fix: create LoRA params inside torch.enable_grad() to escape inference_mode

torch.enable_grad() re-enables grad tracking but nn.Parameters created while
torch.inference_mode() is active are inference tensors that can't enter autograd
regardless. Splitting into _train_inner() and calling it inside enable_grad()
ensures the deepcopy, apply_lora, and the training loop all run with a clean
autograd context.

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

nodes/selva_lora_trainer.py

@@ -365,6 +365,25 @@ class SelvaLoraTrainer:
             raise ValueError("[LoRA Trainer] No clips could be loaded.")
         print(f"[LoRA Trainer] {len(dataset)} clip(s) ready.", flush=True)
 
+        # Everything from here runs inside enable_grad: ComfyUI wraps nodes in
+        # inference_mode, and nn.Parameters created in that context are inference
+        # tensors that can't enter autograd even with requires_grad=True.
+        with torch.enable_grad():
+            return self._train_inner(
+                model, dataset, feature_utils_orig, seq_cfg,
+                device, dtype, variant, mode,
+                data_dir, output_dir, steps, rank, lr,
+                alpha_val, target_suffixes, warmup_steps,
+                grad_accum, save_every, resume_path, seed,
+            )
+
+    def _train_inner(
+        self, model, dataset, feature_utils_orig, seq_cfg,
+        device, dtype, variant, mode,
+        data_dir, output_dir, steps, rank, lr,
+        alpha_val, target_suffixes, warmup_steps,
+        grad_accum, save_every, resume_path, seed,
+    ):
         # --- Prepare generator copy with LoRA ---
         generator = copy.deepcopy(model["generator"]).to(device, dtype)
 
@@ -433,8 +452,6 @@ class SelvaLoraTrainer:
         print(f"\n[LoRA Trainer] Training {remaining} steps "
               f"(step {start_step + 1} → {steps})\n", flush=True)
 
-        # ComfyUI runs nodes inside torch.no_grad() — re-enable gradients for training
-        with torch.enable_grad():
         for step in range(start_step + 1, steps + 1):
             x1_cpu, clip_f_cpu, sync_f_cpu, text_clip_cpu = random.choice(dataset)