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Self Gradient Forcing Plus
Self Gradient Forcing Plus is an autoregressive video generation method that separates context-writing and denoising parameters.
Self Gradient Forcing Plus (SGF+) is a method for autoregressive video generation developed by researchers from Tsinghua University, Joy Future Academy at JD, and The Chinese University of Hong Kong. Autoregressive video models typically struggle because denoising current frames and writing context for future predictions rely on conflicting gradient updates using shared parameters. SGF+ solves this by assigning independent parameter sets to context writing and denoising, training them jointly with the original objective. The approach uses a two-pass training procedure that records a no-gradient rollout and reconstructs forward computation to supervise context writing through differentiable KV states. It improves visual quality, reduces error accumulation, and maintains scene stability during long-horizon generation.
Self Gradient Forcing Plus at a glance
- Best for: Researcher, Software engineer
- Pricing: Free
- Released: October 2026
- Category: AI video tool
- Website: zihan-su.github.io
Key features
- Separates context-writing and denoising parameters
- Trains independent parameter sets jointly with the original objective
- Records a no-gradient autoregressive rollout in the first pass
- Reconstructs forward computation from detached context and noisy targets
- Supervises context writing through differentiable KV states
- Routes gradients to role-specific parameter sets
Use it for
- Generating long-horizon videos up to 24 hours continuously
- Producing autoregressive videos from 5-second training windows
- Performing framewise and chunkwise video generation
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