feat: implement rae autoencoder.

[Ando233]

What does this PR do?

This PR adds a new representation autoencoder implementation, AutoencoderRAE, to diffusers.
Implements diffusers.models.autoencoders.autoencoder_rae.AutoencoderRAE with a frozen pretrained vision encoder (DINOv2 / SigLIP2 / ViT-MAE) and a ViT-MAE style decoder.
The decoder implementation is aligned with the RAE-main GeneralDecoder parameter structure, enabling loading of existing trained decoder checkpoints (e.g. model.pt) without key mismatches when encoder/decoder settings are consistent.
Adds unit/integration tests under diffusers/tests/models/autoencoders/test_models_autoencoder_rae.py.
Registers exports so users can import directly via from diffusers import AutoencoderRAE.

Fixes #13000

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you read the contributor guideline?
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• Was this discussed/approved via a GitHub issue or the forum? Please add a link to it if that's the case: RAE support #13000
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• Did you write any new necessary tests?

Usage

ae = AutoencoderRAE(
    encoder_cls="dinov2",
    encoder_name_or_path=encoder_path,
    image_size=image_size,
    encoder_input_size=image_size,
    patch_size=patch_size,
    num_patches=num_patches,
    decoder_hidden_size=1152,
    decoder_num_hidden_layers=28,
    decoder_num_attention_heads=16,
    decoder_intermediate_size=4096,
).to(device)
ae.eval()

state = torch.load(args.decoder_ckpt, map_location="cpu")
ae.decoder.load_state_dict(state, strict=False)

with torch.no_grad():
    recon = ae(x).sample

Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.