diff --git a/n3fit/src/n3fit/model_gen.py b/n3fit/src/n3fit/model_gen.py
index 33d6ec4e58..959d1929ab 100644
--- a/n3fit/src/n3fit/model_gen.py
+++ b/n3fit/src/n3fit/model_gen.py
@@ -676,8 +676,8 @@ def compute_unnormalized_pdf(x, neural_network, compute_preprocessing_factor):
         pdf_unnormalized = compute_unnormalized_pdf(pdf_input, nn, preprocessing_factor)
         pdf_integration_grid = compute_unnormalized_pdf(integrator_input, nn, preprocessing_factor)
 
-        pdf_normalized = sumrule_layer([pdf_unnormalized, pdf_integration_grid, integrator_input])
-
+        # i_replica argument is necessary to select the right photon integral
+        pdf_normalized = sumrule_layer([pdf_unnormalized, pdf_integration_grid, integrator_input, i_replica])
         if photons:
             pdf_normalized = layer_photon(pdf_normalized, i_replica)
 
diff --git a/n3fit/src/n3fit/msr.py b/n3fit/src/n3fit/msr.py
index 1ad8ce49ac..86b20602de 100644
--- a/n3fit/src/n3fit/msr.py
+++ b/n3fit/src/n3fit/msr.py
@@ -85,16 +85,18 @@ def generate_msr_model_and_grid(
     # 4. Integrate the pdf
     pdf_integrated = xIntegrator(weights_array, input_shape=(nx,))(pdf_integrand)
 
-    # 5. If a photon is given, compute the photon component of the MSR
+    # 5. If a photon is given, retrieve the photon component of the MSR...
     photons_c = None
     if photons:
         photons_c = photons.integral
+        # ... and add the replica number as an input, as the above contains the integrals for all replicas
+        replica_number = Input(shape=(1,), batch_size=1, name="replica_number", dtype="int32")
 
     # 6. Compute the normalization factor
     # For now set the photon component to None
     normalization_factor = MSR_Normalization(
         output_dim, mode, name="msr_weights", photons_contribution=photons_c
-    )(pdf_integrated, ph_replica=None)
+    )(pdf_integrated, ph_replica=replica_number)
 
     # 7. Apply the normalization factor to the pdf
     pdf_normalized = Lambda(lambda pdf_norm: pdf_norm[0] * pdf_norm[1], name="pdf_normalized")(
@@ -105,6 +107,7 @@ def generate_msr_model_and_grid(
         "pdf_x": pdf_x,
         "pdf_xgrid_integration": pdf_xgrid_integration,
         "xgrid_integration": xgrid_integration,
+        "replica_number": replica_number,
     }
     model = MetaModel(inputs, pdf_normalized, name="impose_msr")