Zihan-Su/Self_Gradient_Forcing_Plus

Self Gradient Forcing (SGF) recovers the missing context-gradient path for self-generated causal memory through a bounded two-pass replay, enabling models trained with only a 5-second window to extrapolate to minute-scale videos with stronger identity, layout, and temporal stability.

Official implementation of "Rolling Sink: Bridging Limited-Horizon Training and Open-Ended Testing in Autoregressive Video Diffusion"

[ICLR 2026] Official Repo for Rolling Forcing: Autoregressive Long Video Diffusion in Real Time

[to appear at NeurIPS 2026] Official implementation of "MilliVid: Adaptive Latents for Long-Range Consistency in Video Generation"

[ICML 2026] Official codebase for "Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation" & Causal Forcing++

On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

Official Implementation of LongLive-RAG: A general retrieval-augmented framework for long video generation.

Official Implemenation for V-RAE: Rethinking Video Latent Spaces for Generation

[ICLR 26 Oral] Stable Video Infinity: Infinite-Length Video Generation with Error Recycling