SDXL-Lightning: Progressive Adversarial Diffusion Distillation
We propose a diffusion distillation method that achieves new state-of-the-art
in one-step/few-step 1024px text-to-image generation based on SDXL. Our method
combines progressive and adversarial distillation to achieve a balance between
quality and mode coverage. In this paper, we discuss the theoretical analysis,
discriminator design, model formulation, and training techniques. We
open-source our distilled SDXL-Lightning models both as LoRA and full UNet
weights.
65d6b4071be49a8f1ce303fd
Shanchuan Lin
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2024-02-22T22:27:53.308Z
645863f7dc18eb1a9b5d29df
https://cdn-avatars.huggingface.co/v1/production/uploads/645863f7dc18eb1a9b5d29df/t49Nnyl4tbkUn7CmQqKZh.jpeg
Peter Lin
user
PeterL1n
65d6b4071be49a8f1ce303fe
Anran Wang
65d6b4071be49a8f1ce303ff
Xiao Yang
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2024-02-23T12:25:36.022Z
62b3d2490385d6048985b42b
/avatars/1c4e534d086d53ef1057fbc00334350a.svg
Xiao Yang
user
seravee008
SDXL-Lightning: Progressive Adversarial Diffusion Distillation
We propose a diffusion distillation method that achieves new state-of-the-art
in one-step/few-step 1024px text-to-image generation based on SDXL. Our method
combines progressive and adversarial distillation to achieve a balance between
quality and mode coverage. In this paper, we discuss the theoretical analysis,
discriminator design, model formulation, and training techniques. We
open-source our distilled SDXL-Lightning models both as LoRA and full UNet
weights.
A diffusion distillation method using progressive and adversarial techniques achieves high-quality one-step/few-step text-to-image generation with improved mode coverage and is available as a distilled model.
diffusion distillation progressive distillation adversarial distillation SDXL text-to-image generation discriminator design LoRA UNet mode coverage