Advances in artificial intelligence (ai) and Deep Learning have brought a huge transformation in the way humans interact with computers. With the introduction of diffusion models, generative modeling has demonstrated notable capabilities in various applications, including text generation, image generation, audio synthesis, and video production.
Although diffusion models have shown superior performance, these models frequently have high computational costs, which are mainly related to the cumbersome model size and sequential denoising procedure. These models have a very slow inference speed, to address which researchers have made a number of efforts, including reducing the number of sample steps and reducing the model inference overhead per step using techniques such as pruning, distillation and model quantification.
Conventional methods for compressing diffusion models often require a large amount of retraining, which poses practical and financial difficulties. To overcome these problems, a team of researchers has introduced DeepCache, a new and unique training-free paradigm that optimizes the architecture of diffusion models to accelerate diffusion.
DeepCache takes advantage of the temporal redundancy that is intrinsic to the successive denoising stages of diffusion models. The reason for this redundancy is that some functions are repeated in successive denoising steps. Substantially reduces duplicate calculations by introducing a caching and retrieval method for these properties. The team has shared that this approach is based on the U-Net property, which allows high-level functions to be reused while efficiently and economically updating low-level functions.
DeepCache's creative approach produces a significant speedup factor of 2.3× for Stable Diffusion v1.5 with only a slight CLIP score drop of 0.05. It has also demonstrated an impressive 4.1× speedup for LDM-4-G, albeit with a 0.22 loss in FID on ImageNet.
The team has evaluated DeepCache and experimental comparisons have shown that DeepCache performs better than current pruning and distillation techniques, which typically require retraining. It has even been shown to be compatible with existing sampling methods. It has shown similar, or slightly better, performance with DDIM or PLMS with the same performance and therefore maximizes efficiency without sacrificing the caliber of results produced.
The researchers have summarized the main contributions as follows.
- DeepCache works well with current fast samplers, demonstrating the possibility of achieving similar or even better generation capabilities.
- Improves imaging speed without additional training by dynamically compressing diffusion models at runtime.
- By using cacheable functions, DeepCache reduces duplicate computations by using temporal coherence in high-level functions.
- DeepCache improves the flexibility of function caching by introducing a custom technique for extended caching intervals.
- DeepCache shows better efficiency on DDPM, LDM, and Stable Diffusion models when tested on CIFAR, LSUN-Bedroom/Churches, ImageNet, COCO2017, and PartiPrompt.
- DeepCache performs better than pruning and distillation algorithms that require retraining, maintaining its highest efficiency under the conditions
In conclusion, DeepCache definitely shows great promise as a diffusion model accelerator as it provides a useful and affordable substitute for conventional compression techniques.
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Tanya Malhotra is a final year student of University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with specialization in artificial intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with a burning interest in acquiring new skills, leading groups and managing work in an organized manner.
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