[Bug] Positional Embedding Index Out-of-Bounds in AuraFlow

### Describe the bug

When training the AuraFlow (Pony V7) lora model (via SimpleTuner), the positional embedding selection function
`pe_selection_index_based_on_dim()` can generate **negative** or **out-of-range** indices for the positional embedding table.

This causes:

```
CUDA error: device-side assert triggered
vectorized_gather_kernel: Assertion `ind >=0 && ind < ind_dim_size` failed.
```

Root cause: the code computes a centered crop of the PE grid, but when the input latent grid
(`h // patch_size`, `w // patch_size`) is **larger** than the pretrained positional embedding grid,
the computed indices become invalid (negative start, or exceeding max grid index).

### Reproduction

```python
import torch

# Simulated model parameters
patch_size = 2
pos_embed_max_size = 96 * 96 # 9216 positions
pos_embed = torch.zeros(1, pos_embed_max_size, 3072)

def pe_selection_index_based_on_dim(h, w):
h_p, w_p = h // patch_size, w // patch_size
h_max = w_max = int(pos_embed_max_size ** 0.5)

starth = h_max // 2 - h_p // 2
startw = w_max // 2 - w_p // 2

rows = torch.arange(starth, starth + h_p)
cols = torch.arange(startw, startw + w_p)
row_idx, col_idx = torch.meshgrid(rows, cols, indexing="ij")

return (row_idx * w_max + col_idx).flatten()

# Example that produces invalid indices (real case)
h, w = 196, 196 # latent spatial dims
idx = pe_selection_index_based_on_dim(h, w)

print(idx.min().item(), idx.max().item()) # → negative & >9215
assert (idx >= 0).all() and (idx < pos_embed_max_size).all()
```

### **Observed Output**

```
-97 9312
AssertionError
```

This reproduces the exact index state that triggers the CUDA `vectorized_gather_kernel` failure during training.

### Logs

```shell

```

### System Info

- 🤗 Diffusers version: 0.35.2
- Platform: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.39
- Running on Google Colab?: No
- Python version: 3.12.3
- PyTorch version (GPU?): 2.9.0+cu130 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.36.0
- Transformers version: 4.57.1
- Accelerate version: 1.11.0
- PEFT version: 0.17.1
- Bitsandbytes version: 0.48.2
- Safetensors version: 0.6.2
- xFormers version: not installed
- Accelerator: NVIDIA GeForce RTX 4090, 24564 MiB
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no

### Who can help?

_No response_

Locate pe_selection_index_based_on_dim() in the AuraFlow implementation and run the reproduction from the issue with h and w set to 196. Trace how the centered crop is calculated for grids larger than the pretrained embedding grid; done means the returned indices remain within the positional embedding table and the CUDA gather assertion no longer occurs.

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