[Utils] Adds store() and restore() methods to EMAModel

src/diffusers/training_utils.py

@@ -115,7 +115,7 @@ def __init__(
             deprecate("device", "1.0.0", deprecation_message, standard_warn=False)
             self.to(device=kwargs["device"])
 
-        self.collected_params = None
+        self.temp_stored_params = None
 
         self.decay = decay
         self.min_decay = min_decay
@@ -149,7 +149,6 @@ def save_pretrained(self, path):
         model = self.model_cls.from_config(self.model_config)
         state_dict = self.state_dict()
         state_dict.pop("shadow_params", None)
-        state_dict.pop("collected_params", None)
 
         model.register_to_config(**state_dict)
         self.copy_to(model.parameters())
@@ -248,9 +247,35 @@ def state_dict(self) -> dict:
             "inv_gamma": self.inv_gamma,
             "power": self.power,
             "shadow_params": self.shadow_params,
-            "collected_params": self.collected_params,
         }
 
+    def store(self, parameters: Iterable[torch.nn.Parameter]) -> None:
+        r"""
+        Args:
+        Save the current parameters for restoring later.
+            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+                temporarily stored.
+        """
+        self.temp_stored_params = [param.detach().cpu().clone() for param in parameters]
+
+    def restore(self, parameters: Iterable[torch.nn.Parameter]) -> None:
+        r"""
+        Args:
+        Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without:
+        affecting the original optimization process. Store the parameters before the `copy_to()` method. After
+        validation (or model saving), use this to restore the former parameters.
+            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+                updated with the stored parameters. If `None`, the parameters with which this
+                `ExponentialMovingAverage` was initialized will be used.
+        """
+        if self.temp_stored_params is None:
+            raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`")
+        for c_param, param in zip(self.temp_stored_params, parameters):
+            param.data.copy_(c_param.data)
+
+        # Better memory-wise.
+        self.temp_stored_params = None
+
     def load_state_dict(self, state_dict: dict) -> None:
         r"""
         Args:
@@ -297,12 +322,3 @@ def load_state_dict(self, state_dict: dict) -> None:
                 raise ValueError("shadow_params must be a list")
             if not all(isinstance(p, torch.Tensor) for p in self.shadow_params):
                 raise ValueError("shadow_params must all be Tensors")
-
-        self.collected_params = state_dict.get("collected_params", None)
-        if self.collected_params is not None:
-            if not isinstance(self.collected_params, list):
-                raise ValueError("collected_params must be a list")
-            if not all(isinstance(p, torch.Tensor) for p in self.collected_params):
-                raise ValueError("collected_params must all be Tensors")
-            if len(self.collected_params) != len(self.shadow_params):
-                raise ValueError("collected_params and shadow_params must have the same length")

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -212,7 +212,7 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch", "transformers"])
 
 
-class StableDiffusionSAGPipeline(metaclass=DummyObject):
+class StableDiffusionPix2PixZeroPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 
     def __init__(self, *args, **kwargs):
@@ -227,7 +227,7 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch", "transformers"])
 
 
-class StableDiffusionPix2PixZeroPipeline(metaclass=DummyObject):
+class StableDiffusionSAGPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 
     def __init__(self, *args, **kwargs):