Make the sampling loop of pipelines modular, with designated inputs that can be handled iteratively with functions and a default sampler step function

### Model/Pipeline/Scheduler description

Related to #7761 .

This is an effective replacement for the existing sampling loop function and the many, many kwargs that were made to allow the user to control it or inject callbacks into it.

```py
class SamplingInput:
def __init__(self, img, text_embedding, unet, timestep=None, **kwargs):
self.img = img
self.text_embedding = text_embedding
self.unet = unet
self.timestep = timestep

# ... lots of other code ...

inp = SamplingInput(img, text_embedding, unet)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
inp.timestep = t
for sampling_function in self.sampling_functions:
inp = sampling_function(inp)

output_img = inp.img
```

This will give the end user complete control of the sampling loop, allow the repo to add "official inline sampling functions" like report an image to an endpoint so that the user can view intermediate steps, etc.

We can add an argument sampling_functions: `list[Callable]=[default_sampling_function]` into the `__call__` as a new, backwards compatible kwarg.

This requires a rewrite of all of the pipelines, but as it is a backwards compatible change it can be introduced to any of the more popular pipelines first.

Review the existing sampling loop in the pipelines and their __call__ entry points, then compare how sampling-related kwargs and callbacks are handled. Define the SamplingInput, sampling_functions, and default_sampling_function interfaces against the proposed example. Done means the modular loop is introduced compatibly and the affected popular pipelines are updated without changing existing behavior.

Escrito por el modelo de indexación a partir del texto del issue.