Introduction Welcome to a new era in image retrieval technology! In this article, we'll explore groundbreaking techniques for retrieving realistic images from latent space using SDXL checkpoint models. If you're as excited as I am about pushing the boundaries of what's possible with machine learning and image processing, then you're in the right place. Let’s dive in! Article Under Construction Please Note: This article is currently under construction. We are actively working on updating and refining the content to provide the most accurate and comprehensive information possible. Why Share Now? We are sharing this preliminary version to ensure the techniques and settings are properly licensed and to provide early access to our groundbreaking methods. Your feedback is invaluable to us as we continue to improve and expand upon this work. What to Expect? Continuous Updates: We will regularly update this article with new settings, mathematical methods, and improved techniques. Community Engagement: Your suggestions and feedback are welcome to help us refine and enhance our methods. Stay Tuned! Follow Our Updates: Bookmark this page and check back regularly for the latest updates. Get Involved: Feel free to contact us with any questions, suggestions, or feedback. Credits and Citations We are committed to giving proper credit to all authors whose work we borrow or build upon. If you notice that we have used your work without correct citation, please contact us at conglejobs@gmail.com, and we will update the information promptly.
Authors
This repository contains articles written by Cong Le and Henrik Ha.
Citation Request
If you find this work helpful, please cite it as follows:
For Version 1.0:
@misc{revolutionizing_image_retrieval_v1,
author = {Le, Cong and Ha, Henrik},
title = {Revolutionizing Image Retrieval: New Techniques to Retrieve Realistic Images from Latent Space in SDXL Checkpoint Models},
month = {June},
year = {2024},
url = {https://conglesolutionx.github.io},
note = {Technical article, Version 1.0}
}