LatentCRF: Continuous CRF for Efficient Latent Diffusion

• URL: http://arxiv.org/abs/2412.18596v1
• Date: Tue, 24 Dec 2024 18:51:11 GMT
• Title: LatentCRF: Continuous CRF for Efficient Latent Diffusion
• Authors: Kanchana Ranasinghe, Sadeep Jayasumana, Andreas Veit, Ayan Chakrabarti, Daniel Glasner, Michael S Ryoo, Srikumar Ramalingam, Sanjiv Kumar,
• Abstract summary: We introduce LatentCRF, a continuous Conditional Random Field (CRF) model, implemented as a neural network layer. LatentCRF models the spatial and semantic relationships among the latent vectors in the Latent Diffusion Models. We increase inference efficiency by 33% with no loss in image quality or diversity compared to the full LDM.
• Score: 63.994037257284205
• License:
• Abstract: Latent Diffusion Models (LDMs) produce high-quality, photo-realistic images, however, the latency incurred by multiple costly inference iterations can restrict their applicability. We introduce LatentCRF, a continuous Conditional Random Field (CRF) model, implemented as a neural network layer, that models the spatial and semantic relationships among the latent vectors in the LDM. By replacing some of the computationally-intensive LDM inference iterations with our lightweight LatentCRF, we achieve a superior balance between quality, speed and diversity. We increase inference efficiency by 33% with no loss in image quality or diversity compared to the full LDM. LatentCRF is an easy add-on, which does not require modifying the LDM.

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