Fix regression with FLUX diffusers LoRA models where lora keys were n…

…ot given the expected prefix.

invokeai/backend/lora/conversions/flux_diffusers_lora_conversion_utils.py

@@ -2,6 +2,7 @@
 
 import torch
 
+from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import FLUX_KOHYA_TRANFORMER_PREFIX
 from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
 from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
 from invokeai.backend.lora.layers.lora_layer import LoRALayer
@@ -189,7 +190,9 @@ def add_qkv_lora_layer_if_present(src_keys: list[str], dst_qkv_key: str) -> None
     # Assert that all keys were processed.
     assert len(grouped_state_dict) == 0
 
-    return LoRAModelRaw(layers=layers)
+    layers_with_prefix = {f"{FLUX_KOHYA_TRANFORMER_PREFIX}{k}": v for k, v in layers.items()}
+
+    return LoRAModelRaw(layers=layers_with_prefix)
 
 
 def _group_by_layer(state_dict: Dict[str, torch.Tensor]) -> dict[str, dict[str, torch.Tensor]]:

tests/backend/lora/conversions/test_flux_diffusers_lora_conversion_utils.py

@@ -5,6 +5,7 @@
     is_state_dict_likely_in_flux_diffusers_format,
     lora_model_from_flux_diffusers_state_dict,
 )
+from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import FLUX_KOHYA_TRANFORMER_PREFIX
 from tests.backend.lora.conversions.lora_state_dicts.flux_lora_diffusers_format import (
     state_dict_keys as flux_diffusers_state_dict_keys,
 )
@@ -50,6 +51,7 @@ def test_lora_model_from_flux_diffusers_state_dict():
     concatenated_weights = ["to_k", "to_v", "proj_mlp", "add_k_proj", "add_v_proj"]
     expected_lora_layers = {k for k in expected_lora_layers if not any(w in k for w in concatenated_weights)}
     assert len(model.layers) == len(expected_lora_layers)
+    assert all(k.startswith(FLUX_KOHYA_TRANFORMER_PREFIX) for k in model.layers.keys())
 
 
 def test_lora_model_from_flux_diffusers_state_dict_extra_keys_error():