7 papers · 1 filter
UltraDiff: Transferring High-Fidelity Priors to Compressed Latent Spaces for High-Resolution Image Generation
Jingjing Ren, Haitian Zheng, Haoyu Chen +5
High-resolution synthesis with Latent Diffusion Models (LDMs) faces a fundamental trade-off between computational cost and generative fidelity. This trade off is largely governed b…
Adversarial Training for Pixel Diffusion
Xin Lin, Zhifei Zhang, Yuqian Zhou +4
Pixel diffusion models generate RGB images directly, avoiding the bottleneck of an autoencoder, yet their outputs still systematically underrepresent fine-scale natural-image stati…
DMA: Pixel-space Distribution Matching with Adversarial and Anchor Losses
Xin Lin, Zhifei Zhang, Yuqian Zhou +6
Distribution matching distillation (DMD) provides a general framework for few-step diffusion generation, but its modern text-to-image instantiations have been developed primarily a…
EditStream: A Unified Autoregressive Framework for Interactive Video Generation and Editing
Yuqian Zhou, Zhenghong Zhou, Zongze Wu +5
Interactive video generation and editing are becoming increasingly important for creative design. In this report, we introduce EditStream: a unified framework for interactive video…
PixelControl: Fine-Grained Condition Fidelity in Text-to-Image Diffusion
Xin Lin, Haodong Li, Zhifei Zhang +5
Controllable text-to-image diffusion models can often follow the global layout of spatial conditions, yet still violate fine-grained structures such as object boundaries, thin cont…
Tri-Prompting: Video Diffusion with Unified Control over Scene, Subject, and Motion
Zhenghong Zhou, Xiaohang Zhan, Zhiqin Chen +8
Recent video diffusion models achieve strong visual quality and temporal coherence, but still lack coordinated control over scene layout, customized subject identity, and camera/su…
SNCE: Geometry-Aware Supervision for Scalable Discrete Image Generation
Shufan Li, Jiuxiang Gu, Kangning Liu +3
Recent advancements in discrete image generation showed that scaling the VQ codebook size significantly improves reconstruction fidelity. However, training generative models with a…