目次
こんにちは.高山です.
最近の生成AI関連の盛り上がりはすごいですね (良くも悪くもですが).
高山もこのビッグウェーブに何とかついていこうと,chatGPT や Stable Diffusion を触ってみてるところです (使いこなしているとは言えないですが... (^^;)).
普段 Stable Diffusion 関連のツールは,Docker-Compose でパッケージ化されたサービス (例えば,ブラウザ経由で推論できるサービスや学習できるサービス) を使用しています.
これはこれですごい便利なのですが,良く分からないエラーが出た時に追いづらいという点がありました.
(いえ,追おうと思えば追えるのですが,パッケージ化されている時点で追う気にならないというのが正解かもしれません(^^;))
そこで,普段使っている Docker ベースの開発環境にツール郡を入れて,そこから動かせるように試みたところ,色々とハマりました(^^;).
今後同じようなことが起きたときのためのメモとしてやったことを書き残しておこうと思います.
なお,今回インストールしたツールは下記のとおりです.
- Stable Diffusion web UI: Stable Diffusion を Web ブラウザ上で実行できる GUI アプリケーション
- Kohya's GUI: kohya 氏作成の Stable Diffusion 学習ツールを Web ブラウザ上で実行できる GUI アプリケーション
更新履歴 (大きな変更のみ記載しています)
- 2024/09/18: カテゴリを変更しました
- 2024/09/17: タグを更新しました
1. 主なハマりどころ
今回は最終的にできあがった作業工程を紹介していきますので,下記に主なハマりどころを先に示しておきたいと思います.
- Python3.10 の導入
- Gradio のバージョン設定
- 構成ファイルで指定されていない Python モジュールの設定
Python3.10 の導入
Stable Diffusion 関連のツールは Python3.10 で動作するように実装されています.
個人的に, Docker + virturalenv のように仮想環境を重ねるのはあまり好きではなく,Docker 単体で何とかしようとしたのが間違いの始まりでした(^^;).
Ubuntu20.04 では python3.8 がシステム標準でインストールされるのですが,update-alternative
などを使うと,手動で入れた Python3.10 に切り替えることができます.
ただし他の依存モジュールはこの設定に従ってくれなく,依存モジュールを入れると設定が上書きされて元に戻ってしまうという現象が起きました.
最終的には,Python3.8 をシステム標準としたまま Python3.10 をインストールし,virtualenv で Python3.10 用の仮想環境を作成することで対処することにしました.
Gradio のバージョン設定
今回,Stable Diffusion web UI と Kohya's GUI というツールをインストールしたのですが,これらはそれぞれ違うバージョンの Gradio という GUI モジュールを使用しています.
片方のツールに Gradio のバージョンを合わせると,もう片方のツールが上手く動かないという状況で,原因・対処法の調査に苦労しました.
最終的には,Stable Diffusion web UI の設定に Gradio のバージョンをあわせて (v3.41.2),Kohya's GUI のバージョンを落とす (v23.0.11) ことで解決しています.
なお,Stable Diffusion web UI のソースコードを修正すれば,Gradio のバージョンを Kohya's GUI の設定 (v3.50.2) に合わせることが可能です.
こちらの対処法については,別記事で紹介したいと思います.
構成ファイルで指定されていない Python モジュールの設定
Python モジュールのインストールに際して,各ツールの構成ファイル (requirements.txt
など) を参考にしたのですが,これらの構成ファイルだけではカバーされていないモジュールがいくつかあります.
カバーされていないモジュールは,各ツールの初期化処理で自動インストールされているようになっています.
ただし,初期化処理を起動するとインストール済みの Python モジュールが上書きされる恐れがあります.
結果として,一つずつ手動で対処していったため時間がかかってしまいました.
最終的には,カバーされていないモジュールを含むように自前の構成ファイルを用意することで解決しています.
2. ソースコード (Dockerfile 部分など) のライセンスについて
2.1 ベース Docker イメージ作成用コード
今回は GitHub じゃなくて記事内にソースコードを記述していますので,まずはライセンスについて表記しておきます.
第5節で紹介している,ベース Docker イメージ作成用コードは高山の作成物で,ライセンスは Apatch-2.0 です.
ライセンスに沿ってご利用いただく場合は特にご連絡等は必要ありません.
Apatch-2.0 のライセンス条項を下記に記します.
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2.2 開発環境 Docker イメージ作成用コード
第6節で紹介している開発環境 Docker イメージ作成用コードは,JAremko 氏が公開している配布物を改変しています.
これらは GPL v3.0 ライセンスで配布されており,該当部分はオリジナルのライセンスを継承しておりますので,ご使用の際は注意してください.
GPL v3.0 のライセンス条項を下記に記します.
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{one line to give the program's name and a brief idea of what it does.}
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Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
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For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
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2.3 他の箇所について
他の箇所は基本的に,コマンドの実行やエラーの修正箇所を提示しているだけですのでライセンス等は特にありません.
ご自由にご利用ください (記事全体の著作権を放棄しているわけではありません).
3. PC の環境
今回インストールに使用した PC の環境は下記のとおりです.
- ホストOS: Windows11 Pro, version 23H2, build no. 22631.3296
- CPU: 12th Gen Intel(R) Core(TM) i5-12400 2.50 GHz
- RAM: 64.0GB
- GPU: NVIDIA GeForce RTX 3060 (12GB VRAM)
- 仮想環境: WSL2, version 2.1.5.0
- 仮想OS: Ubuntu20.04
- Docker: Version 26.0.0, community edition
Docker は Windows 上にインストールする Docker デスクトップではなく,Ubuntu に直接インストールして使用しています.
なお,Windows,WSL および Docker のバージョンは今回のハマりどころには関係ありません.
4. 環境構築手順
図1に大まかな環境構築手順を示します.
以降の節では図1の手順に沿って説明していきます.
作業は全て WSL2 上の Ubuntu20.04 内で行っています.
5. ベース Docker イメージの作成
まず最初に,ベースとなる Docker イメージを作成します.
Docker では下記に示すようなビルドスクリプトを用いて,開発やアプリケーションに必要な依存モジュールをインストールしていきます.
Docker について説明すると長くなってしまうので (ただでさえ長文を書きがちなのに...(^^;)),ご興味のある方は公式マニュアル等をご参照ください.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
|
【コード解説】
- 4行目: GPUにアクセスする必要があるため,CUDAがインストール済みのUbuntu20.04をベースに設定
- 6-8行目: GPUの全機能が使えるように環境変数を設定
- 13行目: イメージのビルド中にインタラクティブな操作が入ると処理が停止してしまうので,
インタラクティブ処理を停止
- 14行目: ソースコードのビルドが必要な場合に備えて,作業ディレクトリを設定
(結果的に,今回は必要ありませんでした(^^;))
- 19-33行目: 開発時によく使うツールをまとめてインストール
この中で必須のものは `git` と `python3-dev` です.
- 38-47行目: 日本語の表示や処理が必要な場合に備えて,日本語環境をインストール
- 54-55行目: Pythonのパッケージ管理ツールをインストール
- 57行目: Python用仮想環境構築ツール `virtualenv` をインストール
- 62-73行目: Python3.10をインストール
- 76行目: 作業ディレクトリを削除
Stable Diffusion 関連のツール郡は,Python3.10 向けに作られているものが多いです.
そのため,62-73行目で Python3.10 をインストールしています.
(Ubuntu22.04 の場合は Python3.10 が標準なのでこの工程は必要ありません)
ここで update-alternatives
などで Python3.10 をシステム標準に設定すると,後の操作で設定を書き換えられてうまく行きませんので注意してください.
(正直なところ,ここでかなりハマりました(^^;))
今回はシステム標準は Python3.8 のままにしておいて,virtualenv
で Python3.10 用の仮想環境を作成してその中でツールを動作させるようにします.
なお,Python 向けの仮想環境としては venv
が標準で備わっていますが,こちらはシステム標準の Python でしか使えないので注意してください.
(依存モジュールは切り替えられるが,Python 自体は切り替えられません)
ビルドスクリプトが準備できたら,下記のコードで Docker イメージを作成します.
docker build -t sd_dev/base:ubuntu20.04 -f ./Dockerfile_base ./
6. 開発用の Docker イメージの作成 (Option)
ベースの Docker イメージができたら,それに重ねていく形で開発環境の Docker イメージを作成します.
基本的には高山の開発環境 (Emacs など) のインストールなので飛ばしていただいても構いません.
sudo
やホストと同名のユーザでログインする設定も行っていますので,何か参考になれば幸いです.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
|
【コード解説】
- 12行目: ベースDockerイメージを基にビルドを行うことを指定
- 15行目: GTK (GUIツールキット) による警告を抑制する設定
- 16行目: イメージのビルド中にインタラクティブな操作が入ると処理が停止してしまうので,
インタラクティブ処理を停止
- 21-41行目: 開発環境向けのパッケージをインストール.
34-38行目で`sudo` を使用可能にする設定をしています.
- 46行目: 開発用Pythonモジュールをインストール
- 48-52行目: Dockerコンテナ立ち上げ時の実行コマンドを設定
- 57-62行目: Emacsインストール
最終行に出てきた entrypoint.sh
は Docker コンテナを立ち上げた際に実行されるコマンドです.
下記に示すとおり,ホスト側のユーザ情報を読み取って,同名ユーザでコンテナに入るようにしています.
この処理によって,Docker 内で編集したファイルの権限がホスト側のユーザのものになります.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
|
【コード解説】
- 23-28行目: 同名ユーザ作成
- 30-34行目: ユーザが `sudo` を実行できるように設定
- 37-42行目: ユーザグループの作成
- 45-54行目: ホームディレクトリの作成
- 56-58行目: ホストととの共有ディレクトリを設定
- 60行目: ユーザに `sudo` 権限を与えてコマンドを実行 (通常は/bin/bashなど)
ビルドスクリプトが準備できたら,下記のコードで Docker イメージを作成します.
docker build -t sd_dev/dev:ubuntu20.04 -f ./Dockerfile_dev ./
ここまでの操作で開発環境の Docker イメージが作成できます.
コンテナを立ち上げる際は,下記のように entrypoint.sh
が要求する引数を与えながら docker
コマンドを実行します.
docker run --gpus all -it --rm --net host \
--env UNAME=${USER} \
--env UID=${UID} \
--env GNAME=`id -g -n ${USER}` \
--env GID=`id -g ${USER}` \
--env UHOME=${HOME} \
--env WORKSPACE=${HOME} \
--enb SHELL="/bin/bash" \
sd_dev/dev:ubuntu20.04 bash -l
実際の作業では,この後に一旦コンテナにログインして初期設定 (Emacs や Bash周り) を行い,その後 Docker イメージを更新しています (docker commit
).
今回の話には関係の無い作業なので割愛させていただきます.
7. Git レポジトリのダウンロード
ここからは,ツールをインストールして動作させる作業に入っていきます.
まずは各ツールと依存モジュールのソースコードを GitHub のリポジトリからダウンロードします.
最初に,下記のコマンドでソースコード格納用ディレクトリを作成し,移動します.
mkdir sd_apps
cd sd_apps
次に,各リポジトリからソースコードをダウンロードします.
下記のコマンドで Stable Diffusion web UI に関連するソースコードをダウンロードします.
git clone https://github.com/salesforce/BLIP
git clone https://github.com/Stability-AI/generative-models
git clone https://github.com/crowsonkb/k-diffusion
git clone https://github.com/Stability-AI/stablediffusion
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
さらに,下記のコマンドで Kohya's GUI に関連するソースコードをダウンロードします.
git clone https://github.com/kohya-ss/sd-scripts
git clone https://github.com/bmaltais/kohya_ss -b v23.0.11
Kohya's GUI のバージョンを 23.0.11 に指定している点に注意してください.
冒頭で述べましたが,Kohya's GUI と Stable Diffusion web GUI は,Gradio という GUI モジュールに対してバージョンが競合しています.
ここでは,Stable Diffusion web GUI が指定するバージョン gradio==3.41.2
に合わせて Kohya's GUI のバージョンを設定しています.
8. Python環境の整備
8.1 Python仮想環境のインストール
次は Python 環境の整備を行います.
下記のコマンドで Python3.10 用の仮想環境を作成して起動します.
cd ~
python3 -m virtualenv -p python3.10 py310
source py310/bin/activate
以降の作業は,全て仮想環境内で行っています.
8.2 依存モジュールのインストール
Python 関連の依存モジュールをインストールします.
一個づつ pip
コマンドでインストールするのは大変なので,構成ファイルにインストールするパッケージを記入し,一括でインストールします.
まずは構成ファイルを置くディレクトリを作成します.
mkdir requirements
各リポジトリから構成ファイルをコピーします.
次のコマンドでは,Stable Diffusion web UI 関連の構成ファイルをコピーしています.
cp sd_apps/BLIP/requirements.txt requirements/blip_dependencies.txt
cp sd_apps/generative-models/requirements/pt2.txt requirements/gmodel_dependencies.txt
cp sd_apps/k-diffusion/requirements.txt requirements/kd_dependencies.txt
cp sd_apps/stablediffusion/requirements.txt requirements/sd_dependencies.txt
cp sd_apps/stable-diffusion-webui/requirements.txt requirements/sdw_dependencies.txt
次のコマンドでは,Kohya's GUI 関連の構成ファイルをコピーしています.
cp sd_apps/sd-scripts/requirements.txt requirements/ss_dependencies.txt
cp sd_apps/kohya_ss/requirements.txt requirements/kss_dependencies.txt
各リポジトリの構成ファイルは個別に作成・管理されているので,バージョンがぶつかる可能性があります.
下記の方針で各ファイルを編集します.
- バージョン固定の設定は解除する (例えば
numpy==1.26.0 -> numpy
) - バージョン固定が必須のモジュールは別ファイルに移動する
- 足りないモジュールは別ファイルに記入する
2番目と3番目の条件に該当するモジュールは下記のファイルに記入します.
vi ./requirements/base.txt
中身は次のようになっています.
# Common
gradio==3.41.2
pytorch-lightning==1.9.5
# For stable-diffusion-webui
pydantic==1.10.11
packbits
pytoshop==1.1.0
# For kohya-ss
bitsandbytes
上記の構成ファイル郡を呼び出す親構成ファイルを次のようにして作成します.
vi ./requirements.txt
中身は次のようになっています.
-r ./requirements/base.txt
-r ./requirements/blip_dependencies.txt
-r ./requirements/gmodel_dependencies.txt
-r ./requirements/kd_dependencies.txt
-r ./requirements/kss_dependencies.txt
-r ./requirements/ld_dependencies.txt
-r ./requirements/sd_dependencies.txt
-r ./requirements/sdw_dependencies.txt
-r ./requirements/ss_dependencies.txt
構成ファイルの準備ができましたので,次のコマンドでインストールします.
pip install -r ./requirements.txt
9. ツールのインストール
ここから先はツールのインストール作業を行います.
各ツールには初期設定を自動で行うスクリプトが用意されていますが,インストール済みの Python モジュールを書き換えてしまう恐れがあるため,ここでは手動でインストール作業を行います.
9.1 Stable Diffusion web UI のセットアップ
依存モジュールのパスを通す
まず最初に依存モジュールにパスを通します.
Stable Diffusion web UI では特定のディレクトリに依存モジュールのソースコードを配置することによってパス設定を行っています.
下記のコードで依存モジュールを配置するディレクトリを作成します.
cd sd_apps/steble-diffusion-webui
mkdir repositories
cd repositories
さらに,下記のコードでシンボリックファイルを作成し,HTML 関連の追加リポジトリをダウンロードします.
ln -s ../../BLIP
ln -s ../../generative-models
ln -s ../../k-diffusion
ln -s ../../stablediffusion/ stable-diffusion-stability-ai
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets
以上で,パスの設定は完了です.
エラー箇所の修正
この時点で Stable Diffusion web UI は動作させることが可能ですが,起動すると下記のようなエラーが表示されます.
creating model quickly: OSError
Traceback (most recent call last):
File "C:\stable-diffusion-webui\venv\lib\site-packages\huggingface_hub\utils\_errors.py", line 261, in hf_raise_for_status
response.raise_for_status()
File "C:\stable-diffusion-webui\venv\lib\site-packages\requests\models.py", line 1021, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/None/resolve/main/config.json
このエラーは無視してもツールは動作しますが,気持ち悪いので下記のように修正を行います.
# Solve https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13245
# modules/sd_disable_initialization.py
# Line 67-70
# def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
# res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
# res.name_or_path = pretrained_model_name_or_path
# return res
def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
res = self.CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
res.name_or_path = pretrained_model_name_or_path
return re
なお上記のエラーは,モデルのロード停止を試みたところ,意図せず不正なURLからモデルのダウンロードをする動作となってしまいエラーとなっているようです.
上記のように,self.CLIPTextModel_from_pretrained()
にパスを与えることでエラーは解消されます.
起動確認
それでは,下記のコマンドでツールを起動させてみます.
cd ~/sd_apps/stable-diffusion-webui
python3 webui.py
No module 'xformers'. Proceeding without it.
Loading weights [6ce0161689] from /home/takayaman/sd_apps_stable_gradio3412/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors
Running on local URL: http://127.0.0.1:7860
Creating model from config: /home/takayaman/sd_apps_stable_gradio3412/stable-diffusion-webui/configs/v1-inference.yaml
Applying attention optimization: Doggettx... done.
Model loaded in 2.5s (load weights from disk: 0.5s, create model: 0.5s, apply weights to model: 1.1s, load textual inversion embeddings: 0.2s, calculate empty prompt: 0.1s).
To create a public link, set `share=True` in `launch()`.
Startup time: 7.0s (import torch: 2.4s, import gradio: 0.6s, setup paths: 0.9s, initialize shared: 0.1s, other imports: 0.2s, load scripts: 0.3s, create ui: 0.4s, gradio launch: 2.2s).
起動後はブラウザで localhost:7860
にアクセスするとツールが操作できます.
図2のような画面が表示されれば成功です.
9.2 Kohya's GUI のセットアップ
依存モジュールのパスを通す
次に,Kohya's GUI の設定を行います.
こちらは簡単で,下記のように依存モジュールのパスを通せば良いです.
cd ~/sd_apps/kohya_ss
rmdir sd-scripts
ln -s ../sd-scripts
起動確認
それでは,下記のコマンドでツールを起動させてみます.
cd ~/sd_apps/kohya_ss
python3 kohya_gui.py
16:35:38-563399 INFO headless: False
16:35:38-566199 INFO Load CSS...
Running on local URL: http://127.0.0.1:7860
To create a public link, set `share=True` in `launch()`.
起動後はブラウザで localhost:7860
にアクセスするとツールが操作できます.
図3のような画面が表示されれば成功です.
今回は Stable Diffusion 関連ツールを Docker にインストールする手順を紹介しましたが,如何でしたでしょうか?
正直なところ,ここまで苦戦するとは思っていませんでした(^^;).
正直なところ,Stable Diffusion 関連のツールはメンテナンスが追いついていない印象を受けます.
これも急に注目を集めてプロジェクトが大きくなっていったことの副作用かもしれないですね.
何はともあれ,これで統一間のある環境でツール郡が動かせるようになってスッキリしました.
またツールを触っていく過程で面白いことが見つかれば記事にしていきたいと思います.
今回紹介した話が,これから生成AIを試してみようとお考えの方に何か参考になれば幸いです.