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

FLUX.2 Klein-9B2048² px
FLUX.2 Klein-9B base generation at 2048 square pixels
JoLT (ours)4096² px
JoLT refined generation at 4096 square pixels

Hover either image to compare the same area, then scroll over it to change the magnification.

Controllable high-resolution generation. The master prompt establishes the scene; the detail prompt and feedback strength α control local density and style while JoLT preserves a coherent global composition.

Figure 1 setup. FLUX.2 Klein-9B, four denoising steps, and a fixed seed. The base view is generated at 2048². JoLT produces 4096² with 1024² high-resolution windows, 512 px stride, and α = 0.25.

Abstract

Text-to-image models produce impressive results but still struggle with densely detailed high-resolution images. Existing methods usually generate a low-resolution image first, then upscale or refine it, so high-resolution information cannot influence the global composition as it forms.

We introduce Joint Latent Trajectories (JoLT), a training-free, two-stream generator that jointly denoises low- and high-resolution latent images at each sampling step. The low-resolution trajectory controls overall layout; the tiled high-resolution trajectory adds local detail. Bidirectional exchange keeps both streams synchronized and exposes two direct creative controls: a detail prompt and feedback strength α. The resulting 4096² images are richly detailed while remaining globally coherent.

Method

JoLT keeps a global trajectory and a tiled detail trajectory alive together. At every denoising step, each stream updates the other before sampling continues.

JoLT paper method diagram showing initialization, the low-resolution stream, joint prior, high-resolution stream, and bidirectional conditioning
The complete JoLT pipeline from the paper. A shared Gaussian draw initializes both trajectories. The LR stream denoises the global composition from the master prompt; its clean estimate is decoded, upsampled, and re-encoded as the joint prior for tiled HR denoising. The HR canvas is then randomly subsampled and blended into the next LR update with weight α.

Method setup. FLUX.2 Klein-9B distilled to four sampling steps. Unless stated otherwise: 4096² canvas, 1024² tiled denoising windows, 512 px stride (50% overlap), 1024² LR stream, and constant α = 0.25.

BibTeX

@inproceedings{
anonymous2026jolt,
title={{JoLT}: Joint Latent Trajectories for Context-Guided High-Resolution Tiled Generation},
author={Koroglu, Mathis and Jeanneret, Guillaume and Caselles-Dupr\'e, Hugo and Cord, Matthieu and Dapogny, Arnaud},
booktitle={The 4th AI for Visual Arts Workshop},
year={2026},
url={https://openreview.net/forum?id=9hGsmtZrCI}
}

Acknowledgements

This work has been partially supported by the VISA DEEP chair (grant ANR-20-CHIA-0022), the PostGenAI@Paris cluster (grant ANR-23-IACL-0007; France 2030), and funded by the French National Research Agency (ANR) under the Renaissance project (grant ANR-23-CE23-0023; France 2030).

This project was provided with computing HPC & AI and storage resources by GENCI at IDRIS thanks to grant 2025-AD011017131 on the supercomputer Jean Zay’s H100 partition.