diff --git a/codes/models/audio/music/tfdpc_v5.py b/codes/models/audio/music/tfdpc_v5.py
index 97a78172..9e7d2e29 100644
--- a/codes/models/audio/music/tfdpc_v5.py
+++ b/codes/models/audio/music/tfdpc_v5.py
@@ -215,6 +215,15 @@ class TransformerDiffusionWithPointConditioning(nn.Module):
             left_pt = -1
             cond_right = conditioning_input[:,:,cond_start+N:]
             right_pt = 0
+
+            if self.training:
+                # Arbitrarily restrict the context given. We should support short contexts and without this they are never encountered.
+                arb_context_cap = random.randint(50, 100)
+                if cond_left.shape[-1] > arb_context_cap and random() > .5:
+                    cond_left = cond_left[:,:,-arb_context_cap:]
+                if cond_right.shape[-1] > arb_context_cap and random() > .5:
+                    cond_right = cond_right[:,:,:arb_context_cap]
+
         elif cond_left is None:
             assert conditioning_input.shape[-1] - cond_start - N >= 0, f'Some sort of conditioning misalignment, {conditioning_input.shape[-1], cond_start, N}'
             cond_pre = conditioning_input[:,:,:cond_start]
diff --git a/codes/trainer/eval/music_diffusion_fid.py b/codes/trainer/eval/music_diffusion_fid.py
index b5e010c1..9dfc5986 100644
--- a/codes/trainer/eval/music_diffusion_fid.py
+++ b/codes/trainer/eval/music_diffusion_fid.py
@@ -230,10 +230,11 @@ class MusicDiffusionFid(evaluator.Evaluator):
 
         # 1. Generate the cheater latent using the input as a reference.
         sampler = self.diffuser.ddim_sample_loop if self.ddim else self.diffuser.p_sample_loop
-        output_shape = (1, 256, cheater.shape[-1]-80)
-        gen_cheater = sampler(self.model, output_shape, progress=True,
+        # center-pad the conditioning input (the center isn't actually used). this is hack for giving tfdpc5 a bigger working context.
+        cheater_padded = torch.cat([cheater[:,:,cheater.shape[-1]//2:], torch.zeros(1,256,160, device=cheater.device),  cheater[:,:,:cheater.shape[-1]//2]], dim=-1)
+        gen_cheater = sampler(self.model, cheater.shape, progress=True,
                               causal=self.causal, causal_slope=self.causal_slope,
-                              model_kwargs={'conditioning_input': cheater, 'cond_start': 40})
+                              model_kwargs={'conditioning_input': cheater_padded, 'cond_start': 80})
 
         # 2. Decode the cheater into a MEL
         gen_mel = self.cheater_decoder_diffuser.ddim_sample_loop(self.local_modules['cheater_decoder'].diff.to(audio.device), (1,256,gen_cheater.shape[-1]*16), progress=True,