BitsFusion: 1.99 bits Weight Quantization of Diffusion Model
Diffusion-based image generation models have achieved great success in recent
years by showing the capability of synthesizing high-quality content. However,
these models contain a huge number of parameters, resulting in a significantly
large model size. Saving and transferring them is a major bottleneck for
various applications, especially those running on resource-constrained devices.
In this work, we develop a novel weight quantization method that quantizes the
UNet from Stable Diffusion v1.5 to 1.99 bits, achieving a model with 7.9X
smaller size while exhibiting even better generation quality than the original
one. Our approach includes several novel techniques, such as assigning optimal
bits to each layer, initializing the quantized model for better performance,
and improving the training strategy to dramatically reduce quantization error.
Furthermore, we extensively evaluate our quantized model across various
benchmark datasets and through human evaluation to demonstrate its superior
generation quality.
666273193388180cc4f982ba
Yang Sui
claimed_verified
2024-06-09T20:05:14.872Z
63787b13500186f250ba377c
/avatars/96bb6051662109e9cd25e6df7d738f17.svg
yangsui
user
yangsui
666273193388180cc4f982bb
Yanyu Li
666273193388180cc4f982bc
Anil Kag
666273193388180cc4f982bd
Yerlan Idelbayev
admin_assigned
2024-06-07T12:29:12.106Z
637d7f4a95b847187a701b72
/avatars/214c08ae6941896d35518217f51a924c.svg
Yerlan Idelbayev
user
yerla
666273193388180cc4f982be
Junli Cao
admin_assigned
2024-06-07T12:29:17.461Z
63f54aa73aa49d8cb97b84bc
/avatars/c73c5870039611ab9162daad46a1ba20.svg
junli cao
user
jlcao2
666273193388180cc4f982bf
Ju Hu
666273193388180cc4f982c0
Dhritiman Sagar
666273193388180cc4f982c1
Bo Yuan
claimed_verified
2024-06-09T20:05:10.134Z
66641bd44ca290b4057e849e
/avatars/84bf8f4deddd357f37535925d4ed993c.svg
Bo Yuan
user
yuanrutgers
666273193388180cc4f982c2
Sergey Tulyakov
666273193388180cc4f982c3
Jian Ren
claimed_verified
2024-06-07T09:19:52.592Z
61f19829233c91cbd2f79e70
/avatars/a0735a94542b4f7cda5aed8bc4be0538.svg
Jian Ren
user
alanspike
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model
Diffusion-based image generation models have achieved great success in recent
years by showing the capability of synthesizing high-quality content. However,
these models contain a huge number of parameters, resulting in a significantly
large model size. Saving and transferring them is a major bottleneck for
various applications, especially those running on resource-constrained devices.
In this work, we develop a novel weight quantization method that quantizes the
UNet from Stable Diffusion v1.5 to 1.99 bits, achieving a model with 7.9X
smaller size while exhibiting even better generation quality than the original
one. Our approach includes several novel techniques, such as assigning optimal
bits to each layer, initializing the quantized model for better performance,
and improving the training strategy to dramatically reduce quantization error.
Furthermore, we extensively evaluate our quantized model across various
benchmark datasets and through human evaluation to demonstrate its superior
generation quality.
A weight quantization method reduces the size of diffusion-based image generation models while improving generation quality.
diffusion-based image generation UNet Stable Diffusion weight quantization model size quantization error benchmark datasets human evaluation