make it possible to use checkpoints of different types (SD1, SDXL) in first...

        if not self.enable_hr:

            return samples

        if self.latent_scale_mode is None:

            decoded_samples = decode_first_stage(self.sd_model, samples)

        else:

            decoded_samples = None

        current = shared.sd_model.sd_checkpoint_info

        try:

            if self.hr_checkpoint_info is not None:

                sd_models.reload_model_weights(info=self.hr_checkpoint_info)

            return self.sample_hr_pass(samples, seeds, subseeds, subseed_strength, prompts)

            return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)

        finally:

            sd_models.reload_model_weights(info=current)

    def sample_hr_pass(self, samples, seeds, subseeds, subseed_strength, prompts):

    def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):

        self.is_hr_pass = True

        target_width = self.hr_upscale_to_x

            else:

                image_conditioning = self.txt2img_image_conditioning(samples)

        else:

            decoded_samples = decode_first_stage(self.sd_model, samples)

            lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)

            batch_images = []