speedup when offload_mode enable

diffsynth_engine/models/flux/flux_vae.py

@@ -53,7 +53,8 @@ def __init__(self, device: str = "cuda:0", dtype: torch.dtype = torch.float32):
     def from_state_dict(cls, state_dict: Dict[str, torch.Tensor], device: str, dtype: torch.dtype):
         with no_init_weights():
             model = torch.nn.utils.skip_init(cls, device=device, dtype=dtype)
-        model.load_state_dict(state_dict)
+        model.load_state_dict(state_dict, assign=True)
+        model.to(device=device, dtype=dtype, non_blocking=True)
         return model
 
 
@@ -74,5 +75,6 @@ def __init__(self, device: str, dtype: torch.dtype = torch.float32):
     def from_state_dict(cls, state_dict: Dict[str, torch.Tensor], device: str, dtype: torch.dtype):
         with no_init_weights():
             model = torch.nn.utils.skip_init(cls, device=device, dtype=dtype)
-        model.load_state_dict(state_dict)
+        model.load_state_dict(state_dict, assign=True)
+        model.to(device=device, dtype=dtype, non_blocking=True)
         return model

diffsynth_engine/pipelines/base.py

@@ -43,6 +43,7 @@ def __init__(
         self.dtype = dtype
         self.offload_mode = None
         self.model_names = []
+        self._models_offload_params = {}
 
     @classmethod
     def from_pretrained(
@@ -288,6 +289,10 @@ def _enable_model_cpu_offload(self):
             model = getattr(self, model_name)
             if model is not None:
                 model.to("cpu")
+                self._models_offload_params[model_name] = {}
+                for name, param in model.named_parameters(recurse=True):
+                    param.data = param.data.pin_memory()
+                    self._models_offload_params[model_name][name] = param.data
         self.offload_mode = "cpu_offload"
 
     def _enable_sequential_cpu_offload(self):
@@ -321,12 +326,14 @@ def load_models_to_device(self, load_model_names: List[str] | None = None):
         for model_name in self.model_names:
             if model_name not in load_model_names:
                 model = getattr(self, model_name)
-                if model is not None and (p := next(model.parameters(), None)) is not None and p.device != "cpu":
-                    model.to("cpu")
+                if model is not None and (p := next(model.parameters(), None)) is not None and p.device != torch.device("cpu"):
+                    param_cache = self._models_offload_params[model_name]
+                    for name, param in model.named_parameters(recurse=True):
+                        param.data = param_cache[name]
         # load the needed models to device
         for model_name in load_model_names:
             model = getattr(self, model_name)
-            if model is not None and (p := next(model.parameters(), None)) is not None and p.device != self.device:
+            if model is not None and (p := next(model.parameters(), None)) is not None and p.device != torch.device(self.device):
                 model.to(self.device)
         # fresh the cuda cache
         empty_cache()

diffsynth_engine/pipelines/flux_image.py

@@ -604,7 +604,8 @@ def from_pretrained(
             device=device,
             dtype=model_config.dit_dtype,
         )
-        pipe.enable_cpu_offload(offload_mode)
+        if offload_mode is not None:
+            pipe.enable_cpu_offload(offload_mode)
         if model_config.dit_dtype == torch.float8_e4m3fn:
             pipe.dtype = torch.bfloat16  # running dtype
             pipe.enable_fp8_autocast(

diffsynth_engine/utils/offload.py

@@ -18,7 +18,7 @@ def add_cpu_offload_hook(module: nn.Module, device: str = "cuda", recurse: bool
     def _forward_pre_hook(module: nn.Module, input):
         offload_params = {}
         for name, param in module.named_parameters(recurse=recurse):
-            offload_params[name] = param.data
+            offload_params[name] = param.data.pin_memory()
             param.data = param.data.to(device=device)
         setattr(module, "_offload_params", offload_params)
         return tuple(x.to(device=device) if isinstance(x, torch.Tensor) else x for x in input)