6 papers
Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers
Anh Nguyen, Ngan Nguyen, Duc Vu +11
Modern one-step diffusion models achieve impressive quality through distribution-based timestep distillation. Yet, they rely on a critical assumption: Teacher and Student must inha…
Scale-Wise VAR is Secretly Discrete Diffusion
Amandeep Kumar, Nithin Gopalakrishnan Nair, Vishal M. Patel
Autoregressive (AR) transformers have emerged as a powerful paradigm for visual generation, largely due to their scalability, computational efficiency and unified architecture with…
Scaling Transformer-Based Novel View Synthesis Models with Token Disentanglement and Synthetic Data
Nithin Gopalakrishnan Nair, Srinivas Kaza, Xuan Luo +3
Large transformer-based models have made significant progress in generalizable novel view synthesis (NVS) from sparse input views, generating novel viewpoints without the need for…
Dreamguider: Improved Training free Diffusion-based Conditional Generation
Nithin Gopalakrishnan Nair, Vishal M Patel
Diffusion models have emerged as a formidable tool for training-free conditional generation.However, a key hurdle in inference-time guidance techniques is the need for compute-heav…
MaxFusion: Plug&Play Multi-Modal Generation in Text-to-Image Diffusion Models
Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, Vishal M Patel
Large diffusion-based Text-to-Image (T2I) models have shown impressive generative powers for text-to-image generation as well as spatially conditioned image generation. For most ap…
Diffscaler: Enhancing the Generative Prowess of Diffusion Transformers
Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, Vishal M. Patel
Recently, diffusion transformers have gained wide attention with its excellent performance in text-to-image and text-to-vidoe models, emphasizing the need for transformers as backb…