diff --git a/codes/models/lucidrains/vq.py b/codes/models/lucidrains/vq.py
new file mode 100644
index 00000000..4b1019ec
--- /dev/null
+++ b/codes/models/lucidrains/vq.py
@@ -0,0 +1,444 @@
+import functools
+
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+import torch.distributed as distributed
+from torch.cuda.amp import autocast
+
+from einops import rearrange, repeat
+from contextlib import contextmanager
+
+
+def par(t, nm):
+    print(f'grad report {nm}: {t}')
+    return t
+
+def reg(t, nm):
+    l = torch.tensor([0], requires_grad=True, device=t.device, dtype=torch.float)
+    l.register_hook(functools.partial(par, nm=nm))
+    t = t + l
+    return t
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+def noop(*args, **kwargs):
+    pass
+
+def l2norm(t):
+    return F.normalize(t, p = 2, dim = -1)
+
+def log(t, eps = 1e-20):
+    return torch.log(t.clamp(min = eps))
+
+def gumbel_noise(t):
+    noise = torch.zeros_like(t).uniform_(0, 1)
+    return -log(-log(noise))
+
+def gumbel_sample(t, temperature = 1., dim = -1):
+    if temperature == 0:
+        return t.argmax(dim = dim)
+
+    return ((t / temperature) + gumbel_noise(t)).argmax(dim = dim)
+
+def ema_inplace(moving_avg, new, decay):
+    moving_avg.data.mul_(decay).add_(new, alpha = (1 - decay))
+
+def laplace_smoothing(x, n_categories, eps = 1e-5):
+    return (x + eps) / (x.sum() + n_categories * eps)
+
+def sample_vectors(samples, num):
+    num_samples, device = samples.shape[0], samples.device
+
+    if num_samples >= num:
+        indices = torch.randperm(num_samples, device = device)[:num]
+    else:
+        indices = torch.randint(0, num_samples, (num,), device = device)
+
+    return samples[indices]
+
+def kmeans(samples, num_clusters, num_iters = 10, use_cosine_sim = False):
+    dim, dtype, device = samples.shape[-1], samples.dtype, samples.device
+
+    means = sample_vectors(samples, num_clusters)
+
+    for _ in range(num_iters):
+        if use_cosine_sim:
+            dists = samples @ means.t()
+        else:
+            diffs = rearrange(samples, 'n d -> n () d') \
+                    - rearrange(means, 'c d -> () c d')
+            dists = -(diffs ** 2).sum(dim = -1)
+
+        buckets = dists.max(dim = -1).indices
+        bins = torch.bincount(buckets, minlength = num_clusters)
+        zero_mask = bins == 0
+        bins_min_clamped = bins.masked_fill(zero_mask, 1)
+
+        new_means = buckets.new_zeros(num_clusters, dim, dtype = dtype)
+        new_means.scatter_add_(0, repeat(buckets, 'n -> n d', d = dim), samples)
+        new_means = new_means / bins_min_clamped[..., None]
+
+        if use_cosine_sim:
+            new_means = l2norm(new_means)
+
+        means = torch.where(zero_mask[..., None], means, new_means)
+
+    return means, bins
+
+# regularization losses
+
+def orthgonal_loss_fn(t):
+    # eq (2) from https://arxiv.org/abs/2112.00384
+    n = t.shape[0]
+    normed_codes = l2norm(t)
+    identity = torch.eye(n, device = t.device)
+    cosine_sim = einsum('i d, j d -> i j', normed_codes, normed_codes)
+    return ((cosine_sim - identity) ** 2).sum() / (n ** 2)
+
+# distance types
+
+class EuclideanCodebook(nn.Module):
+    def __init__(
+        self,
+        dim,
+        codebook_size,
+        kmeans_init = False,
+        kmeans_iters = 10,
+        decay = 0.8,
+        eps = 1e-5,
+        threshold_ema_dead_code = 2,
+        use_ddp = False,
+        learnable_codebook = False,
+        sample_codebook_temp = 0
+    ):
+        super().__init__()
+        self.decay = decay
+        init_fn = torch.randn if not kmeans_init else torch.zeros
+        embed = init_fn(codebook_size, dim)
+
+        self.codebook_size = codebook_size
+        self.kmeans_iters = kmeans_iters
+        self.eps = eps
+        self.threshold_ema_dead_code = threshold_ema_dead_code
+        self.sample_codebook_temp = sample_codebook_temp
+
+        self.all_reduce_fn = distributed.all_reduce if use_ddp else noop
+
+        self.register_buffer('initted', torch.Tensor([not kmeans_init]))
+        self.register_buffer('cluster_size', torch.zeros(codebook_size))
+        self.register_buffer('embed_avg', embed.clone())
+
+        self.learnable_codebook = learnable_codebook
+        if learnable_codebook:
+            self.embed = nn.Parameter(embed)
+        else:
+            self.register_buffer('embed', embed)
+
+    @torch.jit.ignore
+    def init_embed_(self, data):
+        if self.initted:
+            return
+
+        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
+        self.embed.data.copy_(embed)
+        self.embed_avg.data.copy_(embed.clone())
+        self.cluster_size.data.copy_(cluster_size)
+        self.initted.data.copy_(torch.Tensor([True]))
+
+    def replace(self, samples, mask):
+        modified_codebook = torch.where(
+            mask[..., None],
+            sample_vectors(samples, self.codebook_size),
+            self.embed
+        )
+        self.embed.data.copy_(modified_codebook)
+
+    def expire_codes_(self, batch_samples):
+        if self.threshold_ema_dead_code == 0:
+            return
+
+        expired_codes = self.cluster_size < self.threshold_ema_dead_code
+        if not torch.any(expired_codes):
+            return
+        batch_samples = rearrange(batch_samples, '... d -> (...) d')
+        self.replace(batch_samples, mask = expired_codes)
+
+    @autocast(enabled = False)
+    def forward(self, x, used_codes=[]):
+        shape, dtype = x.shape, x.dtype
+        flatten = rearrange(x, '... d -> (...) d')
+
+        self.init_embed_(flatten)
+
+        embed = self.embed if not self.learnable_codebook else self.embed.detach()
+        embed = embed.t()
+
+        dist = -(
+            flatten.pow(2).sum(1, keepdim=True)
+            - 2 * flatten @ embed
+            + embed.pow(2).sum(0, keepdim=True)
+        )
+
+        for uc in used_codes:
+            mask = torch.arange(0, self.codebook_size, device=x.device).unsqueeze(0).repeat(x.shape[0],1) == uc.unsqueeze(1)
+            dist[mask] = -torch.inf
+        embed_ind = gumbel_sample(dist, dim = -1, temperature = self.sample_codebook_temp)
+        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
+        embed_ind = embed_ind.view(*shape[:-1])
+        quantize = F.embedding(embed_ind, self.embed)
+
+        # Perform the gumbel trick on the end result (during training)
+        if self.training:
+            quantize = flatten + (quantize - flatten).detach()
+
+        if self.training:
+            cluster_size = embed_onehot.sum(0)
+            self.all_reduce_fn(cluster_size)
+
+            ema_inplace(self.cluster_size, cluster_size, self.decay)
+
+            embed_sum = flatten.t() @ embed_onehot
+            self.all_reduce_fn(embed_sum)
+
+            ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
+            cluster_size = laplace_smoothing(self.cluster_size, self.codebook_size, self.eps) * self.cluster_size.sum()
+            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
+            self.embed.data.copy_(embed_normalized)
+            self.expire_codes_(x)
+
+        return quantize, embed_ind
+
+class CosineSimCodebook(nn.Module):
+    def __init__(
+        self,
+        dim,
+        codebook_size,
+        kmeans_init = False,
+        kmeans_iters = 10,
+        decay = 0.8,
+        eps = 1e-5,
+        threshold_ema_dead_code = 2,
+        use_ddp = False,
+        learnable_codebook = False,
+        sample_codebook_temp = 0.
+    ):
+        super().__init__()
+        self.decay = decay
+
+        if not kmeans_init:
+            embed = l2norm(torch.randn(codebook_size, dim))
+        else:
+            embed = torch.zeros(codebook_size, dim)
+
+        self.codebook_size = codebook_size
+        self.kmeans_iters = kmeans_iters
+        self.eps = eps
+        self.threshold_ema_dead_code = threshold_ema_dead_code
+        self.sample_codebook_temp = sample_codebook_temp
+
+        self.all_reduce_fn = distributed.all_reduce if use_ddp else noop
+        self.register_buffer('initted', torch.Tensor([not kmeans_init]))
+        self.register_buffer('cluster_size', torch.zeros(codebook_size))
+
+        self.learnable_codebook = learnable_codebook
+        if learnable_codebook:
+            self.embed = nn.Parameter(embed)
+        else:
+            self.register_buffer('embed', embed)
+
+    @torch.jit.ignore
+    def init_embed_(self, data):
+        if self.initted:
+            return
+
+        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters,
+                       use_cosine_sim = True)
+        self.embed.data.copy_(embed)
+        self.cluster_size.data.copy_(cluster_size)
+        self.initted.data.copy_(torch.Tensor([True]))
+
+    def replace(self, samples, mask):
+        samples = l2norm(samples)
+        modified_codebook = torch.where(
+            mask[..., None],
+            sample_vectors(samples, self.codebook_size),
+            self.embed
+        )
+        self.embed.data.copy_(modified_codebook)
+
+    def expire_codes_(self, batch_samples):
+        if self.threshold_ema_dead_code == 0:
+            return
+
+        expired_codes = self.cluster_size < self.threshold_ema_dead_code
+        if not torch.any(expired_codes):
+            return
+        batch_samples = rearrange(batch_samples, '... d -> (...) d')
+        self.replace(batch_samples, mask = expired_codes)
+
+    @autocast(enabled = False)
+    def forward(self, x, used_codes=[]):
+        shape, dtype = x.shape, x.dtype
+        flatten = rearrange(x, '... d -> (...) d')
+        flatten = l2norm(flatten)
+
+        self.init_embed_(flatten)
+        embed = self.embed if not self.learnable_codebook else self.embed.detach()
+        embed = l2norm(embed)
+
+        dist = flatten @ embed.t()
+        for uc in used_codes:
+            mask = torch.arange(0, self.codebook_size, device=x.device).unsqueeze(0).repeat(x.shape[0],1) == uc.unsqueeze(1)
+            dist[mask] = -torch.inf
+        embed_ind = gumbel_sample(dist, dim = -1, temperature = self.sample_codebook_temp)
+        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
+        embed_ind = embed_ind.view(*shape[:-1])
+
+        quantize = F.embedding(embed_ind, self.embed)
+        # Perform the gumbel trick on the end result (during training)
+        if self.training:
+            quantize = flatten + (quantize - flatten).detach()
+
+        if self.training:
+            bins = embed_onehot.sum(0)
+            self.all_reduce_fn(bins)
+
+            ema_inplace(self.cluster_size, bins, self.decay)
+
+            zero_mask = (bins == 0)
+            bins = bins.masked_fill(zero_mask, 1.)
+
+            embed_sum = flatten.t() @ embed_onehot
+            self.all_reduce_fn(embed_sum)
+
+            embed_normalized = (embed_sum / bins.unsqueeze(0)).t()
+            embed_normalized = l2norm(embed_normalized)
+            embed_normalized = torch.where(zero_mask[..., None], embed,
+                                           embed_normalized)
+            ema_inplace(self.embed, embed_normalized, self.decay)
+            self.expire_codes_(x)
+
+        return quantize, embed_ind
+
+# main class
+
+class VectorQuantize(nn.Module):
+    def __init__(
+        self,
+        dim,
+        codebook_size,
+        n_embed = None,
+        codebook_dim = None,
+        decay = 0.8,
+        eps = 1e-5,
+        kmeans_init = False,
+        kmeans_iters = 10,
+        use_cosine_sim = False,
+        threshold_ema_dead_code = 0,
+        channel_last = True,
+        accept_image_fmap = False,
+        commitment_weight = None,
+        commitment = 1., # deprecate in next version, turn off by default
+        orthogonal_reg_weight = 0.,
+        orthogonal_reg_active_codes_only = False,
+        orthogonal_reg_max_codes = None,
+        sample_codebook_temp = 0.,
+        sync_codebook = False
+    ):
+        super().__init__()
+        n_embed = default(n_embed, codebook_size)
+
+        codebook_dim = default(codebook_dim, dim)
+        requires_projection = codebook_dim != dim
+        self.project_in = nn.Linear(dim, codebook_dim) if requires_projection \
+                          else nn.Identity()
+        self.project_out = nn.Linear(codebook_dim, dim) if requires_projection \
+                           else nn.Identity()
+
+        self.eps = eps
+        self.commitment_weight = default(commitment_weight, commitment)
+
+        has_codebook_orthogonal_loss = orthogonal_reg_weight > 0
+        self.orthogonal_reg_weight = orthogonal_reg_weight
+        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
+        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
+
+        codebook_class = EuclideanCodebook if not use_cosine_sim \
+                         else CosineSimCodebook
+
+        self._codebook = codebook_class(
+            dim = codebook_dim,
+            codebook_size = n_embed,
+            kmeans_init = kmeans_init,
+            kmeans_iters = kmeans_iters,
+            decay = decay,
+            eps = eps,
+            threshold_ema_dead_code = threshold_ema_dead_code,
+            use_ddp = sync_codebook,
+            learnable_codebook = has_codebook_orthogonal_loss,
+            sample_codebook_temp = sample_codebook_temp
+        )
+
+        self.codebook_size = codebook_size
+
+        self.accept_image_fmap = accept_image_fmap
+        self.channel_last = channel_last
+
+    @property
+    def codebook(self):
+        return self._codebook.embed
+
+    def forward(self, x, used_codes=None):
+        shape, device, codebook_size = x.shape, x.device, self.codebook_size
+
+        need_transpose = not self.channel_last and not self.accept_image_fmap
+
+        if self.accept_image_fmap:
+            height, width = x.shape[-2:]
+            x = rearrange(x, 'b c h w -> b (h w) c')
+
+        if need_transpose:
+            x = rearrange(x, 'b d n -> b n d')
+
+        x = self.project_in(x)
+
+        quantize, embed_ind = self._codebook(x, used_codes)
+
+        loss = torch.tensor([0.], device = device, requires_grad = self.training)
+
+        if self.training:
+            if self.commitment_weight > 0:
+                commit_loss = F.mse_loss(quantize.detach(), x)
+                loss = loss + commit_loss * self.commitment_weight
+
+            if self.orthogonal_reg_weight > 0:
+                codebook = self.codebook
+
+                if self.orthogonal_reg_active_codes_only:
+                    # only calculate orthogonal loss for the activated codes for this batch
+                    unique_code_ids = torch.unique(embed_ind)
+                    codebook = codebook[unique_code_ids]
+
+                num_codes = codebook.shape[0]
+                if exists(self.orthogonal_reg_max_codes) and num_codes > self.orthogonal_reg_max_codes:
+                    rand_ids = torch.randperm(num_codes, device = device)[:self.orthogonal_reg_max_codes]
+                    codebook = codebook[rand_ids]
+
+                orthogonal_reg_loss = orthgonal_loss_fn(codebook)
+                loss = loss + orthogonal_reg_loss * self.orthogonal_reg_weight
+
+        quantize = self.project_out(quantize)
+
+        if need_transpose:
+            quantize = rearrange(quantize, 'b n d -> b d n')
+
+        if self.accept_image_fmap:
+            quantize = rearrange(quantize, 'b (h w) c -> b c h w', h = height, w = width)
+            embed_ind = rearrange(embed_ind, 'b (h w) -> b h w', h = height, w = width)
+
+        return quantize, embed_ind, loss