Comments (1)
teticio
commented on May 26, 2024
Each music sample corresponds to an image, so the length of the sample can be thought of as the x-resolution. This is limited by the GPU memory you have available. As it stands it is limite to short samples of a few seconds, but there are ways to stitch these samples together, which are explored in the sample notebooks. The idea of this repo was to show what could be done with a single commercial grade GPU: hopefully someone with access to much more compute power can do something more impressive.
from audio-diffusion.
Related Issues (20)
- Recommended training hyperparameters for 44.1Khz & 48Khz Samplerate HOT 2
- diffusers v-0.12.0 causes import issues HOT 1
- Diffusers v0.12 removed the `ema_model.averaged_model` attribute HOT 1
- Increasing input size HOT 4
- how does the audio_to_images.py file work? HOT 3
- NameError: name 'transformers' is not defined upon running model via Gradio HOT 2
- Dataset constriants HOT 16
- High fidelity training? HOT 3
- Training own music samples? HOT 1
- Can I input audio file then generate image HOT 2
- Numpy Error HOT 1
- AttributeError: 'AutoencoderKL' object has no attribute 'sample_size' HOT 3
- teticio/audio-diffusion-256 is really good HOT 1
- multi-gpu training HOT 1
- [Little Feedback] Thank you! :) HOT 2
- is it possible to use the train_unet.py script as a regular ldm? HOT 2
- whats the difference between 256 and 512 dataset HOT 1
- Duration of generated audio HOT 4
- WARNING: audio_to_images: No valid audio files were found error! HOT 3
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from audio-diffusion.