Studio diffusion (Phase 16) review fixes: native engine robustness

- sd_cpp_backend: stop truncating explicit seeds to 53 bits (mask to int64);
  a large requested seed was silently collapsed (2**53 -> 0) and distinct seeds
  aliased to the same image. Random seeds stay 53-bit (JS-safe).
- sd_cpp_backend: sanitize empty/whitespace hf_token to None so HfApi/hf_hub
  fall back to anonymous instead of failing auth on a blank token.
- sd_cpp_backend: a superseding load now cancels the in-flight generation, so the
  old sd-cli can no longer return/persist an image from the previous model.
- diffusion_engine_router: run the previous engine's unload() OUTSIDE the lock so a
  slow 10+ GB free / CUDA sync does not block engine selection.
- diffusion_engine_router: probe sd-cli runnability (version()) before committing to
  native, so a present-but-unrunnable binary falls back to diffusers at selection.
- diffusion_device: resolve a torch-free CPU target when torch is unavailable, so a
  CPU-only install can still reach the native sd.cpp engine instead of failing load.
- tests updated for the runnability probe + a not-runnable fallback case.

studio/backend/core/inference/diffusion_device.py

@@ -59,8 +59,24 @@ def resolve_diffusion_device_target() -> DiffusionDeviceTarget:
     (CUDA -> XPU -> MPS -> CPU). On Apple Silicon Studio reports MLX/CPU when its
     product backend is gated on the ``mlx`` package, but diffusers runs on
     PyTorch's MPS backend, so those cases still fall through to the MPS probe.
+
+    Torch is optional here: on a CPU-only install without PyTorch the native
+    stable-diffusion.cpp engine still runs (it shells out to sd-cli), so a missing
+    torch reports a torch-free CPU target instead of crashing the whole
+    ``/images/load`` before the engine router can select the native backend.
     """
-    import torch
+    try:
+        import torch
+    except Exception:
+        return DiffusionDeviceTarget(
+            device = "cpu",
+            dtype = None,
+            backend = "cpu",
+            vendor = None,
+            supports_model_cpu_offload = False,
+            supports_default_torch_compile = False,
+            supports_pinned_transfer = False,
+        )
 
     try:
         from utils.hardware import DeviceType, get_device