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from __future__ import annotations
import argparse
import gc
import json
import resource
import time
from pathlib import Path
import av
import torch
from accelerate import cpu_offload
from diffusers import AutoencoderKLMiniMaxH3, AutoencoderKLMiniMaxH3Audio
from diffusers.modular_pipelines.minimax_h3.packing import (
MINIMAX_H3_PIXEL_MEAN,
MINIMAX_H3_PIXEL_STD,
)
from diffusers.utils.export_utils import _prepare_audio_stream, _write_audio, encode_video
from diffusers.video_processor import VideoProcessor
from checkpoint_io import atomic_json_write
from latent_io import load_latent_bundle, prepare_inference_model
from media_packaging import atomic_media_output
def encode_master_crf1(
frames: list,
*,
fps: int,
output_path: Path,
audio: torch.Tensor,
audio_sample_rate: int,
) -> None:
"""Write the pre-delivery master before any HEVC recompression."""
container = av.open(str(output_path), mode="w", format="mp4")
stream = container.add_stream(
"libx264",
rate=fps,
options={"crf": "1", "preset": "slow"},
)
stream.width = frames[0].width
stream.height = frames[0].height
stream.pix_fmt = "yuv444p"
audio_stream = _prepare_audio_stream(container, audio_sample_rate)
try:
for image in frames:
frame = av.VideoFrame.from_image(image)
for packet in stream.encode(frame):
container.mux(packet)
for packet in stream.encode():
container.mux(packet)
_write_audio(container, audio_stream, audio, audio_sample_rate, av)
finally:
container.close()
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--latents", type=Path, required=True)
parser.add_argument("--vae", type=Path, required=True)
parser.add_argument("--audio-vae", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--preview-output", type=Path, required=True)
parser.add_argument("--disable-spatial-tiling", action="store_true")
args = parser.parse_args()
report_path = args.output.with_suffix(".decode.metrics.json")
report: dict[str, object] = {
"status": "running",
"latent_bundle": str(args.latents),
"vae": str(args.vae),
"vae_dtype": "fp32_source",
"audio_vae": str(args.audio_vae),
"audio_vae_dtype": "fp32_source",
"offload": {
"video_vae": "sequential_cpu_offload",
"audio_vae": "resident_cuda_during_audio_stage",
},
"video_vae_tiling": not args.disable_spatial_tiling,
"master_video": {
"codec": "libx264",
"crf": 1,
"preset": "slow",
"pixel_format": "yuv444p",
},
"gpu": torch.cuda.get_device_name(),
}
report_path.parent.mkdir(parents=True, exist_ok=True)
atomic_json_write(report, report_path)
try:
bundle = load_latent_bundle(args.latents)
metadata = dict(bundle["metadata"])
report["metadata"] = metadata
video_load_started = time.perf_counter()
vae = prepare_inference_model(
AutoencoderKLMiniMaxH3.from_pretrained(
args.vae,
low_cpu_mem_usage=True,
dtype=torch.float32,
)
)
if args.disable_spatial_tiling:
vae.disable_tiling()
else:
vae.enable_tiling()
cpu_offload(
vae,
execution_device=torch.device("cuda"),
offload_buffers=True,
)
report["video_vae_load_seconds"] = time.perf_counter() - video_load_started
torch.cuda.reset_peak_memory_stats()
video_decode_started = time.perf_counter()
video_latents = bundle["video_latents"].to("cuda")
with torch.inference_mode(), torch.autocast(device_type="cuda", dtype=torch.float16):
video = vae.decode(video_latents, return_dict=False)[0]
pixel_mean = torch.tensor(MINIMAX_H3_PIXEL_MEAN, device="cuda").view(
1, -1, 1, 1, 1
)
pixel_std = torch.tensor(MINIMAX_H3_PIXEL_STD, device="cuda").view(
1, -1, 1, 1, 1
)
video = (video.float() * pixel_std + pixel_mean).clamp(0, 1)
videos = VideoProcessor(vae_scale_factor=16, do_normalize=False).postprocess_video(
video,
output_type="pil",
)
torch.cuda.synchronize()
report["video_decode_seconds"] = time.perf_counter() - video_decode_started
report["video_decode_torch_peak_bytes"] = torch.cuda.max_memory_allocated()
report["video_frames"] = len(videos[0])
del vae, video_latents, video, pixel_mean, pixel_std
gc.collect()
torch.cuda.empty_cache()
audio_load_started = time.perf_counter()
audio_vae = prepare_inference_model(
AutoencoderKLMiniMaxH3Audio.from_pretrained(
args.audio_vae,
low_cpu_mem_usage=True,
dtype=torch.float32,
)
).to("cuda")
report["audio_vae_offload"] = "resident_cuda_during_audio_stage"
report["audio_vae_load_seconds"] = time.perf_counter() - audio_load_started
torch.cuda.reset_peak_memory_stats()
audio_decode_started = time.perf_counter()
audio_latents = bundle["audio_latents"].to("cuda")
with torch.inference_mode():
audio = audio_vae.decode(audio_latents, return_dict=False)[0]
audio = audio.float().permute(1, 0, 2)
if not torch.isfinite(audio).all() or torch.count_nonzero(audio) == 0:
raise RuntimeError("source audio VAE produced invalid or silent audio")
torch.cuda.synchronize()
report["audio_decode_seconds"] = time.perf_counter() - audio_decode_started
report["audio_decode_torch_peak_bytes"] = torch.cuda.max_memory_allocated()
report["audio_min"] = float(audio.min())
report["audio_max"] = float(audio.max())
report["audio_std"] = float(audio.std())
preview_started = time.perf_counter()
with atomic_media_output(args.preview_output) as partial:
encode_video(
videos[0],
fps=int(metadata["fps"]),
output_path=str(partial),
audio=audio[0],
audio_sample_rate=int(metadata["sampling_rate"]),
)
report["preview_encode_seconds"] = time.perf_counter() - preview_started
report["preview_output"] = str(args.preview_output)
report["preview_output_bytes"] = args.preview_output.stat().st_size
atomic_json_write(report, report_path)
encode_started = time.perf_counter()
with atomic_media_output(args.output) as partial:
encode_master_crf1(
videos[0],
fps=int(metadata["fps"]),
output_path=partial,
audio=audio[0],
audio_sample_rate=int(metadata["sampling_rate"]),
)
report["encode_seconds"] = time.perf_counter() - encode_started
report["output_bytes"] = args.output.stat().st_size
report["rss_peak_bytes"] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss * 1024
report["status"] = "pass"
except Exception as error:
report["status"] = "fail"
report["error"] = f"{type(error).__name__}: {error}"
raise
finally:
atomic_json_write(report, report_path)
print(json.dumps(report))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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