Refactor BriaFibo classes and update pipeline parameters

- Updated BriaFiboAttnProcessor and BriaFiboAttention classes to reflect changes from Flux equivalents.
- Modified the _unpack_latents method in BriaFiboPipeline to improve clarity.
- Increased the default max_sequence_length to 3000 and added a new optional parameter do_patching.
- Cleaned up test_pipeline_bria_fibo.py by removing unused imports and skipping unsupported tests.

src/diffusers/models/transformers/transformer_bria_fibo.py

@@ -66,8 +66,8 @@ def _get_qkv_projections(attn: "BriaFiboAttention", hidden_states, encoder_hidde
     return _get_projections(attn, hidden_states, encoder_hidden_states)
 
 
+# Copied from diffusers.models.transformers.transformer_flux.FluxAttnProcessor FluxAttnProcessor->BriaFiboAttnProcessor, FluxAttention-> BriaFiboAttention
 class BriaFiboAttnProcessor:
-    # Copied from diffusers.models.transformers.transformer_flux.FluxAttnProcessor
     _attention_backend = None
     _parallel_config = None
 
@@ -134,8 +134,8 @@ class BriaFiboAttnProcessor:
             return hidden_states
 
 
+# Copied from diffusers.models.transformers.transformer_flux.FluxAttention -> BriaFiboAttention
 class BriaFiboAttention(torch.nn.Module, AttentionModuleMixin):
-    # Copied from diffusers.models.transformers.transformer_flux.FluxAttention
     _default_processor_cls = BriaFiboAttnProcessor
     _available_processors = [
         BriaFiboAttnProcessor,

src/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py

@@ -353,8 +353,8 @@ class BriaFiboPipeline(DiffusionPipeline):
         return self._interrupt
 
     @staticmethod
+    # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._unpack_latents
     def _unpack_latents(latents, height, width, vae_scale_factor):
-        # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline
         batch_size, num_patches, channels = latents.shape
 
         height = height // vae_scale_factor
@@ -542,7 +542,8 @@ class BriaFiboPipeline(DiffusionPipeline):
                 The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                 will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                 `._callback_tensor_inputs` attribute of your pipeline class.
-            max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
+            max_sequence_length (`int` defaults to 3000): Maximum sequence length to use with the `prompt`.
+            do_patching (`bool`, *optional*, defaults to `False`): Whether to use patching.
         Examples:
           Returns:
             [`~pipelines.flux.BriaFiboPipelineOutput`] or `tuple`: [`~pipelines.flux.BriaFiboPipelineOutput`] if

tests/pipelines/bria_fibo/test_pipeline_bria_fibo.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import tempfile
 import unittest
 
 import numpy as np
@@ -26,11 +25,10 @@ from diffusers import (
     FlowMatchEulerDiscreteScheduler,
 )
 from diffusers.models.transformers.transformer_bria_fibo import BriaFiboTransformer2DModel
-from tests.pipelines.test_pipelines_common import PipelineTesterMixin, to_np
+from tests.pipelines.test_pipelines_common import PipelineTesterMixin
 
 from ...testing_utils import (
     enable_full_determinism,
-    require_torch_accelerator,
     torch_device,
 )
 
@@ -45,6 +43,7 @@ class BriaFiboPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     test_xformers_attention = False
     test_layerwise_casting = False
     test_group_offloading = False
+    supports_dduf = False
 
     def get_dummy_components(self):
         torch.manual_seed(0)
@@ -107,6 +106,7 @@ class BriaFiboPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         }
         return inputs
 
+    @unittest.skip(reason="will not be supported due to dim-fusion")
     def test_encode_prompt_works_in_isolation(self):
         pass
 
@@ -137,62 +137,3 @@ class BriaFiboPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             image = pipe(**inputs).images[0]
             output_height, output_width, _ = image.shape
             assert (output_height, output_width) == (expected_height, expected_width)
-
-    @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU")
-    @require_torch_accelerator
-    def test_save_load_float16(self, expected_max_diff=1e-2):
-        components = self.get_dummy_components()
-        for name, module in components.items():
-            if hasattr(module, "half"):
-                components[name] = module.to(torch_device).half()
-
-        pipe = self.pipeline_class(**components)
-        for component in pipe.components.values():
-            if hasattr(component, "set_default_attn_processor"):
-                component.set_default_attn_processor()
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(torch_device)
-        output = pipe(**inputs)[0]
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            pipe.save_pretrained(tmpdir)
-            pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)
-            for component in pipe_loaded.components.values():
-                if hasattr(component, "set_default_attn_processor"):
-                    component.set_default_attn_processor()
-            pipe_loaded.to(torch_device)
-            pipe_loaded.set_progress_bar_config(disable=None)
-
-        for name, component in pipe_loaded.components.items():
-            if name == "vae":
-                continue
-            if hasattr(component, "dtype"):
-                self.assertTrue(
-                    component.dtype == torch.float16,
-                    f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.",
-                )
-
-        inputs = self.get_dummy_inputs(torch_device)
-        output_loaded = pipe_loaded(**inputs)[0]
-        max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
-        self.assertLess(
-            max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading."
-        )
-
-    # def test_to_dtype(self):
-    #     components = self.get_dummy_components()
-    #     pipe = self.pipeline_class(**components)
-    #     pipe.set_progress_bar_config(disable=None)
-
-    #     model_dtypes = [component.dtype for component in components.values() if hasattr(component, "dtype")]
-    #     self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))
-
-    #     pipe.to(dtype=torch.float16)
-    #     model_dtypes = [component.dtype for component in components.values() if hasattr(component, "dtype")]
-    #     self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))
-
-    @unittest.skip("")
-    def test_save_load_dduf(self):
-        pass