Merge pipelines and/or checkpoints

### Is your feature request related to a problem? Please describe.
I can't find a way to merge pipelines and/or checkpoints with each other.

### Describe the solution you'd like.
I'd like to merge different Stable Diffusion models using `diffusers`.

AFAIK, `diffusers` doesn't provide that feature yet.

Right now I merge my LORAs with my models in `diffusers`, then save them to `ckpt`, and then use the script below to merge my models.

It would save me a lot of time if I could just merge straight from a `StableDiffusionXLPipeline` and I could skip the step in between.

But, right now, even just adding this script (or a variation of it) to [diffusers/scripts](https://github.com/huggingface/diffusers/tree/main/scripts) would already be an improvement.

### Describe alternatives you've considered.
I currently use the following code from [eyriewow/merge-models](https://github.com/eyriewow/merge-models/) :

```python
import os
import argparse
import torch
from tqdm import tqdm

parser = argparse.ArgumentParser(description="Merge two models")
parser.add_argument("model_0", type=str, help="Path to model 0")
parser.add_argument("model_1", type=str, help="Path to model 1")
parser.add_argument("--alpha", type=float, help="Alpha value, optional, defaults to 0.5", default=0.5, required=False)
parser.add_argument("--output", type=str, help="Output file name, without extension", default="merged", required=False)
parser.add_argument("--device", type=str, help="Device to use, defaults to cpu", default="cpu", required=False)
parser.add_argument("--without_vae", action="store_true", help="Do not merge VAE", required=False)

args = parser.parse_args()

device = args.device
model_0 = torch.load(args.model_0, map_location=device)
model_1 = torch.load(args.model_1, map_location=device)
theta_0 = model_0["state_dict"]
theta_1 = model_1["state_dict"]
alpha = args.alpha

output_file = f'{args.output}-{str(alpha)[2:] + "0"}.ckpt'

# check if output file already exists, ask to overwrite
if os.path.isfile(output_file):
print("Output file already exists. Overwrite? (y/n)")
while True:
overwrite = input()
if overwrite == "y":
break
elif overwrite == "n":
print("Exiting...")
exit()
else:
print("Please enter y or n")

for key in tqdm(theta_0.keys(), desc="Stage 1/2"):
# skip VAE model parameters to get better results(tested for anime models)
# for anime model，with merging VAE model, the result will be worse (dark and blurry)
if args.without_vae and "first_stage_model" in key:
continue

if "model" in key and key in theta_1:
theta_0[key] = (1 - alpha) * theta_0[key] + alpha * theta_1[key]

for key in tqdm(theta_1.keys(), desc="Stage 2/2"):
if "model" in key and key not in theta_0:
theta_0[key] = theta_1[key]

print("Saving...")

torch.save({"state_dict": theta_0}, output_file)

print("Done!")
```

It usually gets the job done for `ckpt` files that are the same size, but that's it.

Yêu cầu này trỏ đến diffusers/scripts và StableDiffusionXLPipeline; hãy bắt đầu bằng việc xem xét các entry point đó cùng với cách xử lý pipeline và checkpoint hiện có. So sánh hành vi merge được đề xuất với script merge-models được liên kết, sau đó xác định các input được hỗ trợ và việc validation cần thiết cho direct merging, bao gồm cả những gì output thành công phải chứa.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.