readme: ---
  inference: true
  tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  license: creativeml-openrail-m
  ---
  
  ## Please Note!
  
  This model is NOT the 19.2M images Characters Model on TrinArt, but an improved version of the original Trin-sama Twitter bot model. This model is intended to retain the original SD's aesthetics as much as possible while nudging the model to anime/manga style.
  
  Other TrinArt models can be found at:
  
  https://huggingface.co/naclbit/trinart_derrida_characters_v2_stable_diffusion
  
  https://huggingface.co/naclbit/trinart_characters_19.2m_stable_diffusion_v1
  
  
  ## Diffusers
  
  The model has been ported to `diffusers` by [ayan4m1](https://huggingface.co/ayan4m1)
  and can easily be run from one of the branches:
  - `revision="diffusers-60k"` for the checkpoint trained on 60,000 steps,
  - `revision="diffusers-95k"` for the checkpoint trained on 95,000 steps,
  - `revision="diffusers-115k"` for the checkpoint trained on 115,000 steps.
  
  For more information, please have a look at [the "Three flavors" section](#three-flavors).
  
  ## Gradio
  
  We also support a [Gradio](https://github.com/gradio-app/gradio) web ui with diffusers to run inside a colab notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RWvik_C7nViiR9bNsu3fvMR3STx6RvDx?usp=sharing)
  
  
  ### Example Text2Image
  
  ```python
  # !pip install diffusers==0.3.0
  from diffusers import StableDiffusionPipeline
  
  # using the 60,000 steps checkpoint
  pipe = StableDiffusionPipeline.from_pretrained("naclbit/trinart_stable_diffusion_v2", revision="diffusers-60k")
  pipe.to("cuda")
  
  image = pipe("A magical dragon flying in front of the Himalaya in manga style").images[0]
  image
  ```
  
  ![dragon](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/a_magical_dragon_himalaya.png)
  
  If you want to run the pipeline faster or on a different hardware, please have a look at the [optimization docs](https://huggingface.co/docs/diffusers/optimization/fp16).
  
  ### Example Image2Image
  
  ```python
  # !pip install diffusers==0.3.0
  from diffusers import StableDiffusionImg2ImgPipeline
  import requests
  from PIL import Image
  from io import BytesIO
  
  url = "https://scitechdaily.com/images/Dog-Park.jpg"
  
  response = requests.get(url)
  init_image = Image.open(BytesIO(response.content)).convert("RGB")
  init_image = init_image.resize((768, 512))
  
  # using the 115,000 steps checkpoint
  pipe = StableDiffusionImg2ImgPipeline.from_pretrained("naclbit/trinart_stable_diffusion_v2", revision="diffusers-115k")
  pipe.to("cuda")
  
  images = pipe(prompt="Manga drawing of Brad Pitt", init_image=init_image, strength=0.75, guidance_scale=7.5).images
  image
  ```
  
  If you want to run the pipeline faster or on a different hardware, please have a look at the [optimization docs](https://huggingface.co/docs/diffusers/optimization/fp16).
  
  
  ## Stable Diffusion TrinArt/Trin-sama AI finetune v2
  
  trinart_stable_diffusion is a SD model finetuned by about 40,000 assorted high resolution manga/anime-style pictures for 8 epochs. This is the same model running on Twitter bot @trinsama (https://twitter.com/trinsama)
  
  Twitterボット「とりんさまAI」@trinsama (https://twitter.com/trinsama) で使用しているSDのファインチューン済モデルです。一定のルールで選別された約4万枚のアニメ・マンガスタイルの高解像度画像を用いて約8エポックの訓練を行いました。
  
  ## Version 2
  
  V2 checkpoint uses dropouts, 10,000 more images and a new tagging strategy and trained longer to improve results while retaining the original aesthetics.
  
  バージョン2は画像を1万枚追加したほか、ドロップアウトの適用、タグ付けの改善とより長いトレーニング時間により、SDのスタイルを保ったまま出力内容の改善を目指しています。
  
  ## Three flavors
  
  Step 115000/95000 checkpoints were trained further, but you may use step 60000 checkpoint instead if style nudging is too much.
  
  ステップ115000/95000のチェックポイントでスタイルが変わりすぎると感じる場合は、ステップ60000のチェックポイントを使用してみてください。
  
  #### img2img
  
  If you want to run **latent-diffusion**'s stock ddim img2img script with this model, **use_ema** must be set to False.
  
  **latent-diffusion** のscriptsフォルダに入っているddim img2imgをこのモデルで動かす場合、use_emaはFalseにする必要があります。
  
  #### Hardware
  
  - 8xNVIDIA A100 40GB
  
  #### Training Info
  
  - Custom dataset loader with augmentations: XFlip, center crop and aspect-ratio locked scaling
  - LR: 1.0e-5
  - 10% dropouts
  
  #### Examples
  
  Each images were diffused using K. Crowson's k-lms (from k-diffusion repo) method for 50 steps.
  
  ![examples](https://pbs.twimg.com/media/FbPO12-VUAAf2CJ?format=jpg&name=900x900)
  ![examples](https://pbs.twimg.com/media/FbPO65cUIAAga8k?format=jpg&name=900x900)
  ![examples](https://pbs.twimg.com/media/FbPO_QuVsAAG6xE?format=png&name=900x900)
  
  #### Credits
  
  - Sta, AI Novelist Dev (https://ai-novel.com/) @ Bit192, Inc.
  - Stable Diffusion - Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bjorn
  
  #### License
  
  CreativeML OpenRAIL-M