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### First Stable-Diffusion v1.5 fine-tuned for 10k steps using [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) upon [Norod78/vintage-blip-captions](https://huggingface.co/datasets/Norod78/vintage-blip-captions) then it underwent further fine tuning with Dreambooth using the same images as the ones in the dataset but rather then having it blip-captioned, it was split into "Vintage style", "Vintage face" and "Pulp cover" concepts.
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### Dreambooth model was trained with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
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## Because the model was first fined-tuned on the whole dataset and only then it was fine-tuned again to learn each individual concept, you can use prompts without Trigger-Words and still get a subtle "Vintage" touch
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# SDv1.5 SD15-VinageStyle model, trained by Norod78 in two parts.
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### First Stable-Diffusion v1.5 fine-tuned for 10k steps using [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) upon [Norod78/vintage-blip-captions](https://huggingface.co/datasets/Norod78/vintage-blip-captions) then it underwent further fine tuning with Dreambooth using the same images as the ones in the dataset but rather then having it blip-captioned, it was split into "Vintage style", "Vintage face" and "Pulp cover" concepts.
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### Dreambooth model was trained with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
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## Because the model was first fined-tuned on the whole dataset and only then it was fine-tuned again to learn each individual concept, you can use prompts without Trigger-Words and still get a subtle "Vintage" touch
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# Trigger-Words are: "Vintage", "Vintage style", "Vintage face", "Pulp cover"
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