up

Author: sayakpaul

tests/lora/utils.py

@@ -12,7 +12,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import copy
 import inspect
 import os
 import re
@@ -292,20 +291,6 @@ def _get_modules_to_save(self, pipe, has_denoiser=False):
 
         return modules_to_save
 
-    def _get_exclude_modules(self, pipe):
-        from diffusers.utils.peft_utils import _derive_exclude_modules
-
-        modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
-        denoiser = "unet" if self.unet_kwargs is not None else "transformer"
-        modules_to_save = {k: v for k, v in modules_to_save.items() if k == denoiser}
-        denoiser_lora_state_dict = self._get_lora_state_dicts(modules_to_save)[f"{denoiser}_lora_layers"]
-        pipe.unload_lora_weights()
-        denoiser_state_dict = pipe.unet.state_dict() if self.unet_kwargs is not None else pipe.transformer.state_dict()
-        exclude_modules = _derive_exclude_modules(
-            denoiser_state_dict, denoiser_lora_state_dict, adapter_name="default"
-        )
-        return exclude_modules
-
     def add_adapters_to_pipeline(self, pipe, text_lora_config=None, denoiser_lora_config=None, adapter_name="default"):
         if text_lora_config is not None:
             if "text_encoder" in self.pipeline_class._lora_loadable_modules:
@@ -2342,58 +2327,6 @@ def test_lora_unload_add_adapter(self):
         )
         _ = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
-    @require_peft_version_greater("0.13.2")
-    def test_lora_exclude_modules(self):
-        """
-        Test to check if `exclude_modules` works or not. It works in the following way:
-        we first create a pipeline and insert LoRA config into it. We then derive a `set`
-        of modules to exclude by investigating its denoiser state dict and denoiser LoRA
-        state dict.
-
-        We then create a new LoRA config to include the `exclude_modules` and perform tests.
-        """
-        scheduler_cls = self.scheduler_classes[0]
-        components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
-        pipe = self.pipeline_class(**components).to(torch_device)
-        _, _, inputs = self.get_dummy_inputs(with_generator=False)
-
-        output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
-        self.assertTrue(output_no_lora.shape == self.output_shape)
-
-        # only supported for `denoiser` now
-        pipe_cp = copy.deepcopy(pipe)
-        pipe_cp, _ = self.add_adapters_to_pipeline(
-            pipe_cp, text_lora_config=text_lora_config, denoiser_lora_config=denoiser_lora_config
-        )
-        denoiser_exclude_modules = self._get_exclude_modules(pipe_cp)
-        pipe_cp.to("cpu")
-        del pipe_cp
-
-        denoiser_lora_config.exclude_modules = denoiser_exclude_modules
-        pipe, _ = self.add_adapters_to_pipeline(
-            pipe, text_lora_config=text_lora_config, denoiser_lora_config=denoiser_lora_config
-        )
-        output_lora_exclude_modules = pipe(**inputs, generator=torch.manual_seed(0))[0]
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
-            lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
-            lora_metadatas = self._get_lora_adapter_metadata(modules_to_save)
-            self.pipeline_class.save_lora_weights(save_directory=tmpdir, **lora_state_dicts, **lora_metadatas)
-            pipe.unload_lora_weights()
-            pipe.load_lora_weights(tmpdir)
-
-            output_lora_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
-
-            self.assertTrue(
-                not np.allclose(output_no_lora, output_lora_exclude_modules, atol=1e-3, rtol=1e-3),
-                "LoRA should change outputs.",
-            )
-            self.assertTrue(
-                np.allclose(output_lora_exclude_modules, output_lora_pretrained, atol=1e-3, rtol=1e-3),
-                "Lora outputs should match.",
-            )
-
     def test_inference_load_delete_load_adapters(self):
         "Tests if `load_lora_weights()` -> `delete_adapters()` -> `load_lora_weights()` works."
         for scheduler_cls in self.scheduler_classes: