diff --git a/CHANGELOG.md b/CHANGELOG.md
index b18dc6a7d..638a23839 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -29,7 +29,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add default `params_to_tune` for `ChangePointsSegmentationTransform`, `ChangePointsTrendTransform`, `ChangePointsLevelTransform`, `TrendTransform`, `LinearTrendTransform`, `TheilSenTrendTransform` and `STLTransform` ([#1243](https://github.com/tinkoff-ai/etna/pull/1243))
 - Add default `params_to_tune` for `TreeFeatureSelectionTransform`, `MRMRFeatureSelectionTransform` and `GaleShapleyFeatureSelectionTransform` ([#1250](https://github.com/tinkoff-ai/etna/pull/1250))
 - Add tuning stage into `Auto.fit` ([#1272](https://github.com/tinkoff-ai/etna/pull/1272))
--
+- Add `params_to_tune` into `Tune` init ([#1282](https://github.com/tinkoff-ai/etna/pull/1282))
 ### Fixed
 -
 - Fix `BaseReconciliator` to work on `pandas==1.1.5` ([#1229](https://github.com/tinkoff-ai/etna/pull/1229))
diff --git a/etna/auto/auto.py b/etna/auto/auto.py
index c6895c309..188b6e4fb 100644
--- a/etna/auto/auto.py
+++ b/etna/auto/auto.py
@@ -12,6 +12,7 @@
 import optuna
 import pandas as pd
 from hydra_slayer import get_from_params
+from optuna.distributions import BaseDistribution
 from optuna.distributions import CategoricalDistribution
 from optuna.distributions import DiscreteUniformDistribution
 from optuna.distributions import IntLogUniformDistribution
@@ -595,6 +596,7 @@ def __init__(
         storage: Optional[BaseStorage] = None,
         metrics: Optional[List[Metric]] = None,
         sampler: Optional[BaseSampler] = None,
+        params_to_tune: Optional[Dict[str, BaseDistribution]] = None,
     ):
         """
         Initialize Tune class.
@@ -622,6 +624,11 @@ def __init__(
             By default, :py:class:`~etna.metrics.metrics.Sign`, :py:class:`~etna.metrics.metrics.SMAPE`,
             :py:class:`~etna.metrics.metrics.MAE`, :py:class:`~etna.metrics.metrics.MSE`,
             :py:class:`~etna.metrics.metrics.MedAE` metrics are used.
+        sampler:
+            Optuna sampler to use. By default, TPE sampler is used.
+        params_to_tune:
+            Parameters of pipeline that should be tuned with corresponding tuning distributions.
+            By default, `pipeline.params_to_tune()` is used.
         """
         super().__init__(
             target_metric=target_metric,
@@ -638,6 +645,10 @@ def __init__(
             self.sampler: BaseSampler = TPESampler()
         else:
             self.sampler = sampler
+        if params_to_tune is None:
+            self.params_to_tune = pipeline.params_to_tune()
+        else:
+            self.params_to_tune = params_to_tune
         self._optuna: Optional[Optuna] = None
 
     def fit(
@@ -677,6 +688,7 @@ def fit(
             objective=self.objective(
                 ts=ts,
                 pipeline=self.pipeline,
+                params_to_tune=self.params_to_tune,
                 target_metric=self.target_metric,
                 metric_aggregation=self.metric_aggregation,
                 metrics=self.metrics,
@@ -696,6 +708,7 @@ def fit(
     def objective(
         ts: TSDataset,
         pipeline: BasePipeline,
+        params_to_tune: Dict[str, BaseDistribution],
         target_metric: Metric,
         metric_aggregation: MetricAggregationStatistics,
         metrics: List[Metric],
@@ -712,6 +725,8 @@ def objective(
             TSDataset to fit on.
         pipeline:
             Pipeline to tune.
+        params_to_tune:
+            Parameters of pipeline that should be tuned with corresponding tuning distributions.
         target_metric:
             Metric to optimize.
         metric_aggregation:
@@ -746,9 +761,6 @@ def objective(
         }
 
         def _objective(trial: Trial) -> float:
-
-            params_to_tune = pipeline.params_to_tune()
-
             # using received optuna.distribution objects to call corresponding trial.suggest_xxx
             params_suggested = {}
             for param_name, param_distr in params_to_tune.items():
diff --git a/tests/test_auto/test_tune.py b/tests/test_auto/test_tune.py
index 116834877..642312ba0 100644
--- a/tests/test_auto/test_tune.py
+++ b/tests/test_auto/test_tune.py
@@ -35,11 +35,13 @@ def test_objective(
     initializer=MagicMock(spec=_Initializer),
     callback=MagicMock(spec=_Callback),
     pipeline=Pipeline(NaiveModel()),
+    params_to_tune={},
 ):
     trial = MagicMock()
     _objective = Tune.objective(
         ts=example_tsds,
         pipeline=pipeline,
+        params_to_tune=params_to_tune,
         target_metric=target_metric,
         metric_aggregation=metric_aggregation,
         metrics=metrics,
@@ -172,14 +174,38 @@ def test_top_k(
 )
 def test_tune_run(example_tsds, optuna_storage, pipeline):
     tune = Tune(
-        pipeline,
-        MAE(),
+        pipeline=pipeline,
+        target_metric=MAE(),
+        metric_aggregation="median",
+        horizon=7,
+        storage=optuna_storage,
+    )
+    tune.fit(ts=example_tsds, n_trials=2)
+
+    assert len(tune._optuna.study.trials) == 2
+    assert len(tune.summary()) == 2
+    assert len(tune.top_k()) == 2
+    assert len(tune.top_k(k=1)) == 1
+
+
+@pytest.mark.parametrize(
+    "pipeline, params_to_tune",
+    [
+        (Pipeline(NaiveModel(1), horizon=7), {"model.lag": IntUniformDistribution(low=1, high=5)}),
+    ],
+)
+def test_tune_run_custom_params_to_tune(example_tsds, optuna_storage, pipeline, params_to_tune):
+    tune = Tune(
+        pipeline=pipeline,
+        params_to_tune=params_to_tune,
+        target_metric=MAE(),
         metric_aggregation="median",
         horizon=7,
         storage=optuna_storage,
     )
     tune.fit(ts=example_tsds, n_trials=2)
 
+    assert tune.params_to_tune == params_to_tune
     assert len(tune._optuna.study.trials) == 2
     assert len(tune.summary()) == 2
     assert len(tune.top_k()) == 2
@@ -205,8 +231,8 @@ def test_tune_hierarchical_run(
     pipeline,
 ):
     tune = Tune(
-        pipeline,
-        MAE(),
+        pipeline=pipeline,
+        target_metric=MAE(),
         metric_aggregation="median",
         horizon=7,
         backtest_params={"n_folds": 2},