Move continuous-time consistency model to experimental module.

Related to #256

As we have not extensively tested their behavior and I have observed
subpar performance in some settings, we move this model class to
experimental for further testing, to see whether it is ready for
broader use, or whether it turns out to be too unstable to be practical.

Author: vpratz

bayesflow/experimental/__init__.py

@@ -19,12 +19,12 @@
 )
 
 
-from ..inference_network import InferenceNetwork
-from ..embeddings import FourierEmbedding
+from bayesflow.networks import InferenceNetwork
+from bayesflow.networks.embeddings import FourierEmbedding
 
 
 @register_keras_serializable(package="bayesflow.networks")
-class ContinuousConsistencyModel(InferenceNetwork):
+class ContinuousTimeConsistencyModel(InferenceNetwork):
     """Implements an sCM (simple, stable, and scalable Consistency Model)
     with continous-time Consistency Training (CT) as described in [1].
     The sampling procedure is taken from [2].

bayesflow/networks/consistency_models/continuous_consistency_model.py renamed to bayesflow/experimental/continuous_time_consistency_model.py

@@ -1,5 +1,5 @@
 from .cif import CIF
-from .consistency_models import ConsistencyModel, ContinuousConsistencyModel
+from .consistency_models import ConsistencyModel
 from .coupling_flow import CouplingFlow
 from .deep_set import DeepSet
 from .flow_matching import FlowMatching

bayesflow/networks/__init__.py

@@ -1,2 +1 @@
 from .consistency_model import ConsistencyModel
-from .continuous_consistency_model import ContinuousConsistencyModel