[SDXL Adapter] Revert load lora

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import inspect
-import os
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -1067,76 +1066,3 @@ def __call__(
             return (image,)
 
         return StableDiffusionXLPipelineOutput(images=image)
-
-    # Overrride to properly handle the loading and unloading of the additional text encoder.
-    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.load_lora_weights
-    def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
-        # We could have accessed the unet config from `lora_state_dict()` too. We pass
-        # it here explicitly to be able to tell that it's coming from an SDXL
-        # pipeline.
-        state_dict, network_alphas = self.lora_state_dict(
-            pretrained_model_name_or_path_or_dict,
-            unet_config=self.unet.config,
-            **kwargs,
-        )
-        self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)
-
-        text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
-        if len(text_encoder_state_dict) > 0:
-            self.load_lora_into_text_encoder(
-                text_encoder_state_dict,
-                network_alphas=network_alphas,
-                text_encoder=self.text_encoder,
-                prefix="text_encoder",
-                lora_scale=self.lora_scale,
-            )
-
-        text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
-        if len(text_encoder_2_state_dict) > 0:
-            self.load_lora_into_text_encoder(
-                text_encoder_2_state_dict,
-                network_alphas=network_alphas,
-                text_encoder=self.text_encoder_2,
-                prefix="text_encoder_2",
-                lora_scale=self.lora_scale,
-            )
-
-    @classmethod
-    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.save_lora_weights
-    def save_lora_weights(
-        self,
-        save_directory: Union[str, os.PathLike],
-        unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
-        text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
-        text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
-        is_main_process: bool = True,
-        weight_name: str = None,
-        save_function: Callable = None,
-        safe_serialization: bool = True,
-    ):
-        state_dict = {}
-
-        def pack_weights(layers, prefix):
-            layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
-            layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
-            return layers_state_dict
-
-        state_dict.update(pack_weights(unet_lora_layers, "unet"))
-
-        if text_encoder_lora_layers and text_encoder_2_lora_layers:
-            state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
-            state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
-
-        self.write_lora_layers(
-            state_dict=state_dict,
-            save_directory=save_directory,
-            is_main_process=is_main_process,
-            weight_name=weight_name,
-            save_function=save_function,
-            safe_serialization=safe_serialization,
-        )
-
-    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._remove_text_encoder_monkey_patch
-    def _remove_text_encoder_monkey_patch(self):
-        self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
-        self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)