Fix text encoder unload skipping models whose pipeline does not expose the text encoder (Qwen-Image-2.1, Z-Image, LTX2), so cached-embedding runs actually free the TE.

toolkit/unloader.py

@@ -40,26 +40,40 @@ def unload_text_encoder(model: "BaseModel"):
 
     if model.text_encoder is not None:
         if isinstance(model.text_encoder, list):
-            text_encoder_list = []
             pipe = model.pipeline
+            freed = set()
+
+            def _free(te):
+                if te is None or isinstance(te, FakeTextEncoder) or id(te) in freed:
+                    return
+                MemoryManager.free(te)
+                freed.add(id(te))
+
+            def _new_fake():
+                return FakeTextEncoder(device=model.device_torch, dtype=model.torch_dtype)
+
+            # model.text_encoder is the source of truth; some pipelines (self-referencing
+            # or built with text_encoder=None) never expose the TE as an attribute
+            for real_te in model.text_encoder:
+                _free(real_te)
+            fakes = [_new_fake() for _ in model.text_encoder]
+            model.text_encoder = fakes
+
+            def _fake(idx):
+                return fakes[idx] if idx < len(fakes) else _new_fake()
 
             # the pipeline stores text encoders like text_encoder, text_encoder_2, text_encoder_3, etc.
             if getattr(pipe, "text_encoder", None) is not None:
-                MemoryManager.free(pipe.text_encoder)
-                te = FakeTextEncoder(device=model.device_torch, dtype=model.torch_dtype)
-                text_encoder_list.append(te)
-                pipe.text_encoder = te
+                _free(pipe.text_encoder)
+                pipe.text_encoder = _fake(0)
 
             i = 2
             while hasattr(pipe, f"text_encoder_{i}"):
                 real_te = getattr(pipe, f"text_encoder_{i}")
                 if real_te is not None:
-                    MemoryManager.free(real_te)
-                    te = FakeTextEncoder(device=model.device_torch, dtype=model.torch_dtype)
-                    text_encoder_list.append(te)
-                    setattr(pipe, f"text_encoder_{i}", te)
+                    _free(real_te)
+                    setattr(pipe, f"text_encoder_{i}", _fake(i - 1))
                 i += 1
-            model.text_encoder = text_encoder_list
         else:
             # only has a single text encoder
             MemoryManager.free(model.text_encoder)