diff --git a/README.md b/README.md
index 7aac6bb..0926110 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,8 @@
 - [Contrastive Neural Ratio Estimation](https://arxiv.org/abs/2210.06170) (short `SNR`)
 - [Neural Approximate Sufficient Statistics](https://arxiv.org/abs/2010.10079) (`SNASS`)
 - [Neural Approximate Slice Sufficient Statistics](https://openreview.net/forum?id=jjzJ768iV1) (`SNASSS`)
-- [Flow matching posterior estimation](https://openreview.net/forum?id=jjzJ768iV1) (`SFMPE`)
+- [Flow matching posterior estimation](https://arxiv.org/abs/2305.17161) (`SFMPE`)
+- [Consistency model posterior estimation](https://arxiv.org/abs/2312.05440) (`SCMPE`)
 
 where the acronyms in parentheses denote the names of the methods in `sbijax`.
 
@@ -53,6 +54,7 @@ pip install git+https://github.com/dirmeier/sbijax@<RELEASE>
 
 ## Acknowledgements
 
+> [!NOTE]
 > 📝 The API of the package is heavily inspired by the excellent Pytorch-based [`sbi`](https://github.com/sbi-dev/sbi) package which is substantially more
 feature-complete and user-friendly, and better documented.
 
diff --git a/docs/index.rst b/docs/index.rst
index 4f3b482..b25e991 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -21,6 +21,7 @@
 - `Neural Approximate Sufficient Statistics <https://arxiv.org/abs/2010.10079>`_ (:code:`SNASS`)
 - `Neural Approximate Slice Sufficient Statistics <https://openreview.net/forum?id=jjzJ768iV1>`_ (:code:`SNASSS`)
 - `Flow matching posterior estimation <https://arxiv.org/abs/2305.17161>`_ (:code:`SFMPE`)
+- `Consistency model posterior estimation <https://arxiv.org/abs/2312.05440>`_ (:code:`SCMPE`)
 
 .. caution::
 
diff --git a/docs/sbijax.rst b/docs/sbijax.rst
index 332fa0c..a074306 100644
--- a/docs/sbijax.rst
+++ b/docs/sbijax.rst
@@ -16,6 +16,7 @@ Methods
     SNP
     SNR
     SFMPE
+    SCMPE
     SNASS
     SNASSS
 
@@ -49,6 +50,12 @@ SFMPE
 ..  autoclass:: SFMPE
     :members: fit, simulate_data_and_possibly_append, sample_posterior
 
+SCMPE
+~~~~~
+
+..  autoclass:: SCMPE
+    :members: fit, simulate_data_and_possibly_append, sample_posterior
+
 SNASS
 ~~~~~
 
diff --git a/examples/bivariate_gaussian_cfmpe.py b/examples/bivariate_gaussian_cfmpe.py
new file mode 100644
index 0000000..01404e4
--- /dev/null
+++ b/examples/bivariate_gaussian_cfmpe.py
@@ -0,0 +1,85 @@
+"""
+Example using consistency model posterior estimation on a bivariate Gaussian
+"""
+
+import distrax
+import haiku as hk
+import matplotlib.pyplot as plt
+import optax
+import seaborn as sns
+from jax import numpy as jnp
+from jax import random as jr
+
+from sbijax import SCMPE
+from sbijax.nn import ConsistencyModel
+
+
+def prior_model_fns():
+    p = distrax.Independent(distrax.Normal(jnp.zeros(2), jnp.ones(2)), 1)
+    return p.sample, p.log_prob
+
+
+def simulator_fn(seed, theta):
+    p = distrax.Normal(jnp.zeros_like(theta), 1.0)
+    y = theta + p.sample(seed=seed)
+    return y
+
+
+def make_model(dim):
+    @hk.transform
+    def _mlp(method, **kwargs):
+        def _c_skip(time):
+            return 1 / ((time - 0.001) ** 2 + 1)
+
+        def _c_out(time):
+            return 1.0 * (time - 0.001) / jnp.sqrt(1 + time**2)
+
+        def _nn(theta, time, context, **kwargs):
+            ins = jnp.concatenate([theta, time, context], axis=-1)
+            outs = hk.nets.MLP([64, 64, dim])(ins)
+            out_skip = _c_skip(time) * theta + _c_out(time) * outs
+            return out_skip
+
+        cm = ConsistencyModel(dim, _nn)
+        return cm(method, **kwargs)
+
+    return _mlp
+
+
+def run():
+    y_observed = jnp.array([2.0, -2.0])
+
+    prior_simulator_fn, prior_logdensity_fn = prior_model_fns()
+    fns = (prior_simulator_fn, prior_logdensity_fn), simulator_fn
+
+    estim = SCMPE(fns, make_model(2))
+    optimizer = optax.adam(1e-3)
+
+    data, params = None, {}
+    for i in range(2):
+        data, _ = estim.simulate_data_and_possibly_append(
+            jr.fold_in(jr.PRNGKey(1), i),
+            params=params,
+            observable=y_observed,
+            data=data,
+        )
+        params, info = estim.fit(
+            jr.fold_in(jr.PRNGKey(2), i),
+            data=data,
+            optimizer=optimizer,
+        )
+
+    rng_key = jr.PRNGKey(23)
+    post_samples, _ = estim.sample_posterior(rng_key, params, y_observed)
+    print(post_samples)
+    fig, axes = plt.subplots(2)
+    for i, ax in enumerate(axes):
+        sns.histplot(post_samples[:, i], color="darkblue", ax=ax)
+        ax.set_xlim([-3.0, 3.0])
+    sns.despine()
+    plt.tight_layout()
+    plt.show()
+
+
+if __name__ == "__main__":
+    run()
diff --git a/sbijax/__init__.py b/sbijax/__init__.py
index 646b951..75ada41 100644
--- a/sbijax/__init__.py
+++ b/sbijax/__init__.py
@@ -2,11 +2,12 @@
 sbijax: Simulation-based inference in JAX
 """
 
-__version__ = "0.1.9"
+__version__ = "0.2.0"
 
 
 from sbijax._src.abc.smc_abc import SMCABC
-from sbijax._src.fmpe import SFMPE
+from sbijax._src.scmpe import SCMPE
+from sbijax._src.sfmpe import SFMPE
 from sbijax._src.snass import SNASS
 from sbijax._src.snasss import SNASSS
 from sbijax._src.snl import SNL
diff --git a/sbijax/_src/nn/consistency_model.py b/sbijax/_src/nn/consistency_model.py
new file mode 100644
index 0000000..24fc43d
--- /dev/null
+++ b/sbijax/_src/nn/consistency_model.py
@@ -0,0 +1,212 @@
+from typing import Callable
+
+import distrax
+import haiku as hk
+import jax
+from jax import numpy as jnp
+
+from sbijax._src.nn.continuous_normalizing_flow import _ResnetBlock
+
+__all__ = ["ConsistencyModel", "make_consistency_model"]
+
+
+class ConsistencyModel(hk.Module):
+    """A consistency model.
+
+    Args:
+        n_dimension: the dimensionality of the modelled space
+        transform: a haiku module. The transform is a callable that has to
+            take as input arguments named 'theta', 'time', 'context' and
+            **kwargs. Theta, time and context are two-dimensional arrays
+            with the same batch dimensions.
+        t_min: minimal time point for ODE integration
+        t_max: maximal time point for ODE integration
+    """
+
+    def __init__(
+        self,
+        n_dimension: int,
+        transform: Callable,
+        t_min: float = 0.001,
+        t_max: float = 50.0,
+    ):
+        """Construct a consistency model.
+
+        Args:
+            n_dimension: the dimensionality of the modelled space
+            transform: a haiku module. The transform is a callable that has to
+                take as input arguments named 'theta', 'time', 'context' and
+                **kwargs. Theta, time and context are two-dimensional arrays
+                with the same batch dimensions.
+            t_min: minimal time point for ODE integration
+            t_max: maximal time point for ODE integration
+        """
+        super().__init__()
+        self._n_dimension = n_dimension
+        self._network = transform
+        self._t_max = t_max
+        self._t_min = t_min
+        self._base_distribution = distrax.Normal(jnp.zeros(n_dimension), 1.0)
+
+    def __call__(self, method, **kwargs):
+        """Aplpy the flow.
+
+        Args:
+            method (str): method to call
+
+        Keyword Args:
+            keyword arguments for the called method:
+        """
+        return getattr(self, method)(**kwargs)
+
+    def sample(self, context, **kwargs):
+        """Sample from the consistency model.
+
+        Args:
+            context: array of conditioning variables
+            kwargs: keyword argumente like 'is_training'
+        """
+        noise = self._base_distribution.sample(
+            seed=hk.next_rng_key(), sample_shape=(context.shape[0],)
+        )
+        y_hat = self.vector_field(noise, self._t_max, context, **kwargs)
+
+        noise = self._base_distribution.sample(
+            seed=hk.next_rng_key(), sample_shape=(y_hat.shape[0],)
+        )
+        tme = self._t_min + (self._t_max - self._t_min) / 2
+        noise = jnp.sqrt(jnp.square(tme) - jnp.square(self._t_min)) * noise
+        y_tme = y_hat + noise
+        y_hat = self.vector_field(y_tme, tme, context, **kwargs)
+
+        return y_hat
+
+    def vector_field(self, theta, time, context, **kwargs):
+        """Compute the vector field.
+
+        Args:
+            theta: array of parameters
+            time: time variables
+            context: array of conditioning variables
+
+        Keyword Args:
+            keyword arguments that aer passed tothe neural network
+        """
+        time = jnp.full((theta.shape[0], 1), time)
+        return self._network(theta=theta, time=time, context=context, **kwargs)
+
+
+# pylint: disable=too-many-arguments,too-many-instance-attributes
+class _CMResnet(hk.Module):
+    """A simplified 1-d residual network."""
+
+    def __init__(
+        self,
+        n_layers: int,
+        n_dimension: int,
+        hidden_size: int,
+        activation: Callable = jax.nn.relu,
+        dropout_rate: float = 0.0,
+        do_batch_norm: bool = False,
+        batch_norm_decay: float = 0.1,
+        t_min: float = 0.001,
+        sigma_data: float = 1.0,
+    ):
+        super().__init__()
+        self.n_layers = n_layers
+        self.n_dimension = n_dimension
+        self.hidden_size = hidden_size
+        self.activation = activation
+        self.do_batch_norm = do_batch_norm
+        self.dropout_rate = dropout_rate
+        self.batch_norm_decay = batch_norm_decay
+        self.sigma_data = sigma_data
+        self.var_data = self.sigma_data**2
+        self.t_min = t_min
+
+    def __call__(self, theta, time, context, is_training, **kwargs):
+        outputs = context
+        t_theta_embedding = jnp.concatenate(
+            [
+                hk.Linear(self.n_dimension)(theta),
+                hk.Linear(self.n_dimension)(time),
+            ],
+            axis=-1,
+        )
+        outputs = hk.Linear(self.hidden_size)(outputs)
+        outputs = self.activation(outputs)
+        for _ in range(self.n_layers):
+            outputs = _ResnetBlock(
+                hidden_size=self.hidden_size,
+                activation=self.activation,
+                dropout_rate=self.dropout_rate,
+                do_batch_norm=self.do_batch_norm,
+                batch_norm_decay=self.batch_norm_decay,
+            )(outputs, context=t_theta_embedding, is_training=is_training)
+        outputs = self.activation(outputs)
+        outputs = hk.Linear(self.n_dimension)(outputs)
+
+        # TODO(simon): dan we choose sigma automatically?
+        out_skip = self._c_skip(time) * theta + self._c_out(time) * outputs
+        return out_skip
+
+    def _c_skip(self, time):
+        return self.var_data / ((time - self.t_min) ** 2 + self.var_data)
+
+    def _c_out(self, time):
+        return (
+            self.sigma_data
+            * (time - self.t_min)
+            / jnp.sqrt(self.var_data + time**2)
+        )
+
+
+def make_consistency_model(
+    n_dimension: int,
+    n_layers: int = 2,
+    hidden_size: int = 64,
+    activation: Callable = jax.nn.tanh,
+    dropout_rate: float = 0.2,
+    do_batch_norm: bool = False,
+    batch_norm_decay: float = 0.2,
+    t_min: float = 0.001,
+    t_max: float = 50.0,
+    sigma_data: float = 1.0,
+):
+    """Create a consistency model.
+
+    The consistency model uses a residual network as score network.
+
+    Args:
+        n_dimension: dimensionality of modelled space
+        n_layers: number of resnet blocks
+        hidden_size: sizes of hidden layers for each resnet block
+        activation: a jax activation function
+        dropout_rate: dropout rate to use in resnet blocks
+        do_batch_norm: use batch normalization or not
+        batch_norm_decay: decay rate of EMA in batch norm layer
+        t_min: minimal time point for ODE integration
+        t_max: maximal time point for ODE integration
+        sigma_data: the standard deviation of the data :)
+
+    Returns:
+        returns a consistency model
+    """
+
+    @hk.transform
+    def _cm(method, **kwargs):
+        nn = _CMResnet(
+            n_layers=n_layers,
+            n_dimension=n_dimension,
+            hidden_size=hidden_size,
+            activation=activation,
+            do_batch_norm=do_batch_norm,
+            dropout_rate=dropout_rate,
+            batch_norm_decay=batch_norm_decay,
+            t_min=t_min,
+            sigma_data=sigma_data,
+        )
+        cm = ConsistencyModel(n_dimension, nn, t_min=t_min, t_max=t_max)
+        return cm(method, **kwargs)
+
+    return _cm
diff --git a/sbijax/_src/nn/continuous_normalizing_flow.py b/sbijax/_src/nn/continuous_normalizing_flow.py
index 1c405e0..741d865 100644
--- a/sbijax/_src/nn/continuous_normalizing_flow.py
+++ b/sbijax/_src/nn/continuous_normalizing_flow.py
@@ -47,7 +47,7 @@ def __call__(self, method, **kwargs):
         """
         return getattr(self, method)(**kwargs)
 
-    def sample(self, context):
+    def sample(self, context, **kwargs):
         """Sample from the pushforward.
 
         Args:
@@ -61,7 +61,7 @@ def ode_func(time, theta_t):
             theta_t = theta_t.reshape(-1, self._n_dimension)
             time = jnp.full((theta_t.shape[0], 1), time)
             ret = self.vector_field(
-                theta=theta_t, time=time, context=context, is_training=False
+                theta=theta_t, time=time, context=context, **kwargs
             )
             return ret.reshape(-1)
 
diff --git a/sbijax/_src/scmpe.py b/sbijax/_src/scmpe.py
new file mode 100644
index 0000000..f9b2d83
--- /dev/null
+++ b/sbijax/_src/scmpe.py
@@ -0,0 +1,247 @@
+from functools import partial
+
+import jax
+import numpy as np
+import optax
+from absl import logging
+from jax import numpy as jnp
+from jax import random as jr
+from tqdm import tqdm
+
+from sbijax._src.sfmpe import SFMPE
+from sbijax._src.util.early_stopping import EarlyStopping
+
+
+def _alpha_t(time):
+    return 1.0 / (_time_schedule(time + 1) - _time_schedule(time))
+
+
+def _time_schedule(n, rho=7, t_min=0.001, t_max=50, n_inters=1000):
+    left = t_min ** (1 / rho)
+    right = t_max ** (1 / rho) - t_min ** (1 / rho)
+    right = (n - 1) / (n_inters - 1) * right
+    return (left + right) ** rho
+
+
+def _discretization_schedule(n_iter, max_iter=1000):
+    s0, s1 = 10, 50
+    nk = (
+        (n_iter / max_iter) * (jnp.square(s1 + 1) - jnp.square(s0))
+        + jnp.square(s0)
+        - 1
+    )
+    nk = jnp.ceil(jnp.sqrt(nk)) + 1
+    return nk
+
+
+# pylint: disable=too-many-locals,too-many-arguments
+def _consistency_loss(
+    params,
+    ema_params,
+    rng_key,
+    apply_fn,
+    n_iter,
+    t_min,
+    t_max,
+    is_training=False,
+    **batch,
+):
+    theta = batch["theta"]
+    nk = _discretization_schedule(n_iter)
+
+    t_key, rng_key = jr.split(rng_key)
+    time_idx = jr.randint(
+        t_key, shape=(theta.shape[0],), minval=1, maxval=nk - 1
+    )
+    tn = _time_schedule(
+        time_idx, t_min=t_min, t_max=t_max, n_inters=nk
+    ).reshape(-1, 1)
+    tnp1 = _time_schedule(
+        time_idx + 1, t_min=t_min, t_max=t_max, n_inters=nk
+    ).reshape(-1, 1)
+
+    noise_key, rng_key = jr.split(rng_key)
+    noise = jr.normal(noise_key, shape=(*theta.shape,))
+
+    train_rng, rng_key = jr.split(rng_key)
+    fnp1 = apply_fn(
+        params,
+        train_rng,
+        method="vector_field",
+        theta=theta + tnp1 * noise,
+        time=tnp1,
+        context=batch["y"],
+        is_training=is_training,
+    )
+    fn = apply_fn(
+        ema_params,
+        train_rng,
+        method="vector_field",
+        theta=theta + tn * noise,
+        time=tn,
+        context=batch["y"],
+        is_training=is_training,
+    )
+    mse = jnp.sqrt(jnp.mean(jnp.square(fnp1 - fn), axis=1))
+    loss = _alpha_t(time_idx) * mse
+    return jnp.mean(loss)
+
+
+# pylint: disable=too-many-arguments,unused-argument,useless-parent-delegation
+class SCMPE(SFMPE):
+    r"""Sequential consistency model posterior estimation.
+
+    Implements a sequential version of the CMPE algorithm introduced in [1]_.
+    For all rounds $r > 1$ parameter samples
+    :math:`\theta \sim \hat{p}^r(\theta)` are drawn from
+    the approximate posterior instead of the prior when computing consistency
+    loss. Note that the implementation does not strictly follow the paper.
+
+    Args:
+        model_fns: a tuple of tuples. The first element is a tuple that
+                consists of functions to sample and evaluate the
+                log-probability of a data point. The second element is a
+                simulator function.
+        network: a neural network
+        t_min: minimal time point for ODE integration
+        t_max: maximal time point for ODE integration
+
+    Examples:
+        >>> import distrax
+        >>> from sbijax import SCMPE
+        >>> from sbijax.nn import make_consistency_model
+        >>>
+        >>> prior = distrax.Normal(0.0, 1.0)
+        >>> s = lambda seed, theta: distrax.Normal(theta, 1.0).sample(seed=seed)
+        >>> fns = (prior.sample, prior.log_prob), s
+        >>> net = make_consistency_model(1)
+        >>>
+        >>> estim = SCMPE(fns, net)
+
+    References:
+        .. [1] Schmitt, Marvin, et al. "Consistency Models for Scalable and
+           Fast Simulation-Based Inference".
+           arXiv preprint arXiv:2312.05440, 2023.
+    """
+
+    def __init__(self, model_fns, network, t_max=50.0, t_min=0.001):
+        """Construct a SCMPE object.
+
+        Args:
+            model_fns: a tuple of tuples. The first element is a tuple that
+                    consists of functions to sample and evaluate the
+                    log-probability of a data point. The second element is a
+                    simulator function.
+            network: network: a neural network
+            t_min: minimal time point for ODE integration
+            t_max: maximal time point for ODE integration
+        """
+        super().__init__(model_fns, network)
+        self._t_min = t_min
+        self._t_max = t_max
+
+    # pylint: disable=undefined-loop-variable
+    def _fit_model_single_round(
+        self,
+        seed,
+        train_iter,
+        val_iter,
+        optimizer,
+        n_iter,
+        n_early_stopping_patience,
+    ):
+        init_key, seed = jr.split(seed)
+        params = self._init_params(init_key, **next(iter(train_iter)))
+        ema_params = params.copy()
+        state = optimizer.init(params)
+
+        loss_fn = jax.jit(
+            partial(
+                _consistency_loss,
+                apply_fn=self.model.apply,
+                is_training=True,
+                t_max=self._t_max,
+                t_min=self._t_min,
+            )
+        )
+
+        @jax.jit
+        def ema_update(params, avg_params):
+            return optax.incremental_update(avg_params, params, step_size=0.01)
+
+        @jax.jit
+        def step(params, ema_params, rng, state, n_iter, **batch):
+            loss, grads = jax.value_and_grad(loss_fn)(
+                params, ema_params, rng, n_iter=n_iter, **batch
+            )
+            updates, new_state = optimizer.update(grads, state, params)
+            new_params = optax.apply_updates(params, updates)
+            new_ema_params = ema_update(new_params, ema_params)
+            return loss, new_params, new_ema_params, new_state
+
+        losses = np.zeros([n_iter, 2])
+        early_stop = EarlyStopping(1e-3, n_early_stopping_patience * 2)
+        best_params, best_loss = None, np.inf
+        logging.info("training model")
+        for i in tqdm(range(n_iter)):
+            train_loss = 0.0
+            rng_key = jr.fold_in(seed, i)
+            for batch in train_iter:
+                train_key, rng_key = jr.split(rng_key)
+                batch_loss, params, ema_params, state = step(
+                    params, ema_params, train_key, state, n_iter + 1, **batch
+                )
+                train_loss += batch_loss * (
+                    batch["y"].shape[0] / train_iter.num_samples
+                )
+            val_key, rng_key = jr.split(rng_key)
+            validation_loss = self._validation_loss(
+                val_key, params, ema_params, n_iter, val_iter
+            )
+            losses[i] = jnp.array([train_loss, validation_loss])
+
+            _, early_stop = early_stop.update(validation_loss)
+            if early_stop.should_stop:
+                logging.info("early stopping criterion found")
+                break
+            if validation_loss < best_loss:
+                best_loss = validation_loss
+                best_params = params.copy()
+
+        losses = jnp.vstack(losses)[: (i + 1), :]
+        return best_params, losses
+
+    def _init_params(self, rng_key, **init_data):
+        times = jr.uniform(jr.PRNGKey(0), shape=(init_data["y"].shape[0], 1))
+        params = self.model.init(
+            rng_key,
+            method="vector_field",
+            theta=init_data["theta"],
+            time=times,
+            context=init_data["y"],
+            is_training=True,
+        )
+        return params
+
+    # pylint: disable=arguments-differ
+    def _validation_loss(self, rng_key, params, ema_params, n_iter, val_iter):
+        loss_fn = jax.jit(
+            partial(
+                _consistency_loss,
+                apply_fn=self.model.apply,
+                is_training=False,
+                t_max=self._t_max,
+                t_min=self._t_min,
+                n_iter=n_iter,
+            )
+        )
+
+        def body_fn(batch_key, **batch):
+            loss = loss_fn(params, ema_params, batch_key, **batch)
+            return loss * (batch["y"].shape[0] / val_iter.num_samples)
+
+        loss = 0.0
+        for batch in val_iter:
+            val_key, rng_key = jr.split(rng_key)
+            loss += body_fn(val_key, **batch)
+        return loss
diff --git a/sbijax/_src/scmpe_test.py b/sbijax/_src/scmpe_test.py
new file mode 100644
index 0000000..8f6a38e
--- /dev/null
+++ b/sbijax/_src/scmpe_test.py
@@ -0,0 +1,60 @@
+# pylint: skip-file
+
+import distrax
+import haiku as hk
+from jax import numpy as jnp
+
+from sbijax import SCMPE
+from sbijax.nn import make_consistency_model
+
+
+def prior_model_fns():
+    p = distrax.Independent(distrax.Normal(jnp.zeros(2), jnp.ones(2)), 1)
+    return p.sample, p.log_prob
+
+
+def simulator_fn(seed, theta):
+    p = distrax.MultivariateNormalDiag(theta, 0.1 * jnp.ones_like(theta))
+    y = p.sample(seed=seed)
+    return y
+
+
+def log_density_fn(theta, y):
+    prior = distrax.Uniform(jnp.full(2, -3.0), jnp.full(2, 3.0))
+    likelihood = distrax.MultivariateNormalDiag(
+        theta, 0.1 * jnp.ones_like(theta)
+    )
+
+    lp = jnp.sum(prior.log_prob(theta)) + jnp.sum(likelihood.log_prob(y))
+    return lp
+
+
+def test_scmpe():
+    rng_seq = hk.PRNGSequence(0)
+    y_observed = jnp.array([-1.0, 1.0])
+
+    prior_simulator_fn, prior_logdensity_fn = prior_model_fns()
+    fns = (prior_simulator_fn, prior_logdensity_fn), simulator_fn
+
+    estim = SCMPE(fns, make_consistency_model(2))
+    data, params = None, {}
+    for i in range(2):
+        data, _ = estim.simulate_data_and_possibly_append(
+            next(rng_seq),
+            params=params,
+            observable=y_observed,
+            data=data,
+            n_simulations=100,
+            n_chains=2,
+            n_samples=200,
+            n_warmup=100,
+        )
+        params, info = estim.fit(next(rng_seq), data=data, n_iter=2)
+    _ = estim.sample_posterior(
+        next(rng_seq),
+        params,
+        y_observed,
+        n_chains=2,
+        n_samples=200,
+        n_warmup=100,
+    )
diff --git a/sbijax/_src/fmpe.py b/sbijax/_src/sfmpe.py
similarity index 98%
rename from sbijax/_src/fmpe.py
rename to sbijax/_src/sfmpe.py
index d426c68..440434d 100644
--- a/sbijax/_src/fmpe.py
+++ b/sbijax/_src/sfmpe.py
@@ -65,7 +65,8 @@ class SFMPE(SNE):
     For all rounds $r > 1$ parameter samples
     :math:`\theta \sim \hat{p}^r(\theta)` are drawn from
     the approximate posterior instead of the prior when computing the flow
-    matching loss.
+    matching loss. Note that the implementation does not strictly follow the
+    paper.
 
     Args:
         model_fns: a tuple of tuples. The first element is a tuple that
@@ -93,7 +94,7 @@ class SFMPE(SNE):
     """
 
     def __init__(self, model_fns, density_estimator):
-        """Construct a FMPE object.
+        """Construct a SFMPE object.
 
         Args:
             model_fns: a tuple of tuples. The first element is a tuple that
@@ -261,6 +262,7 @@ def sample_posterior(
                 sample_key,
                 method="sample",
                 context=jnp.tile(observable, [n_sim, 1]),
+                is_training=False,
             )
             proposal_probs = self.prior_log_density_fn(proposal)
             proposal_accepted = proposal[jnp.isfinite(proposal_probs)]
diff --git a/sbijax/_src/sfmpe_test.py b/sbijax/_src/sfmpe_test.py
new file mode 100644
index 0000000..102ca07
--- /dev/null
+++ b/sbijax/_src/sfmpe_test.py
@@ -0,0 +1,60 @@
+# pylint: skip-file
+
+import distrax
+import haiku as hk
+from jax import numpy as jnp
+
+from sbijax import SFMPE
+from sbijax.nn import make_ccnf
+
+
+def prior_model_fns():
+    p = distrax.Independent(distrax.Normal(jnp.zeros(2), jnp.ones(2)), 1)
+    return p.sample, p.log_prob
+
+
+def simulator_fn(seed, theta):
+    p = distrax.MultivariateNormalDiag(theta, 0.1 * jnp.ones_like(theta))
+    y = p.sample(seed=seed)
+    return y
+
+
+def log_density_fn(theta, y):
+    prior = distrax.Uniform(jnp.full(2, -3.0), jnp.full(2, 3.0))
+    likelihood = distrax.MultivariateNormalDiag(
+        theta, 0.1 * jnp.ones_like(theta)
+    )
+
+    lp = jnp.sum(prior.log_prob(theta)) + jnp.sum(likelihood.log_prob(y))
+    return lp
+
+
+def test_sfmpe():
+    rng_seq = hk.PRNGSequence(0)
+    y_observed = jnp.array([-1.0, 1.0])
+
+    prior_simulator_fn, prior_logdensity_fn = prior_model_fns()
+    fns = (prior_simulator_fn, prior_logdensity_fn), simulator_fn
+
+    estim = SFMPE(fns, make_ccnf(2))
+    data, params = None, {}
+    for i in range(2):
+        data, _ = estim.simulate_data_and_possibly_append(
+            next(rng_seq),
+            params=params,
+            observable=y_observed,
+            data=data,
+            n_simulations=100,
+            n_chains=2,
+            n_samples=200,
+            n_warmup=100,
+        )
+        params, info = estim.fit(next(rng_seq), data=data, n_iter=2)
+    _ = estim.sample_posterior(
+        next(rng_seq),
+        params,
+        y_observed,
+        n_chains=2,
+        n_samples=200,
+        n_warmup=100,
+    )
diff --git a/sbijax/nn/__init__.py b/sbijax/nn/__init__.py
index 635b6f9..1972b41 100644
--- a/sbijax/nn/__init__.py
+++ b/sbijax/nn/__init__.py
@@ -1,5 +1,9 @@
 """Neural network module."""
 
+from sbijax._src.nn.consistency_model import (
+    ConsistencyModel,
+    make_consistency_model,
+)
 from sbijax._src.nn.continuous_normalizing_flow import CCNF, make_ccnf
 from sbijax._src.nn.make_flows import (
     make_affine_maf,