Home Community Meet DeepCache: A Easy and Effective Acceleration Algorithm for Dynamically Compressing Diffusion Models during Runtime

Meet DeepCache: A Easy and Effective Acceleration Algorithm for Dynamically Compressing Diffusion Models during Runtime

Meet DeepCache: A Easy and Effective Acceleration Algorithm for Dynamically Compressing Diffusion Models during Runtime

Advancements in Artificial Intelligence (AI) and Deep Learning have brought an excellent transformation in the best way humans interact with computers. With the introduction of diffusion models, generative modeling has shown remarkable capabilities in various applications, including text generation, picture generation, audio synthesis, and video production. 

Though diffusion models have been showing superior performance, these models regularly have high computational costs, that are mostly related to the cumbersome model size and the sequential denoising procedure. These models have a really slow inference speed, to deal with which numerous efforts have been made by researchers, including reducing the variety of sample steps and lowering the model inference overhead per step using techniques like model pruning, distillation, and quantization.

Conventional methods for compressing diffusion models often need a considerable amount of retraining, which poses practical and financial difficulties. To beat these problems, a team of researchers has introduced DeepCache, a brand new and unique training-free paradigm that optimizes the architecture of diffusion models to speed up diffusion. 

DeepCache takes advantage of the temporal redundancy that’s intrinsic to the successive denoising stages of diffusion models. The rationale for this redundancy is that some features are repeated in successive denoising steps. It substantially reduces duplicate computations by introducing a caching and retrieval method for these properties. The team has shared that this approach relies on the U-Net property, which allows high-level features to be reused while effectively and economically updating low-level features. 

DeepCache’s creative approach yields a major speedup factor of two.3× for Stable Diffusion v1.5 with only a slight CLIP Rating drop of 0.05. It has also demonstrated a powerful speedup of 4.1× for LDM-4-G, albeit with a 0.22 loss in FID on ImageNet.

The team has evaluated DeepCache, and the experimental comparisons have shown that DeepCache performs higher than current pruning and distillation techniques, which often call for retraining. It has even been shown to be compatible with existing sampling methods. It has shown similar, or barely higher, performance with DDIM or PLMS at the identical throughput and thus maximizes efficiency without sacrificing the caliber of produced outputs.

The researchers have summarized the first contributions as follows.

  1. DeepCache works well with current fast samplers, demonstrating the potential of achieving similar and even better-generating capabilities.
  1. It improves image generation speed without the necessity for extra training by dynamically compressing diffusion models during runtime.
  1. Through the use of cacheable features, DeepCache reduces duplicate calculations through the use of temporal consistency in high-level features.
  1. DeepCache improves feature caching flexibility by introducing a customized technique for prolonged caching intervals.
  1. DeepCache exhibits greater efficacy under DDPM, LDM, and Stable Diffusion models when tested on CIFAR, LSUN-Bedroom/Churches, ImageNet, COCO2017, and PartiPrompt.
  1. DeepCache performs higher than retraining-required pruning and distillation algorithms, sustaining its higher efficacy under the

In conclusion, DeepCache definitely shows great promise as a diffusion model accelerator, providing a useful and reasonably priced substitute for conventional compression techniques.

Take a look at the Paper and Github. All credit for this research goes to the researchers of this project. Also, don’t forget to affix our 33k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the most recent AI research news, cool AI projects, and more.

In the event you like our work, you’ll love our newsletter..

Tanya Malhotra is a final yr undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and significant considering, together with an ardent interest in acquiring latest skills, leading groups, and managing work in an organized manner.

🐝 [FREE AI WEBINAR] ‘Beginners Guide to LangChain: Chat with Your Multi-Model Data’ Dec 11, 2023 10 am PST


Please enter your comment!
Please enter your name here