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LongCat-AudioDiT

docs/source/en/api/pipelines/longcat_audio_dit.md

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LongCat-AudioDiT

LongCat-AudioDiT is a text-to-audio diffusion model from Meituan LongCat. The diffusers integration exposes a standard [DiffusionPipeline] interface for text-conditioned audio generation.

This pipeline was adapted from the LongCat-AudioDiT reference implementation: https://github.com/meituan-longcat/LongCat-AudioDiT

This pipeline supports loading from a local directory or Hugging Face Hub repository in diffusers format (containing text_encoder/, transformer/, vae/, tokenizer/, and scheduler/ subfolders).

Usage

py
import soundfile as sf
import torch
from diffusers import LongCatAudioDiTPipeline

pipeline = LongCatAudioDiTPipeline.from_pretrained(
    "ruixiangma/LongCat-AudioDiT-1B-Diffusers",
    torch_dtype=torch.float16,
)
pipeline = pipeline.to("cuda")

prompt = "A calm ocean wave ambience with soft wind in the background."
audio = pipeline(
    prompt,
    audio_duration_s=5.0,
    num_inference_steps=16,
    guidance_scale=4.0,
    generator=torch.Generator("cuda").manual_seed(42),
).audios[0, 0]

sf.write("longcat.wav", audio, pipeline.sample_rate)

Tips

  • audio_duration_s is the most direct way to control output duration.
  • Use generator=torch.Generator("cuda").manual_seed(42) to make generation reproducible.
  • Output shape is (batch, channels, samples) - use .audios[0, 0] to get a single audio sample.
  • The pipeline outputs mono audio (1 channel). If you need stereo, you can duplicate the channel: audio.unsqueeze(0).repeat(1, 2, 1).

LongCatAudioDiTPipeline

[[autodoc]] LongCatAudioDiTPipeline - all - call - from_pretrained