Studio diffusion (Phase 10): reset the global attention backend on native, gate arch-specific kernels, accept sdpa

- apply_attention_backend now restores the native default when no backend is requested or a
  kernel fails. diffusers keeps a process-wide active attention backend that
  set_attention_backend updates, and a fresh transformer's processors follow it, so a load
  that wanted native could silently inherit a backend (e.g. cuDNN) an earlier speed-profile
  load pinned, breaking the bit-identical/off guarantee.
- select_attention_backend drops flash3/flash4 up front when the CUDA capability is below
  Hopper/Blackwell. diffusers only checks the kernels package at set time, so an explicit
  request on the wrong card set fine then crashed mid-generation; it now falls back to native.
- Add the sdpa alias to the attention_backend Literal so an API request with sdpa (already a
  valid alias of native) is accepted instead of 422-rejected by Pydantic.
- Drop the dead replace('-','_') normalization (no alias uses dashes/underscores).
- perf_levers_probe.py output dir is now relative to the script, not a hardcoded path.

scripts/perf_levers_probe.py

@@ -25,7 +25,7 @@ import numpy as np
 
 BASE = "Tongyi-MAI/Z-Image-Turbo"
 PROMPT = "A cinematic photograph of a red fox in a snowy forest at dawn, highly detailed"
-OUT = Path("/mnt/disks/unslothai/ubuntu/workspace_81/outputs/quant_research/perf_levers_images")
+OUT = Path(__file__).resolve().parent.parent / "outputs" / "quant_research" / "perf_levers_images"
 
 
 _LP = {"fn": None}