Query-Selected Global Attention for Text guided Image Style Transfer using Diffusion Model
Bibliographic record
Abstract
Diffusion models have gained tremendous interest in image generation. Additionally, guided text methods for manipulating source images have shown successful progress. However, research on style transfer using diffusion models is still ongoing to address the trade-off between style transfer and content preservation. One representative solution to the issue is contrastive learning in a self-supervised manner, which is useful for extracting specific features from the same location on source and generated images for every pixel. However, there are instances where it is necessary to preserve certain areas, which contain more information from the source image compared to other areas in the image. Therefore, we propose anchoring the areas for preservation and intentionally selecting features at the anchor points through a query-selected global attention method. This enables our method to generate an image that preserves the content of the source while transferring the style without the need for additional fine-tuning or auxiliary network. Our diffusion model follows a simple architecture to enhance image quality and speed up inference time, in comparison to other diffusion methods. Our experimental results also demonstrate superior performance.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".