diff --git a/nodes/sampler.py b/nodes/sampler.py
index 7ae0d4c..60155a3 100644
--- a/nodes/sampler.py
+++ b/nodes/sampler.py
@@ -111,6 +111,20 @@ class PrismAudioSampler:
 
             from prismaudio_core.inference.sampling import sample_discrete_euler
 
+            # Diagnostic: log DIT velocity at first step to verify model is working
+            t_diag = torch.ones([noise.shape[0]], dtype=noise.dtype, device=noise.device)
+            with torch.no_grad():
+                v_diag = diffusion.model(noise, t_diag, **cond_inputs, cfg_scale=cfg_scale, batch_cfg=True)
+                vd = v_diag.float()
+                print(f"[PrismAudio] DIT velocity@t=1: shape={tuple(vd.shape)} mean={vd.mean():.4f} std={vd.std():.4f} min={vd.min():.4f} max={vd.max():.4f}", flush=True)
+                # Also check uncond (cfg_scale=1.0) to verify conditioning is active
+                v_uncond = diffusion.model(noise, t_diag, **cond_inputs, cfg_scale=1.0, batch_cfg=True)
+                vu = v_uncond.float()
+                print(f"[PrismAudio] DIT velocity@t=1 uncond: mean={vu.mean():.4f} std={vu.std():.4f}", flush=True)
+                diff = (vd - vu).abs()
+                print(f"[PrismAudio] DIT cond-uncond diff: mean={diff.mean():.4f} max={diff.max():.4f}", flush=True)
+            del v_diag, v_uncond, vd, vu, diff
+
             def on_step(info):
                 pbar.update(1)
 
diff --git a/nodes/text_only.py b/nodes/text_only.py
index 8053e1e..517e2a4 100644
--- a/nodes/text_only.py
+++ b/nodes/text_only.py
@@ -85,6 +85,19 @@ class PrismAudioTextOnly:
 
             from prismaudio_core.inference.sampling import sample_discrete_euler
 
+            # Diagnostic: log DIT velocity at first step to verify model is working
+            t_diag = torch.ones([noise.shape[0]], dtype=noise.dtype, device=noise.device)
+            with torch.no_grad():
+                v_diag = diffusion.model(noise, t_diag, **cond_inputs, cfg_scale=cfg_scale, batch_cfg=True)
+                vd = v_diag.float()
+                print(f"[PrismAudio] DIT velocity@t=1: shape={tuple(vd.shape)} mean={vd.mean():.4f} std={vd.std():.4f} min={vd.min():.4f} max={vd.max():.4f}", flush=True)
+                v_uncond = diffusion.model(noise, t_diag, **cond_inputs, cfg_scale=1.0, batch_cfg=True)
+                vu = v_uncond.float()
+                print(f"[PrismAudio] DIT velocity@t=1 uncond: mean={vu.mean():.4f} std={vu.std():.4f}", flush=True)
+                diff = (vd - vu).abs()
+                print(f"[PrismAudio] DIT cond-uncond diff: mean={diff.mean():.4f} max={diff.max():.4f}", flush=True)
+            del v_diag, v_uncond, vd, vu, diff
+
             def on_step(info):
                 pbar.update(1)