March 26, 2024, 4:41 a.m. | Ayush Thakur, Rashmi Vashisth

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.16024v1 Announce Type: new
Abstract: This paper presents a comprehensive study on the unified module for accelerating stable-diffusion processes, specifically focusing on the lcm-lora module. Stable-diffusion processes play a crucial role in various scientific and engineering domains, and their acceleration is of paramount importance for efficient computational performance. The standard iterative procedures for solving fixed-source discrete ordinates problems often exhibit slow convergence, particularly in optically thick scenarios. To address this challenge, unconditionally stable diffusion-acceleration methods have been developed, aiming to …

abstract arxiv computational cs.cv cs.gr cs.lg diffusion domains engineering importance iterative lcm-lora lora paper performance processes role scientific standard study type

Senior Machine Learning Engineer

@ GPTZero | Toronto, Canada

Customer Data Analyst with Spanish

@ Michelin | Voluntari

HC Data Analyst - Senior

@ Leidos | 1662 Intelligence Community Campus - Bethesda MD

Healthcare Research & Data Analyst- Infectious, Niche, Rare Disease

@ Clarivate | Remote (121- Massachusetts)

Data Analyst (maternity leave cover)

@ Clarivate | R155-Belgrade

Sales Enablement Data Analyst (Remote)

@ CrowdStrike | USA TX Remote