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Audio Diffusion |
Copied |
import torch |
from IPython.display import Audio |
from diffusers import DiffusionPipeline |
device = "cuda" if torch.cuda.is_available() else "cpu" |
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device) |
output = pipe() |
display(output.images[0]) |
display(Audio(output.audios[0], rate=mel.get_sample_rate())) |
Latent Audio Diffusion |
Copied |
import torch |
from IPython.display import Audio |
from diffusers import DiffusionPipeline |
device = "cuda" if torch.cuda.is_available() else "cpu" |
pipe = DiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device) |
output = pipe() |
display(output.images[0]) |
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) |
Audio Diffusion with DDIM (faster) |
Copied |
import torch |
from IPython.display import Audio |
from diffusers import DiffusionPipeline |
device = "cuda" if torch.cuda.is_available() else "cpu" |
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256").to(device) |
output = pipe() |
display(output.images[0]) |
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) |
Variations, in-painting, out-painting etc. |
Copied |
output = pipe( |
raw_audio=output.audios[0, 0], |
start_step=int(pipe.get_default_steps() / 2), |
mask_start_secs=1, |
mask_end_secs=1, |
) |
display(output.images[0]) |
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) |
AudioDiffusionPipeline |
class diffusers.AudioDiffusionPipeline |
< |
source |
> |
( |
vqvae: AutoencoderKL |
unet: UNet2DConditionModel |
mel: Mel |
scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_ddpm.DDPMScheduler] |
) |
Parameters |
vqae (AutoencoderKL) — Variational AutoEncoder for Latent Audio Diffusion or None |
unet (UNet2DConditionModel) — UNET model |
mel (Mel) — transform audio <-> spectrogram |
scheduler ([DDIMScheduler or DDPMScheduler]) — de-noising scheduler |
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the |
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) |
__call__ |
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