diff --git a/speed.py b/speed.py
index 3cad248..c4ed93e 100644
--- a/speed.py
+++ b/speed.py
@@ -6,10 +6,13 @@ Benchmark the inference speed of each module in LivePortrait.
 TODO: heavy GPT style, need to refactor
 """
 
-import yaml
 import torch
+torch._dynamo.config.suppress_errors = True  # Suppress errors and fall back to eager execution
+
+import yaml
 import time
 import numpy as np
+
 from src.utils.helper import load_model, concat_feat
 from src.config.inference_config import InferenceConfig
 
@@ -47,11 +50,11 @@ def load_and_compile_models(cfg, model_config):
     """
     Load and compile models for inference
     """
-    appearance_feature_extractor = load_model(cfg.checkpoint_F, model_config, cfg.device, 'appearance_feature_extractor')
-    motion_extractor = load_model(cfg.checkpoint_M, model_config, cfg.device, 'motion_extractor')
-    warping_module = load_model(cfg.checkpoint_W, model_config, cfg.device, 'warping_module')
-    spade_generator = load_model(cfg.checkpoint_G, model_config, cfg.device, 'spade_generator')
-    stitching_retargeting_module = load_model(cfg.checkpoint_S, model_config, cfg.device, 'stitching_retargeting_module')
+    appearance_feature_extractor = load_model(cfg.checkpoint_F, model_config, cfg.device_id, 'appearance_feature_extractor')
+    motion_extractor = load_model(cfg.checkpoint_M, model_config, cfg.device_id, 'motion_extractor')
+    warping_module = load_model(cfg.checkpoint_W, model_config, cfg.device_id, 'warping_module')
+    spade_generator = load_model(cfg.checkpoint_G, model_config, cfg.device_id, 'spade_generator')
+    stitching_retargeting_module = load_model(cfg.checkpoint_S, model_config, cfg.device_id, 'stitching_retargeting_module')
 
     models_with_params = [
         ('Appearance Feature Extractor', appearance_feature_extractor),