| --- |
| 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. |
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|
