fix: load model into local var before patching to avoid half-initialised state

Apply local-var-then-assign pattern to chatterbox_backend.py (multilingual)
to match the turbo backend. Also use _current_model_size fallback in
unload, delete, and status endpoints for consistent Qwen model size checks.

backend/backends/chatterbox_backend.py

@@ -136,6 +136,10 @@ class ChatterboxTTSBackend:
             import torch
             from chatterbox.mtl_tts import ChatterboxMultilingualTTS
 
+            # Load into a local variable first, apply all patches, then
+            # assign to self.model.  This avoids leaving a half-initialised
+            # model on self.model if any patch step raises an exception.
+            #
             # Monkey-patch torch.load for CPU loading. The model's .pt files
             # were saved on CUDA; from_pretrained() doesn't pass map_location
             # so loading on CPU fails without this.
@@ -150,13 +154,13 @@ class ChatterboxTTSBackend:
                     with ChatterboxTTSBackend._load_lock:
                         torch.load = _patched_load
                         try:
-                            self.model = ChatterboxMultilingualTTS.from_pretrained(
+                            model = ChatterboxMultilingualTTS.from_pretrained(
                                 device=device,
                             )
                         finally:
                             torch.load = _orig_torch_load
                 else:
-                    self.model = ChatterboxMultilingualTTS.from_pretrained(
+                    model = ChatterboxMultilingualTTS.from_pretrained(
                         device=device,
                     )
             finally:
@@ -165,7 +169,7 @@ class ChatterboxTTSBackend:
             # Fix: transformers >= 4.36 defaults LlamaModel to sdpa attention
             # which doesn't support output_attentions=True (needed by
             # Chatterbox's AlignmentStreamAnalyzer). Force eager attention.
-            t3_tfmr = self.model.t3.tfmr
+            t3_tfmr = model.t3.tfmr
             if hasattr(t3_tfmr, "config") and hasattr(
                 t3_tfmr.config, "_attn_implementation"
             ):
@@ -185,7 +189,7 @@ class ChatterboxTTSBackend:
             import types
 
             # Patch S3Tokenizer (used by s3gen.tokenizer)
-            _tokzr = self.model.s3gen.tokenizer
+            _tokzr = model.s3gen.tokenizer
             _orig_log_mel = _tokzr.log_mel_spectrogram.__func__
 
             def _f32_log_mel(self_tokzr, audio, padding=0):
@@ -197,7 +201,7 @@ class ChatterboxTTSBackend:
             _tokzr.log_mel_spectrogram = types.MethodType(_f32_log_mel, _tokzr)
 
             # Patch VoiceEncoder
-            _ve = self.model.ve
+            _ve = model.ve
             _orig_ve_forward = _ve.forward.__func__
 
             def _f32_ve_forward(self_ve, mels):
@@ -205,6 +209,9 @@ class ChatterboxTTSBackend:
 
             _ve.forward = types.MethodType(_f32_ve_forward, _ve)
 
+            # All patches applied successfully — publish the model
+            self.model = model
+
             logger.info("Chatterbox Multilingual TTS loaded successfully")
 
         except ImportError as e:

backend/backends/chatterbox_turbo_backend.py

@@ -154,6 +154,8 @@ class ChatterboxTurboTTSBackend:
             # Monkey-patch torch.load for CPU loading. The model's .pt files
             # were saved on CUDA; from_local() doesn't pass map_location
             # so loading on CPU fails without this.
+            # Load into a local var, apply patches, then publish to
+            # self.model so a failed patch doesn't leave us half-initialised.
             if device == "cpu":
                 _orig_torch_load = torch.load
 
@@ -164,13 +166,13 @@ class ChatterboxTurboTTSBackend:
                 with ChatterboxTurboTTSBackend._load_lock:
                     torch.load = _patched_load
                     try:
-                        self.model = ChatterboxTurboTTS.from_local(
+                        model = ChatterboxTurboTTS.from_local(
                             local_path, device,
                         )
                     finally:
                         torch.load = _orig_torch_load
             else:
-                self.model = ChatterboxTurboTTS.from_local(
+                model = ChatterboxTurboTTS.from_local(
                     local_path, device,
                 )
 
@@ -191,7 +193,7 @@ class ChatterboxTurboTTSBackend:
             import types
 
             # Patch S3Tokenizer (used by s3gen.tokenizer)
-            _tokzr = self.model.s3gen.tokenizer
+            _tokzr = model.s3gen.tokenizer
             _orig_log_mel = _tokzr.log_mel_spectrogram.__func__
 
             def _f32_log_mel(self_tokzr, audio, padding=0):
@@ -203,7 +205,7 @@ class ChatterboxTurboTTSBackend:
             _tokzr.log_mel_spectrogram = types.MethodType(_f32_log_mel, _tokzr)
 
             # Patch VoiceEncoder
-            _ve = self.model.ve
+            _ve = model.ve
             _orig_ve_forward = _ve.forward.__func__
 
             def _f32_ve_forward(self_ve, mels):
@@ -211,6 +213,9 @@ class ChatterboxTurboTTSBackend:
 
             _ve.forward = types.MethodType(_f32_ve_forward, _ve)
 
+            # Only publish after all patches succeed
+            self.model = model
+
             logger.info("Chatterbox Turbo TTS loaded successfully")
 
         except ImportError as e:

backend/main.py

@@ -1512,7 +1512,10 @@ async def unload_model_by_name(model_name: str):
     try:
         if model_type == "tts":
             tts_model = tts.get_tts_model()
-            if tts_model.is_loaded() and tts_model.model_size == model_size:
+            loaded_size = getattr(
+                tts_model, "_current_model_size", None
+            ) or getattr(tts_model, "model_size", None)
+            if tts_model.is_loaded() and loaded_size == model_size:
                 tts.unload_tts_model()
             else:
                 return {"message": f"Model {model_name} is not loaded"}
@@ -1595,7 +1598,10 @@ async def get_model_status():
         """Check if TTS model is loaded with specific size."""
         try:
             tts_model = tts.get_tts_model()
-            return tts_model.is_loaded() and getattr(tts_model, 'model_size', None) == model_size
+            loaded_size = getattr(
+                tts_model, "_current_model_size", None
+            ) or getattr(tts_model, "model_size", None)
+            return tts_model.is_loaded() and loaded_size == model_size
         except Exception:
             return False
     
@@ -2072,7 +2078,10 @@ async def delete_model(model_name: str):
         # Check if model is loaded and unload it first
         if config["model_type"] == "tts":
             tts_model = tts.get_tts_model()
-            if tts_model.is_loaded() and tts_model.model_size == config["model_size"]:
+            loaded_size = getattr(
+                tts_model, "_current_model_size", None
+            ) or getattr(tts_model, "model_size", None)
+            if tts_model.is_loaded() and loaded_size == config["model_size"]:
                 tts.unload_tts_model()
         elif config["model_type"] == "luxtts":
             from .backends import get_tts_backend_for_engine