LatentCRF: Continuous CRF for Efficient Latent Diffusion

2024-12-24

AI Summary

Latent Diffusion Models (LDMs) suffer from slow inference due to iterative denoising steps. Method: We propose LatentCRF—a lightweight, plug-and-play neural layer embedding a continuous Conditional Random Field (CRF) directly into the latent diffusion process. Unlike prior approaches, LatentCRF explicitly models spatiotemporal and semantic dependencies among latent variables, replacing a subset of denoising iterations without architectural modification to the base LDM. Contribution/Results: This work presents the first end-to-end joint modeling of continuous CRFs and latent diffusion, achieving a 33% inference speedup while preserving image fidelity (FID, LPIPS) and generation diversity (L1-entropy). By simultaneously enhancing efficiency, reconstruction accuracy, and generalization, LatentCRF significantly improves the practical deployability of LDMs—without compromising generative quality or requiring retraining.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Latent Diffusion Models
• Image Generation
• Efficiency

Innovation

• LatentCRF
• Continuous Conditional Random Fields
• Image Generation Optimization