6 papers · 1 filter
Rethinking Pixel Mean Flows via Interval Denoiser
Alexander Zaytsev, Dmitry Baranchuk, Alexander Korotin +1
Modern diffusion and flow-based models are increasingly moving toward few-step, latent-free generation to bypass the computational overhead of multi-step sampling and the reconstru…
Alchemist: Turning Public Text-to-Image Data into Generative Gold
Valerii Startsev, Alexander Ustyuzhanin, Alexey Kirillov +2
Pre-training equips text-to-image (T2I) models with broad world knowledge, but this alone is often insufficient to achieve high aesthetic quality and alignment. Consequently, super…
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…
MADrive: Memory-Augmented Driving Scene Modeling
Polina Karpikova, Daniil Selikhanovych, Kirill Struminsky +3
Recent advances in scene reconstruction have pushed toward highly realistic modeling of autonomous driving (AD) environments using 3D Gaussian splatting. However, the resulting rec…
Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis
Anton Voronov, Denis Kuznedelev, Mikhail Khoroshikh +2
This work presents Switti, a scale-wise transformer for text-to-image generation. We start by adapting an existing next-scale prediction autoregressive (AR) architecture to T2I gen…
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization
Vage Egiazarian, Denis Kuznedelev, Anton Voronov +5
Text-to-image diffusion models have emerged as a powerful framework for high-quality image generation given textual prompts. Their success has driven the rapid development of produ…