arXiv (All)
open access
Spectral-Guided Diffusion: Accelerating Inference via Static Spectral Layer Scheduling
Diffusion inference repeatedly evaluates the same large network. We ask whether pretrained weights alone can identify residual branches that need not be recomputed throughout the trajectory. Our \textbf{Spectral Concentration Ratio (SCR)} measures leading-versus-tail singular-value energy. Combined
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
- Leveraging Inference-Time Compute for Diffusion Models via Global Scheduling of Denoising Trajectories#1037550
- First Learn, Then Memorize: The Spectral Bias of Diffusion Models#1129968
- Learned End-to-End Guidance Schedules for Diffusion Models#1156774
- 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 1077433
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