diff --git a/onediff_diffusers_extensions/examples/text_to_image_ip_adapter.py b/onediff_diffusers_extensions/examples/text_to_image_ip_adapter.py
new file mode 100644
index 000000000..aa2db5ec9
--- /dev/null
+++ b/onediff_diffusers_extensions/examples/text_to_image_ip_adapter.py
@@ -0,0 +1,124 @@
+import argparse
+import json
+from pathlib import Path
+
+import torch
+from onediffx.compilers.diffusion_pipeline_compiler import (
+    convert_pipe_to_memory_format,
+)
+from diffusers import AutoPipelineForText2Image
+from diffusers.utils import load_image
+
+nexfort_options = {
+    "mode": "cudagraphs:benchmark:max-autotune:low-precision:cache-all",
+    "memory_format": "channels_last",
+    "options": {
+        "inductor.optimize_linear_epilogue": False,
+        "overrides.conv_benchmark": True,
+        "overrides.matmul_allow_tf32": True,
+    },
+}
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--base", type=str, default="runwayml/stable-diffusion-v1-5"
+)
+parser.add_argument("--ipadapter", type=str, default="h94/IP-Adapter")
+parser.add_argument("--subfolder", type=str, default="models")
+parser.add_argument("--weight_name", type=str, default="ip-adapter_sd15.bin")
+parser.add_argument("--scale", type=float, default=0.5)
+parser.add_argument(
+    "--input_image",
+    type=str,
+    default="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_1.png",
+)
+parser.add_argument(
+    "--prompt", type=str, default="a cat",
+)
+parser.add_argument("--height", type=int, default=512)
+parser.add_argument("--width", type=int, default=512)
+parser.add_argument("--n_steps", type=int, default=30)
+parser.add_argument(
+    "--saved_image", type=str, required=False, default="ip-adapter-out.png"
+)
+parser.add_argument("--seed", type=int, default=1)
+parser.add_argument("--warmup", type=int, default=1)
+parser.add_argument("--run", type=int, default=3)
+parser.add_argument(
+    "--compile",
+    type=(lambda x: str(x).lower() in ["true", "1", "yes"]),
+    default=True,
+)
+parser.add_argument(
+    "--compile-ipa",
+    type=(lambda x: str(x).lower() in ["true", "1", "yes"]),
+    default=True,
+)
+parser.add_argument("--compile-options", type=str, default=nexfort_options)
+args = parser.parse_args()
+
+# load an image
+image = load_image(args.input_image)
+
+# load stable diffusion and ip-adapter
+pipe = AutoPipelineForText2Image.from_pretrained(
+    args.base, torch_dtype=torch.float16
+)
+pipe.load_ip_adapter(
+    args.ipadapter, subfolder=args.subfolder, weight_name=args.weight_name
+)
+pipe.set_ip_adapter_scale(args.scale)
+pipe.to("cuda")
+
+
+compile_options = args.compile_options
+if isinstance(compile_options, str):
+    compile_options = json.loads(compile_options)
+
+
+memory_format = getattr(
+    torch, compile_options["memory_format"], torch.channels_last
+)
+pipe = convert_pipe_to_memory_format(pipe, memory_format=memory_format)
+compile_options.pop("memory_format", None)
+
+if args.compile:
+    from onediff.infer_compiler import compile
+
+    pipe.unet = compile(pipe.unet, backend="nexfort", options=compile_options)
+    pipe.vae.decoder = compile(
+        pipe.vae.decoder, backend="nexfort", options=compile_options
+    )
+if args.compile_ipa:
+    from onediff.infer_compiler import compile
+
+    pipe.image_encoder = compile(
+        pipe.image_encoder, backend="nexfort", options=compile_options
+    )
+
+
+# generate image
+print("Warmup")
+for i in range(args.warmup):
+    images = pipe(
+        prompt=args.prompt,
+        height=args.height,
+        width=args.width,
+        ip_adapter_image=image,
+        num_inference_steps=args.n_steps,
+        generator=torch.manual_seed(args.seed),
+    ).images
+
+print("Run")
+for i in range(args.run):
+    image = pipe(
+        prompt=args.prompt,
+        height=args.height,
+        width=args.width,
+        ip_adapter_image=image,
+        num_inference_steps=args.n_steps,
+        generator=torch.manual_seed(args.seed),
+    ).images[0]
+    image_path = f"{Path(args.saved_image).stem}_{i}" + Path(args.saved_image).suffix
+    print(f"save output image to {image_path}")
+    image.save(image_path)