Cache text encoder embeds in pipelines

**Is your feature request related to a problem? Please describe.**

When reusing a prompt text encoder embeds are recomputed, this can be time consuming for something like T5-XXL with offloading or on CPU.

Text encoder embeds are relatively small, so keeping them in memory is feasible.
```python
import torch

clip_l = torch.randn([1, 77, 768])
t5_xxl = torch.randn([1, 512, 4096])
>>> clip_l.numel() * clip_l.dtype.itemsize
236544
>>> t5_xxl.numel() * t5_xxl.dtype.itemsize
8388608
```

**Describe the solution you'd like.**

MVP would be reusing the last text encoder embeds if the prompt hasn't changed, this behaviour is supported in community UIs. Ideally, supports multiple prompts, potentially serializable.