diff --git a/src/transformers/models/idefics/modeling_idefics.py b/src/transformers/models/idefics/modeling_idefics.py
index 48cf500b7454..6857fb624c0f 100644
--- a/src/transformers/models/idefics/modeling_idefics.py
+++ b/src/transformers/models/idefics/modeling_idefics.py
@@ -1690,8 +1690,13 @@ def prepare_inputs_for_generation(
             # create position_ids on the fly for batch generation
             position_ids = attention_mask.long().cumsum(-1) - 1
             position_ids.masked_fill_(attention_mask == 0, 1)
+
+            # If past_key_values are present then slice the postion ids for only only the unprocessed tokens.
             if past_key_values:
-                position_ids = position_ids[:, -input_ids.shape[1] :]
+                if inputs_embeds is not None and input_ids.shape[1] == 0:
+                    position_ids = position_ids[:, -inputs_embeds.shape[1] :]
+                else:
+                    position_ids = position_ids[:, -input_ids.shape[1] :]
 
                 # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
                 position_ids = position_ids.clone(memory_format=torch.contiguous_format)
diff --git a/tests/models/idefics/test_modeling_idefics.py b/tests/models/idefics/test_modeling_idefics.py
index 01871e81b30e..cc9efc967db2 100644
--- a/tests/models/idefics/test_modeling_idefics.py
+++ b/tests/models/idefics/test_modeling_idefics.py
@@ -755,6 +755,65 @@ def test_generate_without_input_ids(self):
             )
             self.assertIsNotNone(output_ids_generate)
 
+    @pytest.mark.generate
+    def test_generate_continue_from_inputs_embeds(self):
+        """Overwrite for IDEFICS: Ensure image attention mask is processed while continuing from `inputs_embeds`."""
+
+        for model_class in self.all_generative_model_classes:
+            config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
+            print(inputs)
+
+            model = model_class(config).to(torch_device).eval()
+
+            model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1
+            model.generation_config.forced_eos_token_id = None
+            model.generation_config.use_cache = True
+
+            input_ids = inputs.pop("input_ids")
+            input_embeds = model.get_input_embeddings()(input_ids)
+
+            generation_kwargs = {
+                "return_dict_in_generate": True,
+                "do_sample": False,
+            }
+
+            inputs["inputs_embeds"] = input_embeds
+
+            # Traditional way of generating text, with `return_dict_in_generate` to return the past key values
+            outputs = model.generate(**inputs, max_new_tokens=4, **generation_kwargs)
+            # Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the
+            # inputs may need to be tweaked across `generate` calls (like the attention mask).
+            initial_output = model.generate(**inputs, max_new_tokens=3, **generation_kwargs)
+            inputs["past_key_values"] = initial_output.past_key_values
+
+            new_attention_len = input_ids.shape[1] + initial_output.sequences.shape[-1]
+            continued_embeds = torch.cat([input_embeds, model.get_input_embeddings()(initial_output.sequences)], dim=1)
+            inputs["inputs_embeds"] = continued_embeds
+
+            if "attention_mask" in inputs:
+                inputs["attention_mask"] = torch.nn.functional.pad(
+                    inputs["attention_mask"],
+                    (0, new_attention_len - inputs["attention_mask"].shape[1]),
+                    mode="constant",
+                    value=1,
+                )
+            if "image_attention_mask" in inputs:
+                inputs["image_attention_mask"] = inputs["image_attention_mask"][..., -1:, :]
+
+            cached_output = model.generate(**inputs, max_new_tokens=1, **generation_kwargs)
+
+            # Verify that the combined outputs match the full generation.
+            combined_output_sequences = torch.concat([initial_output.sequences, cached_output.sequences], axis=1)
+            self.assertListEqual(outputs.sequences.tolist(), combined_output_sequences.tolist())
+            for layer_idx in range(len(cached_output.past_key_values)):
+                for kv_idx in range(len(cached_output.past_key_values[layer_idx])):
+                    self.assertTrue(
+                        torch.allclose(
+                            outputs.past_key_values[layer_idx][kv_idx],
+                            cached_output.past_key_values[layer_idx][kv_idx],
+                        )
+                    )
+
     def _check_attentions_for_generate(
         self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
     ):
diff --git a/tests/models/moshi/test_modeling_moshi.py b/tests/models/moshi/test_modeling_moshi.py
index f51d0199156c..09278f0d24c4 100644
--- a/tests/models/moshi/test_modeling_moshi.py
+++ b/tests/models/moshi/test_modeling_moshi.py
@@ -358,7 +358,7 @@ def test_disk_offload_bin(self):
     def test_disk_offload_safetensors(self):
         pass
 
-    @unittest.skip(reason="Test becomes too complex with Moshi requiring multiple modalities input.")
+    @unittest.skip(reason="Test becomes too complex with Moshi requiring multiple input modalities.")
     def test_generate_continue_from_inputs_embeds(self):
         pass
 
@@ -828,6 +828,7 @@ def test_generate_without_input_ids(self):
             output_ids_generate = model.generate(
                 do_sample=False, max_new_tokens=self.max_new_tokens, remove_invalid_values=True
             )
+            print(output_ids_generate)
             self.assertIsNotNone(output_ids_generate)
 
     @unittest.skip(reason="The audio encoder has no gradients.")