Accepted to IEEE Robotics and Automation Letters (RA-L) April 2024
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Updated
Jul 18, 2024 - Python
Accepted to IEEE Robotics and Automation Letters (RA-L) April 2024
Compression via Vector Quantization in PyTorch
Torch implementation of minGPT for images latent code generation
Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders
An educational project dedicated to text-to-image generation with neural networks. VQVAE and BPE autoencoders are used to learn the embedding of text and image respectively. A transformer-based model then is trained to predict the next token in the concatenated sequence of image and text tokens and used for generation.
State of the art of generative models and in-depth study of diffusion models
implementation of VQVAE in pytorch
Applying multiple VQ along the feature axis
Official code for the NeurIPS 2022 paper "Posterior Matching for Arbitrary Conditioning".
Interactive VQ-VAE (Vector-Quantized Variational Autoencoder) in the browser
Implementation of basic autoencodeur, VAE and VQVAE in Flax
VQGAN from LDM without hell of dependencies
Vector-Quantized Generative Adversarial Networks
Image Generation using VQVAE and GPT Models
Tensorflow Implementation of "Theory and Experiments on Vector Quantized Autoencoders"
Large-Scale Bidirectional Training for Zero-Shot Image Captioning
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