Decoupling Complexity from Scale in Latent Diffusion Model

2025-11-20

AI Summary

Existing latent diffusion models (LDMs) tightly couple content complexity with data scale (e.g., resolution, frame rate), causing latent representations to expand linearly with scale—hindering scalability and efficiency. Method: We propose DCS-LDM, the first LDM to explicitly decouple content complexity from data scale. It constructs a hierarchical, scale-invariant latent space where multi-level tokens jointly model structural semantics and fine-grained details. A structure-detail decomposition mechanism and progressive coarse-to-fine synthesis enable adaptive decoding at arbitrary resolutions and frame rates using a fixed-size latent representation, all within a unified framework. Contribution/Results: DCS-LDM achieves state-of-the-art generation quality while supporting multi-scale, multi-resolution, and multi-frame-rate synthesis. Experiments demonstrate significant improvements in deployment flexibility and resource efficiency—enabling high-fidelity generation without scaling latent dimensions—thus establishing a new paradigm for scalable, compute-aware generative modeling.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Existing latent diffusion models typically couple scale with content complexity, using more latent tokens to represent higher-resolution images or higher-frame rate videos. However, the latent capacity required to represent visual data primarily depends on content complexity, with scale serving only as an upper bound. Motivated by this observation, we propose DCS-LDM, a novel paradigm for visual generation that decouples information complexity from scale. DCS-LDM constructs a hierarchical, scale-independent latent space that models sample complexity through multi-level tokens and supports decoding to arbitrary resolutions and frame rates within a fixed latent representation. This latent space enables DCS-LDM to achieve a flexible computation-quality tradeoff. Furthermore, by decomposing structural and detailed information across levels, DCS-LDM supports a progressive coarse-to-fine generation paradigm. Experimental results show that DCS-LDM delivers performance comparable to state-of-the-art methods while offering flexible generation across diverse scales and visual qualities.

Problem

• Decouples content complexity from scale in latent diffusion models
• Enables flexible computation-quality tradeoff for visual generation
• Supports progressive coarse-to-fine generation across diverse scales

Innovation

• Decouples content complexity from scale in latent diffusion
• Uses hierarchical scale-independent latent space with multi-level tokens
• Enables flexible computation-quality tradeoff and progressive generation