Reimu Hakurei
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redo readme
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README.md
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The model originally used for fine-tuning is [Stable Diffusion V1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), which is a latent image diffusion model trained on [LAION2B-en](https://huggingface.co/datasets/laion/laion2B-en).
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The current model
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With [Textual Inversion](https://github.com/rinongal/textual_inversion), the embeddings for the text encoder has been trained to align more with anime-styled images, reducing excessive prompting.
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## Training Data & Annotative Prompting
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The data used for Textual Inversion has come from a random sample of
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Captions are Danbooru-style captions.
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device = "cuda"
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pipe = StableDiffusionPipeline.from_pretrained(model_id,
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pipe = pipe.to(device)
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prompt = "
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with autocast("cuda"):
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image = pipe(prompt, guidance_scale=7.5)["sample"][0]
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## Team Members and Acknowledgements
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This project would not have been possible without the incredible work by the [CompVis Researchers](https://ommer-lab.com/)
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Additionally, the methods presented in the [Textual Inversion](https://github.com/rinongal/textual_inversion) repo was an incredible help.
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- [Anthony Mercurio](https://github.com/harubaru)
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- [Salt](https://github.com/sALTaccount/)
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The model originally used for fine-tuning is [Stable Diffusion V1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), which is a latent image diffusion model trained on [LAION2B-en](https://huggingface.co/datasets/laion/laion2B-en).
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The current model has been fine-tuned with a learning rate of 5.0e-5 for 1 epoch on 56k Danbooru text-image pairs which all have an aesthetic rating greater than `6.0`.
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## Training Data & Annotative Prompting
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The data used for Textual Inversion has come from a random sample of 56k Danbooru images, which were filtered based on [CLIP Aesthetic Scoring](https://github.com/christophschuhmann/improved-aesthetic-predictor) where only images with an aesthetic score greater than `6.0` were used.
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Captions are Danbooru-style captions.
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device = "cuda"
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision='fp16')
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pipe = pipe.to(device)
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prompt = "touhou hakurei_reimu 1girl solo portrait"
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with autocast("cuda"):
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image = pipe(prompt, guidance_scale=7.5)["sample"][0]
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## Team Members and Acknowledgements
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This project would not have been possible without the incredible work by the [CompVis Researchers](https://ommer-lab.com/).
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- [Anthony Mercurio](https://github.com/harubaru)
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- [Salt](https://github.com/sALTaccount/)
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