import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("alppo/vae-conditioned-diffusion-model_v2", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Variational Autoencoder Conditioned Diffusion Model
This model is designed to generate music tracks based on input playlists by extracting the "taste" from the playlists using a combination of a Variational Autoencoder (VAE) and a conditioned diffusion model.
Model Details
- VAE: Learns a compressed latent space representation of the input data, specifically mel spectrogram images of audio samples.
- Diffusion Model: Generates new data points by progressively refining random noise into meaningful data, conditioned on the VAE's latent space.
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