fix: strip inference-mode tensor flags in DITTO before conditions computation

Root cause: net_generator/feature_utils/mel_converter parameters were loaded
in ComfyUI's inference_mode; operations on inference tensors propagate the flag,
so conditions computed from tainted weights were also tainted. checkpoint()
with use_reentrant=False then failed trying to save inference tensors during
the backward recompute pass.

Fix: _strip_inference() clones all params/buffers of all three models before
any forward pass, and _clone_nested() cleans any residual inference flags in
the conditions/empty_conditions output tensors.

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

nodes/selva_ditto_optimizer.py

@@ -283,6 +283,26 @@ def _do_optimize(net_generator, feature_utils, mel_converter,
     clip_f = features["clip_features"].to(device, dtype).clone()
     sync_f = features["sync_features"].to(device, dtype).clone()
 
+    # Strip inference-mode flags from all model weights and buffers BEFORE any
+    # forward pass. Parameters were loaded in ComfyUI's inference_mode context;
+    # operations on inference tensors produce inference tensors, so conditions
+    # computed from tainted weights would also be tainted. clone() outside
+    # inference_mode produces a normal tensor regardless of the source flag.
+    def _strip_inference(module):
+        for mod in module.modules():
+            for name, buf in list(mod._buffers.items()):
+                if buf is not None:
+                    mod._buffers[name] = buf.clone()
+            for name, param in list(mod._parameters.items()):
+                if param is not None:
+                    mod._parameters[name] = torch.nn.Parameter(
+                        param.data.clone(), requires_grad=False
+                    )
+
+    _strip_inference(net_generator)
+    _strip_inference(feature_utils)
+    _strip_inference(mel_converter)
+
     net_generator.update_seq_lengths(
         latent_seq_len=seq_cfg.latent_seq_len,
         clip_seq_len=clip_f.shape[1],
@@ -300,6 +320,20 @@ def _do_optimize(net_generator, feature_utils, mel_converter,
             bs=1, negative_text_features=neg_text_clip
         )
 
+    # Clone all tensors inside conditions/empty_conditions to ensure no inference
+    # flags survived from intermediate computations inside preprocess_conditions.
+    def _clone_nested(obj):
+        if isinstance(obj, torch.Tensor):
+            return obj.clone()
+        elif isinstance(obj, dict):
+            return {k: _clone_nested(v) for k, v in obj.items()}
+        elif isinstance(obj, (list, tuple)):
+            return type(obj)(_clone_nested(v) for v in obj)
+        return obj
+
+    conditions       = _clone_nested(conditions)
+    empty_conditions = _clone_nested(empty_conditions)
+
     # Initial noise — x_0 is the parameter we optimize
     x0_init = torch.randn(
         1, seq_cfg.latent_seq_len, net_generator.latent_dim,