Make diffusion model conditioning more flexible

bayesflow/experimental/diffusion_model/diffusion_model.py

@@ -116,6 +116,7 @@
         if subnet == "mlp":
             subnet_kwargs = DiffusionModel.MLP_DEFAULT_CONFIG | subnet_kwargs
         self.subnet = find_network(subnet, **subnet_kwargs)
+        self._concatenate_subnet_input = kwargs.get("concatenate_subnet_input", True)
 
         self.output_projector = keras.layers.Dense(units=None, bias_initializer="zeros", name="output_projector")
 
@@ -149,6 +150,8 @@
             "prediction_type": self._prediction_type,
             "loss_type": self._loss_type,
             "integrate_kwargs": self.integrate_kwargs,
+            "concatenate_subnet_input": self._concatenate_subnet_input,
+            # we do not need to store subnet_kwargs
         }
         return base_config | serialize(config)
 
@@ -197,6 +200,35 @@
             return (z + sigma_t**2 * pred) / alpha_t
         raise ValueError(f"Unknown prediction type {self._prediction_type}.")
 
+    def _subnet_input(
+        self, xz: Tensor, log_snr: Tensor, conditions: Tensor = None, training: bool = False
+    ) -> Tensor | tuple[Tensor, Tensor, Tensor]:
+        """
+        Prepares the input for the subnet either by concatenating the latent variable `xz`,
+        the signal-to-noise ratio `log_snr`, and optional conditions or by returning them separately.
+
+        Parameters
+        ----------
+        xz : Tensor
+            The noisy input tensor for the diffusion model, typically of shape (..., D), but can vary.
+        log_snr : Tensor
+            The log signal-to-noise ratio tensor, typically of shape (..., 1).
+        conditions : Tensor, optional
+            The optional conditioning tensor (e.g. parameters).
+        training : bool, optional
+            The training mode flag, which can be used to control behavior during training.
+
+        Returns
+        -------
+        Tensor
+            The concatenated input tensor for the subnet or a tuple of tensors if concatenation is disabled.
+        """
+        if self._concatenate_subnet_input:
+            xtc = tensor_utils.concatenate_valid([xz, log_snr, conditions], axis=-1)
+            return self.subnet(xtc, training=training)
+        else:
+            return self.subnet(xz, log_snr, conditions, training=training)
+
     def velocity(
         self,
         xz: Tensor,
@@ -221,7 +253,7 @@
             If True, computes the velocity for the stochastic formulation (SDE).
             If False, uses the deterministic formulation (ODE).
         conditions : Tensor, optional
-            Optional conditional inputs to the network, such as conditioning variables
+            Conditional inputs to the network, such as conditioning variables
             or encoder outputs. Shape must be broadcastable with `xz`. Default is None.
         training : bool, optional
             Whether the model is in training mode. Affects behavior of dropout, batch norm,
@@ -238,12 +270,10 @@
         log_snr_t = ops.broadcast_to(log_snr_t, ops.shape(xz)[:-1] + (1,))
         alpha_t, sigma_t = self.noise_schedule.get_alpha_sigma(log_snr_t=log_snr_t)
 
-        if conditions is None:
-            xtc = tensor_utils.concatenate_valid([xz, self._transform_log_snr(log_snr_t)], axis=-1)
-        else:
-            xtc = tensor_utils.concatenate_valid([xz, self._transform_log_snr(log_snr_t), conditions], axis=-1)
-
-        pred = self.output_projector(self.subnet(xtc, training=training), training=training)
+        subnet_out = self._subnet_input(
+            xz, self._transform_log_snr(log_snr_t), conditions=conditions, training=training
+        )
+        pred = self.output_projector(subnet_out, training=training)
 
         x_pred = self.convert_prediction_to_x(pred=pred, z=xz, alpha_t=alpha_t, sigma_t=sigma_t, log_snr_t=log_snr_t)
 
@@ -461,11 +491,10 @@
         diffused_x = alpha_t * x + sigma_t * eps_t
 
         # calculate output of the network
-        if conditions is None:
-            xtc = tensor_utils.concatenate_valid([diffused_x, self._transform_log_snr(log_snr_t)], axis=-1)
-        else:
-            xtc = tensor_utils.concatenate_valid([diffused_x, self._transform_log_snr(log_snr_t), conditions], axis=-1)
-        pred = self.output_projector(self.subnet(xtc, training=training), training=training)
+        subnet_out = self._subnet_input(
+            diffused_x, self._transform_log_snr(log_snr_t), conditions=conditions, training=training
+        )
+        pred = self.output_projector(subnet_out, training=training)
 
         x_pred = self.convert_prediction_to_x(
             pred=pred, z=diffused_x, alpha_t=alpha_t, sigma_t=sigma_t, log_snr_t=log_snr_t

bayesflow/networks/consistency_models/consistency_model.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 from bayesflow.types import Tensor
-from bayesflow.utils import find_network, layer_kwargs, weighted_mean
+from bayesflow.utils import find_network, layer_kwargs, weighted_mean, tensor_utils, expand_right_as
 from bayesflow.utils.serialization import deserialize, serializable, serialize
 
 from ..inference_network import InferenceNetwork
@@ -77,6 +77,7 @@
         subnet_kwargs = subnet_kwargs or {}
         if subnet == "mlp":
             subnet_kwargs = ConsistencyModel.MLP_DEFAULT_CONFIG | subnet_kwargs
+        self._concatenate_subnet_input = kwargs.get("concatenate_subnet_input", True)
 
         self.subnet = find_network(subnet, **subnet_kwargs)
         self.output_projector = keras.layers.Dense(
@@ -119,6 +120,7 @@
             "eps": self.eps,
             "s0": self.s0,
             "s1": self.s1,
+            "concatenate_subnet_input": self._concatenate_subnet_input,
             # we do not need to store subnet_kwargs
         }
 
@@ -256,6 +258,35 @@
             x = self.consistency_function(x_n, t, conditions=conditions, training=training)
         return x
 
+    def _subnet_input(
+        self, x: Tensor, t: Tensor, conditions: Tensor = None, training: bool = False
+    ) -> Tensor | tuple[Tensor, Tensor, Tensor]:
+        """
+        Prepares the input for the subnet either by concatenating the latent variable `x`,
+        the time `t`, and optional conditions or by returning them separately.
+
+        Parameters
+        ----------
+        x : Tensor
+            The input tensor for the diffusion model, typically of shape (..., D), but can vary.
+        t : Tensor
+            The time tensor, typically of shape (..., 1).
+        conditions : Tensor, optional
+            The optional conditioning tensor (e.g. parameters).
+        training : bool, optional
+            The training mode flag, which can be used to control behavior during training.
+
+        Returns
+        -------
+        Tensor
+            The concatenated input tensor for the subnet or a tuple of tensors if concatenation is disabled.
+        """
+        if self._concatenate_subnet_input:
+            xtc = tensor_utils.concatenate_valid([x, t, conditions], axis=-1)
+            return self.subnet(xtc, training=training)
+        else:
+            return self.subnet(x, t, conditions, training=training)
+
     def consistency_function(self, x: Tensor, t: Tensor, conditions: Tensor = None, training: bool = False) -> Tensor:
         """Compute consistency function.
 
@@ -271,12 +302,8 @@
             Whether internal layers (e.g., dropout) should behave in train or inference mode.
         """
 
-        if conditions is not None:
-            xtc = ops.concatenate([x, t, conditions], axis=-1)
-        else:
-            xtc = ops.concatenate([x, t], axis=-1)
-
-        f = self.output_projector(self.subnet(xtc, training=training))
+        subnet_out = self._subnet_input(x, t, conditions, training=training)
+        f = self.output_projector(subnet_out)
 
         # Compute skip and out parts (vectorized, since self.sigma2 is of shape (1, input_dim)
         # Thus, we can do a cross product with the time vector which is (batch_size, 1) for
@@ -316,8 +343,8 @@
 
         log_p = ops.log(p)
         times = keras.random.categorical(ops.expand_dims(log_p, 0), ops.shape(x)[0], seed=self.seed_generator)[0]
-        t1 = ops.take(discretized_time, times)[..., None]
-        t2 = ops.take(discretized_time, times + 1)[..., None]
+        t1 = expand_right_as(ops.take(discretized_time, times), x)
+        t2 = expand_right_as(ops.take(discretized_time, times + 1), x)
 
         # generate noise vector
         noise = keras.random.normal(keras.ops.shape(x), dtype=keras.ops.dtype(x), seed=self.seed_generator)

bayesflow/networks/flow_matching/flow_matching.py

@@ -12,6 +12,7 @@
     layer_kwargs,
     optimal_transport,
     weighted_mean,
+    tensor_utils,
 )
 from bayesflow.utils.serialization import serialize, deserialize, serializable
 from ..inference_network import InferenceNetwork
@@ -107,6 +108,7 @@
         subnet_kwargs = subnet_kwargs or {}
         if subnet == "mlp":
             subnet_kwargs = FlowMatching.MLP_DEFAULT_CONFIG | subnet_kwargs
+        self._concatenate_subnet_input = kwargs.get("concatenate_subnet_input", True)
 
         self.subnet = find_network(subnet, **subnet_kwargs)
         self.output_projector = keras.layers.Dense(units=None, bias_initializer="zeros", name="output_projector")
@@ -147,22 +149,50 @@
             "loss_fn": self.loss_fn,
             "integrate_kwargs": self.integrate_kwargs,
             "optimal_transport_kwargs": self.optimal_transport_kwargs,
+            "concatenate_subnet_input": self._concatenate_subnet_input,
             # we do not need to store subnet_kwargs
         }
 
         return base_config | serialize(config)
 
+    def _subnet_input(
+        self, x: Tensor, t: Tensor, conditions: Tensor = None, training: bool = False
+    ) -> Tensor | tuple[Tensor, Tensor, Tensor]:
+        """
+        Prepares the input for the subnet either by concatenating the latent variable `x`,
+        the time `t`, and optional conditions or by returning them separately.
+
+        Parameters
+        ----------
+        x : Tensor
+            The input tensor for the diffusion model, typically of shape (..., D), but can vary.
+        t : Tensor
+            The time tensor, typically of shape (..., 1).
+        conditions : Tensor, optional
+            The optional conditioning tensor (e.g. parameters).
+        training : bool, optional
+            The training mode flag, which can be used to control behavior during training.
+
+        Returns
+        -------
+        Tensor
+            The concatenated input tensor for the subnet or a tuple of tensors if concatenation is disabled.
+        """
+        if self._concatenate_subnet_input:
+            t = keras.ops.broadcast_to(t, keras.ops.shape(x)[:-1] + (1,))
+            xtc = tensor_utils.concatenate_valid([x, t, conditions], axis=-1)
+            return self.subnet(xtc, training=training)
+        else:
+            if training is False:
+                t = keras.ops.broadcast_to(t, keras.ops.shape(x)[:-1] + (1,))
+            return self.subnet(x, t, conditions, training=training)
+
     def velocity(self, xz: Tensor, time: float | Tensor, conditions: Tensor = None, training: bool = False) -> Tensor:
         time = keras.ops.convert_to_tensor(time, dtype=keras.ops.dtype(xz))
         time = expand_right_as(time, xz)
-        time = keras.ops.broadcast_to(time, keras.ops.shape(xz)[:-1] + (1,))
-
-        if conditions is None:
-            xtc = keras.ops.concatenate([xz, time], axis=-1)
-        else:
-            xtc = keras.ops.concatenate([xz, time, conditions], axis=-1)
 
-        return self.output_projector(self.subnet(xtc, training=training), training=training)
+        subnet_out = self._subnet_input(xz, time, conditions, training=training)
+        return self.output_projector(subnet_out, training=training)
 
     def _velocity_trace(
         self, xz: Tensor, time: Tensor, conditions: Tensor = None, max_steps: int = None, training: bool = False