diff --git a/docs/notebooks b/docs/notebooks
index 5fa07d4b6..6d7c9bf03 160000
--- a/docs/notebooks
+++ b/docs/notebooks
@@ -1 +1 @@
-Subproject commit 5fa07d4b68f14ee9a26506fb56d87cba4f25f505
+Subproject commit 6d7c9bf032fa395880ea8032c866143fe7dccb19
diff --git a/pyproject.toml b/pyproject.toml
index 71373bdca..8b2d1a50c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -89,6 +89,7 @@ test = [
   "pytest-cov>=6",
   "pytest-mock>=3.14",
   "pytest-xdist",
+  "scvelo>=0.3",
 ]
 docs = [
   "furo>=2024.8.6",
@@ -246,5 +247,7 @@ ignore_roles = [
 ]
 
 [tool.uv]
+# Include test deps in `uv sync` so local dev has them (e.g. adjusttext, pytest)
+default-groups = [ "dev", "test" ]
 # pygpcca 1.0.4 incorrectly pins jinja2==3.0.3; override until next release
 override-dependencies = [ "jinja2>=3.1" ]
diff --git a/src/cellrank/_settings.py b/src/cellrank/_settings.py
index 5103e96d0..b238a6733 100644
--- a/src/cellrank/_settings.py
+++ b/src/cellrank/_settings.py
@@ -19,7 +19,7 @@ def _setup_logger() -> logging.Logger:
     """Set up the ``"cellrank"`` logger with a :class:`~rich.logging.RichHandler`."""
     root = logging.getLogger(_LOGGER_NAME)
     if not root.handlers:
-        console = Console(stderr=True, force_terminal=True)
+        console = Console(stderr=False)
         if console.is_jupyter:
             console.is_jupyter = False
         handler = RichHandler(
@@ -32,6 +32,7 @@ def _setup_logger() -> logging.Logger:
         )
         root.addHandler(handler)
         root.setLevel(logging.INFO)
+    root.propagate = False
     return root
 
 
diff --git a/src/cellrank/estimators/mixins/_fate_probabilities.py b/src/cellrank/estimators/mixins/_fate_probabilities.py
index 4e8aab7a7..31be9e671 100644
--- a/src/cellrank/estimators/mixins/_fate_probabilities.py
+++ b/src/cellrank/estimators/mixins/_fate_probabilities.py
@@ -243,6 +243,7 @@ def plot_fate_probabilities(
         color: str | None = None,
         mode: Literal["embedding", "time"] = PlotMode.EMBEDDING,
         time_key: str | None = None,
+        basis: str = "umap",
         same_plot: bool = True,
         title: str | Sequence[str] | None = None,
         cmap: str = "viridis",
@@ -260,6 +261,8 @@ def plot_fate_probabilities(
             Whether to plot the probabilities in an embedding or along the pseudotime.
         time_key
             Key in :attr:`~anndata.AnnData.obs` where pseudotime is stored. Only used when ``mode = 'time'``.
+        basis
+            Key in :attr:`~anndata.AnnData.obsm` for the embedding to use, e.g. ``'umap'`` or ``'tsne'``.
         title
             Title of the plot.
         same_plot
@@ -268,7 +271,7 @@ def plot_fate_probabilities(
         cmap
             Colormap for continuous annotations.
         kwargs
-            Keyword arguments for :func:`~scvelo.pl.scatter`.
+            Keyword arguments for :func:`~scanpy.pl.embedding`.
 
         Returns
         -------
@@ -285,6 +288,7 @@ def plot_fate_probabilities(
             color=color,
             mode=mode,
             time_key=time_key,
+            basis=basis,
             same_plot=same_plot,
             title=title,
             cmap=cmap,
diff --git a/src/cellrank/estimators/mixins/_lineage_drivers.py b/src/cellrank/estimators/mixins/_lineage_drivers.py
index e2f3bbcbe..c063457c0 100644
--- a/src/cellrank/estimators/mixins/_lineage_drivers.py
+++ b/src/cellrank/estimators/mixins/_lineage_drivers.py
@@ -10,7 +10,6 @@
 import numpy as np
 import pandas as pd
 import scanpy as sc
-import scvelo as scv
 from anndata import AnnData, Raw
 from matplotlib import patheffects, rc_context
 from matplotlib.axes import Axes
@@ -235,7 +234,7 @@ def plot_lineage_drivers(
             with the actual values.
         %(plotting)s
         kwargs
-            Keyword arguments for :func:`~scvelo.pl.scatter`.
+            Keyword arguments for :func:`~scanpy.pl.embedding`.
 
         Returns
         -------
@@ -286,13 +285,17 @@ def prepare_format(
         )
         axes = np.ravel([axes])
 
+        basis = kwargs.pop("basis", "umap")
+        # scvelo compat: "right" means "right margin" in scanpy
+        if kwargs.get("legend_loc") == "right":
+            kwargs["legend_loc"] = "right margin"
         _i = 0
         for _i, (gene, ax) in enumerate(zip(genes.index, axes)):
             data = genes.loc[gene]
-            scv.pl.scatter(
+            sc.pl.embedding(
                 self.adata,
+                basis=basis,
                 color=gene,
-                ncols=ncols,
                 use_raw=use_raw,
                 ax=ax,
                 show=False,
diff --git a/src/cellrank/estimators/terminal_states/_term_states_estimator.py b/src/cellrank/estimators/terminal_states/_term_states_estimator.py
index 72511e842..b63de011b 100644
--- a/src/cellrank/estimators/terminal_states/_term_states_estimator.py
+++ b/src/cellrank/estimators/terminal_states/_term_states_estimator.py
@@ -4,10 +4,11 @@
 from collections.abc import Sequence
 from typing import Any, Literal
 
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import scanpy as sc
 import scipy.sparse as sp
-import scvelo as scv
 from anndata import AnnData
 from matplotlib.colors import to_hex
 from pandas.api.types import infer_dtype
@@ -25,6 +26,7 @@
     _convert_to_categorical_series,
     _merge_categorical_series,
     _unique_order_preserving,
+    save_fig,
 )
 from cellrank.estimators._base_estimator import BaseEstimator
 from cellrank.estimators.mixins._utils import (
@@ -35,6 +37,7 @@
     shadow,
 )
 from cellrank.kernels._base_kernel import KernelExpression
+from cellrank.pl._utils import _plot_color_gradients, _plot_time_scatter
 
 logger = logging.getLogger(__name__)
 __all__ = ["TermStatesEstimator"]
@@ -306,6 +309,7 @@ def plot_macrostates(
         discrete: bool = True,
         mode: Literal["embedding", "time"] = PlotMode.EMBEDDING,
         time_key: str = "latent_time",
+        basis: str = "umap",
         same_plot: bool = True,
         title: str | Sequence[str] | None = None,
         cmap: str = "viridis",
@@ -332,6 +336,8 @@ def plot_macrostates(
             Whether to plot the probabilities in an embedding or along the pseudotime.
         time_key
             Key in :attr:`~anndata.AnnData.obs` where pseudotime is stored. Only used when ``mode = {m.TIME!r}``.
+        basis
+            Key in :attr:`~anndata.AnnData.obsm` for the embedding to use, e.g. ``'umap'`` or ``'tsne'``.
         title
             Title of the plot.
         same_plot
@@ -340,7 +346,7 @@ def plot_macrostates(
         cmap
             Colormap for continuous annotations.
         kwargs
-            Keyword arguments for :func:`~scvelo.pl.scatter`.
+            Keyword arguments for :func:`~scanpy.pl.embedding`.
 
         Returns
         -------
@@ -377,6 +383,7 @@ def plot_macrostates(
                 _title=name,
                 states=states,
                 color=color,
+                basis=basis,
                 same_plot=same_plot,
                 title=title,
                 cmap=cmap,
@@ -390,6 +397,7 @@ def plot_macrostates(
             color=color,
             mode=mode,
             time_key=time_key,
+            basis=basis,
             same_plot=same_plot,
             title=title,
             cmap=cmap,
@@ -403,6 +411,7 @@ def _plot_discrete(
         _title: str | None = None,
         states: str | Sequence[str] | None = None,
         color: str | None = None,
+        basis: str = "umap",
         title: str | Sequence[str] | None = None,
         same_plot: bool = True,
         cmap: str = "viridis",
@@ -434,20 +443,24 @@ def _plot_discrete(
 
         same_plot = same_plot or len(names) == 1
         kwargs.setdefault("legend_loc", "on data")
-        kwargs["color_map"] = cmap
+        # scvelo compat: "right" means "right margin" in scanpy
+        if kwargs.get("legend_loc") == "right":
+            kwargs["legend_loc"] = "right margin"
+        kwargs.pop("color_map", None)
+        kwargs.pop("dpi", None)  # handled at figure level, not by sc.pl.embedding
+        save = kwargs.pop("save", None)
+        show = kwargs.pop("show", None)
+        kwargs["cmap"] = cmap
+        basis = kwargs.pop("basis", basis)
+        size = kwargs.get("size", 120_000 / self.adata.n_obs)
 
         # fmt: off
         with RandomKeys(self.adata, n=1 if same_plot else len(states), where="obs") as keys:
             if same_plot:
-                outline = _data.cat.categories.to_list()
-                _data = _data.cat.add_categories(["nan"]).fillna("nan")
-                states.append("nan")
-                color_mapper["nan"] = "#dedede"
                 self.adata.obs[keys[0]] = _data
                 self.adata.uns[f"{keys[0]}_colors"] = [color_mapper[name] for name in states]
                 title = _title if title is None else title
             else:
-                outline = None
                 for key, cat in zip(keys, states):
                     self.adata.obs[key] = _data.cat.set_categories([cat])
                     self.adata.uns[f"{key}_colors"] = [color_mapper[cat]]
@@ -456,13 +469,43 @@ def _plot_discrete(
             if isinstance(title, str):
                 title = [title]
 
-            scv.pl.scatter(
+            kwargs.setdefault("na_color", "#dedede")
+            kwargs.setdefault("na_in_legend", False)
+            axes = sc.pl.embedding(
                 self.adata,
+                basis=basis,
                 color=color + keys,
                 title=color + title,
-                add_outline=outline,
+                show=False,
+                return_fig=False,
                 **kwargs,
             )
+
+            # Overlay state cells with outlines so they appear on top of NaN cells
+            if same_plot:
+                axes_list = [axes] if not isinstance(axes, list | np.ndarray) else list(np.ravel(axes))
+                mask = _data.notna()
+                if mask.any():
+                    adata_sub = self.adata[mask].copy()
+                    for ax, key in zip(axes_list[len(color):], keys):
+                        ax_title = ax.get_title()
+                        sc.pl.embedding(
+                            adata_sub,
+                            basis=basis,
+                            color=key,
+                            add_outline=True,
+                            show=False,
+                            return_fig=False,
+                            ax=ax,
+                            legend_loc="none",
+                            size=size,
+                        )
+                        ax.set_title(ax_title)
+
+        if save is not None:
+            save_fig(plt.gcf(), save)
+        if show is True or (show is None and save is None):
+            plt.show()
         # fmt: on
 
     def _plot_continuous(
@@ -474,6 +517,7 @@ def _plot_continuous(
         color: str | None = None,
         mode: Literal["embedding", "time"] = PlotMode.EMBEDDING,
         time_key: str = "latent_time",
+        basis: str = "umap",
         title: str | Sequence[str] | None = None,
         same_plot: bool = True,
         cmap: str = "viridis",
@@ -487,7 +531,6 @@ def _plot_continuous(
             states = _data.names
         if not len(states):
             raise ValueError("No lineages have been selected.")
-        is_singleton = _data.shape[1] == 1
         _data = _data[states].copy()
 
         if mode == "time" and same_plot:
@@ -510,6 +553,10 @@ def _plot_continuous(
         # fmt: off
         color = [] if color is None else (color,) if isinstance(color, str) else color
         color = _unique_order_preserving(color)
+        basis = kwargs.pop("basis", basis)
+        kwargs.pop("color_map", None)
+        save = kwargs.pop("save", None)
+        show = kwargs.pop("show", None)
 
         if mode == PlotMode.TIME:
             kwargs.setdefault("legend_loc", "best")
@@ -525,41 +572,47 @@ def _plot_continuous(
             if len(color) and len(color) not in (1, _data_X.shape[1]):
                 raise ValueError(f"Expected `color` to be of length `1` or `{_data_X.shape[1]}`, "
                                  f"found `{len(color)}`.")
-            kwargs["x"] = self.adata.obs[time_key]
-            kwargs["y"] = list(_data_X.T)
-            kwargs["color"] = color if len(color) else None
-            kwargs["xlabel"] = [time_key] * len(states)
-            kwargs["ylabel"] = ["probability"] * len(states)
+            _plot_time_scatter(
+                self.adata, self.adata.obs[time_key].values, list(_data_X.T),
+                color=color if len(color) else None,
+                title=title, xlabel=time_key, ylabel="probability", cmap=cmap,
+                save=save, show=show, **kwargs,
+            )
         elif mode == PlotMode.EMBEDDING:
             kwargs.setdefault("legend_loc", "on data")
+            # scvelo compat: "right" means "right margin" in scanpy
+            if kwargs.get("legend_loc") == "right":
+                kwargs["legend_loc"] = "right margin"
+
             if same_plot:
                 if color:
                     # https://github.com/theislab/scvelo/issues/673
                     logger.warning("Ignoring `color` when `mode='embedding'` and `same_plot=True`")
                 title = [_title] if title is None else title
-                kwargs["color_gradients"] = _data
+                _plot_color_gradients(self.adata, _data, basis=basis, title=title,
+                                      save=save, show=show, **kwargs)
             else:
+                kwargs.pop("dpi", None)  # handled at figure level, not by sc.pl.embedding
                 title = [f"{_title} {state}" for state in states] if title is None else title
                 if isinstance(title, str):
                     title = [title]
                 title = color + title
-                kwargs["color"] = color + list(_data_X.T)
+                # Store probability arrays as temp obs columns (scanpy requires column names)
+                with RandomKeys(self.adata, n=_data_X.shape[1], where="obs") as prob_keys:
+                    for key, col in zip(prob_keys, _data_X.T):
+                        self.adata.obs[key] = col
+                    sc.pl.embedding(
+                        self.adata, basis=basis, color=color + list(prob_keys),
+                        title=title, cmap=cmap, show=False, **kwargs,
+                    )
+                if save is not None:
+                    save_fig(plt.gcf(), save)
+                if show is True or (show is None and save is None):
+                    plt.show()
         else:
             raise NotImplementedError(f"Mode `{mode}` is not yet implemented.")
         # fmt: on
 
-        # e.g. a stationary distribution
-        if is_singleton and not np.allclose(_data_X, 1.0):
-            kwargs.setdefault("perc", [0, 95])
-            _ = kwargs.pop("color_gradients", None)
-
-        scv.pl.scatter(
-            self.adata,
-            title=title,
-            color_map=cmap,
-            **kwargs,
-        )
-
     def _set_categorical_labels(
         self,
         categories: pd.Series | dict[str, Any],
diff --git a/src/cellrank/kernels/_cytotrace_kernel.py b/src/cellrank/kernels/_cytotrace_kernel.py
index 852594f84..37599c888 100644
--- a/src/cellrank/kernels/_cytotrace_kernel.py
+++ b/src/cellrank/kernels/_cytotrace_kernel.py
@@ -57,8 +57,9 @@ class CytoTRACEKernel(PseudotimeKernel):
     --------
     .. code-block::
 
-        import scvelo as scv
+        import scanpy as sc
         import cellrank as cr
+        from cellmapper import CellMapper
 
         adata = cr.datasets.pancreas()
 
@@ -66,18 +67,16 @@ class CytoTRACEKernel(PseudotimeKernel):
         sc.pp.normalize_total(adata)
         sc.pp.log1p(adata)
         sc.pp.highly_variable_genes(adata)
+        sc.pp.pca(adata)
+        sc.pp.neighbors(adata)
 
-        # CytoTRACE by default uses imputed data - a simple way to compute
-        # k-NN imputed data is to use scVelo's moments function.
-        # However, note that this function expects `spliced` counts because
-        # it's designed for RNA velocity, so we're using a simple hack here:
-        if 'spliced' not in adata.layers or 'unspliced' not in adata.layers:
-            adata.layers['spliced'] = adata.X
-            adata.layers['unspliced'] = adata.X
-        scv.pp.moments(adata)
+        # CytoTRACE by default uses imputed data.
+        adata.layers["imputed"] = (
+            CellMapper(adata).map(layer_key="X", use_rep="X_pca").query_imputed.X.copy()
+        )
 
         ctk = cr.kernels.CytoTRACEKernel(adata)
-        ckt = ctk.compute_cytotrace().compute_transition_matrix()
+        ctk = ctk.compute_cytotrace().compute_transition_matrix()
     """
 
     def __init__(
@@ -111,7 +110,7 @@ def _read_from_adata(
     @inject_docs(ct=CytoTRACEAggregation)
     def compute_cytotrace(
         self,
-        layer: str | None = "Ms",
+        layer: str | None = "imputed",
         aggregation: Literal["mean", "median", "hmean", "gmean"] = CytoTRACEAggregation.MEAN,
         use_raw: bool = False,
         n_genes: int = 200,
diff --git a/src/cellrank/kernels/_velocity_kernel.py b/src/cellrank/kernels/_velocity_kernel.py
index 6d748ffef..593261db6 100644
--- a/src/cellrank/kernels/_velocity_kernel.py
+++ b/src/cellrank/kernels/_velocity_kernel.py
@@ -103,7 +103,7 @@ def _read_from_adata(
         if np.any(nans):
             self._xdata = self._xdata[:, ~nans]
             self._vdata = self._vdata[:, ~nans]
-        W = _row_normalize_connectivities(self.adata)
+        W = _row_normalize_connectivities(self.connectivities)
         self._vexp, self._vvar = _knn_moments(W, self._vdata)
         self._vexp = self._vexp.astype(np.float64, copy=False)
         self._vvar = self._vvar.astype(np.float64, copy=False)
diff --git a/src/cellrank/kernels/utils/_moments.py b/src/cellrank/kernels/utils/_moments.py
index b14dee9dc..0488e47c4 100644
--- a/src/cellrank/kernels/utils/_moments.py
+++ b/src/cellrank/kernels/utils/_moments.py
@@ -1,14 +1,9 @@
 """k-NN moment computation formerly imported from scVelo."""
 
-from __future__ import annotations
-
 import warnings
 
 import numpy as np
 import scipy.sparse as sp
-from anndata import AnnData
-
-__all__: list[str] = []
 
 
 def _knn_moments(
@@ -35,8 +30,8 @@ def _knn_moments(
     return mean, var
 
 
-def _row_normalize_connectivities(adata: AnnData) -> sp.csr_matrix:
-    """Build a row-normalized binary connectivities matrix from *adata*.
+def _row_normalize_connectivities(conn: sp.spmatrix) -> sp.csr_matrix:
+    """Build a row-normalized binary connectivities matrix.
 
     The transformation mirrors scVelo's ``get_connectivities``:
     binarize the graph, set the diagonal to 1 (self-loop), then
@@ -44,14 +39,14 @@ def _row_normalize_connectivities(adata: AnnData) -> sp.csr_matrix:
 
     Parameters
     ----------
-    adata
-        Must contain a neighbor graph in ``adata.obsp["connectivities"]``.
+    conn
+        Sparse connectivities matrix of shape ``(n_cells, n_cells)``.
 
     Returns
     -------
     Row-normalized sparse matrix of shape ``(n_cells, n_cells)``.
     """
-    C = adata.obsp["connectivities"].copy()
+    C = conn.copy()
 
     # binarize
     C = (C > 0).astype(np.float32)
diff --git a/src/cellrank/kernels/utils/_plot_utils.py b/src/cellrank/kernels/utils/_plot_utils.py
index 8fb31401e..8011b8fb8 100644
--- a/src/cellrank/kernels/utils/_plot_utils.py
+++ b/src/cellrank/kernels/utils/_plot_utils.py
@@ -1,7 +1,5 @@
 """Private plotting utilities formerly imported from scVelo."""
 
-from __future__ import annotations
-
 from typing import Any
 
 import matplotlib.pyplot as plt
@@ -66,15 +64,19 @@ def _plot_outline(
     y: np.ndarray,
     *,
     outline_color: tuple[str, str] = ("black", "white"),
+    outline_width: tuple[float, float] = (0.3, 0.05),
     kwargs: dict[str, Any] | None = None,
     ax: Axes | None = None,
     **scatter_kwargs: Any,
 ) -> None:
     """Draw outlined scatter points using a double-scatter technique.
 
-    Draws three layers: a large background scatter (``outline_color[0]``),
-    a medium gap scatter (``outline_color[1]``), and optionally the caller
-    adds the actual data scatter on top.
+    Draws two layers: a large background scatter (``outline_color[0]``) and
+    a medium gap scatter (``outline_color[1]``).  The caller typically draws
+    the actual data scatter on top.
+
+    The outline sizes are computed from the base ``s`` value following scVelo's
+    quadratic formula so that ``outline_width`` controls the visual thickness.
 
     Parameters
     ----------
@@ -84,8 +86,10 @@ def _plot_outline(
         Y coordinates.
     outline_color
         Tuple of ``(background_color, gap_color)``.
+    outline_width
+        ``(bg_frac, gap_frac)`` relative to the dot radius.
     kwargs
-        Base keyword arguments for all three scatter calls (e.g. ``s``, ``alpha``).
+        Base keyword arguments for all scatter calls (e.g. ``s``, ``alpha``).
     ax
         Matplotlib axes to draw on. If :obj:`None`, uses current axes.
     **scatter_kwargs
@@ -97,13 +101,14 @@ def _plot_outline(
         kwargs = {}
 
     bg_color, gap_color = outline_color
+    bg_frac, gap_frac = outline_width
+
+    s = kwargs.pop("s", 20)
+    point = np.sqrt(s)
+    gap_size = (2 * point * gap_frac + point) ** 2
+    bg_size = (2 * point * bg_frac + np.sqrt(gap_size)) ** 2
 
-    # background layer (larger points)
-    bg_kwargs = {**kwargs, **scatter_kwargs, "color": bg_color}
-    bg_kwargs["s"] = bg_kwargs.get("s", 20) * 1.4
-    ax.scatter(x, y, **bg_kwargs)
+    base = {**kwargs, **scatter_kwargs, "edgecolors": "none", "marker": "."}
 
-    # gap layer (medium points)
-    gap_kwargs = {**kwargs, **scatter_kwargs, "color": gap_color}
-    gap_kwargs["s"] = gap_kwargs.get("s", 20) * 1.1
-    ax.scatter(x, y, **gap_kwargs)
+    ax.scatter(x, y, s=bg_size, color=bg_color, **base)
+    ax.scatter(x, y, s=gap_size, color=gap_color, **base)
diff --git a/src/cellrank/kernels/utils/_random_walk.py b/src/cellrank/kernels/utils/_random_walk.py
index b94455729..aaafd7737 100644
--- a/src/cellrank/kernels/utils/_random_walk.py
+++ b/src/cellrank/kernels/utils/_random_walk.py
@@ -239,6 +239,9 @@ def plot(
         # scVelo accepted color="none" to skip coloring; scanpy does not.
         if kwargs.get("color") == "none":
             kwargs.pop("color")
+        # scvelo compat: "right" means "right margin" in scanpy
+        if kwargs.get("legend_loc") == "right":
+            kwargs["legend_loc"] = "right margin"
         sc.pl.embedding(self._adata, basis=basis, show=False, ax=ax, **kwargs)
 
         logger.info("Plotting random walks")
diff --git a/src/cellrank/models/_pygam_model.py b/src/cellrank/models/_pygam_model.py
index c86bc3550..125009619 100644
--- a/src/cellrank/models/_pygam_model.py
+++ b/src/cellrank/models/_pygam_model.py
@@ -1,9 +1,10 @@
 import collections
+import contextlib
 import copy
 import enum
+import io
 import logging
 import types
-import warnings
 from collections.abc import Mapping
 from typing import Any, Literal
 
@@ -144,6 +145,7 @@ def __init__(
                 **_filter_kwargs(gam.__init__, **filtered_kwargs),
             )
         super().__init__(adata, model=model)
+        self._converged: bool = True
 
         if grid is None:
             self._grid = None
@@ -174,16 +176,11 @@ def fit(
         """  # noqa: D400
         super().fit(x, y, w, **kwargs)
 
-        with warnings.catch_warnings():
-            warnings.filterwarnings(
-                "ignore",
-                category=DeprecationWarning,
-                message=".* is a deprecated alias for the builtin",
-            )
-            if self._grid is not None:
-                # use default search
-                grid = {} if not isinstance(self._grid, dict) else self._grid
-                try:
+        if self._grid is not None:
+            # use default search
+            grid = {} if not isinstance(self._grid, dict) else self._grid
+            try:
+                with contextlib.redirect_stdout(io.StringIO()):
                     self.model.gridsearch(
                         self.x,
                         self.y,
@@ -193,20 +190,24 @@ def fit(
                         **grid,
                         **kwargs,
                     )
-                    return self
-                except Exception as e:  # noqa: BLE001
-                    # workaround for: https://github.com/dswah/pyGAM/issues/273
+                return self
+            except Exception as e:  # noqa: BLE001
+                # workaround for: https://github.com/dswah/pyGAM/issues/273
+                with contextlib.redirect_stdout(io.StringIO()):
                     self.model.fit(self.x, self.y, weights=self.w, **kwargs)
-                    logger.error("Grid search failed, reason: `%s`. Fitting with default values", e)
+                logger.error("Grid search failed, reason: `%s`. Fitting with default values", e)
 
-            try:
+        try:
+            buf = io.StringIO()
+            with contextlib.redirect_stdout(buf):
                 self.model.fit(self.x, self.y, weights=self.w, **kwargs)
-                return self
-            except Exception as e:  # noqa: BLE001
-                raise RuntimeError(
-                    f"Unable to fit `{type(self).__name__}` for gene "
-                    f"`{self._gene!r}` in lineage `{self._lineage!r}`. Reason: `{e}`"
-                ) from e
+            self._converged = "did not converge" not in buf.getvalue()
+            return self
+        except Exception as e:  # noqa: BLE001
+            raise RuntimeError(
+                f"Unable to fit `{type(self).__name__}` for gene "
+                f"`{self._gene!r}` in lineage `{self._lineage!r}`. Reason: `{e}`"
+            ) from e
 
     @d.dedent
     def predict(
@@ -227,13 +228,7 @@ def predict(
         """  # noqa: D400
         x_test = self._check(key_added, x_test)
 
-        with warnings.catch_warnings():
-            warnings.filterwarnings(
-                "ignore",
-                category=DeprecationWarning,
-                message=".* is a deprecated alias for the builtin",
-            )
-            self._y_test = self.model.predict(x_test, **kwargs)
+        self._y_test = self.model.predict(x_test, **kwargs)
         self._y_test = np.squeeze(self._y_test).astype(self._dtype)
 
         return self.y_test
@@ -251,13 +246,7 @@ def confidence_interval(self, x_test: np.ndarray | None = None, **kwargs: Any) -
         %(base_model_ci.returns)s
         """  # noqa: D400
         x_test = self._check("_x_test", x_test)
-        with warnings.catch_warnings():
-            warnings.filterwarnings(
-                "ignore",
-                category=DeprecationWarning,
-                message=".* is a deprecated alias for the builtin",
-            )
-            self._conf_int = self.model.confidence_intervals(x_test, **kwargs).astype(self._dtype)
+        self._conf_int = self.model.confidence_intervals(x_test, **kwargs).astype(self._dtype)
 
         return self.conf_int
 
diff --git a/src/cellrank/pl/_aggregate_fate_probs.py b/src/cellrank/pl/_aggregate_fate_probs.py
index b9df6eb14..698cf22d1 100644
--- a/src/cellrank/pl/_aggregate_fate_probs.py
+++ b/src/cellrank/pl/_aggregate_fate_probs.py
@@ -11,12 +11,12 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import scanpy as sc
 import scipy.sparse as sp
 import seaborn as sns
 from anndata import AnnData
 from matplotlib import cm, colors
 from scanpy.plotting import violin
-from scvelo.plotting import paga
 
 from cellrank._utils import Lineage
 from cellrank._utils._docs import d, inject_docs
@@ -77,8 +77,8 @@ def aggregate_fate_probabilities(
         Type of plot to show. Valid options are:
 
         - ``{m.BAR!r}`` - barplot, one panel per cluster. The whiskers correspond to the standard error of the mean.
-        - ``{m.PAGA!r}`` - :func:`~scvelo.pl.paga`, one per %(initial_or_terminal)s state, colored in by fate.
-        - ``{m.PAGA_PIE!r}`` - :func:`~scvelo.pl.paga` with pie charts indicating aggregated fates.
+        - ``{m.PAGA!r}`` - :func:`~scanpy.pl.paga`, one per %(initial_or_terminal)s state, colored in by fate.
+        - ``{m.PAGA_PIE!r}`` - :func:`~scanpy.pl.paga` with pie charts indicating aggregated fates.
         - ``{m.VIOLIN!r}`` - violin plots, one per %(initial_or_terminal)s state.
         - ``{m.HEATMAP!r}`` - a heatmap, showing average fates per cluster.
         - ``{m.CLUSTERMAP!r}`` - same as a heatmap, but with a dendrogram.
@@ -109,12 +109,21 @@ def aggregate_fate_probabilities(
         fate probabilities legend.
     %(plotting)s
     kwargs
-        Keyword arguments for :func:`~scvelo.pl.paga`, :func:`~scanpy.pl.violin` or
+        Keyword arguments for :func:`~scanpy.pl.paga`, :func:`~scanpy.pl.violin` or
         :func:`~matplotlib.pyplot.bar`, depending on the ``mode``.
 
     Returns
     -------
     %(just_plots)s
+
+    Notes
+    -----
+    For ``mode = 'paga_pie'``, directed edges are shown only when
+    ``adata.uns['paga']['transitions_confidence']`` is present. This key is
+    produced by scVelo's PAGA extension (:func:`~scvelo.tl.paga`), which
+    augments the standard scanpy PAGA graph with directed transition
+    confidences. When only scanpy's :func:`~scanpy.tl.paga` has been run,
+    edges are undirected.
     """
 
     @valuedispatch
@@ -178,21 +187,30 @@ def _():
         axes = [axes] if not isinstance(axes, np.ndarray) else np.ravel(axes)
         vmin, vmax = np.inf, -np.inf
 
-        if basis is not None:
-            kwargs["basis"] = basis
-            kwargs["scatter_flag"] = True
-            kwargs["color"] = cluster_key
+        # scanpy's paga doesn't support scatter_flag/legend_loc; draw scatter separately
+        kwargs.pop("legend_loc", None)
 
         for i, (ax, lineage_name) in enumerate(zip(axes, probs.names)):
             cols = [v[0][i] for v in d.values()]
+
+            if basis is not None:
+                sc.pl.embedding(
+                    adata,
+                    basis=basis,
+                    color=cluster_key,
+                    ax=ax,
+                    show=False,
+                    legend_loc="none",
+                )
+
             kwargs["ax"] = ax
-            kwargs["colors"] = tuple(cols)
+            kwargs["color"] = tuple(cols)
             kwargs["title"] = f"{direction} {lineage_name}"
 
             vmin = np.min(cols + [vmin])
             vmax = np.max(cols + [vmax])
 
-            paga(adata, **kwargs)
+            sc.pl.paga(adata, **kwargs)
 
         if cbar:
             norm = colors.Normalize(vmin=vmin, vmax=vmax)
@@ -220,26 +238,34 @@ def _():
         kwargs["ax"] = ax
         kwargs["show"] = False
         kwargs["colorbar"] = False  # has to be disabled
-        kwargs["show"] = False
-
-        kwargs["node_colors"] = colors
         kwargs.pop("save", None)  # we will handle saving
 
-        kwargs["transitions"] = kwargs.get("transitions", "transitions_confidence")
-        if "legend_loc" in kwargs:
-            orig_ll = kwargs["legend_loc"]
-            if orig_ll != "on data":
-                kwargs["legend_loc"] = "none"  # we will handle legend
-        else:
-            orig_ll = None
-            kwargs["legend_loc"] = "on data"
+        # Show directed edges when scVelo's PAGA has been run, which stores
+        # directed transition confidences in adata.uns["paga"]. Standard
+        # scanpy PAGA only has undirected connectivities.
+        if "transitions" not in kwargs:
+            paga_uns = adata.uns.get("paga", {})
+            if "transitions_confidence" in paga_uns:
+                kwargs["transitions"] = "transitions_confidence"
+
+        # Handle legend_loc separately (scanpy's paga doesn't accept it)
+        orig_ll = kwargs.pop("legend_loc") if "legend_loc" in kwargs else None
 
+        # Draw scatter background separately (scanpy's paga doesn't support scatter_flag)
         if basis is not None:
-            kwargs["basis"] = basis
-            kwargs["scatter_flag"] = True
-            kwargs["color"] = cluster_key
+            sc.pl.embedding(
+                adata,
+                basis=basis,
+                color=cluster_key,
+                ax=ax,
+                show=False,
+                legend_loc="on data" if orig_ll in (None, "on data") else "none",
+            )
+
+        # Use color for pie chart data (scanpy's paga accepts dict-of-dicts)
+        kwargs["color"] = colors
 
-        ax = paga(adata, **kwargs)
+        ax = sc.pl.paga(adata, **kwargs)
         ax.set_title(kwargs.get("title", cluster_key))
 
         if basis is not None and orig_ll not in ("none", "on data", None):
@@ -259,7 +285,7 @@ def _():
             fig.add_artist(first_legend)
 
         if legend_kwargs.get("loc", None) not in ("none", "on data", None):
-            # we need to use these, because scvelo can have its own handles and
+            # we need to use these, because paga can have its own handles and
             # they would be plotted here
             handles = []
             for lineage_name, color in zip(probs.names, colors[0].keys()):
@@ -465,7 +491,7 @@ def _():
         use_clustermap = True
         mode = mode.HEATMAP
     elif mode in (AggregationMode.PAGA, AggregationMode.PAGA_PIE) and "paga" not in adata.uns:
-        raise KeyError("Compute PAGA first as `scvelo.tl.paga()` or `scanpy.tl.paga()`.")
+        raise KeyError("Compute PAGA first as `scanpy.tl.paga()`.")
 
     fig = plot_violin_no_cluster_key() if mode == AggregationMode.VIOLIN and cluster_key is None else plot(mode)
 
diff --git a/src/cellrank/pl/_circular_projection.py b/src/cellrank/pl/_circular_projection.py
index 02976ad2d..7d83a40df 100644
--- a/src/cellrank/pl/_circular_projection.py
+++ b/src/cellrank/pl/_circular_projection.py
@@ -8,7 +8,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import scvelo as scv
+import scanpy as sc
 from anndata import AnnData
 from matplotlib.collections import LineCollection
 from matplotlib.colors import LinearSegmentedColormap, LogNorm
@@ -148,7 +148,7 @@ def circular_projection(
         ``'X_fate_simplex_{fwd,bwd}'``, based on the ``backward``.
     %(plotting)s
     kwargs
-        Keyword arguments for :func:`~scvelo.pl.scatter`.
+        Keyword arguments for :func:`~scanpy.pl.embedding`.
 
     Returns
     -------
@@ -222,6 +222,11 @@ def circular_projection(
     y = np.sum(X * angle_vec_cos, axis=1)
     adata.obsm[key_added] = np.c_[x, y]
 
+    # scvelo's "right" placed the legend outside the axes; scanpy calls
+    # that "right margin".  Remap so existing user code keeps working.
+    if kwargs.get("legend_loc") == "right":
+        kwargs["legend_loc"] = "right margin"
+
     nrows = int(np.ceil(len(keys) / ncols))
     fig, ax = plt.subplots(
         nrows=nrows,
@@ -250,7 +255,7 @@ def circular_projection(
             # TODO(michalk8): parse the exception
             pass
 
-        scv.pl.scatter(
+        sc.pl.embedding(
             adata,
             basis=key_added,
             color=k,
@@ -258,7 +263,7 @@ def circular_projection(
             ax=ax,
             use_raw=use_raw,
             norm=LogNorm() if set_lognorm else None,
-            colorbar=colorbar,
+            colorbar_loc="right" if colorbar else None,
             **kwargs,
         )
         if colorbar and set_lognorm:
diff --git a/src/cellrank/pl/_utils.py b/src/cellrank/pl/_utils.py
index 688ea2d1a..20b5afa71 100644
--- a/src/cellrank/pl/_utils.py
+++ b/src/cellrank/pl/_utils.py
@@ -13,6 +13,7 @@
 import pandas as pd
 from anndata import AnnData
 from matplotlib import cm, colors
+from matplotlib.colors import to_rgba
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 from pandas.api.types import infer_dtype
 from sklearn.svm import SVR
@@ -230,8 +231,8 @@ def _fit_bulk_helper[Queue](
 
             res[gene][ln] = model if return_models else BulkRes(model.x_test, model.y_test)
 
-        if queue is not None:
-            queue.put(1)
+            if queue is not None:
+                queue.put(1)
 
     if queue is not None:
         queue.put(None)
@@ -297,6 +298,8 @@ def _fit_bulk(
         raise ValueError(f"Expected time ranges to be of length `{len(lineages)}`, found `{len(time_range)}`.")
 
     n_jobs = parallel_kwargs.pop("n_jobs", 1)
+    backend = parallel_kwargs.pop("backend", DEFAULT_BACKEND)
+    show_progress_bar = parallel_kwargs.pop("show_progress_bar", True)
 
     _start = _time.perf_counter()
     logger.info("Computing trends using %d core(s)", n_jobs)
@@ -305,6 +308,8 @@ def _fit_bulk(
         genes,
         unit="gene" if kwargs.get("data_key", "gene") != "obs" else "obs",
         n_jobs=n_jobs,
+        backend=backend,
+        show_progress_bar=show_progress_bar,
         extractor=lambda modelss: {k: v for m in modelss for k, v in m.items()},
     )(
         models=models,
@@ -316,6 +321,12 @@ def _fit_bulk(
     )
     logger.info("    Finish (%.2fs)", _time.perf_counter() - _start)
 
+    n_failed = sum(
+        1 for gene_models in models.values() for m in gene_models.values() if getattr(m, "_converged", True) is False
+    )
+    if n_failed:
+        logger.warning("%d/%d GAM fit(s) did not converge", n_failed, sum(len(v) for v in models.values()))
+
     return _filter_models(models, return_models=return_models, filter_all_failed=filter_all_failed)
 
 
@@ -1023,3 +1034,237 @@ def _get_sorted_colors(
             res.append(np.asarray(adata.obs[ck])[order])
 
     return res
+
+
+def _plot_time_scatter(
+    adata: AnnData,
+    x: np.ndarray,
+    ys: list[np.ndarray],
+    *,
+    color: list[str] | None = None,
+    title: list[str] | None = None,
+    xlabel: str = "",
+    ylabel: str = "probability",
+    cmap: str = "viridis",
+    **kwargs: Any,
+) -> None:
+    """Plot fate probability vs pseudotime as multi-panel scatter.
+
+    Parameters
+    ----------
+    adata
+        Annotated data matrix.
+    x
+        X-axis values (e.g. pseudotime).
+    ys
+        List of arrays, one per panel, for the y-axis.
+    color
+        Column names in ``adata.obs`` to color by, one per panel or one shared.
+    title
+        Title for each panel.
+    xlabel
+        X-axis label.
+    ylabel
+        Y-axis label.
+    cmap
+        Colormap for continuous color values.
+    kwargs
+        Additional keyword arguments (``figsize``, ``dpi``, ``size``, ``show``
+        are extracted; the rest is ignored).
+    """
+    n_panels = len(ys)
+    ncols = min(4, n_panels)
+    nrows = int(np.ceil(n_panels / ncols))
+
+    figsize = kwargs.pop("figsize", (6 * ncols, 4 * nrows))
+    dpi = kwargs.pop("dpi", None)
+    s = kwargs.pop("size", kwargs.pop("s", 1))
+    show = kwargs.pop("show", None)
+    _save = kwargs.pop("save", None)
+    legend_loc = kwargs.pop("legend_loc", "best")
+
+    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize, dpi=dpi, squeeze=False)
+    axes_flat = axes.ravel()
+
+    color_per = None
+    if color is not None:
+        color_per = color * n_panels if len(color) == 1 else list(color)
+
+    for i, (y, ax) in enumerate(zip(ys, axes_flat)):
+        c = color_per[i] if color_per else None
+        if c is not None and c in adata.obs:
+            obs_data = adata.obs[c]
+            if isinstance(obs_data.dtype, pd.CategoricalDtype):
+                palette = adata.uns.get(f"{c}_colors", None)
+                for j, cat in enumerate(obs_data.cat.categories):
+                    mask = (obs_data == cat).values
+                    kw = {"c": palette[j]} if palette is not None and j < len(palette) else {}
+                    ax.scatter(x[mask], y[mask], s=s, alpha=0.8, label=cat, edgecolors="none", **kw)
+                if legend_loc not in (None, "none", "None", False):
+                    legend_kw: dict[str, Any] = {"fontsize": "x-small", "frameon": False}
+                    if legend_loc in ("right", "right margin"):
+                        legend_kw.update(loc="center left", bbox_to_anchor=(1.0, 0.5))
+                    else:
+                        legend_kw["loc"] = legend_loc
+                    ax.legend(**legend_kw)
+            else:
+                scatter = ax.scatter(x, y, c=obs_data.values, cmap=cmap, s=s, alpha=0.8, edgecolors="none")
+                plt.colorbar(scatter, ax=ax)
+        else:
+            scatter = ax.scatter(x, y, c=y, cmap=cmap, s=s, alpha=0.8, edgecolors="none")
+            plt.colorbar(scatter, ax=ax)
+
+        ax.set_xlabel(xlabel)
+        ax.set_ylabel(ylabel)
+        if title and i < len(title):
+            ax.set_title(title[i])
+
+    for j in range(n_panels, len(axes_flat)):
+        axes_flat[j].remove()
+
+    plt.tight_layout()
+    if _save is not None:
+        save_fig(fig, _save)
+    if show is True or (show is None and _save is None):
+        plt.show()
+
+
+def _plot_color_gradients(
+    adata: AnnData,
+    data: Any,
+    *,
+    basis: str = "umap",
+    title: list[str] | None = None,
+    **kwargs: Any,
+) -> None:
+    """Plot fate probabilities as overlapping color gradients on an embedding.
+
+    Uses the same pairwise diverging-colormap technique as scvelo: for each
+    pair of lineages, cells are colored on a diverging scale from
+    ``color_A → transparent → color_B``.  Uncertain cells (low probability
+    for both lineages) become transparent and let the grey background show
+    through.  Only cells whose two dominant lineages match the current pair
+    are drawn, so each cell is plotted at most once per pair.
+
+    Parameters
+    ----------
+    adata
+        Annotated data matrix.
+    data
+        A :class:`~cellrank._utils._lineage.Lineage` with fate probabilities.
+    basis
+        Key in ``adata.obsm`` for the 2-D coordinates.
+    title
+        Plot title (list with a single element or string).
+    kwargs
+        Additional keyword arguments (``figsize``, ``dpi``, ``size``,
+        ``legend_loc``, ``show``, ``save`` are extracted; the rest is
+        ignored).
+    """
+    coords = adata.obsm[f"X_{basis}"]
+
+    figsize = kwargs.pop("figsize", None)
+    dpi = kwargs.pop("dpi", None)
+    s = kwargs.pop("size", kwargs.pop("s", None))
+    if s is None:
+        s = (120_000 / adata.n_obs + 20) / 2
+    show = kwargs.pop("show", None)
+    _save = kwargs.pop("save", None)
+    legend_loc = kwargs.pop("legend_loc", "right")
+
+    fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
+
+    # Probability matrix: cells × lineages, clipped to [0, ∞)
+    vals = np.clip(data.X, 0, None)
+    names = list(data.names)
+    lin_colors = list(data.colors)
+    n_lineages = len(names)
+
+    # Background: all cells in light grey, very faint
+    ax.scatter(coords[:, 0], coords[:, 1], c="lightgrey", s=s, alpha=0.2, marker=".", edgecolors="none")
+
+    # For each cell, find its top-2 lineage indices
+    sorted_idx = np.argsort(vals, axis=1)[:, ::-1][:, :2]  # (n_cells, 2)
+
+    # Pairwise diverging colormap layers (scvelo approach)
+    for id0 in range(n_lineages):
+        for id1 in range(id0 + 1, n_lineages):
+            # Only cells whose top-2 lineages include this pair
+            cell_mask = np.array(
+                [(id0 in row and id1 in row) for row in sorted_idx],
+                dtype=bool,
+            )
+            if np.sum(cell_mask) < 2:
+                continue
+
+            # Diverging value: positive → id1, negative → id0
+            c_vals = vals[cell_mask, id1] - vals[cell_mask, id0]
+            c_coords = coords[cell_mask]
+
+            # Build diverging colormap: color_A → transparent white → color_B
+            rgba_a = np.array(to_rgba(lin_colors[id0]))
+            rgba_b = np.array(to_rgba(lin_colors[id1]))
+            cmap_colors = [
+                (*rgba_a[:3], 1.0),  # fully opaque color A
+                (1.0, 1.0, 1.0, 0.0),  # transparent white in the middle
+                (*rgba_b[:3], 1.0),  # fully opaque color B
+            ]
+            cmap = colors.LinearSegmentedColormap.from_list(f"_cr_{id0}_{id1}", cmap_colors, N=256)
+
+            # Normalise to [-1, 1] symmetric range, then map to [0, 1] for cmap
+            abs_max = np.max(np.abs(c_vals)) if np.max(np.abs(c_vals)) > 0 else 1.0
+            c_normed = (c_vals / abs_max + 1) / 2  # map [-1,1] → [0,1]
+
+            # Sort so high-magnitude cells are on top
+            order = np.argsort(np.abs(c_vals))
+            ax.scatter(
+                c_coords[order, 0],
+                c_coords[order, 1],
+                c=c_normed[order],
+                cmap=cmap,
+                vmin=0,
+                vmax=1,
+                s=s,
+                marker=".",
+                edgecolors="none",
+            )
+
+    # Legend
+    handles = [
+        plt.Line2D(
+            [0],
+            [0],
+            marker="o",
+            color="w",
+            markerfacecolor=c,
+            label=n,
+            markersize=8,
+        )
+        for n, c in zip(names, lin_colors)
+    ]
+    if legend_loc not in (None, "none", "None", False):
+        if legend_loc == "right":
+            ax.legend(handles=handles, frameon=False, loc="center left", bbox_to_anchor=(1.0, 0.5))
+        elif legend_loc == "right margin":
+            ax.legend(handles=handles, frameon=False, loc="center left", bbox_to_anchor=(1.04, 0.5))
+        elif legend_loc == "on data":
+            # Place labels at centroid of high-probability cells
+            for name in data.names:
+                col = data[:, name].X.ravel()
+                top_mask = col > np.percentile(col, 90)
+                if top_mask.any():
+                    cx, cy = coords[top_mask, 0].mean(), coords[top_mask, 1].mean()
+                    ax.text(cx, cy, name, fontsize=8, fontweight="bold", ha="center", va="center")
+        else:
+            ax.legend(handles=handles, frameon=False, loc=legend_loc)
+    if title:
+        ax.set_title(title[0] if isinstance(title, list) else title)
+    ax.set_xticks([])
+    ax.set_yticks([])
+    for spine in ax.spines.values():
+        spine.set_visible(False)
+
+    if _save is not None:
+        save_fig(fig, _save)
+    if show is True or (show is None and _save is None):
+        plt.show()
diff --git a/tests/_ground_truth_figures/all_models_for_1_gene_failed.png b/tests/_ground_truth_figures/all_models_for_1_gene_failed.png
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diff --git a/tests/_ground_truth_figures/transpose_failed_only_off_diagonal.png b/tests/_ground_truth_figures/transpose_failed_only_off_diagonal.png
index b94540cb9..35c1c81fc 100644
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index d22febf20..bcc2ffeaa 100644
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index bbd1a364a..565ab7d8a 100644
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diff --git a/tests/_ground_truth_figures/trends_fate_prob_cmap.png b/tests/_ground_truth_figures/trends_fate_prob_cmap.png
index 60dbdb1dd..157c72909 100644
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index 1a793999f..07e81097c 100644
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diff --git a/tests/_ground_truth_figures/trends_lineage_cell_color_clusters.png b/tests/_ground_truth_figures/trends_lineage_cell_color_clusters.png
index 2f07111bc..731712e91 100644
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diff --git a/tests/_ground_truth_figures/trends_lineage_cell_color_gene.png b/tests/_ground_truth_figures/trends_lineage_cell_color_gene.png
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diff --git a/tests/_ground_truth_figures/trends_lineage_cmap.png b/tests/_ground_truth_figures/trends_lineage_cmap.png
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diff --git a/tests/_ground_truth_figures/trends_margins.png b/tests/_ground_truth_figures/trends_margins.png
index 15805dbab..0370b2022 100644
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diff --git a/tests/_ground_truth_figures/trends_no_cbar.png b/tests/_ground_truth_figures/trends_no_cbar.png
index 914377d56..3b339cd5e 100644
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diff --git a/tests/_ground_truth_figures/trends_perc.png b/tests/_ground_truth_figures/trends_perc.png
index c0b570297..2dc52b164 100644
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diff --git a/tests/_ground_truth_figures/trends_perc_per_lineage.png b/tests/_ground_truth_figures/trends_perc_per_lineage.png
index b9c814b88..cf6e0983c 100644
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diff --git a/tests/_ground_truth_figures/trends_raw.png b/tests/_ground_truth_figures/trends_raw.png
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diff --git a/tests/_ground_truth_figures/trends_show_lineage_ci.png b/tests/_ground_truth_figures/trends_show_lineage_ci.png
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diff --git a/tests/_ground_truth_figures/trends_show_lineage_diff_plot.png b/tests/_ground_truth_figures/trends_show_lineage_diff_plot.png
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diff --git a/tests/_ground_truth_figures/trends_show_lineage_ignores_no_transpose.png b/tests/_ground_truth_figures/trends_show_lineage_ignores_no_transpose.png
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diff --git a/tests/_ground_truth_figures/trends_show_lineage_same_plot.png b/tests/_ground_truth_figures/trends_show_lineage_same_plot.png
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diff --git a/tests/_ground_truth_figures/trends_size.png b/tests/_ground_truth_figures/trends_size.png
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diff --git a/tests/_ground_truth_figures/trends_suptitle.png b/tests/_ground_truth_figures/trends_suptitle.png
index 97caad028..e6ffe2af3 100644
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diff --git a/tests/_ground_truth_figures/trends_time_key.png b/tests/_ground_truth_figures/trends_time_key.png
index 355849276..a9a30a162 100644
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diff --git a/tests/_ground_truth_figures/trends_time_key_del_latent_time.png b/tests/_ground_truth_figures/trends_time_key_del_latent_time.png
index 355849276..a9a30a162 100644
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diff --git a/tests/_ground_truth_figures/trends_time_range.png b/tests/_ground_truth_figures/trends_time_range.png
index d82a7bff6..13246e1b3 100644
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diff --git a/tests/_ground_truth_figures/violin.png b/tests/_ground_truth_figures/violin.png
index 1b60f8cb6..6c6e95e3e 100644
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diff --git a/tests/_ground_truth_figures/violin_cluster_subset.png b/tests/_ground_truth_figures/violin_cluster_subset.png
index baeb6e69a..5ae2e0675 100644
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diff --git a/tests/_ground_truth_figures/violin_lineage_subset.png b/tests/_ground_truth_figures/violin_lineage_subset.png
index 03d420969..db6bccbca 100644
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diff --git a/tests/_ground_truth_figures/violin_no_cluster_key.png b/tests/_ground_truth_figures/violin_no_cluster_key.png
index 390691c2a..1a08604e4 100644
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diff --git a/tests/conftest.py b/tests/conftest.py
index eeeb16d1f..b2c91bcdc 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -22,8 +22,19 @@
 def pytest_sessionstart(session: pytest.Session) -> None:
     matplotlib.use("Agg")
     matplotlib.rcParams["figure.max_open_warning"] = 0
+    matplotlib.rcParams["savefig.transparent"] = True
     np.random.seed(42)  # noqa: NPY002
 
+    _figdir = pathlib.Path(__file__).parent / "figures"
+    cr.settings.figdir = _figdir
+    sc.settings.figdir = _figdir
+    try:
+        import scvelo as scv
+
+        scv.settings.figdir = str(_figdir)
+    except ImportError:
+        pass
+
     # https://github.com/theislab/cellrank/issues/683
     warnings.simplefilter("ignore", NumbaPerformanceWarning)
 
diff --git a/tests/test_gpcca.py b/tests/test_gpcca.py
index 5a78cc994..b13bea163 100644
--- a/tests/test_gpcca.py
+++ b/tests/test_gpcca.py
@@ -1,5 +1,6 @@
 import copy
 import enum
+import logging
 import os
 from collections.abc import Sequence
 from typing import Optional
@@ -1156,7 +1157,19 @@ def test_compute_schur_verbosity(self, adata_large: AnnData, verbose: bool | Non
         vk = VelocityKernel(adata_large).compute_transition_matrix(softmax_scale=4.0)
         g = cr.estimators.GPCCA(vk)
 
-        g.compute_schur(n_components=10, method="krylov", verbose=verbose)
+        # Flush any stdout captured so far (e.g. CellRank INFO logs from
+        # compute_transition_matrix, which go to stdout via RichHandler).
+        capsys.readouterr()
+
+        # Silence CellRank's own log output so we only capture SLEPc's
+        # iteration details, which verbose=False should suppress.
+        cr_logger = logging.getLogger("cellrank")
+        prev_level = cr_logger.level
+        cr_logger.setLevel(logging.WARNING)
+        try:
+            g.compute_schur(n_components=10, method="krylov", verbose=verbose)
+        finally:
+            cr_logger.setLevel(prev_level)
         out, _ = capsys.readouterr()
 
         assert not len(out)
diff --git a/tests/test_kernels.py b/tests/test_kernels.py
index 411520ca5..8aa4b581e 100644
--- a/tests/test_kernels.py
+++ b/tests/test_kernels.py
@@ -379,8 +379,6 @@ class TestKernel:
     )
     @pytest.mark.parametrize("key_added", [None, "foo"])
     def test_kernel_reads_correct_connectivities(self, adata: AnnData, key_added: str | None, clazz: type):
-        if clazz is VelocityKernel and key_added == "foo":
-            pytest.skip("`get_moments` in scVelo doesn't support specifying key")
         del adata.uns["neighbors"]
         del adata.obsp["connectivities"]
         del adata.obsp["distances"]
@@ -1053,12 +1051,12 @@ def test_layer(self, adata: AnnData, layer: str):
 
     @pytest.mark.parametrize("agg", list(CytoTRACEAggregation))
     def test_aggregation(self, adata: AnnData, agg: CytoTRACEAggregation):
-        _ = CytoTRACEKernel(adata).compute_cytotrace(aggregation=agg)
+        _ = CytoTRACEKernel(adata).compute_cytotrace(aggregation=agg, layer="Ms")
         assert adata.uns[Key.cytotrace("params")]["aggregation"] == agg
 
     @pytest.mark.parametrize("use_raw", [False, True])
     def test_raw(self, adata: AnnData, use_raw: bool):
-        _ = CytoTRACEKernel(adata).compute_cytotrace(use_raw=use_raw)
+        _ = CytoTRACEKernel(adata).compute_cytotrace(use_raw=use_raw, layer="Ms")
         assert adata.uns[Key.cytotrace("params")]["use_raw"] == use_raw
 
     @pytest.mark.parametrize("copy", [False, True])
@@ -1096,7 +1094,7 @@ def test_writes_params(self, adata: AnnData):
 
     def test_raw_less_genes(self, adata: AnnData):
         adata.raw = adata.raw.to_adata()[:, :20]
-        _ = CytoTRACEKernel(adata).compute_cytotrace(use_raw=True, n_genes=31)
+        _ = CytoTRACEKernel(adata).compute_cytotrace(use_raw=True, n_genes=31, layer="Ms")
         assert adata.uns[Key.cytotrace("params")] == {
             "layer": "Ms",
             "aggregation": "mean",
@@ -1110,9 +1108,9 @@ def test_n_top_genes(self, adata: AnnData, use_raw: bool, n_genes: int):
         n_genes = min(adata.raw.n_vars if use_raw else adata.n_vars, n_genes)
         if n_genes <= 0:
             with pytest.raises(ValueError, match=r"Expected .* genes to be positive"):
-                _ = CytoTRACEKernel(adata).compute_cytotrace(use_raw=use_raw, n_genes=n_genes)
+                _ = CytoTRACEKernel(adata).compute_cytotrace(use_raw=use_raw, n_genes=n_genes, layer="Ms")
         else:
-            _ = CytoTRACEKernel(adata).compute_cytotrace(use_raw=use_raw, n_genes=n_genes)
+            _ = CytoTRACEKernel(adata).compute_cytotrace(use_raw=use_raw, n_genes=n_genes, layer="Ms")
             assert adata.var[Key.cytotrace("correlates")].sum() == n_genes
             assert adata.uns[Key.cytotrace("params")]["n_genes"] == n_genes
 
@@ -1528,7 +1526,7 @@ def test_from_adata(self, adata: AnnData, clazz: type[Kernel]):
 
         k1 = clazz(adata, **kwargs)
         if isinstance(k1, CytoTRACEKernel):
-            k1 = k1.compute_cytotrace()
+            k1 = k1.compute_cytotrace(layer="Ms")
         k1 = k1.compute_transition_matrix()
         k1.write_to_adata(key=key)
 
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index b54bbab17..c46b90435 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -9,7 +9,6 @@
 import pandas as pd
 import pytest
 import scipy.sparse as sp
-import scvelo as scv
 from anndata import AnnData
 from matplotlib.testing import setup
 from matplotlib.testing.compare import compare_images
@@ -61,11 +60,6 @@
     "Cdh9",
 ]
 
-# TODO(michalk8): move to sessionstart
-cr.settings.figdir = FIGS
-scv.settings.figdir = str(FIGS)
-scv.set_figure_params(transparent=True)
-
 
 def compare(
     *,
@@ -78,11 +72,9 @@ def _compare_images(expected_path: str | pathlib.Path, actual_path: str | pathli
         res = compare_images(expected_path, actual_path, tol=tol)
         assert res is None, res
 
-    # TODO: refactor (we can remove the prefix from scvelo
     def _prepare_fname(func: Callable) -> tuple[str, str]:
         fpath = f"{func.__name__.replace('test_', '')}"
-        # scvelo saves figures as pdf
-        return fpath, str(fpath[7:] + ".png" if fpath.startswith("scvelo_") else fpath)
+        return fpath, fpath
 
     def _assert_equal(fpath: str) -> None:
         if not fpath.endswith(".png"):
@@ -2084,31 +2076,31 @@ def test_mc_spectrum_evals_complex(self, mc: CFLARE, fpath: str):
         mc.plot_spectrum(2, real_only=False, dpi=DPI, save=fpath)
 
     @compare(kind="cflare")
-    def test_scvelo_final_states(self, mc: CFLARE, fpath: str):
+    def test_final_states(self, mc: CFLARE, fpath: str):
         mc.plot_macrostates(which="terminal", dpi=DPI, save=fpath)
 
     @compare(kind="cflare")
-    def test_scvelo_final_states_clusters(self, mc: CFLARE, fpath: str):
+    def test_final_states_clusters(self, mc: CFLARE, fpath: str):
         mc.plot_macrostates(which="terminal", color="clusters", dpi=DPI, save=fpath)
 
     @compare(kind="cflare")
-    def test_scvelo_lin_probs(self, mc: CFLARE, fpath: str):
+    def test_lin_probs(self, mc: CFLARE, fpath: str):
         mc.plot_fate_probabilities(dpi=DPI, save=fpath)
 
     @compare(kind="cflare")
-    def test_scvelo_lin_probs_clusters(self, mc: CFLARE, fpath: str):
+    def test_lin_probs_clusters(self, mc: CFLARE, fpath: str):
         mc.plot_fate_probabilities(color="clusters", dpi=DPI, save=fpath)
 
     @compare(kind="cflare")
-    def test_scvelo_lin_probs_cmap(self, mc: CFLARE, fpath: str):
+    def test_lin_probs_cmap(self, mc: CFLARE, fpath: str):
         mc.plot_fate_probabilities(cmap=cm.inferno, dpi=DPI, save=fpath)
 
     @compare(kind="cflare")
-    def test_scvelo_lin_probs_lineages(self, mc: CFLARE, fpath: str):
+    def test_lin_probs_lineages(self, mc: CFLARE, fpath: str):
         mc.plot_fate_probabilities(states=["0"], dpi=DPI, save=fpath)
 
     @compare(kind="cflare")
-    def test_scvelo_lin_probs_time(self, mc: CFLARE, fpath: str):
+    def test_lin_probs_time(self, mc: CFLARE, fpath: str):
         mc.plot_fate_probabilities(mode="time", time_key="latent_time", dpi=DPI, save=fpath)
 
 
@@ -2186,79 +2178,79 @@ def test_gpcca_coarse_T_no_order(self, mc: GPCCA, fpath: str):
         mc.plot_coarse_T(order=None, dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_meta_states(self, mc: GPCCA, fpath: str):
+    def test_gpcca_meta_states(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="all", dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_meta_states_lineages(self, mc: GPCCA, fpath: str):
+    def test_gpcca_meta_states_lineages(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="all", states=["0"], dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_meta_states_discrete(self, mc: GPCCA, fpath: str):
+    def test_gpcca_meta_states_discrete(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="all", discrete=True, dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_meta_states_cluster_key(self, mc: GPCCA, fpath: str):
+    def test_gpcca_meta_states_cluster_key(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="all", color="clusters", dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_meta_states_no_same_plot(self, mc: GPCCA, fpath: str):
+    def test_gpcca_meta_states_no_same_plot(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="all", same_plot=False, dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_meta_states_cmap(self, mc: GPCCA, fpath: str):
+    def test_gpcca_meta_states_cmap(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="all", cmap=cm.inferno, same_plot=False, dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_meta_states_title(self, mc: GPCCA, fpath: str):
+    def test_gpcca_meta_states_title(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="all", title="foobar", dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_meta_states_time(self, mc: GPCCA, fpath: str):
+    def test_gpcca_meta_states_time(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="all", mode="time", dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_final_states(self, mc: GPCCA, fpath: str):
+    def test_gpcca_final_states(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="terminal", dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_final_states_lineages(self, mc: GPCCA, fpath: str):
+    def test_gpcca_final_states_lineages(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="terminal", states=["0"], dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_final_states_discrete(self, mc: GPCCA, fpath: str):
+    def test_gpcca_final_states_discrete(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="terminal", discrete=True, dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_final_states_cluster_key(self, mc: GPCCA, fpath: str):
+    def test_gpcca_final_states_cluster_key(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="terminal", color="clusters", dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_final_states_no_same_plot(self, mc: GPCCA, fpath: str):
+    def test_gpcca_final_states_no_same_plot(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="terminal", same_plot=False, dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_final_states_cmap(self, mc: GPCCA, fpath: str):
+    def test_gpcca_final_states_cmap(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="terminal", cmap=cm.inferno, same_plot=False, dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_final_states_title(self, mc: GPCCA, fpath: str):
+    def test_gpcca_final_states_title(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="terminal", title="foobar", dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_final_states_time(self, mc: GPCCA, fpath: str):
+    def test_gpcca_final_states_time(self, mc: GPCCA, fpath: str):
         mc.plot_macrostates(which="terminal", mode="time", dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_fate_probs_cont_same_no_clusters(self, mc: GPCCA, fpath: str):
+    def test_gpcca_fate_probs_cont_same_no_clusters(self, mc: GPCCA, fpath: str):
         mc.plot_fate_probabilities(same_plot=True, dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_fate_probs_cont_same_clusters(self, mc: GPCCA, fpath: str):
+    def test_gpcca_fate_probs_cont_same_clusters(self, mc: GPCCA, fpath: str):
         mc.plot_fate_probabilities(color="clusters", same_plot=True, dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
-    def test_scvelo_gpcca_fate_probs_cont_not_same(self, mc: GPCCA, fpath: str):
+    def test_gpcca_fate_probs_cont_not_same(self, mc: GPCCA, fpath: str):
         mc.plot_fate_probabilities(color="clusters", same_plot=False, dpi=DPI, save=fpath)
 
     @compare(kind="gpcca")
@@ -2269,7 +2261,7 @@ def test_scvelo_transition_matrix_projection(self, mc: GPCCA, fpath: str):
             arrow_length=6,
             arrow_size=6,
             dpi=DPI,
-            save=fpath,
+            save=fpath.removeprefix("scvelo_") + ".png",
         )
 
     @compare(kind="gpcca")
@@ -2735,7 +2727,7 @@ def test_proj_extra_keys(self, adata: AnnData, fpath: str):
         assert f"{apk}_entropy" in adata.obs
 
     @compare()
-    def test_proj_scvelo_kwargs(self, adata: AnnData, fpath: str):
+    def test_proj_legend_loc(self, adata: AnnData, fpath: str):
         cr.pl.circular_projection(adata, keys="clusters", legend_loc="upper right", dpi=DPI, save=fpath)
 
     @compare()
@@ -3348,22 +3340,22 @@ class TestProjectionEmbedding:
     def test_scvelo_connectivity_kernel_emb_stream(self, adata: AnnData, fpath: str):
         ck = ConnectivityKernel(adata)
         ck.compute_transition_matrix()
-        ck.plot_projection(dpi=DPI, save=fpath)
+        ck.plot_projection(dpi=DPI, save=fpath.removeprefix("scvelo_") + ".png")
 
     @compare()
     def test_scvelo_pseudotime_kernel_hard_threshold_emb_stream(self, adata: AnnData, fpath: str):
         ptk = PseudotimeKernel(adata, time_key="dpt_pseudotime")
         ptk.compute_transition_matrix(threshold_scheme="hard", frac_to_keep=0.3)
-        ptk.plot_projection(dpi=DPI, save=fpath)
+        ptk.plot_projection(dpi=DPI, save=fpath.removeprefix("scvelo_") + ".png")
 
     @compare()
     def test_scvelo_pseudotime_kernel_soft_threshold_emb_stream(self, adata: AnnData, fpath: str):
         ptk = PseudotimeKernel(adata, time_key="dpt_pseudotime")
         ptk.compute_transition_matrix(threshold_scheme="soft", frac_to_keep=0.3)
-        ptk.plot_projection(dpi=DPI, save=fpath)
+        ptk.plot_projection(dpi=DPI, save=fpath.removeprefix("scvelo_") + ".png")
 
     @compare()
     def test_scvelo_velocity_kernel_emb_stream(self, adata: AnnData, fpath: str):
         vk = VelocityKernel(adata)
         vk.compute_transition_matrix()
-        vk.plot_projection(dpi=DPI, save=fpath)
+        vk.plot_projection(dpi=DPI, save=fpath.removeprefix("scvelo_") + ".png")