Bibliographic record
Abstract
Diffusion models have shown notable success in generating images conditioned on textual prompts, enabling users to edit images at a coarse scale with well-aligned text-to-image models. ControlNet [31] enhances these capabilities by allowing diffusion models to edit aspects such as pose, position, and edges according to reference visual motion information in a qualitative manner. However, diffusion models still face challenges in measurable and quantitative applications, such as applying sharpening or color enhancement effects. We call quantities such as brightness and saturation, attributes. In this work, we introduce Target Attribute Diffusion Models (TADM), which enable diffusion models to incorporate additional conditioning on continuous random variables. Unlike classifier-guidance methods, which require training an explicit classifier [30], TADM supports real-valued conditional variables. We also propose a new architecture called attribute carrier between the text embeddings and the new conditioning variable. Experiments were conducted on three attributes: color saturation, sharpness and human preference. TADM outperformed the baseline algorithm on a single prompt, single attribute experiment. In addition, TADM demonstrates improvement in the multiple prompt experiments with respect to two of the three attributes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".