sdxl-deep-down

Maintainer: fofr

Total Score

47

Last updated 6/9/2024
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PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

sdxl-deep-down is an SDXL model fine-tuned by fofr on underwater imagery. This model is part of a series of SDXL models created by fofr, including sdxl-black-light, sdxl-fresh-ink, sdxl-energy-drink, and sdxl-toy-story-people. The sdxl-deepcache model created by lucataco is another related SDXL model.

Model inputs and outputs

sdxl-deep-down takes a variety of inputs, including a prompt, image, mask, and various parameters to control the output. The model can generate images based on the provided prompt, or can perform inpainting on an input image using the provided mask.

Inputs

  • Prompt: The text prompt that describes the desired output image.
  • Image: An input image for img2img or inpaint mode.
  • Mask: A mask for inpaint mode, where black areas will be preserved and white areas will be inpainted.
  • Seed: A random seed for generating the output.
  • Width/Height: The desired dimensions of the output image.
  • Refine: The refine style to use.
  • Scheduler: The scheduler to use for the diffusion process.
  • LoRA Scale: The additive scale for LoRA, applicable only on trained models.
  • Num Outputs: The number of images to output.
  • Refine Steps: The number of steps to refine for the base_image_refiner.
  • Guidance Scale: The scale for classifier-free guidance.
  • Apply Watermark: Whether to apply a watermark to the generated image.
  • High Noise Frac: The fraction of noise to use for the expert_ensemble_refiner.
  • Negative Prompt: An optional negative prompt to guide the output.
  • Prompt Strength: The strength of the prompt when using img2img or inpaint.
  • Replicate Weights: Optional LoRA weights to use.
  • Num Inference Steps: The number of denoising steps to perform.

Outputs

  • Images: One or more generated images based on the provided inputs.

Capabilities

sdxl-deep-down can generate high-quality images based on provided text prompts, as well as perform inpainting on input images using a provided mask. The model is particularly adept at creating underwater and oceanic-themed imagery, building on the fine-tuning data it was trained on.

What can I use it for?

sdxl-deep-down could be useful for a variety of applications, such as creating concept art for underwater-themed video games or films, designing promotional materials for marine conservation organizations, or generating stock imagery for websites and publications focused on aquatic themes. The model's ability to perform inpainting could also be leveraged for tasks like restoring damaged underwater photographs or creating digital artwork inspired by the ocean.

Things to try

Experiment with different prompts and input images to see the range of outputs the sdxl-deep-down model can produce. Try combining the model with other AI-powered tools, such as those for 3D modeling or animation, to create more complex and immersive underwater scenes. You can also experiment with the various input parameters, such as the guidance scale and number of inference steps, to find the settings that work best for your specific use case.



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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