A hierarchical VQ-VAE implementation in Flax
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Updated
Aug 25, 2024 - Python
A hierarchical VQ-VAE implementation in Flax
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Branch of the original Project "dome272/VQGAN-pytorch" adding an inference file for the VQGAN (Not for the VQGAN Transformers)
Python toolkit for speech processing
Code for the paper "Jukebox: A Generative Model for Music"
This is the official implementation of MusER (AAAI'24).
[ICML 2023] Official PyTorch Implementation of "Hierarchical Neural Coding for Controllable CAD Model Generation".
A generative machine learning model that generates noval foley sounds
Official Implement of Multi-Stage Multi-Codebook (MSMC) TTS
Vector-Quantized Generative Adversarial Networks
Generative modeling and representation learning through reconstruction
Conditional Video/GIF Synthesis implementation using PyTorch Lightning and Hydra. This method utilizes Vector Quantization Variational AutoEncoder (VQ-VAE) with Discrete Denoising Diffusion Probabilistic Models (D3PM) to generate novel videos.
(CVPR 2023) Pytorch implementation of “T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations”
Implementation of VQ-VAE with a GPT-style sampler in the JAX and Haiku ecosystem.
Implementation of Vector Quantised VAE (VQ-VAE) with PixelCNN prior in Tensorflow.
Hercules: Attributable and Scalable Opinion Summarization (ACL 2023)
Official PyTorch code for "Transformer-based out-of-distribution detection for clinically safe segmentation"
A Bach music generator with Artificial Intelligence. This model is made by a VQ-VAE + Transformer (decoder-only). Sequences of midi 1 quarter length are compressed into 16 codebooks via VQ-VAE and a transformer learns how to generate the codebooks sequence to obtain a midi score.
Easy generative modeling in PyTorch.
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