Nitro-Diffusion

Maintainer: nitrosocke

Total Score

378

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

Nitro-Diffusion is a fine-tuned Stable Diffusion model trained by nitrosocke on three distinct art styles simultaneously, allowing for high control of mixing, weighting, and single-style use. The model was trained on images from the Arcane, modern disney, and an archer style. Users can incorporate these styles into their prompts using the tokens _archer style, arcane style or modern disney style_.

Model inputs and outputs

The Nitro-Diffusion model takes text prompts as input and generates corresponding images as output. The model was trained using the Stable Diffusion architecture, which utilizes a diffusion process to transform noise into photorealistic images based on the provided text prompt.

Inputs

  • Text prompt: A natural language description of the desired image, which can incorporate the style tokens to guide the generated output.

Outputs

  • Image: A high-quality, photorealistic image that matches the provided text prompt, with the specified artistic style(s) applied.

Capabilities

The Nitro-Diffusion model can generate a wide variety of scenes and characters in multiple art styles, from realistic to stylized. Examples include fantasy characters, futuristic landscapes, and whimsical scenes. By using the style tokens, users can create images that blend different artistic influences or focus on a single style.

What can I use it for?

The Nitro-Diffusion model can be used for a variety of creative and artistic applications, such as concept art, character design, and illustration. The ability to mix and match styles makes it a versatile tool for designers, artists, and hobbyists alike. Additionally, the model's high-quality output makes it suitable for use in commercial projects, such as game development, movie production, and marketing materials.

Things to try

Experiment with different combinations of the style tokens to see how they influence the generated images. Try blending the styles in various ways, or focus on a single style to create a more cohesive look. Additionally, explore the model's capabilities by providing detailed, complex prompts to see the level of detail and realism it can achieve.



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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