aipicasso
/

picasso-diffusion-1-1

+Copyright (c) 2022 AI Picasso Inc, and contributors
+CreativeML Open RAIL++-M-NC License
+dated February 5, 2023
+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:
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+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
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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.
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+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 CHANGED Viewed

@@ -1,3 +1,176 @@
 ---
 license: other
 ---

 ---
 license: other
+tags:
+  - stable-diffusion
+  - text-to-image
+inference: false
 ---
+# Picasso Diffusion 1.1 Model Card
+English version is [here](README_en.md).
+# はじめに
+Picasso Diffusionは、約7000GPU時間をかけ開発したAIアートに特化した画像生成AIです。
+# 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。
+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り、お使いください。
+# 法律について
+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。
+# 使い方
+手軽に楽しみたい方は、こちらの[Space](https://huggingface.co/spaces/aipicasso/demo)をお使いください。
+モデルは[safetensor形式](v1-1.safetensor)や[ckpt形式](v1-1.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
+- **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
+Stable Diffusion v2 の使い方と同じく、ckpt形式、またはsafetensor形式のモデルファイルとyaml形式の設定ファイルをモデルフォルダに入れてください。
+詳しいインストール方法は、[こちらの記事](https://note.com/it_navi/n/n6ffb66513769)を参照してください。
+なお、xformersをインストールし、--xformers --disable-nan-checkオプションをオンにすることをおすすめします。そうでない場合は--no-halfオプションをオンにしてください。
+### 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
+```
+次のスクリプトを実行し、画像を生成してください。
+```python
+from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
+import torch
+model_id = "alfredplpl/picasso-diffusion-1-1"
+scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+prompt = "anime, masterpiece, a portrait of a girl, good pupil, 4k, detailed"
+negative_prompt="deformed, blurry, bad anatomy, bad pupil, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, bad hands, fused fingers, messy drawing, broken legs censor, low quality, mutated hands and fingers, long body, mutation, poorly drawn, bad eyes, ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts, 3d, cg, text, japanese kanji"
+images = pipe(prompt,negative_prompt=negative_prompt, num_inference_steps=20).images
+images[0].save("girl.png")
+```
+**注意**:
+- [xformers](https://github.com/facebookresearch/xformers) を使うと早くなります。
+- 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
+- 自己表現
+    - このAIを使い、「あなた」らしさを発信すること
+- 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。
+- 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
+    - 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
+- 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）
+- 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
+- 拡散モデルや大規模言語モデルは、いまだに未知の部分が多く、その限界は判明していない。
+### バイアス
+- 拡散モデルや大規模言語モデルは、いまだに未知の部分が多く、バイアスは判明していない。
+## 学習
+**学習データ**
+Danbooruなどの無断転載サイトを除く、国内法に準拠したデータとモデル。
+**学習プロセス**
+- **ハードウェア:** A100 80GB, V100
+## 評価結果
+第三者による評価を求めています。
+## 環境への影響
+- **ハードウェアタイプ:** A100 80GB, V100
+- **使用時間（単位は時間）:** 7000
+- **学習した場所:** 日本
+## 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、AI Picasso株式会社がかきました。

README_en.md ADDED Viewed

	@@ -0,0 +1,76 @@

+# Picasso Diffusion 1.1 Model Card
+# Introduction
+Picasso Diffusion is the latent diffusion model made for AI art.
+# 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.
+# Usage
+You can try the model by our [Space](https://huggingface.co/spaces/aipicasso/demo).
+I recommend to use the model by Web UI.
+You can download the model [here](v1-1.ckpt). Safetensor version is [here](v1-1.safetensor).
+## 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](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
+**Run with --no-half option. I recommend to install [xformers](https://github.com/facebookresearch/xformers).**
+Download the model [here](v1-1.ckpt).
+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 Picassso Diffusion 1.0 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, EulerAncestralDiscreteScheduler
+import torch
+model_id = "alfredplpl/picasso-diffusion-1-1"
+scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+prompt = "anime, masterpiece, a portrait of a girl, good pupil, 4k, detailed"
+negative_prompt="deformed, blurry, bad anatomy, bad pupil, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, bad hands, fused fingers, messy drawing, broken legs censor, low quality, mutated hands and fingers, long body, mutation, poorly drawn, bad eyes, ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts, 3d, cg, text, japanese kanji"
+images = pipe(prompt,negative_prompt=negative_prompt, num_inference_steps=20).images
+images[0].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: AI Picasso Inc. and is based on the [Stable Diffusion v2](https://huggingface.co/stabilityai/stable-diffusion-2/raw/main/README.md)