[Tests] [LoRA] clean up the serialization stuff. (#9512)

* clean up the serialization stuff.

* better

tests/lora/utils.py

@@ -201,6 +201,32 @@ class PeftLoraLoaderMixinTests:
         prepared_inputs["input_ids"] = inputs
         return prepared_inputs
 
+    def _get_lora_state_dicts(self, modules_to_save):
+        state_dicts = {}
+        for module_name, module in modules_to_save.items():
+            if module is not None:
+                state_dicts[f"{module_name}_lora_layers"] = get_peft_model_state_dict(module)
+        return state_dicts
+
+    def _get_modules_to_save(self, pipe, has_denoiser=False):
+        modules_to_save = {}
+        lora_loadable_modules = self.pipeline_class._lora_loadable_modules
+
+        if "text_encoder" in lora_loadable_modules and hasattr(pipe, "text_encoder"):
+            modules_to_save["text_encoder"] = pipe.text_encoder
+
+        if "text_encoder_2" in lora_loadable_modules and hasattr(pipe, "text_encoder_2"):
+            modules_to_save["text_encoder_2"] = pipe.text_encoder_2
+
+        if has_denoiser:
+            if "unet" in lora_loadable_modules and hasattr(pipe, "unet"):
+                modules_to_save["unet"] = pipe.unet
+
+            if "transformer" in lora_loadable_modules and hasattr(pipe, "transformer"):
+                modules_to_save["transformer"] = pipe.transformer
+
+        return modules_to_save
+
     def test_simple_inference(self):
         """
         Tests a simple inference and makes sure it works as expected
@@ -420,45 +446,21 @@ class PeftLoraLoaderMixinTests:
             images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
             with tempfile.TemporaryDirectory() as tmpdirname:
-                text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder)
-                if self.has_two_text_encoders or self.has_three_text_encoders:
-                    if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                        text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)
+                modules_to_save = self._get_modules_to_save(pipe)
+                lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
 
                 self.pipeline_class.save_lora_weights(
-                            save_directory=tmpdirname,
-                            text_encoder_lora_layers=text_encoder_state_dict,
-                            text_encoder_2_lora_layers=text_encoder_2_state_dict,
-                            safe_serialization=False,
-                        )
-                else:
-                    self.pipeline_class.save_lora_weights(
-                        save_directory=tmpdirname,
-                        text_encoder_lora_layers=text_encoder_state_dict,
-                        safe_serialization=False,
-                    )
-
-                if self.has_two_text_encoders:
-                    if "text_encoder_2" not in self.pipeline_class._lora_loadable_modules:
-                        self.pipeline_class.save_lora_weights(
-                            save_directory=tmpdirname,
-                            text_encoder_lora_layers=text_encoder_state_dict,
-                            safe_serialization=False,
+                    save_directory=tmpdirname, safe_serialization=False, **lora_state_dicts
                 )
 
                 self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
                 pipe.unload_lora_weights()
-
                 pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.bin"))
 
-            images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
-            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+            for module_name, module in modules_to_save.items():
+                self.assertTrue(check_if_lora_correctly_set(module), f"Lora not correctly set in {module_name}")
 
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
             self.assertTrue(
                 np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),
@@ -614,54 +616,20 @@ class PeftLoraLoaderMixinTests:
             images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
             with tempfile.TemporaryDirectory() as tmpdirname:
-                text_encoder_state_dict = (
-                    get_peft_model_state_dict(pipe.text_encoder)
-                    if "text_encoder" in self.pipeline_class._lora_loadable_modules
-                    else None
+                modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
+                lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
+                self.pipeline_class.save_lora_weights(
+                    save_directory=tmpdirname, safe_serialization=False, **lora_state_dicts
                 )
 
-                denoiser_state_dict = get_peft_model_state_dict(denoiser)
-
-                saving_kwargs = {
-                    "save_directory": tmpdirname,
-                    "safe_serialization": False,
-                }
-
-                if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                    saving_kwargs.update({"text_encoder_lora_layers": text_encoder_state_dict})
-
-                if self.unet_kwargs is not None:
-                    saving_kwargs.update({"unet_lora_layers": denoiser_state_dict})
-                else:
-                    saving_kwargs.update({"transformer_lora_layers": denoiser_state_dict})
-
-                if self.has_two_text_encoders or self.has_three_text_encoders:
-                    if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                        text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)
-                        saving_kwargs.update({"text_encoder_2_lora_layers": text_encoder_2_state_dict})
-
-                self.pipeline_class.save_lora_weights(**saving_kwargs)
-
                 self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
                 pipe.unload_lora_weights()
-
                 pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.bin"))
 
-            images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
-
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            for module_name, module in modules_to_save.items():
+                self.assertTrue(check_if_lora_correctly_set(module), f"Lora not correctly set in {module_name}")
 
+            images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(
                 np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),
                 "Loading from saved checkpoints should give same results.",