fix(mlx): fold Qwen3 LLM load+generate into one worker submission; bind model locally in generate closures

Review follow-ups on the thread-affinity PR: MLXQwenLLMBackend.generate
awaited load_model as one worker submission and then submitted
_generate_sync as a second, leaving the same load/generate gap the TTS
and STT paths close (a concurrent load_model for another size or an
unload could swap or null out self.model/self.tokenizer in between).
Give the LLM backend the same _op_lock + single _reload_and_generate_sync
shape. The TTS/STT/LLM sync closures now bind the model to a local once,
so an inline unload_model() from the event loop mid-generation cannot
turn a later self.model read (the voice-clone fallback path in
particular) into an AttributeError.

backend/backends/mlx_backend.py

@@ -138,9 +138,9 @@ class MLXTTSBackend:
         through its global allocator, no stream needed), and routing it
         through the single worker would block the caller — usually the
         FastAPI event loop, via /models/unload — until any in-flight
-        generation on that worker finishes. A generation still running keeps
-        its own reference to the model, so it completes normally and the
-        next generate() reloads via _reload_and_generate_sync.
+        generation on that worker finishes. A generation still running bound
+        the model to a local before it started, so it completes normally and
+        the next generate() reloads via _reload_and_generate_sync.
         """
         self._unload_model_sync()
 
@@ -231,6 +231,10 @@ class MLXTTSBackend:
 
         def _generate_sync():
             """Run synchronous generation in thread pool."""
+            # Bind the model once so an inline unload_model() from another
+            # thread mid-generation cannot turn a later self.model read into
+            # None (the fallback path below runs seconds into a request).
+            model = self.model
             # MLX generate() returns a generator yielding GenerationResult objects
             audio_chunks = []
             sample_rate = 24000
@@ -264,26 +268,26 @@ class MLXTTSBackend:
                     # Check if generate accepts ref_audio parameter
                     import inspect
 
-                    sig = inspect.signature(self.model.generate)
+                    sig = inspect.signature(model.generate)
                     if "ref_audio" in sig.parameters:
                         # Generate with voice cloning
-                        for result in self.model.generate(text, ref_audio=ref_audio, ref_text=ref_text, lang_code=lang):
+                        for result in model.generate(text, ref_audio=ref_audio, ref_text=ref_text, lang_code=lang):
                             audio_chunks.append(np.array(result.audio))
                             sample_rate = result.sample_rate
                     else:
                         # Fallback: generate without voice cloning
-                        for result in self.model.generate(text, lang_code=lang):
+                        for result in model.generate(text, lang_code=lang):
                             audio_chunks.append(np.array(result.audio))
                             sample_rate = result.sample_rate
                 else:
                     # No voice prompt, generate normally
-                    for result in self.model.generate(text, lang_code=lang):
+                    for result in model.generate(text, lang_code=lang):
                         audio_chunks.append(np.array(result.audio))
                         sample_rate = result.sample_rate
             except Exception as e:
                 # If voice cloning fails, try without it
                 logger.warning("Voice cloning failed, generating without voice prompt: %s", e)
-                for result in self.model.generate(text, lang_code=lang):
+                for result in model.generate(text, lang_code=lang):
                     audio_chunks.append(np.array(result.audio))
                     sample_rate = result.sample_rate
 
@@ -402,6 +406,7 @@ class MLXSTTBackend:
             """Run synchronous transcription in thread pool."""
             # MLX Whisper transcription using generate method
             # The generate method accepts audio path directly
+            model = self.model
             decode_options = {}
             if language:
                 decode_options["language"] = language
@@ -409,7 +414,7 @@ class MLXSTTBackend:
             # Inference runs with the process's default HF_HUB_OFFLINE
             # state — see the comment in MLXTTSBackend.generate for the
             # regression this revert fixes (issue #462).
-            result = self.model.generate(str(audio_path), **decode_options)
+            result = model.generate(str(audio_path), **decode_options)
 
             # Extract text from result
             if isinstance(result, str):