---
license: cc-by-4.0
pretty_name: MGFlow-T2I
language:
- en
task_categories:
- text-to-image
size_categories:
- 100K<n<1M
tags:
- mgflow
- synthetic
- image
- text
- webdataset
---

# MGFlow-T2I

**Anonymous dataset release for ICLR 2027 review.**

Synthetic image-prompt pairs used as the reference distribution for MGFlow
text-to-image post-training in
Unifying Distributional Training for One-Step Visual Generation.
The same reference images support both image-only and joint image-text matching.

Project page: https://mgflow-repo.github.io

Model checkpoints and fitted reference distributions:
[MGFlow/MGFlow](https://huggingface.co/MGFlow/MGFlow).

## Dataset contents

The dataset contains **301,706 synthetic images at 512×512** with English prompts.
All images were generated with the official distilled
[FLUX.2 [klein] 4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B)
model using four sampling steps and guidance scale 1.

| Subset | Prompt source | Images | TAR shards |
| --- | --- | ---: | ---: |
| COCO | 82,783 COCO train2014 captions | 248,349 | 105 |
| GenEval | 553 GenEval prompts | 53,357 | 19 |
| Total | | 301,706 | 124 |

Reference construction follows the procedure used by iRDM and AMFD:

- **COCO:** use the first caption in annotation order for each train2014 image,
  generate 24 candidates per caption, and retain the three with the highest PickScore.
- **GenEval:** retain up to 100 candidates per prompt that pass the official
  GenEval correctness test, considering up to 1,000 candidates when needed.
  Some prompts yield fewer than 100 passing images.

The release contains generated images, not original COCO photographs.
See the paper's text-to-image training details for the full construction protocol,
and [ATTRIBUTION.md](ATTRIBUTION.md) for image and prompt sources.

## File structure

```text
data/coco/train-*.tar       COCO-derived image-prompt pairs
data/geneval/train-*.tar    GenEval image-prompt pairs
metadata.jsonl             One record per image: id, prompt, subset, shard, image
manifest.json              Image counts, shard sizes, and checksums
ATTRIBUTION.md             Source attribution and license notices
```

Each TAR shard stores pairs named `<id>.png` and `<id>.json` in WebDataset format.
`metadata.jsonl` maps every image ID to its prompt and location in a shard;
`manifest.json` lists the per-shard counts and SHA-256 checksums.

## Read a sample

Install `huggingface_hub` and `pillow`. This example streams the first metadata
record and its image without downloading the full dataset or a complete shard:

```python
import json
import tarfile
from urllib.request import urlopen

from huggingface_hub import hf_hub_url
from PIL import Image

repo_id = "MGFlow/MGFlow-T2I"
metadata_url = hf_hub_url(repo_id, "metadata.jsonl", repo_type="dataset")
with urlopen(metadata_url) as response:
    sample = json.loads(response.readline())

shard_url = hf_hub_url(repo_id, sample["shard"], repo_type="dataset")
with urlopen(shard_url) as response:
    with tarfile.open(fileobj=response, mode="r|") as archive:
        for member in archive:
            if member.name == sample["image"]:
                with archive.extractfile(member) as image_file:
                    image = Image.open(image_file).convert("RGB")
                break

print(sample["prompt"])
print(image.size)
```

## License

Generated images and MGFlow-authored metadata are released under CC BY 4.0.
Third-party prompts retain their original licenses; see [LICENSE.md](LICENSE.md)
and [ATTRIBUTION.md](ATTRIBUTION.md).