Integration of ImageBind and StableUnCLIPImg2ImgPipeline for audio2image generation

### Model/Pipeline/Scheduler description

For anyone who need, here is a simple demo to illustrate how to integrate ImageBind and StableUnCLIPImg2ImgPipeline for audio2image generation.

### Open source status

- [X] The model implementation is available
- [X] The model weights are available (Only relevant if addition is not a scheduler).

### Provide useful links for the implementation

See also, https://github.com/Zeqiang-Lai/Anything2Image

```python
import imagebind
import torch
from diffusers import StableUnCLIPImg2ImgPipeline

# construct models
device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
)
pipe = pipe.to(device)

model = imagebind.imagebind_huge(pretrained=True)
model.eval()
model.to(device)

# generate image
with torch.no_grad():
audio_paths=["assets/wav/bird_audio.wav"]
embeddings = model.forward({
imagebind.ModalityType.AUDIO: imagebind.load_and_transform_audio_data(audio_paths, device),
})
embeddings = embeddings[imagebind.ModalityType.AUDIO]
images = pipe(image_embeds=embeddings.half()).images
images[0].save("bird_audio.png")
```