c708aecb18
Signed-off-by: Zhang Jian <jianmusings@gmail.com> Signed-off-by: Zhang <jianmusings@gmail.com> Signed-off-by: Zhang <zhang.jian@u.nus.edu> Signed-off-by: Zhang Jian <e0322744@u.nus.edu> Signed-off-by: 汪志鹏 <wangzhipeng628@gmail.com> Co-authored-by: Zhang Jian <e0322744@u.nus.edu> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>
96 lines
3.1 KiB
Markdown
96 lines
3.1 KiB
Markdown
# AudioX
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> AudioX MMDiT for unified audio + music generation: t2a / t2m / v2a / v2m / tv2a / tv2m
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## Summary
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- Vendor: HKUSTAudio (project), `zhangj1an/AudioX` weight bundle
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- Model: `zhangj1an/AudioX`
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- Task: Text/video → audio or music. Six tasks: `t2a`, `t2m`, `v2a`, `v2m`, `tv2a`, `tv2m`.
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- Mode: Offline inference + online serving (pure diffusion)
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- Maintainer: Community
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## When to use this recipe
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Use this recipe to run AudioX for sound-effect (`*2a`) or music (`*2m`) generation
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from a text prompt and/or video clip. AudioX is a unified diffusion transformer
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that produces stereo 44.1 kHz audio up to ~10 s per call.
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## References
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- Project page: <https://zeyuet.github.io/AudioX/>
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- vLLM-Omni weight bundle: <https://huggingface.co/zhangj1an/AudioX>
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- Pipeline: `vllm_omni.diffusion.models.audiox.pipeline_audiox.AudioXPipeline`
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- Input transforms: `vllm_omni.transformers_utils.processors.audiox`
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- Offline example: [`examples/offline_inference/audiox/`](../../examples/offline_inference/audiox/)
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- Online example: [`examples/online_serving/audiox/`](../../examples/online_serving/audiox/)
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## Hardware Support
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## GPU
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### 1x L4 24GB
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#### Environment
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- OS: Ubuntu 22.04
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- Python: 3.10+
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- Driver / runtime: CUDA 12.4
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- vLLM version: 0.20.0
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- vLLM-Omni version: 0.1.x
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#### Command
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Offline (text-to-audio):
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```bash
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huggingface-cli download zhangj1an/AudioX --local-dir ./audiox_weights
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python examples/offline_inference/audiox/end2end.py \
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--model ./audiox_weights \
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--tasks t2a \
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--num-inference-steps 250 \
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--seconds-total 10
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```
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Online:
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```bash
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bash examples/online_serving/audiox/run_server.sh
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python examples/online_serving/audiox/openai_chat_client.py \
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--task t2a \
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--prompt "Fireworks burst twice, followed by a clock ticking." \
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--output t2a.wav
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```
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#### Verification
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```bash
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# Health check
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curl http://localhost:8099/health
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# Listen to the saved file (stereo, 44.1 kHz, sigma_min=0.03, sigma_max=1000 — upstream defaults)
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ffprobe t2a.wav
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```
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#### Notes
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- Memory usage: ~10 GB peak with `num_inference_steps=250`, 10 s of audio.
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- Output rate: 44.1 kHz stereo, regardless of `--sample-rate` (resampled in the example
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script if requested).
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- Supported tasks: `t2a`, `t2m`, `v2a`, `v2m`, `tv2a`, `tv2m`. Pass via
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`extra_args["audiox_task"]` (offline) or the `extra_args` field in the OpenAI
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chat-completions body (online).
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- Video conditioning: `v2*` and `tv2*` require a video file; the online client
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attaches it as an OpenAI `video_url` content item (data URI for local files).
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- Cache acceleration is **not** supported (AudioXPipeline is in `_NO_CACHE_ACCELERATION`).
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- Tensor parallelism is supported via `--tensor-parallel-size` (DiT QKV is sharded with
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`QKVParallelLinear`); cross-attention K/V is also TP-sharded.
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### Known limitations
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- Inference uses an inlined DPM-Solver++(3M) SDE sampler (k-diffusion port). Replacing it with
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diffusers' `EDMDPMSolverMultistepScheduler` introduces a fixed ~861 Hz resonance and is not
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recommended.
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- Generation is fixed at 10 s (configured by the bundle's `sample_size`); longer outputs require
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a different bundle.
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