JoLT: Joint Latent Trajectories for Context-Guided High-Resolution Tiled Generation

2026-08-15

AI Summary

This study addresses the challenge of generating detail-rich, high-resolution images with text-to-image models by proposing a Joint Latent Trajectory method. The approach introduces a dual-stream synchronous denoising mechanism that dynamically integrates low-resolution layouts with high-resolution details through cross-branch information exchange during sampling, combined with a patch-wise generation strategy for coordinated control. Experimental results demonstrate that this method effectively resolves the structural-textural imbalance inherent in high-definition generation, producing visually appealing images with rich details. Significantly outperforming existing baselines, this work establishes a novel paradigm for high-resolution image synthesis.

Abstract

Although text-to-image generative models produce impressive results, they struggle to generate densely detailed, high-resolution (HR) images. Current literature addresses this issue with a low-to-high-resolution approach. First, a low-resolution (LR) image is generated. Then, an upsampled version is generated using the LR image as an additional cue. In this paper, we present Joint Latent Trajectories (JoLT). To generate an image, JoLT uses two streams that jointly denoise LR and HR latent images at each sampling step. The LR latent controls the overall layout, while the HR latent controls the details. We interconnect both branches to jointly integrate their information. We extensively validate our method, demonstrating its advantages over competing baselines. The resulting images are not only richly detailed but also visually pleasing, opening new avenues for artistic creation.

Problem

• Text-to-Image Generation
• High-Resolution Image Synthesis
• Dense Detail Generation

Innovation

• Joint Latent Trajectories
• High-Resolution Generation
• Dual-Stream Denoising
• Context-Guided Generation
• Tiled Generation