2 citations · 3 across the 11 of their papers we have counts for
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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…
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…
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…
Infinity-RoPE: Action-Controllable Infinite Video Generation Emerges From Autoregressive Self-Rollout
Hidir Yesiltepe, Tuna Han Salih Meral, Adil Kaan Akan +2
Current autoregressive video diffusion models are constrained by three core bottlenecks: (i) the finite temporal horizon imposed by the base model's 3D Rotary Positional Embedding…
Dynamic View Synthesis as an Inverse Problem
Hidir Yesiltepe, Pinar Yanardag
In this work, we address dynamic view synthesis from monocular videos as an inverse problem in a training-free setting. By redesigning the noise initialization phase of a pre-train…
LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers
Yusuf Dalva, Hidir Yesiltepe, Pinar Yanardag
We introduce LoRAShop, the first framework for multi-concept image editing with LoRA models. LoRAShop builds on a key observation about the feature interaction patterns inside Flux…
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…
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…
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…
RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models
Ozgur Kara, Bariscan Kurtkaya, Hidir Yesiltepe +2
Recent advancements in diffusion-based models have demonstrated significant success in generating images from text. However, video editing models have not yet reached the same leve…