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@@ -3,6 +3,7 @@ license: creativeml-openrail-m
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  ---
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  **Update:** Arcane Diffusion v3 coming soon (already in training)!
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  This is the fine-tuned Stable Diffusion model trained on images from the TV Show Arcane.
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  Use the tokens **arcane style** in your prompts for the effect.
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@@ -12,7 +13,7 @@ Sample images used for training:
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  Sample images from the model:
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  ![output Samples](https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-output-images.jpg)
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- Version 2 (arcane-diffusion-v2): This uses the diffusers based dreambooth training and prior-preservation loss is way more effective. The diffusers where then converted with a script to a ckpt file in order to work with automatics repo.
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  Training was done with 5k steps for a direct comparison to v1 and results show that it needs more steps for a more prominent result. Version 3 will be tested with 11k steps.
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- Version 1 (arcane-diffusion-5k): This model was trained using _Unfrozen Model Textual Inversion_ utilizing the _Training with prior-preservation loss_ methods. There is still a slight shift towards the style, while not using the arcane token.
 
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  ---
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  **Update:** Arcane Diffusion v3 coming soon (already in training)!
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+ **Arcane Diffusion**
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  This is the fine-tuned Stable Diffusion model trained on images from the TV Show Arcane.
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  Use the tokens **arcane style** in your prompts for the effect.
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  Sample images from the model:
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  ![output Samples](https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-output-images.jpg)
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+ **Version 2** (arcane-diffusion-v2): This uses the diffusers based dreambooth training and prior-preservation loss is way more effective. The diffusers where then converted with a script to a ckpt file in order to work with automatics repo.
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  Training was done with 5k steps for a direct comparison to v1 and results show that it needs more steps for a more prominent result. Version 3 will be tested with 11k steps.
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+ **Version 1** (arcane-diffusion-5k): This model was trained using _Unfrozen Model Textual Inversion_ utilizing the _Training with prior-preservation loss_ methods. There is still a slight shift towards the style, while not using the arcane token.