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Vqvae
V
Vqvae
Developed by hpcai-tech
VQVAE is a video generation model based on the VQ-VAE architecture, cloned from the VideoGPT project, aimed at converting the model to Hugging Face format for easier loading.
Downloads 179
Release Time : 2/20/2024
Model Overview
This model is based on the VQ-VAE (Vector Quantized Variational Autoencoder) architecture, focusing on video generation tasks by encoding video data into discrete latent representations for efficient generation.
Model Features
Efficient Video Representation
Encodes videos into discrete latent representations via the VQ-VAE architecture for efficient storage and processing.
Hugging Face Compatibility
The model has been converted to Hugging Face format for easy loading and use within the Hugging Face ecosystem.
Video Generation Capability
Capable of generating high-quality video content based on latent representations.
Model Capabilities
Video Encoding
Video Generation
Latent Representation Learning
Use Cases
Creative Content Generation
Short Video Generation
Generate creative short video content
Can produce coherent short video sequences
Data Augmentation
Video Data Expansion
Generate additional training data for video recognition tasks
Can extend limited video datasets
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