Jean-Marc Valin
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b0620c0bf9
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Using sparse GRUs in DRED decoder
Saves ~270 kB of weights in the decoder
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2023-11-15 04:08:50 -05:00 |
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Jean-Marc Valin
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77594bf158
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Dumping RDOVAE stats from XML
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2023-11-08 17:32:43 -05:00 |
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Jean-Marc Valin
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222662dac8
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DRED: quantize scale and dead zone to 8 bits
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2023-11-07 18:10:50 -05:00 |
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Jean-Marc Valin
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0ab0640d4a
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Split stats in two and remove useless dimensions
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2023-11-07 00:07:14 -05:00 |
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Jean-Marc Valin
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544b3e576c
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DRED: quantize r and p0 parameters with 8 bits
Only code non-degenerate symbols, which makes the encoder faster
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2023-11-06 03:16:43 -05:00 |
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Jan Buethe
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1accd2472e
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finalized quantization option in export_rdovae_weights.py
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2023-10-20 14:14:31 +02:00 |
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Jean-Marc Valin
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d720955d61
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Marking RDOVAE layers to quantize
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2023-10-19 16:06:52 -04:00 |
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Jan Buethe
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60ac1c6c99
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prepared quantization implementation for DRED
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2023-10-19 21:54:39 +02:00 |
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Jean-Marc Valin
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27663d3641
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Using a DenseNet for DRED
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2023-10-02 01:43:44 -04:00 |
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Jan Buethe
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eb72d29a15
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Support for dumping LinearLayer in weight-exchange
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2023-07-27 19:55:17 -04:00 |
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Marcus Asteborg
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f36685fc97
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Remove trailing whitespace in dnn
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2023-06-22 13:58:37 -07:00 |
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jbuethe
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aa474553b5
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updated torch framework to include quantization
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2023-01-13 11:48:04 +00:00 |
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jbuethe
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fdb04d0eef
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added pytorch implementation of RDOVAE
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2022-11-23 11:02:29 +00:00 |
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