Instructions to use alppo/vae-conditioned-diffusion-model_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alppo/vae-conditioned-diffusion-model_v2 with Diffusers:
pip install -U diffusers transformers accelerate
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] - Notebooks
- Google Colab
- Kaggle
metadata
datasets:
- teticio/audio-diffusion-256
library_name: diffusers
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.