Autoregressive video generation stops sharing one weight set for context writing and denoising: 5-second training windows sustain 24 hours of continuous generation
In autoregressive video diffusion, writing generated frames into key-value context and denoising the current frame previously shared one set of parameters; SGF+ gives each role its own parameters while keeping them coupled through causal attention, and still trains both with the original generation objective. On VBench at 5s, 60s, and 240s horizons, SGF+ scores above Self Forcing and Self Gradient Forcing on most quality and long-horizon consistency metrics in both framewise and chunkwise generation; trained only on 5s clips, it generates continuously for up to 24 hours without long-video fine-tuning.