fix: handle LoRA rank mismatch during inference in lora_ft_webui

Pass the selected LoRA checkpoint to load_model() on first load so the
model initializes with the correct rank from lora_config.json instead of
always defaulting to r=32.

On subsequent LoRA hot-swaps, detect rank incompatibility and
automatically reload the model with the new checkpoint's config,
preventing tensor shape mismatch errors (fixes #283).

Made-with: Cursor

lora_ft_webui.py

@@ -281,27 +281,48 @@ def run_inference(text, prompt_wav, prompt_text, lora_selection, cfg_scale, step
                     print(f"Warning: Failed to read base_model from LoRA config: {e}", file=sys.stderr)
 
         # 加载模型
+        lora_to_load = lora_selection if lora_selection and lora_selection != "None" else None
         try:
             print(f"Loading base model: {base_model_path}", file=sys.stderr)
-            load_model(base_model_path)
+            load_model(base_model_path, lora_to_load)
-            if lora_selection and lora_selection != "None":
+            if lora_to_load:
-                print(f"Model loaded for LoRA: {lora_selection}", file=sys.stderr)
+                print(f"Model loaded with LoRA: {lora_selection}", file=sys.stderr)
         except Exception as e:
             error_msg = f"Failed to load model from {base_model_path}: {str(e)}"
             print(error_msg, file=sys.stderr)
             return None, error_msg
+        lora_just_loaded = lora_to_load
+    else:
+        lora_just_loaded = None
 
     # Handle LoRA hot-swapping
     assert current_model is not None, "Model must be loaded before inference"
     if lora_selection and lora_selection != "None":
         full_lora_path = os.path.join("lora", lora_selection)
+
+        if lora_just_loaded != lora_selection:
+            new_lora_config, new_base_model = load_lora_config_from_checkpoint(full_lora_path)
+            current_r = current_model.tts_model.lora_config.r if current_model.tts_model.lora_config else None
+            new_r = new_lora_config.r if new_lora_config else None
+
+            if new_r is not None and current_r is not None and new_r != current_r:
+                print(f"LoRA rank mismatch (model r={current_r}, checkpoint r={new_r}), reloading...", file=sys.stderr)
+                reload_base = (
+                    new_base_model if new_base_model and os.path.exists(new_base_model)
+                    else (pretrained_path if pretrained_path and pretrained_path.strip() else default_pretrained_path)
+                )
+                try:
+                    load_model(reload_base, lora_selection)
+                except Exception as e:
+                    return None, f"Failed to reload model for LoRA rank change: {e}"
+            else:
                 print(f"Hot-loading LoRA: {full_lora_path}", file=sys.stderr)
                 try:
                     current_model.load_lora(full_lora_path)
-            current_model.set_lora_enabled(True)
                 except Exception as e:
                     print(f"Error loading LoRA: {e}", file=sys.stderr)
                     return None, f"Error loading LoRA: {e}"
+        current_model.set_lora_enabled(True)
     else:
         print("Disabling LoRA", file=sys.stderr)
         current_model.set_lora_enabled(False)