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UID:532@fds.yale.edu
DTSTART;TZID=America/New_York:20240401T160000
DTEND;TZID=America/New_York:20240401T170000
DTSTAMP:20250916T142132Z
URL:https://fds.yale.edu/events/sds-seminar-mingyuan-zhou-university-of-te
 xas-at-austin/
SUMMARY:S&DS Seminar: Mingyuan Zhou (UTexas at Austin)\, "Score identity Di
 stillation: Exponentially Fast Distillation of Pretrained Diffusion Models
  for One-Step Generation"
DESCRIPTION:3:30pm - Pre-talk meet and greet teatime - 219 Prospect Street\
 , 13 floor\, there will be light snacks and beverages in the kitchen area.
 \nAbstract: Diffusion-based models\, key to advancing generative AI with t
 heir photorealistic outputs\, face a major hurdle: slow generation speed. 
 Our Score identity Distillation (SiD) method challenges the belief that qu
 ality in diffusion models requires iterative refinement\, offering a groun
 dbreaking solution. SiD streamlines the generative process into a single\,
  swift step\, achieving rapid improvements in Fréchet Inception Distance 
 (FID) during the distillation process and\, in many cases\, exceeding the 
 quality of the original models\, which require extensive steps\, from doze
 ns to hundreds. By reinterpreting the forward diffusion process with semi-
 implicit distributions and three novel score-based identities\, we introdu
 ce a unique loss mechanism. This allows for quick FID reductions by traini
 ng the generator with its synthesized images\, eliminating the need for re
 al data or conventional reverse diffusion\, all within a significantly red
 uced generation timeframe. Evaluated across four benchmark datasets\, SiD 
 demonstrates unparalleled efficiency and superior quality compared to curr
 ent generative methods\, setting new standards for diffusion model distill
 ation and expanding the potential of diffusion-based generation. This inno
 vation makes high-quality generative processes more accessible and feasibl
 e for various applications\, opening new research and application avenues 
 in generative AI.\n\nClick for more information.\n\n\n
CATEGORIES:FDS Events,Statistics & Data Science Seminar,Seminar Series
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