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X-WR-CALNAME:Conjugate Integrators for Fast Sampling in Diffusion Models
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UID:2024-10-14-kushagra-pandey@cml.ics.uci.edu
DTSTAMP:20241014T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20241014T130000
DTEND;TZID=America/Los_Angeles:20241014T140000
SUMMARY:[CML Seminar] Kushagra Pandey: Conjugate Integrators for Fast Sampl
ing in Diffusion Models
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Kushagra Pandey\, PhD Student\, Department of Computer Science\
, University of California\, Irvine\n\nTitle: Conjugate Integrators for Fa
st Sampling in Diffusion Models\n\nAbstract: Diffusion models exhibit exce
llent sample quality across multiple-generation tasks. However\, their inf
erence process is iterative and often requires hundreds of function evalua
tions. Moreover\, it is unclear if existing methods for accelerating diffu
sion model sampling can generalize well across different types of diffusio
n processes. In the first part of my talk\, I will introduce Conjugate Int
egrators\, which project unconditional diffusion dynamics to an alternate
space that is more amenable to faster sampling. In the second part of my t
alk\, I will extend the idea of Conjugate Integrators from unconditional s
ampling to conditional diffusion sampling in the context of solving invers
e problems. Empirically\, on challenging inverse problems like 4x super-re
solution on the ImageNet-256 dataset\, conditional Conjugate Integrators c
an generate high-quality samples in as few as 5 conditional sampling steps
.\n\nhttps://cml.ics.uci.edu/seminars/2024-10-14-kushagra-pandey
X-ALT-DESC;FMTTYPE=text/html:Kushagra Pandey\, PhD Stude
nt\, Department of Computer Science\, University of California\, Irvine
Title: Conjugate Integrators for Fast Sampling in Diffusion Mo
dels
Abstract: Diffusion models exhibit excellent sample qua
lity across multiple-generation tasks. However\, their inference process i
s iterative and often requires hundreds of function evaluations. Moreover\
, it is unclear if existing methods for accelerating diffusion model sampl
ing can generalize well across different types of diffusion processes. In
the first part of my talk\, I will introduce Conjugate Integrators\, which
project unconditional diffusion dynamics to an alternate space that is mo
re amenable to faster sampling. In the second part of my talk\, I will ext
end the idea of Conjugate Integrators from unconditional sampling to condi
tional diffusion sampling in the context of solving inverse problems. Empi
rically\, on challenging inverse problems like 4x super-resolution on the
ImageNet-256 dataset\, conditional Conjugate Integrators can generate high
-quality samples in as few as 5 conditional sampling steps.
https://cml
.ics.uci.edu/seminars/2024-10-14-kushagra-pandey
URL:https://cml.ics.uci.edu/seminars/2024-10-14-kushagra-pandey
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