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catvton-train-reconstruction

Use when implementing, reviewing, or extending CatVTON-style training in this repository. Focus on DressCode-based training, agnostic mask generation from local dataset annotations, and attention checkpoint export compatible with CatVTON inference layouts.

Install

mkdir -p .claude/skills/catvton-train-reconstruction-hello-hoy && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/14777" && unzip -o skill.zip -d .claude/skills/catvton-train-reconstruction-hello-hoy && rm skill.zip

Installs to .claude/skills/catvton-train-reconstruction-hello-hoy

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Use when implementing, reviewing, or extending CatVTON-style training in this repository. Focus on DressCode-based training, agnostic mask generation from local dataset annotations, and attention checkpoint export compatible with CatVTON inference layouts.
256 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)

About this skill

CatVTON Train Reconstruction

Use this skill for work inside CatVTON_practice.

What this repo is for

This project is a practice implementation of CatVTON-style training centered on the local DressCode dataset under data/DressCode.

The important local constraint is that DressCode here does not already contain agnostic_masks, so training must either generate them on the fly or cache them before use.

Workflow

  1. Read references/repo-notes.md.
  2. Keep the implementation scoped to this repo before reaching back into the original CatVTON repo.
  3. Preserve CatVTON-compatible checkpoint layout:
    • <output>/<dataset_tag>/attention
  4. Prefer self-attention-only training unless the user explicitly asks for a different fine-tuning target.
  5. When changing dataset logic, validate against the actual files under data/DressCode.

Validation rules

  • Confirm train_pairs.txt and test pair files still parse.
  • Confirm masks are generated or loaded consistently.
  • Confirm the saved checkpoint contains attention weights that can be reloaded later.

References

  • references/repo-notes.md

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