3 papers
cs.CV2026
ReCache: Learning Budget-Aware Caching Schedules for Diffusion Models via REINFORCE
Mishan Aliev, Eva Neudachina, Ilya Bykov +4
Modern diffusion models generate high-quality images and videos, but their iterative denoising process makes inference expensive. Feature caching accelerates sampling by reusing or…
cs.CV2026
GAS: Improving Discretization of Diffusion ODEs via Generalized Adversarial Solver
Aleksandr Oganov, Ilya Bykov, Eva Neudachina +7
While diffusion models achieve state-of-the-art generation quality, they still suffer from computationally expensive sampling. Recent works address this issue with gradient-based o…
cs.CV2026
CasTex: Cascaded Text-to-Texture Synthesis via Explicit Texture Maps and Physically-Based Shading
Mishan Aliev, Dmitry Baranchuk, Kirill Struminsky
This work investigates text-to-texture synthesis using diffusion models to generate physically-based texture maps. We aim to achieve realistic model appearances under varying light…