document the new SimVQ and ResidualSimVQ

Author: lucidrains

README.md

@@ -276,6 +276,47 @@ indices = quantizer(x)
 
 This repository should also automatically synchronizing the codebooks in a multi-process setting. If somehow it isn't, please open an issue. You can override whether to synchronize codebooks or not by setting `sync_codebook = True | False`
 
+### Sim VQ
+
+<img src="./images/simvq.png" width="400px"></img>
+
+A <a href="https://arxiv.org/abs/2411.02038">new ICLR 2025 paper</a> proposes a scheme where the codebook is frozen, and the codebook is implicitly generated through a linear projection. The author claims this setup leads to less codebook collapse as well as easier convergence. I have found this to perform even better when paired with <a href="https://arxiv.org/abs/2410.06424">rotation trick</a> from Fifty et al., and expanding the linear projection to a small one layer MLP. You can experiment with it as so
+
+```python
+import torch
+from vector_quantize_pytorch import SimVQ
+
+sim_vq = SimVQ(
+    dim = 512,
+    codebook_size = 1024
+)
+
+x = torch.randn(1, 1024, 512)
+quantized, indices, commit_loss = sim_vq(x)
+
+assert x.shape == quantized.shape
+assert torch.allclose(quantized, sim_vq.indices_to_codes(indices), atol = 1e-6)
+```
+
+For the residual flavor, just import `ResidualSimVQ` instead
+
+```python
+import torch
+from vector_quantize_pytorch import SimVQ, ResidualSimVQ
+
+residual_sim_vq = ResidualSimVQ(
+    dim = 512,
+    num_quantizers = 4,
+    codebook_size = 1024
+)
+
+x = torch.randn(1, 1024, 512)
+quantized, indices, commit_loss = residual_sim_vq(x)
+
+assert x.shape == quantized.shape
+assert torch.allclose(quantized, residual_sim_vq.get_output_from_indices(indices), atol = 1e-6)
+```
+
 ### Finite Scalar Quantization
 
 <img src="./images/fsq.png" width="500px"></img>