4 citations · 14 across the 38 of their papers we have counts for
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Explaining in Diffusion: Explaining a Classifier Through Hierarchical Semantics with Text-to-Image Diffusion Models
Tahira Kazimi, Ritika Allada, Pinar Yanardag
Classifiers are important components in many computer vision tasks, serving as the foundational backbone of a wide variety of models employed across diverse applications. However,…
FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers
Yusuf Dalva, Kavana Venkatesh, Pinar Yanardag
Rectified flow models have emerged as a dominant approach in image generation, showcasing impressive capabilities in high-quality image synthesis. However, despite their effectiven…
MotionShop: Zero-Shot Motion Transfer in Video Diffusion Models with Mixture of Score Guidance
Hidir Yesiltepe, Tuna Han Salih Meral, Connor Dunlop +1
In this work, we propose the first motion transfer approach in diffusion transformer through Mixture of Score Guidance (MSG), a theoretically-grounded framework for motion transfer…
MotionFlow: Attention-Driven Motion Transfer in Video Diffusion Models
Tuna Han Salih Meral, Hidir Yesiltepe, Connor Dunlop +1
Text-to-video models have demonstrated impressive capabilities in producing diverse and captivating video content, showcasing a notable advancement in generative AI. However, these…
LayerFusion: Harmonized Multi-Layer Text-to-Image Generation with Generative Priors
Yusuf Dalva, Yijun Li, Qing Liu +4
Large-scale diffusion models have achieved remarkable success in generating high-quality images from textual descriptions, gaining popularity across various applications. However,…
PRIMA: Multi-Image Vision-Language Models for Reasoning Segmentation
Muntasir Wahed, Kiet A. Nguyen, Adheesh Sunil Juvekar +6
Despite significant advancements in Large Vision-Language Models (LVLMs)' capabilities, existing pixel-grounding models operate in single-image settings, limiting their ability to…
LoRACLR: Contrastive Adaptation for Customization of Diffusion Models
Enis Simsar, Thomas Hofmann, Federico Tombari +1
Recent advances in text-to-image customization have enabled high-fidelity, context-rich generation of personalized images, allowing specific concepts to appear in a variety of scen…
RAVEL: Rare Concept Generation and Editing via Graph-driven Relational Guidance
Kavana Venkatesh, Yusuf Dalva, Ismini Lourentzou +1
Despite impressive visual fidelity, current text-to-image (T2I) diffusion models struggle to depict rare, complex, or culturally nuanced concepts due to training data limitations.…
ORACLE: Leveraging Mutual Information for Consistent Character Generation with LoRAs in Diffusion Models
Kiymet Akdemir, Pinar Yanardag
Text-to-image diffusion models have recently taken center stage as pivotal tools in promoting visual creativity across an array of domains such as comic book artistry, children's l…
Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models
Matthew Zheng, Enis Simsar, Hidir Yesiltepe +3
Text-to-image models are becoming increasingly popular, revolutionizing the landscape of digital art creation by enabling highly detailed and creative visual content generation. Th…
The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP
Hidir Yesiltepe, Yusuf Dalva, Pinar Yanardag
Diffusion models have become prominent in creating high-quality images. However, unlike GAN models celebrated for their ability to edit images in a disentangled manner, diffusion-b…
MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models
Hidir Yesiltepe, Kiymet Akdemir, Pinar Yanardag
Diffusion-based text-to-image models have rapidly gained popularity for their ability to generate detailed and realistic images from textual descriptions. However, these models oft…
Contrastive Test-Time Composition of Multiple LoRA Models for Image Generation
Tuna Han Salih Meral, Enis Simsar, Federico Tombari +1
Low-Rank Adaptation (LoRA) has emerged as a powerful and popular technique for personalization, enabling efficient adaptation of pre-trained image generation models for specific ta…
Learn Once, Edit Anywhere: Visual Direction Transfer for Diffusion Models
Yusuf Dalva, Hidir Yesiltepe, Pinar Yanardag
The rapid advancement of diffusion models has enabled the generation of high-fidelity images from textual prompts, yet achieving precise, disentangled control over specific attribu…