class StableAudioPipeline(DiffusionPipeline):
    r"""
    Pipeline for text-to-audio generation using StableAudio.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
    implemented for all pipelines (downloading, saving, running on a particular device, etc.).

    Args:
        vae ([`AutoencoderOobleck`]):
            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
        text_encoder ([`~transformers.T5EncoderModel`]):
            Frozen text-encoder. StableAudio uses the encoder of
            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
            [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) variant.
        projection_model ([`StableAudioProjectionModel`]):
            A trained model used to linearly project the hidden-states from the text encoder model and the start and
            end seconds. The projected hidden-states from the encoder and the conditional seconds are concatenated to
            give the input to the transformer model.
        tokenizer ([`~transformers.T5Tokenizer`]):
            Tokenizer to tokenize text for the frozen text-encoder.
        transformer ([`StableAudioDiTModel`]):
            A `StableAudioDiTModel` to denoise the encoded audio latents.
        scheduler ([`EDMDPMSolverMultistepScheduler`]):
            A scheduler to be used in combination with `transformer` to denoise the encoded audio latents.
    """

    model_cpu_offload_seq = "text_encoder->projection_model->transformer->vae"

    def __init__(
        self,
        vae: AutoencoderOobleck,
        text_encoder: T5EncoderModel,
        projection_model: StableAudioProjectionModel,
        tokenizer: Union[T5Tokenizer, T5TokenizerFast],
        transformer: StableAudioDiTModel,
        scheduler: EDMDPMSolverMultistepScheduler,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            projection_model=projection_model,
            tokenizer=tokenizer,
            transformer=transformer,
            scheduler=scheduler,
        )
        self.rotary_embed_dim = self.transformer.config.attention_head_dim // 2

    # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_slicing
    def enable_vae_slicing(self):
        r"""
        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
        """
        self.vae.enable_slicing()

    # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.disable_vae_slicing
    def disable_vae_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_slicing()

    def encode_prompt(
        self,
        prompt,
        device,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        negative_attention_mask: Optional[torch.LongTensor] = None,
    ):
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if prompt_embeds is None:
            # 1. Tokenize text
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            attention_mask = text_inputs.attention_mask
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(
                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
                )
                logger.warning(
                    f"The following part of your input was truncated because {self.text_encoder.config.model_type} can "
                    f"only handle sequences up to {self.tokenizer.model_max_length} tokens: {removed_text}"
                )

            text_input_ids = text_input_ids.to(device)
            attention_mask = attention_mask.to(device)

            # 2. Text encoder forward
            self.text_encoder.eval()
            prompt_embeds = self.text_encoder(
                text_input_ids,
                attention_mask=attention_mask,
            )
            prompt_embeds = prompt_embeds[0]

        if do_classifier_free_guidance and negative_prompt is not None:
            uncond_tokens: List[str]
            if type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            # 1. Tokenize text
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )

            uncond_input_ids = uncond_input.input_ids.to(device)
            negative_attention_mask = uncond_input.attention_mask.to(device)

            # 2. Text encoder forward
            self.text_encoder.eval()
            negative_prompt_embeds = self.text_encoder(
                uncond_input_ids,
                attention_mask=negative_attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

            if negative_attention_mask is not None:
                # set the masked tokens to the null embed
                negative_prompt_embeds = torch.where(
                    negative_attention_mask.to(torch.bool).unsqueeze(2), negative_prompt_embeds, 0.0
                )

        # 3. Project prompt_embeds and negative_prompt_embeds
        if do_classifier_free_guidance and negative_prompt_embeds is not None:
            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the negative and text embeddings into a single batch
            # to avoid doing two forward passes
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
            if attention_mask is not None and negative_attention_mask is None:
                negative_attention_mask = torch.ones_like(attention_mask)
            elif attention_mask is None and negative_attention_mask is not None:
                attention_mask = torch.ones_like(negative_attention_mask)

            if attention_mask is not None:
                attention_mask = torch.cat([negative_attention_mask, attention_mask])

        prompt_embeds = self.projection_model(
            text_hidden_states=prompt_embeds,
        ).text_hidden_states
        if attention_mask is not None:
            prompt_embeds = prompt_embeds * attention_mask.unsqueeze(-1).to(prompt_embeds.dtype)
            prompt_embeds = prompt_embeds * attention_mask.unsqueeze(-1).to(prompt_embeds.dtype)

        return prompt_embeds

    def encode_duration(
        self,
        audio_start_in_s,
        audio_end_in_s,
        device,
        do_classifier_free_guidance,
        batch_size,
    ):
        audio_start_in_s = audio_start_in_s if isinstance(audio_start_in_s, list) else [audio_start_in_s]
        audio_end_in_s = audio_end_in_s if isinstance(audio_end_in_s, list) else [audio_end_in_s]

        if len(audio_start_in_s) == 1:
            audio_start_in_s = audio_start_in_s * batch_size
        if len(audio_end_in_s) == 1:
            audio_end_in_s = audio_end_in_s * batch_size

        # Cast the inputs to floats
        audio_start_in_s = [float(x) for x in audio_start_in_s]
        audio_start_in_s = torch.tensor(audio_start_in_s).to(device)

        audio_end_in_s = [float(x) for x in audio_end_in_s]
        audio_end_in_s = torch.tensor(audio_end_in_s).to(device)

        projection_output = self.projection_model(
            start_seconds=audio_start_in_s,
            end_seconds=audio_end_in_s,
        )
        seconds_start_hidden_states = projection_output.seconds_start_hidden_states
        seconds_end_hidden_states = projection_output.seconds_end_hidden_states

        # For classifier free guidance, we need to do two forward passes.
        # Here we repeat the audio hidden states to avoid doing two forward passes
        if do_classifier_free_guidance:
            seconds_start_hidden_states = torch.cat([seconds_start_hidden_states, seconds_start_hidden_states], dim=0)
            seconds_end_hidden_states = torch.cat([seconds_end_hidden_states, seconds_end_hidden_states], dim=0)

        return seconds_start_hidden_states, seconds_end_hidden_states

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def check_inputs(
        self,
        prompt,
        audio_start_in_s,
        audio_end_in_s,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        attention_mask=None,
        negative_attention_mask=None,
        initial_audio_waveforms=None,
        initial_audio_sampling_rate=None,
    ):
        if audio_end_in_s < audio_start_in_s:
            raise ValueError(
                f"`audio_end_in_s={audio_end_in_s}' must be higher than 'audio_start_in_s={audio_start_in_s}` but "
            )

        if (
            audio_start_in_s < self.projection_model.config.min_value
            or audio_start_in_s > self.projection_model.config.max_value
        ):
            raise ValueError(
                f"`audio_start_in_s` must be greater than or equal to {self.projection_model.config.min_value}, and lower than or equal to {self.projection_model.config.max_value} but "
                f"is {audio_start_in_s}."
            )

        if (
            audio_end_in_s < self.projection_model.config.min_value
            or audio_end_in_s > self.projection_model.config.max_value
        ):
            raise ValueError(
                f"`audio_end_in_s` must be greater than or equal to {self.projection_model.config.min_value}, and lower than or equal to {self.projection_model.config.max_value} but "
                f"is {audio_end_in_s}."
            )

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and (prompt_embeds is None):
            raise ValueError(
                "Provide either `prompt`, or `prompt_embeds`. Cannot leave"
                "`prompt` undefined without specifying `prompt_embeds`."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )
            if attention_mask is not None and attention_mask.shape != prompt_embeds.shape[:2]:
                raise ValueError(
                    "`attention_mask should have the same batch size and sequence length as `prompt_embeds`, but got:"
                    f"`attention_mask: {attention_mask.shape} != `prompt_embeds` {prompt_embeds.shape}"
                )

        if initial_audio_sampling_rate is None and initial_audio_waveforms is not None:
            raise ValueError(
                "`initial_audio_waveforms' is provided but the sampling rate is not. Make sure to pass `initial_audio_sampling_rate`."
            )

        if initial_audio_sampling_rate is not None and initial_audio_sampling_rate != self.vae.sampling_rate:
            raise ValueError(
                f"`initial_audio_sampling_rate` must be {self.vae.hop_length}' but is `{initial_audio_sampling_rate}`."
                "Make sure to resample the `initial_audio_waveforms` and to correct the sampling rate. "
            )

    def prepare_latents(
        self,
        batch_size,
        num_channels_vae,
        sample_size,
        dtype,
        device,
        generator,
        latents=None,
        initial_audio_waveforms=None,
        num_waveforms_per_prompt=None,
        audio_channels=None,
    ):
        shape = (batch_size, num_channels_vae, sample_size)
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma

        # encode the initial audio for use by the model
        if initial_audio_waveforms is not None:
            # check dimension
            if initial_audio_waveforms.ndim == 2:
                initial_audio_waveforms = initial_audio_waveforms.unsqueeze(1)
            elif initial_audio_waveforms.ndim != 3:
                raise ValueError(
                    f"`initial_audio_waveforms` must be of shape `(batch_size, num_channels, audio_length)` or `(batch_size, audio_length)` but has `{initial_audio_waveforms.ndim}` dimensions"
                )

            audio_vae_length = int(self.transformer.config.sample_size) * self.vae.hop_length
            audio_shape = (batch_size // num_waveforms_per_prompt, audio_channels, audio_vae_length)

            # check num_channels
            if initial_audio_waveforms.shape[1] == 1 and audio_channels == 2:
                initial_audio_waveforms = initial_audio_waveforms.repeat(1, 2, 1)
            elif initial_audio_waveforms.shape[1] == 2 and audio_channels == 1:
                initial_audio_waveforms = initial_audio_waveforms.mean(1, keepdim=True)

            if initial_audio_waveforms.shape[:2] != audio_shape[:2]:
                raise ValueError(
                    f"`initial_audio_waveforms` must be of shape `(batch_size, num_channels, audio_length)` or `(batch_size, audio_length)` but is of shape `{initial_audio_waveforms.shape}`"
                )

            # crop or pad
            audio_length = initial_audio_waveforms.shape[-1]
            if audio_length < audio_vae_length:
                logger.warning(
                    f"The provided input waveform is shorter ({audio_length}) than the required audio length ({audio_vae_length}) of the model and will thus be padded."
                )
            elif audio_length > audio_vae_length:
                logger.warning(
                    f"The provided input waveform is longer ({audio_length}) than the required audio length ({audio_vae_length}) of the model and will thus be cropped."
                )

            audio = initial_audio_waveforms.new_zeros(audio_shape)
            audio[:, :, : min(audio_length, audio_vae_length)] = initial_audio_waveforms[:, :, :audio_vae_length]

            encoded_audio = self.vae.encode(audio).latent_dist.sample(generator)
            encoded_audio = encoded_audio.repeat((num_waveforms_per_prompt, 1, 1))
            latents = encoded_audio + latents
        return latents

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        audio_end_in_s: Optional[float] = None,
        audio_start_in_s: Optional[float] = 0.0,
        num_inference_steps: int = 100,
        guidance_scale: float = 7.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_waveforms_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        initial_audio_waveforms: Optional[torch.Tensor] = None,
        initial_audio_sampling_rate: Optional[torch.Tensor] = None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        negative_attention_mask: Optional[torch.LongTensor] = None,
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
        callback_steps: Optional[int] = 1,
        output_type: Optional[str] = "pt",
    ):
        r"""
        The call function to the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide audio generation. If not defined, you need to pass `prompt_embeds`.
            audio_end_in_s (`float`, *optional*, defaults to 47.55):
                Audio end index in seconds.
            audio_start_in_s (`float`, *optional*, defaults to 0):
                Audio start index in seconds.
            num_inference_steps (`int`, *optional*, defaults to 100):
                The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.0):
                A higher guidance scale value encourages the model to generate audio that is closely linked to the text
                `prompt` at the expense of lower sound quality. Guidance scale is enabled when `guidance_scale > 1`.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in audio generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
            num_waveforms_per_prompt (`int`, *optional*, defaults to 1):
                The number of waveforms to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            latents (`torch.Tensor`, *optional*):
                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for audio
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor is generated by sampling using the supplied random `generator`.
            initial_audio_waveforms (`torch.Tensor`, *optional*):
                Optional initial audio waveforms to use as the initial audio waveform for generation. Must be of shape
                `(batch_size, num_channels, audio_length)` or `(batch_size, audio_length)`, where `batch_size`
                corresponds to the number of prompts passed to the model.
            initial_audio_sampling_rate (`int`, *optional*):
                Sampling rate of the `initial_audio_waveforms`, if they are provided. Must be the same as the model.
            prompt_embeds (`torch.Tensor`, *optional*):
                Pre-computed text embeddings from the text encoder model. Can be used to easily tweak text inputs,
                *e.g.* prompt weighting. If not provided, text embeddings will be computed from `prompt` input
                argument.
            negative_prompt_embeds (`torch.Tensor`, *optional*):
                Pre-computed negative text embeddings from the text encoder model. Can be used to easily tweak text
                inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be computed from
                `negative_prompt` input argument.
            attention_mask (`torch.LongTensor`, *optional*):
                Pre-computed attention mask to be applied to the `prompt_embeds`. If not provided, attention mask will
                be computed from `prompt` input argument.
            negative_attention_mask (`torch.LongTensor`, *optional*):
                Pre-computed attention mask to be applied to the `negative_text_audio_duration_embeds`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is called with the
                following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function is called. If not specified, the callback is called at
                every step.
            output_type (`str`, *optional*, defaults to `"pt"`):
                The output format of the generated audio. Choose between `"np"` to return a NumPy `np.ndarray` or
                `"pt"` to return a PyTorch `torch.Tensor` object. Set to `"latent"` to return the latent diffusion
                model (LDM) output.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
                otherwise a `tuple` is returned where the first element is a list with the generated audio.
        """
        # 0. Convert audio input length from seconds to latent length
        downsample_ratio = self.vae.hop_length

        max_audio_length_in_s = self.transformer.config.sample_size * downsample_ratio / self.vae.config.sampling_rate
        if audio_end_in_s is None:
            audio_end_in_s = max_audio_length_in_s

        if audio_end_in_s - audio_start_in_s > max_audio_length_in_s:
            raise ValueError(
                f"The total audio length requested ({audio_end_in_s-audio_start_in_s}s) is longer than the model maximum possible length ({max_audio_length_in_s}). Make sure that 'audio_end_in_s-audio_start_in_s<={max_audio_length_in_s}'."
            )

        waveform_start = int(audio_start_in_s * self.vae.config.sampling_rate)
        waveform_end = int(audio_end_in_s * self.vae.config.sampling_rate)
        waveform_length = int(self.transformer.config.sample_size)

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            audio_start_in_s,
            audio_end_in_s,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
            attention_mask,
            negative_attention_mask,
            initial_audio_waveforms,
            initial_audio_sampling_rate,
        )

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        prompt_embeds = self.encode_prompt(
            prompt,
            device,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
            attention_mask,
            negative_attention_mask,
        )

        # Encode duration
        seconds_start_hidden_states, seconds_end_hidden_states = self.encode_duration(
            audio_start_in_s,
            audio_end_in_s,
            device,
            do_classifier_free_guidance and (negative_prompt is not None or negative_prompt_embeds is not None),
            batch_size,
        )

        # Create text_audio_duration_embeds and audio_duration_embeds
        text_audio_duration_embeds = torch.cat(
            [prompt_embeds, seconds_start_hidden_states, seconds_end_hidden_states], dim=1
        )

        audio_duration_embeds = torch.cat([seconds_start_hidden_states, seconds_end_hidden_states], dim=2)

        # In case of classifier free guidance without negative prompt, we need to create unconditional embeddings and
        # to concatenate it to the embeddings
        if do_classifier_free_guidance and negative_prompt_embeds is None and negative_prompt is None:
            negative_text_audio_duration_embeds = torch.zeros_like(
                text_audio_duration_embeds, device=text_audio_duration_embeds.device
            )
            text_audio_duration_embeds = torch.cat(
                [negative_text_audio_duration_embeds, text_audio_duration_embeds], dim=0
            )
            audio_duration_embeds = torch.cat([audio_duration_embeds, audio_duration_embeds], dim=0)

        bs_embed, seq_len, hidden_size = text_audio_duration_embeds.shape
        # duplicate audio_duration_embeds and text_audio_duration_embeds for each generation per prompt, using mps friendly method
        text_audio_duration_embeds = text_audio_duration_embeds.repeat(1, num_waveforms_per_prompt, 1)
        text_audio_duration_embeds = text_audio_duration_embeds.view(
            bs_embed * num_waveforms_per_prompt, seq_len, hidden_size
        )

        audio_duration_embeds = audio_duration_embeds.repeat(1, num_waveforms_per_prompt, 1)
        audio_duration_embeds = audio_duration_embeds.view(
            bs_embed * num_waveforms_per_prompt, -1, audio_duration_embeds.shape[-1]
        )

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        num_channels_vae = self.transformer.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_waveforms_per_prompt,
            num_channels_vae,
            waveform_length,
            text_audio_duration_embeds.dtype,
            device,
            generator,
            latents,
            initial_audio_waveforms,
            num_waveforms_per_prompt,
            audio_channels=self.vae.config.audio_channels,
        )

        # 6. Prepare extra step kwargs
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Prepare rotary positional embedding
        rotary_embedding = get_1d_rotary_pos_embed(
            self.rotary_embed_dim,
            latents.shape[2] + audio_duration_embeds.shape[1],
            use_real=True,
            repeat_interleave_real=False,
        )

        # 8. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                # predict the noise residual
                noise_pred = self.transformer(
                    latent_model_input,
                    t.unsqueeze(0),
                    encoder_hidden_states=text_audio_duration_embeds,
                    global_hidden_states=audio_duration_embeds,
                    rotary_embedding=rotary_embedding,
                    return_dict=False,
                )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

                if XLA_AVAILABLE:
                    xm.mark_step()

        # 9. Post-processing
        if not output_type == "latent":
            audio = self.vae.decode(latents).sample
        else:
            return AudioPipelineOutput(audios=latents)

        audio = audio[:, :, waveform_start:waveform_end]

        if output_type == "np":
            audio = audio.cpu().float().numpy()

        self.maybe_free_model_hooks()

        if not return_dict:
            return (audio,)

        return AudioPipelineOutput(audios=audio)
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py