aryan5v
62fc354824
Expose the exported H3 NVFP4 VAE decoder through ComponentConfig and map it to FastVideoArgs, alongside the existing text-encoder weight override. Add example flags and docs for the 36L quality and 8L fast decoder tiers, including the 8L depth-cut and no-NVFP4-VAE-encoder caveats. Add compat translation tests.
194 lines
7.5 KiB
Python
194 lines
7.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Generate synchronized video/audio with Pika H3 Flex.
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Five task shapes share one model release:
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``t2va`` text only
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``i2va`` first image
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``l2va`` last image
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``fl2va`` first and last images
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``ref2va`` ordered image/video/audio references
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The six-forward ladder is part of the adapter contract; this script deliberately
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does not expose a step-count flag. ``--lossless-accel`` turns on the existing
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training-free FastVideo switches (bit-identical kernels, reference memo,
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parallel VAE tiles) before the generator starts. ``--nvfp4-decoder`` and
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``--nvfp4-text-encoder`` opt into the existing Blackwell FP4 weight paths;
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they change weights, not the six-forward sampler. Kernel/VSA work is separate.
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Examples:
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python examples/inference/basic/basic_pika_h3_flex.py --task t2va \
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--prompt "a paper boat drifting down a rainy street"
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python examples/inference/basic/basic_pika_h3_flex.py --task fl2va \
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--image first.png --last-image last.png --prompt "the flower opens"
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python examples/inference/basic/basic_pika_h3_flex.py --task ref2va \
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--reference image:person.png --reference video:dance.mp4 \
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--reference audio:voice.wav --prompt "the person dances to the music"
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"""
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from __future__ import annotations
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import argparse
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import os
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from pathlib import Path
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from typing import Any
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from fastvideo import VideoGenerator
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from fastvideo.api import (
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CompileConfig,
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ComponentConfig,
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EngineConfig,
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GenerationRequest,
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GeneratorConfig,
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InputConfig,
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OffloadConfig,
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OutputConfig,
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ParallelismConfig,
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PipelineSelection,
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SamplingConfig,
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)
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from fastvideo.models.vision_utils import load_image
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from fastvideo.pipelines.basic.minimax_h3 import MiniMaxH3Reference
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# Training-free switches FastVideo already exposes. Workers inherit the
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# environment, so they must be set before VideoGenerator starts.
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LOSSLESS_ACCEL_ENV = {
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"FASTVIDEO_MINIMAX_H3_EXACT_KERNELS": "all",
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"FASTVIDEO_H3_REF2VA_MEMO_ENTRIES": "16",
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"FASTVIDEO_H3_VAE_TILE_PARALLEL": "1",
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"FASTVIDEO_ULYSSES_A2A": "auto",
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}
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TASKS = ("t2va", "i2va", "l2va", "fl2va", "ref2va")
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def _reference(value: str) -> MiniMaxH3Reference:
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try:
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media_type, source = value.split(":", 1)
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except ValueError as error:
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raise argparse.ArgumentTypeError("A reference must be image:PATH, video:PATH, or audio:PATH.") from error
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if media_type not in ("image", "video", "audio"):
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raise argparse.ArgumentTypeError(f"Unknown reference type {media_type!r}.")
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return MiniMaxH3Reference(source=source, media_type=media_type)
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
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parser.add_argument("--model-path", default="Pika-Labs/Pika-H3-Flex")
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parser.add_argument("--task", choices=TASKS, default="t2va")
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parser.add_argument("--prompt", required=True)
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parser.add_argument("--image", help="first frame (i2va/fl2va)")
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parser.add_argument("--last-image", help="last frame (l2va/fl2va)")
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parser.add_argument("--reference",
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action="append",
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type=_reference,
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default=[],
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help="ordered reference (repeatable; ref2va)")
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parser.add_argument("--height", type=int, default=768)
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parser.add_argument("--width", type=int, default=1344)
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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("--num-gpus", type=int, default=1)
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parser.add_argument("--compile", action="store_true", help="torch.compile the DiT")
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parser.add_argument("--lossless-accel", action="store_true", help="enable bit-identical speed-ups")
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parser.add_argument("--low-vram", action="store_true", help="stream the DiT layerwise")
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parser.add_argument("--nvfp4-decoder",
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help="Blackwell-only NVFP4 H3 decoder (quality-36L or fast-8L decoder-nvfp4.safetensors)")
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parser.add_argument("--nvfp4-text-encoder",
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help="Blackwell-only serialized NVFP4 Qwen3-VL text_encoder directory")
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parser.add_argument("--output", default="outputs/pika_h3_flex")
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args = parser.parse_args()
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if args.task in ("i2va", "fl2va") and not args.image:
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parser.error(f"--task {args.task} needs --image")
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if args.task in ("l2va", "fl2va") and not args.last_image:
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parser.error(f"--task {args.task} needs --last-image")
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if args.task == "ref2va" and not args.reference:
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parser.error("--task ref2va needs at least one --reference")
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if args.task != "ref2va" and args.reference:
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parser.error("--reference is only valid with --task ref2va")
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return args
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def _inputs(args: argparse.Namespace) -> InputConfig:
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if args.task == "ref2va":
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return InputConfig(references=args.reference)
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# The request adapter copies image fields verbatim; the stage loads
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# ``image_path`` itself but requires ``last_image`` to already be a PIL image.
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last_image = load_image(args.last_image) if args.last_image else None
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return InputConfig(image_path=args.image, last_image=last_image)
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def _pipeline(args: argparse.Namespace) -> PipelineSelection:
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components = ComponentConfig()
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if args.task == "ref2va":
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components.override_pipeline_cls_name = "PikaH3FlexRef2VAModularPipeline"
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if args.nvfp4_decoder:
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components.h3_vae_nvfp4_decoder = args.nvfp4_decoder
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if args.nvfp4_text_encoder:
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components.text_encoder_weights = args.nvfp4_text_encoder
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return PipelineSelection(
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workload_type="t2v" if args.task == "t2va" else "i2v",
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components=components,
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vae_tiling=True,
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)
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def main() -> None:
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args = parse_args()
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if args.lossless_accel:
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for key, value in LOSSLESS_ACCEL_ENV.items():
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os.environ.setdefault(key, value)
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output_path = Path(args.output) / f"pika_h3_flex_{args.task}.mp4"
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output_path.parent.mkdir(parents=True, exist_ok=True)
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offload = OffloadConfig(
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dit=False,
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dit_layerwise=args.low_vram,
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text_encoder=True,
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vae=True,
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pin_cpu_memory=not args.low_vram,
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lazy_module_load=True,
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)
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generator = VideoGenerator.from_config(
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GeneratorConfig(
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model_path=args.model_path,
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engine=EngineConfig(
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num_gpus=args.num_gpus,
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use_fsdp_inference=args.num_gpus > 1,
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parallelism=ParallelismConfig(tp_size=1, sp_size=args.num_gpus),
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offload=offload,
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compile=CompileConfig(enabled=args.compile),
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),
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pipeline=_pipeline(args),
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))
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try:
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result = generator.generate(
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GenerationRequest(
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prompt=args.prompt,
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negative_prompt="",
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inputs=_inputs(args),
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sampling=SamplingConfig(
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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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fps=24,
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num_inference_steps=6,
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guidance_scale=1.0,
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batch_cfg=False,
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seed=args.seed,
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),
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output=OutputConfig(
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output_path=str(output_path),
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save_video=True,
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return_frames=False,
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),
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))
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print(f"Output written to: {result.video_path}")
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finally:
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generator.shutdown()
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if __name__ == "__main__":
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main()
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