README.md

# FlowGuard Dataset
<div align="center">
  <img src="assets/main_fig.png" alt="main">
</div>

## 📦 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.

---
## 🚀 Loading and Usage

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

```text
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

```python
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

```python
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

```python
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

```python
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:

<div align="center">
  <img src="assets/nsfw_pie.png" alt="NSFW 7分类分布饼图" style="width:70%; max-width:600px; height:auto;">
</div>

---
## 📊 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**     |

---

## 🛡️ 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

---

## 📌 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