Maintainer: nitrosocke

Total Score


Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

Future-Diffusion is a fine-tuned version of the Stable Diffusion 2.0 base model, trained by nitrosocke on high-quality 3D images with a futuristic sci-fi theme. This model allows users to generate images with a distinct "future style" by incorporating the future style token into their prompts. Compared to similar models like redshift-diffusion-768, Future-Diffusion has a 512x512 resolution, while the redshift model has a higher 768x768 resolution. The Ghibli-Diffusion and Arcane-Diffusion models, on the other hand, are fine-tuned on anime and Arcane-themed images respectively, producing outputs with those distinct visual styles.

Model inputs and outputs

Future-Diffusion is a text-to-image model, taking text prompts as input and generating corresponding images as output. The model was trained using the diffusers-based dreambooth training approach with prior-preservation loss and the train-text-encoder flag.


  • Text prompts: Users provide text descriptions to guide the image generation, such as future style [subject] Negative Prompt: duplicate heads bad anatomy for character generation or future style city market street level at night Negative Prompt: blurry fog soft for landscapes.


  • Images: The model generates 512x512 or 1024x576 pixel images based on the provided text prompts, with a futuristic sci-fi style.


Future-Diffusion can generate a wide range of images with a distinct futuristic aesthetic, including human characters, animals, vehicles, and landscapes. The model's ability to capture this specific style sets it apart from more generic text-to-image models.

What can I use it for?

The Future-Diffusion model can be useful for various creative and commercial applications, such as:

  • Generating concept art for science fiction stories, games, or films
  • Designing futuristic product visuals or packaging
  • Creating promotional materials or marketing assets with a futuristic flair
  • Exploring and experimenting with novel visual styles and aesthetics

Things to try

One interesting aspect of Future-Diffusion is the ability to combine the "future style" token with other style tokens, such as those from the Ghibli-Diffusion or Arcane-Diffusion models. This can result in unique and unexpected hybrid styles, allowing users to expand their creative possibilities.

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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