MONET (Massive, Open, Non-redundant and Enriched Text-to-image dataset) is a large-scale, curated image-text dataset designed for training text-to-image (T2I) systems. It contains 103.8 million high-quality image-text pairs distilled from 2.9 billion raw pairs across nine heterogeneous open sources (6 real and 3 synthetic) through successive stages of safety filtering, domain-based filtering, exact and near-duplicate removal, and re-captioning with multiple vision-language models, and is further augmented with synthetically generated samples. Each image is released with pre-computed embeddings, structured annotations and pre-encoded VAE latents to accelerate downstream use. A 4B-parameter…
SAVRN Model Hub · Datasets by Task
Text to Image Datasets
2 open-weight text to image datasets in the SAVRN Model Hub, with Sway and Jasper AI publishing the most.
2Datasets
2Publishers
2Licenses
Most Downloaded
| Dataset | Publisher | License | Monthly downloads |
|---|---|---|---|
| preprocessed_commoncatalog-cc-by_DCAE | Sway | cc | 112k |
| monet | Jasper AI | apache-2.0 | 101.4k |
Licenses
| License | Datasets | Commercial use |
|---|---|---|
| cc | 1 | Read the license |
| apache-2.0 | 1 | Yes |
Who Publishes Them
| Publisher | Datasets |
|---|---|
| Sway | 1 |
| Jasper AI | 1 |
All 2 Datasets
The images are resized and then encoded with the DC-AE f32 autoencoder. The resizing is done with a bucketmanager with base resolution 512x512, minimum side length 256, maximum side length 1024, all sides are divisible by 32 ofcourse as they needed to be encoded by the DCAEf32 encoder. The captions are generated with moondream2, encoded with siglip and bert. (Bert embeddings variance is very high, so use a norm layer). The text embeddings are padded to 64 tokens, but i have provided the