SolitaryThinker
bbbb7ab021
Move all examples/inference and scripts off the legacy
VideoGenerator.from_pretrained(model, **kwargs) +
generate_video(prompt, **kwargs) surface onto the typed
from_config(GeneratorConfig(...)) + generate(GenerationRequest(...))
convention, including the Kandinsky-5 and DreamX-World examples added
on main after the original migration. git-mv api/compat.py to
api/translation.py ('compat' implied a temporary shim; the forward
translation is its honest permanent role) and repoint all importers,
tests, and doc links.
87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
"""Generate one LTX2 video and score its audio track.
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LTX2 produces video + audio and muxes both into the output mp4. The
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eval suite reads the audio track straight out of that mp4 (librosa
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decodes via ffmpeg under the hood), so we just point the audio
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metrics at the same path.
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Only the reference-free audio metrics run here — they're the ones
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that make sense for a single one-shot generation:
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* ``audio.clap_score`` — CLAP text↔audio cosine similarity
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* ``audio.audiobox_aesthetics`` — AudioBox 4-axis quality score
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The other audio metrics need extra inputs we don't have for a
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one-shot run: ``audio.frechet_distance`` and ``audio.kl_divergence``
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need a reference audio, ``audio.wer`` needs a ground-truth transcript.
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Install: ``uv pip install -e .[eval-audio]`` covers both metrics here
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(and the rest of the audio suite).
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"""
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import torch
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from fastvideo import VideoGenerator
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from fastvideo.api import (
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EngineConfig, GenerationRequest, GeneratorConfig, OutputConfig,
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SamplingConfig,
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)
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from fastvideo.eval import create_evaluator
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PROMPT = (
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"A warm sunny backyard. The camera starts in a tight cinematic close-up "
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"of a woman and a man in their 30s, facing each other with serious "
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"expressions. The woman, emotional and dramatic, says softly, \"That's "
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"it... Dad's lost it. And we've lost Dad.\" The man exhales, slightly "
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"annoyed: \"Stop being so dramatic, Jess.\" A beat. He glances aside, "
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"then mutters defensively, \"He's just having fun.\""
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)
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METRICS = [
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"audio.clap_score",
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"audio.audiobox_aesthetics",
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]
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def main() -> None:
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generator = VideoGenerator.from_config(
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GeneratorConfig(
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model_path="Davids048/LTX2-Base-Diffusers",
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engine=EngineConfig(num_gpus=1),
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))
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output_path = "outputs_video/ltx2_audio_eval/output.mp4"
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generator.generate(
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GenerationRequest(
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prompt=PROMPT,
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sampling=SamplingConfig(
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num_frames=121,
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height=1088,
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width=1920,
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),
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output=OutputConfig(
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output_path=output_path,
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save_video=True,
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),
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))
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generator.shutdown()
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torch.cuda.empty_cache()
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print(f"\n[eval] building evaluator: {METRICS}")
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evaluator = create_evaluator(metrics=METRICS)
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results = evaluator.evaluate(audio=output_path, text_prompt=PROMPT)
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print("\n=== Audio scores ===")
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for name in METRICS:
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r = results[name]
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if r.score is None:
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print(f" {name}: SKIPPED ({r.details.get('skipped', 'no score')})")
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else:
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print(f" {name}: {r.score:.4f}")
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if r.details:
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for k, v in r.details.items():
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print(f" {k}: {v}")
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if __name__ == "__main__":
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main()
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