ac48bb3cd1
Co-authored-by: Aryan Kumar <aryank@Aryans-Mac-Studio.local> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: CodeRabbit <noreply@coderabbit.ai>
109 lines
4.6 KiB
Python
109 lines
4.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""End-to-end MiniMax-H3 (FastH3) generation with the Apple Silicon MLX runtime.
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Accepts a text prompt and produces an MP4 with H.264 video at 24 fps and
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stereo AAC audio at 32 kHz. One heavyweight model phase is resident at a time.
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python examples/inference/basic/mlx_fasth3.py \
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--model-root ~/models/FastH3-Preview-v0.2 \
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--mlx-checkpoint ~/models/FastH3-MLX/int8 \
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--prompt '(S1) A red panda says <d>[English] Fast H3 is amazing.</d>' \
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--height 480 --width 832 --num-frames 124 --seed 2026 \
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--output-path ~/fasth3_outputs/int8.mp4
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Conditioning uses the streamed Qwen3-VL text encoder on first use and caches
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the resulting embeddings under --prompt-cache-dir for instant reuse.
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``--fast`` is temporal fast mode. It keeps full-duration audio while
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denoising fewer video frames, then uses MLX RIFE 4.25 to reconstruct the
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requested frame count. A 1280x720 request runs on H3's 1280x736 grid and is
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center-cropped after decode.
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This entrypoint currently supports text-to-video-with-audio only. It does not
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yet wire FL2VA, Ref2VA, spatial fast mode, or two-pass refinement.
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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parser.add_argument("--model-root", type=Path, default=Path.home() / "models/FastH3-Preview-v0.2",
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help="H3 snapshot root (vae/, audio_vae/, text_encoder/, tokenizer/)")
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parser.add_argument("--mlx-checkpoint", type=Path, required=True,
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help="pre-quantized MLX DiT directory (int8/int6/int4 mlx_h3_dit format)")
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parser.add_argument(
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"--prompt",
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required=True,
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help="H3 text prompt; use (S1) and <d>[Language] words</d> for explicit dialogue",
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)
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parser.add_argument("--output-path", type=Path, required=True)
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parser.add_argument("--height", type=int, default=480)
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parser.add_argument("--width", type=int, default=832)
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parser.add_argument("--num-frames", type=int, default=124)
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--steps", type=int, default=4, help="denoise steps (trained ladder = 4)")
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parser.add_argument(
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"--fast",
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action=argparse.BooleanOptionalAction,
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default=False,
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help="denoise fewer video frames, then use MLX RIFE to restore the target frame count; audio stays full length",
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)
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parser.add_argument("--fast-factor", type=int, default=2,
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help="temporal reduction target for --fast (default: 2)")
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parser.add_argument("--fast-sharpen", type=float, default=0.6,
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help="unsharp strength after RIFE interpolation (0 disables)")
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parser.add_argument("--rife-weights-dir", type=Path, default=None,
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help="optional local mlx-community/RIFE-4.25 snapshot")
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parser.add_argument("--vae-dtype", choices=("fp32", "fp16", "bf16"), default="fp32")
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parser.add_argument("--prompt-cache-dir", type=Path, default=None,
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help="directory for reusable prompt embedding caches")
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parser.add_argument(
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"--tiled-video-decode",
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action=argparse.BooleanOptionalAction,
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default=True,
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help="decode with the reference 256px overlapping VAE tiles (disable only for diagnostics)",
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)
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return parser.parse_args()
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def main() -> None:
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args = parse_args()
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from fastvideo.mlx_runtime.minimax_h3_pipeline import MiniMaxH3MLXPipeline
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pipeline = MiniMaxH3MLXPipeline(
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model_root=args.model_root,
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mlx_dit_checkpoint=args.mlx_checkpoint,
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vae_dtype=args.vae_dtype,
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prompt_cache_dir=args.prompt_cache_dir,
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)
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result = pipeline.generate(
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args.prompt,
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output_path=args.output_path,
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height=args.height,
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width=args.width,
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num_frames=args.num_frames,
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seed=args.seed,
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num_steps=args.steps,
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tiled_video_decode=args.tiled_video_decode,
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fast=args.fast,
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fast_factor=args.fast_factor,
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fast_sharpen=args.fast_sharpen,
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rife_weights_dir=args.rife_weights_dir,
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)
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print(json.dumps({
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"video_path": result.video_path,
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"timings_s": {k: round(v, 2) for k, v in result.timings.items()},
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"peak_memory_gib": {k: round(v, 2) for k, v in result.peak_memory_gib.items()},
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"audio_samples": int(result.waveform.shape[-1]),
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}, indent=2))
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if __name__ == "__main__":
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main()
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