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trinart01

Text-to-Image
Diffusers
StableDiffusionPipeline
Inference Endpoints
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trinart01
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---
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