Enable AIMDO DynamicVRAM and async offload on Intel XPU

- main.py: extend the DynamicVRAM enablement gate to is_intel_xpu() (was Nvidia-only)
- model_management.py: add XPU-safe host_register/host_unregister helpers (no CUDA host-registration API on XPU; pinnable buffers are already Level Zero host USM) and route the cudaHostRegister/Unregister sites through them
- model_management.py: add is_intel_xpu_discrete() which queries Level Zero (ZE_DEVICE_PROPERTY_FLAG_INTEGRATED) via ctypes on both Windows (ze_loader.dll) and Linux (libze_loader.so.1), matching the active torch device by PCI deviceId; fail-closed on any error or ambiguity
- model_management.py: enable async weight-offload streams (NUM_STREAMS=2) by default on discrete Intel XPU; user --async-offload/--disable-async-offload overrides preserved
- model_patcher.py, pinned_memory.py: route remaining host (un)register calls through the XPU-safe helpers

device_supports_non_blocking() is unchanged (XPU stays blocking): the ~15% async win comes from stream overlap, not non-blocking copies.

Validated end-to-end on a discrete Intel Arc B570 (Windows, torch 2.10.0+xpu).

Amp-Thread-ID: https://ampcode.com/threads/T-019ef7fa-0c6c-743e-b9c6-f9597ddcfa75
Co-authored-by: Amp <amp@ampcode.com>

main.py

@@ -236,7 +236,7 @@ import hook_breaker_ac10a0
 import comfy.memory_management
 import comfy.model_patcher
 
-if args.enable_dynamic_vram or (enables_dynamic_vram() and comfy.model_management.is_nvidia() and not comfy.model_management.is_wsl()):
+if args.enable_dynamic_vram or (enables_dynamic_vram() and (comfy.model_management.is_nvidia() or comfy.model_management.is_intel_xpu()) and not comfy.model_management.is_wsl()):
     if (not args.enable_dynamic_vram) and (comfy.model_management.torch_version_numeric < (2, 8)):
         logging.warning("Unsupported Pytorch detected. DynamicVRAM support requires Pytorch version 2.8 or later. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows")
     else: