multigpu: use unet_manual_cast for SelectModelDevice compute dtype

comfy_extras/nodes_multigpu.py

@@ -48,17 +48,14 @@ def execute(cls, model: ModelPatcher, max_gpus: int) -> io.NodeOutput:
         return io.NodeOutput(model)
 
 
-def _force_fp32_cpu_compute(patcher: ModelPatcher):
-    """Force fp32 inference dtype for CPU.
-
-    PyTorch's CPU conv2d kernels fall back to software emulation for fp16/bf16
-    and run ~500-600x slower than fp32, which makes a normal-sized workflow
-    look frozen for hours. Routing through set_model_compute_dtype leaves the
-    weights as-is and casts at use, so peak memory does not blow up."""
-    dtype = patcher.model_dtype()
-    if dtype in (torch.float16, torch.bfloat16):
-        logging.info(f"Select Model Device: using fp32 compute dtype for CPU inference (model dtype was {dtype}).")
-        patcher.set_model_compute_dtype(torch.float32)
+def _force_supported_compute_dtype(patcher: ModelPatcher, device: torch.device):
+    """Cast compute dtype to one the device supports; no-op if already supported."""
+    weight_dtype = patcher.model_dtype()
+    cast_dtype = comfy.model_management.unet_manual_cast(weight_dtype, device)
+    if cast_dtype is None:
+        return
+    logging.info(f"Select Model Device: using {cast_dtype} compute dtype on {device} (model weight dtype was {weight_dtype}).")
+    patcher.set_model_compute_dtype(cast_dtype)
 
 
 def _remember_base_devices(patcher: ModelPatcher):
@@ -229,8 +226,7 @@ def execute(cls, model: ModelPatcher, device: str = "default") -> io.NodeOutput:
             logging.warning(f"Select Model Device: cannot retarget model, passing through unchanged. ({e})")
             return io.NodeOutput(model)
         if resolved is not None:
-            if resolved.type == "cpu":
-                _force_fp32_cpu_compute(model)
+            _force_supported_compute_dtype(model, resolved)
             _prune_multigpu_collision(model, model.load_device)
         return io.NodeOutput(model)