diff --git a/codes/models/audio/music/transformer_diffusion13.py b/codes/models/audio/music/transformer_diffusion13.py
index 0ed685b8..9dd67410 100644
--- a/codes/models/audio/music/transformer_diffusion13.py
+++ b/codes/models/audio/music/transformer_diffusion13.py
@@ -126,7 +126,8 @@ class TransformerDiffusion(nn.Module):
         )
         self.resolution_embed = nn.Embedding(resolution_steps, model_channels)
         self.conditioning_encoder = ConditioningEncoder(in_channels, model_channels, resolution_steps, num_attn_heads=model_channels//64)
-        self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,6))
+        self.unconditioned_prior = nn.Parameter(torch.zeros(1,in_channels,1))
 
         self.inp_block = conv_nd(1, in_channels+input_vec_dim, model_channels, 3, 1, 1)
         self.layers = TimestepEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, num_heads, dropout) for _ in range(num_layers)])
@@ -163,10 +164,17 @@ class TransformerDiffusion(nn.Module):
         return groups
 
     def input_to_random_resolution_and_window(self, x, x_prior):
+        """
+        This function MUST be applied to the target *before* noising. It returns the reduced, re-scoped target as well
+        as caches an internal prior for the rescoped target which will be useud in training.
+        Args:
+            x: Diffusion target
+            x_prior: Prior input, which is generally just {x}
+        """
         assert x.shape == x_prior.shape, f'{x.shape} {x_prior.shape}'
-        resolution = randrange(0, self.resolution_steps)
+        resolution = randrange(1, self.resolution_steps)
         resolution_scale = 2 ** resolution
-        s = F.interpolate(x, scale_factor=1/resolution_scale, mode='linear', align_corners=True)
+        s = F.interpolate(x, scale_factor=1/resolution_scale, mode='nearest', align_corners=True)
         s_prior = F.interpolate(x_prior, scale_factor=1/resolution_scale, mode='linear', align_corners=True)
         s_diff = s.shape[-1] - self.max_window
         if s_diff > 1:
@@ -179,7 +187,6 @@ class TransformerDiffusion(nn.Module):
         return s
 
     def forward(self, x, timesteps, x_prior=None, resolution=None, conditioning_input=None, conditioning_free=False):
-        unused_params = []
         conditioning_input = x_prior if conditioning_input is None else conditioning_input
 
         h = x
@@ -202,9 +209,14 @@ class TransformerDiffusion(nn.Module):
                 gap = conditioning_input.shape[-1] - clen
                 cstart = randrange(0, gap)
                 conditioning_input = conditioning_input[:,:,cstart:cstart+clen]
-
             code_emb = self.conditioning_encoder(conditioning_input, resolution)
-            unused_params.append(self.unconditioned_embedding)
+
+        # Mask out the conditioning input and x_prior inputs for whole batch elements, implementing something similar to classifier-free guidance.
+        if self.training and self.unconditioned_percentage > 0:
+            unconditioned_batches = torch.rand((h.shape[0], 1, 1),
+                                               device=h.device) < self.unconditioned_percentage
+            h_sub = torch.where(unconditioned_batches, self.unconditioned_prior.repeat(h_sub.shape[0], 1, h_sub.shape[-1]), h_sub)
+            code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(code_emb.shape[0], 1, 1), code_emb)
 
         with torch.autocast(x.device.type, enabled=self.enable_fp16):
             time_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
@@ -219,7 +231,8 @@ class TransformerDiffusion(nn.Module):
         h = h.float()
         out = self.out(h)
 
-        # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
+        # Defensively involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
+        unused_params = [self.unconditioned_prior, self.unconditioned_embedding]
         extraneous_addition = 0
         for p in unused_params:
             extraneous_addition = extraneous_addition + p.mean()
@@ -238,11 +251,19 @@ def test_tfd():
     cond = torch.randn(2,256,10336)
     ts = torch.LongTensor([600, 600])
     model = TransformerDiffusion(in_channels=256, model_channels=1024, contraction_dim=512,
-                                 num_heads=512//64, input_vec_dim=256, num_layers=12, dropout=.1)
+                                 num_heads=512//64, input_vec_dim=256, num_layers=12, dropout=.1,
+                                 unconditioned_percentage=.6)
     for k in range(100):
         x = model.input_to_random_resolution_and_window(clip, x_prior=clip)
         model(x, ts, clip)
 
 
+def remove_conditioning(sd_path):
+    sd = torch.load(sd_path)
+    del sd['unconditioned_embedding']
+    torch.save(sd, sd_path.replace('.pth', '') + '_fixed.pth')
+
+
 if __name__ == '__main__':
+    remove_conditioning('X:\\dlas\\experiments\\train_music_diffusion_multilevel_sr\\models\\12000_generator.pth')
     test_tfd()