LatentCRF: Continuous CRF for Efficient Latent Diffusion
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915078671958016 |
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| author | Ranasinghe, Kanchana Jayasumana, Sadeep Veit, Andreas Chakrabarti, Ayan Glasner, Daniel Ryoo, Michael S Ramalingam, Srikumar Kumar, Sanjiv |
| author_facet | Ranasinghe, Kanchana Jayasumana, Sadeep Veit, Andreas Chakrabarti, Ayan Glasner, Daniel Ryoo, Michael S Ramalingam, Srikumar Kumar, Sanjiv |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LatentCRF: Continuous CRF for Efficient Latent Diffusion Ranasinghe, Kanchana Jayasumana, Sadeep Veit, Andreas Chakrabarti, Ayan Glasner, Daniel Ryoo, Michael S Ramalingam, Srikumar Kumar, Sanjiv Computer Vision and Pattern Recognition 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. |
| title | LatentCRF: Continuous CRF for Efficient Latent Diffusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.18596 |