FloralMarble

Maintainer: spaablauw

Total Score

58

Last updated 5/28/2024

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

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Model overview

FloralMarble is a Stable Diffusion model created by the maintainer spaablauw that generates images of marble statues with a floral, abstract effect. It works well with prompts using the words "flower petals" and can be combined with other embeddings like PhotoHelper and Hyperfluid for additional effects. The model has a strong bias towards humans, so it may require some tinkering to get good results with other subject matters.

Model inputs and outputs

Inputs

  • Text prompts using words like "flower petals" that work well with the model
  • Ability to combine with other embeddings like PhotoHelper and Hyperfluid for more control over the output

Outputs

  • Images of marble statues with an abstract, floral effect
  • Outputs are particularly well-suited for human portraits and figures, though the model can be tweaked to work with other subject matter

Capabilities

FloralMarble can generate striking, artistic images of marble statues enhanced with abstract floral elements. The model excels at producing dreamy, ethereal portraits and figures, often with water or petal effects. While the output is biased towards humans, the maintainer notes the model can be adjusted to generate more universal results.

What can I use it for?

The FloralMarble model could be useful for digital artists, designers, and content creators looking to add a unique, artistic flair to their work. The model's strength in portraiture makes it well-suited for creating album covers, book covers, or other editorial imagery. With some tweaking, the model could also be applied to generate abstract, sculptural art pieces or fantasy character designs.

Things to try

Experiment with combining FloralMarble with other embeddings like PhotoHelper and Hyperfluid to unlock new visual possibilities. Try prompts that push the model beyond its human bias, such as landscapes, animals, or abstract concepts, to see how it performs. The maintainer also suggests trying the 150 or 250 iterations of the model for more controllable results.



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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