diff --git a/src/lightning/pytorch/CHANGELOG.md b/src/lightning/pytorch/CHANGELOG.md
index 6e70119b65e99..2da11d8673ad8 100644
--- a/src/lightning/pytorch/CHANGELOG.md
+++ b/src/lightning/pytorch/CHANGELOG.md
@@ -17,6 +17,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - For cross-device local checkpoints, instruct users to install `fsspec>=2025.5.0` if unavailable ([#20780](https://github.com/Lightning-AI/pytorch-lightning/pull/20780))
 
+- Default to RichProgressBar and RichModelSummary if the rich package is available. Fallback to TQDMProgressBar and ModelSummary otherwise. ([#9580](https://github.com/Lightning-AI/pytorch-lightning/pull/9580))
+
 
 ### Changed
 
diff --git a/src/lightning/pytorch/callbacks/progress/rich_progress.py b/src/lightning/pytorch/callbacks/progress/rich_progress.py
index 7bb98e8a9058c..dab436bf569ef 100644
--- a/src/lightning/pytorch/callbacks/progress/rich_progress.py
+++ b/src/lightning/pytorch/callbacks/progress/rich_progress.py
@@ -17,15 +17,14 @@
 from datetime import timedelta
 from typing import Any, Optional, Union, cast
 
-from lightning_utilities.core.imports import RequirementCache
+import torch
 from typing_extensions import override
 
 import lightning.pytorch as pl
 from lightning.pytorch.callbacks.progress.progress_bar import ProgressBar
+from lightning.pytorch.utilities.imports import _RICH_AVAILABLE
 from lightning.pytorch.utilities.types import STEP_OUTPUT
 
-_RICH_AVAILABLE = RequirementCache("rich>=10.2.2")
-
 if _RICH_AVAILABLE:
     from rich import get_console, reconfigure
     from rich.console import Console, RenderableType
@@ -171,7 +170,7 @@ def render(self, task: "Task") -> Text:
                 return Text()
             if self._trainer.training and task.id not in self._tasks:
                 self._tasks[task.id] = "None"
-                if self._renderable_cache:
+                if self._renderable_cache and self._current_task_id in self._renderable_cache:
                     self._current_task_id = cast(TaskID, self._current_task_id)
                     self._tasks[self._current_task_id] = self._renderable_cache[self._current_task_id][1]
                 self._current_task_id = task.id
@@ -185,7 +184,10 @@ def render(self, task: "Task") -> Text:
         def _generate_metrics_texts(self) -> Generator[str, None, None]:
             for name, value in self._metrics.items():
                 if not isinstance(value, str):
-                    value = f"{value:{self._metrics_format}}"
+                    try:
+                        value = f"{value:{self._metrics_format}}"
+                    except (TypeError, ValueError):
+                        value = str(value)
                 yield f"{name}: {value}"
 
 
@@ -448,17 +450,12 @@ def _add_task(self, total_batches: Union[int, float], description: str, visible:
         )
 
     def _update(self, progress_bar_id: Optional["TaskID"], current: int, visible: bool = True) -> None:
-        if self.progress is not None and self.is_enabled:
-            assert progress_bar_id is not None
+        if self.progress is not None and self.is_enabled and progress_bar_id is not None:
             total = self.progress.tasks[progress_bar_id].total
             assert total is not None
             if not self._should_update(current, total):
                 return
-
-            leftover = current % self.refresh_rate
-            advance = leftover if (current == total and leftover != 0) else self.refresh_rate
-            self.progress.update(progress_bar_id, advance=advance, visible=visible)
-            self.refresh()
+            self.progress.update(progress_bar_id, completed=current, visible=visible)
 
     def _should_update(self, current: int, total: Union[int, float]) -> bool:
         return current % self.refresh_rate == 0 or current == total
@@ -552,9 +549,13 @@ def on_validation_batch_end(
         if self.is_disabled:
             return
         if trainer.sanity_checking:
-            self._update(self.val_sanity_progress_bar_id, batch_idx + 1)
-        elif self.val_progress_bar_id is not None:
-            self._update(self.val_progress_bar_id, batch_idx + 1)
+            if self.val_sanity_progress_bar_id is not None:
+                self._update(self.val_sanity_progress_bar_id, batch_idx + 1)
+            return
+
+        if self.val_progress_bar_id is None:
+            return
+        self._update(self.val_progress_bar_id, batch_idx + 1)
         self.refresh()
 
     @override
@@ -567,9 +568,8 @@ def on_test_batch_end(
         batch_idx: int,
         dataloader_idx: int = 0,
     ) -> None:
-        if self.is_disabled:
+        if self.is_disabled or self.test_progress_bar_id is None:
             return
-        assert self.test_progress_bar_id is not None
         self._update(self.test_progress_bar_id, batch_idx + 1)
         self.refresh()
 
@@ -583,9 +583,8 @@ def on_predict_batch_end(
         batch_idx: int,
         dataloader_idx: int = 0,
     ) -> None:
-        if self.is_disabled:
+        if self.is_disabled or self.predict_progress_bar_id is None:
             return
-        assert self.predict_progress_bar_id is not None
         self._update(self.predict_progress_bar_id, batch_idx + 1)
         self.refresh()
 
@@ -612,6 +611,17 @@ def _reset_progress_bar_ids(self) -> None:
         self.test_progress_bar_id = None
         self.predict_progress_bar_id = None
 
+    @override
+    def get_metrics(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"
+    ) -> dict[str, Union[int, str, float, dict[str, float]]]:
+        items = super().get_metrics(trainer, pl_module)
+        # convert all metrics to float before sending to rich
+        for k, v in items.items():
+            if isinstance(v, torch.Tensor):
+                items[k] = v.item()
+        return items
+
     def _update_metrics(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         metrics = self.get_metrics(trainer, pl_module)
         if self._metric_component:
diff --git a/src/lightning/pytorch/callbacks/progress/tqdm_progress.py b/src/lightning/pytorch/callbacks/progress/tqdm_progress.py
index 4ef260f00006d..942ba3627efc0 100644
--- a/src/lightning/pytorch/callbacks/progress/tqdm_progress.py
+++ b/src/lightning/pytorch/callbacks/progress/tqdm_progress.py
@@ -274,7 +274,7 @@ def on_train_batch_end(
         self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int
     ) -> None:
         n = batch_idx + 1
-        if self._should_update(n, self.train_progress_bar.total):
+        if self.train_progress_bar is not None and self._should_update(n, self.train_progress_bar.total):
             _update_n(self.train_progress_bar, n)
             self.train_progress_bar.set_postfix(self.get_metrics(trainer, pl_module))
 
@@ -322,7 +322,7 @@ def on_validation_batch_end(
         dataloader_idx: int = 0,
     ) -> None:
         n = batch_idx + 1
-        if self._should_update(n, self.val_progress_bar.total):
+        if self.val_progress_bar is not None and self._should_update(n, self.val_progress_bar.total):
             _update_n(self.val_progress_bar, n)
 
     @override
@@ -363,7 +363,7 @@ def on_test_batch_end(
         dataloader_idx: int = 0,
     ) -> None:
         n = batch_idx + 1
-        if self._should_update(n, self.test_progress_bar.total):
+        if self.test_progress_bar is not None and self._should_update(n, self.test_progress_bar.total):
             _update_n(self.test_progress_bar, n)
 
     @override
@@ -402,7 +402,7 @@ def on_predict_batch_end(
         dataloader_idx: int = 0,
     ) -> None:
         n = batch_idx + 1
-        if self._should_update(n, self.predict_progress_bar.total):
+        if self.predict_progress_bar is not None and self._should_update(n, self.predict_progress_bar.total):
             _update_n(self.predict_progress_bar, n)
 
     @override
diff --git a/src/lightning/pytorch/callbacks/rich_model_summary.py b/src/lightning/pytorch/callbacks/rich_model_summary.py
index 817aeeb655a7a..ce00ae06890f6 100644
--- a/src/lightning/pytorch/callbacks/rich_model_summary.py
+++ b/src/lightning/pytorch/callbacks/rich_model_summary.py
@@ -16,7 +16,7 @@
 from typing_extensions import override
 
 from lightning.pytorch.callbacks import ModelSummary
-from lightning.pytorch.callbacks.progress.rich_progress import _RICH_AVAILABLE
+from lightning.pytorch.utilities.imports import _RICH_AVAILABLE
 from lightning.pytorch.utilities.model_summary import get_human_readable_count
 
 
diff --git a/src/lightning/pytorch/loops/evaluation_loop.py b/src/lightning/pytorch/loops/evaluation_loop.py
index 7f033dbd8e2c2..b1e9edfaf7220 100644
--- a/src/lightning/pytorch/loops/evaluation_loop.py
+++ b/src/lightning/pytorch/loops/evaluation_loop.py
@@ -25,7 +25,6 @@
 
 import lightning.pytorch as pl
 from lightning.fabric.utilities.data import _set_sampler_epoch
-from lightning.pytorch.callbacks.progress.rich_progress import _RICH_AVAILABLE
 from lightning.pytorch.loops.fetchers import _DataFetcher, _DataLoaderIterDataFetcher
 from lightning.pytorch.loops.loop import _Loop
 from lightning.pytorch.loops.progress import _BatchProgress
@@ -44,6 +43,7 @@
 from lightning.pytorch.utilities.combined_loader import CombinedLoader
 from lightning.pytorch.utilities.data import has_len_all_ranks
 from lightning.pytorch.utilities.exceptions import SIGTERMException
+from lightning.pytorch.utilities.imports import _RICH_AVAILABLE
 from lightning.pytorch.utilities.model_helpers import _ModuleMode, is_overridden
 from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
 
diff --git a/src/lightning/pytorch/trainer/connectors/callback_connector.py b/src/lightning/pytorch/trainer/connectors/callback_connector.py
index 5c351aeebc564..e95f196d9ae43 100644
--- a/src/lightning/pytorch/trainer/connectors/callback_connector.py
+++ b/src/lightning/pytorch/trainer/connectors/callback_connector.py
@@ -37,6 +37,7 @@
 from lightning.pytorch.callbacks.timer import Timer
 from lightning.pytorch.trainer import call
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
+from lightning.pytorch.utilities.imports import _RICH_AVAILABLE
 from lightning.pytorch.utilities.model_helpers import is_overridden
 from lightning.pytorch.utilities.rank_zero import rank_zero_info
 
@@ -125,14 +126,8 @@ def _configure_model_summary_callback(self, enable_model_summary: bool) -> None:
             )
             return
 
-        progress_bar_callback = self.trainer.progress_bar_callback
-        is_progress_bar_rich = isinstance(progress_bar_callback, RichProgressBar)
-
         model_summary: ModelSummary
-        if progress_bar_callback is not None and is_progress_bar_rich:
-            model_summary = RichModelSummary()
-        else:
-            model_summary = ModelSummary()
+        model_summary = RichModelSummary() if _RICH_AVAILABLE else ModelSummary()
         self.trainer.callbacks.append(model_summary)
 
     def _configure_progress_bar(self, enable_progress_bar: bool = True) -> None:
@@ -157,7 +152,7 @@ def _configure_progress_bar(self, enable_progress_bar: bool = True) -> None:
             )
 
         if enable_progress_bar:
-            progress_bar_callback = TQDMProgressBar()
+            progress_bar_callback = RichProgressBar() if _RICH_AVAILABLE else TQDMProgressBar()
             self.trainer.callbacks.append(progress_bar_callback)
 
     def _configure_timer_callback(self, max_time: Optional[Union[str, timedelta, dict[str, int]]] = None) -> None:
diff --git a/src/lightning/pytorch/utilities/imports.py b/src/lightning/pytorch/utilities/imports.py
index 6c0815a6af9dc..cb3290b3d6275 100644
--- a/src/lightning/pytorch/utilities/imports.py
+++ b/src/lightning/pytorch/utilities/imports.py
@@ -28,6 +28,7 @@
 
 _OMEGACONF_AVAILABLE = package_available("omegaconf")
 _TORCHVISION_AVAILABLE = RequirementCache("torchvision")
+_RICH_AVAILABLE = RequirementCache("rich>=10.2.2")
 
 
 @functools.lru_cache(maxsize=128)
diff --git a/src/lightning/pytorch/utilities/testing/_runif.py b/src/lightning/pytorch/utilities/testing/_runif.py
index 9c46913681143..b84503365b68b 100644
--- a/src/lightning/pytorch/utilities/testing/_runif.py
+++ b/src/lightning/pytorch/utilities/testing/_runif.py
@@ -17,9 +17,8 @@
 
 from lightning.fabric.utilities.testing import _runif_reasons as fabric_run_if
 from lightning.pytorch.accelerators.cpu import _PSUTIL_AVAILABLE
-from lightning.pytorch.callbacks.progress.rich_progress import _RICH_AVAILABLE
 from lightning.pytorch.core.module import _ONNX_AVAILABLE
-from lightning.pytorch.utilities.imports import _OMEGACONF_AVAILABLE
+from lightning.pytorch.utilities.imports import _OMEGACONF_AVAILABLE, _RICH_AVAILABLE
 
 _SKLEARN_AVAILABLE = RequirementCache("scikit-learn")
 
diff --git a/tests/tests_pytorch/callbacks/progress/test_tqdm_progress_bar.py b/tests/tests_pytorch/callbacks/progress/test_tqdm_progress_bar.py
index d93bf1cf60e9c..538f1bce57ce0 100644
--- a/tests/tests_pytorch/callbacks/progress/test_tqdm_progress_bar.py
+++ b/tests/tests_pytorch/callbacks/progress/test_tqdm_progress_bar.py
@@ -18,7 +18,7 @@
 from collections import defaultdict
 from typing import Union
 from unittest import mock
-from unittest.mock import ANY, Mock, PropertyMock, call
+from unittest.mock import ANY, Mock, PropertyMock, call, patch
 
 import pytest
 import torch
@@ -109,6 +109,7 @@ def test_tqdm_progress_bar_misconfiguration():
         Trainer(callbacks=TQDMProgressBar(), enable_progress_bar=False)
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 @pytest.mark.parametrize("num_dl", [1, 2])
 def test_tqdm_progress_bar_totals(tmp_path, num_dl):
     """Test that the progress finishes with the correct total steps processed."""
@@ -203,6 +204,7 @@ def predict_step(self, batch, batch_idx, dataloader_idx=0):
     assert pbar.predict_progress_bar.leave
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_tqdm_progress_bar_fast_dev_run(tmp_path):
     model = BoringModel()
 
@@ -323,6 +325,7 @@ def test_tqdm_progress_bar_default_value(tmp_path):
 
 
 @mock.patch.dict(os.environ, {"COLAB_GPU": "1"})
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_tqdm_progress_bar_value_on_colab(tmp_path):
     """Test that Trainer will override the default in Google COLAB."""
     trainer = Trainer(default_root_dir=tmp_path)
@@ -411,6 +414,7 @@ def test_test_progress_bar_update_amount(tmp_path, test_batches: int, refresh_ra
     assert progress_bar.test_progress_bar.n_values == updates
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_tensor_to_float_conversion(tmp_path):
     """Check tensor gets converted to float."""
 
@@ -424,7 +428,13 @@ def training_step(self, batch, batch_idx):
     trainer = Trainer(
         default_root_dir=tmp_path, max_epochs=1, limit_train_batches=2, logger=False, enable_checkpointing=False
     )
-    trainer.fit(TestModel())
+
+    with mock.patch.object(sys.stdout, "write") as mock_write:
+        trainer.fit(TestModel())
+    bar_updates = "".join(call.args[0] for call in mock_write.call_args_list)
+    assert "a=0.123" in bar_updates
+    assert "b=1.000" in bar_updates
+    assert "c=2.000" in bar_updates
 
     torch.testing.assert_close(trainer.progress_bar_metrics["a"], 0.123)
     assert trainer.progress_bar_metrics["b"] == 1.0
@@ -616,6 +626,7 @@ def test_progress_bar_max_val_check_interval(
     assert pbar_callback.is_enabled
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 @RunIf(min_cuda_gpus=2, standalone=True)
 @pytest.mark.parametrize("val_check_interval", [0.2, 0.5])
 def test_progress_bar_max_val_check_interval_ddp(tmp_path, val_check_interval):
@@ -703,7 +714,7 @@ def get_metrics(self, trainer, pl_module):
             del items["v_num"]
             # this is equivalent to mocking `set_postfix` as this method gets called every time
             self.calls[trainer.state.fn].append((
-                trainer.state.stage,
+                trainer.state.stage.value,
                 trainer.current_epoch,
                 trainer.global_step,
                 items,
diff --git a/tests/tests_pytorch/callbacks/test_callbacks.py b/tests/tests_pytorch/callbacks/test_callbacks.py
index 53ea109b6ddf3..34749087bfb97 100644
--- a/tests/tests_pytorch/callbacks/test_callbacks.py
+++ b/tests/tests_pytorch/callbacks/test_callbacks.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 from pathlib import Path
 from re import escape
-from unittest.mock import Mock
+from unittest.mock import Mock, patch
 
 import pytest
 from lightning_utilities.test.warning import no_warning_call
@@ -119,6 +119,7 @@ def load_state_dict(self, state_dict) -> None:
         self.state = state_dict["state"]
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_resume_callback_state_saved_by_type_stateful(tmp_path):
     """Test that a legacy checkpoint that didn't use a state key before can still be loaded, using
     state_dict/load_state_dict."""
diff --git a/tests/tests_pytorch/trainer/connectors/test_callback_connector.py b/tests/tests_pytorch/trainer/connectors/test_callback_connector.py
index 94b5fcba652be..54fbd065fa919 100644
--- a/tests/tests_pytorch/trainer/connectors/test_callback_connector.py
+++ b/tests/tests_pytorch/trainer/connectors/test_callback_connector.py
@@ -14,7 +14,7 @@
 import contextlib
 import logging
 from unittest import mock
-from unittest.mock import MagicMock, Mock
+from unittest.mock import MagicMock, Mock, patch
 
 import pytest
 import torch
@@ -35,13 +35,14 @@
 from lightning.pytorch.trainer.connectors.callback_connector import _CallbackConnector
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_checkpoint_callbacks_are_last(tmp_path):
-    """Test that checkpoint callbacks always get moved to the end of the list, with preserved order."""
-    checkpoint1 = ModelCheckpoint(tmp_path, monitor="foo")
-    checkpoint2 = ModelCheckpoint(tmp_path, monitor="bar")
-    model_summary = ModelSummary()
+    """Test that checkpoint callbacks always come last."""
+    checkpoint1 = ModelCheckpoint(tmp_path / "path1", filename="ckpt1", monitor="val_loss_c1")
+    checkpoint2 = ModelCheckpoint(tmp_path / "path2", filename="ckpt2", monitor="val_loss_c2")
     early_stopping = EarlyStopping(monitor="foo")
     lr_monitor = LearningRateMonitor()
+    model_summary = ModelSummary()
     progress_bar = TQDMProgressBar()
 
     # no model reference
@@ -71,7 +72,7 @@ def test_checkpoint_callbacks_are_last(tmp_path):
     # with model-specific callbacks that substitute ones in Trainer
     model = LightningModule()
     model.configure_callbacks = lambda: [checkpoint1, early_stopping, model_summary, checkpoint2]
-    trainer = Trainer(callbacks=[progress_bar, lr_monitor, ModelCheckpoint(tmp_path)])
+    trainer = Trainer(callbacks=[progress_bar, lr_monitor, ModelCheckpoint(tmp_path, filename="ckpt_trainer")])
     trainer.strategy._lightning_module = model
     cb_connector = _CallbackConnector(trainer)
     cb_connector._attach_model_callbacks()
@@ -161,6 +162,7 @@ def test_all_callback_states_saved_before_checkpoint_callback(tmp_path):
     )
 
 
+@patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
 def test_attach_model_callbacks():
     """Test that the callbacks defined in the model and through Trainer get merged correctly."""
 
diff --git a/tests/tests_pytorch/trainer/connectors/test_rich_integration.py b/tests/tests_pytorch/trainer/connectors/test_rich_integration.py
new file mode 100644
index 0000000000000..62926d26018ba
--- /dev/null
+++ b/tests/tests_pytorch/trainer/connectors/test_rich_integration.py
@@ -0,0 +1,169 @@
+# Copyright The Lightning AI team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from lightning.pytorch import Trainer
+from lightning.pytorch.callbacks import ModelSummary, ProgressBar, RichModelSummary, RichProgressBar, TQDMProgressBar
+from lightning.pytorch.demos.boring_classes import BoringModel
+
+
+class TestRichIntegration:
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
+    def test_no_rich_defaults_tqdm_and_model_summary(self, tmp_path):
+        trainer = Trainer(default_root_dir=tmp_path, logger=False, enable_checkpointing=False)
+        assert any(isinstance(cb, TQDMProgressBar) for cb in trainer.callbacks)
+        assert any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
+        assert not any(isinstance(cb, RichProgressBar) for cb in trainer.callbacks)
+        assert not any(isinstance(cb, RichModelSummary) for cb in trainer.callbacks)
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
+    def test_no_rich_respects_user_provided_tqdm_progress_bar(self, tmp_path):
+        user_progress_bar = TQDMProgressBar()
+        trainer = Trainer(
+            default_root_dir=tmp_path, callbacks=[user_progress_bar], logger=False, enable_checkpointing=False
+        )
+        assert user_progress_bar in trainer.callbacks
+        assert sum(isinstance(cb, ProgressBar) for cb in trainer.callbacks) == 1
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
+    def test_no_rich_respects_user_provided_rich_progress_bar(self, tmp_path):
+        # If user explicitly provides RichProgressBar, it should be used,
+        # even if _RICH_AVAILABLE is False (simulating our connector logic).
+        # RequirementCache would normally prevent RichProgressBar instantiation if rich is truly not installed.
+        user_progress_bar = RichProgressBar()
+        trainer = Trainer(
+            default_root_dir=tmp_path, callbacks=[user_progress_bar], logger=False, enable_checkpointing=False
+        )
+        assert user_progress_bar in trainer.callbacks
+        assert sum(isinstance(cb, ProgressBar) for cb in trainer.callbacks) == 1
+        assert isinstance(trainer.progress_bar_callback, RichProgressBar)
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
+    def test_no_rich_respects_user_provided_model_summary(self, tmp_path):
+        user_model_summary = ModelSummary()
+        trainer = Trainer(
+            default_root_dir=tmp_path, callbacks=[user_model_summary], logger=False, enable_checkpointing=False
+        )
+        assert user_model_summary in trainer.callbacks
+        assert sum(isinstance(cb, ModelSummary) for cb in trainer.callbacks) == 1
+        # Check that the specific instance is the one from the trainer's list of ModelSummary callbacks
+        assert trainer.callbacks[trainer.callbacks.index(user_model_summary)] == user_model_summary
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
+    def test_no_rich_respects_user_provided_rich_model_summary(self, tmp_path):
+        user_model_summary = RichModelSummary()
+        trainer = Trainer(
+            default_root_dir=tmp_path, callbacks=[user_model_summary], logger=False, enable_checkpointing=False
+        )
+        assert user_model_summary in trainer.callbacks
+        assert sum(isinstance(cb, ModelSummary) for cb in trainer.callbacks) == 1
+        # Check that the specific instance is the one from the trainer's list of ModelSummary callbacks
+        model_summary_callbacks = [cb for cb in trainer.callbacks if isinstance(cb, ModelSummary)]
+        assert user_model_summary in model_summary_callbacks
+        assert isinstance(model_summary_callbacks[0], RichModelSummary)
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", True)
+    def test_rich_available_defaults_rich_progress_and_summary(self, tmp_path):
+        trainer = Trainer(default_root_dir=tmp_path, logger=False, enable_checkpointing=False)
+        assert any(isinstance(cb, RichProgressBar) for cb in trainer.callbacks)
+        assert any(isinstance(cb, RichModelSummary) for cb in trainer.callbacks)
+        assert not any(isinstance(cb, TQDMProgressBar) for cb in trainer.callbacks)
+        # Ensure the only ModelSummary is the RichModelSummary
+        model_summaries = [cb for cb in trainer.callbacks if isinstance(cb, ModelSummary)]
+        assert len(model_summaries) == 1
+        assert isinstance(model_summaries[0], RichModelSummary)
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", True)
+    def test_rich_available_respects_user_tqdm_progress_bar(self, tmp_path):
+        user_progress_bar = TQDMProgressBar()
+        trainer = Trainer(
+            default_root_dir=tmp_path, callbacks=[user_progress_bar], logger=False, enable_checkpointing=False
+        )
+        assert user_progress_bar in trainer.callbacks
+        assert sum(isinstance(cb, ProgressBar) for cb in trainer.callbacks) == 1
+        assert isinstance(trainer.progress_bar_callback, TQDMProgressBar)
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", True)
+    def test_rich_available_respects_user_model_summary(self, tmp_path):
+        user_model_summary = ModelSummary()  # Non-rich
+        trainer = Trainer(
+            default_root_dir=tmp_path, callbacks=[user_model_summary], logger=False, enable_checkpointing=False
+        )
+        assert user_model_summary in trainer.callbacks
+        model_summaries = [cb for cb in trainer.callbacks if isinstance(cb, ModelSummary)]
+        assert len(model_summaries) == 1
+        assert isinstance(model_summaries[0], ModelSummary)
+        assert not isinstance(model_summaries[0], RichModelSummary)
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
+    def test_progress_bar_disabled_no_rich(self, tmp_path):
+        trainer = Trainer(
+            default_root_dir=tmp_path, enable_progress_bar=False, logger=False, enable_checkpointing=False
+        )
+        assert not any(isinstance(cb, ProgressBar) for cb in trainer.callbacks)
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", True)
+    def test_progress_bar_disabled_with_rich(self, tmp_path):
+        trainer = Trainer(
+            default_root_dir=tmp_path, enable_progress_bar=False, logger=False, enable_checkpointing=False
+        )
+        assert not any(isinstance(cb, ProgressBar) for cb in trainer.callbacks)
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", False)
+    def test_model_summary_disabled_no_rich(self, tmp_path):
+        trainer = Trainer(
+            default_root_dir=tmp_path, enable_model_summary=False, logger=False, enable_checkpointing=False
+        )
+        assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", True)
+    def test_model_summary_disabled_with_rich(self, tmp_path):
+        trainer = Trainer(
+            default_root_dir=tmp_path, enable_model_summary=False, logger=False, enable_checkpointing=False
+        )
+        assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
+
+    @patch("lightning.pytorch.trainer.connectors.callback_connector._RICH_AVAILABLE", True)
+    def test_rich_progress_bar_tensor_metric(self, tmp_path):
+        """Test that tensor metrics are converted to float for RichProgressBar."""
+
+        class MyModel(BoringModel):
+            def training_step(self, batch, batch_idx):
+                self.log("my_tensor_metric", torch.tensor(1.23), prog_bar=True)
+                return super().training_step(batch, batch_idx)
+
+        model = MyModel()
+        trainer = Trainer(
+            default_root_dir=tmp_path,
+            limit_train_batches=1,
+            limit_val_batches=0,
+            max_epochs=1,
+            logger=False,
+            enable_checkpointing=False,
+        )
+
+        with patch("lightning.pytorch.callbacks.progress.rich_progress.MetricsTextColumn.update") as mock_update:
+            trainer.fit(model)
+
+        assert mock_update.call_count > 0
+        # The metrics are updated multiple times, check the last call
+        last_call_metrics = mock_update.call_args[0][0]
+        assert "my_tensor_metric" in last_call_metrics
+        metric_val = last_call_metrics["my_tensor_metric"]
+        assert isinstance(metric_val, float)
+        assert metric_val == pytest.approx(1.23)
diff --git a/tests/tests_pytorch/trainer/logging_/test_eval_loop_logging.py b/tests/tests_pytorch/trainer/logging_/test_eval_loop_logging.py
index be6de37ddff3a..576497ae19f77 100644
--- a/tests/tests_pytorch/trainer/logging_/test_eval_loop_logging.py
+++ b/tests/tests_pytorch/trainer/logging_/test_eval_loop_logging.py
@@ -27,12 +27,12 @@
 from torch import Tensor
 
 from lightning.pytorch import Trainer, callbacks
-from lightning.pytorch.callbacks.progress.rich_progress import _RICH_AVAILABLE
 from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
 from lightning.pytorch.loggers import TensorBoardLogger
 from lightning.pytorch.loops import _EvaluationLoop
 from lightning.pytorch.trainer.states import RunningStage
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
+from lightning.pytorch.utilities.imports import _RICH_AVAILABLE
 from tests_pytorch.helpers.runif import RunIf
 
 if _RICH_AVAILABLE:
@@ -534,7 +534,7 @@ def test_step(self, batch, batch_idx):
         max_epochs=2,
     )
 
-    # Train the model ⚡
+    # Train the model
     trainer.fit(model)
 
     assert set(trainer.callback_metrics) == {