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Diffusion

13,848 papers tagged archive 2025-07-28

Introduced by Jonathan Ho et al. in Denoising Diffusion Probabilistic Models

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

Diffusion models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).

PaperSource

Papers archive 2025-07-28

30 shown of 13,848, 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

20 shown of 1,474 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
Image Generation2,525
Denoising2,447
Video Generation666
Diversity549
Text-to-Image Generation543
Text to Image Generation485
Super-Resolution399
Object345
model333
Data Augmentation326
Semantic Segmentation318
Language Modelling313
Decoder310
Segmentation283
3D Generation263
Language Modeling235
Attribute222
GPU203
Image Restoration201
Text to 3D197

Usage over time archive 2025-07-28

Papers per year tagged with Diffusion: 2020 to 2025, peak 5,829 5,829 0 2020: 167 papers 2020 2021: 434 papers 2021 2022: 857 papers 2022 2023: 3354 papers 2023 2024: 5829 papers 2024 2025: 3207 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (13,848 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

Language ModelsImage Generation Models

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