| --- |
| 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). |
|
|
