Stable Diffusion
Stable Diffusion is a powerful class of generative AI models primarily used for creating high-quality images from text prompts. Current research focuses on improving efficiency (e.g., through quantization and model compression), enhancing control and customization (e.g., via ControlNet and fine-tuning techniques like LoRA), and mitigating biases and safety concerns present in generated images. These advancements are significant for various applications, including image editing, compression, and design, as well as for advancing our understanding of generative models and their societal impact.
141papers
Papers
February 28, 2025
PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
Amar Kumar, Anita Kriz, Mohammad Havaei, Tal ArbelMcGill University●MILA (Quebec AI institute)●Google ResearchAdvancing AI-Powered Medical Image Synthesis: Insights from MedVQA-GI Challenge Using CLIP, Fine-Tuned Stable Diffusion, and Dream-Booth + LoRA
Ojonugwa Oluwafemi Ejiga Peter, Md Mahmudur Rahman, Fahmi KhalifaMorgan State University
December 24, 2024
December 17, 2024
December 11, 2024
December 9, 2024
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning
Mehdi Noroozi, Isma Hadji, Victor Escorcia, Anestis Zaganidis, Brais Martinez, Georgios TzimiropoulosNo Annotations for Object Detection in Art through Stable Diffusion
Patrick Ramos, Nicolas Gonthier, Selina Khan, Yuta Nakashima, Noa GarciaRendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data
Kartik Patwari, David Schneider, Xiaoxiao Sun, Chen-Nee Chuah, Lingjuan Lyu, Vivek Sharma