Characterizing and Scheduling of Diffusion Process for Text-to-Image Generation in Edge Networks
Bibliographic record
Abstract
Artificial Intelligence-Generated Content (AIGC) technology is transforming content creation by enabling diverse customized and quality services. However, the limited computing resources on mobile devices hinder the provisioning of AIGC services at scale, pose challenges in guaranteeing user-satisfied content quality requirement. To address these challenges, we first investigate the characteristics of prompt category and inference models in Text-to-Image (T2I) diffusion process. It is observed that, model size, denoising steps, and computing resource, are three deciding factors to image generation utility. Based on this insight, we first design an edge-assisted AIGC service system to efficiently process multi-user T2I generative requests, employing a multi-flow queuing model to capture multi-user dynamics and characterize the impact of diffusion scheduling on service latency. The system schedules the diffusion process of T2I generation across edge-deployed models, balancing service quality and computing resource. To maximize generation utility under resource constraints, we propose a Monte Carlo Tree Search-based diffusion scheduling algorithm embedded with adaptive computing resource allocation subroutine. This algorithm ensures that, resource allocation dynamically adapts to scheduling decisions in real time, enabling an effective trade-off between service quality and latency. Extensive experimental comparison against baseline approaches demonstrates that, the proposed system can enhance the generation utility by up to 7.3$\%$, achieving a 2.9$\%$improvement in quality score and a 33.3$\%$reduction in service latency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".