luna-diffusion
Maintainer: proximasanfinetuning
45
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
luna-diffusion is a fine-tuned version of Stable Diffusion 1.5 created by proximacentaurib. It was trained on a few hundred mostly hand-captioned high-resolution images to produce an ethereal, painterly aesthetic. Similar models include Dreamlike Diffusion 1.0, which is also a fine-tuned version of Stable Diffusion, and Hitokomoru Diffusion, which has been fine-tuned on Japanese artwork.
Model inputs and outputs
luna-diffusion is a text-to-image generation model that takes a text prompt as input and produces an image as output. The model was fine-tuned on high-resolution images, so it works best at 768x768, 512x768, or 768x512 pixel resolutions. The model also supports adding "painting" to the prompt to increase the painterly effect, and "illustration" to get more vector art-style images.
Inputs
- Text prompt: A natural language description of the desired image, such as "painting of a beautiful woman with red hair, 8k, high quality"
Outputs
- Image: A generated image matching the provided text prompt, saved as a JPEG or PNG file
Capabilities
luna-diffusion can generate high-quality, painterly-style images based on text prompts. The model produces ethereal, soft-focus images with a focus on detailed scenes and figures. It works particularly well for prompts involving people, nature, and fantasy elements.
What can I use it for?
luna-diffusion is well-suited for applications in art, design, and creative expression. You could use it to generate concept art, illustrations, or other visual assets for things like games, books, marketing materials, and more. The model's unique aesthetic could also make it useful for mood boards, visual inspiration, or other creative projects.
Things to try
To get the best results from luna-diffusion, try experimenting with different aspect ratios and resolutions. The model was trained on 768x768 images, so that size or similar ratios like 512x768 or 768x512 tend to work well. You can also play with the "painting" and "illustration" keywords in your prompts to adjust the style. Additionally, the DPM++ 2M sampler often produces crisp, clear results, while the Euler_a sampler gives a softer look.
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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