Remove unrelated changes.

Author: hameerabbasi

tests/lora/utils.py

@@ -2188,17 +2188,6 @@ def test_correct_lora_configs_with_different_ranks(self):
         self.assertTrue(not np.allclose(original_output, lora_output_diff_alpha, atol=1e-3, rtol=1e-3))
         self.assertTrue(not np.allclose(lora_output_diff_alpha, lora_output_same_rank, atol=1e-3, rtol=1e-3))
 
-    @property
-    def supports_text_encoder_lora(self):
-        return (
-            len(
-                {"text_encoder", "text_encoder_2", "text_encoder_3"}.intersection(
-                    self.pipeline_class._lora_loadable_modules
-                )
-            )
-            != 0
-        )
-
     def test_layerwise_casting_inference_denoiser(self):
         from diffusers.hooks.layerwise_casting import DEFAULT_SKIP_MODULES_PATTERN, SUPPORTED_PYTORCH_LAYERS
 
@@ -2251,15 +2240,13 @@ def initialize_pipeline(storage_dtype=None, compute_dtype=torch.float32):
         pipe_fp32 = initialize_pipeline(storage_dtype=None)
         pipe_fp32(**inputs, generator=torch.manual_seed(0))[0]
 
-        # MPS doesn't support float8 yet.
-        if torch_device not in {"mps"}:
-            pipe_float8_e4m3_fp32 = initialize_pipeline(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.float32)
-            pipe_float8_e4m3_fp32(**inputs, generator=torch.manual_seed(0))[0]
+        pipe_float8_e4m3_fp32 = initialize_pipeline(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.float32)
+        pipe_float8_e4m3_fp32(**inputs, generator=torch.manual_seed(0))[0]
 
-            pipe_float8_e4m3_bf16 = initialize_pipeline(
-                storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16
-            )
-            pipe_float8_e4m3_bf16(**inputs, generator=torch.manual_seed(0))[0]
+        pipe_float8_e4m3_bf16 = initialize_pipeline(
+            storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16
+        )
+        pipe_float8_e4m3_bf16(**inputs, generator=torch.manual_seed(0))[0]
 
     @require_peft_version_greater("0.14.0")
     def test_layerwise_casting_peft_input_autocast_denoiser(self):
@@ -2284,7 +2271,7 @@ def test_layerwise_casting_peft_input_autocast_denoiser(self):
             apply_layerwise_casting,
         )
 
-        storage_dtype = torch.float8_e4m3fn if not torch_device == "mps" else torch.bfloat16
+        storage_dtype = torch.float8_e4m3fn
         compute_dtype = torch.float32
 
         def check_module(denoiser):