diff --git a/scripts/evaluation/inference.py b/scripts/evaluation/inference.py
index 2beec8d..05a90a1 100644
--- a/scripts/evaluation/inference.py
+++ b/scripts/evaluation/inference.py
@@ -13,7 +13,13 @@
 from funcs import load_model_checkpoint, load_prompts, load_image_batch, get_filelist, save_videos
 from funcs import batch_ddim_sampling
 from utils.utils import instantiate_from_config
+import logging
 
+def setup_logging():
+    """Set up basic configuration for logging."""
+    logging.basicConfig(filename='inference_log.txt', level=logging.INFO,
+                        format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+    logging.info("Logging is configured. Starting the program...")
 
 def get_parser():
     parser = argparse.ArgumentParser()
@@ -61,6 +67,8 @@ def run_inference(args, gpu_num, gpu_no, **kwargs):
     ## saving folders
     os.makedirs(args.savedir, exist_ok=True)
 
+    logging.info(f"[rank:{gpu_no}] Model and configuration are loaded.")
+
     ## step 2: load data
     ## -----------------------------------------------------------------
     assert os.path.exists(args.prompt_file), "Error: prompt file NOT Found!"
@@ -70,7 +78,7 @@ def run_inference(args, gpu_num, gpu_no, **kwargs):
 
     samples_split = num_samples // gpu_num
     residual_tail = num_samples % gpu_num
-    print(f'[rank:{gpu_no}] {samples_split}/{num_samples} samples loaded.')
+    logging.info(f"[rank:{gpu_no}] {samples_split}/{num_samples} samples loaded.")
     indices = list(range(samples_split*gpu_no, samples_split*(gpu_no+1)))
     if gpu_no == 0 and residual_tail != 0:
         indices = indices + list(range(num_samples-residual_tail, num_samples))
@@ -86,13 +94,14 @@ def run_inference(args, gpu_num, gpu_no, **kwargs):
 
     filename_list_rank = [filename_list[i] for i in indices]
 
+
     ## step 3: run over samples
     ## -----------------------------------------------------------------
     start = time.time()
     n_rounds = len(prompt_list_rank) // args.bs
     n_rounds = n_rounds+1 if len(prompt_list_rank) % args.bs != 0 else n_rounds
     for idx in range(0, n_rounds):
-        print(f'[rank:{gpu_no}] batch-{idx+1} ({args.bs})x{args.n_samples} ...')
+        logging.info(f'[rank:{gpu_no}] batch-{idx+1} ({args.bs})x{args.n_samples} ...')
         idx_s = idx*args.bs
         idx_e = min(idx_s+args.bs, len(prompt_list_rank))
         batch_size = idx_e - idx_s
@@ -124,12 +133,13 @@ def run_inference(args, gpu_num, gpu_no, **kwargs):
         ## b,samples,c,t,h,w
         save_videos(batch_samples, args.savedir, filenames, fps=args.savefps)
 
-    print(f"Saved in {args.savedir}. Time used: {(time.time() - start):.2f} seconds")
+    logging.info(f"Saved in {args.savedir}. Time used: {(time.time() - start):.2f} seconds")
 
 
 if __name__ == '__main__':
+    setup_logging()
     now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
-    print("@CoLVDM Inference: %s"%now)
+    logging.info(f"@CoLVDM Inference: {now}")
     parser = get_parser()
     args = parser.parse_args()
     seed_everything(args.seed)