Resnet only use contiguous in training mode.

src/diffusers/models/resnet.py

@@ -366,7 +366,12 @@ def forward(self, input_tensor: torch.Tensor, temb: torch.Tensor, *args, **kwarg
  hidden_states = self.conv2(hidden_states)
 
  if self.conv_shortcut is not None:
- input_tensor = self.conv_shortcut(input_tensor.contiguous())
+ # Only use contiguous() during training to avoid DDP gradient stride mismatch warning.
+ # In inference mode (eval or no_grad), skip contiguous() for better performance, especially on CPU.
+ # Issue: https://github.com/huggingface/diffusers/issues/12975
+ if self.training:
+ input_tensor = input_tensor.contiguous()
+ input_tensor = self.conv_shortcut(input_tensor)
 
  output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
 

tests/pipelines/kandinsky2_2/test_kandinsky_inpaint.py

@@ -248,6 +248,9 @@ def test_inference_batch_single_identical(self):
  def test_float16_inference(self):
  super().test_float16_inference(expected_max_diff=5e-1)
 
+ def test_save_load_dduf(self):
+ super().test_save_load_dduf(atol=1e-3, rtol=1e-3)
+
  @is_flaky()
  def test_model_cpu_offload_forward_pass(self):
  super().test_inference_batch_single_identical(expected_max_diff=8e-4)

tests/pipelines/kandinsky3/test_kandinsky3_img2img.py

@@ -191,6 +191,9 @@ def test_float16_inference(self):
  def test_inference_batch_single_identical(self):
  super().test_inference_batch_single_identical(expected_max_diff=1e-2)
 
+ def test_save_load_dduf(self):
+ super().test_save_load_dduf(atol=1e-3, rtol=1e-3)
+
 
 @slow
 @require_torch_accelerator