arXiv (All)
open access
ForcingBooster: Adaptive Patch-wise Denoising for Fast Autoregressive Video Diffusion
Autoregressive video diffusion models have become a leading approach to real-time interactive video generation and video world modeling. However, existing methods typically allocate computation uniformly across space when generating the next video chunk, updating all patches with the same denoising
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Causal Forcing++: Scalable Few-Step Autoregressive Diffusion Distillation for Real-Time Interactive Video Generation#1200937
- SGF+: Decoupling Gradient Flows for Autoregressive Video Generation#1252657
- SGF+: Decoupling Gradient Flows for Autoregressive Video Generation#1234854
- Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration#1142633
- TGuide: Faster Autoregressive Video Diffusion via Historical Trajectory Guidance#1325553
Record · ID 1296632
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive