Original title: Latency-efficient cloud-edge cultural pattern generation via lossless lightweight condition transmission and bandwidth-adaptive scheduling
Wei Zhao, Jiaheng Zeng · September 2026
Sending full pictures to online AI systems takes time and uses lots of data, even though tools like ControlNetA system that adds extra input conditions (like edge maps) to a diffusion model to control what the generated image looks like. only need simple structural outlines to guide image creation. To solve this, researchers designed a cloud-edge setup for generating cultural patterns. Instead of sending an entire raw image, a local edge device pulls out just the necessary outline map, compresses it without losing any detail, and uploads only that small file. A powerful cloud computer then runs the full Stable Diffusion pipeline to make the art. Because internet speeds constantly change, a smart scheduling program chooses the best compression settings on the fly. This method cut average upload sizes by about 94% and slashed transmission pipeline overhead by about 90%. Best of all, the final images came out completely identical to pictures made using full raw uploads, keeping image quality and visual details totally intact.
You are mailing a detailed blueprint to a builder. Normally you send the entire full-size blueprint, which takes time and costs more postage. But the builder only needs the outline to reconstruct the exact same building. Here, you trace just the outline, compress that thin drawing without any loss, and mail only that tiny sheet. The builder recreates the building from it. You also adjust how thick you draw the outline based on how fast the mail is, so it always arrives quickly.
Cloud-based image generation is slow on weak internet connections because uploading full reference images takes time. This paper shows that sending only a tiny, losslessly compressed condition map saves 94% of data and 90% of transmission time, with no quality loss. The system also adapts to internet speed, so it works well on everything from slow mobile to fast fiber.
The abstract reports latencies excluding cloud diffusion time, so end-to-end savings may differ. The framework is tested on only 112 cultural pattern images, so generalization to other domains or larger scales is not shown. The RL scheduler matches but does not outperform baselines, meaning improvement over simpler adaptive policies is not demonstrated. Also, the approach relies on the condition map being losslessly compressible; for images where the condition map is large, savings may be less.
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Latency-efficient cloud-edge cultural pattern generation via lossless lightweight condition transmission and bandwidth-adaptive scheduling — Wei Zhao, Jiaheng Zeng — Discover Artificial Intelligence — doi:10.1007/s44163-026-02208-w
Abstract licence: CC BY
Explanation generated by Synthesis from the abstract — it is not the original paper.