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Change README to show how to use it with diffusers
Browse filesAdds a couple of lines to the README to show how to use the model with diffusers
README.md
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@@ -13,9 +13,60 @@ This model is NOT the 19.2M images Characters Model on TrinArt, but an improved
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このモデルはTrinArtのキャラクターズモデル(1920万枚再学習モデル)ではありません! とりんさまAIボットのモデルの改良版です。このモデルはオリジナルのSD v1.4モデルのアートスタイルをできる限り残したまま、アニメ・マンガ方向に調整することを意図しています。
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## Stable Diffusion TrinArt/Trin-sama AI finetune v2
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このモデルはTrinArtのキャラクターズモデル(1920万枚再学習モデル)ではありません! とりんさまAIボットのモデルの改良版です。このモデルはオリジナルのSD v1.4モデルのアートスタイルをできる限り残したまま、アニメ・マンガ方向に調整することを意図しています。
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## Diffusers
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The model has been ported to `diffusers` by [ayan4m1](https://huggingface.co/ayan4m1] (thanks!)
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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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### 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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![dragon](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/a_magical_dragon_himalaya.png)
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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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![brad_pitt](https://huggingface.co/datasets/patrickvonplaten/images/blob/main/manga_man.png)
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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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