remove redundant hijacks

modules/processing_callbacks.py

@@ -37,8 +37,6 @@ def diffusers_callback(pipe, step: int, timestep: int, kwargs: dict):
     if p is None:
         return kwargs
     latents = kwargs.get('latents', None)
-    if torch.is_tensor(latents) and latents.device.type == "privateuseone":
-        torch.dml.synchronize_tensor(latents) # DML synchronize
     debug_callback(f'Callback: step={step} timestep={timestep} latents={latents.shape if latents is not None else None} kwargs={list(kwargs)}')
     shared.state.sampling_step = step
     if shared.state.interrupted or shared.state.skipped: