---
inference: false
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 trinsama 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.

The first version of characters model has been publicly released on 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