diff --git a/src/brevitas_examples/stable_diffusion/main.py b/src/brevitas_examples/stable_diffusion/main.py
index fef5900f2..e2b640495 100644
--- a/src/brevitas_examples/stable_diffusion/main.py
+++ b/src/brevitas_examples/stable_diffusion/main.py
@@ -586,6 +586,7 @@ def sdpa_zp_stats_type():
                     layerwise=True,
                     blacklist_layers=blacklist if args.exclude_blacklist_act_eq else None,
                     add_mul_node=True):
+
                 for (inp_args, inp_kwargs) in vae_calibration:
                     input_args = tuple([
                         input_arg.cpu() if isinstance(input_arg, torch.Tensor) else input_arg
@@ -594,6 +595,8 @@ def sdpa_zp_stats_type():
                         k: (v.cpu() if isinstance(v, torch.Tensor) else v)
                         for (k, v) in input_kwargs.items()}
                     pipe.vae.decode(*inp_args, **inp_kwargs)
+                    if args.dry_run or args.vae_load_checkpoint is not None:
+                        break
 
         quantizers = generate_quantizers(
             dtype=dtype,
@@ -646,7 +649,15 @@ def sdpa_zp_stats_type():
                 k: (v.cuda() if isinstance(v, torch.Tensor) else v)
                 for (k, v) in vae_calibration[0][1].items()}
             pipe.vae.decode(*input_args, **input_kwargs)
-        if needs_calibration:
+
+        if args.vae_load_checkpoint is not None:
+            with load_quant_model_mode(pipe.vae):
+                pipe = pipe.to('cpu')
+                print(f"Loading checkpoint: {args.vae_load_checkpoint}... ", end="")
+                pipe.vae.load_state_dict(torch.load(args.vae_load_checkpoint, map_location='cpu'))
+                print(f"Checkpoint loaded!")
+            pipe = pipe.to(args.device)
+        if needs_calibration and not (args.dry_run or args.vae_load_checkpoint is not None):
             print("Applying activation calibration")
             with torch.no_grad(), calibration_mode(pipe.vae):
                 for (inp_args, inp_kwargs) in vae_calibration:
@@ -658,7 +669,7 @@ def sdpa_zp_stats_type():
                         for (k, v) in input_kwargs.items()}
                     pipe.vae.decode(*inp_args, **inp_kwargs)
 
-        if args.vae_gptq:
+        if args.vae_gptq and not (args.dry_run or args.vae_load_checkpoint is not None):
             print("Applying GPTQ")
             with torch.no_grad(), gptq_mode(pipe.vae,
                         create_weight_orig=False,
@@ -673,7 +684,7 @@ def sdpa_zp_stats_type():
                         k: (v.cuda() if isinstance(v, torch.Tensor) else v)
                         for (k, v) in input_kwargs.items()}
                     pipe.vae.decode(*inp_args, **inp_kwargs)
-        if args.vae_bias_correction:
+        if args.vae_bias_correction and not (args.dry_run or args.vae_load_checkpoint is not None):
             print("Applying Bias Correction")
             with torch.no_grad(), bias_correction_mode(pipe.vae):
                 for inp_args, inp_kwargs in vae_calibration:
@@ -830,6 +841,11 @@ def sdpa_zp_stats_type():
         type=str,
         default=None,
         help='Path to checkpoint to load. If provided, PTQ techniques are skipped.')
+    parser.add_argument(
+        '--vae-load-checkpoint',
+        type=str,
+        default=None,
+        help='Path to checkpoint vae to load. If provided, PTQ techniques are skipped.')
     parser.add_argument(
         '--path-to-latents',
         type=str,