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A newer version of the Gradio SDK is available: 6.30.0
title: SGF+ Video Generation
emoji: 🎬
colorFrom: purple
colorTo: yellow
sdk: gradio
sdk_version: 6.29.1
app_file: app.py
python_version: '3.10'
startup_duration_timeout: 1h
short_description: Few-step autoregressive text-to-video with SGF+ dual experts
SGF+: Decoupling Gradient Flows for Autoregressive Video Generation
Interactive demo of SGF+ (chunkwise Self Gradient Forcing Plus), a few-step autoregressive text-to-video model built on Wan2.1-T2V-1.3B. Each latent block (3 latents = 12 pixel frames) is denoised in only 4 steps by the generation expert, then written back to the KV/cross-attention caches through a dedicated memory expert — the paper's core contribution — before the block is decoded and streamed to your browser as MPEG-TS chunks.
Usage
Type a prompt (or pick an example) and press Generate video. Frames start streaming as soon as the first block is ready; 7 blocks produce an 81-frame 480×832 clip at 16 fps (~5 s of video). Use the Blocks slider for shorter clips and the seed for reproducible generations.
The inference loop follows the authors' inference.py (chunkwise config
sgf_plus_chunkwise.yaml, EMA generator weights, warped 4-step denoising
schedule, per-block memory-expert KV refresh) — capped to the 21-latent clip
length the model was trained on, rather than the paper's long-horizon rollout.
Credits
SGF+ code and prompts are from the Self_Gradient_Forcing_Plus repository (Apache-2.0). Base model: Wan-AI/Wan2.1-T2V-1.3B.