diff --git a/src/pytorch_lightning/CHANGELOG.md b/src/pytorch_lightning/CHANGELOG.md
index 6856c4b21c..b34d2a2f9b 100644
--- a/src/pytorch_lightning/CHANGELOG.md
+++ b/src/pytorch_lightning/CHANGELOG.md
@@ -139,6 +139,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - The `WandbLogger` will now use the run name in the logs folder if it is provided, and otherwise the project name  ([#12604](https://github.com/PyTorchLightning/pytorch-lightning/pull/12604))
 
 
+- Enabled using any Sampler in distributed environment in Lite ([#13646](https://github.com/PyTorchLightning/pytorch-lightning/pull/13646))
+
+
 -
 
 
diff --git a/src/pytorch_lightning/lite/lite.py b/src/pytorch_lightning/lite/lite.py
index 4dfcde177f..f5cebcd89a 100644
--- a/src/pytorch_lightning/lite/lite.py
+++ b/src/pytorch_lightning/lite/lite.py
@@ -22,7 +22,7 @@ import torch
 import torch.nn as nn
 from torch import Tensor
 from torch.optim import Optimizer
-from torch.utils.data import DataLoader, DistributedSampler, RandomSampler, SequentialSampler
+from torch.utils.data import DataLoader, DistributedSampler
 
 from pytorch_lightning.accelerators.accelerator import Accelerator
 from pytorch_lightning.lite.wrappers import _LiteDataLoader, _LiteModule, _LiteOptimizer
@@ -223,13 +223,6 @@ class LightningLite(ABC):
         """
         sampler = dataloader.sampler
         if replace_sampler and self._requires_distributed_sampler(dataloader):
-            if not isinstance(sampler, (SequentialSampler, RandomSampler)):
-                raise MisconfigurationException(
-                    "You seem to have configured a sampler in your DataLoader. This will be replaced "
-                    " by `DistributedSampler` since `replace_sampler_ddp` is True and you are using"
-                    " distributed training. Either remove the sampler from your DataLoader or set"
-                    " `replace_sampler=False` if you want to use your custom sampler."
-                )
             sampler = self._get_distributed_sampler(dataloader, **self._strategy.distributed_sampler_kwargs)
 
         # the dataloader needs to be re-instantiated because we want to update the input arguments (e.g., sampler)