diff --git a/src/transformers/pipelines/text_generation.py b/src/transformers/pipelines/text_generation.py
index c0f14663f..0a8e6e845 100644
--- a/src/transformers/pipelines/text_generation.py
+++ b/src/transformers/pipelines/text_generation.py
@@ -3,11 +3,13 @@ import itertools
 import types
 from typing import Dict
 
-from ..utils import add_end_docstrings, is_tf_available, is_torch_available
+from ..utils import ModelOutput, add_end_docstrings, is_tf_available, is_torch_available
 from .base import Pipeline, build_pipeline_init_args
 
 
 if is_torch_available():
+    import torch
+
     from ..models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
     from .pt_utils import KeyDataset
 
@@ -380,13 +382,44 @@ class TextGenerationPipeline(Pipeline):
         if "generation_config" not in generate_kwargs:
             generate_kwargs["generation_config"] = self.generation_config
 
-        generated_sequence = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, **generate_kwargs)
+        output = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, **generate_kwargs)
+
+        if isinstance(output, ModelOutput):
+            generated_sequence = output.sequences
+            other_outputs = {k: v for k, v in output.items() if k != "sequences"}
+            out_b = generated_sequence.shape[0]
+
+            if self.framework == "pt":
+                for key, value in other_outputs.items():
+                    if isinstance(value, torch.Tensor) and value.shape[0] == out_b:
+                        other_outputs[key] = value.reshape(in_b, out_b // in_b, *value.shape[1:])
+                    if isinstance(value, tuple) and len(value[0]) == out_b:
+                        value = torch.stack(value).swapaxes(0, 1)
+                        other_outputs[key] = value
+            elif self.framework == "tf":
+                for key, value in other_outputs.items():
+                    if isinstance(value, tf.Tensor) and value.shape[0] == out_b:
+                        other_outputs[key] = tf.reshape(value, (in_b, out_b // in_b, *value.shape[1:]))
+                    if isinstance(value, tuple) and len(value[0]) == out_b:
+                        value = tf.stack(value).swapaxes(0, 1)
+                        other_outputs[key] = value
+        else:
+            generated_sequence = output
+            other_outputs = {}
+
         out_b = generated_sequence.shape[0]
         if self.framework == "pt":
             generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:])
         elif self.framework == "tf":
             generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:]))
-        return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
+
+        model_outputs = {
+            "generated_sequence": generated_sequence,
+            "input_ids": input_ids,
+            "prompt_text": prompt_text,
+        }
+        model_outputs.update(other_outputs)
+        return model_outputs
 
     def postprocess(
         self,
@@ -400,7 +433,19 @@ class TextGenerationPipeline(Pipeline):
         prompt_text = model_outputs["prompt_text"]
         generated_sequence = generated_sequence.numpy().tolist()
         records = []
-        for sequence in generated_sequence:
+        other_outputs = model_outputs.get("additional_outputs", {})
+        splitted_keys = {}
+        if other_outputs:
+            if self.framework == "pt":
+                for k, v in other_outputs.items():
+                    if isinstance(v, torch.Tensor) and v.shape[0] == len(generated_sequence):
+                        splitted_keys[k] = v.numpy().tolist()
+            elif self.framework == "tf":
+                for k, v in other_outputs.items():
+                    if isinstance(v, tf.Tensor) and v.shape[0] == len(generated_sequence):
+                        splitted_keys[k] = v.numpy().tolist()
+
+        for idx, sequence in enumerate(generated_sequence):
             if return_type == ReturnType.TENSORS:
                 record = {"generated_token_ids": sequence}
             elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
@@ -444,6 +489,8 @@ class TextGenerationPipeline(Pipeline):
                             # When we're not starting from a prefill, the output is a new assistant message
                             all_text = list(prompt_text.messages) + [{"role": "assistant", "content": all_text}]
                 record = {"generated_text": all_text}
+                for key, values in splitted_keys.items():
+                    record[key] = values[idx]
             records.append(record)
 
         return records
diff --git a/tests/pipelines/test_pipelines_text_generation.py b/tests/pipelines/test_pipelines_text_generation.py
index 7504ae009..5c5d3de17 100644
--- a/tests/pipelines/test_pipelines_text_generation.py
+++ b/tests/pipelines/test_pipelines_text_generation.py
@@ -653,6 +653,31 @@ class TextGenerationPipelineTests(unittest.TestCase):
             _ = text_generator(prompt, max_length=10)
         self.assertNotIn(logger_msg, cl.out)
 
+    def test_return_dict_in_generate(self):
+        text_generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-gpt2", max_new_tokens=16)
+        out = text_generator(
+            ["This is great !", "Something else"], return_dict_in_generate=True, output_logits=True, output_scores=True
+        )
+        self.assertEqual(
+            out,
+            [
+                [
+                    {
+                        "generated_text": ANY(str),
+                        "logits": ANY(list),
+                        "scores": ANY(list),
+                    },
+                ],
+                [
+                    {
+                        "generated_text": ANY(str),
+                        "logits": ANY(list),
+                        "scores": ANY(list),
+                    },
+                ],
+            ],
+        )
+
     @require_torch
     def test_pipeline_assisted_generation(self):
         """Tests that we can run assisted generation in the pipeline"""