[tests] refactor b* pipeline tests

tests/pipelines/bria_fibo/test_pipeline_bria_fibo.py

@@ -27,7 +27,9 @@
 from ...testing_utils import assert_tensors_close, torch_device
 from ..testing_utils import (
     BasePipelineTesterConfig,
-    PipelineOffloadTesterMixin,
+    LoraMemoryTesterMixin,
+    LoraTesterMixin,
+    MemoryTesterMixin,
     PipelineTesterMixin,
 )
 
@@ -69,7 +71,17 @@ def get_dummy_components(self):
         scheduler = FlowMatchEulerDiscreteScheduler()
 
         torch.manual_seed(0)
-        text_encoder = SmolLM3ForCausalLM(SmolLM3Config(hidden_size=32))
+        text_encoder = SmolLM3ForCausalLM(
+            SmolLM3Config(
+                hidden_size=32,
+                intermediate_size=64,
+                num_hidden_layers=2,
+                num_attention_heads=2,
+                num_key_value_heads=1,
+                # `vocab_size` stays at the SmolLM3 default: the pipeline hardcodes the beginning-of-text id
+                # (128000) for empty prompts, so a smaller vocabulary would not be a valid text encoder here.
+            )
+        )
         tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
 
         return {
@@ -105,7 +117,7 @@ def test_inference(self):
         assert generated_image.shape == self.output_shape
 
         # fmt: off
-        expected_slice = torch.tensor([0.4025, 0.4722, 0.4377, 0.6178, 0.3643, 0.4914, 0.3694, 0.5096, 0.5980, 0.5516, 0.5228, 0.4731, 0.6202, 0.2424, 0.6280, 0.3556])
+        expected_slice = torch.tensor([0.3804, 0.4799, 0.5112, 0.5784, 0.3970, 0.4709, 0.4027, 0.4882, 0.5627, 0.4635, 0.4718, 0.3876, 0.5741, 0.2936, 0.5912, 0.4014])
         # fmt: on
 
         generated_slice = generated_image.flatten()
@@ -141,5 +153,22 @@ def test_image_output_shape(self):
             assert (output_height, output_width) == (height, width)
 
 
-class TestBriaFiboPipelineMemory(BriaFiboPipelineTesterConfig, PipelineOffloadTesterMixin):
-    pass
+class TestBriaFiboPipelineMemory(BriaFiboPipelineTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests (CPU offload, group offload, layerwise casting) for the Bria FIBO pipeline."""
+
+
+class TestBriaFiboPipelineLoRA(BriaFiboPipelineTesterConfig, LoraTesterMixin):
+    """LoRA tests for the Bria FIBO pipeline."""
+
+    @pytest.mark.skip(
+        "`_load_lora_into_text_encoder` only infers per-module ranks for CLIP-style names "
+        "(`.q_proj`/`.k_proj`/`.v_proj`/`.out_proj`/`.fc1`/`.fc2`, see `src/diffusers/loaders/lora_base.py`), so the "
+        "LLaMA-style `.o_proj` on the SmolLM3 text encoder falls back to the default rank and the non-uniform "
+        "`rank_pattern` this test builds cannot round-trip."
+    )
+    def test_simple_inference_with_partial_text_lora(self):
+        pass
+
+
+class TestBriaFiboPipelineLoRAMemory(BriaFiboPipelineTesterConfig, LoraMemoryTesterMixin):
+    """LoRA x memory-optimization tests (group offload, CPU offload) for the Bria FIBO pipeline."""

tests/pipelines/bria_fibo_edit/test_pipeline_bria_fibo_edit.py

@@ -29,7 +29,9 @@
 from ...testing_utils import assert_tensors_close, torch_device
 from ..testing_utils import (
     BasePipelineTesterConfig,
-    PipelineOffloadTesterMixin,
+    LoraMemoryTesterMixin,
+    LoraTesterMixin,
+    MemoryTesterMixin,
     PipelineTesterMixin,
 )
 
@@ -73,7 +75,17 @@ def get_dummy_components(self):
             z_dim=16,
         )
         scheduler = FlowMatchEulerDiscreteScheduler()
-        text_encoder = SmolLM3ForCausalLM(SmolLM3Config(hidden_size=32))
+        text_encoder = SmolLM3ForCausalLM(
+            SmolLM3Config(
+                hidden_size=32,
+                intermediate_size=64,
+                num_hidden_layers=2,
+                num_attention_heads=2,
+                num_key_value_heads=1,
+                # `vocab_size` stays at the SmolLM3 default: the pipeline hardcodes the beginning-of-text id
+                # (128000) for empty prompts, so a smaller vocabulary would not be a valid text encoder here.
+            )
+        )
         tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
 
         return {
@@ -111,7 +123,7 @@ def test_inference(self):
         assert generated_image.shape == self.output_shape
 
         # fmt: off
-        expected_slice = torch.tensor([0.5615, 0.4469, 0.4043, 0.4312, 0.3783, 0.4426, 0.4081, 0.4446, 0.6549, 0.6302, 0.6324, 0.5871, 0.6117, 0.6647, 0.5871, 0.6246])
+        expected_slice = torch.tensor([0.5594, 0.4469, 0.4011, 0.4329, 0.3747, 0.4408, 0.4074, 0.4452, 0.6472, 0.6353, 0.6258, 0.5867, 0.6104, 0.6624, 0.5824, 0.6277])
         # fmt: on
 
         generated_slice = generated_image.flatten()
@@ -233,5 +245,22 @@ def test_bria_fibo_edit_mask_no_image(self):
             pipe(**inputs)
 
 
-class TestBriaFiboEditPipelineMemory(BriaFiboEditPipelineTesterConfig, PipelineOffloadTesterMixin):
-    pass
+class TestBriaFiboEditPipelineMemory(BriaFiboEditPipelineTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests (CPU offload, group offload, layerwise casting) for the Bria FIBO Edit pipeline."""
+
+
+class TestBriaFiboEditPipelineLoRA(BriaFiboEditPipelineTesterConfig, LoraTesterMixin):
+    """LoRA tests for the Bria FIBO Edit pipeline."""
+
+    @pytest.mark.skip(
+        "`_load_lora_into_text_encoder` only infers per-module ranks for CLIP-style names "
+        "(`.q_proj`/`.k_proj`/`.v_proj`/`.out_proj`/`.fc1`/`.fc2`, see `src/diffusers/loaders/lora_base.py`), so the "
+        "LLaMA-style `.o_proj` on the SmolLM3 text encoder falls back to the default rank and the non-uniform "
+        "`rank_pattern` this test builds cannot round-trip."
+    )
+    def test_simple_inference_with_partial_text_lora(self):
+        pass
+
+
+class TestBriaFiboEditPipelineLoRAMemory(BriaFiboEditPipelineTesterConfig, LoraMemoryTesterMixin):
+    """LoRA x memory-optimization tests (group offload, CPU offload) for the Bria FIBO Edit pipeline."""

tests/pipelines/testing_utils/lora.py

@@ -62,6 +62,7 @@ def check_module_lora_metadata(parsed_metadata: dict, lora_metadatas: dict, modu
 # Keyed by `config.model_type` — both `CLIPTextModel` and `CLIPTextModelWithProjection` report `clip_text_model`.
 TEXT_ENCODER_TARGET_MODULES = {
     "clip_text_model": ["q_proj", "k_proj", "v_proj", "out_proj"],
+    "smollm3": ["q_proj", "k_proj", "v_proj", "o_proj"],
 }
 
 
@@ -447,9 +448,16 @@ def test_simple_inference_with_text_denoiser_lora_and_scale(self, base_pipe_outp
             msg="Lora + 0 scale should lead to same result as no LoRA",
         )
 
-        if self.text_encoder_components:
-            text_encoder_root = getattr(pipe.text_encoder, "text_model", pipe.text_encoder)
-            assert text_encoder_root.encoder.layers[0].self_attn.q_proj.scaling["default"] == 1.0, (
+        for name in self.text_encoder_components:
+            # Walk the modules rather than indexing a fixed path: text encoder architectures nest their attention
+            # layers differently (CLIP under `text_model.encoder.layers`, decoder-only ones under `model.layers`).
+            scalings = [
+                module.scaling["default"]
+                for module in getattr(pipe, name).modules()
+                if hasattr(module, "lora_A") and "default" in module.scaling
+            ]
+            assert scalings, f"No LoRA layers found on {name}"
+            assert all(scaling == 1.0 for scaling in scalings), (
                 "The scaling parameter has not been correctly restored!"
             )