{"library":"vector-quantize-pytorch","type":"library","category":null,"description":"A vector quantization library for PyTorch, originally transcribed from Deepmind's TensorFlow implementation. It focuses on using exponential moving averages to update the dictionary and has been applied successfully in generative models for images (VQ-VAE-2) and music (Jukebox). The library is actively maintained with frequent micro-releases, often incorporating new research techniques.","language":"python","status":"active","version":"1.28.1","tags":["pytorch","vector quantization","machine learning","deep learning","generative models"],"last_verified":"Thu May 21","install":[{"cmd":"pip install vector-quantize-pytorch","imports":["from vector_quantize_pytorch import VectorQuantize","from vector_quantize_pytorch import ResidualVQ","from vector_quantize_pytorch import ResidualFSQ"]}],"homepage":"https://pypi.org/project/vector-quantize-pytorch/","github":"https://github.com/lucidrains/vector-quantizer-pytorch","docs":null,"changelog":null,"pypi":"https://pypi.org/project/vector-quantize-pytorch/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":20,"avg_install_s":66.9,"avg_import_s":11.96,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/vector-quantize-pytorch/compatibility"}}