Revert "instruct-pix2pix support"

        return conditioning

    def edit_image_conditioning(self, source_image):

        conditioning_image = self.sd_model.encode_first_stage(source_image).mode()

        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))

        return conditioning_image

        batch_size = len(conds_list)

        repeats = [len(conds_list[i]) for i in range(batch_size)]

        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])

        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])

        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [image_cond])

        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])

        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])

        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])

        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)

        cfg_denoiser_callback(denoiser_params)

        sigma_in = denoiser_params.sigma

        if tensor.shape[1] == uncond.shape[1]:

            cond_in = torch.cat([tensor, uncond, uncond])

            cond_in = torch.cat([tensor, uncond])

            if shared.batch_cond_uncond:

                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})