diff --git a/codes/models/audio/music/tfdpc_v3.py b/codes/models/audio/music/tfdpc_v3.py
index 59531c32..3c150516 100644
--- a/codes/models/audio/music/tfdpc_v3.py
+++ b/codes/models/audio/music/tfdpc_v3.py
@@ -215,8 +215,8 @@ class TransformerDiffusionWithConditioningEncoder(nn.Module):
         self.diff = TransformerDiffusionWithPointConditioning(**kwargs)
         self.conditioning_encoder = ConditioningEncoder(256, kwargs['model_channels'])
 
-    def forward(self, x, timesteps, true_cheater, conditioning_input=None, disable_diversity=False, conditioning_free=False):
-        cond = self.conditioning_encoder(true_cheater)
+    def forward(self, x, timesteps, conditioning_input=None, disable_diversity=False, conditioning_free=False):
+        cond = self.conditioning_encoder(conditioning_input)
         diff = self.diff(x, timesteps, conditioning_input=cond, conditioning_free=conditioning_free)
         return diff
 
diff --git a/codes/models/audio/music/tfdpc_v5.py b/codes/models/audio/music/tfdpc_v5.py
index cf6c5141..09230b99 100644
--- a/codes/models/audio/music/tfdpc_v5.py
+++ b/codes/models/audio/music/tfdpc_v5.py
@@ -196,16 +196,16 @@ class TransformerDiffusionWithPointConditioning(nn.Module):
         unused_params = []
 
         time_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
-        cond_enc = self.conditioning_encoder(conditioning_input, time_emb)
-        cs = cond_enc[:,:,cond_start]
-        ce = cond_enc[:,:,x.shape[-1]+cond_start]
-        cond_enc = torch.cat([cs.unsqueeze(-1), ce.unsqueeze(-1)], dim=-1)
-        cond_enc = F.interpolate(cond_enc, size=(x.shape[-1],), mode='linear').permute(0,2,1)
 
         if conditioning_free:
             cond = self.unconditioned_embedding
+            cond = cond.repeat(1,x.shape[-1],1)
         else:
-            cond = cond_enc
+            cond_enc = self.conditioning_encoder(conditioning_input, time_emb)
+            cs = cond_enc[:,:,cond_start]
+            ce = cond_enc[:,:,x.shape[-1]+cond_start]
+            cond_enc = torch.cat([cs.unsqueeze(-1), ce.unsqueeze(-1)], dim=-1)
+            cond = F.interpolate(cond_enc, size=(x.shape[-1],), mode='linear').permute(0,2,1)
             # 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((cond.shape[0], 1, 1),
diff --git a/codes/trainer/eval/music_diffusion_fid.py b/codes/trainer/eval/music_diffusion_fid.py
index e29d9409..dfc2681e 100644
--- a/codes/trainer/eval/music_diffusion_fid.py
+++ b/codes/trainer/eval/music_diffusion_fid.py
@@ -301,14 +301,14 @@ class MusicDiffusionFid(evaluator.Evaluator):
 
 
 if __name__ == '__main__':
-    diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_cheater_gen.yml', 'generator',
+    diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_cheater_gen_r8.yml', 'generator',
                                        also_load_savepoint=False,
-                                       load_path='X:\\dlas\\experiments\\train_music_cheater_gen_v4\\models\\28000_generator_ema.pth'
+                                       load_path='X:\\dlas\\experiments\\train_music_cheater_gen_v5\\models\\18000_generator_ema.pth'
                                        ).cuda()
     opt_eval = {'path': 'Y:\\split\\yt-music-eval',  # eval music, mostly electronica. :)
                 #'path': 'E:\\music_eval',  # this is music from the training dataset, including a lot more variety.
                 'diffusion_steps': 32,
-                'conditioning_free': False, 'conditioning_free_k': 1, 'clip_audio': False, 'use_ddim': True,
+                'conditioning_free': True, 'conditioning_free_k': 1, 'clip_audio': False, 'use_ddim': True,
                 'diffusion_schedule': 'linear', 'diffusion_type': 'cheater_gen',
                 #'partial_low': 128, 'partial_high': 192
     }