glid-3-xl

Maintainer: jack000

Total Score

45

Last updated 5/17/2024
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Model LinkView on Replicate
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Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

glid-3-xl is a 1.4B parameter text-to-image model developed by CompVis and fine-tuned by jack000. It is a back-ported version of CompVis' latent diffusion model to the guided diffusion codebase. Unlike the original stable-diffusion model, glid-3-xl has been split into three checkpoints, allowing for fine-tuning on new datasets and additional tasks like inpainting and super-resolution.

Model inputs and outputs

The glid-3-xl model takes in a text prompt, an optional init image, and various parameters to control the image generation process. It outputs one or more generated images that match the given text prompt.

Inputs

  • Prompt: Your text prompt describing the image you want to generate.
  • Negative Prompt: (Optional) Text to negatively influence the model's prediction.
  • Init Image: (Optional) An initial image to use as a starting point for the generation.
  • Seed: (Optional) A seed value for the random number generator.
  • Steps: The number of diffusion steps to run, controlling the quality and detail of the output.
  • Guidance Scale: A value controlling the trade-off between faithfulness to the prompt and sample diversity.
  • Width/Height: The target size of the generated image.
  • Batch Size: The number of images to generate at once.

Outputs

  • Image(s): One or more generated images that match the given text prompt.

Capabilities

glid-3-xl is capable of generating high-quality, photorealistic images from text prompts. It can handle a wide range of subjects and styles, from realistic scenes to abstract and surreal compositions. The model has also been fine-tuned for inpainting, allowing you to edit and modify existing images.

What can I use it for?

You can use glid-3-xl to generate custom images for a variety of applications, such as:

  • Illustration and concept art
  • Product visualizations
  • Social media content
  • Advertising and marketing materials
  • Educational resources
  • Personal creative projects

The ability to fine-tune the model on new datasets also opens up possibilities for domain-specific applications, such as generating medical illustrations or architectural visualizations.

Things to try

One interesting aspect of glid-3-xl is the ability to use an init image and apply human-guided diffusion to iteratively refine the generation. This allows you to start with a basic image and progressively edit it to better match your desired prompt. You can also experiment with the various sampling techniques, such as PLMS and classifier-free guidance, to find the approach that works best for your 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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