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Wan2.2 TI2V-5B Usage

This page documents the FlashRT official-pipeline route for Wan2.2-TI2V-5B on RTX SM120.

FlashRT exposes a stable set_prompt() / infer() API around the official Wan Python pipeline. ComfyUI support is handled outside the core repository through custom nodes that call FlashRT public APIs.

Requirements

Use the original Wan2.2-TI2V-5B checkpoint layout:

Wan2.2-TI2V-5B/
  diffusion_pytorch_model-00001-of-00003.safetensors
  diffusion_pytorch_model-00002-of-00003.safetensors
  diffusion_pytorch_model-00003-of-00003.safetensors
  diffusion_pytorch_model.safetensors.index.json
  Wan2.2_VAE.pth
  models_t5_umt5-xxl-enc-bf16.pth
  google/umt5-xxl/

The official Wan Python package must be importable. Either install the Wan source package, add it to PYTHONPATH, or set one of:

export FLASH_RT_WAN22_ROOT=/path/to/Wan2.2/source
export MOTUS_ROOT=/path/to/Motus        # works when Wan is vendored in bak/wan

API

import flash_rt

model = flash_rt.load_model(
    "/path/to/Wan2.2-TI2V-5B",
    framework="torch",
    config="wan22_ti2v_5b",
    hardware="rtx_sm120",
)

model.set_prompt(
    "A cinematic shot of a blue sphere rolling across a wooden table",
)

out = model.infer(
    mode="t2v",
    width=832,
    height=480,
    frames=81,
    steps=20,
    shift=5.0,
    guide_scale=5.0,
    seed=1234,
    return_metadata=True,
)

video = out["video"]       # torch.Tensor [C, F, H, W]
metadata = out["metadata"]

TeaCache is available as an explicit experimental acceleration option:

out = model.infer(
    mode="t2v",
    width=1280,
    height=704,
    frames=121,
    steps=20,
    shift=5.0,
    guide_scale=5.0,
    teacache=True,
    teacache_threshold=0.3,
    teacache_start_step=1,
    teacache_end_step=-1,
    teacache_cache_device="cuda",
    return_metadata=True,
)

print(out["metadata"]["teacache"])

teacache_threshold controls the speed/quality trade-off. Larger values skip more DiT steps and may change composition or motion. The Wan2.2 5B route uses training-free relative-L1 TeaCache without model-specific coefficients, so treat it as a validation tool until the threshold is qualified for your prompt class.

For image-to-video:

model.set_prompt("A handheld camera shot, smooth motion")
video = model.infer(
    mode="i2v",
    image=start_image,     # PIL.Image.Image
    width=832,
    height=480,
    frames=81,
    steps=20,
    shift=5.0,
    guide_scale=5.0,
)

model.predict() is not part of the Wan API because predict() is the VLA action-output convenience wrapper.

Recommended Baselines

Community 480p performance baseline:

width=832
height=480
frames=81
steps=20
shift=5.0
guide_scale=5.0
sample_solver=unipc

Official quality baseline:

width=1280
height=704
frames=121
steps=20 or 50
shift=5.0
guide_scale=5.0
sample_solver=unipc

Benchmarks

RTX 5090, official Wan2.2-TI2V-5B checkpoint, T2V, 1280x704, frames=121, steps=20, shift=5.0, guide_scale=5.0, sample_solver=unipc. Timings are infer_seconds, which includes text encoding, DiT sampling, and VAE decode but excludes checkpoint load.

Path TeaCache threshold DiT calls Time Peak VRAM Note
FlashRT official pipeline off 20/20 178.6 s 24.37 GiB baseline
FlashRT official pipeline 0.3 8/20 114.2 s 24.37 GiB 1.56x faster; visible quality drift on the test prompt
Upstream public reference off n/a under 9 min n/a Wan2.2 TI2V-5B model-card 720p single consumer GPU reference

Use teacache_threshold=0.15-0.30 as a starting search range. In local testing 0.03 did not skip steps on a small smoke run, while 0.3 skipped aggressively and changed the result. Keep the no-TeaCache output as the quality reference for each prompt class.

Upstream reference: Wan2.2-TI2V-5B model card.

Keep ComfyUI wall time separate from this official-pipeline timing. ComfyUI adds graph-node scheduling, model-file repackaging, optional FP8/GGUF/Sage attention paths, and video-output overhead.