تقرير
Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss
العنوان: | Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss |
---|---|
المؤلفون: | Gupta, Yatharth, Jaddipal, Vishnu V., Prabhala, Harish, Paul, Sayak, Von Platen, Patrick |
سنة النشر: | 2024 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence |
الوصف: | Stable Diffusion XL (SDXL) has become the best open source text-to-image model (T2I) for its versatility and top-notch image quality. Efficiently addressing the computational demands of SDXL models is crucial for wider reach and applicability. In this work, we introduce two scaled-down variants, Segmind Stable Diffusion (SSD-1B) and Segmind-Vega, with 1.3B and 0.74B parameter UNets, respectively, achieved through progressive removal using layer-level losses focusing on reducing the model size while preserving generative quality. We release these models weights at https://hf.co/Segmind. Our methodology involves the elimination of residual networks and transformer blocks from the U-Net structure of SDXL, resulting in significant reductions in parameters, and latency. Our compact models effectively emulate the original SDXL by capitalizing on transferred knowledge, achieving competitive results against larger multi-billion parameter SDXL. Our work underscores the efficacy of knowledge distillation coupled with layer-level losses in reducing model size while preserving the high-quality generative capabilities of SDXL, thus facilitating more accessible deployment in resource-constrained environments. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2401.02677 |
رقم الأكسشن: | edsarx.2401.02677 |
قاعدة البيانات: | arXiv |
كن أول من يترك تعليقا!