fix: pad/trim clip and sync features to fixed seq_len at dataset load time

Clips from shorter videos produce fewer CLIP frames (e.g. 2s → 16 frames,
8s → 64 frames). Mixed-length datasets would cause torch.stack() to fail
during batching. Normalize to seq_cfg.clip_seq_len / sync_seq_len at load,
same as latents are already normalized to latent_seq_len.

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

train_lora.py

@@ -288,7 +288,24 @@ def main():
             elif x1.shape[1] > tgt:
                 x1 = x1[:, :tgt, :]
             text_clip = encode_text_clip(clip_model, tokenizer_clip, [prompt], device).cpu()
-            dataset.append((x1, bundle["clip_features"], bundle["sync_features"], text_clip))
+
+            # Pad/trim clip and sync features to fixed seq lengths — shorter clips
+            # have fewer frames and would cause stack() to fail during batching
+            clip_f = bundle["clip_features"]  # [1, N_clip, 1024]
+            c_tgt  = seq_cfg.clip_seq_len
+            if clip_f.shape[1] < c_tgt:
+                clip_f = F.pad(clip_f, (0, 0, 0, c_tgt - clip_f.shape[1]))
+            elif clip_f.shape[1] > c_tgt:
+                clip_f = clip_f[:, :c_tgt, :]
+
+            sync_f = bundle["sync_features"]  # [1, N_sync, 768]
+            s_tgt  = seq_cfg.sync_seq_len
+            if sync_f.shape[1] < s_tgt:
+                sync_f = F.pad(sync_f, (0, 0, 0, s_tgt - sync_f.shape[1]))
+            elif sync_f.shape[1] > s_tgt:
+                sync_f = sync_f[:, :s_tgt, :]
+
+            dataset.append((x1, clip_f, sync_f, text_clip))
         except Exception as e:
             print(f"  [LoRA] Warning: failed to process {npz_path.name}: {e}")