IDEAS home Printed from https://ideas.repec.org/a/aes/infoec/v28y2024i1p25-38.html
   My bibliography  Save this article

Empowering Local Image Generation: Harnessing Stable Diffusion for Machine Learning and AI

Author

Listed:
  • Ahmed Imran KABIR
  • Limon MAHOMUD
  • Abdullah Al Fahad
  • Ridwan AHMED

Abstract

This paper examines the ability to use Stable Diffusion's diffusion models to get state-of-the-art synthesis results on image data and other types of data. Also, a guiding interface can be used to control the process of making images by converting text to images and image to image. But because these models usually work directly in pixel space, optimizing strong DMs often needs more GPU VRAM to run. Using Stable Diffusion and diffusion models on local hardware like this lets more information and depth be added while generating images, which greatly improves the quality detail of the image. By combining diffusion models to model architecture, I have made diffusion models into powerful and flexible producers for general conditioning inputs, such as when using XL-XDXL 1.0 and LoRA models. Overall, the paper highlights how a normal person can run their own Midjourney like AI image generation with the help of machine learning and generative AI.

Suggested Citation

  • Ahmed Imran KABIR & Limon MAHOMUD & Abdullah Al Fahad & Ridwan AHMED, 2024. "Empowering Local Image Generation: Harnessing Stable Diffusion for Machine Learning and AI," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 28(1), pages 25-38.
    • Handle: RePEc:aes:infoec:v:28:y:2024:i:1:p:25-38
    as

    Download full text from publisher

    File URL: https://www.revistaie.ase.ro/content/109/03%20-%20kabir,%20mahomud,%20fadad,%20ahmed.pdf
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:infoec:v:28:y:2024:i:1:p:25-38. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Paul Pocatilu (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.