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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",
    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