[agents docs] add float64 gotcha (#13472)

* [docs] add float64 + runtime weight-dtype gotchas to models.md

Document two dtype pitfalls surfaced by Ernie-Image follow-up #13464:
unconditional torch.float64 in RoPE/precompute (breaks MPS/NPU) and
reading a child module's weight dtype at runtime (breaks gguf/quant).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* update claude config to allow .ai folder

* [ci] fetch default branch before .ai/ checkout in claude_review

When triggered by pull_request_review_comment, actions/checkout lands
on the PR head and fetch-depth=1 means origin/main isn't tracked, so
the follow-up `git checkout origin/main -- .ai/` fails with exit 128.
Fetch the default branch explicitly first.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* combine #10 into #8

* Apply suggestions from code review

Co-authored-by: YiYi Xu <yixu310@gmail.com>

---------

Co-authored-by: yiyi@huggingface.co <yiyi@ip-26-0-160-103.ec2.internal>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: 3 people

.ai/models.md

@@ -73,4 +73,14 @@ Consult the implementations in `src/diffusers/models/transformers/` if you need
 
 7. **Forgetting to update `_import_structure` and `_lazy_modules`.** The top-level `src/diffusers/__init__.py` has both -- missing either one causes partial import failures.
 
-8. **Hardcoded dtype in model forward.** Don't hardcode `torch.float32` or `torch.bfloat16` in the model's forward pass. Use the dtype of the input tensors or `self.dtype` so the model works with any precision.
+8. **Hardcoded dtype in model forward.** Don't hardcode `torch.float32` or `torch.bfloat16`, and don't cast activations by reading a weight's dtype (`self.linear.weight.dtype`) — the stored weight dtype isn't the compute dtype under gguf / quantized loading. Always derive the cast target from the input tensor's dtype or `self.dtype`.
+
+9. **`torch.float64` anywhere in the model.** MPS and several NPU backends don't support float64 -- ops will either error out or silently fall back. Reference repos commonly reach for float64 in RoPE frequency bases, timestep embeddings, sinusoidal position encodings, and similar "precision-sensitive" precompute code (`torch.arange(..., dtype=torch.float64)`, `.double()`, `torch.float64` literals). When porting a model, grep for `float64` / `double()` up front and resolve as follows:
+    - **Default: just use `torch.float32`.** For inference it is almost always sufficient -- the precision difference in RoPE angles, timestep embeddings, etc. is immaterial to image/video quality. Flip it and move on.
+    - **Only if float32 visibly degrades output, fall back to the device-gated pattern** we use in the repo:
+      ```python
+      is_mps = hidden_states.device.type == "mps"
+      is_npu = hidden_states.device.type == "npu"
+      freqs_dtype = torch.float32 if (is_mps or is_npu) else torch.float64
+      ```
+      See `transformer_flux.py`, `transformer_flux2.py`, `transformer_wan.py`, `unet_2d_condition.py` for reference usages. Never leave an unconditional `torch.float64` in the model.