1.58-bit FLUX
We present 1.58-bit FLUX, the first successful approach to quantizing the
state-of-the-art text-to-image generation model, FLUX.1-dev, using 1.58-bit
weights (i.e., values in {-1, 0, +1}) while maintaining comparable performance
for generating 1024 x 1024 images. Notably, our quantization method operates
without access to image data, relying solely on self-supervision from the
FLUX.1-dev model. Additionally, we develop a custom kernel optimized for
1.58-bit operations, achieving a 7.7x reduction in model storage, a 5.1x
reduction in inference memory, and improved inference latency. Extensive
evaluations on the GenEval and T2I Compbench benchmarks demonstrate the
effectiveness of 1.58-bit FLUX in maintaining generation quality while
significantly enhancing computational efficiency.
6772d6ff33efe31653f02b0b
Chenglin Yang
6772d6ff33efe31653f02b0c
Celong Liu
admin_assigned
2024-12-30T20:46:38.723Z
64364ba31adb261e94e0e5b4
/avatars/cb6ef2155d88b1e64c4ff36bd3a8f255.svg
Celong Liu
user
goddice
6772d6ff33efe31653f02b0d
Xueqing Deng
admin_assigned
2024-12-30T20:46:44.300Z
65ca9b1743207e438a95e90c
/avatars/8f7bde1c44d8e665a29ee08ce7fedfa4.svg
Xueqing Deng
user
xdeng77
6772d6ff33efe31653f02b0e
Dongwon Kim
claimed_verified
2025-01-16T08:31:52.344Z
64dc5208c38427829de81b16
/avatars/43a08e46a7a78f1e3d1f6645a9b1d26b.svg
Dongwon
user
kdwon
6772d6ff33efe31653f02b0f
Xing Mei
admin_assigned
2024-12-30T20:47:17.287Z
642e748858ba61373f2ea217
/avatars/e9f002cd662f07bd213c31a45176d5a5.svg
Xing Mei
user
xm2023
6772d6ff33efe31653f02b10
Xiaohui Shen
admin_assigned
2024-12-30T20:47:23.529Z
6430aa1b32a732121cd81f98
/avatars/5419f8d6d4d36fa5ac83e30667b9fd99.svg
Xiaohui Shen
user
XiaohuiShen
6772d6ff33efe31653f02b11
Liang-Chieh Chen
1.58-bit FLUX
We present 1.58-bit FLUX, the first successful approach to quantizing the
state-of-the-art text-to-image generation model, FLUX.1-dev, using 1.58-bit
weights (i.e., values in {-1, 0, +1}) while maintaining comparable performance
for generating 1024 x 1024 images. Notably, our quantization method operates
without access to image data, relying solely on self-supervision from the
FLUX.1-dev model. Additionally, we develop a custom kernel optimized for
1.58-bit operations, achieving a 7.7x reduction in model storage, a 5.1x
reduction in inference memory, and improved inference latency. Extensive
evaluations on the GenEval and T2I Compbench benchmarks demonstrate the
effectiveness of 1.58-bit FLUX in maintaining generation quality while
significantly enhancing computational efficiency.
1.58-bit quantization of the FLUX.1-dev text-to-image model achieves comparable generation quality with reduced storage and inference overhead using self-supervised methods.
quantizing self-supervision custom kernel GenEval T2I Compbench