---
pretty_name: DC-Gen Data
language:
  - en
tags:
  - dc-gen
  - training-examples
  - image-generation
  - image-editing
  - video-generation
  - evaluation
---

# DC-Gen Data

This repository contains **example training data** and **processed evaluation data** for [DC-Gen](https://github.com/dc-ai-projects/DC-Gen). The training examples are small subsets for exercising data preparation and model adaptation workflows; they are not the complete datasets used to train the released models. The evaluation inputs are prepared test data for Wan I2V benchmarking.

## Contents

| Subfolder | Contents | Intended use |
| --- | --- | --- |
| `train_example/flux/` | 6 original TAR shards, including an `align/` subset | FLUX RGB metadata preparation, teacher/student latent extraction, and adaptation training examples |
| `train_example/zimage/` | 5 original TAR shards, including an `align/` subset | Z-Image RGB metadata preparation, teacher/student latent extraction, and adaptation training examples |
| `train_example/qwen-image-edit/qwen_image_gen/` | 1 TAR of Qwen-Image-Edit generated examples | Embedding alignment and patch/head training examples |
| `train_example/qwen-image-edit/single_turn_processed/` | 1 TAR of processed Pico-Banana examples | Subsequent image-editing training examples |
| `train_example/wan/fusionX_480p/` | 1 original TAR under `align/shift_2/` | Wan/DC-AE-V latent extraction and T2V/I2V adaptation training examples |
| `eval/wan-i2v/` | 355 images at 832x480 plus `VBench_i2v_extended_full_info.json` | Prepared VBench I2V test inputs with extended prompts |

Original TAR filenames, contents, and internal directory structure are preserved. Metadata indexes and latent archives are generated locally using the project recipes. Model weights are distributed separately.

## Download only the required subfolder

Install `huggingface_hub` if needed. Use `HfFileSystem.get` with a trailing `/` on the remote subfolder to download its contents directly into the directory already used by your training or evaluation commands. Only the selected subfolder is downloaded; the remote `train_example/` or `eval/` prefix is not added to the local destination.

For the processed Wan I2V evaluation data:

```bash
python - <<'PY_DOWNLOAD'
from huggingface_hub import HfFileSystem

HfFileSystem().get(
    "datasets/dc-ai/DC-Gen-Data/eval/wan-i2v/",
    "assets/data/vbench",
    recursive=True,
)
PY_DOWNLOAD
```

This creates `assets/data/vbench/VBench_i2v_extended_full_info.json` and `assets/data/vbench/vbench_i2v_imgs/832-480/`, matching the existing `VBenchImagePrompt` defaults. No trainer path overrides are needed.

For the Wan training examples:

```bash
python - <<'PY_DOWNLOAD'
from huggingface_hub import HfFileSystem

HfFileSystem().get(
    "datasets/dc-ai/DC-Gen-Data/train_example/wan/fusionX_480p/",
    "assets/dataset/fusionX_480p",
    recursive=True,
)
PY_DOWNLOAD
```

For other training examples, choose the same local directory already used by the project's data preparation commands:

| Remote subfolder (append to `datasets/dc-ai/DC-Gen-Data/`) | Local destination |
| --- | --- |
| `train_example/flux/` | Your FLUX RGB TAR directory (`<path/to/rgb/tar/shards>`) |
| `train_example/zimage/` | Your Z-Image RGB TAR directory (`<path/to/rgb/tar/shards>`) |
| `train_example/qwen-image-edit/single_turn_processed/` | `$PICO_BANANA_DATA_PATH/single_turn_processed` |
| `train_example/qwen-image-edit/qwen_image_gen/` | `$PICO_BANANA_DATA_PATH/qwen_image_gen` |
| `train_example/wan/fusionX_480p/` | `assets/dataset/fusionX_480p` |
| `eval/wan-i2v/` | `assets/data/vbench` |

Keep the trailing `/` on the remote subfolder so the selected contents map directly into the destination, including nested directories such as `align/`. Download the Qwen subfolders separately when only one is needed.

Training follows the sequence RGB TARs → metadata indexes → latent archives → latent metadata → training. Ordinary manual-prompt inference uses model weights and user inputs and does not require these training examples or benchmark inputs.

## Sources and evaluation processing

The training examples are migrated from the DC-Gen project's existing shared data folders. `single_turn_processed` contains processed Pico-Banana examples; `qwen_image_gen` contains the corresponding project's Qwen-generated training examples. This release preserves the supplied sample archives rather than generating additional training data.

The I2V test images are derived from [VBench's I2V benchmark](https://github.com/Vchitect/VBench/tree/master/vbench2_beta_i2v), prepared at a 26:15 aspect ratio and 832x480 resolution. The accompanying JSON contains the project's extended prompts. This repository distributes those processed inputs; it does not include the official `origin.zip`, `crop.zip`, or other VBench release archives. Refer to the upstream projects for original data terms and attribution.

The JSON is the version paired with the project's shared prepared images. The separate [VBench-I2V-Prompt-Extended](https://huggingface.co/datasets/dc-ai/VBench-I2V-Prompt-Extended) repository is retained unchanged.

`checksums.sha256` records SHA-256 checksums for every data file in this release.