diff --git a/codes/trainer/eval/sr_diffusion_fid.py b/codes/trainer/eval/sr_diffusion_fid.py
index 172d2561..7730f8cb 100644
--- a/codes/trainer/eval/sr_diffusion_fid.py
+++ b/codes/trainer/eval/sr_diffusion_fid.py
@@ -9,7 +9,7 @@ import trainer.eval.evaluator as evaluator
 
 from pytorch_fid import fid_score
 from data import create_dataset
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, DistributedSampler, SequentialSampler
 
 from trainer.injectors.gaussian_diffusion_injector import GaussianDiffusionInferenceInjector
 from utils.util import opt_get
@@ -23,6 +23,10 @@ class SrDiffusionFidEvaluator(evaluator.Evaluator):
         self.fid_batch_size = opt_get(opt_eval, ['fid_batch_size'], 64)
         assert self.batch_sz is not None
         self.dataset = create_dataset(opt_eval['dataset'])
+        if torch.distributed.is_available() and torch.distributed.is_initialized():
+            self.sampler = DistributedSampler(self.dataset, shuffle=False, drop_last=True)
+        else:
+            self.sampler = SequentialSampler(self.dataset)
         self.fid_real_samples = opt_eval['dataset']['paths']  # This is assumed to exist for the given dataset.
         assert isinstance(self.fid_real_samples, str)
         self.gd = GaussianDiffusionInferenceInjector(opt_eval['diffusion_params'], env)
@@ -31,7 +35,7 @@ class SrDiffusionFidEvaluator(evaluator.Evaluator):
     def perform_eval(self):
         # Attempt to make the dataset deterministic.
         self.dataset.reset_random()
-        dataloader = DataLoader(self.dataset, self.batch_sz, shuffle=False, num_workers=0)
+        dataloader = DataLoader(self.dataset, self.batch_sz, sampler=self.sampler, num_workers=0)
 
         fid_fake_path = osp.join(self.env['base_path'], "..", "fid", str(self.env["step"]))
         os.makedirs(fid_fake_path, exist_ok=True)