fix(loaders): point at FunPack LoRA Loader, which applies on its own

Every message about loading the chunk-causal adapter named FunPack Apply LoRA
Weights. That is the wrong node: it is the optional stack producer for
prompt-specific trained strengths. FunPack LoRA Loader loads AND applies from
its own file list, and always has — so the instruction sent the user to add a
node they do not need.

Its own description read the same way ("Loads LoRAs from a FunPack LoRA
stack"), which is where the confusion came from. Both it and the docstring now
say the stack is the narrow case, not the way in.

Tests: 1767 passed (+5).

loaders.py

@@ -174,8 +174,8 @@ class FunPackDiffusionModelLoader:
                                "about the weights changes and an ordinary generation is "
                                "unaffected — the model simply gains the ability to read a "
                                "key/value cache. It still needs a LoRA trained for that "
-                               "pattern (load it with FunPack Apply LoRA Weights like any "
-                               "other): the base H3 weights have never seen a cache, so "
+                               "pattern — load it with FunPack LoRA Loader like any other "
+                               "LoRA: the base H3 weights have never seen a cache, so "
                                "without one the chunked lane runs out of distribution. Use "
                                "the full non-pruned checkpoint."}),
                 "fp16_accumulation": ("BOOLEAN", {

model_management.py

@@ -718,13 +718,19 @@ class FunPackApplyLoraWeights:
 
 
 class FunPackLoraLoader:
-    """Loads the LoRA stack prepared by FunPack Apply LoRA Weights."""
+    """Loads and applies LoRAs. Complete on its own — the stack input is optional.
+
+    The `lora_list` on this node is the ordinary way in: pick files, they are applied. A
+    FunPack Apply LoRA Weights stack is only needed when the strengths are prompt-specific
+    and trained, which is a narrower case than it used to read as.
+    """
 
     CATEGORY = "FunPack/Model Management"
     RETURN_TYPES = ("MODEL", "CLIP", LORA_STACK_TYPE, "STRING")
     RETURN_NAMES = ("MODEL", "CLIP", "lora_stack", "status")
     FUNCTION = "load_loras"
-    DESCRIPTION = "Loads LoRAs from a FunPack LoRA stack without doing any learning."
+    DESCRIPTION = ("Loads and applies LoRAs. Pick files in its own list — a stack node is "
+                   "only needed for prompt-specific trained strengths.")
 
     def __init__(self):
         self.raw_lora_cache = OrderedDict()

raven_causal.py

@@ -294,7 +294,8 @@ def build_session(model, positive, latent, *, sink=2, window=2, storage="cpu_pin
     if not hasattr(diffusion, "forward_chunk") or not hasattr(diffusion, "prefill_text"):
         return None, ("the loaded model is a stock bidirectional H3. Switch on `chunk_causal` "
                       "on FunPack Diffusion Model Loader, and load a LoRA trained for the "
-                      "chunked pattern with FunPack Apply LoRA Weights.")
+                      "chunked pattern with FunPack LoRA Loader — it applies on its own, no "
+                      "stack node needed.")
     blocks = getattr(diffusion, "blocks", None)
     if not blocks:
         return None, "the loaded model exposes no DiT blocks to cache"

tests/test_h3_causal_loader.py

@@ -111,3 +111,44 @@ def test_a_comfy_without_h3_declines_instead_of_raising(monkeypatch):
     fake.model.diffusion_model = diffusion
     ok, note = hc.make_causal(fake)
     assert ok is False and "no MiniMax H3" in note
+
+
+# ── which node the user is actually sent to ─────────────────────────────────
+#
+# FunPack LoRA Loader loads AND applies, from its own file list. FunPack Apply LoRA Weights is
+# the optional stack producer for prompt-specific trained strengths. Naming the second one as
+# the way in sends the user to add a node they do not need.
+
+def test_the_loader_message_names_the_lora_loader():
+    tip = _fields()["chunk_causal"][1]["tooltip"]
+    assert "FunPack LoRA Loader" in tip
+    assert "Apply LoRA Weights" not in tip
+
+
+def test_the_sampler_message_names_the_lora_loader():
+    import inspect
+    import raven_causal
+    src = inspect.getsource(raven_causal.build_session)
+    assert "FunPack LoRA Loader" in src
+    assert "Apply LoRA Weights" not in src
+
+
+def test_the_lora_loader_does_not_advertise_itself_as_stack_only():
+    """Its description used to read as though a stack node were required."""
+    import model_management
+    text = model_management.FunPackLoraLoader.DESCRIPTION
+    assert "only needed" in text or "own list" in text
+
+
+def test_the_stack_input_is_optional():
+    """If it were required, 'the loader applies them on its own' would be false."""
+    import model_management
+    spec = model_management.FunPackLoraLoader.INPUT_TYPES()
+    assert "lora_stack" in spec.get("optional", {})
+    assert "lora_stack" not in spec.get("required", {})
+
+
+def test_the_file_list_is_the_required_way_in():
+    import model_management
+    spec = model_management.FunPackLoraLoader.INPUT_TYPES()
+    assert "lora_list" in spec.get("required", {})