diff --git a/codes/models/audio/music/transformer_diffusion2.py b/codes/models/audio/music/transformer_diffusion2.py
index 9b565254..3b37b019 100644
--- a/codes/models/audio/music/transformer_diffusion2.py
+++ b/codes/models/audio/music/transformer_diffusion2.py
@@ -1,18 +1,10 @@
-import os
-import random
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import torchvision
-from torch import autocast
 
-from models.arch_util import ResBlock
 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
-from scripts.audio.gen.use_mel2vec_codes import collapse_codegroups
-from trainer.injectors.audio_injectors import normalize_mel
+from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
 from trainer.networks import register_model
 from utils.util import checkpoint
 
@@ -34,16 +26,26 @@ class MultiGroupEmbedding(nn.Module):
         return torch.cat(h, dim=-1)
 
 
+class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
+    def forward(self, x, emb, rotary_emb):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb, rotary_emb)
+            else:
+                x = layer(x, rotary_emb)
+        return x
+
+
 class AttentionBlock(TimestepBlock):
     def __init__(self, dim, heads, dropout):
         super().__init__()
         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, glu=True)
+        self.ff = FeedForward(dim, mult=2, dropout=dropout, zero_init_output=True)
         self.rms_scale_norm = RMSScaleShiftNorm(dim)
 
-    def forward(self, x, emb):
-        h = self.rms_scale_norm(x, norm_scale_shift_inp=emb)
-        h, _, _, _ = checkpoint(self.attn, h)
+    def forward(self, x, timestep_emb, rotary_emb):
+        h = self.rms_scale_norm(x, 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
 
@@ -59,6 +61,7 @@ class TransformerDiffusion(nn.Module):
             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
@@ -133,11 +136,12 @@ class TransformerDiffusion(nn.Module):
         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.top_layers = TimestepEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers//4)])
+        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
+        self.top_layers = TimestepRotaryEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers//4)])
         self.mid_intg = nn.Linear(model_channels*2, model_channels, bias=False)
-        self.mid_layers = TimestepEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers//2)])
+        self.mid_layers = TimestepRotaryEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers//2)])
         self.final_intg = nn.Linear(model_channels*2, model_channels, bias=False)
-        self.final_layers = TimestepEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers//4)])
+        self.final_layers = TimestepRotaryEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers//4)])
 
         self.out = nn.Sequential(
             normalization(model_channels),
@@ -190,10 +194,8 @@ class TransformerDiffusion(nn.Module):
             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):
+    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."
@@ -219,13 +221,14 @@ class TransformerDiffusion(nn.Module):
         blk_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + cond_emb
         x = self.inp_block(x).permute(0,2,1)
 
-        xt = self.top_layers(x, blk_emb)
+        rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
+        xt = self.top_layers(x, blk_emb, rotary_pos_emb)
         xm = torch.cat([xt, code_emb], dim=2)
         xm = self.mid_intg(xm)
-        xm = self.mid_layers(xm, blk_emb)
+        xm = self.mid_layers(xm, blk_emb, rotary_pos_emb)
         xb = torch.cat([xt, xm], dim=2)
         xb = self.final_intg(xb)
-        x = self.final_layers(xb, blk_emb)
+        x = self.final_layers(xb, blk_emb, rotary_pos_emb)
 
         x = x.float().permute(0,2,1)
         out = self.out(x)