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--- |
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inference: true |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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license: creativeml-openrail-m |
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--- |
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## Please Note! |
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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. |
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Other TrinArt models can be found at: |
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https://huggingface.co/naclbit/trinart_derrida_characters_v2_stable_diffusion |
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https://huggingface.co/naclbit/trinart_characters_19.2m_stable_diffusion_v1 |
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## Diffusers |
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The model has been ported to `diffusers` by [ayan4m1](https://huggingface.co/ayan4m1) |
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and can easily be run from one of the branches: |
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- `revision="diffusers-60k"` for the checkpoint trained on 60,000 steps, |
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- `revision="diffusers-95k"` for the checkpoint trained on 95,000 steps, |
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- `revision="diffusers-115k"` for the checkpoint trained on 115,000 steps. |
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For more information, please have a look at [the "Three flavors" section](#three-flavors). |
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## Gradio |
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We also support a [Gradio](https://github.com/gradio-app/gradio) web ui with diffusers to run inside a colab notebook: [](https://colab.research.google.com/drive/1RWvik_C7nViiR9bNsu3fvMR3STx6RvDx?usp=sharing) |
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### Example Text2Image |
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```python |
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# !pip install diffusers==0.3.0 |
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from diffusers import StableDiffusionPipeline |
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# using the 60,000 steps checkpoint |
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pipe = StableDiffusionPipeline.from_pretrained("naclbit/trinart_stable_diffusion_v2", revision="diffusers-60k") |
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pipe.to("cuda") |
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image = pipe("A magical dragon flying in front of the Himalaya in manga style").images[0] |
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image |
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``` |
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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). |
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### Example Image2Image |
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```python |
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# !pip install diffusers==0.3.0 |
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from diffusers import StableDiffusionImg2ImgPipeline |
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import requests |
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from PIL import Image |
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from io import BytesIO |
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url = "https://scitechdaily.com/images/Dog-Park.jpg" |
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response = requests.get(url) |
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init_image = Image.open(BytesIO(response.content)).convert("RGB") |
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init_image = init_image.resize((768, 512)) |
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# using the 115,000 steps checkpoint |
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained("naclbit/trinart_stable_diffusion_v2", revision="diffusers-115k") |
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pipe.to("cuda") |
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images = pipe(prompt="Manga drawing of Brad Pitt", init_image=init_image, strength=0.75, guidance_scale=7.5).images |
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image |
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``` |
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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). |
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## Stable Diffusion TrinArt/Trin-sama AI finetune v2 |
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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) |
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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。 |
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## Version 2 |
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V2 checkpoint uses dropouts, 10,000 more images and a new tagging strategy and trained longer to improve results while retaining the original aesthetics. |
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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。 |
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## Three flavors |
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Step 115000/95000 checkpoints were trained further, but you may use step 60000 checkpoint instead if style nudging is too much. |
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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。 |
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#### img2img |
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If you want to run **latent-diffusion**'s stock ddim img2img script with this model, **use_ema** must be set to False. |
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**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。 |
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#### Hardware |
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- 8xNVIDIA A100 40GB |
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#### Training Info |
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- Custom dataset loader with augmentations: XFlip, center crop and aspect-ratio locked scaling |
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- LR: 1.0e-5 |
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- 10% dropouts |
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#### Examples |
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Each images were diffused using K. Crowson's k-lms (from k-diffusion repo) method for 50 steps. |
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#### Credits |
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- Sta, AI Novelist Dev (https://ai-novel.com/) @ Bit192, Inc. |
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- Stable Diffusion - Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bjorn |
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#### License |
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CreativeML OpenRAIL-M |
