diff --git a/codes/trainer/injectors/gaussian_diffusion_injector.py b/codes/trainer/injectors/gaussian_diffusion_injector.py
index 292bf1e1..ce34ba66 100644
--- a/codes/trainer/injectors/gaussian_diffusion_injector.py
+++ b/codes/trainer/injectors/gaussian_diffusion_injector.py
@@ -1,3 +1,6 @@
+import random
+import time
+
 import torch
 
 from models.diffusion.gaussian_diffusion import GaussianDiffusion, get_named_beta_schedule
@@ -26,6 +29,12 @@ class GaussianDiffusionInjector(Injector):
     def forward(self, state):
         gen = self.env['generators'][self.opt['generator']]
         hq = state[self.input]
+
+        # In eval mode, seed torch with a deterministic seed for reproducibility.
+        if not gen.trainable:
+            torch.manual_seed(0)
+            random.seed(0)
+
         model_inputs = {k: state[v] for k, v in self.model_input_keys.items()}
         t, weights = self.schedule_sampler.sample(hq.shape[0], hq.device)
         diffusion_outputs = self.diffusion.training_losses(gen, hq, t, model_kwargs=model_inputs)
@@ -40,6 +49,12 @@ class GaussianDiffusionInjector(Injector):
         out.update({self.output: diffusion_outputs['mse'],
                 self.output_variational_bounds_key: diffusion_outputs['vb'],
                 self.output_x_start_key: diffusion_outputs['x_start_predicted']})
+
+        # Absolutely critical to undo the above seed.
+        if not gen.trainable:
+            torch.manual_seed(int(time.time()))
+            random.seed(int(time.time()))
+
         return out