Distributed latent encoding for AIGC art and digital media retrieval in cloud environments

The rapid proliferation of AI-generated content (AIGC) and large-scale digital media assets has created an urgent need for efficient, scalable, and cloud-compatible retrieval mechanisms. Traditional retrieval frameworks struggle to maintain performance when facing massive cloud-hosted repositories and increasingly complex multimodal data. To address these challenges, we propose a distributed latent encoding framework designed to support high-throughput AIGC and digital media retrieval in cloud environments. The core idea is to transform raw media features into compact, semantically aligned latent representations that can be efficiently processed across distributed cloud infrastructures. The framework integrates distribution-regularized latent modeling to enhance robustness in large-scale scenarios and adopts a structure-aligned representation mechanism to preserve semantic consistency across heterogeneous AIGC modalities. Furthermore, a cloud-native distributed indexing strategy enables low-latency retrieval and elastic scaling under varying system loads. Experiments on six benchmark datasets show that the proposed method reduces retrieval latency and memory-related overhead while maintaining a competitive level of retrieval accuracy. The results suggest that compact latent encoding can be a practical choice for cloud-oriented retrieval tasks where response time and scalability are important constraints. Rather than replacing high-capacity real-valued models in all scenarios, DMR provides an efficiency-oriented alternative for large-scale media retrieval pipelines.