glid-3
Maintainer: nicholascelestin
3
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | No paper link provided |
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Model overview
glid-3 is a combination of OpenAI's GLIDE, Latent Diffusion, and CLIP. It uses the same text conditioning as GLIDE, but instead of training a new text transformer, it uses the existing one from OpenAI CLIP. Instead of upsampling, it does diffusion in the latent diffusion space and adds classifier-free guidance.
Similar models include glid-3-xl-stable, which has more powerful in-painting and out-painting capabilities, and glid-3-xl, which is a CompVis latent-diffusion text2im model fine-tuned for inpainting. Another related model is icons, which is fine-tuned to generate slick icons and flat pop constructivist graphics. The well-known stable-diffusion is also a similar latent text-to-image diffusion model.
Model inputs and outputs
glid-3 takes in a text prompt and outputs a generated image. The model can generate images quickly, though the image quality may not be ideal as the model is still a work in progress.
Inputs
- Prompt: The text prompt describing the image you want to generate.
- Negative: An optional negative prompt to guide the model away from generating certain elements.
- Batch Size: The number of images to generate at once, up to 20.
Outputs
- Array of image URLs: The generated images, returned as an array of image URLs.
Capabilities
glid-3 can generate a wide variety of photographic images based on text prompts. While it may not work as well for illustrations or artwork, it can create compelling images of scenes, objects, and people described in the prompt.
What can I use it for?
You can use glid-3 to quickly generate images for various applications, such as marketing materials, blog posts, social media, or even as a creative tool for ideation. The model's ability to translate text into visual concepts can be a powerful asset for content creators and designers.
Things to try
One interesting aspect of glid-3 is its use of latent diffusion, which allows for more efficient generation compared to upsampling approaches. You could experiment with different prompts and techniques, such as using classifier-free guidance, to see how it affects the quality and creativity of the generated images.
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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