diff --git a/codes/models/audio/music/transformer_diffusion4.py b/codes/models/audio/music/transformer_diffusion4.py
new file mode 100644
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+++ b/codes/models/audio/music/transformer_diffusion4.py
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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import TimestepEmbedSequential, TimestepBlock
+from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
+from trainer.networks import register_model
+from utils.util import checkpoint
+
+
+def is_latent(t):
+    return t.dtype == torch.float
+
+def is_sequence(t):
+    return t.dtype == torch.long
+
+
+class MultiGroupEmbedding(nn.Module):
+    def __init__(self, tokens, groups, dim):
+        super().__init__()
+        self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
+
+    def forward(self, x):
+        h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
+        return torch.cat(h, dim=-1)
+
+
+class SpecialSequential(nn.Sequential, TimestepBlock):
+    def forward(self, x, aligned_emb, point_emb, rotary_emb):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, aligned_emb, point_emb, rotary_emb)
+            else:
+                x = layer(x, aligned_emb, rotary_emb)
+        return x
+
+
+class AttentionBlock(TimestepBlock):
+    def __init__(self, dim, heads, dropout):
+        super().__init__()
+        self.intg = nn.Linear(dim*2, dim)
+        self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout, zero_init_output=False)
+        self.ff = FeedForward(dim, mult=2, dropout=dropout, zero_init_output=True)
+        self.rms_scale_norm = RMSScaleShiftNorm(dim)
+
+    def forward(self, x, aligned_emb, timestep_emb, rotary_emb):
+        h = self.intg(torch.cat([x, aligned_emb], dim=-1))
+        h = self.rms_scale_norm(h, norm_scale_shift_inp=timestep_emb)
+        h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
+        h = checkpoint(self.ff, h)
+        return h + x
+
+
+class TransformerDiffusion(nn.Module):
+    """
+    A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
+    """
+
+    def __init__(
+            self,
+            model_channels=512,
+            num_layers=8,
+            in_channels=256,
+            in_latent_channels=512,
+            rotary_emb_dim=32,
+            token_count=8,
+            in_groups=None,
+            out_channels=512,  # mean and variance
+            dropout=0,
+            use_fp16=False,
+            # Parameters for regularization.
+            unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.dropout = dropout
+        self.unconditioned_percentage = unconditioned_percentage
+        self.enable_fp16 = use_fp16
+        heads = model_channels//64
+
+        self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
+
+        self.time_embed = nn.Sequential(
+            linear(model_channels, model_channels),
+            nn.SiLU(),
+            linear(model_channels, model_channels),
+        )
+        self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2),
+                                                   nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2))
+        self.conditioning_encoder = Encoder(
+                    dim=model_channels,
+                    depth=4,
+                    heads=heads,
+                    ff_dropout=dropout,
+                    attn_dropout=dropout,
+                    use_rmsnorm=True,
+                    ff_glu=True,
+                    rotary_pos_emb=True,
+                )
+
+        # Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
+        # This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
+        # complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
+        # transformer network.
+        if in_groups is None:
+            self.embeddings = nn.Embedding(token_count, model_channels)
+        else:
+            self.embeddings = MultiGroupEmbedding(token_count, in_groups, model_channels)
+        self.latent_conditioner = nn.Sequential(
+            nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
+            Encoder(
+                    dim=model_channels,
+                    depth=2,
+                    heads=heads,
+                    ff_dropout=dropout,
+                    attn_dropout=dropout,
+                    use_rmsnorm=True,
+                    ff_glu=True,
+                    rotary_pos_emb=True,
+                )
+        )
+        self.latent_fade = nn.Parameter(torch.zeros(1,1,model_channels))
+        self.code_converter = Encoder(
+                    dim=model_channels,
+                    depth=3,
+                    heads=heads,
+                    ff_dropout=dropout,
+                    attn_dropout=dropout,
+                    use_rmsnorm=True,
+                    ff_glu=True,
+                    rotary_pos_emb=True,
+                )
+
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
+        self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
+
+        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
+        self.layers = SpecialSequential(*[AttentionBlock(model_channels, model_channels // 64, dropout) for _ in range(num_layers)])
+
+        self.out = nn.Sequential(
+            normalization(model_channels),
+            nn.SiLU(),
+            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
+        )
+
+        self.debug_codes = {}
+
+    def get_grad_norm_parameter_groups(self):
+        groups = {
+            'contextual_embedder': list(self.conditioning_embedder.parameters()),
+            'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
+            'code_converters': list(self.embeddings.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()),
+            'time_embed': list(self.time_embed.parameters()),
+        }
+        return groups
+
+    def timestep_independent(self, codes, conditioning_input, expected_seq_len, prenet_latent=None, return_code_pred=False):
+        cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
+        cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
+
+        code_emb = self.embeddings(codes)
+        if prenet_latent is not None:
+            latent_conditioning = self.latent_conditioner(prenet_latent)
+            code_emb = code_emb + latent_conditioning * self.latent_fade
+
+        unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
+        # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
+        if self.training and self.unconditioned_percentage > 0:
+            unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
+                                               device=code_emb.device) < self.unconditioned_percentage
+            code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(codes.shape[0], 1, 1),
+                                   code_emb)
+        code_emb = self.code_converter(code_emb)
+
+        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
+        if not return_code_pred:
+            return expanded_code_emb, cond_emb
+        else:
+            # Perform the mel_head computation on the pre-exanded code embeddings, then interpolate it separately.
+            mel_pred = self.mel_head(code_emb.permute(0,2,1))
+            mel_pred = F.interpolate(mel_pred, size=expected_seq_len, mode='nearest')
+            # Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches.
+            # This is because we don't want that gradient being used to train parameters through the codes_embedder as
+            # it unbalances contributions to that network from the MSE loss.
+            mel_pred = mel_pred * unconditioned_batches.logical_not()
+            return expanded_code_emb, cond_emb, mel_pred
+
+
+    def forward(self, x, timesteps, codes=None, conditioning_input=None, prenet_latent=None, precomputed_code_embeddings=None,
+                precomputed_cond_embeddings=None, conditioning_free=False, return_code_pred=False):
+        if precomputed_code_embeddings is not None:
+            assert precomputed_cond_embeddings is not None, "Must specify both precomputed embeddings if one is specified"
+            assert codes is None and conditioning_input is None and prenet_latent is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
+        assert not (return_code_pred and precomputed_code_embeddings is not None), "I cannot compute a code_pred output for you."
+
+        unused_params = []
+        if not return_code_pred:
+            unused_params.extend(list(self.mel_head.parameters()))
+        if conditioning_free:
+            code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
+            unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
+            unused_params.extend(list(self.latent_conditioner.parameters()))
+        else:
+            if precomputed_code_embeddings is not None:
+                code_emb = precomputed_code_embeddings
+                cond_emb = precomputed_cond_embeddings
+            else:
+                code_emb, cond_emb, mel_pred = self.timestep_independent(codes, conditioning_input, x.shape[-1], prenet_latent, True)
+                if prenet_latent is None:
+                    unused_params.extend(list(self.latent_conditioner.parameters()) + [self.latent_fade])
+            unused_params.append(self.unconditioned_embedding)
+
+        blk_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + cond_emb
+        x = self.inp_block(x).permute(0,2,1)
+
+        rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
+        x = self.layers(x, code_emb, blk_emb, rotary_pos_emb)
+
+        x = x.float().permute(0,2,1)
+        out = self.out(x)
+        # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
+        extraneous_addition = 0
+        for p in unused_params:
+            extraneous_addition = extraneous_addition + p.mean()
+        out = out + extraneous_addition * 0
+
+        if return_code_pred:
+            return out, mel_pred
+        return out
+
+
+@register_model
+def register_transformer_diffusion4(opt_net, opt):
+    return TransformerDiffusion(**opt_net['kwargs'])
+
+
+if __name__ == '__main__':
+    clip = torch.randn(2, 256, 400)
+    aligned_latent = torch.randn(2,100,512)
+    aligned_sequence = torch.randint(0,8,(2,100,8))
+    cond = torch.randn(2, 256, 400)
+    ts = torch.LongTensor([600, 600])
+    model = TransformerDiffusion(512, unconditioned_percentage=.5, in_groups=8)
+    o = model(clip, ts, aligned_sequence, cond, return_code_pred=True)
+    #o = model(clip, ts, aligned_sequence, cond, aligned_latent)
+