Stochastic Interpolants via Conditional Dependent Coupling

• URL: http://arxiv.org/abs/2509.23122v1
• Date: Sat, 27 Sep 2025 05:03:08 GMT
• Title: Stochastic Interpolants via Conditional Dependent Coupling
• Abstract summary: Existing image generation models face critical challenges regarding the trade-off between computation and fidelity.<n>We introduce a unified multistage generative framework based on our proposed Conditional Dependent Coupling strategy.<n>It decomposes the generative process into interpolant trajectories at multiple stages, ensuring accurate distribution learning while enabling end-to-end optimization.
• Score: 36.84747986070112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing image generation models face critical challenges regarding the trade-off between computation and fidelity. Specifically, models relying on a pretrained Variational Autoencoder (VAE) suffer from information loss, limited detail, and the inability to support end-to-end training. In contrast, models operating directly in the pixel space incur prohibitive computational cost. Although cascade models can mitigate computational cost, stage-wise separation prevents effective end-to-end optimization, hampers knowledge sharing, and often results in inaccurate distribution learning within each stage. To address these challenges, we introduce a unified multistage generative framework based on our proposed Conditional Dependent Coupling strategy. It decomposes the generative process into interpolant trajectories at multiple stages, ensuring accurate distribution learning while enabling end-to-end optimization. Importantly, the entire process is modeled as a single unified Diffusion Transformer, eliminating the need for disjoint modules and also enabling knowledge sharing. Extensive experiments demonstrate that our method achieves both high fidelity and efficiency across multiple resolutions.

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