[Community pipeline] Echo-Memory overlay for Wan 2.1 1.3B

### Model/Pipeline/Scheduler description

Request to add a **GitHub community pipeline** that overlays the released Echo-Memory `context_k1` DiT fine-tune onto official Diffusers Wan 2.1 1.3B.

This is not a new official Wan checkpoint and not the full Echo-Memory multi-chunk / camera-action / SSM research stack. It remaps original DiffSynth/Wan transformer keys onto `WanPipeline.transformer` (825 / 825 official keys) so users can load:

```python
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
custom_pipeline="pipeline_echo_memory",
torch_dtype=torch.bfloat16,
)
pipe.load_echo_memory_weights()
```

PR: https://github.com/huggingface/diffusers/pull/14471

### Open source status

- [x] The model implementation is available.
- [x] The model weights are available (Only relevant if addition is not a scheduler).

### Provide useful links for the implementation

- Paper: https://arxiv.org/abs/2606.09803
- Code: https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Memory
- Original keys: https://huggingface.co/Echo-Team/Echo-Memory (`context_k1/epoch-0.safetensors`)
- Converted transformer + Hub custom pipeline: https://huggingface.co/Wayne-King/echo-memory-diffusers
- Authors: @WayneJin0918 / Echo Team

Revisar el PR #14471 junto con el código de Echo-Memory y el transformer/custom pipeline convertido enlazado en el issue. Verificar que la pipeline de la comunidad cargue el modelo oficial Wan 2.1 1.3B, reasigne las 825 transformer keys y que load_echo_memory_weights() funcione con el ejemplo documentado de DiffusionPipeline.

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