arcane-diffusion

Maintainer: nitrosocke

Total Score

36

Last updated 9/5/2024
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Model overview

Arcane-Diffusion is a fine-tuned version of the Stable Diffusion model, trained on images from the TV show Arcane. This model can produce images in the distinctive "Arcane style" by using the tokens

arcane style
in your prompts. The maintainer nitrosocke has also created other fine-tuned Stable Diffusion models, such as mo-di-diffusion which is trained on images in a "modern Disney style".

Model inputs and outputs

Arcane-Diffusion is a text-to-image model that takes a text prompt as input and generates a corresponding image as output. The model can be used just like the original Stable Diffusion model, with the addition of the

arcane style
token to produce images in the Arcane aesthetic.

Inputs

  • Text prompt: A text description of the desired image, including the
    arcane style
    token.

Outputs

  • Generated image: An image that corresponds to the input text prompt, rendered in the Arcane art style.

Capabilities

Arcane-Diffusion can generate a wide variety of Arcane-themed images, from fantastical characters and creatures to elaborate environments and scenes. The model is able to capture the distinct visual style of the Arcane universe, including its unique color palette, lighting, and artistic flourishes.

What can I use it for?

Arcane-Diffusion can be used to create original artwork and illustrations inspired by the Arcane universe. This could include character designs, background environments, promotional materials, and more. The model can also be used to generate images for creative projects, such as fanart, game assets, or digital art commissions.

Things to try

One interesting aspect of Arcane-Diffusion is its ability to blend the Arcane art style with other elements. Try combining the

arcane style
token with prompts that introduce other themes, such as "a magical princess with golden hair, arcane style" or "a cyberpunk city at night, arcane style". This can lead to unique and unexpected results that push the boundaries of the model's capabilities.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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