diff --git a/CHANGELOG.md b/CHANGELOG.md
index e6a45c98671fd..dd874c7c724a8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -181,6 +181,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for `torch.autograd.set_detect_anomaly` through `Trainer` constructor argument `detect_anomaly` ([#9848](https://github.com/PyTorchLightning/pytorch-lightning/pull/9848))
 
 
+- Added `enable_model_summary` flag to Trainer ([#9699](https://github.com/PyTorchLightning/pytorch-lightning/pull/9699))
+
+
 ### Changed
 
 - Module imports are now catching `ModuleNotFoundError` instead of `ImportError` ([#9867](https://github.com/PyTorchLightning/pytorch-lightning/pull/9867))
@@ -344,6 +347,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `pytorch_lightning.core.decorators.parameter_validation` in favor of `pytorch_lightning.utilities.parameter_tying.set_shared_parameters` ([#9525](https://github.com/PyTorchLightning/pytorch-lightning/pull/9525))
 
 
+- Deprecated passing `weights_summary` to the `Trainer` constructor in favor of adding the `ModelSummary` callback with `max_depth` directly to the list of callbacks ([#9699](https://github.com/PyTorchLightning/pytorch-lightning/pull/9699))
+
+
 ### Removed
 
 - Removed deprecated `metrics` ([#8586](https://github.com/PyTorchLightning/pytorch-lightning/pull/8586/))
diff --git a/benchmarks/test_basic_parity.py b/benchmarks/test_basic_parity.py
index e9442dd26e65b..2144be39394cb 100644
--- a/benchmarks/test_basic_parity.py
+++ b/benchmarks/test_basic_parity.py
@@ -159,7 +159,7 @@ def lightning_loop(cls_model, idx, device_type: str = "cuda", num_epochs=10):
         # as the first run is skipped, no need to run it long
         max_epochs=num_epochs if idx > 0 else 1,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
         gpus=1 if device_type == "cuda" else 0,
         checkpoint_callback=False,
         logger=False,
diff --git a/docs/source/common/debugging.rst b/docs/source/common/debugging.rst
index 7a11863c0e1bf..6e5a721dd092a 100644
--- a/docs/source/common/debugging.rst
+++ b/docs/source/common/debugging.rst
@@ -95,11 +95,14 @@ Print a summary of your LightningModule
 ---------------------------------------
 Whenever the ``.fit()`` function gets called, the Trainer will print the weights summary for the LightningModule.
 By default it only prints the top-level modules. If you want to show all submodules in your network, use the
-`'full'` option:
+``max_depth`` option:
 
 .. testcode::
 
-    trainer = Trainer(weights_summary="full")
+    from pytorch_lightning.callbacks import ModelSummary
+
+    trainer = Trainer(callbacks=[ModelSummary(max_depth=-1)])
+
 
 You can also display the intermediate input- and output sizes of all your layers by setting the
 ``example_input_array`` attribute in your LightningModule. It will print a table like this
@@ -115,8 +118,9 @@ You can also display the intermediate input- and output sizes of all your layers
 when you call ``.fit()`` on the Trainer. This can help you find bugs in the composition of your layers.
 
 See Also:
-    - :paramref:`~pytorch_lightning.trainer.trainer.Trainer.weights_summary` Trainer argument
-    - :class:`~pytorch_lightning.core.memory.ModelSummary`
+    - :class:`~pytorch_lightning.callbacks.model_summary.ModelSummary`
+    - :func:`~pytorch_lightning.utilities.model_summary.summarize`
+    - :class:`~pytorch_lightning.utilities.model_summary.ModelSummary`
 
 ----------------
 
diff --git a/docs/source/common/trainer.rst b/docs/source/common/trainer.rst
index 42615383b5c1f..41fd89c3e7ee0 100644
--- a/docs/source/common/trainer.rst
+++ b/docs/source/common/trainer.rst
@@ -1589,6 +1589,11 @@ Example::
 weights_summary
 ^^^^^^^^^^^^^^^
 
+.. warning:: `weights_summary` is deprecated in v1.5 and will be removed in v1.7. Please pass :class:`~pytorch_lightning.callbacks.model_summary.ModelSummary`
+    directly to the Trainer's ``callbacks`` argument instead. To disable the model summary,
+    pass ``enable_model_summary = False`` to the Trainer.
+
+
 .. raw:: html
 
     <video width="50%" max-width="400px" controls
@@ -1611,6 +1616,25 @@ Options: 'full', 'top', None.
     # don't print a summary
     trainer = Trainer(weights_summary=None)
 
+
+enable_model_summary
+^^^^^^^^^^^^^^^^^^^^
+
+Whether to enable or disable the model summarization. Defaults to True.
+
+.. testcode::
+
+    # default used by the Trainer
+    trainer = Trainer(enable_model_summary=True)
+
+    # disable summarization
+    trainer = Trainer(enable_model_summary=False)
+
+    # enable custom summarization
+    from pytorch_lightning.callbacks import ModelSummary
+
+    trainer = Trainer(enable_model_summary=True, callbacks=[ModelSummary(max_depth=-1)])
+
 -----
 
 Trainer class API
diff --git a/pl_examples/bug_report_model.py b/pl_examples/bug_report_model.py
index f83cc70d44526..270b0cd2abe8d 100644
--- a/pl_examples/bug_report_model.py
+++ b/pl_examples/bug_report_model.py
@@ -55,7 +55,7 @@ def run():
         limit_val_batches=1,
         num_sanity_val_steps=0,
         max_epochs=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
     trainer.test(model, dataloaders=test_data)
diff --git a/pl_examples/domain_templates/computer_vision_fine_tuning.py b/pl_examples/domain_templates/computer_vision_fine_tuning.py
index 2cfeaf655f819..631832bd5de4b 100644
--- a/pl_examples/domain_templates/computer_vision_fine_tuning.py
+++ b/pl_examples/domain_templates/computer_vision_fine_tuning.py
@@ -272,7 +272,7 @@ def add_arguments_to_parser(self, parser):
         parser.set_defaults(
             {
                 "trainer.max_epochs": 15,
-                "trainer.weights_summary": None,
+                "trainer.enable_model_summary": False,
                 "trainer.num_sanity_val_steps": 0,
             }
         )
diff --git a/pytorch_lightning/trainer/connectors/callback_connector.py b/pytorch_lightning/trainer/connectors/callback_connector.py
index f6a8a252b7c5b..2f63e65340760 100644
--- a/pytorch_lightning/trainer/connectors/callback_connector.py
+++ b/pytorch_lightning/trainer/connectors/callback_connector.py
@@ -45,6 +45,7 @@ def on_trainer_init(
         process_position: int,
         default_root_dir: Optional[str],
         weights_save_path: Optional[str],
+        enable_model_summary: bool,
         weights_summary: Optional[str],
         stochastic_weight_avg: bool,
         max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None,
@@ -101,7 +102,7 @@ def on_trainer_init(
             self.trainer._progress_bar_callback = None
 
         # configure the ModelSummary callback
-        self._configure_model_summary_callback(weights_summary)
+        self._configure_model_summary_callback(enable_model_summary, weights_summary)
 
         # accumulated grads
         self._configure_accumulated_gradients(accumulate_grad_batches)
@@ -159,24 +160,50 @@ def _configure_checkpoint_callbacks(self, checkpoint_callback: Optional[bool], e
         if not self._trainer_has_checkpoint_callbacks() and enable_checkpointing is True:
             self.trainer.callbacks.append(ModelCheckpoint())
 
-    def _configure_model_summary_callback(self, weights_summary: Optional[str] = None) -> None:
-        if any(isinstance(cb, ModelSummary) for cb in self.trainer.callbacks):
+    def _configure_model_summary_callback(
+        self, enable_model_summary: bool, weights_summary: Optional[str] = None
+    ) -> None:
+        if weights_summary is None:
+            rank_zero_deprecation(
+                "Setting `Trainer(weights_summary=None)` is deprecated in v1.5 and will be removed"
+                " in v1.7. Please set `Trainer(enable_model_summary=False)` instead."
+            )
+            return
+        if not enable_model_summary:
+            return
+
+        model_summary_cbs = [type(cb) for cb in self.trainer.callbacks if isinstance(cb, ModelSummary)]
+        if model_summary_cbs:
+            rank_zero_info(
+                f"Trainer already configured with model summary callbacks: {model_summary_cbs}."
+                " Skipping setting a default `ModelSummary` callback."
+            )
             return
-        if weights_summary is not None:
+
+        if weights_summary == "top":
+            # special case the default value for weights_summary to preserve backward compatibility
+            max_depth = 1
+        else:
+            rank_zero_deprecation(
+                f"Setting `Trainer(weights_summary={weights_summary})` is deprecated in v1.5 and will be removed"
+                " in v1.7. Please pass `pytorch_lightning.callbacks.model_summary.ModelSummary` with"
+                " `max_depth` directly to the Trainer's `callbacks` argument instead."
+            )
             if weights_summary not in ModelSummaryMode.supported_types():
                 raise MisconfigurationException(
                     f"`weights_summary` can be None, {', '.join(ModelSummaryMode.supported_types())}",
                     f" but got {weights_summary}",
                 )
             max_depth = ModelSummaryMode.get_max_depth(weights_summary)
-            if self.trainer._progress_bar_callback is not None and isinstance(
-                self.trainer._progress_bar_callback, RichProgressBar
-            ):
-                model_summary = RichModelSummary(max_depth=max_depth)
-            else:
-                model_summary = ModelSummary(max_depth=max_depth)
-            self.trainer.callbacks.append(model_summary)
-            self.trainer.weights_summary = weights_summary
+
+        is_progress_bar_rich = isinstance(self.trainer._progress_bar_callback, RichProgressBar)
+
+        if self.trainer._progress_bar_callback is not None and is_progress_bar_rich:
+            model_summary = RichModelSummary(max_depth=max_depth)
+        else:
+            model_summary = ModelSummary(max_depth=max_depth)
+        self.trainer.callbacks.append(model_summary)
+        self.trainer._weights_summary = weights_summary
 
     def _configure_swa_callbacks(self):
         if not self.trainer._stochastic_weight_avg:
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index e37df73a4b98f..244f6ce7099d1 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -157,6 +157,7 @@ def __init__(
         accelerator: Optional[Union[str, Accelerator]] = None,
         sync_batchnorm: bool = False,
         precision: Union[int, str] = 32,
+        enable_model_summary: bool = True,
         weights_summary: Optional[str] = "top",
         weights_save_path: Optional[str] = None,
         num_sanity_val_steps: int = 2,
@@ -373,8 +374,16 @@ def __init__(
             val_check_interval: How often to check the validation set. Use float to check within a training epoch,
                 use int to check every n steps (batches).
 
+            enable_model_summary: Whether to enable model summarization by default.
+
             weights_summary: Prints a summary of the weights when training begins.
 
+                .. deprecated:: v1.5
+                    ``weights_summary`` has been deprecated in v1.5 and will be removed in v1.7.
+                    To disable the summary, pass ``enable_model_summary = False`` to the Trainer.
+                    To customize the summary, pass :class:`~pytorch_lightning.callbacks.model_summary.ModelSummary`
+                    directly to the Trainer's ``callbacks`` argument.
+
             weights_save_path: Where to save weights if specified. Will override default_root_dir
                 for checkpoints only. Use this if for whatever reason you need the checkpoints
                 stored in a different place than the logs written in `default_root_dir`.
@@ -467,6 +476,9 @@ def __init__(
         self.tested_ckpt_path: Optional[str] = None
         self.predicted_ckpt_path: Optional[str] = None
 
+        # todo: remove in v1.7
+        self._weights_summary: Optional[str] = None
+
         # init callbacks
         # Declare attributes to be set in callback_connector on_trainer_init
         self.callback_connector.on_trainer_init(
@@ -478,6 +490,7 @@ def __init__(
             process_position,
             default_root_dir,
             weights_save_path,
+            enable_model_summary,
             weights_summary,
             stochastic_weight_avg,
             max_time,
@@ -2036,6 +2049,16 @@ def _exit_gracefully_on_signal(self) -> None:
             class_name = caller[0].f_locals["self"].__class__.__name__
             raise ExitGracefullyException(f"Exiting gracefully on {class_name}:{caller.function}")
 
+    @property
+    def weights_summary(self) -> Optional[str]:
+        rank_zero_deprecation("`Trainer.weights_summary` is deprecated in v1.5 and will be removed in v1.7.")
+        return self._weights_summary
+
+    @weights_summary.setter
+    def weights_summary(self, val: Optional[str]) -> None:
+        rank_zero_deprecation("Setting `Trainer.weights_summary` is deprecated in v1.5 and will be removed in v1.7.")
+        self._weights_summary = val
+
     """
     Other
     """
diff --git a/tests/accelerators/test_multi_nodes_gpu.py b/tests/accelerators/test_multi_nodes_gpu.py
index a1abed776c12d..a0362642e9fbf 100644
--- a/tests/accelerators/test_multi_nodes_gpu.py
+++ b/tests/accelerators/test_multi_nodes_gpu.py
@@ -54,7 +54,7 @@ def validation_step(self, batch, batch_idx):
         limit_train_batches=1,
         limit_val_batches=1,
         max_epochs=2,
-        weights_summary=None,
+        enable_model_summary=False,
         accelerator="ddp",
         gpus=1,
         num_nodes=2,
@@ -101,7 +101,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
         accelerator="ddp",
         gpus=1,
         num_nodes=2,
diff --git a/tests/callbacks/test_callback_hook_outputs.py b/tests/callbacks/test_callback_hook_outputs.py
index 7e52c4f49e5e6..d55313fde37e6 100644
--- a/tests/callbacks/test_callback_hook_outputs.py
+++ b/tests/callbacks/test_callback_hook_outputs.py
@@ -53,7 +53,7 @@ def training_epoch_end(self, outputs) -> None:
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     assert any(isinstance(c, CB) for c in trainer.callbacks)
@@ -74,7 +74,7 @@ def on_epoch_end(self, trainer, pl_module):
         limit_train_batches=2,
         limit_val_batches=2,
         max_epochs=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     trainer.fit(model)
diff --git a/tests/callbacks/test_gradient_accumulation_scheduler.py b/tests/callbacks/test_gradient_accumulation_scheduler.py
index da3b28b5c3795..25ce1e843b80e 100644
--- a/tests/callbacks/test_gradient_accumulation_scheduler.py
+++ b/tests/callbacks/test_gradient_accumulation_scheduler.py
@@ -31,7 +31,7 @@ def test_trainer_accumulate_grad_batches_zero_grad(tmpdir, accumulate_grad_batch
             limit_train_batches=20,
             limit_val_batches=1,
             max_epochs=1,
-            weights_summary=None,
+            enable_model_summary=False,
             accumulate_grad_batches=accumulate_grad_batches,
         )
         assert trainer.accumulate_grad_batches == accumulate_grad_batches
@@ -56,7 +56,7 @@ def test_trainer_accumulate_grad_batches_dict_zero_grad(tmpdir, accumulate_grad_
             limit_train_batches=10,
             limit_val_batches=1,
             max_epochs=4,
-            weights_summary=None,
+            enable_model_summary=False,
             accumulate_grad_batches=accumulate_grad_batches,
         )
         assert trainer.accumulate_grad_batches == accumulate_grad_batches.get(0, 1)
@@ -74,7 +74,7 @@ def test_trainer_accumulate_grad_batches_with_callback(tmpdir):
             limit_train_batches=10,
             limit_val_batches=1,
             max_epochs=4,
-            weights_summary=None,
+            enable_model_summary=False,
             callbacks=[GradientAccumulationScheduler({1: 2, 3: 4})],
         )
         assert trainer.accumulate_grad_batches == 1
diff --git a/tests/callbacks/test_lr_monitor.py b/tests/callbacks/test_lr_monitor.py
index ff92c83871162..a51b994797ba3 100644
--- a/tests/callbacks/test_lr_monitor.py
+++ b/tests/callbacks/test_lr_monitor.py
@@ -252,7 +252,7 @@ def configure_optimizers(self):
         limit_train_batches=0.5,
         callbacks=[lr_monitor],
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(TestModel())
     assert lr_monitor.lr_sch_names == list(lr_monitor.lrs.keys()) == ["my_logging_name"]
@@ -273,7 +273,7 @@ def configure_optimizers(self):
         limit_train_batches=2,
         callbacks=[lr_monitor],
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(TestModel())
     assert lr_monitor.lr_sch_names == ["lr-SGD"]
@@ -311,7 +311,7 @@ def configure_optimizers(self):
         limit_train_batches=2,
         callbacks=[lr_monitor],
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     with pytest.raises(
@@ -389,7 +389,7 @@ def finetune_function(self, pl_module, epoch: int, optimizer, opt_idx: int):
         limit_train_batches=2,
         callbacks=[TestFinetuning(), lr_monitor, Check()],
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
         enable_checkpointing=False,
     )
     model = TestModel()
diff --git a/tests/callbacks/test_model_summary.py b/tests/callbacks/test_model_summary.py
index a0264186d9969..b2a57b4e192d0 100644
--- a/tests/callbacks/test_model_summary.py
+++ b/tests/callbacks/test_model_summary.py
@@ -36,6 +36,15 @@ def test_model_summary_callback_with_weights_summary_none():
     trainer = Trainer(weights_summary=None)
     assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
 
+    trainer = Trainer(enable_model_summary=False)
+    assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
+
+    trainer = Trainer(enable_model_summary=False, weights_summary="full")
+    assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
+
+    trainer = Trainer(enable_model_summary=True, weights_summary=None)
+    assert not any(isinstance(cb, ModelSummary) for cb in trainer.callbacks)
+
 
 def test_model_summary_callback_with_weights_summary():
 
diff --git a/tests/callbacks/test_progress_bar.py b/tests/callbacks/test_progress_bar.py
index cd6a5cf2a0cad..f56d52041c36b 100644
--- a/tests/callbacks/test_progress_bar.py
+++ b/tests/callbacks/test_progress_bar.py
@@ -547,7 +547,7 @@ def _test_progress_bar_max_val_check_interval(
         default_root_dir=tmpdir,
         num_sanity_val_steps=0,
         max_epochs=1,
-        weights_summary=None,
+        enable_model_summary=False,
         val_check_interval=val_check_interval,
         gpus=world_size,
         accelerator="ddp",
diff --git a/tests/callbacks/test_pruning.py b/tests/callbacks/test_pruning.py
index 0f79132f9748b..44eaabcf58a32 100644
--- a/tests/callbacks/test_pruning.py
+++ b/tests/callbacks/test_pruning.py
@@ -108,7 +108,7 @@ def train_with_pruning_callback(
     trainer = Trainer(
         default_root_dir=tmpdir,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
         enable_checkpointing=False,
         logger=False,
         limit_train_batches=10,
@@ -226,7 +226,7 @@ def apply_lottery_ticket_hypothesis(self):
     trainer = Trainer(
         default_root_dir=tmpdir,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
         enable_checkpointing=False,
         logger=False,
         limit_train_batches=10,
@@ -253,7 +253,7 @@ def test_multiple_pruning_callbacks(tmpdir, caplog, make_pruning_permanent: bool
     trainer = Trainer(
         default_root_dir=tmpdir,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
         enable_checkpointing=False,
         logger=False,
         limit_train_batches=10,
diff --git a/tests/checkpointing/test_checkpoint_callback_frequency.py b/tests/checkpointing/test_checkpoint_callback_frequency.py
index 46551df660b8d..b0191d1d7ef4c 100644
--- a/tests/checkpointing/test_checkpoint_callback_frequency.py
+++ b/tests/checkpointing/test_checkpoint_callback_frequency.py
@@ -40,7 +40,7 @@ def test_default_checkpoint_freq(save_mock, tmpdir, epochs: int, val_check_inter
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=epochs,
-        weights_summary=None,
+        enable_model_summary=False,
         val_check_interval=val_check_interval,
         limit_val_batches=1,
         enable_progress_bar=False,
@@ -75,7 +75,7 @@ def training_step(self, batch, batch_idx):
         callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor="my_loss", save_top_k=k, save_last=save_last)],
         default_root_dir=tmpdir,
         max_epochs=epochs,
-        weights_summary=None,
+        enable_model_summary=False,
         val_check_interval=val_check_interval,
     )
     trainer.fit(model)
@@ -121,7 +121,7 @@ def training_epoch_end(self, outputs) -> None:
         default_root_dir=tmpdir,
         enable_progress_bar=False,
         max_epochs=epochs,
-        weights_summary=None,
+        enable_model_summary=False,
         val_check_interval=val_check_interval,
         accelerator="ddp",
         gpus=2,
diff --git a/tests/checkpointing/test_model_checkpoint.py b/tests/checkpointing/test_model_checkpoint.py
index 5513339409ab8..7f4e983b995b4 100644
--- a/tests/checkpointing/test_model_checkpoint.py
+++ b/tests/checkpointing/test_model_checkpoint.py
@@ -881,8 +881,8 @@ def validation_step(self, batch, batch_idx):
         limit_val_batches=2,
         limit_test_batches=2,
         callbacks=[checkpoint_callback],
-        weights_summary=None,
         enable_progress_bar=False,
+        enable_model_summary=False,
     )
     trainer.fit(model)
     assert os.listdir(tmpdir) == ["epoch=00.ckpt"]
@@ -897,8 +897,8 @@ def validation_step(self, batch, batch_idx):
             limit_val_batches=2,
             limit_test_batches=2,
             resume_from_checkpoint=checkpoint_callback.best_model_path,
-            weights_summary=None,
             enable_progress_bar=False,
+            enable_model_summary=False,
         )
         trainer.fit(model)
         trainer.test(model, verbose=False)
@@ -1032,8 +1032,8 @@ def test_val_check_interval_checkpoint_files(tmpdir):
         limit_train_batches=10,
         callbacks=[model_checkpoint],
         logger=False,
-        weights_summary=None,
         enable_progress_bar=False,
+        enable_model_summary=False,
     )
     trainer.fit(model)
     files = {p.basename for p in tmpdir.listdir()}
@@ -1056,8 +1056,8 @@ def training_step(self, *args):
         limit_val_batches=1,
         callbacks=[model_checkpoint],
         logger=False,
-        weights_summary=None,
         enable_progress_bar=False,
+        enable_model_summary=False,
     )
     trainer.fit(TestModel())
     assert model_checkpoint.current_score == 0.3
@@ -1089,8 +1089,8 @@ def training_step(self, *args):
         limit_val_batches=1,
         callbacks=[model_checkpoint],
         logger=False,
-        weights_summary=None,
         enable_progress_bar=False,
+        enable_model_summary=False,
     )
     trainer.fit(TestModel())
     expected = float("inf" if mode == "min" else "-inf")
@@ -1113,8 +1113,8 @@ def __init__(self, hparams):
         limit_val_batches=1,
         callbacks=[model_checkpoint],
         logger=False,
-        weights_summary=None,
         enable_progress_bar=False,
+        enable_model_summary=False,
     )
     hp = {"test_hp_0": 1, "test_hp_1": 2}
     hp = OmegaConf.create(hp) if hparams_type == Container else Namespace(**hp)
@@ -1141,8 +1141,8 @@ def test_ckpt_version_after_rerun_new_trainer(tmpdir):
             default_root_dir=tmpdir,
             callbacks=[mc],
             logger=False,
-            weights_summary=None,
             enable_progress_bar=False,
+            enable_model_summary=False,
         )
         trainer.fit(BoringModel())
 
@@ -1167,8 +1167,8 @@ def test_ckpt_version_after_rerun_same_trainer(tmpdir):
         default_root_dir=tmpdir,
         callbacks=[mc],
         logger=False,
-        weights_summary=None,
         enable_progress_bar=False,
+        enable_model_summary=False,
     )
     trainer.fit(BoringModel())
     trainer.fit_loop.max_epochs = 4
@@ -1198,7 +1198,7 @@ def test_check_val_every_n_epochs_top_k_integration(tmpdir):
         max_epochs=5,
         check_val_every_n_epoch=2,
         callbacks=mc,
-        weights_summary=None,
+        enable_model_summary=False,
         logger=False,
     )
     trainer.fit(model)
diff --git a/tests/core/test_datamodules.py b/tests/core/test_datamodules.py
index 2f84032593472..1da50978f1fa7 100644
--- a/tests/core/test_datamodules.py
+++ b/tests/core/test_datamodules.py
@@ -280,7 +280,7 @@ def test_train_loop_only(tmpdir):
     model.test_step_end = None
     model.test_epoch_end = None
 
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, weights_summary=None)
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, enable_model_summary=False)
 
     # fit model
     trainer.fit(model, datamodule=dm)
@@ -298,7 +298,7 @@ def test_train_val_loop_only(tmpdir):
     model.validation_step_end = None
     model.validation_epoch_end = None
 
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, weights_summary=None)
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, enable_model_summary=False)
 
     # fit model
     trainer.fit(model, datamodule=dm)
@@ -329,7 +329,7 @@ def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
         max_epochs=1,
         limit_train_batches=2,
         limit_val_batches=1,
-        weights_summary=None,
+        enable_model_summary=False,
         callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor="early_stop_on")],
     )
 
@@ -348,7 +348,7 @@ def test_full_loop(tmpdir):
     dm = ClassifDataModule()
     model = ClassificationModel()
 
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, weights_summary=None, deterministic=True)
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, enable_model_summary=False, deterministic=True)
 
     # fit model
     trainer.fit(model, dm)
diff --git a/tests/core/test_lightning_optimizer.py b/tests/core/test_lightning_optimizer.py
index 3f9ac37c2cb1c..f4f1287c122d8 100644
--- a/tests/core/test_lightning_optimizer.py
+++ b/tests/core/test_lightning_optimizer.py
@@ -35,7 +35,7 @@ def configure_optimizers(self):
 
     model = TestModel()
     trainer = Trainer(
-        default_root_dir=tmpdir, limit_train_batches=1, limit_val_batches=1, max_epochs=1, weights_summary=None
+        default_root_dir=tmpdir, limit_train_batches=1, limit_val_batches=1, max_epochs=1, enable_model_summary=False
     )
     trainer.fit(model)
 
@@ -59,7 +59,7 @@ def configure_optimizers(self):
 
     model = TestModel()
     trainer = Trainer(
-        default_root_dir=tmpdir, limit_train_batches=1, limit_val_batches=1, max_epochs=1, weights_summary=None
+        default_root_dir=tmpdir, limit_train_batches=1, limit_val_batches=1, max_epochs=1, enable_model_summary=False
     )
     trainer.fit(model)
 
@@ -108,7 +108,7 @@ def configure_optimizers(self):
     model.training_step_end = None
     model.training_epoch_end = None
     trainer = Trainer(
-        default_root_dir=tmpdir, limit_train_batches=8, limit_val_batches=1, max_epochs=1, weights_summary=None
+        default_root_dir=tmpdir, limit_train_batches=8, limit_val_batches=1, max_epochs=1, enable_model_summary=False
     )
 
     with patch.multiple(torch.optim.SGD, zero_grad=DEFAULT, step=DEFAULT) as sgd, patch.multiple(
@@ -197,7 +197,7 @@ def configure_optimizers(self):
 
     model = TestModel()
     trainer = Trainer(
-        default_root_dir=tmpdir, limit_train_batches=20, limit_val_batches=1, max_epochs=1, weights_summary=None
+        default_root_dir=tmpdir, limit_train_batches=20, limit_val_batches=1, max_epochs=1, enable_model_summary=False
     )
 
     with patch("torch.optim.Adam.zero_grad") as adam_zero_grad, patch("torch.optim.SGD.zero_grad") as sgd_zero_grad:
@@ -241,7 +241,7 @@ def configure_optimizers(self):
         limit_train_batches=limit_train_batches,
         limit_val_batches=1,
         max_epochs=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     with patch.multiple(torch.optim.SGD, zero_grad=DEFAULT, step=DEFAULT) as sgd, patch.multiple(
@@ -266,7 +266,7 @@ def configure_optimizers(self):
 
     model = TestModel()
     trainer = Trainer(
-        default_root_dir=tmpdir, limit_train_batches=1, limit_val_batches=1, max_epochs=1, weights_summary=None
+        default_root_dir=tmpdir, limit_train_batches=1, limit_val_batches=1, max_epochs=1, enable_model_summary=False
     )
 
     with patch("torch.optim.LBFGS.zero_grad") as zero_grad:
diff --git a/tests/deprecated_api/test_remove_1-7.py b/tests/deprecated_api/test_remove_1-7.py
index af6781f3b609e..3d0990e33db01 100644
--- a/tests/deprecated_api/test_remove_1-7.py
+++ b/tests/deprecated_api/test_remove_1-7.py
@@ -341,3 +341,18 @@ def test_v1_7_0_deprecate_parameter_validation():
         match="Using `pytorch_lightning.core.decorators.parameter_validation` is deprecated in v1.5"
     ):
         from pytorch_lightning.core.decorators import parameter_validation  # noqa: F401
+
+
+def test_v1_7_0_weights_summary_trainer(tmpdir):
+    with pytest.deprecated_call(match=r"Setting `Trainer\(weights_summary=full\)` is deprecated in v1.5"):
+        t = Trainer(weights_summary="full")
+
+    with pytest.deprecated_call(match=r"Setting `Trainer\(weights_summary=None\)` is deprecated in v1.5"):
+        t = Trainer(weights_summary=None)
+
+    t = Trainer(weights_summary="top")
+    with pytest.deprecated_call(match=r"`Trainer.weights_summary` is deprecated in v1.5"):
+        _ = t.weights_summary
+
+    with pytest.deprecated_call(match=r"Setting `Trainer.weights_summary` is deprecated in v1.5"):
+        t.weights_summary = "blah"
diff --git a/tests/loggers/test_base.py b/tests/loggers/test_base.py
index d535905930dd2..7b02d6d72b0fc 100644
--- a/tests/loggers/test_base.py
+++ b/tests/loggers/test_base.py
@@ -320,7 +320,7 @@ class _Test:
         num_sanity_val_steps=0,
         enable_checkpointing=False,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     # there should be no exceptions raised for the same key/value pair in the hparams of both
     # the lightning module and data module
@@ -344,7 +344,7 @@ class _Test:
         num_sanity_val_steps=0,
         enable_checkpointing=False,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     with pytest.raises(MisconfigurationException, match="Error while merging hparams"):
         trainer.fit(model, dm)
@@ -361,7 +361,7 @@ class _Test:
         num_sanity_val_steps=0,
         enable_checkpointing=False,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     with pytest.raises(MisconfigurationException, match="Error while merging hparams"):
         trainer.fit(model, dm)
diff --git a/tests/loops/batch/test_truncated_bptt.py b/tests/loops/batch/test_truncated_bptt.py
index de38800917398..6a05f64f376c1 100644
--- a/tests/loops/batch/test_truncated_bptt.py
+++ b/tests/loops/batch/test_truncated_bptt.py
@@ -99,7 +99,7 @@ def on_train_batch_start(self, *_, **__) -> None:
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=2,
-        weights_summary=None,
+        enable_model_summary=False,
         logger=False,
         enable_checkpointing=False,
     )
@@ -140,7 +140,7 @@ def training_epoch_end(self, training_step_outputs):
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
-        weights_summary=None,
+        enable_model_summary=False,
         logger=False,
         enable_checkpointing=False,
     )
@@ -165,7 +165,7 @@ def training_step(self, *args, **kwargs):
         default_root_dir=tmpdir,
         max_epochs=2,
         log_every_n_steps=2,
-        weights_summary=None,
+        enable_model_summary=False,
         enable_checkpointing=False,
     )
     trainer.fit(model)
diff --git a/tests/loops/test_evaluation_loop.py b/tests/loops/test_evaluation_loop.py
index 75e873e7aa083..2b67dec18de34 100644
--- a/tests/loops/test_evaluation_loop.py
+++ b/tests/loops/test_evaluation_loop.py
@@ -30,7 +30,7 @@ def test_on_evaluation_epoch_end(eval_epoch_end_mock, tmpdir):
     model = BoringModel()
 
     trainer = Trainer(
-        default_root_dir=tmpdir, limit_train_batches=2, limit_val_batches=2, max_epochs=2, weights_summary=None
+        default_root_dir=tmpdir, limit_train_batches=2, limit_val_batches=2, max_epochs=2, enable_model_summary=False
     )
 
     trainer.fit(model)
@@ -55,7 +55,7 @@ def on_validation_end(self):
             order.append("on_validation_end")
             super().on_validation_end()
 
-    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1, weights_summary=None, num_sanity_val_steps=0)
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1, enable_model_summary=False, num_sanity_val_steps=0)
     trainer.fit(LessBoringModel())
 
     assert order == ["log_epoch_metrics", "on_validation_end"]
@@ -99,7 +99,9 @@ def validation_step(self, batch, batch_idx):
             return super().validation_step(batch, batch_idx)
 
     torch.cuda.empty_cache()
-    trainer = Trainer(gpus=1, default_root_dir=tmpdir, fast_dev_run=2, move_metrics_to_cpu=True, weights_summary=None)
+    trainer = Trainer(
+        gpus=1, default_root_dir=tmpdir, fast_dev_run=2, move_metrics_to_cpu=True, enable_model_summary=False
+    )
     trainer.fit(BoringLargeBatchModel())
 
 
diff --git a/tests/loops/test_evaluation_loop_flow.py b/tests/loops/test_evaluation_loop_flow.py
index d927262021d82..5a9d0a737350c 100644
--- a/tests/loops/test_evaluation_loop_flow.py
+++ b/tests/loops/test_evaluation_loop_flow.py
@@ -52,7 +52,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -117,7 +117,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -188,7 +188,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     trainer.fit(model)
@@ -246,7 +246,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     trainer.fit(model)
diff --git a/tests/loops/test_flow_warnings.py b/tests/loops/test_flow_warnings.py
index d7860c807ac5b..02cdf309f6801 100644
--- a/tests/loops/test_flow_warnings.py
+++ b/tests/loops/test_flow_warnings.py
@@ -35,7 +35,7 @@ def test_no_depre_without_epoch_end(tmpdir):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     with warnings.catch_warnings(record=True) as w:
diff --git a/tests/loops/test_loops.py b/tests/loops/test_loops.py
index e22e653827a0f..ed648897400ec 100644
--- a/tests/loops/test_loops.py
+++ b/tests/loops/test_loops.py
@@ -682,7 +682,7 @@ def test_fit_loop_reset(tmpdir):
         max_epochs=2,
         callbacks=[checkpoint_callback],
         logger=False,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
diff --git a/tests/loops/test_training_loop.py b/tests/loops/test_training_loop.py
index a6bc414e0e93f..d491db3bbc91c 100644
--- a/tests/loops/test_training_loop.py
+++ b/tests/loops/test_training_loop.py
@@ -54,7 +54,7 @@ def training_epoch_end(self, outputs):
         limit_train_batches=2,
         limit_test_batches=1,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
diff --git a/tests/loops/test_training_loop_flow_dict.py b/tests/loops/test_training_loop_flow_dict.py
index b8061610a49bb..657e8d31b8f64 100644
--- a/tests/loops/test_training_loop_flow_dict.py
+++ b/tests/loops/test_training_loop_flow_dict.py
@@ -42,7 +42,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -81,7 +81,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -128,7 +128,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -181,7 +181,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
diff --git a/tests/loops/test_training_loop_flow_scalar.py b/tests/loops/test_training_loop_flow_scalar.py
index d7dbecc82eaaf..0501cbdf529db 100644
--- a/tests/loops/test_training_loop_flow_scalar.py
+++ b/tests/loops/test_training_loop_flow_scalar.py
@@ -47,7 +47,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -86,7 +86,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -131,7 +131,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -205,7 +205,7 @@ def backward(self, loss, optimizer, optimizer_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -295,7 +295,7 @@ def training_step(self, batch, batch_idx):
         limit_train_batches=4,
         limit_val_batches=1,
         max_epochs=4,
-        weights_summary=None,
+        enable_model_summary=False,
         logger=False,
         enable_checkpointing=False,
     )
@@ -341,7 +341,7 @@ def train_dataloader(self):
         limit_train_batches=4,
         limit_val_batches=1,
         max_epochs=4,
-        weights_summary=None,
+        enable_model_summary=False,
         logger=False,
         enable_checkpointing=False,
     )
diff --git a/tests/models/test_cpu.py b/tests/models/test_cpu.py
index 18663562df626..8020021b34714 100644
--- a/tests/models/test_cpu.py
+++ b/tests/models/test_cpu.py
@@ -160,7 +160,6 @@ def __init__(self, optimizer_name, learning_rate):
         default_root_dir=tmpdir,
         max_epochs=1,
         enable_progress_bar=False,
-        weights_summary="top",
         limit_train_batches=0.2,
         limit_val_batches=0.2,
     )
diff --git a/tests/models/test_hooks.py b/tests/models/test_hooks.py
index 305b8213b7780..1575ea1daaec3 100644
--- a/tests/models/test_hooks.py
+++ b/tests/models/test_hooks.py
@@ -198,7 +198,7 @@ def train_dataloader(self):
         limit_train_batches=2,
         limit_val_batches=0,
         max_epochs=1,
-        weights_summary=None,
+        enable_model_summary=False,
         accelerator="ddp",
         gpus=2,
     )
@@ -472,7 +472,7 @@ def training_step(self, batch, batch_idx):
         limit_train_batches=train_batches,
         limit_val_batches=val_batches,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
         callbacks=[callback],
         **kwargs,
     )
@@ -579,7 +579,7 @@ def test_trainer_model_hook_system_fit_no_val_and_resume(tmpdir):
         max_steps=1,
         limit_val_batches=0,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
         callbacks=[HookedCallback([])],
     )
     trainer.fit(model)
@@ -596,7 +596,7 @@ def test_trainer_model_hook_system_fit_no_val_and_resume(tmpdir):
         max_steps=(1 + train_batches),
         limit_val_batches=0,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
         resume_from_checkpoint=best_model_path,
         callbacks=[callback],
     )
@@ -691,7 +691,7 @@ def test_trainer_model_hook_system_eval(tmpdir, batches, verb, noun, dataloader,
         limit_val_batches=batches,
         limit_test_batches=batches,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
         callbacks=[callback],
     )
     assert called == [
@@ -858,7 +858,7 @@ def call(hook, fn, *args, **kwargs):
         limit_test_batches=batches,
         limit_predict_batches=batches,
         enable_progress_bar=False,
-        weights_summary=None,
+        enable_model_summary=False,
         reload_dataloaders_every_epoch=True,
     )
 
diff --git a/tests/models/test_horovod.py b/tests/models/test_horovod.py
index e58bd391feb82..7be7b8cf9dbe1 100644
--- a/tests/models/test_horovod.py
+++ b/tests/models/test_horovod.py
@@ -306,7 +306,7 @@ def training_epoch_end(self, outputs) -> None:
             limit_val_batches=2,
             max_epochs=1,
             log_every_n_steps=1,
-            weights_summary=None,
+            enable_model_summary=False,
             logger=False,
         )
 
diff --git a/tests/trainer/connectors/test_callback_connector.py b/tests/trainer/connectors/test_callback_connector.py
index 34d5b8fa97afa..949723b022750 100644
--- a/tests/trainer/connectors/test_callback_connector.py
+++ b/tests/trainer/connectors/test_callback_connector.py
@@ -144,7 +144,10 @@ def _attach_callbacks(trainer_callbacks, model_callbacks):
         model = LightningModule()
         model.configure_callbacks = lambda: model_callbacks
         trainer = Trainer(
-            enable_checkpointing=False, enable_progress_bar=False, weights_summary=None, callbacks=trainer_callbacks
+            enable_checkpointing=False,
+            enable_progress_bar=False,
+            enable_model_summary=None,
+            callbacks=trainer_callbacks,
         )
         trainer.model = model
         cb_connector = CallbackConnector(trainer)
diff --git a/tests/trainer/dynamic_args/test_multiple_eval_dataloaders.py b/tests/trainer/dynamic_args/test_multiple_eval_dataloaders.py
index 7c49b4b12534b..b4378f34c1211 100644
--- a/tests/trainer/dynamic_args/test_multiple_eval_dataloaders.py
+++ b/tests/trainer/dynamic_args/test_multiple_eval_dataloaders.py
@@ -70,7 +70,7 @@ def val_dataloader(self):
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     trainer.fit(model)
@@ -100,7 +100,7 @@ def val_dataloader(self):
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     trainer.fit(model)
@@ -157,7 +157,7 @@ def configure_optimizers(self):
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     trainer.fit(model)
diff --git a/tests/trainer/flags/test_min_max_epochs.py b/tests/trainer/flags/test_min_max_epochs.py
index ecfdb8bedf6e6..4201c89350c21 100644
--- a/tests/trainer/flags/test_min_max_epochs.py
+++ b/tests/trainer/flags/test_min_max_epochs.py
@@ -26,7 +26,7 @@ def test_min_max_steps_epochs(tmpdir, min_epochs, max_epochs, min_steps, max_ste
         max_epochs=max_epochs,
         min_steps=min_steps,
         max_steps=max_steps,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
diff --git a/tests/trainer/flags/test_overfit_batches.py b/tests/trainer/flags/test_overfit_batches.py
index f9411c1eeea00..76c8b37405b47 100644
--- a/tests/trainer/flags/test_overfit_batches.py
+++ b/tests/trainer/flags/test_overfit_batches.py
@@ -38,7 +38,7 @@ def val_dataloader(self):
     model = TestModel()
 
     trainer = Trainer(
-        default_root_dir=tmpdir, max_epochs=2, overfit_batches=1, log_every_n_steps=1, weights_summary=None
+        default_root_dir=tmpdir, max_epochs=2, overfit_batches=1, log_every_n_steps=1, enable_model_summary=False
     )
 
     trainer.fit(model)
@@ -50,6 +50,6 @@ def test_overfit_basic(tmpdir, overfit):
 
     model = BoringModel()
 
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, overfit_batches=overfit, weights_summary=None)
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, overfit_batches=overfit, enable_model_summary=False)
 
     trainer.fit(model)
diff --git a/tests/trainer/logging_/test_distributed_logging.py b/tests/trainer/logging_/test_distributed_logging.py
index ac71de39757b9..54c599f47a26e 100644
--- a/tests/trainer/logging_/test_distributed_logging.py
+++ b/tests/trainer/logging_/test_distributed_logging.py
@@ -71,7 +71,7 @@ def test_all_rank_logging_ddp_cpu(tmpdir):
         limit_train_batches=1,
         limit_val_batches=1,
         max_epochs=1,
-        weights_summary=None,
+        enable_model_summary=False,
         logger=all_rank_logger,
         log_every_n_steps=1,
     )
@@ -92,7 +92,7 @@ def test_all_rank_logging_ddp_spawn(tmpdir):
         limit_val_batches=1,
         max_epochs=1,
         logger=all_rank_logger,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
diff --git a/tests/trainer/logging_/test_eval_loop_logging.py b/tests/trainer/logging_/test_eval_loop_logging.py
index 99145972f3561..11a77f5c2a09a 100644
--- a/tests/trainer/logging_/test_eval_loop_logging.py
+++ b/tests/trainer/logging_/test_eval_loop_logging.py
@@ -51,7 +51,7 @@ def validation_step(self, batch, batch_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -89,7 +89,7 @@ def validation_epoch_end(self, outputs):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -117,7 +117,7 @@ def validation_epoch_end(self, outputs):
         limit_val_batches=batches,
         max_epochs=max_epochs,
         log_every_n_steps=log_interval,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -149,7 +149,7 @@ def validation_step(self, batch, batch_idx):
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -180,7 +180,7 @@ def validation_epoch_end(self, outputs) -> None:
         limit_val_batches=3,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
         num_sanity_val_steps=0,
     )
     trainer.fit(model)
@@ -210,7 +210,7 @@ def test_epoch_end(self, outputs):
         max_epochs=max_epochs,
         limit_test_batches=batches,
         log_every_n_steps=log_interval,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     results = trainer.test(model)
 
@@ -244,7 +244,7 @@ def test_dataloader(self):
         limit_test_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     results = trainer.test(model)
 
@@ -655,6 +655,6 @@ def val_dataloader(self):
         val_check_interval=val_check_interval,
         max_epochs=3,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
diff --git a/tests/trainer/logging_/test_train_loop_logging.py b/tests/trainer/logging_/test_train_loop_logging.py
index 57776c05efd72..cbc97c751f094 100644
--- a/tests/trainer/logging_/test_train_loop_logging.py
+++ b/tests/trainer/logging_/test_train_loop_logging.py
@@ -78,7 +78,7 @@ def training_step(self, batch, batch_idx):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
         callbacks=[ModelCheckpoint(monitor="l_se")],
     )
     trainer.fit(model)
@@ -116,7 +116,7 @@ def training_epoch_end(self, outputs):
         limit_val_batches=2,
         max_epochs=2,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -156,7 +156,7 @@ def training_epoch_end(self, outputs):
         limit_val_batches=batches,
         max_epochs=max_epochs,
         log_every_n_steps=log_interval,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -194,7 +194,7 @@ def validation_step(self, batch, batch_idx):
         limit_train_batches=batches,
         limit_val_batches=batches,
         max_epochs=2,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -232,7 +232,7 @@ def val_dataloader(self):
         limit_train_batches=1,
         limit_val_batches=2,
         max_epochs=1,
-        weights_summary=None,
+        enable_model_summary=False,
         fast_dev_run=True,
     )
     trainer.fit(model)
@@ -391,7 +391,7 @@ def test_logging_sync_dist_true(tmpdir, gpus):
         default_root_dir=tmpdir,
         limit_train_batches=3,
         limit_val_batches=3,
-        weights_summary=None,
+        enable_model_summary=False,
         gpus=gpus,
     )
     trainer.fit(model)
@@ -439,7 +439,7 @@ def validation_step(self, batch, batch_idx):
         limit_train_batches=1,
         limit_val_batches=1,
         max_epochs=2,
-        weights_summary=None,
+        enable_model_summary=False,
         accelerator="ddp",
         gpus=2,
         profiler="pytorch",
@@ -483,7 +483,7 @@ def on_epoch_end(self, trainer: Trainer, model: LightningModule):
         limit_val_batches=0,
         enable_checkpointing=False,
         logger=False,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     model = TestModel()
     trainer.fit(model)
@@ -519,7 +519,7 @@ def on_train_epoch_end(self, trainer, pl_module):
         limit_train_batches=1,
         limit_val_batches=1,
         max_epochs=1,
-        weights_summary=None,
+        enable_model_summary=False,
         callbacks=[LoggingCallback()],
     )
     trainer.fit(model)
diff --git a/tests/trainer/optimization/test_manual_optimization.py b/tests/trainer/optimization/test_manual_optimization.py
index 7fc1fba9c1940..a7f1ba2257802 100644
--- a/tests/trainer/optimization/test_manual_optimization.py
+++ b/tests/trainer/optimization/test_manual_optimization.py
@@ -118,7 +118,7 @@ def on_train_end(self):
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
         **kwargs,
     )
 
@@ -160,7 +160,7 @@ def training_epoch_end(self, outputs) -> None:
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     with mock.patch.object(Accelerator, "backward", wraps=trainer.accelerator.backward) as bwd_mock:
@@ -187,7 +187,7 @@ def training_epoch_end(self, outputs) -> None:
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
 
     with mock.patch.object(Accelerator, "backward", wraps=trainer.accelerator.backward) as bwd_mock:
@@ -208,7 +208,7 @@ def test_multiple_optimizers_manual_native_amp(tmpdir):
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
         precision=16,
         gpus=1,
     )
@@ -447,7 +447,7 @@ def training_epoch_end(self, outputs) -> None:
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
         precision=16,
         amp_backend="native",
         gpus=1,
@@ -1048,7 +1048,7 @@ def configure_optimizers(self):
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
         gpus=1,
         precision=precision,
     )
diff --git a/tests/trainer/optimization/test_multiple_optimizers.py b/tests/trainer/optimization/test_multiple_optimizers.py
index 5c29bbbac2d7e..30ee7c635b0bf 100644
--- a/tests/trainer/optimization/test_multiple_optimizers.py
+++ b/tests/trainer/optimization/test_multiple_optimizers.py
@@ -45,7 +45,7 @@ def training_step(self, batch, batch_idx, optimizer_idx):
 
     # Initialize a trainer
     trainer = pl.Trainer(
-        default_root_dir=tmpdir, max_epochs=1, limit_train_batches=5, limit_val_batches=5, weights_summary=None
+        default_root_dir=tmpdir, max_epochs=1, limit_train_batches=5, limit_val_batches=5, enable_model_summary=False
     )
     trainer.fit(model)
 
@@ -77,7 +77,7 @@ def training_epoch_end(self, outputs) -> None:
         limit_val_batches=2,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -117,7 +117,7 @@ def training_epoch_end(self, outputs) -> None:
     model.val_dataloader = None
 
     trainer = pl.Trainer(
-        default_root_dir=tmpdir, limit_train_batches=2, max_epochs=1, log_every_n_steps=1, weights_summary=None
+        default_root_dir=tmpdir, limit_train_batches=2, max_epochs=1, log_every_n_steps=1, enable_model_summary=False
     )
     trainer.fit(model)
 
@@ -189,7 +189,7 @@ def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_c
         limit_train_batches=limit_train_batches,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
     assert len(model.training_step_called) == len(model.optimizer_step_called) == len(model.optimizers())
diff --git a/tests/trainer/optimization/test_optimizers.py b/tests/trainer/optimization/test_optimizers.py
index 9872d9088070e..bc97b5667fae1 100644
--- a/tests/trainer/optimization/test_optimizers.py
+++ b/tests/trainer/optimization/test_optimizers.py
@@ -370,7 +370,7 @@ def configure_optimizers(self):
         limit_train_batches=1,
         limit_val_batches=2,
         max_epochs=1,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
diff --git a/tests/trainer/test_dataloaders.py b/tests/trainer/test_dataloaders.py
index 9a3c79bdd3cf6..eed006e1de47b 100644
--- a/tests/trainer/test_dataloaders.py
+++ b/tests/trainer/test_dataloaders.py
@@ -890,7 +890,9 @@ def gen(self):
     model = TestModel()
     train_dataloader = DataLoader(TestDataset(model.gen), batch_size=2)
     trainer = Trainer(
-        default_root_dir=os.getcwd(), max_epochs=2, weights_summary=None  # we expect the second epoch to be skipped
+        default_root_dir=os.getcwd(),
+        max_epochs=2,
+        enable_model_summary=False,  # we expect the second epoch to be skipped
     )
     trainer.fit(model, train_dataloader=train_dataloader)
     assert trainer.global_step == 2 * yield_at_all
diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py
index ba7be93127ff5..213eac01fce68 100644
--- a/tests/trainer/test_trainer.py
+++ b/tests/trainer/test_trainer.py
@@ -429,7 +429,7 @@ def on_load_checkpoint(self, _):
         val_check_interval=1.0,
         enable_progress_bar=False,
         logger=False,
-        weights_summary=None,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -621,8 +621,8 @@ def test_trainer_max_steps_accumulate_batches(tmpdir):
         max_steps=num_train_samples + 10,
         accumulate_grad_batches=10,
         logger=False,
-        weights_summary=None,
         enable_progress_bar=False,
+        enable_model_summary=False,
     )
     trainer.fit(model)
 
@@ -1389,7 +1389,7 @@ def predict(
         default_root_dir=tmpdir,
         max_epochs=1,
         log_every_n_steps=1,
-        weights_summary=None,
+        enable_model_summary=False,
         accelerator=accelerator,
         gpus=gpus,
         num_processes=num_processes,
diff --git a/tests/utilities/test_auto_restart.py b/tests/utilities/test_auto_restart.py
index 709ee1100a1b4..1000aefe2eeb2 100644
--- a/tests/utilities/test_auto_restart.py
+++ b/tests/utilities/test_auto_restart.py
@@ -941,8 +941,8 @@ def test_dataset_rng_states_restart_with_lightning(tmpdir, dataset_classes, mult
     trainer_kwargs = dict(
         default_root_dir=tmpdir,
         max_epochs=3,
-        weights_summary=None,
         enable_progress_bar=False,
+        enable_model_summary=False,
         multiple_trainloader_mode=multiple_trainloader_mode,
     )
 
diff --git a/tests/utilities/test_cli.py b/tests/utilities/test_cli.py
index 0e8e0dba768eb..af6a0bd757a84 100644
--- a/tests/utilities/test_cli.py
+++ b/tests/utilities/test_cli.py
@@ -125,7 +125,7 @@ def _raise():
         ("--tpu_cores=1,", dict(tpu_cores="1,")),
         ("--limit_train_batches=100", dict(limit_train_batches=100)),
         ("--limit_train_batches 0.8", dict(limit_train_batches=0.8)),
-        ("--weights_summary=null", dict(weights_summary=None)),
+        ("--enable_model_summary FALSE", dict(enable_model_summary=False)),
         (
             "",
             dict(
@@ -337,7 +337,7 @@ def test_lightning_cli_args(tmpdir):
         f"--data.data_dir={tmpdir}",
         f"--trainer.default_root_dir={tmpdir}",
         "--trainer.max_epochs=1",
-        "--trainer.weights_summary=null",
+        "--trainer.enable_model_summary=False",
         "--seed_everything=1234",
     ]