Fix HunyuanFoley: save text_feats to disk inside GPU worker

ZeroGPU forbids CUDA tensor deserialization in the main process. The previous
fix resolved the ModuleNotFoundError but text_feats contains CUDA tensors;
unpickling them in main triggers torch.cuda._lazy_init() which ZeroGPU blocks.

Fix: save text_feats via torch.save() inside the GPU worker, return the file
path string instead. Main process receives only numpy arrays + a string path.
Update _hunyuan_extras to use the pre-saved path directly.

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

app.py

@@ -1322,7 +1322,13 @@ def _hunyuan_gpu_infer_impl(video_file, prompt, negative_prompt, seed_val,
 
         _log_inference_timing("HunyuanFoley", time.perf_counter() - _t_hny_start,
                               len(segments), int(num_steps), HUNYUAN_SECS_PER_STEP)
-        results.append((seg_wavs, sr, text_feats))
+
+        # Save text_feats to disk inside the GPU worker so we never pickle a CUDA
+        # tensor back to the main process (ZeroGPU forbids CUDA init in main process).
+        text_feats_path = os.path.join(tmp_dir, f"hunyuan_{sample_idx}_text_feats.pt")
+        torch.save(text_feats, text_feats_path)
+        print(f"[HunyuanFoley] text_feats saved to {text_feats_path}")
+        results.append((seg_wavs, sr, text_feats_path))
 
         # Free GPU memory between samples to prevent VRAM fragmentation
         if torch.cuda.is_available():
@@ -1352,10 +1358,10 @@ def generate_hunyuan(video_file, prompt, negative_prompt, seed_val,
 
     # ── CPU post-processing (no GPU needed) ──
     def _hunyuan_extras(sample_idx, result, td):
-        _, _sr, text_feats = result
-        path = os.path.join(td, f"hunyuan_{sample_idx}_text_feats.pt")
-        torch.save(text_feats, path)
-        return {"text_feats_path": path}
+        # text_feats was saved to disk inside the GPU worker (to avoid pickling CUDA
+        # tensors across the ZeroGPU process boundary); result[2] is the file path.
+        _, _sr, text_feats_path = result
+        return {"text_feats_path": text_feats_path}
 
     outputs = _post_process_samples(
         results, model="hunyuan", tmp_dir=tmp_dir,