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README.md
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@@ -22,7 +22,7 @@ Why is the Model even existing? There are loads of Stable Diffusion model out th
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Well, is there any models trained with resolution base resolution (`base_res`) 768 even 1024 before? Don't think so.
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Here it is, the BPModel, a Stable Diffusion model you may love or hate.
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Trained with 5k high quality images that suit my taste (not necessary yours unfortunately) from [Sankaku Complex](https://chan.sankakucomplex.com) with annotations.
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The dataset is public in [Crosstyan/BPDataset](https://huggingface.co/datasets/Crosstyan/BPDataset) for full disclosure.
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Pure combination of tags may not be the optimal way to describe the image,
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but I don't need to do extra work.
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And no, I won't feed any AI generated image
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@@ -97,7 +97,7 @@ better than some artist style DreamBooth model which only train with a few
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hundred images or even less. I also oppose changing style by merging model since You
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could apply different style by training with proper captions and prompting.
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Besides some of images in my dataset
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be misinterpreted by CLIP when tokenizing. For example, *as109* will be tokenized as `[as, 1, 0, 9]` and
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*fuzichoco* will become `[fu, z, ic, hoco]`. Romanized Japanese suffers from the problem a lot and
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I don't have a good solution to fix it other than changing the artist name in the caption, which is
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Well, is there any models trained with resolution base resolution (`base_res`) 768 even 1024 before? Don't think so.
|
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Here it is, the BPModel, a Stable Diffusion model you may love or hate.
|
24 |
Trained with 5k high quality images that suit my taste (not necessary yours unfortunately) from [Sankaku Complex](https://chan.sankakucomplex.com) with annotations.
|
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+
The dataset is public in [Crosstyan/BPDataset](https://huggingface.co/datasets/Crosstyan/BPDataset) for the sake of full disclosure .
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Pure combination of tags may not be the optimal way to describe the image,
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but I don't need to do extra work.
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And no, I won't feed any AI generated image
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hundred images or even less. I also oppose changing style by merging model since You
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98 |
could apply different style by training with proper captions and prompting.
|
99 |
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100 |
+
Besides some of images in my dataset have the artist name in the caption, however some artist name will
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be misinterpreted by CLIP when tokenizing. For example, *as109* will be tokenized as `[as, 1, 0, 9]` and
|
102 |
*fuzichoco* will become `[fu, z, ic, hoco]`. Romanized Japanese suffers from the problem a lot and
|
103 |
I don't have a good solution to fix it other than changing the artist name in the caption, which is
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