Davids048andClaude Opus 5.5
991fa391ff
The schema class MiniMaxH3Config had the same name as the MiniMax-H3 DiT config class in fastvideo/configs/models/dits/minimax_h3.py. The family partitions of PipelineSelection are renamed LTX2Options, LTX2RefineOptions, MiniMaxH3Options, and LongCatOptions, so their names cannot be confused with the model and pipeline config classes. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Profile one FastH3 Preview generation with NVTX after warmup."""
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from __future__ import annotations
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import argparse
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from pathlib import Path
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import torch
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from huggingface_hub import hf_hub_download
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from fastvideo import VideoGenerator
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from fastvideo.api import (
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AttentionConfig,
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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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MiniMaxH3Options,
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OffloadConfig,
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OutputConfig,
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ParallelismConfig,
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PipelineSelection,
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QuantizationConfig,
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SamplingConfig,
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)
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from fastvideo.profiler import nvtx_range
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VARIANT_BACKENDS = {
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"dense-datafree": "FLASH_ATTN",
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"vsa-datafree": "VIDEO_SPARSE_ATTN_H3",
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"vsa-synthetic-step1300": "VIDEO_SPARSE_ATTN_H3",
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"vsa-synthetic-step1900": "VIDEO_SPARSE_ATTN_H3",
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}
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("variant", choices=VARIANT_BACKENDS)
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parser.add_argument("--prompt", required=True)
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parser.add_argument("--output", required=True)
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parser.add_argument("--num-frames", type=int, default=345)
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parser.add_argument("--warmup-runs", type=int, default=3)
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return parser.parse_args()
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def main() -> None:
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"""Warm the selected FastH3 recipe, then profile one identical generation."""
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args = parse_args()
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output_dir = Path(args.output)
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output_dir.mkdir(parents=True, exist_ok=True)
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attention_backend = VARIANT_BACKENDS[args.variant]
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use_vsa = attention_backend == "VIDEO_SPARSE_ATTN_H3"
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adapter_path = hf_hub_download(
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repo_id="FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA",
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filename=f"{args.variant}/adapter_model.safetensors",
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)
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generator = VideoGenerator.from_config(
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GeneratorConfig(
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model_path="MiniMaxAI/MiniMax-H3",
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pipeline=PipelineSelection(
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components=ComponentConfig(lora_path=adapter_path, lora_strength=1.0),
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minimax_h3=MiniMaxH3Options(vae_parallel_decode=True, vae_parallel_decode_strategy="gather"),
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),
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engine=EngineConfig(
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num_gpus=4,
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parallelism=ParallelismConfig(tp_size=1, sp_size=4),
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offload=OffloadConfig(dit=False, dit_layerwise=False),
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compile=CompileConfig(vae_enabled=True, regional=attention_backend == "FLASH_ATTN"),
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attention=AttentionConfig(
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backend=attention_backend,
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vsa_sparsity=0.9 if use_vsa else None,
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vsa_tile_size=64 if use_vsa else None,
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),
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quantization=QuantizationConfig(transformer_quant="MXFP8"),
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),
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)
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)
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request = GenerationRequest(
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prompt=args.prompt,
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negative_prompt="",
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sampling=SamplingConfig(
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height=768,
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width=1344,
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num_frames=args.num_frames,
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fps=24,
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num_inference_steps=5,
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guidance_scale=1.0,
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batch_cfg=False,
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seed=1000,
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),
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output=OutputConfig(
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output_path=str(output_dir / "fasth3_profile.mp4"),
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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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try:
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for warmup_index in range(args.warmup_runs):
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warmup_result = generator.generate(request)
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print(f"Warmup {warmup_index + 1}/{args.warmup_runs}: {warmup_result.video_path}")
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torch.cuda.profiler.start()
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try:
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with nvtx_range("fasth3.profiled_generation"):
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measured_result = generator.generate(request)
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finally:
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torch.cuda.profiler.stop()
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print(f"Profiled output: {measured_result.video_path}")
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if measured_result.generation_time is not None:
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print(f"Generation time: {measured_result.generation_time:.2f}s")
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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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