hao-ai-lab-FastVideo/tests/local_tests/lingbotworld_fast

LingBot-World-Fast local verification

Local-only numerical parity and port evidence for FastVideo/LingBot-World-Fast-Diffusers.

Scope

• Official implementation: Robbyant/lingbot-world, wan/modules/model_fast.py
• Released fast checkpoint: robbyant/lingbot-world-fast
• FastVideo bundle: FastVideo/LingBot-World-Fast-Diffusers
• Workload: causal image-to-video with camera poses
• Released sampling contract:
  • global self-attention (local_attn_size=-1)
  • three latent frames per chunk
  • four fixed scheduler indices: (0, 179, 358, 679)
  • flow shift 10.0

Setup

Use the current FastVideo environment. The official source needs easydict in addition to FastVideo's dependencies.

git clone https://github.com/Robbyant/lingbot-world.git \
  /tmp/lingbot-world-reference
uv pip install --python .venv/bin/python easydict

Resolve a local snapshot of the FastVideo bundle:

LINGBOTWORLD_FAST_MODEL_DIR=$(
  .venv/bin/python - <<'PY'
from huggingface_hub import snapshot_download
print(snapshot_download("FastVideo/LingBot-World-Fast-Diffusers"))
PY
)
export LINGBOTWORLD_FAST_MODEL_DIR
export LINGBOTWORLD_FAST_REFERENCE_DIR=/tmp/lingbot-world-reference

Do not store Hugging Face tokens in this directory.

Full transformer parity

The parity test loads the same released transformer weights into both:

1. the official WanModelFast implementation; and
2. FastVideo's production TransformerLoader.

It runs the models sequentially to stay within memory limits. Both sides use the mathematical SDPA backend. The official cross-attention helper is routed through the official source's own SDPA-capable dispatcher because the direct helper only supports FlashAttention.

The deterministic input is one real three-frame causal chunk with:

• 16 noisy latent channels;
• 20 image-conditioning channels;
• 4096-wide text context;
• packed 384-channel camera rays;
• real per-layer self- and cross-attention caches.

Run:

FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA \
LINGBOTWORLD_FAST_MODEL_DIR="$LINGBOTWORLD_FAST_MODEL_DIR" \
LINGBOTWORLD_FAST_REFERENCE_DIR="$LINGBOTWORLD_FAST_REFERENCE_DIR" \
.venv/bin/python -m pytest \
  tests/local_tests/lingbotworld_fast/test_lingbotworld_fast_transformer_parity.py \
  -v -s

This is valid evidence only when the full-transformer test reports PASSED, not SKIPPED.

Lightweight runtime-contract tests

These tests cover backend dispatch, the fixed-step preset contract, and exact registry/pipeline resolution without loading the 18.5B transformer:

.venv/bin/python -m pytest \
  fastvideo/tests/attention/test_lingbotworld_fast_backend.py \
  fastvideo/tests/api/test_presets.py::TestLingBotWorldFastPresets \
  -v

SSIM

The package test is:

pytest fastvideo/tests/ssim/test_lingbot_fast_similarity.py -v -s

It requires two GPUs and device-specific references. New L40S references must be generated with the repository's seed-ssim-references workflow, reviewed visually, and uploaded to FastVideo/ssim-reference-videos. A GB10 output is not a valid replacement for the L40S CI reference.

See PORT_STATUS.md for the latest measured results and remaining blockers.