Multimodality · 2022
LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models
Released an openly available dataset of 5.85 billion image-text pairs, giving the research community the web-scale multimodal data that had previously been locked inside a few large labs and enabling open text-to-image and vision-language models.
Editorial record
Plain-language summary
LAION-5B is a filtered, CLIP-scored collection of billions of image-caption pairs scraped from the web and released openly by the LAION non-profit. It supplied the training data behind Stable Diffusion and many open CLIP and vision-language models, shifting a major bottleneck -- data -- from proprietary to public. It also foregrounded the governance, consent, and safety questions that come with web-scale datasets.
Knowledge graph
Relationships
Antecedents
EnablesEvidence: Strongly supported
Latent Diffusion (Stable Diffusion) / Diffusion Transformers (DiT)
LAION-5B provided the image-text data used to train Stable Diffusion
LAION
Descendants
Conceptual ancestorEvidence: Strongly supported
CLIP: Learning Transferable Visual Models from NL Supervision
LAION set out to openly reproduce CLIP-scale image-text data
LAION
Source record
Provenance
- Record ID
- P-369
- Record created
- 2026-07-13
- Last reviewed
- 2026-07-14
- Record version
- 2
- https://arxiv.org/abs/2210.08402
- arXiv:2210.08402
Citation caveat: Citation metadata is approximate and marked unverified in the source dataset.