This commit adds support for 5-channel multispectral data in the VAE architecture
while maintaining compatibility with Stable Diffusion 3's latent space requirements.

Key changes:
- Add AutoencoderKLMultispectral5Ch implementation with 5 input/output channels
- Implement 8x downsampling to match SD3's latent space requirements
- Add comprehensive test suite for multispectral VAE functionality
- Add training script for 5-channel multispectral data
- Update documentation with detailed implementation notes

Technical details:
- Uses 4 downsampling blocks for 8x downsampling
- Maintains 4-channel latent space for SD3 compatibility
- Implements group normalization (32 groups) for stable training
- Preserves spectral information through careful normalization
- Handles 16-bit multispectral data with proper scaling

Files changed:
- src/diffusers/models/autoencoders/autoencoder_kl_multispectral_5ch.py
- tests/models/autoencoders/test_models_autoencoder_kl_multispectral_5ch.py
- examples/multispectral/train_multispectral_vae_5ch.py

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