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main/matryoshka.py

@@ -868,7 +868,7 @@ class CrossAttnDownBlock2D(nn.Module):
         blocks = list(zip(self.resnets, self.attentions))
 
         for i, (resnet, attn) in enumerate(blocks):
-            if self.training and self.gradient_checkpointing:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
 
                 def create_custom_forward(module, return_dict=None):
                     def custom_forward(*inputs):
@@ -1029,7 +1029,7 @@ class UNetMidBlock2DCrossAttn(nn.Module):
 
         hidden_states = self.resnets[0](hidden_states, temb)
         for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            if self.training and self.gradient_checkpointing:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
 
                 def create_custom_forward(module, return_dict=None):
                     def custom_forward(*inputs):
@@ -1191,7 +1191,7 @@ class CrossAttnUpBlock2D(nn.Module):
 
             hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
 
-            if self.training and self.gradient_checkpointing:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
 
                 def create_custom_forward(module, return_dict=None):
                     def custom_forward(*inputs):
@@ -1364,7 +1364,7 @@ class MatryoshkaTransformer2DModel(LegacyModelMixin, LegacyConfigMixin):
 
         # Blocks
         for block in self.transformer_blocks:
-            if self.training and self.gradient_checkpointing:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
 
                 def create_custom_forward(module, return_dict=None):
                     def custom_forward(*inputs):