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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…
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,…
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.…
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…
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…