HCM: Icon art design based on diffusion model
Bibliographic record
Abstract
With the advancement of AI-generated image technology, the field of icon design has increasingly incorporated computational methods as design references. Compared to direct design by human designers, AI technology can significantly reduce time and labor costs. In response to this, this research propose the High-Quality Customized Model (HCM) for controllable stylized icon design. This research introduce the Icon-ControlNet module to achieve precise control over icon generation, ensuring high levels of customization. Additionally, article employ the IconIC module to reduce computational resource consumption and enhance generation efficiency. This article have also constructed the IconData dataset, comprising 25,000 finely annotated medium-sized images. Through extensive ablation experiments, the results were evaluated by FID and IS, effectively demonstrating the advantages of HCM in terms of icon clarity, style transfer, and diversity. This model provides a novel solution for the automation and personalization of icon design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".