[LoRA] strip the stale text_model. prefix from Kohya CLIP text-encoder LoRAs on transformers >= 5.6

src/diffusers/loaders/lora_base.py

@@ -383,6 +383,14 @@ def _load_lora_into_text_encoder(
         # convert state dict
         state_dict = convert_state_dict_to_peft(state_dict)
 
+        # Under transformers>=5, CLIPTextModel was flattened and no longer has the
+        # `text_model.` wrapper module, so named_modules() returns unprefixed names
+        # like "encoder.layers.0.self_attn.q_proj". Kohya-sourced LoRA state dict
+        # keys still carry the "text_model." prefix from the old module layout.
+        # Strip it so that rank-key matching works regardless of the transformers version.
+        if not hasattr(text_encoder, "text_model"):
+            state_dict = {k.removeprefix("text_model."): v for k, v in state_dict.items()}
+
         for name, _ in text_encoder.named_modules():
             if name.endswith((".q_proj", ".k_proj", ".v_proj", ".out_proj", ".fc1", ".fc2")):
                 rank_key = f"{name}.lora_B.weight"

tests/pipelines/flux/test_pipeline_flux.py

@@ -242,6 +242,24 @@ class TestFluxPipelineMagCache(FluxPipelineTesterConfig, MagCacheTesterMixin):
 class TestFluxPipelineLoRA(FluxPipelineTesterConfig, LoraTesterMixin):
     """LoRA tests for the Flux pipeline."""
 
+    def test_kohya_clip_text_encoder_lora_under_flattened_clip(self):
+        # Regression for huggingface/diffusers#13984: transformers >= 5.6 removed the
+        # `text_model.` wrapper from CLIPTextModel, but kohya-converted state dicts still
+        # carry that prefix, which left the rank dict empty and raised IndexError.
+        pipe = self.get_pipeline().to(torch_device)
+        if hasattr(pipe.text_encoder, "text_model"):
+            pytest.skip("transformers still exposes the text_model wrapper")
+
+        state_dict = {}
+        for name, module in pipe.text_encoder.named_modules():
+            if name.endswith((".q_proj", ".k_proj", ".v_proj", ".out_proj", ".fc1", ".fc2")):
+                state_dict[f"text_encoder.text_model.{name}.lora_A.weight"] = torch.randn(2, module.in_features)
+                state_dict[f"text_encoder.text_model.{name}.lora_B.weight"] = torch.randn(module.out_features, 2)
+
+        pipe.load_lora_weights(state_dict)
+        assert check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
+        pipe.unload_lora_weights()
+
     def test_with_alpha_in_state_dict(self, tmp_path):
         pipe = self.get_pipeline().to(torch_device)
         self.add_adapters_to_pipeline(pipe, components=["transformer"])