736 citations · 1.1k across the 52 of their papers we have counts for
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Diffusion Models and Representation Learning: A Survey
Michael Fuest, Pingchuan Ma, Ming Gui +3
Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention. They can be considered a unique instance of self-supervised lear…
Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation
Jiajun Wang, Morteza Ghahremani, Yitong Li +2
Controllable text-to-image (T2I) diffusion models have shown impressive performance in generating high-quality visual content through the incorporation of various conditions. Curre…
CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models
Nick Stracke, Stefan Andreas Baumann, Joshua M. Susskind +2
Text-to-image generative models have become a prominent and powerful tool that excels at generating high-resolution realistic images. However, guiding the generative process of the…
Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions
Stefan Andreas Baumann, Felix Krause, Michael Neumayr +4
Recent advances in text-to-image (T2I) diffusion models have significantly improved the quality of generated images. However, providing efficient control over individual subjects,…
CAGE: Unsupervised Visual Composition and Animation for Controllable Video Generation
Aram Davtyan, Sepehr Sameni, Björn Ommer +1
The field of video generation has expanded significantly in recent years, with controllable and compositional video generation garnering considerable interest. Most methods rely on…
ZigMa: A DiT-style Zigzag Mamba Diffusion Model
Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui +4
The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long…