title: DeepCache: Accelerating Diffusion Models for Free
summary: Diffusion models have recently gained unprecedented attention in the field of
  image synthesis due to their remarkable generative capabilities.
  Notwithstanding their prowess, these models often incur substantial
  computational costs, primarily attributed to the sequential denoising process
  and cumbersome model size. Traditional methods for compressing diffusion models
  typically involve extensive retraining, presenting cost and feasibility
  challenges. In this paper, we introduce DeepCache, a novel training-free
  paradigm that accelerates diffusion models from the perspective of model
  architecture. DeepCache capitalizes on the inherent temporal redundancy
  observed in the sequential denoising steps of diffusion models, which caches
  and retrieves features across adjacent denoising stages, thereby curtailing
  redundant computations. Utilizing the property of the U-Net, we reuse the
  high-level features while updating the low-level features in a very cheap way.
  This innovative strategy, in turn, enables a speedup factor of 2.3times for
  Stable Diffusion v1.5 with only a 0.05 decline in CLIP Score, and 4.1times
  for LDM-4-G with a slight decrease of 0.22 in FID on ImageNet. Our experiments
  also demonstrate DeepCache's superiority over existing pruning and distillation
  methods that necessitate retraining and its compatibility with current sampling
  techniques. Furthermore, we find that under the same throughput, DeepCache
  effectively achieves comparable or even marginally improved results with DDIM
  or PLMS. The code is available at https://github.com/horseee/DeepCache
paper_authors._id: 656ea1e3228bbe9eb46c76e4
paper_authors.name: Xinyin Ma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-05T08:39:01.637Z
paper_authors._id: 64396ebc21221ac7411852b3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64396ebc21221ac7411852b3/SR0dC8N0bdj9tZFxYPpSf.jpeg
paper_authors.fullname: Xinyin Ma
paper_authors.type: user
paper_authors.user: horseee
paper_authors._id: 656ea1e3228bbe9eb46c76e5
paper_authors.name: Gongfan Fang
paper_authors._id: 656ea1e3228bbe9eb46c76e6
paper_authors.name: Xinchao Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-05T15:08:52.796Z
paper_authors._id: 63fc03a50aab060792ffef39
paper_authors.avatarUrl: /avatars/9d5b1bb2a41928e08176b703935133ab.svg
paper_authors.fullname: Wangxinchao
paper_authors.type: user
paper_authors.user: wxcTest
paper_title: DeepCache: Accelerating Diffusion Models for Free
paper_summary: Diffusion models have recently gained unprecedented attention in the field of
  image synthesis due to their remarkable generative capabilities.
  Notwithstanding their prowess, these models often incur substantial
  computational costs, primarily attributed to the sequential denoising process
  and cumbersome model size. Traditional methods for compressing diffusion models
  typically involve extensive retraining, presenting cost and feasibility
  challenges. In this paper, we introduce DeepCache, a novel training-free
  paradigm that accelerates diffusion models from the perspective of model
  architecture. DeepCache capitalizes on the inherent temporal redundancy
  observed in the sequential denoising steps of diffusion models, which caches
  and retrieves features across adjacent denoising stages, thereby curtailing
  redundant computations. Utilizing the property of the U-Net, we reuse the
  high-level features while updating the low-level features in a very cheap way.
  This innovative strategy, in turn, enables a speedup factor of 2.3times for
  Stable Diffusion v1.5 with only a 0.05 decline in CLIP Score, and 4.1times
  for LDM-4-G with a slight decrease of 0.22 in FID on ImageNet. Our experiments
  also demonstrate DeepCache's superiority over existing pruning and distillation
  methods that necessitate retraining and its compatibility with current sampling
  techniques. Furthermore, we find that under the same throughput, DeepCache
  effectively achieves comparable or even marginally improved results with DDIM
  or PLMS. The code is available at https://github.com/horseee/DeepCache
paper_ai_keywords: diffusion models image synthesis sequential denoising process model compression training-free paradigm DeepCache temporal redundancy U-Net high-level features low-level features Stable Diffusion v1.5 CLIP Score LDM-4-G FID Score ImageNet pruning distillation sampling techniques DDIM PLMS