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hao-ai-lab-FastVideo/examples/inference/basic/v2_basic_ltx2_3_distilled.py
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SolitaryThinker c6d2976fc2 [feat] v2 VideoGenerator: A/V convenience path (generate_video -> T2VS -> mp4 + 24kHz wav)
Makes the 'Full A/V' LTX-2.3 deliverable reachable from the user-facing entrypoint, not just the
engine. A model advertising TEXT_TO_VIDEO_SOUND (LTX-2.3) now auto-issues a T2VS request, so generate()
/ generate_video() return BOTH modalities in one joint pass:
- VideoGenerator stores the resident instance + a supports_av flag (from card.capabilities); generate()
  gains want_audio (None=auto-by-capability, True/False to force) and routes T2V vs T2VS+{video,audio}.
- _result saves the stereo waveform as a sibling .wav at the vocoder's REAL rate (24000) — read off the
  built audio_vae adapter (TorchLTX2AudioVAE.sample_rate = Vocoder.output_sample_rate), since the
  AudioArtifact default rate is a placeholder. Populates GenerationResult.audio/.audio_sample_rate and
  extra['audio_path']. scipy IEEE-float WAV; [channels,samples] auto-transposed.
- Rewrote v2_basic_ltx2_3_distilled.py: registry routes to build_ltx2_3_card (its own joint T2VS A/V
  card, not the LTX-2 base/2-stage card); the example prints both the mp4 and the wav.
- GPU-verified via the convenience API: ev.mp4 + ev.wav (24000 Hz, stereo 61920x2, nonzero, std 0.043).
  CPU mini green (202 passed, 2 skipped); engine/program/toy paths untouched (test_ltx2_av pins 44100).
2026-07-04 17:15:34 +00:00

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"""v2 port of basic_ltx2_3_distilled.py — LTX-2.3 Distilled (single-stage, joint A/V) through the v2
VideoGenerator.
Unlike LTX-2.0 distilled (two-stage, video-only), LTX-2.3 is a single-stage *audio+video* model. The
shared registry (v2/registry.py) maps ``FastVideo/LTX-2.3-Distilled-Diffusers`` to its OWN card,
``build_ltx2_3_card`` — distinct from the LTX-2 base/2-stage cards — which wires the 2.3-specific path:
* SEPARATE video + audio text connectors (the Gemma encoder projects the prompt to two embeddings,
2048-dim for audio, 4096-dim for video) plus gated attention;
* a JOINT DiT forward where video and audio latents cross-attend in a single denoise per step;
* a video VAE decode + an AudioDecoder→Vocoder decode → video frames AND a stereo waveform @24kHz.
Because the model advertises TEXT_TO_VIDEO_SOUND, the VideoGenerator issues a T2VS request by default,
so ``generate_video`` returns BOTH modalities: the mp4 plus a sibling ``.wav`` (and ``result.audio`` /
``result.audio_sample_rate`` in memory). Being distilled, it wants FEW steps (8). GPU-verified on the
rebuilt x86 stack: video (3,33,256,384) + stereo audio (2×61920 @ 24kHz).
"""
from v2 import VideoGenerator
PROMPT = "ocean waves crashing on rocks at sunset, seagulls calling in the distance, cinematic, highly detailed"
def main() -> None:
generator = VideoGenerator.from_pretrained("FastVideo/LTX-2.3-Distilled-Diffusers", num_gpus=1)
# audio=None auto-enables sound for this A/V model (pass audio=False to force video-only).
result = generator.generate_video(
prompt=PROMPT, output_path="v2_video_samples_ltx2_3", output_video_name="ltx2_3_ocean",
save_video=True, num_frames=33, height=512, width=768, num_inference_steps=8, seed=1)
print(f"Video: {result.video_path}")
audio_path = result.extra.get("audio_path")
if audio_path:
print(f"Audio: {audio_path} ({result.audio_sample_rate} Hz)")
generator.shutdown()
if __name__ == "__main__":
main()