Method get_dummy_components

tests/lora/utils.py:138–223

(self, scheduler_cls=None, use_dora=False, lora_alpha=None)

        return self.cached_non_lora_output

    def get_dummy_components(self, scheduler_cls=None, use_dora=False, lora_alpha=None):
        if self.unet_kwargs and self.transformer_kwargs:
            raise ValueError("Both `unet_kwargs` and `transformer_kwargs` cannot be specified.")
        if self.has_two_text_encoders and self.has_three_text_encoders:
            raise ValueError("Both `has_two_text_encoders` and `has_three_text_encoders` cannot be True.")

        scheduler_cls = scheduler_cls if scheduler_cls is not None else self.scheduler_cls
        rank = 4
        lora_alpha = rank if lora_alpha is None else lora_alpha

        torch.manual_seed(0)
        if self.unet_kwargs is not None:
            unet = UNet2DConditionModel(**self.unet_kwargs)
        else:
            transformer = self.transformer_cls(**self.transformer_kwargs)

        scheduler = scheduler_cls(**self.scheduler_kwargs)

        torch.manual_seed(0)
        vae = self.vae_cls(**self.vae_kwargs)

        text_encoder = self.text_encoder_cls.from_pretrained(
            self.text_encoder_id, subfolder=self.text_encoder_subfolder
        )
        tokenizer = self.tokenizer_cls.from_pretrained(self.tokenizer_id, subfolder=self.tokenizer_subfolder)

        if self.text_encoder_2_cls is not None:
            text_encoder_2 = self.text_encoder_2_cls.from_pretrained(
                self.text_encoder_2_id, subfolder=self.text_encoder_2_subfolder
            )
            tokenizer_2 = self.tokenizer_2_cls.from_pretrained(
                self.tokenizer_2_id, subfolder=self.tokenizer_2_subfolder
            )

        if self.text_encoder_3_cls is not None:
            text_encoder_3 = self.text_encoder_3_cls.from_pretrained(
                self.text_encoder_3_id, subfolder=self.text_encoder_3_subfolder
            )
            tokenizer_3 = self.tokenizer_3_cls.from_pretrained(
                self.tokenizer_3_id, subfolder=self.tokenizer_3_subfolder
            )

        text_lora_config = LoraConfig(
            r=rank,
            lora_alpha=lora_alpha,
            target_modules=self.text_encoder_target_modules,
            init_lora_weights=False,
            use_dora=use_dora,
        )

        denoiser_lora_config = LoraConfig(
            r=rank,
            lora_alpha=lora_alpha,
            target_modules=self.denoiser_target_modules,
            init_lora_weights=False,
            use_dora=use_dora,
        )