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---
license: mit
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
---
[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is now available in 🧨 Diffusers.
## FP32
```python
# !pip install diffusers[torch] accelerate scipy
from diffusers import DiffusionPipeline
import scipy
model_id = "harmonai/maestro-150k"
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline = pipeline.to("cuda")
audios = pipeline(audio_length_in_s=4.0).audios
# To save locally
for audio in audios:
scipy.io.wavfile.write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose())
# To dislay in google colab
import IPython.display as ipd
for audio in audios:
display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
```
## FP16
Faster at a small loss of quality
```python
# !pip install diffusers[torch] accelerate scipy
from diffusers import DiffusionPipeline
import scipy
import torch
model_id = "harmonai/maestro-150k"
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")
audios = pipeline(audio_length_in_s=4.0).audios
# To save locally
for i, audio in enumerate(audios):
scipy.io.wavfile.write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose())
# To dislay in google colab
import IPython.display as ipd
for audio in audios:
display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
``` |