Add support for images embeddings as one of the **input parameters
### Feature request

[Owl-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)
**Current flow** for inference for OWL ViT is to accept image and text, run it through a processor and then give that as an input to the Model for inference. Result: Getting detections with some confidences.
```python
model = OwlViTForObjectDetection.from_pretrained(...)
processor = OwlViTProcessor.from_pretrained(...)

inputs = processor(text=texts, images=image, return_tensors="pt")
outputs = model(**inputs)
```

**Additional flow**
It would be useful to have additional capability to support images embeddings as an input (either for a processor or the model itself), so the additional flow would look like this:
take 100 (e.g.) images -> run to calculate images embeddings, save (a dataframe, or files) -> use model(embeddings_dir, text_terms) to infer detections.
```python
model = OwlViTForObjectDetection.from_pretrained(...)
processor = OwlViTProcessor.from_pretrained(...)

# using images_embeddings instead of images
inputs = processor(text=texts, images_embeddings=image_embedding, return_tensors="pt")
outputs = model(**inputs)
```

### Motivation

Creating a cache, precomputed set of images embeddings for faster inference / search by text.
It is possible, that this functionality already exists, but because of the way owl-vit is structured, it might be tricky to perform: currently **inputs contain embeddings for bboxes and text.

### Your contribution

Would be difficult, as I'm not familiar with the infra.
cc @alaradirik and @NielsRogge <|||||>Hi @ramanova, thanks for the suggestion! 

This is doable but might be a bit tricky. `OwlViTProcessor` preprocesses images (resizing, cropping, etc.) and doesn't compute embeddings so you would need compute the embeddings using the base `OwlViTModel`. 

@NielsRogge do you know if there are any other models that provide this kind of functionality?<|||||>Several NLP models, like BERT, provides the `inputs_embeds` argument as seen [here](https://github.com/huggingface/transformers/blob/d066c3731bed1755f93ea64f0f00981b805532de/src/transformers/models/bert/modeling_bert.py#L919), which allows you to provide embeddings yourself rather than `input_ids`. So the use case here is similar, I assume.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.