EdgeFusion: On-Device Text-to-Image Generation
The intensive computational burden of Stable Diffusion (SD) for text-to-image
generation poses a significant hurdle for its practical application. To tackle
this challenge, recent research focuses on methods to reduce sampling steps,
such as Latent Consistency Model (LCM), and on employing architectural
optimizations, including pruning and knowledge distillation. Diverging from
existing approaches, we uniquely start with a compact SD variant, BK-SDM. We
observe that directly applying LCM to BK-SDM with commonly used crawled
datasets yields unsatisfactory results. It leads us to develop two strategies:
(1) leveraging high-quality image-text pairs from leading generative models and
(2) designing an advanced distillation process tailored for LCM. Through our
thorough exploration of quantization, profiling, and on-device deployment, we
achieve rapid generation of photo-realistic, text-aligned images in just two
steps, with latency under one second on resource-limited edge devices.
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Thibault Castells
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2024-04-19T08:44:11.536Z
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Thibault Castells
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thibcastells
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Hyoung-Kyu Song
claimed_verified
2024-04-19T07:38:29.972Z
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Hyoung-Kyu Song
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deepkyu
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Tairen Piao
admin_assigned
2024-04-19T08:44:18.421Z
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Tairen Piao
user
tairenpiao
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Shinkook Choi
admin_assigned
2024-04-19T08:44:28.222Z
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shinkookchoi
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skchoi
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Bo-Kyeong Kim
claimed_verified
2024-04-19T07:38:27.688Z
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Bo-Kyeong Kim
user
bokyeong1015
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Hanyoung Yim
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Changgwun Lee
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Jae Gon Kim
claimed_verified
2024-04-25T07:37:22.295Z
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Jae Gon Kim
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jgon9031
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Tae-Ho Kim
admin_assigned
2024-04-19T08:44:57.310Z
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Tae-Ho Kim
user
ktho22
EdgeFusion: On-Device Text-to-Image Generation
The intensive computational burden of Stable Diffusion (SD) for text-to-image
generation poses a significant hurdle for its practical application. To tackle
this challenge, recent research focuses on methods to reduce sampling steps,
such as Latent Consistency Model (LCM), and on employing architectural
optimizations, including pruning and knowledge distillation. Diverging from
existing approaches, we uniquely start with a compact SD variant, BK-SDM. We
observe that directly applying LCM to BK-SDM with commonly used crawled
datasets yields unsatisfactory results. It leads us to develop two strategies:
(1) leveraging high-quality image-text pairs from leading generative models and
(2) designing an advanced distillation process tailored for LCM. Through our
thorough exploration of quantization, profiling, and on-device deployment, we
achieve rapid generation of photo-realistic, text-aligned images in just two
steps, with latency under one second on resource-limited edge devices.
Researchers achieve efficient text-to-image generation with reduced sampling steps and architectural optimizations, specifically using a compact Stable Diffusion variant, high-quality datasets, and an advanced distillation process for rapid and accurate image generation on edge devices.
Stable Diffusion text-to-image generation Latent Consistency Model pruning knowledge distillation BK-SDM high-quality image-text pairs quantization profiling on-device deployment photo-realistic text-aligned images