fix hf upload #1196

Save state on train end

README.md

@@ -33,6 +33,7 @@ Most of the documents are written in Japanese.
 * [Training guide - common](./docs/train_README-ja.md) : data preparation, options etc... 
   * [Chinese version](./docs/train_README-zh.md)
 * [Dataset config](./docs/config_README-ja.md) 
+  * [English version](./docs/config_README-en.md)
 * [DreamBooth training guide](./docs/train_db_README-ja.md)
 * [Step by Step fine-tuning guide](./docs/fine_tune_README_ja.md):
 * [training LoRA](./docs/train_network_README-ja.md)
@@ -249,15 +250,139 @@ ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [docum
 
 ## Change History
 
+### Working in progress
+
+- Colab seems to stop with log output. Try specifying `--console_log_simple` option in the training script to disable rich logging.
+- The `.toml` file for the dataset config is now read in UTF-8 encoding. PR [#1167](https://github.com/kohya-ss/sd-scripts/pull/1167) Thanks to Horizon1704!
+- `train_network.py` and `sdxl_train_network.py` are modified to record some dataset settings in the metadata of the trained model (`caption_prefix`, `caption_suffix`, `keep_tokens_separator`, `secondary_separator`, `enable_wildcard`).
+- Some features are added to the dataset subset settings.
+  - `secondary_separator` is added to specify the tag separator that is not the target of shuffling or dropping. 
+    - Specify `secondary_separator=";;;"`. When you specify `secondary_separator`, the part is not shuffled or dropped. See the example below.
+  - `enable_wildcard` is added. When set to `true`, the wildcard notation `{aaa|bbb|ccc}` can be used. See the example below.
+  - `keep_tokens_separator` is updated to be used twice in the caption. When you specify `keep_tokens_separator="|||"`, the part divided by the second `|||` is not shuffled or dropped and remains at the end.
+  - The existing features `caption_prefix` and `caption_suffix` can be used together. `caption_prefix` and `caption_suffix` are processed first, and then `enable_wildcard`, `keep_tokens_separator`, shuffling and dropping, and `secondary_separator` are processed in order.
+  - The examples are [shown below](#example-of-dataset-settings--データセット設定の記述例).
+- The support for v3 repositories is added to `tag_image_by_wd14_tagger.py` (`--onnx` option only). PR [#1192](https://github.com/kohya-ss/sd-scripts/pull/1192) Thanks to sdbds!
+  - Onnx may need to be updated. Onnx is not installed by default, so please install or update it with `pip install onnx==1.15.0 onnxruntime-gpu==1.17.1` etc. Please also check the comments in `requirements.txt`.
+- The model is now saved in the subdirectory as `--repo_id` in `tag_image_by_wd14_tagger.py` . This caches multiple repo_id models. Please delete unnecessary files under `--model_dir`.
+- The options `--noise_offset_random_strength` and `--ip_noise_gamma_random_strength` are added to each training script. These options can be used to vary the noise offset and ip noise gamma in the range of 0 to the specified value. PR [#1177](https://github.com/kohya-ss/sd-scripts/pull/1177) Thanks to KohakuBlueleaf!
+- The [English version of the dataset settings documentation](./docs/config_README-en.md) is added. PR [#1175](https://github.com/kohya-ss/sd-scripts/pull/1175) Thanks to darkstorm2150!
+- The options `--save_state_on_train_end` are added to each training script. PR [#1168](https://github.com/kohya-ss/sd-scripts/pull/1168) Thanks to gesen2egee!
+
+
+- Colab での動作時、ログ出力で停止してしまうようです。学習スクリプトに `--console_log_simple` オプションを指定し、rich のロギングを無効してお試しください。
+- データセット設定の `.toml` ファイルが UTF-8 encoding で読み込まれるようになりました。PR [#1167](https://github.com/kohya-ss/sd-scripts/pull/1167) Horizon1704 氏に感謝します。
+- `train_network.py` および `sdxl_train_network.py` で、学習したモデルのメタデータに一部のデータセット設定が記録されるよう修正しました（`caption_prefix`、`caption_suffix`、`keep_tokens_separator`、`secondary_separator`、`enable_wildcard`）。
+- データセットのサブセット設定にいくつかの機能を追加しました。
+  - シャッフルの対象とならないタグ分割識別子の指定 `secondary_separator` を追加しました。`secondary_separator=";;;"` のように指定します。`secondary_separator` で区切ることで、その部分はシャッフル、drop 時にまとめて扱われます。詳しくは記述例をご覧ください。
+  - `enable_wildcard` を追加しました。`true` にするとワイルドカード記法 `{aaa|bbb|ccc}` が使えます。詳しくは記述例をご覧ください。
+  - `keep_tokens_separator` をキャプション内に 2 つ使えるようにしました。たとえば `keep_tokens_separator="|||"` と指定したとき、`1girl, hatsune miku, vocaloid ||| stage, mic ||| best quality, rating: general` とキャプションを指定すると、二番目の `|||` で分割された部分はシャッフル、drop されず末尾に残ります。
+  - 既存の機能 `caption_prefix` と `caption_suffix` とあわせて使えます。`caption_prefix` と `caption_suffix` は一番最初に処理され、その後、ワイルドカード、`keep_tokens_separator`、シャッフルおよび drop、`secondary_separator` の順に処理されます。
+- `tag_image_by_wd14_tagger.py` で v3 のリポジトリがサポートされました（`--onnx` 指定時のみ有効）。 PR [#1192](https://github.com/kohya-ss/sd-scripts/pull/1192) sdbds 氏に感謝します。
+  - Onnx のバージョンアップが必要になるかもしれません。デフォルトでは Onnx はインストールされていませんので、`pip install onnx==1.15.0 onnxruntime-gpu==1.17.1` 等でインストール、アップデートしてください。`requirements.txt` のコメントもあわせてご確認ください。
+- `tag_image_by_wd14_tagger.py` で、モデルを`--repo_id` のサブディレクトリに保存するようにしました。これにより複数のモデルファイルがキャッシュされます。`--model_dir` 直下の不要なファイルは削除願います。
+- 各学習スクリプトに、noise offset、ip noise gammaを、それぞれ 0~指定した値の範囲で変動させるオプション `--noise_offset_random_strength` および `--ip_noise_gamma_random_strength` が追加されました。 PR [#1177](https://github.com/kohya-ss/sd-scripts/pull/1177) KohakuBlueleaf 氏に感謝します。
+- データセット設定の[英語版ドキュメント](./docs/config_README-en.md) が追加されました。PR [#1175](https://github.com/kohya-ss/sd-scripts/pull/1175) darkstorm2150 氏に感謝します。
+- 各学習スクリプトに、学習終了時に state を保存する `--save_state_on_train_end` オプションが追加されました。 PR [#1168](https://github.com/kohya-ss/sd-scripts/pull/1168) gesen2egee 氏に感謝します。
+
+
+#### Example of dataset settings / データセット設定の記述例:
+
+```toml
+[general]
+flip_aug = true
+color_aug = false
+resolution = [1024, 1024]
+
+[[datasets]]
+batch_size = 6
+enable_bucket = true
+bucket_no_upscale = true
+caption_extension = ".txt"
+keep_tokens_separator= "|||"
+shuffle_caption = true
+caption_tag_dropout_rate = 0.1
+secondary_separator = ";;;" # subset 側に書くこともできます / can be written in the subset side
+enable_wildcard = true # 同上 / same as above
+
+  [[datasets.subsets]]
+  image_dir = "/path/to/image_dir"
+  num_repeats = 1
+
+  # ||| の前後はカンマは不要です（自動的に追加されます） / No comma is required before and after ||| (it is added automatically)
+  caption_prefix = "1girl, hatsune miku, vocaloid |||" 
+  
+  # ||| の後はシャッフル、drop されず残ります / After |||, it is not shuffled or dropped and remains
+  # 単純に文字列として連結されるので、カンマなどは自分で入れる必要があります / It is simply concatenated as a string, so you need to put commas yourself
+  caption_suffix = ", anime screencap ||| masterpiece, rating: general"
+```
+
+#### Example of caption, secondary_separator notation: `secondary_separator = ";;;"`
+
+```txt
+1girl, hatsune miku, vocaloid, upper body, looking at viewer, sky;;;cloud;;;day, outdoors
+```
+The part `sky;;;cloud;;;day` is replaced with `sky,cloud,day` without shuffling or dropping. When shuffling and dropping are enabled, it is processed as a whole (as one tag). For example, it becomes `vocaloid, 1girl, upper body, sky,cloud,day, outdoors, hatsune miku` (shuffled) or `vocaloid, 1girl, outdoors, looking at viewer, upper body, hatsune miku` (dropped).
+
+#### Example of caption, enable_wildcard notation: `enable_wildcard = true`
+
+```txt
+1girl, hatsune miku, vocaloid, upper body, looking at viewer, {simple|white} background
+```
+`simple` or `white` is randomly selected, and it becomes `simple background` or `white background`.
+
+```txt
+1girl, hatsune miku, vocaloid, {{retro style}}
+```
+If you want to include `{` or `}` in the tag string, double them like `{{` or `}}` (in this example, the actual caption used for training is `{retro style}`).
+
+#### Example of caption, `keep_tokens_separator` notation: `keep_tokens_separator = "|||"`
+
+```txt
+1girl, hatsune miku, vocaloid ||| stage, microphone, white shirt, smile ||| best quality, rating: general
+```
+It becomes `1girl, hatsune miku, vocaloid, microphone, stage, white shirt, best quality, rating: general` or `1girl, hatsune miku, vocaloid, white shirt, smile, stage, microphone, best quality, rating: general` etc.
+
+
+#### キャプション記述例、secondary_separator 記法：`secondary_separator = ";;;"` の場合
+
+```txt
+1girl, hatsune miku, vocaloid, upper body, looking at viewer, sky;;;cloud;;;day, outdoors
+```
+`sky;;;cloud;;;day` の部分はシャッフル、drop されず `sky,cloud,day` に置換されます。シャッフル、drop が有効な場合、まとめて（一つのタグとして）処理されます。つまり `vocaloid, 1girl, upper body, sky,cloud,day, outdoors, hatsune miku` （シャッフル）や `vocaloid, 1girl, outdoors, looking at viewer, upper body, hatsune miku` （drop されたケース）などになります。
+
+#### キャプション記述例、ワイルドカード記法： `enable_wildcard = true` の場合
+
+```txt
+1girl, hatsune miku, vocaloid, upper body, looking at viewer, {simple|white} background
+```
+ランダムに `simple` または `white` が選ばれ、`simple background` または `white background` になります。
+
+```txt
+1girl, hatsune miku, vocaloid, {{retro style}}
+```
+タグ文字列に `{` や `}` そのものを含めたい場合は `{{` や `}}` のように二つ重ねてください（この例では実際に学習に用いられるキャプションは `{retro style}` になります）。
+
+#### キャプション記述例、`keep_tokens_separator` 記法： `keep_tokens_separator = "|||"` の場合
+
+```txt
+1girl, hatsune miku, vocaloid ||| stage, microphone, white shirt, smile ||| best quality, rating: general
+```
+`1girl, hatsune miku, vocaloid, microphone, stage, white shirt, best quality, rating: general` や `1girl, hatsune miku, vocaloid, white shirt, smile, stage, microphone, best quality, rating: general` などになります。
+
+
 ### Mar 15, 2024 / 2024/3/15: v0.8.5
 
 - Fixed a bug that the value of timestep embedding during SDXL training was incorrect.
+  - Please update for SDXL training.
   - The inference with the generation script is also fixed.
-  - The impact is unknown, but please update for SDXL training.
+  - This fix appears to resolve an issue where unintended artifacts occurred in trained models under certain conditions. 
+We would like to express our deep gratitude to Mark Saint (cacoe) from leonardo.ai, for reporting the issue and cooperating with the verification, and to gcem156 for the advice provided in identifying the part of the code that needed to be fixed.
 
 - SDXL 学習時の timestep embedding の値が誤っていたのを修正しました。
+  - SDXL の学習時にはアップデートをお願いいたします。
   - 生成スクリプトでの推論時についてもあわせて修正しました。
-  - 影響の度合いは不明ですが、SDXL の学習時にはアップデートをお願いいたします。
+  - この修正により、特定の条件下で学習されたモデルに意図しないアーティファクトが発生する問題が解消されるようです。問題を報告いただき、また検証にご協力いただいた leonardo.ai の Mark Saint (cacoe) 氏、および修正点の特定に関するアドバイスをいただいた gcem156 氏に深く感謝いたします。
 
 ### Feb 24, 2024 / 2024/2/24: v0.8.4
 
@@ -314,64 +439,6 @@ ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [docum
 - 複数 GPU での学習時に `network_multiplier` を指定するとクラッシュする不具合が修正されました。 PR [#1084](https://github.com/kohya-ss/sd-scripts/pull/1084) fireicewolf 氏に感謝します。
 - ControlNet-LLLite の学習がエラーになる不具合を修正しました。 
 
-### Jan 23, 2024 / 2024/1/23: v0.8.2
-
-- [Experimental] The `--fp8_base` option is added to the training scripts for LoRA etc. The base model (U-Net, and Text Encoder when training modules for Text Encoder) can be trained with fp8. PR [#1057](https://github.com/kohya-ss/sd-scripts/pull/1057) Thanks to KohakuBlueleaf!
-  - Please specify `--fp8_base` in `train_network.py` or `sdxl_train_network.py`.
-  - PyTorch 2.1 or later is required.
-  - If you use xformers with PyTorch 2.1, please see [xformers repository](https://github.com/facebookresearch/xformers) and install the appropriate version according to your CUDA version.
-  - The sample image generation during training consumes a lot of memory. It is recommended to turn it off.
-
-- [Experimental] The network multiplier can be specified for each dataset in the training scripts for LoRA etc.
-  - This is an experimental option and may be removed or changed in the future.
-  - For example, if you train with state A as `1.0` and state B as `-1.0`, you may be able to generate by switching between state A and B depending on the LoRA application rate.
-  - Also, if you prepare five states and train them as `0.2`, `0.4`, `0.6`, `0.8`, and `1.0`, you may be able to generate by switching the states smoothly depending on the application rate.
-  - Please specify `network_multiplier` in `[[datasets]]` in `.toml` file.
-- Some options are added to `networks/extract_lora_from_models.py` to reduce the memory usage.
-  - `--load_precision` option can be used to specify the precision when loading the model. If the model is saved in fp16, you can reduce the memory usage by specifying `--load_precision fp16` without losing precision.
-  - `--load_original_model_to` option can be used to specify the device to load the original model. `--load_tuned_model_to` option can be used to specify the device to load the derived model. The default is `cpu` for both options, but you can specify `cuda` etc. You can reduce the memory usage by loading one of them to GPU. This option is available only for SDXL.
-
-- The gradient synchronization in LoRA training with multi-GPU is improved. PR [#1064](https://github.com/kohya-ss/sd-scripts/pull/1064) Thanks to KohakuBlueleaf!
-- The code for Intel IPEX support is improved. PR [#1060](https://github.com/kohya-ss/sd-scripts/pull/1060) Thanks to akx!
-- Fixed a bug in multi-GPU Textual Inversion training.
-
-- （実験的）　LoRA等の学習スクリプトで、ベースモデル（U-Net、および Text Encoder のモジュール学習時は Text Encoder も）の重みを fp8 にして学習するオプションが追加されました。 PR [#1057](https://github.com/kohya-ss/sd-scripts/pull/1057) KohakuBlueleaf 氏に感謝します。
-  - `train_network.py` または `sdxl_train_network.py` で `--fp8_base` を指定してください。
-  - PyTorch 2.1 以降が必要です。
-  - PyTorch 2.1 で xformers を使用する場合は、[xformers のリポジトリ](https://github.com/facebookresearch/xformers) を参照し、CUDA バージョンに応じて適切なバージョンをインストールしてください。
-  - 学習中のサンプル画像生成はメモリを大量に消費するため、オフにすることをお勧めします。
-- (実験的)　LoRA 等の学習で、データセットごとに異なるネットワーク適用率を指定できるようになりました。 
-  - 実験的オプションのため、将来的に削除または仕様変更される可能性があります。
-  - たとえば状態 A を `1.0`、状態 B を `-1.0` として学習すると、LoRA の適用率に応じて状態 A と B を切り替えつつ生成できるかもしれません。
-  - また、五段階の状態を用意し、それぞれ `0.2`、`0.4`、`0.6`、`0.8`、`1.0` として学習すると、適用率でなめらかに状態を切り替えて生成できるかもしれません。 
-  - `.toml` ファイルで `[[datasets]]` に `network_multiplier` を指定してください。
-- `networks/extract_lora_from_models.py` に使用メモリ量を削減するいくつかのオプションを追加しました。 
-  - `--load_precision` で読み込み時の精度を指定できます。モデルが fp16 で保存されている場合は `--load_precision fp16` を指定して精度を変えずにメモリ量を削減できます。
-  - `--load_original_model_to` で元モデルを読み込むデバイスを、`--load_tuned_model_to` で派生モデルを読み込むデバイスを指定できます。デフォルトは両方とも `cpu` ですがそれぞれ `cuda` 等を指定できます。片方を GPU に読み込むことでメモリ量を削減できます。SDXL の場合のみ有効です。
-- マルチ GPU での LoRA 等の学習時に勾配の同期が改善されました。 PR [#1064](https://github.com/kohya-ss/sd-scripts/pull/1064) KohakuBlueleaf 氏に感謝します。
-- Intel IPEX サポートのコードが改善されました。PR [#1060](https://github.com/kohya-ss/sd-scripts/pull/1060) akx 氏に感謝します。
-- マルチ GPU での Textual Inversion 学習の不具合を修正しました。
-
-- `.toml` example for network multiplier / ネットワーク適用率の `.toml` の記述例
-
-```toml
-[general]
-[[datasets]]
-resolution = 512
-batch_size = 8
-network_multiplier = 1.0
-
-... subset settings ...
-
-[[datasets]]
-resolution = 512
-batch_size = 8
-network_multiplier = -1.0
-
-... subset settings ...
-```
-
-
 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。
 

docs/config_README-en.md

@@ -0,0 +1,279 @@
+Original Source by kohya-ss
+
+A.I Translation by Model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO, editing by Darkstorm2150
+
+# Config Readme
+
+This README is about the configuration files that can be passed with the `--dataset_config` option.
+
+## Overview
+
+By passing a configuration file, users can make detailed settings.
+
+* Multiple datasets can be configured
+   * For example, by setting `resolution` for each dataset, they can be mixed and trained.
+   * In training methods that support both the DreamBooth approach and the fine-tuning approach, datasets of the DreamBooth method and the fine-tuning method can be mixed.
+* Settings can be changed for each subset
+   * A subset is a partition of the dataset by image directory or metadata. Several subsets make up a dataset.
+   * Options such as `keep_tokens` and `flip_aug` can be set for each subset. On the other hand, options such as `resolution` and `batch_size` can be set for each dataset, and their values are common among subsets belonging to the same dataset. More details will be provided later.
+
+The configuration file format can be JSON or TOML. Considering the ease of writing, it is recommended to use [TOML](https://toml.io/ja/v1.0.0-rc.2). The following explanation assumes the use of TOML.
+
+
+Here is an example of a configuration file written in TOML.
+
+```toml
+[general]
+shuffle_caption = true
+caption_extension = '.txt'
+keep_tokens = 1
+
+# This is a DreamBooth-style dataset
+[[datasets]]
+resolution = 512
+batch_size = 4
+keep_tokens = 2
+
+  [[datasets.subsets]]
+  image_dir = 'C:\hoge'
+  class_tokens = 'hoge girl'
+  # This subset uses keep_tokens = 2 (the value of the parent datasets)
+
+  [[datasets.subsets]]
+  image_dir = 'C:\fuga'
+  class_tokens = 'fuga boy'
+  keep_tokens = 3
+
+  [[datasets.subsets]]
+  is_reg = true
+  image_dir = 'C:\reg'
+  class_tokens = 'human'
+  keep_tokens = 1
+
+# This is a fine-tuning dataset
+[[datasets]]
+resolution = [768, 768]
+batch_size = 2
+
+  [[datasets.subsets]]
+  image_dir = 'C:\piyo'
+  metadata_file = 'C:\piyo\piyo_md.json'
+  # This subset uses keep_tokens = 1 (the value of [general])
+```
+
+In this example, three directories are trained as a DreamBooth-style dataset at 512x512 (batch size 4), and one directory is trained as a fine-tuning dataset at 768x768 (batch size 2).
+
+## Settings for datasets and subsets
+
+Settings for datasets and subsets are divided into several registration locations.
+
+* `[general]`
+    * This is where options that apply to all datasets or all subsets are specified.
+    * If there are options with the same name in the dataset-specific or subset-specific settings, the dataset-specific or subset-specific settings take precedence.
+* `[[datasets]]`
+    * `datasets` is where settings for datasets are registered. This is where options that apply individually to each dataset are specified.
+	* If there are subset-specific settings, the subset-specific settings take precedence.
+* `[[datasets.subsets]]`
+    * `datasets.subsets` is where settings for subsets are registered. This is where options that apply individually to each subset are specified.
+
+Here is an image showing the correspondence between image directories and registration locations in the previous example.
+
+```
+C:\
+├─ hoge  ->  [[datasets.subsets]] No.1  ┐                        ┐
+├─ fuga  ->  [[datasets.subsets]] No.2  |->  [[datasets]] No.1   |->  [general]
+├─ reg   ->  [[datasets.subsets]] No.3  ┘                        |
+└─ piyo  ->  [[datasets.subsets]] No.4  -->  [[datasets]] No.2   ┘
+```
+
+The image directory corresponds to each `[[datasets.subsets]]`. Then, multiple `[[datasets.subsets]]` are combined to form one `[[datasets]]`. All `[[datasets]]` and `[[datasets.subsets]]` belong to `[general]`.
+
+The available options for each registration location may differ, but if the same option is specified, the value in the lower registration location will take precedence. You can check how the `keep_tokens` option is handled in the previous example for better understanding.
+
+Additionally, the available options may vary depending on the method that the learning approach supports.
+
+* Options specific to the DreamBooth method
+* Options specific to the fine-tuning method
+* Options available when using the caption dropout technique
+
+When using both the DreamBooth method and the fine-tuning method, they can be used together with a learning approach that supports both.
+When using them together, a point to note is that the method is determined based on the dataset, so it is not possible to mix DreamBooth method subsets and fine-tuning method subsets within the same dataset.
+In other words, if you want to use both methods together, you need to set up subsets of different methods belonging to different datasets.
+
+In terms of program behavior, if the `metadata_file` option exists, it is determined to be a subset of fine-tuning. Therefore, for subsets belonging to the same dataset, as long as they are either "all have the `metadata_file` option" or "all have no `metadata_file` option," there is no problem.
+
+Below, the available options will be explained. For options with the same name as the command-line argument, the explanation will be omitted in principle. Please refer to other READMEs.
+
+### Common options for all learning methods
+
+These are options that can be specified regardless of the learning method.
+
+#### Data set specific options
+
+These are options related to the configuration of the data set. They cannot be described in `datasets.subsets`.
+
+
+| Option Name | Example Setting | `[general]` | `[[datasets]]` |
+| ---- | ---- | ---- | ---- |
+| `batch_size` | `1` | o | o |
+| `bucket_no_upscale` | `true` | o | o |
+| `bucket_reso_steps` | `64` | o | o |
+| `enable_bucket` | `true` | o | o |
+| `max_bucket_reso` | `1024` | o | o |
+| `min_bucket_reso` | `128` | o | o |
+| `resolution` | `256`, `[512, 512]` | o | o |
+
+* `batch_size`
+    * This corresponds to the command-line argument `--train_batch_size`.
+
+These settings are fixed per dataset. That means that subsets belonging to the same dataset will share these settings. For example, if you want to prepare datasets with different resolutions, you can define them as separate datasets as shown in the example above, and set different resolutions for each.
+
+#### Options for Subsets
+
+These options are related to subset configuration.
+
+| Option Name | Example | `[general]` | `[[datasets]]` | `[[dataset.subsets]]` |
+| ---- | ---- | ---- | ---- | ---- |
+| `color_aug` | `false` | o | o | o |
+| `face_crop_aug_range` | `[1.0, 3.0]` | o | o | o |
+| `flip_aug` | `true` | o | o | o |
+| `keep_tokens` | `2` | o | o | o |
+| `num_repeats` | `10` | o | o | o |
+| `random_crop` | `false` | o | o | o |
+| `shuffle_caption` | `true` | o | o | o |
+| `caption_prefix` | `"masterpiece, best quality, "` | o | o | o |
+| `caption_suffix` | `", from side"` | o | o | o |
+
+* `num_repeats`
+    * Specifies the number of repeats for images in a subset. This is equivalent to `--dataset_repeats` in fine-tuning but can be specified for any training method.
+* `caption_prefix`, `caption_suffix`
+    * Specifies the prefix and suffix strings to be appended to the captions. Shuffling is performed with these strings included. Be cautious when using `keep_tokens`.
+
+### DreamBooth-specific options
+
+DreamBooth-specific options only exist as subsets-specific options.
+
+#### Subset-specific options
+
+Options related to the configuration of DreamBooth subsets.
+
+| Option Name | Example Setting | `[general]` | `[[datasets]]` | `[[dataset.subsets]]` |
+| ---- | ---- | ---- | ---- | ---- |
+| `image_dir` | `'C:\hoge'` | - | - | o (required) |
+| `caption_extension` | `".txt"` | o | o | o |
+| `class_tokens` | `"sks girl"` | - | - | o |
+| `is_reg` | `false` | - | - | o |
+
+Firstly, note that for `image_dir`, the path to the image files must be specified as being directly in the directory. Unlike the previous DreamBooth method, where images had to be placed in subdirectories, this is not compatible with that specification. Also, even if you name the folder something like "5_cat", the number of repeats of the image and the class name will not be reflected. If you want to set these individually, you will need to explicitly specify them using `num_repeats` and `class_tokens`.
+
+* `image_dir`
+    * Specifies the path to the image directory. This is a required option.
+    * Images must be placed directly under the directory.
+* `class_tokens`
+    * Sets the class tokens.
+    * Only used during training when a corresponding caption file does not exist. The determination of whether or not to use it is made on a per-image basis. If `class_tokens` is not specified and a caption file is not found, an error will occur.
+* `is_reg`
+    * Specifies whether the subset images are for normalization. If not specified, it is set to `false`, meaning that the images are not for normalization.
+
+### Fine-tuning method specific options
+
+The options for the fine-tuning method only exist for subset-specific options.
+
+#### Subset-specific options
+
+These options are related to the configuration of the fine-tuning method's subsets.
+
+| Option name | Example setting | `[general]` | `[[datasets]]` | `[[dataset.subsets]]` |
+| ---- | ---- | ---- | ---- | ---- |
+| `image_dir` | `'C:\hoge'` | - | - | o |
+| `metadata_file` | `'C:\piyo\piyo_md.json'` | - | - | o (required) |
+
+* `image_dir`
+    * Specify the path to the image directory. Unlike the DreamBooth method, specifying it is not mandatory, but it is recommended to do so.
+        * The case where it is not necessary to specify is when the `--full_path` is added to the command line when generating the metadata file.
+    * The images must be placed directly under the directory.
+* `metadata_file`
+    * Specify the path to the metadata file used for the subset. This is a required option.
+        * It is equivalent to the command-line argument `--in_json`.
+    * Due to the specification that a metadata file must be specified for each subset, it is recommended to avoid creating a metadata file with images from different directories as a single metadata file. It is strongly recommended to prepare a separate metadata file for each image directory and register them as separate subsets.
+
+### Options available when caption dropout method can be used
+
+The options available when the caption dropout method can be used exist only for subsets. Regardless of whether it's the DreamBooth method or fine-tuning method, if it supports caption dropout, it can be specified.
+
+#### Subset-specific options
+
+Options related to the setting of subsets that caption dropout can be used for.
+
+| Option Name | `[general]` | `[[datasets]]` | `[[dataset.subsets]]` |
+| ---- | ---- | ---- | ---- |
+| `caption_dropout_every_n_epochs` | o | o | o |
+| `caption_dropout_rate` | o | o | o |
+| `caption_tag_dropout_rate` | o | o | o |
+
+## Behavior when there are duplicate subsets
+
+In the case of the DreamBooth dataset, if there are multiple `image_dir` directories with the same content, they are considered to be duplicate subsets. For the fine-tuning dataset, if there are multiple `metadata_file` files with the same content, they are considered to be duplicate subsets. If duplicate subsets exist in the dataset, subsequent subsets will be ignored.
+
+However, if they belong to different datasets, they are not considered duplicates. For example, if you have subsets with the same `image_dir` in different datasets, they will not be considered duplicates. This is useful when you want to train with the same image but with different resolutions.
+
+```toml
+# If data sets exist separately, they are not considered duplicates and are both used for training.
+
+[[datasets]]
+resolution = 512
+
+  [[datasets.subsets]]
+  image_dir = 'C:\hoge'
+
+[[datasets]]
+resolution = 768
+
+  [[datasets.subsets]]
+  image_dir = 'C:\hoge'
+```
+
+## Command Line Argument and Configuration File
+
+There are options in the configuration file that have overlapping roles with command line argument options.
+
+The following command line argument options are ignored if a configuration file is passed:
+
+* `--train_data_dir`
+* `--reg_data_dir`
+* `--in_json`
+
+The following command line argument options are given priority over the configuration file options if both are specified simultaneously. In most cases, they have the same names as the corresponding options in the configuration file.
+
+| Command Line Argument Option   | Prioritized Configuration File Option |
+| ------------------------------- | ------------------------------------- |
+| `--bucket_no_upscale`           |                                       |
+| `--bucket_reso_steps`           |                                       |
+| `--caption_dropout_every_n_epochs` |                                       |
+| `--caption_dropout_rate`        |                                       |
+| `--caption_extension`           |                                       |
+| `--caption_tag_dropout_rate`    |                                       |
+| `--color_aug`                   |                                       |
+| `--dataset_repeats`             | `num_repeats`                          |
+| `--enable_bucket`               |                                       |
+| `--face_crop_aug_range`         |                                       |
+| `--flip_aug`                    |                                       |
+| `--keep_tokens`                 |                                       |
+| `--min_bucket_reso`              |                                       |
+| `--random_crop`                 |                                       |
+| `--resolution`                  |                                       |
+| `--shuffle_caption`             |                                       |
+| `--train_batch_size`            | `batch_size`                           |
+
+## Error Guide
+
+Currently, we are using an external library to check if the configuration file is written correctly, but the development has not been completed, and there is a problem that the error message is not clear. In the future, we plan to improve this problem.
+
+As a temporary measure, we will list common errors and their solutions. If you encounter an error even though it should be correct or if the error content is not understandable, please contact us as it may be a bug.
+
+* `voluptuous.error.MultipleInvalid: required key not provided @ ...`: This error occurs when a required option is not provided. It is highly likely that you forgot to specify the option or misspelled the option name.
+  * The error location is indicated by `...` in the error message. For example, if you encounter an error like `voluptuous.error.MultipleInvalid: required key not provided @ data['datasets'][0]['subsets'][0]['image_dir']`, it means that the `image_dir` option does not exist in the 0th `subsets` of the 0th `datasets` setting.
+* `voluptuous.error.MultipleInvalid: expected int for dictionary value @ ...`: This error occurs when the specified value format is incorrect. It is highly likely that the value format is incorrect. The `int` part changes depending on the target option. The example configurations in this README may be helpful.
+* `voluptuous.error.MultipleInvalid: extra keys not allowed @ ...`: This error occurs when there is an option name that is not supported. It is highly likely that you misspelled the option name or mistakenly included it.
+
+

fine_tune.py

@@ -457,7 +457,7 @@ def train(args):
 
     accelerator.end_training()
 
-    if args.save_state and is_main_process:
+    if is_main_process and (args.save_state or args.save_state_on_train_end):        
         train_util.save_state_on_train_end(args, accelerator)
 
     del accelerator  # この後メモリを使うのでこれは消す

finetune/tag_images_by_wd14_tagger.py

@@ -12,8 +12,10 @@ from tqdm import tqdm
 
 import library.train_util as train_util
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
 # from wd14 tagger
@@ -79,34 +81,42 @@ def collate_fn_remove_corrupted(batch):
 
 
 def main(args):
+    # model location is model_dir + repo_id
+    # repo id may be like "user/repo" or "user/repo/branch", so we need to remove slash
+    model_location = os.path.join(args.model_dir, args.repo_id.replace("/", "_"))
+
     # 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:
+    if not os.path.exists(model_location) or args.force_download:
+        os.makedirs(args.model_dir, exist_ok=True)
         logger.info(f"downloading wd14 tagger model from hf_hub. id: {args.repo_id}")
         files = FILES
         if args.onnx:
+            files = ["selected_tags.csv"]
             files += FILES_ONNX
+        else:
+            for file in SUB_DIR_FILES:
+                hf_hub_download(
+                    args.repo_id,
+                    file,
+                    subfolder=SUB_DIR,
+                    cache_dir=os.path.join(model_location, SUB_DIR),
+                    force_download=True,
+                    force_filename=file,
+                )
         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,
-            )
+            hf_hub_download(args.repo_id, file, cache_dir=model_location, force_download=True, force_filename=file)
     else:
         logger.info("using existing wd14 tagger model")
 
     # 画像を読み込む
     if args.onnx:
+        import torch
         import onnx
         import onnxruntime as ort
 
-        onnx_path = f"{args.model_dir}/model.onnx"
+        onnx_path = f"{model_location}/model.onnx"
         logger.info("Running wd14 tagger with onnx")
         logger.info(f"loading onnx model: {onnx_path}")
 
@@ -123,7 +133,7 @@ def main(args):
         except:
             batch_size = model.graph.input[0].type.tensor_type.shape.dim[0].dim_param
 
-        if args.batch_size != batch_size and type(batch_size) != str:
+        if args.batch_size != batch_size and type(batch_size) != str and batch_size > 0:
             # some rebatch model may use 'N' as dynamic axes
             logger.warning(
                 f"Batch size {args.batch_size} doesn't match onnx model batch size {batch_size}, use model batch size {batch_size}"
@@ -134,19 +144,19 @@ def main(args):
 
         ort_sess = ort.InferenceSession(
             onnx_path,
-            providers=["CUDAExecutionProvider"]
-            if "CUDAExecutionProvider" in ort.get_available_providers()
-            else ["CPUExecutionProvider"],
+            providers=(
+                ["CUDAExecutionProvider"] if "CUDAExecutionProvider" in ort.get_available_providers() else ["CPUExecutionProvider"]
+            ),
         )
     else:
         from tensorflow.keras.models import load_model
 
-        model = load_model(f"{args.model_dir}")
+        model = load_model(f"{model_location}")
 
     # label_names = pd.read_csv("2022_0000_0899_6549/selected_tags.csv")
     # 依存ライブラリを増やしたくないので自力で読むよ
 
-    with open(os.path.join(args.model_dir, CSV_FILE), "r", encoding="utf-8") as f:
+    with open(os.path.join(model_location, CSV_FILE), "r", encoding="utf-8") as f:
         reader = csv.reader(f)
         l = [row for row in reader]
         header = l[0]  # tag_id,name,category,count
@@ -172,8 +182,8 @@ def main(args):
         imgs = np.array([im for _, im in path_imgs])
 
         if args.onnx:
-            if len(imgs) < args.batch_size:
-                imgs = np.concatenate([imgs, np.zeros((args.batch_size - len(imgs), IMAGE_SIZE, IMAGE_SIZE, 3))], axis=0)
+            # if len(imgs) < args.batch_size:
+            #     imgs = np.concatenate([imgs, np.zeros((args.batch_size - len(imgs), IMAGE_SIZE, IMAGE_SIZE, 3))], axis=0)
             probs = ort_sess.run(None, {input_name: imgs})[0]  # onnx output numpy
             probs = probs[: len(path_imgs)]
         else:
@@ -314,7 +324,9 @@ def setup_parser() -> argparse.ArgumentParser:
         help="directory to store wd14 tagger model / wd14 taggerのモデルを格納するディレクトリ",
     )
     parser.add_argument(
-        "--force_download", action="store_true", help="force downloading wd14 tagger models / wd14 taggerのモデルを再ダウンロードします"
+        "--force_download",
+        action="store_true",
+        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("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
     parser.add_argument(
@@ -329,8 +341,12 @@ def setup_parser() -> argparse.ArgumentParser:
         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")
-    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(
+        "--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"
+    )
+    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(
         "--general_threshold",
         type=float,
@@ -343,7 +359,9 @@ def setup_parser() -> argparse.ArgumentParser:
         default=None,
         help="threshold of confidence to add a tag for character category, same as --thres if omitted / characterカテゴリのタグを追加するための確信度の閾値、省略時は --thresh と同じ",
     )
-    parser.add_argument("--recursive", action="store_true", help="search for images in subfolders recursively / サブフォルダを再帰的に検索する")
+    parser.add_argument(
+        "--recursive", action="store_true", help="search for images in subfolders recursively / サブフォルダを再帰的に検索する"
+    )
     parser.add_argument(
         "--remove_underscore",
         action="store_true",
@@ -356,9 +374,13 @@ def setup_parser() -> argparse.ArgumentParser:
         default="",
         help="comma-separated list of undesired tags to remove from the output / 出力から除外したいタグのカンマ区切りのリスト",
     )
-    parser.add_argument("--frequency_tags", action="store_true", help="Show frequency of tags for images / 画像ごとのタグの出現頻度を表示する")
+    parser.add_argument(
+        "--frequency_tags", action="store_true", help="Show frequency of tags for images / 画像ごとのタグの出現頻度を表示する"
+    )
     parser.add_argument("--onnx", action="store_true", help="use onnx model for inference / onnxモデルを推論に使用する")
-    parser.add_argument("--append_tags", action="store_true", help="Append captions instead of overwriting / 上書きではなくキャプションを追記する")
+    parser.add_argument(
+        "--append_tags", action="store_true", help="Append captions instead of overwriting / 上書きではなくキャプションを追記する"
+    )
     parser.add_argument(
         "--caption_separator",
         type=str,

library/config_util.py

@@ -60,6 +60,8 @@ class BaseSubsetParams:
     caption_separator: str = (",",)
     keep_tokens: int = 0
     keep_tokens_separator: str = (None,)
+    secondary_separator: Optional[str] = None
+    enable_wildcard: bool = False
     color_aug: bool = False
     flip_aug: bool = False
     face_crop_aug_range: Optional[Tuple[float, float]] = None
@@ -181,6 +183,8 @@ class ConfigSanitizer:
         "shuffle_caption": bool,
         "keep_tokens": int,
         "keep_tokens_separator": str,
+        "secondary_separator": str,
+        "enable_wildcard": bool,
         "token_warmup_min": int,
         "token_warmup_step": Any(float, int),
         "caption_prefix": str,
@@ -504,6 +508,8 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
           shuffle_caption: {subset.shuffle_caption}
           keep_tokens: {subset.keep_tokens}
           keep_tokens_separator: {subset.keep_tokens_separator}
+          secondary_separator: {subset.secondary_separator}
+          enable_wildcard: {subset.enable_wildcard}
           caption_dropout_rate: {subset.caption_dropout_rate}
           caption_dropout_every_n_epoches: {subset.caption_dropout_every_n_epochs}
           caption_tag_dropout_rate: {subset.caption_tag_dropout_rate}

library/huggingface_util.py

@@ -5,10 +5,13 @@ import argparse
 import os
 from library.utils import fire_in_thread
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
+
 def exists_repo(repo_id: str, repo_type: str, revision: str = "main", token: str = None):
     api = HfApi(
         token=token,
@@ -44,19 +47,14 @@ def upload(
 
     def uploader():
         try:
+            # 自前でスレッド化しているので run_as_future は明示的に False にする（Hub APIのバグかもしれない）
             if is_folder:
                 api.upload_folder(
-                    repo_id=repo_id,
-                    repo_type=repo_type,
-                    folder_path=src,
-                    path_in_repo=path_in_repo,
+                    repo_id=repo_id, repo_type=repo_type, folder_path=src, path_in_repo=path_in_repo, run_as_future=False
                 )
             else:
                 api.upload_file(
-                    repo_id=repo_id,
-                    repo_type=repo_type,
-                    path_or_fileobj=src,
-                    path_in_repo=path_in_repo,
+                    repo_id=repo_id, repo_type=repo_type, path_or_fileobj=src, path_in_repo=path_in_repo, run_as_future=False
                 )
         except Exception as e:  # RuntimeErrorを確認済みだが他にあると困るので
             logger.error("===========================================")

library/train_util.py

@@ -364,6 +364,8 @@ class BaseSubset:
         caption_separator: str,
         keep_tokens: int,
         keep_tokens_separator: str,
+        secondary_separator: Optional[str],
+        enable_wildcard: bool,
         color_aug: bool,
         flip_aug: bool,
         face_crop_aug_range: Optional[Tuple[float, float]],
@@ -382,6 +384,8 @@ class BaseSubset:
         self.caption_separator = caption_separator
         self.keep_tokens = keep_tokens
         self.keep_tokens_separator = keep_tokens_separator
+        self.secondary_separator = secondary_separator
+        self.enable_wildcard = enable_wildcard
         self.color_aug = color_aug
         self.flip_aug = flip_aug
         self.face_crop_aug_range = face_crop_aug_range
@@ -410,6 +414,8 @@ class DreamBoothSubset(BaseSubset):
         caption_separator: str,
         keep_tokens,
         keep_tokens_separator,
+        secondary_separator,
+        enable_wildcard,
         color_aug,
         flip_aug,
         face_crop_aug_range,
@@ -431,6 +437,8 @@ class DreamBoothSubset(BaseSubset):
             caption_separator,
             keep_tokens,
             keep_tokens_separator,
+            secondary_separator,
+            enable_wildcard,
             color_aug,
             flip_aug,
             face_crop_aug_range,
@@ -466,6 +474,8 @@ class FineTuningSubset(BaseSubset):
         caption_separator,
         keep_tokens,
         keep_tokens_separator,
+        secondary_separator,
+        enable_wildcard,
         color_aug,
         flip_aug,
         face_crop_aug_range,
@@ -487,6 +497,8 @@ class FineTuningSubset(BaseSubset):
             caption_separator,
             keep_tokens,
             keep_tokens_separator,
+            secondary_separator,
+            enable_wildcard,
             color_aug,
             flip_aug,
             face_crop_aug_range,
@@ -519,6 +531,8 @@ class ControlNetSubset(BaseSubset):
         caption_separator,
         keep_tokens,
         keep_tokens_separator,
+        secondary_separator,
+        enable_wildcard,
         color_aug,
         flip_aug,
         face_crop_aug_range,
@@ -540,6 +554,8 @@ class ControlNetSubset(BaseSubset):
             caption_separator,
             keep_tokens,
             keep_tokens_separator,
+            secondary_separator,
+            enable_wildcard,
             color_aug,
             flip_aug,
             face_crop_aug_range,
@@ -675,15 +691,41 @@ class BaseDataset(torch.utils.data.Dataset):
         if is_drop_out:
             caption = ""
         else:
+            # process wildcards
+            if subset.enable_wildcard:
+                # wildcard is like '{aaa|bbb|ccc...}'
+                # escape the curly braces like {{ or }}
+                replacer1 = "⦅"
+                replacer2 = "⦆"
+                while replacer1 in caption or replacer2 in caption:
+                    replacer1 += "⦅"
+                    replacer2 += "⦆"
+
+                caption = caption.replace("{{", replacer1).replace("}}", replacer2)
+
+                # replace the wildcard
+                def replace_wildcard(match):
+                    return random.choice(match.group(1).split("|"))
+
+                caption = re.sub(r"\{([^}]+)\}", replace_wildcard, caption)
+
+                # unescape the curly braces
+                caption = caption.replace(replacer1, "{").replace(replacer2, "}")
+
             if subset.shuffle_caption or subset.token_warmup_step > 0 or subset.caption_tag_dropout_rate > 0:
                 fixed_tokens = []
                 flex_tokens = []
+                fixed_suffix_tokens = []
                 if (
                     hasattr(subset, "keep_tokens_separator")
                     and subset.keep_tokens_separator
                     and subset.keep_tokens_separator in caption
                 ):
                     fixed_part, flex_part = caption.split(subset.keep_tokens_separator, 1)
+                    if subset.keep_tokens_separator in flex_part:
+                        flex_part, fixed_suffix_part = flex_part.split(subset.keep_tokens_separator, 1)
+                        fixed_suffix_tokens = [t.strip() for t in fixed_suffix_part.split(subset.caption_separator) if t.strip()]
+
                     fixed_tokens = [t.strip() for t in fixed_part.split(subset.caption_separator) if t.strip()]
                     flex_tokens = [t.strip() for t in flex_part.split(subset.caption_separator) if t.strip()]
                 else:
@@ -718,7 +760,11 @@ class BaseDataset(torch.utils.data.Dataset):
 
                 flex_tokens = dropout_tags(flex_tokens)
 
-                caption = ", ".join(fixed_tokens + flex_tokens)
+                caption = ", ".join(fixed_tokens + flex_tokens + fixed_suffix_tokens)
+
+            # process secondary separator
+            if subset.secondary_separator:
+                caption = caption.replace(subset.secondary_separator, subset.caption_separator)
 
             # textual inversion対応
             for str_from, str_to in self.replacements.items():
@@ -1774,6 +1820,8 @@ class ControlNetDataset(BaseDataset):
                 subset.caption_separator,
                 subset.keep_tokens,
                 subset.keep_tokens_separator,
+                subset.secondary_separator,
+                subset.enable_wildcard,
                 subset.color_aug,
                 subset.flip_aug,
                 subset.face_crop_aug_range,
@@ -2888,7 +2936,12 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
     parser.add_argument(
         "--save_state",
         action="store_true",
-        help="save training state additionally (including optimizer states etc.) / optimizerなど学習状態も含めたstateを追加で保存する",
+        help="save training state additionally (including optimizer states etc.) when saving model / optimizerなど学習状態も含めたstateをモデル保存時に追加で保存する",
+    )
+    parser.add_argument(
+        "--save_state_on_train_end",
+        action="store_true",
+        help="save training state (including optimizer states etc.) on train end / optimizerなど学習状態も含めたstateを学習完了時に保存する",
     )
     parser.add_argument("--resume", type=str, default=None, help="saved state to resume training / 学習再開するモデルのstate")
 
@@ -3039,6 +3092,11 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         default=None,
         help="enable noise offset with this value (if enabled, around 0.1 is recommended) / Noise offsetを有効にしてこの値を設定する（有効にする場合は0.1程度を推奨）",
     )
+    parser.add_argument(
+        "--noise_offset_random_strength",
+        action="store_true",
+        help="use random strength between 0~noise_offset for noise offset. / noise offsetにおいて、0からnoise_offsetの間でランダムな強度を使用します。",
+    )
     parser.add_argument(
         "--multires_noise_iterations",
         type=int,
@@ -3052,6 +3110,12 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         help="enable input perturbation noise. used for regularization. recommended value: around 0.1 (from arxiv.org/abs/2301.11706) "
         + "/  input perturbation noiseを有効にする。正則化に使用される。推奨値: 0.1程度 (arxiv.org/abs/2301.11706 より)",
     )
+    parser.add_argument(
+        "--ip_noise_gamma_random_strength",
+        action="store_true",
+        help="Use random strength between 0~ip_noise_gamma for input perturbation noise."
+        + "/ input perturbation noiseにおいて、0からip_noise_gammaの間でランダムな強度を使用します。",
+    )
     # parser.add_argument(
     #     "--perlin_noise",
     #     type=int,
@@ -3284,6 +3348,18 @@ def add_dataset_arguments(
         help="A custom separator to divide the caption into fixed and flexible parts. Tokens before this separator will not be shuffled. If not specified, '--keep_tokens' will be used to determine the fixed number of tokens."
         + " / captionを固定部分と可変部分に分けるためのカスタム区切り文字。この区切り文字より前のトークンはシャッフルされない。指定しない場合、'--keep_tokens'が固定部分のトークン数として使用される。",
     )
+    parser.add_argument(
+        "--secondary_separator",
+        type=str,
+        default=None,
+        help="a secondary separator for caption. This separator is replaced to caption_separator after dropping/shuffling caption"
+        + " / captionのセカンダリ区切り文字。この区切り文字はcaptionのドロップやシャッフル後にcaption_separatorに置き換えられる",
+    )
+    parser.add_argument(
+        "--enable_wildcard",
+        action="store_true",
+        help="enable wildcard for caption (e.g. '{image|picture|rendition}') / captionのワイルドカードを有効にする（例：'{image|picture|rendition}'）",
+    )
     parser.add_argument(
         "--caption_prefix",
         type=str,
@@ -3474,7 +3550,7 @@ def read_config_from_file(args: argparse.Namespace, parser: argparse.ArgumentPar
         exit(1)
 
     logger.info(f"Loading settings from {config_path}...")
-    with open(config_path, "r") as f:
+    with open(config_path, "r", encoding="utf-8") as f:
         config_dict = toml.load(f)
 
     # combine all sections into one
@@ -4596,7 +4672,11 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
     # Sample noise that we'll add to the latents
     noise = torch.randn_like(latents, device=latents.device)
     if args.noise_offset:
-        noise = custom_train_functions.apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale)
+        if args.noise_offset_random_strength:
+            noise_offset = torch.rand(1, device=latents.device) * args.noise_offset
+        else:
+            noise_offset = args.noise_offset
+        noise = custom_train_functions.apply_noise_offset(latents, noise, noise_offset, args.adaptive_noise_scale)
     if args.multires_noise_iterations:
         noise = custom_train_functions.pyramid_noise_like(
             noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount
@@ -4613,7 +4693,11 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
     # Add noise to the latents according to the noise magnitude at each timestep
     # (this is the forward diffusion process)
     if args.ip_noise_gamma:
-        noisy_latents = noise_scheduler.add_noise(latents, noise + args.ip_noise_gamma * torch.randn_like(latents), timesteps)
+        if args.ip_noise_gamma_random_strength:
+            strength = torch.rand(1, device=latents.device) * args.ip_noise_gamma
+        else:
+            strength = args.ip_noise_gamma
+        noisy_latents = noise_scheduler.add_noise(latents, noise + strength * torch.randn_like(latents), timesteps)
     else:
         noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 

requirements.txt

@@ -22,9 +22,12 @@ huggingface-hub==0.20.1
 # for WD14 captioning (tensorflow)
 # tensorflow==2.10.1
 # for WD14 captioning (onnx)
-# onnx==1.14.1
-# onnxruntime-gpu==1.16.0
-# onnxruntime==1.16.0
+# onnx==1.15.0
+# onnxruntime-gpu==1.17.1
+# onnxruntime==1.17.1
+# for cuda 12.1(default 11.8)
+# onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
+
 # this is for onnx: 
 # protobuf==3.20.3
 # open clip for SDXL

sdxl_train.py

@@ -712,7 +712,7 @@ def train(args):
 
     accelerator.end_training()
 
-    if args.save_state:  # and is_main_process:
+    if args.save_state or args.save_state_on_train_end:        
         train_util.save_state_on_train_end(args, accelerator)
 
     del accelerator  # この後メモリを使うのでこれは消す

sdxl_train_control_net_lllite.py

@@ -549,7 +549,7 @@ def train(args):
 
     accelerator.end_training()
 
-    if is_main_process and args.save_state:
+    if is_main_process and (args.save_state or args.save_state_on_train_end):
         train_util.save_state_on_train_end(args, accelerator)
 
     if is_main_process:

train_controlnet.py

@@ -565,7 +565,7 @@ def train(args):
 
     accelerator.end_training()
 
-    if is_main_process and args.save_state:
+    if is_main_process and (args.save_state or args.save_state_on_train_end):
         train_util.save_state_on_train_end(args, accelerator)
 
     # del accelerator  # この後メモリを使うのでこれは消す→printで使うので消さずにおく

train_db.py

@@ -444,7 +444,7 @@ def train(args):
 
     accelerator.end_training()
 
-    if args.save_state and is_main_process:
+    if is_main_process and (args.save_state or args.save_state_on_train_end):
         train_util.save_state_on_train_end(args, accelerator)
 
     del accelerator  # この後メモリを使うのでこれは消す

train_network.py

@@ -564,6 +564,11 @@ class NetworkTrainer:
                         "random_crop": bool(subset.random_crop),
                         "shuffle_caption": bool(subset.shuffle_caption),
                         "keep_tokens": subset.keep_tokens,
+                        "keep_tokens_separator": subset.keep_tokens_separator,
+                        "secondary_separator": subset.secondary_separator,
+                        "enable_wildcard": bool(subset.enable_wildcard),
+                        "caption_prefix": subset.caption_prefix,
+                        "caption_suffix": subset.caption_suffix,
                     }
 
                     image_dir_or_metadata_file = None
@@ -935,7 +940,7 @@ class NetworkTrainer:
 
         accelerator.end_training()
 
-        if is_main_process and args.save_state:
+        if is_main_process and (args.save_state or args.save_state_on_train_end):
             train_util.save_state_on_train_end(args, accelerator)
 
         if is_main_process:

train_textual_inversion.py

@@ -732,7 +732,7 @@ class TextualInversionTrainer:
 
         accelerator.end_training()
 
-        if args.save_state and is_main_process:
+        if is_main_process and (args.save_state or args.save_state_on_train_end):
             train_util.save_state_on_train_end(args, accelerator)
 
         if is_main_process:

train_textual_inversion_XTI.py

@@ -586,7 +586,7 @@ def train(args):
 
     accelerator.end_training()
 
-    if args.save_state and is_main_process:
+    if is_main_process and (args.save_state or args.save_state_on_train_end):
         train_util.save_state_on_train_end(args, accelerator)
 
     updated_embs = text_encoder.get_input_embeddings().weight[token_ids_XTI].data.detach().clone()