This is the fine-tuned Stable Diffusion model trained on images from the TV Show Arcane. Use the tokens arcane style in your prompts for the effect.
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This model can be used just like any other Stable Diffusion model. For more information, please have a look at the Stable Diffusion.
You can also export the model to ONNX, MPS and/or FLAX/JAX.
#!pip install diffusers transformers scipy torch from diffusers import StableDiffusionPipeline import torch model_id = "nitrosocke/Arcane-Diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "arcane style, a magical princess with golden hair" image = pipe(prompt).images image.save("./magical_princess.png")
Gradio & Colab
We also support a Gradio Web UI and Colab with Diffusers to run fine-tuned Stable Diffusion models:
Sample images from v3:
Sample images from the model:
Sample images used for training:
Version 3 (arcane-diffusion-v3): This version uses the new train-text-encoder setting and improves the quality and edibility of the model immensely. Trained on 95 images from the show in 8000 steps.
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. 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.
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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