Accurate Compression of Text-to-Image Diffusion Models via Vector
  Quantization
Text-to-image diffusion models have emerged as a powerful framework for
high-quality image generation given textual prompts. Their success has driven
the rapid development of production-grade diffusion models that consistently
increase in size and already contain billions of parameters. As a result,
state-of-the-art text-to-image models are becoming less accessible in practice,
especially in resource-limited environments. Post-training quantization (PTQ)
tackles this issue by compressing the pretrained model weights into lower-bit
representations. Recent diffusion quantization techniques primarily rely on
uniform scalar quantization, providing decent performance for the models
compressed to 4 bits. This work demonstrates that more versatile vector
quantization (VQ) may achieve higher compression rates for large-scale
text-to-image diffusion models. Specifically, we tailor vector-based PTQ
methods to recent billion-scale text-to-image models (SDXL and SDXL-Turbo), and
show that the diffusion models of 2B+ parameters compressed to around 3 bits
using VQ exhibit the similar image quality and textual alignment as previous
4-bit compression techniques.
66d7fbe9b61dd11022037abc
Vage Egiazarian
admin_assigned
2024-09-04T09:57:06.617Z
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Vage Egiazarian
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Vahe527887
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Denis Kuznedelev
admin_assigned
2024-09-04T09:57:13.118Z
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Denis Kuznedelev
user
SpiridonSunRotator
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Anton Voronov
admin_assigned
2024-09-04T09:57:35.968Z
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Anton Voronov
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AntonVoronov
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Ruslan Svirschevski
admin_assigned
2024-09-04T09:57:41.538Z
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ruslan.svirschevski
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rsw01
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Michael Goin
admin_assigned
2024-09-04T09:58:10.768Z
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Michael Goin
user
mgoin
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Daniil Pavlov
66d7fbe9b61dd11022037ac2
Dan Alistarh
admin_assigned
2024-09-04T10:08:39.943Z
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Dan Alistarh
user
dalistarh
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Dmitry Baranchuk
admin_assigned
2024-09-04T10:08:48.711Z
62b6cc49752323892323bc04
https://cdn-avatars.huggingface.co/v1/production/uploads/62b6cc49752323892323bc04/gGBld1KJIP9AIpd81L3PC.jpeg
Dmitry Baranchuk
user
dbaranchuk
Accurate Compression of Text-to-Image Diffusion Models via Vector
  Quantization
Text-to-image diffusion models have emerged as a powerful framework for
high-quality image generation given textual prompts. Their success has driven
the rapid development of production-grade diffusion models that consistently
increase in size and already contain billions of parameters. As a result,
state-of-the-art text-to-image models are becoming less accessible in practice,
especially in resource-limited environments. Post-training quantization (PTQ)
tackles this issue by compressing the pretrained model weights into lower-bit
representations. Recent diffusion quantization techniques primarily rely on
uniform scalar quantization, providing decent performance for the models
compressed to 4 bits. This work demonstrates that more versatile vector
quantization (VQ) may achieve higher compression rates for large-scale
text-to-image diffusion models. Specifically, we tailor vector-based PTQ
methods to recent billion-scale text-to-image models (SDXL and SDXL-Turbo), and
show that the diffusion models of 2B+ parameters compressed to around 3 bits
using VQ exhibit the similar image quality and textual alignment as previous
4-bit compression techniques.
Vector quantization techniques improve the compression rate of large text-to-image diffusion models without compromising their image quality and textual alignment.
text-to-image diffusion models parameter compression post-training quantization uniform scalar quantization vector quantization image quality textual alignment SDXL SDXL-Turbo