存')
+  parser.add_argument('--copy_associated_files', action='store_true',
+                      help='Copy files with same base name to images (captions etc) / 画像と同じファイル名（拡張子を除く）のファイルもコピーする')
+
+  args = parser.parse_args()
+  resize_images(args.src_img_folder, args.dst_img_folder, args.max_resolution,
+                args.divisible_by, args.interpolation, args.save_as_png, args.copy_associated_files)
+
+
+if __name__ == '__main__':
+  main()

train_db.py

@@ -35,10 +35,16 @@ def train(args):
 
   train_dataset = DreamBoothDataset(args.train_batch_size, args.train_data_dir, args.reg_data_dir,
                                     tokenizer, args.max_token_length, args.caption_extension, args.shuffle_caption, args.keep_tokens,
-                                    args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso, args.prior_loss_weight,
-                                    args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop, args.debug_dataset)
+                                    args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
+                                    args.bucket_reso_steps, args.bucket_no_upscale,
+                                    args.prior_loss_weight, args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop, args.debug_dataset)
+
   if args.no_token_padding:
     train_dataset.disable_token_padding()
+
+  # 学習データのdropout率を設定する
+  train_dataset.set_caption_dropout(args.caption_dropout_rate, args.caption_dropout_every_n_epochs, args.caption_tag_dropout_rate)
+
   train_dataset.make_buckets()
 
   if args.debug_dataset:
@@ -92,10 +98,7 @@ def train(args):
     gc.collect()
 
   # 学習を準備する：モデルを適切な状態にする
-  if args.stop_text_encoder_training is None:
-    args.stop_text_encoder_training = args.max_train_steps + 1                # do not stop until end
-
-  train_text_encoder = args.stop_text_encoder_training >= 0
+  train_text_encoder = args.stop_text_encoder_training is None or args.stop_text_encoder_training >= 0
   unet.requires_grad_(True)                   # 念のため追加
   text_encoder.requires_grad_(train_text_encoder)
   if not train_text_encoder:
@@ -112,35 +115,31 @@ def train(args):
 
   # 学習に必要なクラスを準備する
   print("prepare optimizer, data loader etc.")
-
-  # 8-bit Adamを使う
-  if args.use_8bit_adam:
-    try:
-      import bitsandbytes as bnb
-    except ImportError:
-      raise ImportError("No bitsand bytes / bitsandbytesがインストールされていないようです")
-    print("use 8-bit Adam optimizer")
-    optimizer_class = bnb.optim.AdamW8bit
-  else:
-    optimizer_class = torch.optim.AdamW
-
   if train_text_encoder:
     trainable_params = (itertools.chain(unet.parameters(), text_encoder.parameters()))
   else:
     trainable_params = unet.parameters()
 
-  # betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略
-  optimizer = optimizer_class(trainable_params, lr=args.learning_rate)
+  _, _, 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(8, os.cpu_count() - 1)      # cpu_count-1 ただし最大8
+  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
   train_dataloader = torch.utils.data.DataLoader(
-      train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers)
+      train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
 
-  # lr schedulerを用意する
-  lr_scheduler = diffusers.optimization.get_scheduler(
-      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps)
+  # 学習ステップ数を計算する
+  if args.max_train_epochs is not None:
+    args.max_train_steps = args.max_train_epochs * len(train_dataloader)
+    print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
+
+  if args.stop_text_encoder_training is None:
+    args.stop_text_encoder_training = args.max_train_steps + 1                # do not stop until end
+
+  # lr schedulerを用意する TODO gradient_accumulation_stepsの扱いが何かおかしいかもしれない。後で確認する
+  lr_scheduler = train_util.get_scheduler_fix(args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
+                                              num_training_steps=args.max_train_steps,
+                                              num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
 
   # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
   if args.full_fp16:
@@ -171,6 +170,8 @@ def train(args):
   # epoch数を計算する
   num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
   num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+  if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
+    args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
 
   # 学習する
   total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
@@ -193,8 +194,11 @@ def train(args):
   if accelerator.is_main_process:
     accelerator.init_trackers("dreambooth")
 
+  loss_list = []
+  loss_total = 0.0
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
+    train_dataset.set_current_epoch(epoch + 1)
 
     # 指定したステップ数までText Encoderを学習する：epoch最初の状態
     unet.train()
@@ -202,7 +206,6 @@ def train(args):
     if args.gradient_checkpointing or global_step < args.stop_text_encoder_training:
       text_encoder.train()
 
-    loss_total = 0
     for step, batch in enumerate(train_dataloader):
       # 指定したステップ数でText Encoderの学習を止める
       if global_step == args.stop_text_encoder_training:
@@ -219,10 +222,13 @@ def train(args):
           else:
             latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
           latents = latents * 0.18215
+        b_size = latents.shape[0]
 
         # Sample noise that we'll add to the latents
         noise = torch.randn_like(latents, device=latents.device)
-        b_size = latents.shape[0]
+        if args.noise_offset:
+          # https://www.crosslabs.org//blog/diffusion-with-offset-noise
+          noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
 
         # Get the text embedding for conditioning
         with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):
@@ -256,12 +262,12 @@ def train(args):
         loss = loss.mean()                # 平均なのでbatch_sizeで割る必要なし
 
         accelerator.backward(loss)
-        if accelerator.sync_gradients:
+        if accelerator.sync_gradients and args.max_grad_norm != 0.0:
           if train_text_encoder:
             params_to_clip = (itertools.chain(unet.parameters(), text_encoder.parameters()))
           else:
             params_to_clip = unet.parameters()
-          accelerator.clip_grad_norm_(params_to_clip, 1.0)  # args.max_grad_norm)
+          accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
         optimizer.step()
         lr_scheduler.step()
@@ -274,11 +280,18 @@ def train(args):
 
       current_loss = loss.detach().item()
       if args.logging_dir is not None:
-        logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
+        logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
+        if args.optimizer_type.lower() == "DAdaptation".lower():  # tracking d*lr value
+          logs["lr/d*lr"] = lr_scheduler.optimizers[0].param_groups[0]['d']*lr_scheduler.optimizers[0].param_groups[0]['lr']
         accelerator.log(logs, step=global_step)
 
+      if epoch == 0:
+        loss_list.append(current_loss)
+      else:
+        loss_total -= loss_list[step]
+        loss_list[step] = current_loss
       loss_total += current_loss
-      avr_loss = loss_total / (step+1)
+      avr_loss = loss_total / len(loss_list)
       logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
       progress_bar.set_postfix(**logs)
 
@@ -286,7 +299,7 @@ def train(args):
         break
 
     if args.logging_dir is not None:
-      logs = {"epoch_loss": loss_total / len(train_dataloader)}
+      logs = {"loss/epoch": loss_total / len(loss_list)}
       accelerator.log(logs, step=epoch+1)
 
     accelerator.wait_for_everyone()
@@ -319,9 +332,10 @@ if __name__ == '__main__':
   parser = argparse.ArgumentParser()
 
   train_util.add_sd_models_arguments(parser)
-  train_util.add_dataset_arguments(parser, True, False)
+  train_util.add_dataset_arguments(parser, True, False, True)
   train_util.add_training_arguments(parser, True)
   train_util.add_sd_saving_arguments(parser)
+  train_util.add_optimizer_arguments(parser)
 
   parser.add_argument("--no_token_padding", action="store_true",
                       help="disable token padding (same as Diffuser's DreamBooth) / トークンのpaddingを無効にする（Diffusers版DreamBoothと同じ動作）")

train_db_README-ja.md

@@ -72,7 +72,7 @@ identifierとclassを使い、たとえば「shs dog」などでモデルを学
 ※LoRA等の追加ネットワークを学習する場合のコマンドは ``train_db.py`` ではなく ``train_network.py`` となります。また追加でnetwork_\*オプションが必要となりますので、LoRAのガイドを参照してください。
 
 ```
-accelerate launch --num_cpu_threads_per_process 8 train_db.py 
+accelerate launch --num_cpu_threads_per_process 1 train_db.py 
     --pretrained_model_name_or_path=<.ckptまたは.safetensordまたはDiffusers版モデルのディレクトリ> 
     --train_data_dir=<学習用データのディレクトリ> 
     --reg_data_dir=<正則化画像のディレクトリ> 
@@ -89,7 +89,7 @@ accelerate launch --num_cpu_threads_per_process 8 train_db.py
     --gradient_checkpointing
 ```
 
-num_cpu_threads_per_processにはCPUコア数を指定するとよいようです。
+num_cpu_threads_per_processには通常は1を指定するとよいようです。
 
 pretrained_model_name_or_pathに追加学習を行う元となるモデルを指定します。Stable Diffusionのcheckpointファイル（.ckptまたは.safetensors）、Diffusersのローカルディスクにあるモデルディレクトリ、DiffusersのモデルID（"stabilityai/stable-diffusion-2"など）が指定できます。学習後のモデルの保存形式はデフォルトでは元のモデルと同じになります（save_model_asオプションで変更できます）。
 
@@ -159,7 +159,7 @@ v2.xモデルでWebUIで画像生成する場合、モデルの仕様が記述
 
 ![image](https://user-images.githubusercontent.com/52813779/210776915-061d79c3-6582-42c2-8884-8b91d2f07313.png)
 
-各yamlファイルは[https://github.com/Stability-AI/stablediffusion/tree/main/configs/stable-diffusion](Stability AIのSD2.0のリポジトリ)にあります。
+各yamlファイルは[Stability AIのSD2.0のリポジトリ](https://github.com/Stability-AI/stablediffusion/tree/main/configs/stable-diffusion)にあります。
 
 # その他の学習オプション
 

train_network.py

@@ -1,8 +1,13 @@
+from torch.cuda.amp import autocast
+from torch.nn.parallel import DistributedDataParallel as DDP
 import importlib
 import argparse
 import gc
 import math
 import os
+import random
+import time
+import json
 
 from tqdm import tqdm
 import torch
@@ -18,7 +23,27 @@ def collate_fn(examples):
   return examples[0]
 
 
+# TODO 他のスクリプトと共通化する
+def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
+  logs = {"loss/current": current_loss, "loss/average": avr_loss}
+
+  if args.network_train_unet_only:
+    logs["lr/unet"] = float(lr_scheduler.get_last_lr()[0])
+  elif args.network_train_text_encoder_only:
+    logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])
+  else:
+    logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])
+    logs["lr/unet"] = float(lr_scheduler.get_last_lr()[-1])          # may be same to textencoder
+
+  if args.optimizer_type.lower() == "DAdaptation".lower():  # tracking d*lr value of unet.
+    logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]['d']*lr_scheduler.optimizers[-1].param_groups[0]['lr']
+
+  return logs
+
+
 def train(args):
+  session_id = random.randint(0, 2**32)
+  training_started_at = time.time()
   train_util.verify_training_args(args)
   train_util.prepare_dataset_args(args, True)
 
@@ -35,22 +60,29 @@ def train(args):
     print("Use DreamBooth method.")
     train_dataset = DreamBoothDataset(args.train_batch_size, args.train_data_dir, args.reg_data_dir,
                                       tokenizer, args.max_token_length, args.caption_extension, args.shuffle_caption, args.keep_tokens,
-                                      args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso, args.prior_loss_weight,
-                                      args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop, args.debug_dataset)
+                                      args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
+                                      args.bucket_reso_steps, args.bucket_no_upscale,
+                                      args.prior_loss_weight, args.flip_aug, args.color_aug, args.face_crop_aug_range,
+                                      args.random_crop, args.debug_dataset)
   else:
     print("Train with captions.")
     train_dataset = FineTuningDataset(args.in_json, args.train_batch_size, args.train_data_dir,
                                       tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens,
                                       args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
+                                      args.bucket_reso_steps, args.bucket_no_upscale,
                                       args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop,
                                       args.dataset_repeats, args.debug_dataset)
+
+  # 学習データのdropout率を設定する
+  train_dataset.set_caption_dropout(args.caption_dropout_rate, args.caption_dropout_every_n_epochs, args.caption_tag_dropout_rate)
+
   train_dataset.make_buckets()
 
   if args.debug_dataset:
     train_util.debug_dataset(train_dataset)
     return
   if len(train_dataset) == 0:
-    print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
+    print("No data found. Please verify arguments (train_data_dir must be the parent of folders with images) / 画像がありません。引数指定を確認してください（train_data_dirには画像があるフォルダではなく、画像があるフォルダの親フォルダを指定する必要があります）")
     return
 
   # acceleratorを準備する
@@ -63,6 +95,11 @@ def train(args):
   # モデルを読み込む
   text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
 
+  # work on low-ram device
+  if args.lowram:
+    text_encoder.to("cuda")
+    unet.to("cuda")
+
   # モデルに xformers とか memory efficient attention を組み込む
   train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
 
@@ -88,7 +125,8 @@ def train(args):
       key, value = net_arg.split('=')
       net_kwargs[key] = value
 
-  network = network_module.create_network(1.0, args.network_dim, vae, text_encoder, unet, **net_kwargs)
+  # if a new network is added in future, add if ~ then blocks for each network (;'∀')
+  network = network_module.create_network(1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, **net_kwargs)
   if network is None:
     return
 
@@ -108,31 +146,24 @@ def train(args):
   # 学習に必要なクラスを準備する
   print("prepare optimizer, data loader etc.")
 
-  # 8-bit Adamを使う
-  if args.use_8bit_adam:
-    try:
-      import bitsandbytes as bnb
-    except ImportError:
-      raise ImportError("No bitsand bytes / bitsandbytesがインストールされていないようです")
-    print("use 8-bit Adam optimizer")
-    optimizer_class = bnb.optim.AdamW8bit
-  else:
-    optimizer_class = torch.optim.AdamW
-
   trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
-
-  # betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略
-  optimizer = optimizer_class(trainable_params, lr=args.learning_rate)
+  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(8, os.cpu_count() - 1)      # cpu_count-1 ただし最大8
+  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
   train_dataloader = torch.utils.data.DataLoader(
-      train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers)
+      train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
+
+  # 学習ステップ数を計算する
+  if args.max_train_epochs is not None:
+    args.max_train_steps = args.max_train_epochs * len(train_dataloader)
+    print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
   # lr schedulerを用意する
-  lr_scheduler = diffusers.optimization.get_scheduler(
-      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
+  lr_scheduler = train_util.get_scheduler_fix(args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
+                                              num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+                                              num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
 
   # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
   if args.full_fp16:
@@ -157,14 +188,26 @@ def train(args):
   unet.requires_grad_(False)
   unet.to(accelerator.device, dtype=weight_dtype)
   text_encoder.requires_grad_(False)
-  text_encoder.to(accelerator.device, dtype=weight_dtype)
+  text_encoder.to(accelerator.device)
   if args.gradient_checkpointing:                       # according to TI example in Diffusers, train is required
     unet.train()
     text_encoder.train()
+
+    # set top parameter requires_grad = True for gradient checkpointing works
+    if type(text_encoder) == DDP:
+      text_encoder.module.text_model.embeddings.requires_grad_(True)
+    else:
+      text_encoder.text_model.embeddings.requires_grad_(True)
   else:
     unet.eval()
     text_encoder.eval()
 
+  # support DistributedDataParallel
+  if type(text_encoder) == DDP:
+    text_encoder = text_encoder.module
+    unet = unet.module
+    network = network.module
+
   network.prepare_grad_etc(text_encoder, unet)
 
   if not cache_latents:
@@ -184,6 +227,8 @@ def train(args):
   # epoch数を計算する
   num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
   num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+  if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
+    args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
 
   # 学習する
   total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
@@ -198,21 +243,26 @@ def train(args):
   print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
 
   metadata = {
+      "ss_session_id": session_id,            # random integer indicating which group of epochs the model came from
+      "ss_training_started_at": training_started_at,          # unix timestamp
+      "ss_output_name": args.output_name,
       "ss_learning_rate": args.learning_rate,
       "ss_text_encoder_lr": args.text_encoder_lr,
       "ss_unet_lr": args.unet_lr,
-      "ss_num_train_images": train_dataset.num_train_images,          # includes repeating TODO more detailed data
+      "ss_num_train_images": train_dataset.num_train_images,          # includes repeating
       "ss_num_reg_images": train_dataset.num_reg_images,
       "ss_num_batches_per_epoch": len(train_dataloader),
       "ss_num_epochs": num_train_epochs,
       "ss_batch_size_per_device": args.train_batch_size,
       "ss_total_batch_size": total_batch_size,
+      "ss_gradient_checkpointing": args.gradient_checkpointing,
       "ss_gradient_accumulation_steps": args.gradient_accumulation_steps,
       "ss_max_train_steps": args.max_train_steps,
       "ss_lr_warmup_steps": args.lr_warmup_steps,
       "ss_lr_scheduler": args.lr_scheduler,
       "ss_network_module": args.network_module,
-      "ss_network_dim": args.network_dim,      # None means default because another network than LoRA may have another default dim
+      "ss_network_dim": args.network_dim,          # None means default because another network than LoRA may have another default dim
+      "ss_network_alpha": args.network_alpha,      # some networks may not use this value
       "ss_mixed_precision": args.mixed_precision,
       "ss_full_fp16": bool(args.full_fp16),
       "ss_v2": bool(args.v2),
@@ -224,10 +274,27 @@ def train(args):
       "ss_random_crop": bool(args.random_crop),
       "ss_shuffle_caption": bool(args.shuffle_caption),
       "ss_cache_latents": bool(args.cache_latents),
-      "ss_enable_bucket": bool(train_dataset.enable_bucket),        # TODO move to BaseDataset from DB/FT
-      "ss_min_bucket_reso": args.min_bucket_reso,                   # TODO get from dataset
-      "ss_max_bucket_reso": args.max_bucket_reso,
-      "ss_seed": args.seed
+      "ss_enable_bucket": bool(train_dataset.enable_bucket),
+      "ss_bucket_no_upscale": bool(train_dataset.bucket_no_upscale),
+      "ss_min_bucket_reso": train_dataset.min_bucket_reso,
+      "ss_max_bucket_reso": train_dataset.max_bucket_reso,
+      "ss_seed": args.seed,
+      "ss_lowram": args.lowram,
+      "ss_keep_tokens": args.keep_tokens,
+      "ss_noise_offset": args.noise_offset,
+      "ss_dataset_dirs": json.dumps(train_dataset.dataset_dirs_info),
+      "ss_reg_dataset_dirs": json.dumps(train_dataset.reg_dataset_dirs_info),
+      "ss_tag_frequency": json.dumps(train_dataset.tag_frequency),
+      "ss_bucket_info": json.dumps(train_dataset.bucket_info),
+      "ss_training_comment": args.training_comment,       # will not be updated after training
+      "ss_sd_scripts_commit_hash": train_util.get_git_revision_hash(),
+      "ss_optimizer": optimizer_name + (f"({optimizer_args})" if len(optimizer_args) > 0 else ""),
+      "ss_max_grad_norm": args.max_grad_norm,
+      "ss_caption_dropout_rate": args.caption_dropout_rate,
+      "ss_caption_dropout_every_n_epochs": args.caption_dropout_every_n_epochs,
+      "ss_caption_tag_dropout_rate": args.caption_tag_dropout_rate,
+      "ss_face_crop_aug_range": args.face_crop_aug_range,
+      "ss_prior_loss_weight": args.prior_loss_weight,
   }
 
   # uncomment if another network is added
@@ -238,6 +305,7 @@ def train(args):
     sd_model_name = args.pretrained_model_name_or_path
     if os.path.exists(sd_model_name):
       metadata["ss_sd_model_hash"] = train_util.model_hash(sd_model_name)
+      metadata["ss_new_sd_model_hash"] = train_util.calculate_sha256(sd_model_name)
       sd_model_name = os.path.basename(sd_model_name)
     metadata["ss_sd_model_name"] = sd_model_name
 
@@ -245,6 +313,7 @@ def train(args):
     vae_name = args.vae
     if os.path.exists(vae_name):
       metadata["ss_vae_hash"] = train_util.model_hash(vae_name)
+      metadata["ss_new_vae_hash"] = train_util.calculate_sha256(vae_name)
       vae_name = os.path.basename(vae_name)
     metadata["ss_vae_name"] = vae_name
 
@@ -259,13 +328,16 @@ def train(args):
   if accelerator.is_main_process:
     accelerator.init_trackers("network_train")
 
+  loss_list = []
+  loss_total = 0.0
   for epoch in range(num_train_epochs):
     print(f"epoch {epoch+1}/{num_train_epochs}")
+    train_dataset.set_current_epoch(epoch + 1)
+
     metadata["ss_epoch"] = str(epoch+1)
 
     network.on_epoch_start(text_encoder, unet)
 
-    loss_total = 0
     for step, batch in enumerate(train_dataloader):
       with accelerator.accumulate(network):
         with torch.no_grad():
@@ -284,6 +356,9 @@ def train(args):
 
         # Sample noise that we'll add to the latents
         noise = torch.randn_like(latents, device=latents.device)
+        if args.noise_offset:
+          # https://www.crosslabs.org//blog/diffusion-with-offset-noise
+          noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
 
         # Sample a random timestep for each image
         timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
@@ -294,7 +369,8 @@ def train(args):
         noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
         # Predict the noise residual
-        noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+        with accelerator.autocast():
+          noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
 
         if args.v_parameterization:
           # v-parameterization training
@@ -311,9 +387,9 @@ def train(args):
         loss = loss.mean()                # 平均なのでbatch_sizeで割る必要なし
 
         accelerator.backward(loss)
-        if accelerator.sync_gradients:
+        if accelerator.sync_gradients and args.max_grad_norm != 0.0:
           params_to_clip = network.get_trainable_params()
-          accelerator.clip_grad_norm_(params_to_clip, 1.0)  # args.max_grad_norm)
+          accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
         optimizer.step()
         lr_scheduler.step()
@@ -325,20 +401,25 @@ def train(args):
         global_step += 1
 
       current_loss = loss.detach().item()
-      if args.logging_dir is not None:
-        logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
-        accelerator.log(logs, step=global_step)
-
+      if epoch == 0:
+        loss_list.append(current_loss)
+      else:
+        loss_total -= loss_list[step]
+        loss_list[step] = current_loss
       loss_total += current_loss
-      avr_loss = loss_total / (step+1)
+      avr_loss = loss_total / len(loss_list)
       logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
       progress_bar.set_postfix(**logs)
 
+      if args.logging_dir is not None:
+        logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
+        accelerator.log(logs, step=global_step)
+
       if global_step >= args.max_train_steps:
         break
 
     if args.logging_dir is not None:
-      logs = {"epoch_loss": loss_total / len(train_dataloader)}
+      logs = {"loss/epoch": loss_total / len(loss_list)}
       accelerator.log(logs, step=epoch+1)
 
     accelerator.wait_for_everyone()
@@ -349,6 +430,7 @@ def train(args):
       def save_func():
         ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + '.' + args.save_model_as
         ckpt_file = os.path.join(args.output_dir, ckpt_name)
+        metadata["ss_training_finished_at"] = str(time.time())
         print(f"saving checkpoint: {ckpt_file}")
         unwrap_model(network).save_weights(ckpt_file, save_dtype, None if args.no_metadata else metadata)
 
@@ -359,13 +441,14 @@ def train(args):
           print(f"removing old checkpoint: {old_ckpt_file}")
           os.remove(old_ckpt_file)
 
-      saving, remove_epoch_no = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
+      saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
       if saving and args.save_state:
-        train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1, remove_epoch_no)
+        train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
 
     # end of epoch
 
   metadata["ss_epoch"] = str(num_train_epochs)
+  metadata["ss_training_finished_at"] = str(time.time())
 
   is_main_process = accelerator.is_main_process
   if is_main_process:
@@ -394,12 +477,13 @@ if __name__ == '__main__':
   parser = argparse.ArgumentParser()
 
   train_util.add_sd_models_arguments(parser)
-  train_util.add_dataset_arguments(parser, True, True)
+  train_util.add_dataset_arguments(parser, True, True, True)
   train_util.add_training_arguments(parser, True)
+  train_util.add_optimizer_arguments(parser)
 
   parser.add_argument("--no_metadata", action='store_true', help="do not save metadata in output model / メタデータを出力先モデルに保存しない")
-  parser.add_argument("--save_model_as", type=str, default="pt", choices=[None, "ckpt", "pt", "safetensors"],
-                      help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）")
+  parser.add_argument("--save_model_as", type=str, default="safetensors", choices=[None, "ckpt", "pt", "safetensors"],
+                      help="format to save the model (default is .safetensors) / モデル保存時の形式（デフォルトはsafetensors）")
 
   parser.add_argument("--unet_lr", type=float, default=None, help="learning rate for U-Net / U-Netの学習率")
   parser.add_argument("--text_encoder_lr", type=float, default=None, help="learning rate for Text Encoder / Text Encoderの学習率")
@@ -409,11 +493,15 @@ if __name__ == '__main__':
   parser.add_argument("--network_module", type=str, default=None, help='network module to train / 学習対象のネットワークのモジュール')
   parser.add_argument("--network_dim", type=int, default=None,
                       help='network dimensions (depends on each network) / モジュールの次元数（ネットワークにより定義は異なります）')
+  parser.add_argument("--network_alpha", type=float, default=1,
+                      help='alpha for LoRA weight scaling, default 1 (same as network_dim for same behavior as old version) / LoRaの重み調整のalpha値、デフォルト1（旧バージョンと同じ動作をするにはnetwork_dimと同じ値を指定）')
   parser.add_argument("--network_args", type=str, default=None, nargs='*',
                       help='additional argmuments for network (key=value) / ネットワークへの追加の引数')
   parser.add_argument("--network_train_unet_only", action="store_true", help="only training U-Net part / U-Net関連部分のみ学習する")
   parser.add_argument("--network_train_text_encoder_only", action="store_true",
                       help="only training Text Encoder part / Text Encoder関連部分のみ学習する")
+  parser.add_argument("--training_comment", type=str, default=None,
+                      help="arbitrary comment string stored in metadata / メタデータに記録する任意のコメント文字列")
 
   args = parser.parse_args()
   train(args)

train_network_README-ja.md

@@ -10,7 +10,7 @@
 
 cloneofsimo氏のリポジトリ、およびd8ahazard氏の[Dreambooth Extension for Stable-Diffusion-WebUI](https://github.com/d8ahazard/sd_dreambooth_extension)とは、現時点では互換性がありません。いくつかの機能拡張を行っているためです（後述）。
 
-WebUI等で画像生成する場合には、学習したLoRAのモデルを学習元のStable Diffusionのモデルにこのリポジトリ内のスクリプトであらかじめマージしておくか、こちらの[WebUI用extention](https://github.com/kohya-ss/sd-webui-additional-networks)を使ってください。
+WebUI等で画像生成する場合には、学習したLoRAのモデルを学習元のStable Diffusionのモデルにこのリポジトリ内のスクリプトであらかじめマージしておくか、こちらの[WebUI用extension](https://github.com/kohya-ss/sd-webui-additional-networks)を使ってください。
 
 ## 学習方法
 
@@ -24,7 +24,7 @@ DreamBoothの手法（identifier（sksなど）とclass、オプションで正
 
 [DreamBoothのガイド](./train_db_README-ja.md) を参照してデータを用意してください。
 
-学習するとき、train_db.pyの代わりにtrain_network.pyを指定してください。
+学習するとき、train_db.pyの代わりにtrain_network.pyを指定してください。そして「LoRAの学習のためのオプション」にあるようにLoRA関連のオプション（``network_dim``や``network_alpha``など）を追加してください。
 
 ほぼすべてのオプション（Stable Diffusionのモデル保存関係を除く）が使えますが、stop_text_encoder_trainingはサポートしていません。
 
@@ -32,7 +32,7 @@ DreamBoothの手法（identifier（sksなど）とclass、オプションで正
 
 [fine-tuningのガイド](./fine_tune_README_ja.md) を参照し、各手順を実行してください。
 
-学習するとき、fine_tune.pyの代わりにtrain_network.pyを指定してください。ほぼすべてのオプション（モデル保存関係を除く）がそのまま使えます。
+学習するとき、fine_tune.pyの代わりにtrain_network.pyを指定してください。ほぼすべてのオプション（モデル保存関係を除く）がそのまま使えます。そして「LoRAの学習のためのオプション」にあるようにLoRA関連のオプション（``network_dim``や``network_alpha``など）を追加してください。
 
 なお「latentsの事前取得」は行わなくても動作します。VAEから学習時（またはキャッシュ時）にlatentを取得するため学習速度は遅くなりますが、代わりにcolor_augが使えるようになります。
 
@@ -45,22 +45,26 @@ train_network.pyでは--network_moduleオプションに、学習対象のモジ
 以下はコマンドラインの例です（DreamBooth手法）。
 
 ```
-accelerate launch --num_cpu_threads_per_process 12 train_network.py 
+accelerate launch --num_cpu_threads_per_process 1 train_network.py 
     --pretrained_model_name_or_path=..\models\model.ckpt 
     --train_data_dir=..\data\db\char1 --output_dir=..\lora_train1 
     --reg_data_dir=..\data\db\reg1 --prior_loss_weight=1.0 
     --resolution=448,640 --train_batch_size=1 --learning_rate=1e-4 
-    --max_train_steps=400 --use_8bit_adam --xformers --mixed_precision=fp16 
+    --max_train_steps=400 --optimizer_type=AdamW8bit --xformers --mixed_precision=fp16 
     --save_every_n_epochs=1 --save_model_as=safetensors --clip_skip=2 --seed=42 --color_aug 
     --network_module=networks.lora
 ```
 
---output_dirオプションで指定したディレクトリに、LoRAのモデルが保存されます。
+（2023/2/22:オプティマイザの指定方法が変わりました。[こちら](#オプティマイザの指定について）をご覧ください。）
+
+--output_dirオプションで指定したフォルダに、LoRAのモデルが保存されます。
 
 その他、以下のオプションが指定できます。
 
 * --network_dim
-  * LoRAの次元数を指定します（``--networkdim=4``など）。省略時は4になります。数が多いほど表現力は増しますが、学習に必要なメモリ、時間は増えます。また闇雲に増やしても良くないようです。
+  * LoRAのRANKを指定します（``--networkdim=4``など）。省略時は4になります。数が多いほど表現力は増しますが、学習に必要なメモリ、時間は増えます。また闇雲に増やしても良くないようです。
+* --network_alpha
+  *  アンダーフローを防ぎ安定して学習するための ``alpha`` 値を指定します。デフォルトは1です。``network_dim``と同じ値を指定すると以前のバージョンと同じ動作になります。
 * --network_weights
   * 学習前に学習済みのLoRAの重みを読み込み、そこから追加で学習します。
 * --network_train_unet_only
@@ -74,6 +78,42 @@ accelerate launch --num_cpu_threads_per_process 12 train_network.py
 
 --network_train_unet_onlyと--network_train_text_encoder_onlyの両方とも未指定時（デフォルト）はText EncoderとU-Netの両方のLoRAモジュールを有効にします。
 
+## オプティマイザの指定について
+
+--optimizer_type オプションでオプティマイザの種類を指定します。以下が指定できます。
+
+- AdamW : [torch.optim.AdamW](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html)
+  - 過去のバージョンのオプション未指定時と同じ
+- AdamW8bit : 引数は同上
+  - 過去のバージョンの--use_8bit_adam指定時と同じ
+- Lion : https://github.com/lucidrains/lion-pytorch
+  - 過去のバージョンの--use_lion_optimizer指定時と同じ
+- SGDNesterov : [torch.optim.SGD](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html), nesterov=True
+- SGDNesterov8bit : 引数は同上
+- DAdaptation : https://github.com/facebookresearch/dadaptation
+- AdaFactor : [Transformers AdaFactor](https://huggingface.co/docs/transformers/main_classes/optimizer_schedules)
+- 任意のオプティマイザ
+
+オプティマイザのオプション引数は--optimizer_argsオプションで指定してください。key=valueの形式で、複数の値が指定できます。また、valueはカンマ区切りで複数の値が指定できます。たとえばAdamWオプティマイザに引数を指定する場合は、``--optimizer_args weight_decay=0.01 betas=.9,.999``のようになります。
+
+オプション引数を指定する場合は、それぞれのオプティマイザの仕様をご確認ください。
+
+一部のオプティマイザでは必須の引数があり、省略すると自動的に追加されます（SGDNesterovのmomentumなど）。コンソールの出力を確認してください。
+
+D-Adaptationオプティマイザは学習率を自動調整します。学習率のオプションに指定した値は学習率そのものではなくD-Adaptationが決定した学習率の適用率になりますので、通常は1.0を指定してください。Text EncoderにU-Netの半分の学習率を指定したい場合は、``--text_encoder_lr=0.5 --unet_lr=1.0``と指定します。
+
+AdaFactorオプティマイザはrelative_step=Trueを指定すると学習率を自動調整できます（省略時はデフォルトで追加されます）。自動調整する場合は学習率のスケジューラにはadafactor_schedulerが強制的に使用されます。またscale_parameterとwarmup_initを指定するとよいようです。
+
+自動調整する場合のオプション指定はたとえば ``--optimizer_args "relative_step=True" "scale_parameter=True" "warmup_init=True"`` のようになります。
+
+学習率を自動調整しない場合はオプション引数 ``relative_step=False`` を追加してください。その場合、学習率のスケジューラにはconstant_with_warmupが、また勾配のclip normをしないことが推奨されているようです。そのため引数は ``--optimizer_type=adafactor --optimizer_args "relative_step=False" --lr_scheduler="constant_with_warmup" --max_grad_norm=0.0`` のようになります。
+
+### 任意のオプティマイザを使う
+
+``torch.optim`` のオプティマイザを使う場合にはクラス名のみを（``--optimizer_type=RMSprop``など）、他のモジュールのオプティマイザを使う時は「モジュール名.クラス名」を指定してください（``--optimizer_type=bitsandbytes.optim.lamb.LAMB``など）。
+
+（内部でimportlibしているだけで動作は未確認です。必要ならパッケージをインストールしてください。）
+
 ## マージスクリプトについて
 
 merge_lora.pyでStable DiffusionのモデルにLoRAの学習結果をマージしたり、複数のLoRAモデルをマージしたりできます。
@@ -126,7 +166,7 @@ python networks\merge_lora.py
 
 --ratiosにそれぞれのモデルの比率（どのくらい重みを元モデルに反映するか）を0~1.0の数値で指定します。二つのモデルを一対一でマージす場合は、「0.5 0.5」になります。「1.0 1.0」では合計の重みが大きくなりすぎて、恐らく結果はあまり望ましくないものになると思われます。
 
-v1で学習したLoRAとv2で学習したLoRA、次元数の異なるLoRAはマージできません。U-NetだけのLoRAとU-Net+Text EncoderのLoRAはマージできるはずですが、結果は未知数です。
+v1で学習したLoRAとv2で学習したLoRA、rank（次元数）や``alpha``の異なるLoRAはマージできません。U-NetだけのLoRAとU-Net+Text EncoderのLoRAはマージできるはずですが、結果は未知数です。
 
 
 ### その他のオプション
@@ -138,7 +178,7 @@ v1で学習したLoRAとv2で学習したLoRA、次元数の異なるLoRAはマ
 
 ## 当リポジトリ内の画像生成スクリプトで生成する
 
-gen_img_diffusers.pyに、--network_module、--network_weights、--network_dim（省略可）の各オプションを追加してください。意味は学習時と同様です。
+gen_img_diffusers.pyに、--network_module、--network_weightsの各オプションを追加してください。意味は学習時と同様です。
 
 --network_mulオプションで0~1.0の数値を指定すると、LoRAの適用率を変えられます。
 
@@ -176,6 +216,38 @@ Text Encoderが二つのモデルで同じ場合にはLoRAはU-NetのみのLoRA
 - --save_precision
   - LoRAの保存形式を"float", "fp16", "bf16"から指定します。省略時はfloatになります。
 
+## 画像リサイズスクリプト
+
+（のちほどドキュメントを整理しますがとりあえずここに説明を書いておきます。）
+
+Aspect Ratio Bucketingの機能拡張で、小さな画像については拡大しないでそのまま教師データとすることが可能になりました。元の教師画像を縮小した画像を、教師データに加えると精度が向上したという報告とともに前処理用のスクリプトをいただきましたので整備して追加しました。bmaltais氏に感謝します。
+
+### スクリプトの実行方法
+
+以下のように指定してください。元の画像そのまま、およびリサイズ後の画像が変換先フォルダに保存されます。リサイズ後の画像には、ファイル名に ``+512x512`` のようにリサイズ先の解像度が付け加えられます（画像サイズとは異なります）。リサイズ先の解像度より小さい画像は拡大されることはありません。
+
+```
+python tools\resize_images_to_resolution.py --max_resolution 512x512,384x384,256x256 --save_as_png 
+    --copy_associated_files 元画像フォルダ 変換先フォルダ
+```
+
+元画像フォルダ内の画像ファイルが、指定した解像度（複数指定可）と同じ面積になるようにリサイズされ、変換先フォルダに保存されます。画像以外のファイルはそのままコピーされます。
+
+``--max_resolution`` オプションにリサイズ先のサイズを例のように指定してください。面積がそのサイズになるようにリサイズします。複数指定すると、それぞれの解像度でリサイズされます。``512x512,384x384,256x256``なら、変換先フォルダの画像は、元サイズとリサイズ後サイズ×3の計4枚になります。
+
+``--save_as_png`` オプションを指定するとpng形式で保存します。省略するとjpeg形式（quality=100）で保存されます。
+
+``--copy_associated_files`` オプションを指定すると、拡張子を除き画像と同じファイル名（たとえばキャプションなど）のファイルが、リサイズ後の画像のファイル名と同じ名前でコピーされます。
+
+
+### その他のオプション
+
+- divisible_by
+  - リサイズ後の画像のサイズ（縦、横のそれぞれ）がこの値で割り切れるように、画像中心を切り出します。
+- interpolation
+  - 縮小時の補完方法を指定します。``area, cubic, lanczos4``から選択可能で、デフォルトは``area``です。
+
+
 ## 追加情報
 
 ### cloneofsimo氏のリポジトリとの違い

train_textual_inversion.py

@@ -0,0 +1,495 @@
+import importlib
+import argparse
+import gc
+import math
+import os
+
+from tqdm import tqdm
+import torch
+from accelerate.utils import set_seed
+import diffusers
+from diffusers import DDPMScheduler
+
+import library.train_util as train_util
+from library.train_util import DreamBoothDataset, FineTuningDataset
+
+imagenet_templates_small = [
+    "a photo of a {}",
+    "a rendering of a {}",
+    "a cropped photo of the {}",
+    "the photo of a {}",
+    "a photo of a clean {}",
+    "a photo of a dirty {}",
+    "a dark photo of the {}",
+    "a photo of my {}",
+    "a photo of the cool {}",
+    "a close-up photo of a {}",
+    "a bright photo of the {}",
+    "a cropped photo of a {}",
+    "a photo of the {}",
+    "a good photo of the {}",
+    "a photo of one {}",
+    "a close-up photo of the {}",
+    "a rendition of the {}",
+    "a photo of the clean {}",
+    "a rendition of a {}",
+    "a photo of a nice {}",
+    "a good photo of a {}",
+    "a photo of the nice {}",
+    "a photo of the small {}",
+    "a photo of the weird {}",
+    "a photo of the large {}",
+    "a photo of a cool {}",
+    "a photo of a small {}",
+]
+
+imagenet_style_templates_small = [
+    "a painting in the style of {}",
+    "a rendering in the style of {}",
+    "a cropped painting in the style of {}",
+    "the painting in the style of {}",
+    "a clean painting in the style of {}",
+    "a dirty painting in the style of {}",
+    "a dark painting in the style of {}",
+    "a picture in the style of {}",
+    "a cool painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a bright painting in the style of {}",
+    "a cropped painting in the style of {}",
+    "a good painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a rendition in the style of {}",
+    "a nice painting in the style of {}",
+    "a small painting in the style of {}",
+    "a weird painting in the style of {}",
+    "a large painting in the style of {}",
+]
+
+
+def collate_fn(examples):
+  return examples[0]
+
+
+def train(args):
+  if args.output_name is None:
+    args.output_name = args.token_string
+  use_template = args.use_object_template or args.use_style_template
+
+  train_util.verify_training_args(args)
+  train_util.prepare_dataset_args(args, True)
+
+  cache_latents = args.cache_latents
+  use_dreambooth_method = args.in_json is None
+
+  if args.seed is not None:
+    set_seed(args.seed)
+
+  tokenizer = train_util.load_tokenizer(args)
+
+  # acceleratorを準備する
+  print("prepare accelerator")
+  accelerator, unwrap_model = train_util.prepare_accelerator(args)
+
+  # mixed precisionに対応した型を用意しておき適宜castする
+  weight_dtype, save_dtype = train_util.prepare_dtype(args)
+
+  # モデルを読み込む
+  text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
+
+  # Convert the init_word to token_id
+  if args.init_word is not None:
+    init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False)
+    if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token:
+      print(
+          f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: length {len(init_token_ids)}")
+  else:
+    init_token_ids = None
+
+  # add new word to tokenizer, count is num_vectors_per_token
+  token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
+  num_added_tokens = tokenizer.add_tokens(token_strings)
+  assert num_added_tokens == args.num_vectors_per_token, f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: {args.token_string}"
+
+  token_ids = tokenizer.convert_tokens_to_ids(token_strings)
+  print(f"tokens are added: {token_ids}")
+  assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
+  assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
+
+  # Resize the token embeddings as we are adding new special tokens to the tokenizer
+  text_encoder.resize_token_embeddings(len(tokenizer))
+
+  # Initialise the newly added placeholder token with the embeddings of the initializer token
+  token_embeds = text_encoder.get_input_embeddings().weight.data
+  if init_token_ids is not None:
+    for i, token_id in enumerate(token_ids):
+      token_embeds[token_id] = token_embeds[init_token_ids[i % len(init_token_ids)]]
+      # print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
+
+  # load weights
+  if args.weights is not None:
+    embeddings = load_weights(args.weights)
+    assert len(token_ids) == len(
+        embeddings), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}"
+    # print(token_ids, embeddings.size())
+    for token_id, embedding in zip(token_ids, embeddings):
+      token_embeds[token_id] = embedding
+      # print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
+    print(f"weighs loaded")
+
+  print(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
+
+  # データセットを準備する
+  if use_dreambooth_method:
+    print("Use DreamBooth method.")
+    train_dataset = DreamBoothDataset(args.train_batch_size, args.train_data_dir, args.reg_data_dir,
+                                      tokenizer, args.max_token_length, args.caption_extension, args.shuffle_caption, args.keep_tokens,
+                                      args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
+                                      args.bucket_reso_steps, args.bucket_no_upscale,
+                                      args.prior_loss_weight, args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop, args.debug_dataset)
+  else:
+    print("Train with captions.")
+    train_dataset = FineTuningDataset(args.in_json, args.train_batch_size, args.train_data_dir,
+                                      tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens,
+                                      args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
+                                      args.bucket_reso_steps, args.bucket_no_upscale,
+                                      args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop,
+                                      args.dataset_repeats, args.debug_dataset)
+
+  # make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
+  if use_template:
+    print("use template for training captions. is object: {args.use_object_template}")
+    templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small
+    replace_to = " ".join(token_strings)
+    captions = []
+    for tmpl in templates:
+      captions.append(tmpl.format(replace_to))
+    train_dataset.add_replacement("", captions)
+  elif args.num_vectors_per_token > 1:
+    replace_to = " ".join(token_strings)
+    train_dataset.add_replacement(args.token_string, replace_to)
+
+  train_dataset.make_buckets()
+
+  if args.debug_dataset:
+    train_util.debug_dataset(train_dataset, show_input_ids=True)
+    return
+  if len(train_dataset) == 0:
+    print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
+    return
+
+  # モデルに xformers とか memory efficient attention を組み込む
+  train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
+
+  # 学習を準備する
+  if cache_latents:
+    vae.to(accelerator.device, dtype=weight_dtype)
+    vae.requires_grad_(False)
+    vae.eval()
+    with torch.no_grad():
+      train_dataset.cache_latents(vae)
+    vae.to("cpu")
+    if torch.cuda.is_available():
+      torch.cuda.empty_cache()
+    gc.collect()
+
+  if args.gradient_checkpointing:
+    unet.enable_gradient_checkpointing()
+    text_encoder.gradient_checkpointing_enable()
+
+  # 学習に必要なクラスを準備する
+  print("prepare optimizer, data loader etc.")
+  trainable_params = text_encoder.get_input_embeddings().parameters()
+  _, _, 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
+  train_dataloader = torch.utils.data.DataLoader(
+      train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
+
+  # 学習ステップ数を計算する
+  if args.max_train_epochs is not None:
+    args.max_train_steps = args.max_train_epochs * len(train_dataloader)
+    print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
+
+  # lr schedulerを用意する
+  lr_scheduler = train_util.get_scheduler_fix(args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
+                                              num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+                                              num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
+
+  # acceleratorがなんかよろしくやってくれるらしい
+  text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+      text_encoder, optimizer, train_dataloader, lr_scheduler)
+
+  index_no_updates = torch.arange(len(tokenizer)) < token_ids[0]
+  # print(len(index_no_updates), torch.sum(index_no_updates))
+  orig_embeds_params = unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
+
+  # Freeze all parameters except for the token embeddings in text encoder
+  text_encoder.requires_grad_(True)
+  text_encoder.text_model.encoder.requires_grad_(False)
+  text_encoder.text_model.final_layer_norm.requires_grad_(False)
+  text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
+  # text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)
+
+  unet.requires_grad_(False)
+  unet.to(accelerator.device, dtype=weight_dtype)
+  if args.gradient_checkpointing:                       # according to TI example in Diffusers, train is required
+    unet.train()
+  else:
+    unet.eval()
+
+  if not cache_latents:
+    vae.requires_grad_(False)
+    vae.eval()
+    vae.to(accelerator.device, dtype=weight_dtype)
+
+  # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
+  if args.full_fp16:
+    train_util.patch_accelerator_for_fp16_training(accelerator)
+    text_encoder.to(weight_dtype)
+
+  # resumeする
+  if args.resume is not None:
+    print(f"resume training from state: {args.resume}")
+    accelerator.load_state(args.resume)
+
+  # epoch数を計算する
+  num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+  num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+  if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
+    args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
+
+  # 学習する
+  total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+  print("running training / 学習開始")
+  print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset.num_train_images}")
+  print(f"  num reg images / 正則化画像の数: {train_dataset.num_reg_images}")
+  print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
+  print(f"  num epochs / epoch数: {num_train_epochs}")
+  print(f"  batch size per device / バッチサイズ: {args.train_batch_size}")
+  print(f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
+  print(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+  print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
+
+  progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
+  global_step = 0
+
+  noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
+                                  num_train_timesteps=1000, clip_sample=False)
+
+  if accelerator.is_main_process:
+    accelerator.init_trackers("textual_inversion")
+
+  for epoch in range(num_train_epochs):
+    print(f"epoch {epoch+1}/{num_train_epochs}")
+    train_dataset.set_current_epoch(epoch + 1)
+
+    text_encoder.train()
+
+    loss_total = 0
+    bef_epo_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone()
+    for step, batch in enumerate(train_dataloader):
+      with accelerator.accumulate(text_encoder):
+        with torch.no_grad():
+          if "latents" in batch and batch["latents"] is not None:
+            latents = batch["latents"].to(accelerator.device)
+          else:
+            # latentに変換
+            latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
+          latents = latents * 0.18215
+        b_size = latents.shape[0]
+
+        # Get the text embedding for conditioning
+        input_ids = batch["input_ids"].to(accelerator.device)
+        # weight_dtype) use float instead of fp16/bf16 because text encoder is float
+        encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.float)
+
+        # Sample noise that we'll add to the latents
+        noise = torch.randn_like(latents, device=latents.device)
+        if args.noise_offset:
+          # https://www.crosslabs.org//blog/diffusion-with-offset-noise
+          noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
+
+        # Sample a random timestep for each image
+        timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
+        timesteps = timesteps.long()
+
+        # Add noise to the latents according to the noise magnitude at each timestep
+        # (this is the forward diffusion process)
+        noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+        # Predict the noise residual
+        noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+        if args.v_parameterization:
+          # v-parameterization training
+          target = noise_scheduler.get_velocity(latents, noise, timesteps)
+        else:
+          target = noise
+
+        loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
+        loss = loss.mean([1, 2, 3])
+
+        loss_weights = batch["loss_weights"]                      # 各sampleごとのweight
+        loss = loss * loss_weights
+
+        loss = loss.mean()                # 平均なのでbatch_sizeで割る必要なし
+
+        accelerator.backward(loss)
+        if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+          params_to_clip = text_encoder.get_input_embeddings().parameters()
+          accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
+
+        optimizer.step()
+        lr_scheduler.step()
+        optimizer.zero_grad(set_to_none=True)
+
+        # Let's make sure we don't update any embedding weights besides the newly added token
+        with torch.no_grad():
+          unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = orig_embeds_params[index_no_updates]
+
+      # Checks if the accelerator has performed an optimization step behind the scenes
+      if accelerator.sync_gradients:
+        progress_bar.update(1)
+        global_step += 1
+
+      current_loss = loss.detach().item()
+      if args.logging_dir is not None:
+        logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
+        if args.optimizer_type.lower() == "DAdaptation".lower():  # tracking d*lr value
+          logs["lr/d*lr"] = lr_scheduler.optimizers[0].param_groups[0]['d']*lr_scheduler.optimizers[0].param_groups[0]['lr']
+        accelerator.log(logs, step=global_step)
+
+      loss_total += current_loss
+      avr_loss = loss_total / (step+1)
+      logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
+      progress_bar.set_postfix(**logs)
+
+      if global_step >= args.max_train_steps:
+        break
+
+    if args.logging_dir is not None:
+      logs = {"loss/epoch": loss_total / len(train_dataloader)}
+      accelerator.log(logs, step=epoch+1)
+
+    accelerator.wait_for_everyone()
+
+    updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone()
+    # d = updated_embs - bef_epo_embs
+    # print(bef_epo_embs.size(), updated_embs.size(), d.mean(), d.min())
+
+    if args.save_every_n_epochs is not None:
+      model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name
+
+      def save_func():
+        ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + '.' + args.save_model_as
+        ckpt_file = os.path.join(args.output_dir, ckpt_name)
+        print(f"saving checkpoint: {ckpt_file}")
+        save_weights(ckpt_file, updated_embs, save_dtype)
+
+      def remove_old_func(old_epoch_no):
+        old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + '.' + args.save_model_as
+        old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
+        if os.path.exists(old_ckpt_file):
+          print(f"removing old checkpoint: {old_ckpt_file}")
+          os.remove(old_ckpt_file)
+
+      saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
+      if saving and args.save_state:
+        train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
+
+    # end of epoch
+
+  is_main_process = accelerator.is_main_process
+  if is_main_process:
+    text_encoder = unwrap_model(text_encoder)
+
+  accelerator.end_training()
+
+  if args.save_state:
+    train_util.save_state_on_train_end(args, accelerator)
+
+  updated_embs = text_encoder.get_input_embeddings().weight[token_ids].data.detach().clone()
+
+  del accelerator                         # この後メモリを使うのでこれは消す
+
+  if is_main_process:
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
+    ckpt_name = model_name + '.' + args.save_model_as
+    ckpt_file = os.path.join(args.output_dir, ckpt_name)
+
+    print(f"save trained model to {ckpt_file}")
+    save_weights(ckpt_file, updated_embs, save_dtype)
+    print("model saved.")
+
+
+def save_weights(file, updated_embs, save_dtype):
+  state_dict = {"emb_params": updated_embs}
+
+  if save_dtype is not None:
+    for key in list(state_dict.keys()):
+      v = state_dict[key]
+      v = v.detach().clone().to("cpu").to(save_dtype)
+      state_dict[key] = v
+
+  if os.path.splitext(file)[1] == '.safetensors':
+    from safetensors.torch import save_file
+    save_file(state_dict, file)
+  else:
+    torch.save(state_dict, file)                    # can be loaded in Web UI
+
+
+def load_weights(file):
+  if os.path.splitext(file)[1] == '.safetensors':
+    from safetensors.torch import load_file
+    data = load_file(file)
+  else:
+    # compatible to Web UI's file format
+    data = torch.load(file, map_location='cpu')
+    if type(data) != dict:
+      raise ValueError(f"weight file is not dict / 重みファイルがdict形式ではありません: {file}")
+
+    if 'string_to_param' in data:                           # textual inversion embeddings
+      data = data['string_to_param']
+      if hasattr(data, '_parameters'):                      # support old PyTorch?
+        data = getattr(data, '_parameters')
+
+  emb = next(iter(data.values()))
+  if type(emb) != torch.Tensor:
+    raise ValueError(f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {file}")
+
+  if len(emb.size()) == 1:
+    emb = emb.unsqueeze(0)
+
+  return emb
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+
+  train_util.add_sd_models_arguments(parser)
+  train_util.add_dataset_arguments(parser, True, True, False)
+  train_util.add_training_arguments(parser, True)
+  train_util.add_optimizer_arguments(parser)
+
+  parser.add_argument("--save_model_as", type=str, default="pt", choices=[None, "ckpt", "pt", "safetensors"],
+                      help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）")
+
+  parser.add_argument("--weights", type=str, default=None,
+                      help="embedding weights to initialize / 学習するネットワークの初期重み")
+  parser.add_argument("--num_vectors_per_token", type=int, default=1,
+                      help='number of vectors per token / トークンに割り当てるembeddingsの要素数')
+  parser.add_argument("--token_string", type=str, default=None,
+                      help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること")
+  parser.add_argument("--init_word", type=str, default=None,
+                      help="words to initialize vector / ベクトルを初期化に使用する単語、複数可")
+  parser.add_argument("--use_object_template", action='store_true',
+                      help="ignore caption and use default templates for object / キャプションは使わずデフォルトの物体用テンプレートで学習する")
+  parser.add_argument("--use_style_template", action='store_true',
+                      help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する")
+
+  args = parser.parse_args()
+  train(args)

train_ti_README-ja.md

@@ -0,0 +1,63 @@
+## Textual Inversionの学習について
+
+[Textual Inversion](https://textual-inversion.github.io/)です。実装に当たっては https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion を大いに参考にしました。
+
+学習したモデルはWeb UIでもそのまま使えます。
+
+なお恐らくSD2.xにも対応していますが現時点では未テストです。
+
+## 学習方法
+
+``train_textual_inversion.py`` を用います。
+
+データの準備については ``train_network.py`` と全く同じですので、[そちらのドキュメント](./train_network_README-ja.md)を参照してください。
+
+## オプション
+
+以下はコマンドラインの例です（DreamBooth手法）。
+
+```
+accelerate launch --num_cpu_threads_per_process 1 train_textual_inversion.py 
+    --pretrained_model_name_or_path=..\models\model.ckpt 
+    --train_data_dir=..\data\db\char1 --output_dir=..\ti_train1 
+    --resolution=448,640 --train_batch_size=1 --learning_rate=1e-4 
+    --max_train_steps=400 --use_8bit_adam --xformers --mixed_precision=fp16 
+    --save_every_n_epochs=1 --save_model_as=safetensors --clip_skip=2 --seed=42 --color_aug 
+    --token_string=mychar4 --init_word=cute --num_vectors_per_token=4
+```
+
+``--token_string`` に学習時のトークン文字列を指定します。__学習時のプロンプトは、この文字列を含むようにしてください（token_stringがmychar4なら、``mychar4 1girl`` など）__。プロンプトのこの文字列の部分が、Textual Inversionの新しいtokenに置換されて学習されます。
+
+プロンプトにトークン文字列が含まれているかどうかは、``--debug_dataset`` で置換後のtoken idが表示されますので、以下のように ``49408`` 以降のtokenが存在するかどうかで確認できます。
+
+```
+input ids: tensor([[49406, 49408, 49409, 49410, 49411, 49412, 49413, 49414, 49415, 49407,
+         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
+         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
+         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
+         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
+         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
+         49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407, 49407,
+         49407, 49407, 49407, 49407, 49407, 49407, 49407]])
+```
+
+tokenizerがすでに持っている単語（一般的な単語）は使用できません。
+
+``--init_word`` にembeddingsを初期化するときのコピー元トークンの文字列を指定します。学ばせたい概念が近いものを選ぶとよいようです。二つ以上のトークンになる文字列は指定できません。
+
+``--num_vectors_per_token`` にいくつのトークンをこの学習で使うかを指定します。多いほうが表現力が増しますが、その分多くのトークンを消費します。たとえばnum_vectors_per_token=8の場合、指定したトークン文字列は（一般的なプロンプトの77トークン制限のうち）8トークンを消費します。
+
+
+その他、以下のオプションが指定できます。
+
+* --weights
+  * 学習前に学習済みのembeddingsを読み込み、そこから追加で学習します。
+* --use_object_template
+  * キャプションではなく既定の物体用テンプレート文字列（``a photo of a {}``など）で学習します。公式実装と同じになります。キャプションは無視されます。
+* --use_style_template
+  * キャプションではなく既定のスタイル用テンプレート文字列で学習します（``a painting in the style of {}``など）。公式実装と同じになります。キャプションは無視されます。
+
+## 当リポジトリ内の画像生成スクリプトで生成する
+
+gen_img_diffusers.pyに、``--textual_inversion_embeddings`` オプションで学習したembeddingsファイルを指定してください（複数可）。プロンプトでembeddingsファイルのファイル名（拡張子を除く）を使うと、そのembeddingsが適用されます。
+