# SPDX-License-Identifier: Apache-2.0
"""Minimal user-runnable example for the daVinci-MagiHuman base AV pipeline.

Produces an mp4 with both video (Wan 2.2 TI2V-5B VAE) and audio (Stable
Audio Open 1.0 VAE, first-class FastVideo port in
`fastvideo/models/vaes/oobleck.py`) muxed together via PyAV.

Prerequisites (one-off):

  # 1) Accept terms of use on the gated HF repos with your HF_TOKEN:
  #    - https://huggingface.co/google/t5gemma-9b-9b-ul2
  #    - https://huggingface.co/stabilityai/stable-audio-open-1.0
  #    All four cross-variant shared components — Wan 2.2 VAE, T5-Gemma
  #    encoder + tokenizer, and Stable Audio VAE — are lazy-loaded from
  #    their canonical upstream HF repos on first build, so a single
  #    cache (~25 GB) is shared across every MagiHuman variant.
  # 2) Convert the raw MagiHuman base DiT to a minimal Diffusers layout
  #    (~5 GB; only transformer/ + scheduler/ + model_index.json):
  python scripts/checkpoint_conversion/convert_magi_human_to_diffusers.py \\
      --source GAIR/daVinci-MagiHuman \\
      --subfolder base \\
      --output converted_weights/magi_human_base
  # Pass --bundle-vae / --bundle-audio-vae / --bundle-text-encoder only
  # if you want a self-contained snapshot.
"""
from fastvideo import VideoGenerator


PROMPT = (
    "A warm afternoon scene: a person sits on a park bench reading a book, "
    "surrounded by softly swaying trees."
)


def main() -> None:
    generator = VideoGenerator.from_pretrained(
        "converted_weights/magi_human_base",
        num_gpus=1,
    )
    output_path = "outputs_video/magi_human_basic/output_magi_human.mp4"
    generator.generate_video(
        prompt=PROMPT,
        output_path=output_path,
        save_video=True,
        # Defaults pulled from the registered preset (magi_human_base):
        # height=256, width=448, fps=25, num_inference_steps=32, seed=42.
        # Override here only if you have a specific QA scenario.
    )
    generator.shutdown()


if __name__ == "__main__":
    main()