Comments (2)
fighting-Zhang
commented on June 22, 2024
LFQ seems not to support shared_codebook, resulting in the same index in mesh codes corresponding to different meanings. Could this affect the model's learning?
Additionally, the paper utilizes RVQ-VAE, indicating that RVQ should also possess a certain capability. However, in practical training, the performance of RVQ is very poor. Could this be an issue with the code?
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lucidrains
commented on June 22, 2024
@fighting-Zhang i think scalar quantization is the future. you aren't the only one reporting great results without loss of generalization
LFQ has a fixed codebook, so it doesn't matter whether it is shared or not
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Related Issues (20)
- Sliding window for transformer HOT 1
- Mesh intra face vertex id ordering convention HOT 16
- Classifier-Free Guidance, cond_drop_prob=1.0, attn_mask=False: Error!!! HOT 2
- GroupNorm in ResnetBlocks HOT 13
- TypeError: MessagePassing.__init__() got an unexpected keyword argument 'sageconv_dropout' HOT 12
- ImportError: cannot import name 'MeshDataset' from 'meshgpt_pytorch' HOT 5
- Loss=nan when training transformertrainer HOT 5
- distirbution training HOT 1
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- Guide for generating novel meshes based on the trained models
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- [Critical] Very high loss rate at first few tokens (classifier free guidance not working) HOT 66
- Seeking Collaboration HOT 3
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