title: Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer
    Level Loss
summary: 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.
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paper_authors.name: Yatharth Gupta
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paper_authors.name: Vishnu V. Jaddipal
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paper_authors.fullname: Vishnu V Jaddipal
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paper_authors.name: Harish Prabhala
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paper_authors.fullname: Harish Segmind
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paper_authors.user: harishseg
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paper_authors.name: Sayak Paul
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paper_authors.fullname: Sayak Paul
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paper_authors.name: Patrick Von Platen
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paper_authors.fullname: Patrick von Platen
paper_authors.type: user
paper_authors.user: patrickvonplaten
paper_title: Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer
    Level Loss
paper_summary: 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.
paper_ai_summary: Two scaled-down variants of Stable Diffusion XL (SDXL), achieved through layer-level loss-driven pruning, maintain competitive image quality with reduced computational demands.
paper_ai_keywords: Stable Diffusion XL text-to-image T2I parameter-efficient U-Net residual networks transformer blocks knowledge distillation layer-level losses generative quality resource-constrained environments