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20212026
most citedStylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models

2 citations · 8 across the 35 of their papers we have counts for

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cs.CV2026

RankT2I: A Submodular Framework for Discovering Interpretable and Diverse Semantics in Text-to-Image Models

Ritika Allada, Pinar Yanardag

Recent advances in text-to-image (T2I) models have revolutionized the field of image generation and editing. However, identifying semantics that a T2I model can successfully edit i…

cs.CV2026

From Zero to Hero: Training-Free Custom Concept Spawning in World Models

Kiymet Akdemir, Pinar Yanardag

Autoregressive world models have emerged as a powerful paradigm for interactive video generation, allowing users to navigate dynamically generated environments through actions. The…

cs.CV2026

DTG-Restore: Training-Free Diffusion Refinement for Generative Video Super-Resolution

Hidir Yesiltepe, Koutilya PNVR, Gaurav Pathak +4

Recent progress in video diffusion models has enabled remarkable generative fidelity, yet leveraging these priors for restoration remains limited by the strong coupling between con…

cs.CV2026

VideoMLA: Low-Rank Latent KV Cache for Minute-Scale Autoregressive Video Diffusion

Hidir Yesiltepe, Jiazhen Hu, Tuna Han Salih Meral +4

Long-rollout causal video diffusion has converged on a fixed-size sliding-window KV cache, with recent progress innovating within this layout by changing which tokens occupy the wi…

cs.CV2026

AdaState: Self-Evolving Anchors for Streaming Video Generation

Yusuf Dalva, Pinar Yanardag

Autoregressive video diffusion models generate streaming video by producing frames sequentially, conditioning each chunk on previously generated content. These models are structura…

cs.CV2026

Aligning Latent Geometry for Spherical Flow Matching in Image Generation

Tuna Han Salih Meral, Kaan Oktay, Hidir Yesiltepe +2

Latent flow matching for image generation usually transports Gaussian noise to variational autoencoder latents along linear paths. Both endpoints, however, concentrate in thin sphe…