Wuerstchen: Efficient Pretraining of Text-to-Image Models
We introduce Wuerstchen, a novel technique for text-to-image synthesis that
unites competitive performance with unprecedented cost-effectiveness and ease
of training on constrained hardware. Building on recent advancements in machine
learning, our approach, which utilizes latent diffusion strategies at strong
latent image compression rates, significantly reduces the computational burden,
typically associated with state-of-the-art models, while preserving, if not
enhancing, the quality of generated images. Wuerstchen achieves notable speed
improvements at inference time, thereby rendering real-time applications more
viable. One of the key advantages of our method lies in its modest training
requirements of only 9,200 GPU hours, slashing the usual costs significantly
without compromising the end performance. In a comparison against the
state-of-the-art, we found the approach to yield strong competitiveness. This
paper opens the door to a new line of research that prioritizes both
performance and computational accessibility, hence democratizing the use of
sophisticated AI technologies. Through Wuerstchen, we demonstrate a compelling
stride forward in the realm of text-to-image synthesis, offering an innovative
path to explore in future research.
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Pablo Pernias
extracted_confirmed
2023-06-02T10:09:25.495Z
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Pablo Pernías
user
babbleberns
64797b91bb9a5693c48cff57
Dominic Rampas
admin_assigned
2023-06-02T08:00:18.333Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/634cb5eefb80cc6bcaf63c3e/2ml5Jri2owN2obinOvFch.png
Dominic Rampas
user
dome272
64797b91bb9a5693c48cff58
Marc Aubreville
extracted_confirmed
2023-06-02T05:20:20.976Z
6305bf83660f01f1509ce1c2
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Marc Aubreville
user
maubreville
Wuerstchen: Efficient Pretraining of Text-to-Image Models
We introduce Wuerstchen, a novel technique for text-to-image synthesis that
unites competitive performance with unprecedented cost-effectiveness and ease
of training on constrained hardware. Building on recent advancements in machine
learning, our approach, which utilizes latent diffusion strategies at strong
latent image compression rates, significantly reduces the computational burden,
typically associated with state-of-the-art models, while preserving, if not
enhancing, the quality of generated images. Wuerstchen achieves notable speed
improvements at inference time, thereby rendering real-time applications more
viable. One of the key advantages of our method lies in its modest training
requirements of only 9,200 GPU hours, slashing the usual costs significantly
without compromising the end performance. In a comparison against the
state-of-the-art, we found the approach to yield strong competitiveness. This
paper opens the door to a new line of research that prioritizes both
performance and computational accessibility, hence democratizing the use of
sophisticated AI technologies. Through Wuerstchen, we demonstrate a compelling
stride forward in the realm of text-to-image synthesis, offering an innovative
path to explore in future research.
Wuerstchen employs latent diffusion strategies for efficient text-to-image synthesis with reduced computational requirements and improved inference speed.
latent diffusion strategies text-to-image synthesis computational accessibility real-time applications