Methods › Computer Vision › Generative Models › Self-Attention Guidance

Self-Attention Guidance

7 papers tagged archive 2025-07-28

Introduced by Susung Hong et al. in Improving Sample Quality of Diffusion Models Using Self-Attention Guidance

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

The archive carries no description for this method.

PaperSource

Papers archive 2025-07-28

7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

17 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Denoising2
Image Generation2
Super-Resolution2
Data Augmentation1
Demosaicking1
Diversity1
Image Colorization1
Metric Learning1
Sketch Colorization1
Supervised Video Summarization1
Surface Reconstruction1
Texture Classification1
Texture Synthesis1
Video Deinterlacing1
Video Restoration1
Video Summarization1
Video Super-Resolution1

Usage over time archive 2025-07-28

Papers per year tagged with Self-Attention Guidance: 2022 to 2025, peak 5 5 0 2022: 1 paper 2022 2023: 0 papers 2023 2024: 5 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (7 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Generative Models

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