@@ -262,6 +262,9 @@ def encode_prompt(
         prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
         prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
 
+        if prompt_embeds_mask is not None and prompt_embeds_mask.all():
+            prompt_embeds_mask = None
+
         return prompt_embeds, prompt_embeds_mask
 
     def check_inputs(
 
@@ -324,6 +324,9 @@ def encode_prompt(
         prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
         prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
 
+        if prompt_embeds_mask is not None and prompt_embeds_mask.all():
+            prompt_embeds_mask = None
+
         return prompt_embeds, prompt_embeds_mask
 
     def check_inputs(
 
@@ -305,6 +305,9 @@ def encode_prompt(
         prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
         prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
 
+        if prompt_embeds_mask is not None and prompt_embeds_mask.all():
+            prompt_embeds_mask = None
+
         return prompt_embeds, prompt_embeds_mask
 
     def check_inputs(
 
@@ -309,6 +309,9 @@ def encode_prompt(
         prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
         prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
 
+        if prompt_embeds_mask is not None and prompt_embeds_mask.all():
+            prompt_embeds_mask = None
+
         return prompt_embeds, prompt_embeds_mask
 
     def check_inputs(
 
@@ -321,6 +321,9 @@ def encode_prompt(
         prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
         prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
 
+        if prompt_embeds_mask is not None and prompt_embeds_mask.all():
+            prompt_embeds_mask = None
+
         return prompt_embeds, prompt_embeds_mask
 
     # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_inpaint.QwenImageInpaintPipeline.check_inputs
 
@@ -323,6 +323,9 @@ def encode_prompt(
         prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
         prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
 
+        if prompt_embeds_mask is not None and prompt_embeds_mask.all():
+            prompt_embeds_mask = None
+
         return prompt_embeds, prompt_embeds_mask
 
     # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_edit.QwenImageEditPipeline.check_inputs
 
@@ -305,6 +305,9 @@ def encode_prompt(
         prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
         prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
 
+        if prompt_embeds_mask is not None and prompt_embeds_mask.all():
+            prompt_embeds_mask = None
+
         return prompt_embeds, prompt_embeds_mask
 
     def check_inputs(
 
@@ -316,6 +316,9 @@ def encode_prompt(
         prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
         prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
 
+        if prompt_embeds_mask is not None and prompt_embeds_mask.all():
+            prompt_embeds_mask = None
+
         return prompt_embeds, prompt_embeds_mask
 
     def check_inputs(
 
@@ -328,6 +328,9 @@ def encode_prompt(
         prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
         prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
 
+        if prompt_embeds_mask is not None and prompt_embeds_mask.all():
+            prompt_embeds_mask = None
+
         return prompt_embeds, prompt_embeds_mask
 
     def get_image_caption(self, prompt_image, use_en_prompt=True, device=None):
 
@@ -276,3 +276,74 @@ def prepare_dummy_input(self, height, width):
 
     def test_torch_compile_recompilation_and_graph_break(self):
         super().test_torch_compile_recompilation_and_graph_break()
+
+    def test_torch_compile_with_and_without_mask(self):
+        """Test that torch.compile works with both None mask and padding mask."""
+        init_dict, inputs = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).to(torch_device)
+        model.eval()
+        model.compile(mode="default", fullgraph=True)
+
+        # Test 1: Run with None mask (no padding, all tokens are valid)
+        inputs_no_mask = inputs.copy()
+        inputs_no_mask["encoder_hidden_states_mask"] = None
+
+        # First run to allow compilation
+        with torch.no_grad():
+            output_no_mask = model(**inputs_no_mask)
+
+        # Second run to verify no recompilation
+        with (
+            torch._inductor.utils.fresh_inductor_cache(),
+            torch._dynamo.config.patch(error_on_recompile=True),
+            torch.no_grad(),
+        ):
+            output_no_mask_2 = model(**inputs_no_mask)
+
+        self.assertEqual(output_no_mask.sample.shape[1], inputs["hidden_states"].shape[1])
+        self.assertEqual(output_no_mask_2.sample.shape[1], inputs["hidden_states"].shape[1])
+
+        # Test 2: Run with all-ones mask (should behave like None)
+        inputs_all_ones = inputs.copy()
+        # Keep the all-ones mask
+        self.assertTrue(inputs_all_ones["encoder_hidden_states_mask"].all().item())
+
+        # First run to allow compilation
+        with torch.no_grad():
+            output_all_ones = model(**inputs_all_ones)
+
+        # Second run to verify no recompilation
+        with (
+            torch._inductor.utils.fresh_inductor_cache(),
+            torch._dynamo.config.patch(error_on_recompile=True),
+            torch.no_grad(),
+        ):
+            output_all_ones_2 = model(**inputs_all_ones)
+
+        self.assertEqual(output_all_ones.sample.shape[1], inputs["hidden_states"].shape[1])
+        self.assertEqual(output_all_ones_2.sample.shape[1], inputs["hidden_states"].shape[1])
+
+        # Test 3: Run with actual padding mask (has zeros)
+        inputs_with_padding = inputs.copy()
+        mask_with_padding = inputs["encoder_hidden_states_mask"].clone()
+        mask_with_padding[:, 4:] = 0  # Last 3 tokens are padding
+
+        inputs_with_padding["encoder_hidden_states_mask"] = mask_with_padding
+
+        # First run to allow compilation
+        with torch.no_grad():
+            output_with_padding = model(**inputs_with_padding)
+
+        # Second run to verify no recompilation
+        with (
+            torch._inductor.utils.fresh_inductor_cache(),
+            torch._dynamo.config.patch(error_on_recompile=True),
+            torch.no_grad(),
+        ):
+            output_with_padding_2 = model(**inputs_with_padding)
+
+        self.assertEqual(output_with_padding.sample.shape[1], inputs["hidden_states"].shape[1])
+        self.assertEqual(output_with_padding_2.sample.shape[1], inputs["hidden_states"].shape[1])
+
+        # Verify that outputs are different (mask should affect results)
+        self.assertFalse(torch.allclose(output_no_mask.sample, output_with_padding.sample, atol=1e-3))