Convert MusicLDM (#4579)

* from audioldm

* fix vae

* move to new pipeline

* copied from audioldm

* remove redundant control flow

* iterate

* fix docstring

* finish pipeline

* tests: from audioldm2

* iterate

* finish fast tests

* finish slow integration tests

* add docs

* remove dtype test

* update toctree

* "copied from" in conversion (where possible)

* Update docs/source/en/api/pipelines/musicldm.md

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix docstring

* make nightly

* style

* fix dtype test

---------

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/en/_toctree.yml

@@ -224,6 +224,8 @@
       title: Latent Diffusion
     - local: api/pipelines/panorama
       title: MultiDiffusion
+    - local: api/pipelines/musicldm
+      title: MusicLDM
     - local: api/pipelines/paint_by_example
       title: PaintByExample
     - local: api/pipelines/paradigms

docs/source/en/api/pipelines/musicldm.md

@@ -0,0 +1,57 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# MusicLDM
+
+MusicLDM was proposed in [MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies](https://huggingface.co/papers/2308.01546) by Ke Chen, Yusong Wu, Haohe Liu, Marianna Nezhurina, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
+MusicLDM takes a text prompt as input and predicts the corresponding music sample. 
+
+Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview) and [AudioLDM](https://huggingface.co/docs/diffusers/api/pipelines/audioldm/overview),
+MusicLDM is a text-to-music _latent diffusion model (LDM)_ that learns continuous audio representations from [CLAP](https://huggingface.co/docs/transformers/main/model_doc/clap)
+latents.
+
+MusicLDM is trained on a corpus of 466 hours of music data. Beat-synchronous data augmentation strategies are applied to 
+the music samples, both in the time domain and in the latent space. Using beat-synchronous data augmentation strategies 
+encourages the model to interpolate between the training samples, but stay within the domain of the training data. The 
+result is generated music that is more diverse while staying faithful to the corresponding style.
+
+The abstract of the paper is the following:
+
+*In this paper, we present MusicLDM, a state-of-the-art text-to-music model that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, to encourage the model to generate music more diverse while still staying faithful to the corresponding style.*
+
+This pipeline was contributed by [sanchit-gandhi](https://huggingface.co/sanchit-gandhi).
+
+## Tips
+
+When constructing a prompt, keep in mind:
+
+* Descriptive prompt inputs work best; use adjectives to describe the sound (for example, "high quality" or "clear") and make the prompt context specific where possible (e.g. "melodic techno with a fast beat and synths" works better than "techno").
+* Using a *negative prompt* can significantly improve the quality of the generated audio. Try using a negative prompt of "low quality, average quality".
+
+During inference:
+
+* The _quality_ of the generated audio sample can be controlled by the `num_inference_steps` argument; higher steps give higher quality audio at the expense of slower inference.
+* Multiple waveforms can be generated in one go: set `num_waveforms_per_prompt` to a value greater than 1 to enable. Automatic scoring will be performed between the generated waveforms and prompt text, and the audios ranked from best to worst accordingly.
+* The _length_ of the generated audio sample can be controlled by varying the `audio_length_in_s` argument.
+
+<Tip>
+
+Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between 
+scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) 
+section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+## MusicLDMPipeline
+[[autodoc]] MusicLDMPipeline
+	- all
+	- __call__

src/diffusers/__init__.py

@@ -163,6 +163,7 @@ else:
         KandinskyV22PriorEmb2EmbPipeline,
         KandinskyV22PriorPipeline,
         LDMTextToImagePipeline,
+        MusicLDMPipeline,
         PaintByExamplePipeline,
         SemanticStableDiffusionPipeline,
         ShapEImg2ImgPipeline,

src/diffusers/pipelines/__init__.py

@@ -83,6 +83,7 @@ else:
         KandinskyV22PriorPipeline,
     )
     from .latent_diffusion import LDMTextToImagePipeline
+    from .musicldm import MusicLDMPipeline
     from .paint_by_example import PaintByExamplePipeline
     from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
     from .shap_e import ShapEImg2ImgPipeline, ShapEPipeline

src/diffusers/pipelines/musicldm/__init__.py

@@ -0,0 +1,17 @@
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    is_torch_available,
+    is_transformers_available,
+    is_transformers_version,
+)
+
+
+try:
+    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import (
+        MusicLDMPipeline,
+    )
+else:
+    from .pipeline_musicldm import MusicLDMPipeline

src/diffusers/pipelines/musicldm/pipeline_musicldm.py

@@ -0,0 +1,607 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers import (
+    ClapFeatureExtractor,
+    ClapModel,
+    ClapTextModelWithProjection,
+    RobertaTokenizer,
+    RobertaTokenizerFast,
+    SpeechT5HifiGan,
+)
+
+from ...models import AutoencoderKL, UNet2DConditionModel
+from ...schedulers import KarrasDiffusionSchedulers
+from ...utils import is_librosa_available, logging, randn_tensor, replace_example_docstring
+from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
+
+
+if is_librosa_available():
+    import librosa
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> from diffusers import MusicLDMPipeline
+        >>> import torch
+        >>> import scipy
+
+        >>> repo_id = "cvssp/audioldm-s-full-v2"
+        >>> pipe = MusicLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
+        >>> pipe = pipe.to("cuda")
+
+        >>> prompt = "Techno music with a strong, upbeat tempo and high melodic riffs"
+        >>> audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]
+
+        >>> # save the audio sample as a .wav file
+        >>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
+        ```
+"""
+
+
+class MusicLDMPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-audio generation using MusicLDM.
+
+    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 ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
+        text_encoder ([`~transformers.ClapModel`]):
+            Frozen text-audio embedding model (`ClapTextModel`), specifically the
+            [laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant.
+        tokenizer ([`PreTrainedTokenizer`]):
+            A [`~transformers.RobertaTokenizer`] to tokenize text.
+        feature_extractor ([`~transformers.ClapFeatureExtractor`]):
+            Feature extractor to compute mel-spectrograms from audio waveforms.
+        unet ([`UNet2DConditionModel`]):
+            A `UNet2DConditionModel` to denoise the encoded audio latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        vocoder ([`~transformers.SpeechT5HifiGan`]):
+            Vocoder of class `SpeechT5HifiGan`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: Union[ClapTextModelWithProjection, ClapModel],
+        tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],
+        feature_extractor: Optional[ClapFeatureExtractor],
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        vocoder: SpeechT5HifiGan,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            unet=unet,
+            scheduler=scheduler,
+            vocoder=vocoder,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.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.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.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,
+        num_waveforms_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device (`torch.device`):
+                torch device
+            num_waveforms_per_prompt (`int`):
+                number of waveforms that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the audio generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+        """
+        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:
+            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(
+                    "The following part of your input was truncated because CLAP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            prompt_embeds = self.text_encoder.get_text_features(
+                text_input_ids.to(device),
+                attention_mask=attention_mask.to(device),
+            )
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.text_model.dtype, device=device)
+
+        (
+            bs_embed,
+            seq_len,
+        ) = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_waveforms_per_prompt)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif 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
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            uncond_input_ids = uncond_input.input_ids.to(device)
+            attention_mask = uncond_input.attention_mask.to(device)
+
+            negative_prompt_embeds = self.text_encoder.get_text_features(
+                uncond_input_ids,
+                attention_mask=attention_mask,
+            )
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.text_model.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        return prompt_embeds
+
+    # Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.mel_spectrogram_to_waveform
+    def mel_spectrogram_to_waveform(self, mel_spectrogram):
+        if mel_spectrogram.dim() == 4:
+            mel_spectrogram = mel_spectrogram.squeeze(1)
+
+        waveform = self.vocoder(mel_spectrogram)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        waveform = waveform.cpu().float()
+        return waveform
+
+    # Copied from diffusers.pipelines.audioldm2.pipeline_audioldm2.AudioLDM2Pipeline.score_waveforms
+    def score_waveforms(self, text, audio, num_waveforms_per_prompt, device, dtype):
+        if not is_librosa_available():
+            logger.info(
+                "Automatic scoring of the generated audio waveforms against the input prompt text requires the "
+                "`librosa` package to resample the generated waveforms. Returning the audios in the order they were "
+                "generated. To enable automatic scoring, install `librosa` with: `pip install librosa`."
+            )
+            return audio
+        inputs = self.tokenizer(text, return_tensors="pt", padding=True)
+        resampled_audio = librosa.resample(
+            audio.numpy(), orig_sr=self.vocoder.config.sampling_rate, target_sr=self.feature_extractor.sampling_rate
+        )
+        inputs["input_features"] = self.feature_extractor(
+            list(resampled_audio), return_tensors="pt", sampling_rate=self.feature_extractor.sampling_rate
+        ).input_features.type(dtype)
+        inputs = inputs.to(device)
+
+        # compute the audio-text similarity score using the CLAP model
+        logits_per_text = self.text_encoder(**inputs).logits_per_text
+        # sort by the highest matching generations per prompt
+        indices = torch.argsort(logits_per_text, dim=1, descending=True)[:, :num_waveforms_per_prompt]
+        audio = torch.index_select(audio, 0, indices.reshape(-1).cpu())
+        return audio
+
+    # 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
+
+    # Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        audio_length_in_s,
+        vocoder_upsample_factor,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor
+        if audio_length_in_s < min_audio_length_in_s:
+            raise ValueError(
+                f"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but "
+                f"is {audio_length_in_s}."
+            )
+
+        if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0:
+            raise ValueError(
+                f"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the "
+                f"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of "
+                f"{self.vae_scale_factor}."
+            )
+
+        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 both `prompt` and `prompt_embeds` undefined."
+            )
+        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}."
+                )
+
+    # Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            height // self.vae_scale_factor,
+            self.vocoder.config.model_in_dim // self.vae_scale_factor,
+        )
+        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
+        return latents
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        audio_length_in_s: Optional[float] = None,
+        num_inference_steps: int = 200,
+        guidance_scale: float = 2.0,
+        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.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        output_type: Optional[str] = "np",
+    ):
+        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_length_in_s (`int`, *optional*, defaults to 10.24):
+                The length of the generated audio sample in seconds.
+            num_inference_steps (`int`, *optional*, defaults to 200):
+                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 2.0):
+                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. If `num_waveforms_per_prompt > 1`, the text encoding
+                model is a joint text-audio model ([`~transformers.ClapModel`]), and the tokenizer is a
+                `[~transformers.ClapProcessor]`, then automatic scoring will be performed between the generated outputs
+                and the input text. This scoring ranks the generated waveforms based on their cosine similarity to text
+                input in the joint text-audio embedding space.
+            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.FloatTensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                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`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.AudioPipelineOutput`] 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.FloatTensor)`.
+            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.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            output_type (`str`, *optional*, defaults to `"np"`):
+                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.AudioPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] 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 spectrogram height
+        vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate
+
+        if audio_length_in_s is None:
+            audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor
+
+        height = int(audio_length_in_s / vocoder_upsample_factor)
+
+        original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate)
+        if height % self.vae_scale_factor != 0:
+            height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor
+            logger.info(
+                f"Audio length in seconds {audio_length_in_s} is increased to {height * vocoder_upsample_factor} "
+                f"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the "
+                f"denoising process."
+            )
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            audio_length_in_s,
+            vocoder_upsample_factor,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+
+        # 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,
+            num_waveforms_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_waveforms_per_prompt,
+            num_channels_latents,
+            height,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. 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.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=None,
+                    class_labels=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    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:
+                        callback(i, t, latents)
+
+        # 8. Post-processing
+        if not output_type == "latent":
+            latents = 1 / self.vae.config.scaling_factor * latents
+            mel_spectrogram = self.vae.decode(latents).sample
+        else:
+            return AudioPipelineOutput(audios=latents)
+
+        audio = self.mel_spectrogram_to_waveform(mel_spectrogram)
+
+        audio = audio[:, :original_waveform_length]
+
+        # 9. Automatic scoring
+        if num_waveforms_per_prompt > 1 and prompt is not None:
+            audio = self.score_waveforms(
+                text=prompt,
+                audio=audio,
+                num_waveforms_per_prompt=num_waveforms_per_prompt,
+                device=device,
+                dtype=prompt_embeds.dtype,
+            )
+
+        if output_type == "np":
+            audio = audio.numpy()
+
+        if not return_dict:
+            return (audio,)
+
+        return AudioPipelineOutput(audios=audio)

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -482,6 +482,21 @@ class LDMTextToImagePipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class MusicLDMPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class PaintByExamplePipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/musicldm/test_musicldm.py

@@ -0,0 +1,465 @@
+# coding=utf-8
+# Copyright 2023 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import gc
+import unittest
+
+import numpy as np
+import torch
+from transformers import (
+    ClapAudioConfig,
+    ClapConfig,
+    ClapFeatureExtractor,
+    ClapModel,
+    ClapTextConfig,
+    RobertaTokenizer,
+    SpeechT5HifiGan,
+    SpeechT5HifiGanConfig,
+)
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    LMSDiscreteScheduler,
+    MusicLDMPipeline,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.utils import is_xformers_available, torch_device
+from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu
+
+from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
+from ..test_pipelines_common import PipelineTesterMixin
+
+
+enable_full_determinism()
+
+
+class MusicLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = MusicLDMPipeline
+    params = TEXT_TO_AUDIO_PARAMS
+    batch_params = TEXT_TO_AUDIO_BATCH_PARAMS
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "num_waveforms_per_prompt",
+            "generator",
+            "latents",
+            "output_type",
+            "return_dict",
+            "callback",
+            "callback_steps",
+        ]
+    )
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        unet = UNet2DConditionModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=4,
+            out_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
+            cross_attention_dim=(32, 64),
+            class_embed_type="simple_projection",
+            projection_class_embeddings_input_dim=32,
+            class_embeddings_concat=True,
+        )
+        scheduler = DDIMScheduler(
+            beta_start=0.00085,
+            beta_end=0.012,
+            beta_schedule="scaled_linear",
+            clip_sample=False,
+            set_alpha_to_one=False,
+        )
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            block_out_channels=[32, 64],
+            in_channels=1,
+            out_channels=1,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+        )
+        torch.manual_seed(0)
+        text_branch_config = ClapTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=16,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=2,
+            num_hidden_layers=2,
+            pad_token_id=1,
+            vocab_size=1000,
+        )
+        audio_branch_config = ClapAudioConfig(
+            spec_size=64,
+            window_size=4,
+            num_mel_bins=64,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            depths=[2, 2],
+            num_attention_heads=[2, 2],
+            num_hidden_layers=2,
+            hidden_size=192,
+            patch_size=2,
+            patch_stride=2,
+            patch_embed_input_channels=4,
+        )
+        text_encoder_config = ClapConfig.from_text_audio_configs(
+            text_config=text_branch_config, audio_config=audio_branch_config, projection_dim=32
+        )
+        text_encoder = ClapModel(text_encoder_config)
+        tokenizer = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta", model_max_length=77)
+        feature_extractor = ClapFeatureExtractor.from_pretrained(
+            "hf-internal-testing/tiny-random-ClapModel", hop_length=7900
+        )
+
+        torch.manual_seed(0)
+        vocoder_config = SpeechT5HifiGanConfig(
+            model_in_dim=8,
+            sampling_rate=16000,
+            upsample_initial_channel=16,
+            upsample_rates=[2, 2],
+            upsample_kernel_sizes=[4, 4],
+            resblock_kernel_sizes=[3, 7],
+            resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],
+            normalize_before=False,
+        )
+
+        vocoder = SpeechT5HifiGan(vocoder_config)
+
+        components = {
+            "unet": unet,
+            "scheduler": scheduler,
+            "vae": vae,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "feature_extractor": feature_extractor,
+            "vocoder": vocoder,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+        inputs = {
+            "prompt": "A hammer hitting a wooden surface",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 6.0,
+        }
+        return inputs
+
+    def test_musicldm_ddim(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+
+        components = self.get_dummy_components()
+        musicldm_pipe = MusicLDMPipeline(**components)
+        musicldm_pipe = musicldm_pipe.to(torch_device)
+        musicldm_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        output = musicldm_pipe(**inputs)
+        audio = output.audios[0]
+
+        assert audio.ndim == 1
+        assert len(audio) == 256
+
+        audio_slice = audio[:10]
+        expected_slice = np.array(
+            [-0.0027, -0.0036, -0.0037, -0.0020, -0.0035, -0.0019, -0.0037, -0.0020, -0.0038, -0.0019]
+        )
+
+        assert np.abs(audio_slice - expected_slice).max() < 1e-4
+
+    def test_musicldm_prompt_embeds(self):
+        components = self.get_dummy_components()
+        musicldm_pipe = MusicLDMPipeline(**components)
+        musicldm_pipe = musicldm_pipe.to(torch_device)
+        musicldm_pipe = musicldm_pipe.to(torch_device)
+        musicldm_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        inputs["prompt"] = 3 * [inputs["prompt"]]
+
+        # forward
+        output = musicldm_pipe(**inputs)
+        audio_1 = output.audios[0]
+
+        inputs = self.get_dummy_inputs(torch_device)
+        prompt = 3 * [inputs.pop("prompt")]
+
+        text_inputs = musicldm_pipe.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=musicldm_pipe.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_inputs = text_inputs["input_ids"].to(torch_device)
+
+        prompt_embeds = musicldm_pipe.text_encoder.get_text_features(text_inputs)
+
+        inputs["prompt_embeds"] = prompt_embeds
+
+        # forward
+        output = musicldm_pipe(**inputs)
+        audio_2 = output.audios[0]
+
+        assert np.abs(audio_1 - audio_2).max() < 1e-2
+
+    def test_musicldm_negative_prompt_embeds(self):
+        components = self.get_dummy_components()
+        musicldm_pipe = MusicLDMPipeline(**components)
+        musicldm_pipe = musicldm_pipe.to(torch_device)
+        musicldm_pipe = musicldm_pipe.to(torch_device)
+        musicldm_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        negative_prompt = 3 * ["this is a negative prompt"]
+        inputs["negative_prompt"] = negative_prompt
+        inputs["prompt"] = 3 * [inputs["prompt"]]
+
+        # forward
+        output = musicldm_pipe(**inputs)
+        audio_1 = output.audios[0]
+
+        inputs = self.get_dummy_inputs(torch_device)
+        prompt = 3 * [inputs.pop("prompt")]
+
+        embeds = []
+        for p in [prompt, negative_prompt]:
+            text_inputs = musicldm_pipe.tokenizer(
+                p,
+                padding="max_length",
+                max_length=musicldm_pipe.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_inputs = text_inputs["input_ids"].to(torch_device)
+
+            text_embeds = musicldm_pipe.text_encoder.get_text_features(
+                text_inputs,
+            )
+            embeds.append(text_embeds)
+
+        inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
+
+        # forward
+        output = musicldm_pipe(**inputs)
+        audio_2 = output.audios[0]
+
+        assert np.abs(audio_1 - audio_2).max() < 1e-2
+
+    def test_musicldm_negative_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
+        musicldm_pipe = MusicLDMPipeline(**components)
+        musicldm_pipe = musicldm_pipe.to(device)
+        musicldm_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        negative_prompt = "egg cracking"
+        output = musicldm_pipe(**inputs, negative_prompt=negative_prompt)
+        audio = output.audios[0]
+
+        assert audio.ndim == 1
+        assert len(audio) == 256
+
+        audio_slice = audio[:10]
+        expected_slice = np.array(
+            [-0.0027, -0.0036, -0.0037, -0.0019, -0.0035, -0.0018, -0.0037, -0.0021, -0.0038, -0.0018]
+        )
+
+        assert np.abs(audio_slice - expected_slice).max() < 1e-4
+
+    def test_musicldm_num_waveforms_per_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
+        musicldm_pipe = MusicLDMPipeline(**components)
+        musicldm_pipe = musicldm_pipe.to(device)
+        musicldm_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "A hammer hitting a wooden surface"
+
+        # test num_waveforms_per_prompt=1 (default)
+        audios = musicldm_pipe(prompt, num_inference_steps=2).audios
+
+        assert audios.shape == (1, 256)
+
+        # test num_waveforms_per_prompt=1 (default) for batch of prompts
+        batch_size = 2
+        audios = musicldm_pipe([prompt] * batch_size, num_inference_steps=2).audios
+
+        assert audios.shape == (batch_size, 256)
+
+        # test num_waveforms_per_prompt for single prompt
+        num_waveforms_per_prompt = 2
+        audios = musicldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios
+
+        assert audios.shape == (num_waveforms_per_prompt, 256)
+
+        # test num_waveforms_per_prompt for batch of prompts
+        batch_size = 2
+        audios = musicldm_pipe(
+            [prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt
+        ).audios
+
+        assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
+
+    def test_musicldm_audio_length_in_s(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        musicldm_pipe = MusicLDMPipeline(**components)
+        musicldm_pipe = musicldm_pipe.to(torch_device)
+        musicldm_pipe.set_progress_bar_config(disable=None)
+        vocoder_sampling_rate = musicldm_pipe.vocoder.config.sampling_rate
+
+        inputs = self.get_dummy_inputs(device)
+        output = musicldm_pipe(audio_length_in_s=0.016, **inputs)
+        audio = output.audios[0]
+
+        assert audio.ndim == 1
+        assert len(audio) / vocoder_sampling_rate == 0.016
+
+        output = musicldm_pipe(audio_length_in_s=0.032, **inputs)
+        audio = output.audios[0]
+
+        assert audio.ndim == 1
+        assert len(audio) / vocoder_sampling_rate == 0.032
+
+    def test_musicldm_vocoder_model_in_dim(self):
+        components = self.get_dummy_components()
+        musicldm_pipe = MusicLDMPipeline(**components)
+        musicldm_pipe = musicldm_pipe.to(torch_device)
+        musicldm_pipe.set_progress_bar_config(disable=None)
+
+        prompt = ["hey"]
+
+        output = musicldm_pipe(prompt, num_inference_steps=1)
+        audio_shape = output.audios.shape
+        assert audio_shape == (1, 256)
+
+        config = musicldm_pipe.vocoder.config
+        config.model_in_dim *= 2
+        musicldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)
+        output = musicldm_pipe(prompt, num_inference_steps=1)
+        audio_shape = output.audios.shape
+        # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
+        assert audio_shape == (1, 256)
+
+    def test_attention_slicing_forward_pass(self):
+        self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)
+
+    def test_inference_batch_single_identical(self):
+        self._test_inference_batch_single_identical(test_mean_pixel_difference=False)
+
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_attention_forwardGenerator_pass(self):
+        self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
+
+    def test_to_dtype(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.set_progress_bar_config(disable=None)
+
+        # The method component.dtype returns the dtype of the first parameter registered in the model, not the
+        # dtype of the entire model. In the case of CLAP, the first parameter is a float64 constant (logit scale)
+        model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, "dtype")}
+
+        # Without the logit scale parameters, everything is float32
+        model_dtypes.pop("text_encoder")
+        self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes.values()))
+
+        # the CLAP sub-models are float32
+        model_dtypes["clap_text_branch"] = components["text_encoder"].text_model.dtype
+        self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes.values()))
+
+        # Once we send to fp16, all params are in half-precision, including the logit scale
+        pipe.to(torch_dtype=torch.float16)
+        model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, "dtype")}
+        self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes.values()))
+
+
+@nightly
+@require_torch_gpu
+class MusicLDMPipelineNightlyTests(unittest.TestCase):
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
+        generator = torch.Generator(device=generator_device).manual_seed(seed)
+        latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
+        latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
+        inputs = {
+            "prompt": "A hammer hitting a wooden surface",
+            "latents": latents,
+            "generator": generator,
+            "num_inference_steps": 3,
+            "guidance_scale": 2.5,
+        }
+        return inputs
+
+    def test_musicldm(self):
+        musicldm_pipe = MusicLDMPipeline.from_pretrained("cvssp/musicldm")
+        musicldm_pipe = musicldm_pipe.to(torch_device)
+        musicldm_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_inputs(torch_device)
+        inputs["num_inference_steps"] = 25
+        audio = musicldm_pipe(**inputs).audios[0]
+
+        assert audio.ndim == 1
+        assert len(audio) == 81952
+
+        # check the portion of the generated audio with the largest dynamic range (reduces flakiness)
+        audio_slice = audio[8680:8690]
+        expected_slice = np.array(
+            [-0.1042, -0.1068, -0.1235, -0.1387, -0.1428, -0.136, -0.1213, -0.1097, -0.0967, -0.0945]
+        )
+        max_diff = np.abs(expected_slice - audio_slice).max()
+        assert max_diff < 1e-3
+
+    def test_musicldm_lms(self):
+        musicldm_pipe = MusicLDMPipeline.from_pretrained("cvssp/musicldm")
+        musicldm_pipe.scheduler = LMSDiscreteScheduler.from_config(musicldm_pipe.scheduler.config)
+        musicldm_pipe = musicldm_pipe.to(torch_device)
+        musicldm_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_inputs(torch_device)
+        audio = musicldm_pipe(**inputs).audios[0]
+
+        assert audio.ndim == 1
+        assert len(audio) == 81952
+
+        # check the portion of the generated audio with the largest dynamic range (reduces flakiness)
+        audio_slice = audio[58020:58030]
+        expected_slice = np.array([0.3592, 0.3477, 0.4084, 0.4665, 0.5048, 0.5891, 0.6461, 0.5579, 0.4595, 0.4403])
+        max_diff = np.abs(expected_slice - audio_slice).max()
+        assert max_diff < 1e-3