Rapid development and deployment of GPU based computation has led to an improvement in diffusion generation of video and images. Further, a rapid reduction in the effective cost of compression using NNC techniques provides opportunities to compress images and videos in new ways. The overall structure of diffusion based generative video and images is leveraged to take advantage of the compressed latent to lower overall compression costs and latency. This paper presents an architecture to compress a latent for transmission and reduce overall latency and cost as compared to alternatives using traditional Codecs or NNC on the raw image. It explores a proof of concept based on image compression of a latent. It further presents computational cost, quantitative and perceptual quality, and latency for this architecture as compared to the alternatives.
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