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[AudioLDM] Update docs to use updated ckpt (#3240)
* [AudioLDM] Update docs to use updated ckpt * make style
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docs/source/en/api/pipelines/audioldm.mdx

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@@ -25,14 +25,14 @@ This pipeline was contributed by [sanchit-gandhi](https://huggingface.co/sanchit
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## Text-to-Audio
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The [`AudioLDMPipeline`] can be used to load pre-trained weights from [cvssp/audioldm](https://huggingface.co/cvssp/audioldm) and generate text-conditional audio outputs:
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The [`AudioLDMPipeline`] can be used to load pre-trained weights from [cvssp/audioldm-s-full-v2](https://huggingface.co/cvssp/audioldm-s-full-v2) and generate text-conditional audio outputs:
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```python
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from diffusers import AudioLDMPipeline
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import torch
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import scipy
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repo_id = "cvssp/audioldm"
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repo_id = "cvssp/audioldm-s-full-v2"
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pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
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pipe = pipe.to("cuda")
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### How to load and use different schedulers
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The AudioLDM pipeline uses [`DDIMScheduler`] scheduler by default. But `diffusers` provides many other schedulers
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that can be used with the AudioLDM pipeline such as [`PNDMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`],
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that can be used with the AudioLDM pipeline such as [`PNDMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`],
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[`EulerAncestralDiscreteScheduler`] etc. We recommend using the [`DPMSolverMultistepScheduler`] as it's currently the fastest
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scheduler there is.
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>>> from diffusers import AudioLDMPipeline, DPMSolverMultistepScheduler
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>>> import torch
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>>> pipeline = AudioLDMPipeline.from_pretrained("cvssp/audioldm", torch_dtype=torch.float16)
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>>> pipeline = AudioLDMPipeline.from_pretrained("cvssp/audioldm-s-full-v2", torch_dtype=torch.float16)
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>>> pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
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>>> # or
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>>> dpm_scheduler = DPMSolverMultistepScheduler.from_pretrained("cvssp/audioldm", subfolder="scheduler")
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>>> pipeline = AudioLDMPipeline.from_pretrained("cvssp/audioldm", scheduler=dpm_scheduler, torch_dtype=torch.float16)
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>>> dpm_scheduler = DPMSolverMultistepScheduler.from_pretrained("cvssp/audioldm-s-full-v2", subfolder="scheduler")
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>>> pipeline = AudioLDMPipeline.from_pretrained(
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... "cvssp/audioldm-s-full-v2", scheduler=dpm_scheduler, torch_dtype=torch.float16
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... )
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```
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## AudioLDMPipeline

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