data-preparation
Routes ALAE dataset preparation, TFRecord layout validation, sample-image setup, style-mixing image layout, and face-alignment workflows.
来源信息
- 仓库
- VectorSpaceLab/AREX-Skill
- 最近来源活动
- 2026年8月26日 16:31
- 检测到的 SKILL.md 语言
- 英语
- 星标
- 12
- 分支
- 2
安装方式
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
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SKILL.md
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- data-preparation
- description
- Routes ALAE dataset preparation, TFRecord layout validation, sample-image setup, style-mixing image layout, and face-alignment workflows.
- disable-model-invocation
- true
- metadata
- {"disco-role":"operating"}
- license
- NO_LICENSE
# ALAE Data Preparation
Use this sub-skill when a task asks for ALAE data inputs: TFRecord conversion or splitting, config dataset paths, sample-image directories, style-mixing `src`/`dst` image folders, or face alignment for FFHQ/CelebA-style inputs.
## Route by request
- **Validate a config or checkout layout first:** read [data-layouts](references/data-layouts.md), then run `scripts/validate_alae_data_layout.py` against the user's config. This is the safest first step before training, reconstruction, style mixing, or metrics.
- **Prepare TFRecords:** read [TFRecord preparation](references/tfrecord-preparation.md). The original dataset scripts are TensorFlow 1.x, raw-data dependent, and often large or network-bound; treat commands there as explicit, checkout-root operations.
- **Align face images:** read [face alignment](references/face-alignment.md), then use `scripts/align_faces_alae.py` with explicit `--input-dir`, `--output-dir`, and `--predictor` paths.
- **Debug data errors:** read [troubleshooting](references/troubleshooting.md) for TensorFlow 1.x API failures, `PYTHONPATH` issues, missing dlib predictors, stale `/data/datasets` assumptions, disk/network side effects, and malformed style/sample layouts.
## Boundaries
- This sub-skill owns dataset paths from ALAE configs: `DATASET.PATH`, `DATASET.PATH_TEST`, `PART_COUNT`, `PART_COUNT_TEST`, `SAMPLES_PATH`, `STYLE_MIX_PATH`, `MAX_RESOLUTION_LEVEL`, and `OUTPUT_DIR` as they affect data readiness.
- Route full training launches and checkpoint interpretation to the training sub-skill (`../training/SKILL.md`).
- Route pretrained model downloads, generation, reconstruction, and style-mixing execution to the generation sub-skill (`../generation/SKILL.md`) after this sub-skill validates input layouts.
- Route FID/PPL/LPIPS metric execution to the metrics sub-skill (`../metrics/SKILL.md`) after TFRecords and paths are validated here.
## Safe bundled helpers
```bash
python scripts/validate_alae_data_layout.py --help
python scripts/align_faces_alae.py --help
```
Both helpers avoid network, training, and hard-coded checkout paths by default. Pass an ALAE checkout root or explicit input/output paths when validating or transforming user data.
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