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Unleashing the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing
Zizheng Yang, Hu Yu, Bing Li +3
Diffusion models have recently been investigated as powerful generative solvers for image dehazing, owing to their remarkable capability to model the data distribution. However, th…
VideoMAR: Autoregressive Video Generatio with Continuous Tokens
Hu Yu, Biao Gong, Hangjie Yuan +5
Masked-based autoregressive models have demonstrated promising image generation capability in continuous space. However, their potential for video generation remains under-explored…
SynBoost: A Synergistic Framework for Fast Sampling of Diffusion Models
Hu Yu, Hao Luo, Xueyang Fu +3
Diffusion probabilistic models (DPMs) have demonstrated remarkable success in visual generation. However, their iterative sampling mechanism results in slow inference speeds. While…
FEB-Cache: Frequency-Guided Exposure Bias Reduction for Enhancing Diffusion Transformer Caching
Zhen Zou, Feng Zhao
Diffusion Transformer (DiT) has exhibited impressive generation capabilities but faces great challenges due to its high computational complexity. To address this issue, various met…
Frequency Autoregressive Image Generation with Continuous Tokens
Hu Yu, Hao Luo, Hangjie Yuan +3
Autoregressive (AR) models for image generation typically adopt a two-stage paradigm of vector quantization and raster-scan ``next-token prediction", inspired by its great success…
AnyLogo: Symbiotic Subject-Driven Diffusion System with Gemini Status
Jinghao Zhang, Wen Qian, Hao Luo +2
Diffusion models have made compelling progress on facilitating high-throughput daily production. Nevertheless, the appealing customized requirements are remain suffered from instan…