def _load_lora_adapter()

in docker_images/diffusers/app/lora.py [0:0]

• 54 lines of code
• 13 McCabe index (conditional complexity)

    def _load_lora_adapter(self, kwargs):
        adapter = kwargs.pop("lora_adapter", None)
        if adapter is not None:
            logger.info("LoRA adapter %s requested", adapter)
            if adapter != self.current_lora_adapter:
                model_data = self._hub_model_info(adapter)
                if not self._is_lora(model_data):
                    msg = f"Requested adapter {adapter:s} is not a LoRA adapter"
                    logger.error(msg)
                    raise ValueError(msg)
                base_model = model_data.cardData["base_model"]
                is_list = isinstance(base_model, list)
                if (is_list and (self.model_id not in base_model)) or (
                    not is_list and self.model_id != base_model
                ):
                    msg = f"Requested adapter {adapter:s} is not a LoRA adapter for base model {self.model_id:s}"
                    logger.error(msg)
                    raise ValueError(msg)
                logger.info(
                    "LoRA adapter %s needs to be replaced with compatible adapter %s",
                    self.current_lora_adapter,
                    adapter,
                )
                if self.current_lora_adapter is not None:
                    self.ldm.unfuse_lora()
                    self.ldm.unload_lora_weights()
                    self._unload_textual_embeddings()
                    self.current_lora_adapter = None
                logger.info("LoRA weights unloaded, loading new weights")
                weight_name = self._get_lora_weight_name(model_data=model_data)

                self.ldm.load_lora_weights(
                    adapter, weight_name=weight_name, use_auth_token=self.use_auth_token
                )
                self.current_lora_adapter = adapter
                self._fuse_or_raise()
                logger.info("LoRA weights loaded for adapter %s", adapter)
                self._load_textual_embeddings(adapter, model_data)
            else:
                logger.info("LoRA adapter %s already loaded", adapter)
                # Needed while a LoRA is loaded w/ model
                model_data = self._hub_model_info(adapter)
                if (
                    self._is_pivotal_tuning_lora(model_data)
                    and self.current_tokens_loaded == 0
                ):
                    self._load_textual_embeddings(adapter, model_data)
        elif self.current_lora_adapter is not None:
            logger.info(
                "No LoRA adapter requested, unloading weights and using base model %s",
                self.model_id,
            )
            self.ldm.unfuse_lora()
            self.ldm.unload_lora_weights()
            self._unload_textual_embeddings()
            self.current_lora_adapter = None