debug: add latent and audio stats logging to T2A node

Print fakes latent stats (mean/std/min/max) and audio pre-norm stats
to diagnose whether diffusion output is numerically reasonable.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nodes/text_only.py

@@ -90,6 +90,9 @@ class PrismAudioTextOnly:
                 batch_cfg=True,
             )
 
+            fakes_f = fakes.float()
+            print(f"[PrismAudio] latent stats: shape={tuple(fakes_f.shape)} mean={fakes_f.mean():.4f} std={fakes_f.std():.4f} min={fakes_f.min():.4f} max={fakes_f.max():.4f}", flush=True)
+
             if strategy == "offload_to_cpu":
                 diffusion.model.to(get_offload_device())
                 diffusion.conditioner.to(get_offload_device())
@@ -98,7 +101,7 @@ class PrismAudioTextOnly:
 
             # VAE decode in fp32 (snake activations overflow in fp16)
             with torch.amp.autocast(device_type=device.type, enabled=False):
-                audio = diffusion.pretransform.decode(fakes.float())
+                audio = diffusion.pretransform.decode(fakes_f)
 
             if strategy == "offload_to_cpu":
                 diffusion.pretransform.to(get_offload_device())
@@ -106,8 +109,12 @@ class PrismAudioTextOnly:
 
         # Peak normalize then clamp
         audio = audio.float()
+        pre_norm_std = audio.std().item()
+        pre_norm_peak = audio.abs().max().item()
         peak = audio.abs().max().clamp(min=1e-8)
         audio = (audio / peak).clamp(-1, 1)
+        print(f"[PrismAudio] audio stats (pre-norm): std={pre_norm_std:.4f} peak={pre_norm_peak:.4f}", flush=True)
+        print(f"[PrismAudio] audio shape: {tuple(audio.shape)}", flush=True)
 
         return ({"waveform": audio.cpu(), "sample_rate": SAMPLE_RATE},)