[AudioLDM2] Doc fixes (#4739)

* [AudioLDM2] Doc fixes

* update docstrings

* fix unet docstring

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

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

docs/source/en/api/pipelines/audioldm2.md

@@ -20,10 +20,10 @@ Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelin
 is a text-to-audio _latent diffusion model (LDM)_ that learns continuous audio representations from text embeddings. Two 
 text encoder models are used to compute the text embeddings from a prompt input: the text-branch of [CLAP](https://huggingface.co/docs/transformers/main/en/model_doc/clap)
 and the encoder of [Flan-T5](https://huggingface.co/docs/transformers/main/en/model_doc/flan-t5). These text embeddings 
-are then projected to a shared embedding space by an [AudioLDM2ProjectionModel](https://huggingface.co/docs/diffusers/api/pipelines/audioldm2/AudioLDM2ProjectionModel). 
+are then projected to a shared embedding space by an [AudioLDM2ProjectionModel](https://huggingface.co/docs/diffusers/main/api/pipelines/audioldm2#diffusers.AudioLDM2ProjectionModel). 
 A [GPT2](https://huggingface.co/docs/transformers/main/en/model_doc/gpt2) _language model (LM)_ is used to auto-regressively 
 predict eight new embedding vectors, conditional on the projected CLAP and Flan-T5 embeddings. The generated embedding 
-vectors and Flan-T5 text embeddings are used as cross-attention conditioning in the LDM. The [UNet](https://huggingface.co/docs/diffusers/api/pipelines/audioldm2/AudioLDM2UNet2DConditionModel) 
+vectors and Flan-T5 text embeddings are used as cross-attention conditioning in the LDM. The [UNet](https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2#diffusers.AudioLDM2UNet2DConditionModel) 
 of AudioLDM 2 is unique in the sense that it takes **two** cross-attention embeddings, as opposed to one cross-attention 
 conditioning, as in most other LDMs.
 
@@ -38,13 +38,17 @@ found at [haoheliu/audioldm2](https://github.com/haoheliu/audioldm2).
 
 ### Choosing a checkpoint
 
-AudioLDM2 comes in three variants. Two of these checkpoints are applicable to the general task of text-to-audio generation. The third checkpoint is trained exclusively on text-to-music generation. See table below for details on the three official checkpoints:
+AudioLDM2 comes in three variants. Two of these checkpoints are applicable to the general task of text-to-audio 
+generation. The third checkpoint is trained exclusively on text-to-music generation.
 
-| Checkpoint                                                      | Task          | Model Size | Training Data / h |
-|-----------------------------------------------------------------|---------------|------------|-------------------|
-| [audioldm2](https://huggingface.co/cvssp/audioldm2)             | Text-to-audio | 1.1B       | 1150k             |
-| [audioldm2-music](https://huggingface.co/cvssp/audioldm2-music) | Text-to-music | 1.1B       | 665k              |
-| [audioldm2-large](https://huggingface.co/cvssp/audioldm2-large) | Text-to-audio | 1.5B       | 1150k             |
+All checkpoints share the same model size for the text encoders and VAE. They differ in the size and depth of the UNet. 
+See table below for details on the three checkpoints:
+
+| Checkpoint                                                      | Task          | UNet Model Size | Total Model Size | Training Data / h |
+|-----------------------------------------------------------------|---------------|-----------------|------------------|-------------------|
+| [audioldm2](https://huggingface.co/cvssp/audioldm2)             | Text-to-audio | 350M            | 1.1B             | 1150k             |
+| [audioldm2-large](https://huggingface.co/cvssp/audioldm2-large) | Text-to-audio | 750M            | 1.5B             | 1150k             |
+| [audioldm2-music](https://huggingface.co/cvssp/audioldm2-music) | Text-to-music | 350M            | 1.1B             | 665k              |
 
 ### Constructing a prompt
 
@@ -62,37 +66,7 @@ AudioLDM2 comes in three variants. Two of these checkpoints are applicable to th
 * The quality of the generated waveforms can vary significantly based on the seed. Try generating with different seeds until you find a satisfactory generation
 * Multiple waveforms can be generated in one go: set `num_waveforms_per_prompt` to a value greater than 1. Automatic scoring will be performed between the generated waveforms and prompt text, and the audios ranked from best to worst accordingly.
 
-The following example demonstrates how to construct good music generation using the aforementioned tips: 
-
-```python
-import scipy
-import torch
-from diffusers import AudioLDM2Pipeline
-
-# load the best weights for music generation
-repo_id = "cvssp/audioldm2-music"
-pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
-
-# define the prompts
-prompt = "Techno music with a strong, upbeat tempo and high melodic riffs"
-negative_prompt = "Low quality."
-
-# set the seed
-generator = torch.Generator("cuda").manual_seed(0)
-
-# run the generation
-audio = pipe(
-    prompt,
-    negative_prompt=negative_prompt,
-    num_inference_steps=200,
-    audio_length_in_s=10.0,
-    num_waveforms_per_prompt=3,
-).audios
-
-# save the best audio sample (index 0) as a .wav file
-scipy.io.wavfile.write("techno.wav", rate=16000, data=audio[0])
-```
+The following example demonstrates how to construct good music generation using the aforementioned tips: [example](https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2#diffusers.AudioLDM2Pipeline.__call__.example).
 
 <Tip>
 
@@ -114,3 +88,6 @@ section to learn how to efficiently load the same components into multiple pipel
 ## AudioLDM2UNet2DConditionModel
 [[autodoc]] AudioLDM2UNet2DConditionModel
 	- forward
+
+## AudioPipelineOutput
+[[autodoc]] pipelines.AudioPipelineOutput
\ No newline at end of file

src/diffusers/pipelines/audioldm2/modeling_audioldm2.py

@@ -659,7 +659,7 @@ class AudioLDM2UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoad
         encoder_attention_mask_1: Optional[torch.Tensor] = None,
     ) -> Union[UNet2DConditionOutput, Tuple]:
         r"""
-        The [`UNet2DConditionModel`] forward method.
+        The [`AudioLDM2UNet2DConditionModel`] forward method.
 
         Args:
             sample (`torch.FloatTensor`):

src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py

@@ -51,19 +51,33 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
-        >>> from diffusers import AudioLDM2Pipeline
-        >>> import torch
         >>> import scipy
+        >>> import torch
+        >>> from diffusers import AudioLDM2Pipeline
 
         >>> repo_id = "cvssp/audioldm2"
         >>> pipe = AudioLDM2Pipeline.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=200, audio_length_in_s=10.0).audios[0]
+        >>> # define the prompts
+        >>> prompt = "The sound of a hammer hitting a wooden surface."
+        >>> negative_prompt = "Low quality."
 
-        >>> # save the audio sample as a .wav file
-        >>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
+        >>> # set the seed for generator
+        >>> generator = torch.Generator("cuda").manual_seed(0)
+
+        >>> # run the generation
+        >>> audio = pipe(
+        ...     prompt,
+        ...     negative_prompt=negative_prompt,
+        ...     num_inference_steps=200,
+        ...     audio_length_in_s=10.0,
+        ...     num_waveforms_per_prompt=3,
+        ...     generator=generator,
+        ... ).audios
+
+        >>> # save the best audio sample (index 0) as a .wav file
+        >>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio[0])
         ```
 """
 
@@ -315,6 +329,7 @@ class AudioLDM2Pipeline(DiffusionPipeline):
         Example:
 
         ```python
+        >>> import scipy
         >>> import torch
         >>> from diffusers import AudioLDM2Pipeline
 
@@ -337,6 +352,9 @@ class AudioLDM2Pipeline(DiffusionPipeline):
         ...     num_inference_steps=200,
         ...     audio_length_in_s=10.0,
         ... ).audios[0]
+
+        >>> # save generated audio sample
+        >>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
         ```"""
         if prompt is not None and isinstance(prompt, str):
             batch_size = 1