Toward Lightweight and Fast Decoders for Diffusion Models in Image and Video Generation
This work addresses efficiency issues for large-scale inference scenarios like generating 100K images, though it is incremental as it builds on existing diffusion models with custom decoders.
The paper tackled the problem of high inference time and memory footprint in stable diffusion models by introducing lightweight decoders for image and video generation, achieving up to 15% overall speed-ups for images and 20 times faster decoding in sub-modules.
We investigate methods to reduce inference time and memory footprint in stable diffusion models by introducing lightweight decoders for both image and video synthesis. Traditional latent diffusion pipelines rely on large Variational Autoencoder decoders that can slow down generation and consume considerable GPU memory. We propose custom-trained decoders using lightweight Vision Transformer and Taming Transformer architectures. Experiments show up to 15% overall speed-ups for image generation on COCO2017 and up to 20 times faster decoding in the sub-module, with additional gains on UCF-101 for video tasks. Memory requirements are moderately reduced, and while there is a small drop in perceptual quality compared to the default decoder, the improvements in speed and scalability are crucial for large-scale inference scenarios such as generating 100K images. Our work is further contextualized by advances in efficient video generation, including dual masking strategies, illustrating a broader effort to improve the scalability and efficiency of generative models.