YeQingWen/FlowGuard-Dataset

FlowGuard Dataset

📦 Overview

The FlowGuard Dataset is designed to support the training and evaluation of the FlowGuard framework, with a focus on safety-aware image generation and detection.

To ensure scalability and efficient storage, the dataset is:

• organized by model architecture
• packed into size-balanced tar shards
• filtered to retain only essential supervision signals

We include generations from 9 different diffusion / generative architectures, enabling diverse and robust evaluation.

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🚀 Loading and Usage

The dataset is stored on Hugging Face as Parquet shards organized by split, model, and label:

train/{model}/{label}/*.parquet
test/{model}/{label}/*.parquet

Each row contains one image sample with metadata such as model name, split, safety label, case ID, step type, and step index.

Load the full dataset

from datasets import load_dataset

dataset = load_dataset(
    "parquet",
    data_files={
        "train": "hf://datasets/YeQingWen/FlowGuard-Dataset/train/*/*/*.parquet",
        "test": "hf://datasets/YeQingWen/FlowGuard-Dataset/test/*/*/*.parquet",
    }
)

print(dataset)
print(dataset["train"][0])

Load a specific model

from datasets import load_dataset

dataset = load_dataset(
    "parquet",
    data_files={
        "train": "hf://datasets/YeQingWen/FlowGuard-Dataset/train/flux1/*/*.parquet",
        "test": "hf://datasets/YeQingWen/FlowGuard-Dataset/test/flux1/*/*.parquet",
    }
)

Load a specific model and label

from datasets import load_dataset

safe_flux1_train = load_dataset(
    "parquet",
    data_files={
        "train": "hf://datasets/YeQingWen/FlowGuard-Dataset/train/flux1/safe/*.parquet"
    },
    split="train"
)

Access an image

example = dataset["train"][0]

image = example["image"]
label = example["label"]
model = example["model"]
step_type = example["step_type"]
step_index = example["step_index"]

image.show()

Each example contains:

• model: generation architecture
• split: train or test
• label: safe or unsafe
• case_id: generation case identifier
• step_type: linear_step or step49
• step_index: diffusion step index
• image: decoded image object

NSFW Category Distribution

The NSFW category is shown below:

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📊 Statistics

The table below reflects the full, unbalanced dataset distribution.

⚠️ During training, we subsample to achieve a balanced distribution.
For details, see: https://arxiv.org/abs/2604.07879

Model	Train Safe	Train Unsafe	Train Total	Test Safe	Test Unsafe	Test Total	Overall Total
flux1	2,687	1,953	4,640	200	237	437	5,077
flux2	671	2,017	2,688	227	181	408	3,096
pixart	2,725	4,246	6,971	195	231	426	7,397
Qwen-Image	2,643	2,055	4,698	196	496	692	5,390
sd3	1,250	1,293	2,543	200	191	391	2,934
sd3.5	N/A	N/A	N/A	391	317	708	708
sdv1.5	2,659	3,537	6,196	199	253	452	6,648
sdxl	1,899	1,759	3,658	243	282	525	4,183
Zimage	2,676	1,910	4,586	199	248	447	5,033
Total	17,210	18,770	35,980	2,050	2,436	4,486	40,466

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🛡️ Auditing

To ensure dataset quality:

• The training set is automatically audited using LlavaGuard-7B
• The test set is curated under strict human supervision

This hybrid pipeline ensures both scalability and high-quality evaluation signals.

For implementation details (e.g., prompts and hyperparameters), please refer to:

https://arxiv.org/abs/2604.07879

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📌 Notes

• Each sample corresponds to a generation case (subdirectory)
• Each case contains:
  • multiple linear_step outputs
  • one final image
• .pt files are excluded to reduce storage overhead