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checkpoint-and-setup

checkpoint-and-setup

Guides HunyuanVideo installation, CUDA dependency choices, checkpoint layout validation, text encoder preparation, and model-root troubleshooting.

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Documentation de la Skill

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name: checkpoint-and-setup description: "Guides HunyuanVideo installation, CUDA dependency choices, checkpoint layout validation, text encoder preparation, and model-root troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

HunyuanVideo Checkpoint and Setup

Use this sub-skill when the task is about installing HunyuanVideo, preparing ckpts/, validating model files, diagnosing missing text encoders/VAE/DIT weights, or deciding whether CUDA hardware is sufficient before generation.

Read first

  • references/installation.md for Python, CUDA, Docker, and dependency guidance.
  • references/checkpoint-layout.md for the expected checkpoint tree and text encoder preprocessing.
  • references/troubleshooting.md for setup failures and memory/backend triage.
  • scripts/validate_checkpoint_layout.py to validate file presence without downloading or loading weights.

Safe workflow

  1. Confirm the environment has the repo-pinned dependencies and CUDA if generation is required. From the root skill, run:
python scripts/check_hunyuan_video_env.py --check-optional
  1. Validate model files before launching a long generation job:
python sub-skills/checkpoint-and-setup/scripts/validate_checkpoint_layout.py --model-base ckpts
  1. If using FP8, validate the companion map file too:
python sub-skills/checkpoint-and-setup/scripts/validate_checkpoint_layout.py --model-base ckpts --require-fp8 --dit-weight ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states_fp8.pt
  1. Route to ../inference/SKILL.md for single-GPU command construction, ../parallel-and-optimization/SKILL.md for FP8/xDiT commands, or ../web-demo/SKILL.md for Gradio launch.

Important constraints

  • --model-base defaults to ckpts; if that directory does not exist, the canonical sampling script fails before model loading.
  • The default constants also read a MODEL_BASE environment variable at import time. Keep MODEL_BASE and --model-base consistent when customizing paths.
  • Full generation is not proven by parser or layout checks. It requires downloaded checkpoint files, CUDA, and enough VRAM.
  • The repository documents Linux, Python 3.10.9, PyTorch 2.6.0, CUDA 12.4 or 11.8, flash-attn, and optional xDiT.
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