diff --git a/README.md b/README.md
index c653509..b7c923b 100644
--- a/README.md
+++ b/README.md
@@ -682,34 +682,38 @@ image = pipe(prompt, num_inference_steps=4).images[0]
 You can use `cache-dit` to further speedup FLUX model, different configurations of compute blocks (F12B12, etc.) can be customized in cache-dit: DBCache. Please check [cache-dit](https://github.com/vipshop/cache-dit) for more details. For example:
 
 ```python
-# Install: pip install -U cache-dit
+# Install: pip install git+https://github.com/vipshop/cache-dit.git
+import cache_dit
 from diffusers import FluxPipeline
-from cache_dit.cache_factory import apply_cache_on_pipe, CacheType
 
 pipeline = FluxPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-dev",
     torch_dtype=torch.bfloat16,
 ).to("cuda")
 
-# cache-dit: DBCache configs
-cache_options = {
-    "cache_type": CacheType.DBCache,
-    "warmup_steps": 0,
-    "max_cached_steps": -1,  # -1 means no limit
-    "Fn_compute_blocks": 1,  # Fn, F1, F12, etc.
-    "Bn_compute_blocks": 0,  # Bn, B0, B12, etc.
-    "residual_diff_threshold": 0.12,
-    # TaylorSeer options
-    "enable_taylorseer": True,
-    "enable_encoder_taylorseer": True,
-    # Taylorseer cache type cache be hidden_states or residual
-    "taylorseer_cache_type": "residual",
-    "taylorseer_kwargs": {
-         "n_derivatives": 2,
-    },
-}
-
-apply_cache_on_pipe(pipeline, **cache_options)
+# Default options, F8B0, 8 warmup steps, and unlimited cached 
+# steps for good balance between performance and precision
+cache_dit.enable_cache(pipe_or_adapter)
+
+# Or using custom options via cache configs
+from cache_dit import BasicCacheConfig, TaylorSeerCalibratorConfig
+
+cache_dit.enable_cache(
+    pipeline,
+    # Basic DBCache w/ FnBn configurations
+    cache_config=BasicCacheConfig(
+        max_warmup_steps=0,  # steps do not cache
+        max_cached_steps=-1, # -1 means no limit
+        Fn_compute_blocks=1, # Fn, F1, etc.
+        Bn_compute_blocks=0, # Bn, B0, etc.
+        residual_diff_threshold=0.12,
+    ),
+    # Then, you can use the TaylorSeer Calibrator to approximate 
+    # the values in cached steps, taylorseer_order default is 1.
+    calibrator_config=TaylorSeerCalibratorConfig(
+        taylorseer_order=1,
+    ),
+)
 ```
 
 By the way, `cache-dit` is designed to work compatibly with torch.compile. You can easily use `cache-dit` with torch.compile to further achieve a better performance. For example:
diff --git a/cache_config.yaml b/cache_config.yaml
index 844e1d9..2e8b939 100644
--- a/cache_config.yaml
+++ b/cache_config.yaml
@@ -1,11 +1,10 @@
-cache_type: DBCache
-warmup_steps: 0
+max_warmup_steps: 0
 max_cached_steps: -1
+max_continuous_cached_steps: 2
 Fn_compute_blocks: 1
 Bn_compute_blocks: 0
-residual_diff_threshold: 0.12
+residual_diff_threshold: 0.30
 enable_taylorseer: true
 enable_encoder_taylorseer: true
 taylorseer_cache_type: residual
-taylorseer_kwargs:
-  n_derivatives: 2
+taylorseer_order: 2
\ No newline at end of file
diff --git a/run_benchmark.py b/run_benchmark.py
index a897a86..6298e5b 100644
--- a/run_benchmark.py
+++ b/run_benchmark.py
@@ -54,6 +54,13 @@ def main(args):
     print('time mean/var:', timings, timings.mean().item(), timings.var().item())
     image.save(args.output_file)
 
+    if args.cache_dit_config is not None:
+        try:
+            import cache_dit 
+            cache_dit.summary(pipeline)
+        except ImportError:
+            print("cache-dit not installed, please install it to see cache-dit summary")
+
     # optionally generate PyTorch Profiler trace
     # this is done after benchmarking because tracing introduces overhead
     if args.trace_file is not None:
diff --git a/utils/pipeline_utils.py b/utils/pipeline_utils.py
index c40b111..e19d8f4 100644
--- a/utils/pipeline_utils.py
+++ b/utils/pipeline_utils.py
@@ -407,12 +407,11 @@ def optimize(pipeline, args):
             )
         try:
             # docs: https://github.com/vipshop/cache-dit
-            from cache_dit.cache_factory import apply_cache_on_pipe
-            from cache_dit.cache_factory import load_cache_options_from_yaml
-            cache_options = load_cache_options_from_yaml(
-                args.cache_dit_config
+            import cache_dit
+            
+            cache_dit.enable_cache(
+                pipeline, **cache_dit.load_options(args.cache_dit_config),
             )
-            apply_cache_on_pipe(pipeline, **cache_options)
         except ImportError as e:
             print(
                 "You have passed the '--cache_dit_config' flag, but we cannot "