Datasets:

udgamlabs
/

chitra-tiny-data

---
pretty_name: Chitra Tiny training set
license: other
license_name: cc0-pdm-cdla-permissive-2.0-mit
license_link: https://huggingface.co/datasets/udgamlabs/chitra-tiny-data/blob/main/LICENSES/README.md
language:
- en
task_categories:
- text-to-image
size_categories:
- 1M<n<10M
tags:
- udgamlabs
- image
- webdataset
- public-domain
- cc0
- latents
configs:
- config_name: default
  data_files:
  - split: train
    path: train/*.tar
  - split: validation
    path: validation/*.tar
---
![Chitra Tiny training set · Udgam Labs](./banner.png)

**UDGAM LABS** · Image-text dataset · 4.7 million images

# Chitra Tiny training set

The images that [Chitra Tiny](https://huggingface.co/udgamlabs/Chitra-Tiny), our 212M-parameter text-to-image model,
was trained on: 4,691,814 pictures at 256×256, each with its caption and its SD-VAE latent, plus a held-out split of
14,384 images that the model never saw. Every image was dedicated under CC0 or marked public domain (PDM) by the
source. The images come from two public collections curated by Spawning, PD12M and Megalith-CC0. We resized and encoded
them, and before release we scanned every image and removed 29,460 that failed a safety or licence check.

**See the model:** [Chitra Tiny on Hugging Face](https://huggingface.co/udgamlabs/Chitra-Tiny) ·
[in your browser](https://huggingface.co/spaces/udgamlabs/Chitra-Tiny) ·
[code on GitHub](https://github.com/udgamlabs/chitra-tiny) ·
[udgamlabs.com/models/chitra-tiny](https://udgamlabs.com/models/chitra-tiny)

## Quick start

Stream a few samples without downloading the whole set (159 GB):

```python
import numpy as np
from datasets import load_dataset
ds = load_dataset("udgamlabs/chitra-tiny-data", split="train", streaming=True)
for s in ds.take(3):
    image, caption = s["jpg"], s["txt"]                       # PIL image (256×256), str
    latent = np.asarray(s["npy"], dtype=np.float16)           # (4, 32, 32)
    print(s["__key__"], s["json"]["license"], image.size, latent.shape, caption[:60])
```

Or read the tar shards directly with [webdataset](https://github.com/webdataset/webdataset):

```python
import webdataset as wds
url = "https://huggingface.co/datasets/udgamlabs/chitra-tiny-data/resolve/main/train/train-{00000..00143}.tar"
ds = wds.WebDataset(url, shardshuffle=True).shuffle(5000).decode("pil").to_tuple("jpg", "txt", "npy", "json")
```

Decode a latent back to pixels with the same VAE:

```python
import torch
from diffusers import AutoencoderKL
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
x = vae.decode(torch.from_numpy(latent).float()[None] / 0.18215).sample   # (1, 3, 256, 256), values in [-1, 1]
```

## What is in it

| Split | Images | From PD12M | From Megalith-CC0 | Shards |
|---|---|---|---|---|
| train | 4,691,814 | 2,491,765 | 2,200,049 | 144 (`train/train-00000.tar` … `train-00143.tar`) |
| validation | 14,384 | 7,476 | 6,908 | 2 (`validation/validation-00000.tar`, `-00001.tar`) |

Each sample has four files with the same key (the source dataset's own image id):

| File | What it is |
|---|---|
| `KEY.jpg` | The image: 256×256 RGB, short side resized to 256 and centre-cropped, JPEG quality 90 |
| `KEY.txt` | The caption, unchanged from the source |
| `KEY.npy` | The image's latent from [stabilityai/sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse): the posterior mean × 0.18215, float16, shape 4×32×32 |
| `KEY.json` | `source_dataset` (PD12M or Megalith-CC0) and `license` (CC0-1.0 or PDM-1.0) |

**Licence index.** `index/train.parquet` and `index/validation.parquet` have one row per image: `key`, `split`, `shard`,
`source_dataset`, `license`, `license_url`, `image_url` (the source's copy of the original), `source_collection` (for
PD12M, the institution or site it came from), `original_width`, `original_height` and `source_hash`. Load it with
`load_dataset("parquet", data_files="hf://datasets/udgamlabs/chitra-tiny-data/index/train.parquet")` or with pandas.

Licences: 3,706,589 images (78.8%) are CC0 and 999,609 (21.2%) carry the Public Domain Mark.
Within PD12M the largest sources are Wikimedia Commons (56.5%), iNaturalist (20.5%), Rijksmuseum (1.3%).

## Where it falls short

- **Narrow in what it shows.** PD12M is mostly Wikimedia Commons, iNaturalist and museum and archive collections, so it
  leans towards historic, Western and museum imagery, plants and animals. Megalith-CC0 is everyday Flickr photographs,
  many of them of people.
- **Small and cropped.** 256 px centre crops lose the edges of wide and tall images and all fine detail.
- **An older latent space.** The latents are for SD-VAE ft-mse (8× downsampling, 4 channels). Most newer image models use
  other autoencoders, so you may need to re-encode the images.
- **Machine-written captions.** PD12M's captions were written by Florence-2-large. The source does not say which model
  wrote Megalith-CC0's captions; they are much longer (in the validation split, a median of 89 words against
  37 for PD12M) and often start "The image shows". Both can be wrong about what is in a picture.
- **The safety scan is automatic and imperfect.** Two classifiers scored every image. They rarely agree on which images
  to flag (2,657 images were flagged by both, 26,946 by either), which suggests both miss some unsafe images and
  flag some harmless ones. We removed anything either flagged. No image was checked against lists of known abuse images
  (hash matching); report anything you find (see Takedown below).
- **Licence status is the source's label, not a guarantee.** PD12M's authors note they cannot guarantee the licence of
  every work. The Public Domain Mark records the source's view that a work's copyright has expired, and that can depend on
  the country.
- **A frozen copy.** The sources remove items they learn are infringing or unsafe. Those removals do not reach this copy
  by themselves; we apply them when we hear of them.

## How it was made

**Sources.** [PD12M](https://huggingface.co/datasets/Spawning/PD12M) (Spawning): metadata files 000–024, the first 2.5
million of its 12.4 million rows, for training, and file 123 for the held-out split.
[Megalith-CC0](https://huggingface.co/datasets/Spawning/megalith-cc0) (Spawning, a CC0-only selection of
[Megalith-10m](https://huggingface.co/datasets/madebyollin/megalith-10m)): all nine parts, with the first 10,000 rows of
part 8 held out. Images were downloaded from the sources' own hosted copies.

**Selection.** Kept rows whose licence is CC0 or the Public Domain Mark and whose caption has at least 8 words. Images
that failed to download were skipped (about 6% of Megalith-CC0, evenly across its files). Chitra Tiny was trained on the
resulting 4,721,166 images: 52.9% from PD12M and 47.1% from Megalith-CC0.

**Processing.** Each image was decoded, resized so its short side is 256 px (bicubic), centre-cropped to 256×256 and saved
as JPEG quality 90; the latent is the SD-VAE ft-mse posterior mean, scaled by 0.18215. Apart from the clean-up below, this
is the data Chitra Tiny was trained and evaluated on. Its FID numbers use 5,000 images from the validation split; the
clean-up took out 108 validation images, so a few of those may be missing here.

**Release clean-up and safety scan (October 2026).** Before publishing we went over all 4,735,658 images once:

| Step | What it did | Images removed |
|---|---|---|
| Hidden text in image files | Removed JPEG comment and metadata segments from 246,489 files at the byte level; the pixels are unchanged (checked for each file) | — |
| Copyright notice | Removed images whose embedded comment carried a copyright notice, which conflicts with their CC0 or PDM label | 865 |
| Safety classifiers | Scored every image with [Falconsai/nsfw_image_detection](https://huggingface.co/Falconsai/nsfw_image_detection) and [AdamCodd/vit-base-nsfw-detector](https://huggingface.co/AdamCodd/vit-base-nsfw-detector) (both Apache-2.0); removed every image either scored above 0.5 | 26,946 |
| Caption rule | Removed every image whose caption pairs a word for a child with a word for nudity, underwear or swimwear. No image was viewed; the rule is applied to the text only | 1,347 |
| Personal data in captions | Removed images whose caption contains an email address, a phone number or a key-like string | 567 |
| Licence check | Matched every image to the source's metadata; removed any whose id or licence did not check out | 0 |
| Damaged samples | Removed undecodable images or unreadable latents | 0 |
| **Total** | Some images failed more than one check | **29,460** (0.6%) |

## Intended use and limitations

For training and studying small text-to-image and latent-diffusion models, for teaching, and for reproducing Chitra
Tiny. Not for identifying or profiling the people who appear in the photos, and not as a source of facts about what an
image shows. If you build a public product on it, add your own safety filtering.

## Takedown

If an image or caption should not be here (an unsafe image, a person who wants their photo removed, a work that is not
in the public domain), open a discussion on this dataset's
[Community tab](https://huggingface.co/datasets/udgamlabs/chitra-tiny-data/discussions) or email [jitesh@udgamlabs.com](mailto:jitesh@udgamlabs.com).
We take reported items out of this copy, and also items that PD12M or Megalith-CC0 take out of theirs.

## Licence

Each part has its own terms; see [`LICENSES/README.md`](LICENSES/README.md). In short:

| Part | Terms |
|---|---|
| Images | Dedicated under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) or marked public domain ([PDM 1.0](https://creativecommons.org/publicdomain/mark/1.0/)) by the source; per image in the index |
| PD12M captions and metadata | [CDLA-Permissive-2.0](LICENSES/CDLA-Permissive-2.0.txt) |
| Megalith-CC0 captions and metadata | [MIT](LICENSES/MIT-Megalith-CC0.txt) |
| What Udgam Labs adds (latents, index, per-sample `.json`, this card) | [CDLA-Permissive-2.0](LICENSES/CDLA-Permissive-2.0.txt) |

CC0 and the Public Domain Mark cover copyright only. They do not cover the privacy or publicity rights of people shown in
the images, or trademarks, logos and insignia.

## Citation

```bibtex
@misc{chitratinydata2026,
  title  = {Chitra Tiny training set: 4.7M public-domain and CC0 images at 256 px with captions and SD-VAE latents},
  author = {Udgam Labs},
  year   = {2026},
  url    = {https://huggingface.co/datasets/udgamlabs/chitra-tiny-data}
}
```

Please also cite the sources:

```bibtex
@misc{meyer2024publicdomain12mhighly,
  title         = {Public Domain 12M: A Highly Aesthetic Image-Text Dataset with Novel Governance Mechanisms},
  author        = {Jordan Meyer and Nick Padgett and Cullen Miller and Laura Exline},
  year          = {2024},
  eprint        = {2410.23144},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2410.23144}
}
```

Megalith-CC0: https://huggingface.co/datasets/Spawning/megalith-cc0, derived from Megalith-10m by madebyollin
(https://huggingface.co/datasets/madebyollin/megalith-10m).

**Thanks** to Spawning for PD12M and Megalith-CC0, to madebyollin for Megalith-10m, to Microsoft for Florence-2,
to Stability AI for the SD-VAE, and to the authors of the two safety classifiers.

---

**About Udgam Labs.** Udgam Labs is an independent AI research lab. We work from first principles: before we build, we understand why things work, whether we are training a model, fine-tuning one or applying AI to a new problem. Udgam (उद्गम) is Sanskrit for origin. [udgamlabs.com](https://udgamlabs.com)

	---
	pretty_name: Chitra Tiny training set
	license: other
	license_name: cc0-pdm-cdla-permissive-2.0-mit
	license_link: https://huggingface.co/datasets/udgamlabs/chitra-tiny-data/blob/main/LICENSES/README.md
	language:
	- en
	task_categories:
	- text-to-image
	size_categories:
	- 1M<n<10M
	tags:
	- udgamlabs
	- image
	- webdataset
	- public-domain
	- cc0
	- latents
	configs:
	- config_name: default
	data_files:
	- split: train
	path: train/*.tar
	- split: validation
	path: validation/*.tar
	---

	![Chitra Tiny training set · Udgam Labs](./banner.png)

	UDGAM LABS · Image-text dataset · 4.7 million images

	# Chitra Tiny training set

	The images that [Chitra Tiny](https://huggingface.co/udgamlabs/Chitra-Tiny), our 212M-parameter text-to-image model,
	was trained on: 4,691,814 pictures at 256×256, each with its caption and its SD-VAE latent, plus a held-out split of
	14,384 images that the model never saw. Every image was dedicated under CC0 or marked public domain (PDM) by the
	source. The images come from two public collections curated by Spawning, PD12M and Megalith-CC0. We resized and encoded
	them, and before release we scanned every image and removed 29,460 that failed a safety or licence check.

	See the model: [Chitra Tiny on Hugging Face](https://huggingface.co/udgamlabs/Chitra-Tiny) ·
	[in your browser](https://huggingface.co/spaces/udgamlabs/Chitra-Tiny) ·
	[code on GitHub](https://github.com/udgamlabs/chitra-tiny) ·
	[udgamlabs.com/models/chitra-tiny](https://udgamlabs.com/models/chitra-tiny)

	## Quick start

	Stream a few samples without downloading the whole set (159 GB):

	```python
	import numpy as np
	from datasets import load_dataset

	ds = load_dataset("udgamlabs/chitra-tiny-data", split="train", streaming=True)
	for s in ds.take(3):
	image, caption = s["jpg"], s["txt"] # PIL image (256×256), str
	latent = np.asarray(s["npy"], dtype=np.float16) # (4, 32, 32)
	print(s["__key__"], s["json"]["license"], image.size, latent.shape, caption[:60])
	```

	Or read the tar shards directly with [webdataset](https://github.com/webdataset/webdataset):

	```python
	import webdataset as wds

	url = "https://huggingface.co/datasets/udgamlabs/chitra-tiny-data/resolve/main/train/train-{00000..00143}.tar"
	ds = wds.WebDataset(url, shardshuffle=True).shuffle(5000).decode("pil").to_tuple("jpg", "txt", "npy", "json")
	```

	Decode a latent back to pixels with the same VAE:

	```python
	import torch
	from diffusers import AutoencoderKL

	vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
	x = vae.decode(torch.from_numpy(latent).float()[None] / 0.18215).sample # (1, 3, 256, 256), values in [-1, 1]
	```

	## What is in it

	\| Split \| Images \| From PD12M \| From Megalith-CC0 \| Shards \|
	\|---\|---\|---\|---\|---\|
	\| train \| 4,691,814 \| 2,491,765 \| 2,200,049 \| 144 (`train/train-00000.tar` … `train-00143.tar`) \|
	\| validation \| 14,384 \| 7,476 \| 6,908 \| 2 (`validation/validation-00000.tar`, `-00001.tar`) \|

	Each sample has four files with the same key (the source dataset's own image id):

	\| File \| What it is \|
	\|---\|---\|
	\| `KEY.jpg` \| The image: 256×256 RGB, short side resized to 256 and centre-cropped, JPEG quality 90 \|
	\| `KEY.txt` \| The caption, unchanged from the source \|
	\| `KEY.npy` \| The image's latent from [stabilityai/sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse): the posterior mean × 0.18215, float16, shape 4×32×32 \|
	\| `KEY.json` \| `source_dataset` (PD12M or Megalith-CC0) and `license` (CC0-1.0 or PDM-1.0) \|

	Licence index. `index/train.parquet` and `index/validation.parquet` have one row per image: `key`, `split`, `shard`,
	`source_dataset`, `license`, `license_url`, `image_url` (the source's copy of the original), `source_collection` (for
	PD12M, the institution or site it came from), `original_width`, `original_height` and `source_hash`. Load it with
	`load_dataset("parquet", data_files="hf://datasets/udgamlabs/chitra-tiny-data/index/train.parquet")` or with pandas.

	Licences: 3,706,589 images (78.8%) are CC0 and 999,609 (21.2%) carry the Public Domain Mark.
	Within PD12M the largest sources are Wikimedia Commons (56.5%), iNaturalist (20.5%), Rijksmuseum (1.3%).

	## Where it falls short

	- Narrow in what it shows. PD12M is mostly Wikimedia Commons, iNaturalist and museum and archive collections, so it
	leans towards historic, Western and museum imagery, plants and animals. Megalith-CC0 is everyday Flickr photographs,
	many of them of people.
	- Small and cropped. 256 px centre crops lose the edges of wide and tall images and all fine detail.
	- An older latent space. The latents are for SD-VAE ft-mse (8× downsampling, 4 channels). Most newer image models use
	other autoencoders, so you may need to re-encode the images.
	- Machine-written captions. PD12M's captions were written by Florence-2-large. The source does not say which model
	wrote Megalith-CC0's captions; they are much longer (in the validation split, a median of 89 words against
	37 for PD12M) and often start "The image shows". Both can be wrong about what is in a picture.
	- The safety scan is automatic and imperfect. Two classifiers scored every image. They rarely agree on which images
	to flag (2,657 images were flagged by both, 26,946 by either), which suggests both miss some unsafe images and
	flag some harmless ones. We removed anything either flagged. No image was checked against lists of known abuse images
	(hash matching); report anything you find (see Takedown below).
	- Licence status is the source's label, not a guarantee. PD12M's authors note they cannot guarantee the licence of
	every work. The Public Domain Mark records the source's view that a work's copyright has expired, and that can depend on
	the country.
	- A frozen copy. The sources remove items they learn are infringing or unsafe. Those removals do not reach this copy
	by themselves; we apply them when we hear of them.

	## How it was made

	Sources. [PD12M](https://huggingface.co/datasets/Spawning/PD12M) (Spawning): metadata files 000–024, the first 2.5
	million of its 12.4 million rows, for training, and file 123 for the held-out split.
	[Megalith-CC0](https://huggingface.co/datasets/Spawning/megalith-cc0) (Spawning, a CC0-only selection of
	[Megalith-10m](https://huggingface.co/datasets/madebyollin/megalith-10m)): all nine parts, with the first 10,000 rows of
	part 8 held out. Images were downloaded from the sources' own hosted copies.

	Selection. Kept rows whose licence is CC0 or the Public Domain Mark and whose caption has at least 8 words. Images
	that failed to download were skipped (about 6% of Megalith-CC0, evenly across its files). Chitra Tiny was trained on the
	resulting 4,721,166 images: 52.9% from PD12M and 47.1% from Megalith-CC0.

	Processing. Each image was decoded, resized so its short side is 256 px (bicubic), centre-cropped to 256×256 and saved
	as JPEG quality 90; the latent is the SD-VAE ft-mse posterior mean, scaled by 0.18215. Apart from the clean-up below, this
	is the data Chitra Tiny was trained and evaluated on. Its FID numbers use 5,000 images from the validation split; the
	clean-up took out 108 validation images, so a few of those may be missing here.

	Release clean-up and safety scan (October 2026). Before publishing we went over all 4,735,658 images once:

	\| Step \| What it did \| Images removed \|
	\|---\|---\|---\|
	\| Hidden text in image files \| Removed JPEG comment and metadata segments from 246,489 files at the byte level; the pixels are unchanged (checked for each file) \| — \|
	\| Copyright notice \| Removed images whose embedded comment carried a copyright notice, which conflicts with their CC0 or PDM label \| 865 \|
	\| Safety classifiers \| Scored every image with [Falconsai/nsfw_image_detection](https://huggingface.co/Falconsai/nsfw_image_detection) and [AdamCodd/vit-base-nsfw-detector](https://huggingface.co/AdamCodd/vit-base-nsfw-detector) (both Apache-2.0); removed every image either scored above 0.5 \| 26,946 \|
	\| Caption rule \| Removed every image whose caption pairs a word for a child with a word for nudity, underwear or swimwear. No image was viewed; the rule is applied to the text only \| 1,347 \|
	\| Personal data in captions \| Removed images whose caption contains an email address, a phone number or a key-like string \| 567 \|
	\| Licence check \| Matched every image to the source's metadata; removed any whose id or licence did not check out \| 0 \|
	\| Damaged samples \| Removed undecodable images or unreadable latents \| 0 \|
	\| Total \| Some images failed more than one check \| 29,460 (0.6%) \|

	## Intended use and limitations

	For training and studying small text-to-image and latent-diffusion models, for teaching, and for reproducing Chitra
	Tiny. Not for identifying or profiling the people who appear in the photos, and not as a source of facts about what an
	image shows. If you build a public product on it, add your own safety filtering.

	## Takedown

	If an image or caption should not be here (an unsafe image, a person who wants their photo removed, a work that is not
	in the public domain), open a discussion on this dataset's
	[Community tab](https://huggingface.co/datasets/udgamlabs/chitra-tiny-data/discussions) or email [jitesh@udgamlabs.com](mailto:jitesh@udgamlabs.com).
	We take reported items out of this copy, and also items that PD12M or Megalith-CC0 take out of theirs.

	## Licence

	Each part has its own terms; see [`LICENSES/README.md`](LICENSES/README.md). In short:

	\| Part \| Terms \|
	\|---\|---\|
	\| Images \| Dedicated under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) or marked public domain ([PDM 1.0](https://creativecommons.org/publicdomain/mark/1.0/)) by the source; per image in the index \|
	\| PD12M captions and metadata \| [CDLA-Permissive-2.0](LICENSES/CDLA-Permissive-2.0.txt) \|
	\| Megalith-CC0 captions and metadata \| [MIT](LICENSES/MIT-Megalith-CC0.txt) \|
	\| What Udgam Labs adds (latents, index, per-sample `.json`, this card) \| [CDLA-Permissive-2.0](LICENSES/CDLA-Permissive-2.0.txt) \|

	CC0 and the Public Domain Mark cover copyright only. They do not cover the privacy or publicity rights of people shown in
	the images, or trademarks, logos and insignia.

	## Citation

	```bibtex
	@misc{chitratinydata2026,
	title = {Chitra Tiny training set: 4.7M public-domain and CC0 images at 256 px with captions and SD-VAE latents},
	author = {Udgam Labs},
	year = {2026},
	url = {https://huggingface.co/datasets/udgamlabs/chitra-tiny-data}
	}
	```

	Please also cite the sources:

	```bibtex
	@misc{meyer2024publicdomain12mhighly,
	title = {Public Domain 12M: A Highly Aesthetic Image-Text Dataset with Novel Governance Mechanisms},
	author = {Jordan Meyer and Nick Padgett and Cullen Miller and Laura Exline},
	year = {2024},
	eprint = {2410.23144},
	archivePrefix = {arXiv},
	primaryClass = {cs.AI},
	url = {https://arxiv.org/abs/2410.23144}
	}
	```

	Megalith-CC0: https://huggingface.co/datasets/Spawning/megalith-cc0, derived from Megalith-10m by madebyollin
	(https://huggingface.co/datasets/madebyollin/megalith-10m).

	Thanks to Spawning for PD12M and Megalith-CC0, to madebyollin for Megalith-10m, to Microsoft for Florence-2,
	to Stability AI for the SD-VAE, and to the authors of the two safety classifiers.

	---

	About Udgam Labs. Udgam Labs is an independent AI research lab. We work from first principles: before we build, we understand why things work, whether we are training a model, fine-tuning one or applying AI to a new problem. Udgam (उद्गम) is Sanskrit for origin. [udgamlabs.com](https://udgamlabs.com)