The October 2025 paper introduces **Paris**, a novel open-weight diffusion model for text-to-image generation that was trained using a completely **decentralized** methodology without requiring any communication between its expert components. This approach overcomes the need for expensive, specialized hardware clusters and synchronized gradient updates, which are typically required for training large-scale models like Stable Diffusion. The system functions by partitioning the training data into semantically distinct clusters, allowing **eight expert models** to train in isolation, with a separate, lightweight **router network** dynamically selecting the most appropriate expert(s) during inference. Empirical results demonstrate that Paris achieves competitive generation quality while using substantially fewer computational resources and training data compared to prior decentralized benchmarks, making large generative AI models more accessible on **heterogeneous, fragmented compute infrastructure**.

Source:

https://arxiv.org/pdf/2510.03434