init

MODEL-LICENSE

@@ -0,0 +1,84 @@
+Copyright (c) 2022 Stability AI, Alfred Increment, and contributors
+
+CreativeML Open RAIL++-M-NC License
+dated December 11, 2022
+
+Section I: PREAMBLE
+
+Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
+
+Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
+
+In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
+
+Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
+
+This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
+
+NOW THEREFORE, You and Licensor agree as follows:
+
+1. Definitions
+
+- "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
+- "Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
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+- "Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material.
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+
+Section II: INTELLECTUAL PROPERTY RIGHTS
+
+Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
+
+2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
+3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed.
+
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+
+4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
+Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
+You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License;
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+5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
+6. The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
+
+Section IV: OTHER PROVISIONS
+
+7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License.
+8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
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+10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+
+END OF TERMS AND CONDITIONS
+
+
+
+
+Attachment A
+
+Use Restrictions
+
+You agree not to use the Model or Derivatives of the Model:
+
+- For commercial purposes except for news reporting about image generated AI;
+- In any way that violates any applicable national, federal, state, local or international law or regulation;
+- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
+- To generate or disseminate personal identifiable information that can be used to harm an individual;
+- To defame, disparage or otherwise harass others;
+- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
+- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
+- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
+- To provide medical advice and medical results interpretation;
+- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).

README.md

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 ---
 license: other
+tags:
+  - stable-diffusion
+  - text-to-image
 ---
+
+# Cool Japan Diffusion 2.1.1.1 Model Card
+
+![アイキャッチ](eyecatch.jpg)
+
+[注意事项。中国将对图像生成的人工智能实施法律限制。 ](http://www.cac.gov.cn/2022-12/11/c_1672221949318230.htm) （中国国内にいる人への警告）
+
+English version is [here](README_en.md).
+
+# はじめに
+Cool Japan Diffusion (for learning) はStable Diffsionをファインチューニングして、アニメやマンガ、ゲームなどのクールジャパンを表現することに特化したモデルです。なお、内閣府のクールジャパン戦略とは特に関係はありません。
+
+# ライセンスについて
+ライセンスについては、もとのライセンス CreativeML Open RAIL++-M License に例外を除き商用利用禁止を追加しただけです。
+例外を除き商用利用禁止を追加した理由は創作業界に悪影響を及ぼしかねないという懸念からです。
+この懸念が払拭されれば、次のバージョンから元のライセンスに戻し、商用利用可能とします。
+ちなみに、元のライセンスの日本語訳は[こちら](https://qiita.com/robitan/items/887d9f3153963114823d)になります。 
+営利企業にいる方は法務部にいる人と相談してください。
+趣味で利用する方はあまり気にしなくても一般常識を守れば大丈夫なはずです。
+なお、ライセンスにある通り、このモデルを改造しても、このライセンスを引き継ぐ必要があります。
+
+# 法律や倫理について
+本モデルは日本にて作成されました。したがって、日本の法律が適用されます。
+本モデルの学習は、著作権法第30条の4に基づき、合法であると主張します。
+また、本モデルの配布については、著作権法や刑法175条に照らしてみても、
+正犯や幇助犯にも該当しないと主張します。詳しくは柿沼弁護士の[見解](https://twitter.com/tka0120/status/1601483633436393473?s=20&t=yvM9EX0Em-_7lh8NJln3IQ)を御覧ください。
+ただし、ライセンスにもある通り、本モデルの生成物は各種法令に従って取り扱って下さい。
+
+しかし、本モデルを配布する行為が倫理的に良くないとは作者は思っています。
+これは学習する著作物に対して著作者の許可を得ていないためです。
+ただし、学習するには著作者の許可は法律上必要もなく、検索エンジンと同様法律上は問題はありません。
+したがって、法的な側面ではなく、倫理的な側面を調査する目的も本配布は兼ねていると考えてください。
+
+# 使い方
+手軽に楽しみたい方は、こちらの[Space](https://huggingface.co/spaces/alfredplpl/cool-japan-diffusion-2-1-0)をお使いください。
+詳しい本モデルの取り扱い方は[こちらの取扱説明書](https://alfredplpl.hatenablog.com/entry/2022/12/30/102636)にかかれています。
+モデルは[ここ](https://huggingface.co/aipicasso/cool-japan-diffusion-2-1-0/resolve/main/v2-1-0.ckpt)からダウンロードできます。
+
+以下、一般的なモデルカードの日本語訳です。
+
+## モデル詳細
+- **開発者:** Robin Rombach, Patrick Esser, Alfred Increment
+- **モデルタイプ:** 拡散モデルベースの text-to-image 生成モデル
+- **言語:** 日本語
+- **ライセンス:** CreativeML Open RAIL++-M-NC License
+- **モデルの説明:** このモデルはプロンプトに応じて適切な画像を生成することができます。アルゴリズムは [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) と [OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip) です。
+- **補足:**
+- **参考文献:**
+
+      @InProceedings{Rombach_2022_CVPR,
+          author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
+          title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
+          booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+          month     = {June},
+          year      = {2022},
+          pages     = {10684-10695}
+      }
+
+## モデルの使用例
+
+Stable Diffusion v2と同じ使い方です。
+たくさんの方法がありますが、２つのパターンを提供します。
+- Web UI
+- Diffusers
+
+### Web UIの場合
+
+こちらの[取扱説明書](https://alfredplpl.hatenablog.com/entry/2022/12/30/102636)に従って作成してください。
+
+### Diffusersの場合
+
+[🤗's Diffusers library](https://github.com/huggingface/diffusers) を使ってください。
+
+まずは、以下のスクリプトを実行し、ライブラリをいれてください。
+
+```bash
+pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy
+```
+
+次の���クリプトを実行し、画像を生成してください。
+
+```python
+from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
+import torch
+
+model_id = "aipicasso/cool-japan-diffusion-2-1-1-beta"
+
+scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "anime, a portrait of a girl with black short hair and red eyes, kimono, full color illustration, official art, 4k, detailed"
+negative_prompt="low quality, bad face, bad anatomy, bad hand, lowres, jpeg artifacts, 2d, 3d, cg, text"
+image = pipe(prompt,negative_prompt=negative_prompt).images[0]
+
+image.save("girl.png")
+
+```
+
+**注意**:
+- [xformers](https://github.com/facebookresearch/xformers) を使うと早くなるらしいです。
+- GPUを使う際にGPUのメモリが少ない人は `pipe.enable_attention_slicing()` を使ってください。
+
+#### 想定される用途
+
+- コンテスト
+    - [AIアートグランプリ](https://www.aiartgrandprix.com/)への投稿
+        - ファインチューニングに用いた全データを開示し、審査基準を満たしていることを判断してもらうようにします。
+        - コンテストに向けて、要望があれば、Hugging Face の Community などで私に伝えてください。
+- 画像生成AIに関する報道
+    - 公共放送だけでなく、営利企業でも可能
+        - 画像合成AIに関する情報を「知る権利」は創作業界に悪影響を及ぼさないと判断したためです。また、報道の自由などを尊重しました。
+- クールジャパンの紹介
+    - 他国の人にクールジャパンとはなにかを説明すること。
+        - 他国の留学生はクールジャパンに惹かれて日本に来ることがおおくあります。そこで、クールジャパンが日本では「クールでない」とされていることにがっかりされることがとても多いとAlfred Incrementは感じております。他国の人が憧れる自国の文化をもっと誇りに思ってください。
+- 研究開発
+    - Discord上でのモデルの利用
+        - プロンプトエンジニアリング
+        - ファインチューニング（追加学習とも）
+            - DreamBooth など
+        - 他のモデルとのマージ
+    - Latent Diffusion Modelとクールジャパンとの相性
+    - 本モデルの性能をFIDなどで調べること
+    - 本モデルがStable Diffusion以外のモデルとは独立であることをチェックサムやハッシュ関数などで調べること
+- 教育
+    - 美大生や専門学校生の卒業制作
+    - 大学生の卒業論文や課題制作
+    - 先生が画像生成AIの現状を伝えること
+- 自己表現
+    - SNS上で自分の感情や思考を表現すること
+- Hugging Face の Community にかいてある用途
+    - 日本語か英語で質問してください
+
+#### 想定されない用途
+- 物事を事実として表現するようなこと
+- 収益化されているYouTubeなどのコンテンツへの使用
+- 商用のサービスとして直接提供すること
+- 先生を困らせるようなこと
+- その他、創作業界に悪影響を及ぼすこと
+
+# 使用してはいけない用途や悪意のある用途
+- デジタル贋作 ([Digital Forgery](https://arxiv.org/abs/2212.03860)) は公開しないでください（著作権法に違反するおそれ）
+    - 特に既存のキャラクターは公開しないでください（著作権法に違反するおそれ）
+        - なお、学習していない[キャラクターも生成できる](https://twitter.com/ThePioneerJPnew/status/1609074173892235264?s=20&t=-rY1ufzNeIDT3Fm5YdME6g)そうです。（このツイート自体は研究目的として許可しています。）
+- 他人の作品を無断でImage-to-Imageしないでください（著作権法に違反するおそれ）
+- わいせつ物を頒布しないでください (刑法175条に違反するおそれ）
+    - いわゆる業界のマナーを守らないようなこと
+- 事実に基づかないことを事実のように語らないようにしてください（威力業務妨害罪が適用されるおそれ）
+    - フェイクニュース
+
+## モデルの限界やバイアス
+
+### モデルの限界
+
+- よくわかっていない
+
+### バイアス
+
+Stable Diffusionと同じバイアスが掛かっています。
+気をつけてください。
+
+## 学習
+
+**学習データ**
+
+次のデータを主に使ってStable Diffusionをファインチューニングしています。
+
+- VAEについて
+    - Danbooruなどの無断転載サイトを除いた日本の国内法を遵守したデータ: 60万種類 （データ拡張により無限枚作成）
+- U-Netについて
+    - Danbooruなどの無断転載サイトを除いた日本の国内法を遵守したデータ: 100万ペア
+
+**学習プロセス**
+
+Stable DiffusionのVAEとU-Netをファインチューニングしました。
+
+- **ハードウェア:** RTX 3090, A6000
+- **オプティマイザー:** AdamW
+- **Gradient Accumulations**: 1
+- **バッチサイズ:** 1
+
+## 評価結果
+
+## 環境への影響
+
+ほとんどありません。
+
+- **ハードウェアタイプ:** RTX 3090, A6000
+- **使用時間（単位は時間）:** 700
+- **クラウド事業者:** なし
+- **学習した場所:** 日本
+- **カーボン排出量:** そんなにない
+
+## 参考文献
+    @InProceedings{Rombach_2022_CVPR,
+        author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
+        title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
+        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+        month     = {June},
+        year      = {2022},
+        pages     = {10684-10695}
+    }
+
+*このモデルカードは [Stable Diffusion v2](https://huggingface.co/stabilityai/stable-diffusion-2/raw/main/README.md) に基づいて、Alfred Incrementがかきました。

README_en.md

@@ -0,0 +1,81 @@
+# Cool Japan Diffusion 2.1.1.1 Model Card
+
+![eyecatch](eyecatch.jpg)
+
+# Introduction
+Cool Japan Diffusion (for learning) is the latent diffusion model created from Stable Diffsion.
+Cool Japan Diffusion is suitable for genetrating Cool Japan images such as Anime, Manga, and Game.
+
+# Legal and ethical information
+We create this model legally.
+However, we think that this model have ethical problems.
+Therefore, we cannot use the model for commercially except for news reporting.
+
+TBA.
+
+# Usage
+You can try the model by our [Space](https://huggingface.co/spaces/alfredplpl/cool-japan-diffusion-2-1-0).
+I recommend to use the model by Web UI.
+You can download the model [here](https://huggingface.co/aipicasso/cool-japan-diffusion-2-1-0/resolve/main/v2-1-0.ckpt).
+
+## Model Details
+- **Developed by:** Robin Rombach, Patrick Esser, Alfred Increment
+- **Model type:** Diffusion-based text-to-image generation model
+- **Language(s):** English
+- **License:** [CreativeML Open RAIL++-M-NC License](https://huggingface.co/aipicasso/cool-japan-diffusion-2-1-1-beta/blob/main/MODEL-LICENSE)
+- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
+- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
+- **Cite as:**
+
+      @InProceedings{Rombach_2022_CVPR,
+          author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
+          title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
+          booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+          month     = {June},
+          year      = {2022},
+          pages     = {10684-10695}
+      }
+
+## Examples
+
+- Web UI
+- Diffusers
+
+## Web UI
+Download the model [here]().
+Then, install [Web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) by AUTIMATIC1111.
+
+## Diffusers
+
+Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Cool Japan Diffusion 2.1.1 Beta in a simple and efficient manner.
+
+```bash
+pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy
+```
+
+Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to EulerDiscreteScheduler):
+
+```python
+from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
+import torch
+
+model_id = "aipicasso/cool-japan-diffusion-2-1-1-beta"
+
+scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "anime, a portrait of a girl with black short hair and red eyes, kimono, full color illustration, official art, 4k, detailed"
+negative_prompt="low quality, bad face, bad anatomy, bad hand, lowres, jpeg artifacts, 2d, 3d, cg, text"
+image = pipe(prompt,negative_prompt=negative_prompt).images[0]
+
+image.save("girl.png")
+
+```
+
+**Notes**:
+- Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
+- If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)
+
+
+*This model card was written by: Alfred Increment and is based on the [Stable Diffusion v2](https://huggingface.co/stabilityai/stable-diffusion-2/raw/main/README.md)