Added store/restore to EMAModel, restore non-EMA weights after saving checkpoint

diffusers_trainer.py

@@ -462,6 +462,33 @@ class EMAModel:
         for s_param, param in zip(self.shadow_params, parameters):
             param.data.copy_(s_param.data)
 
+    # From CompVis LitEMA implementation
+    def store(self, parameters):
+        """
+        Save the current parameters for restoring later.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            temporarily stored.
+        """
+        self.collected_params = [param.clone() for param in parameters]
+
+    def restore(self, parameters):
+        """
+        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.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored parameters.
+        """
+        for c_param, param in zip(self.collected_params, parameters):
+            param.data.copy_(c_param.data)
+
+        del self.collected_params
+        gc.collect()
+
     def to(self, device=None, dtype=None) -> None:
         r"""Move internal buffers of the ExponentialMovingAverage to `device`.
         Args:
@@ -602,6 +629,7 @@ def main():
     def save_checkpoint(global_step):
         if rank == 0:
             if args.use_ema:
+                ema_unet.store(unet.parameters())
                 ema_unet.copy_to(unet.parameters())
             pipeline = StableDiffusionPipeline(
                 text_encoder=text_encoder,
@@ -616,6 +644,9 @@ def main():
             )
             print(f'saving checkpoint to: {args.output_path}/{args.run_name}_{global_step}')
             pipeline.save_pretrained(f'{args.output_path}/{args.run_name}_{global_step}')
+
+            if args.use_ema:
+                ema_unet.restore(unet.parameters())
         # barrier
         torch.distributed.barrier()