arXiv (All)
open access
Uncertainty-Aware Consistency Distillation for Few-Step Video Generation
We study few-step video generation, i.e., distilling a multi-step video generator, which typically requires tens of sampling steps, incurring substantial latency and compute, into a few-step student. Consistency distillation is a common recipe, in which a multi-step teacher provides the consistency
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
- ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation#1076579
- Uncertainty DMD: Restoring Diversity in Few-Step Autoregressive Video Distillation#998806
- Enhancing Autoregressive Video Generation via Representation Adversarial Distillation#1145204
- EpaCache: Error-Propagation-Aware Caching for Accelerating Diffusion-Based Visual Generation#794073
- EpaCache: Error-Propagation-Aware Caching for Accelerating Diffusion-Based Visual Generation#790054
Record · ID 1144099
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