diff --git a/examples/EntityControl/entity_transfer.py b/examples/EntityControl/entity_transfer.py
new file mode 100644
index 0000000..372e68e
--- /dev/null
+++ b/examples/EntityControl/entity_transfer.py
@@ -0,0 +1,88 @@
+from diffsynth import ModelManager, FluxImagePipeline, download_customized_models
+from diffsynth.data.video import crop_and_resize
+from modelscope import dataset_snapshot_download
+from examples.EntityControl.utils import visualize_masks
+from PIL import Image
+import numpy as np
+import torch
+
+
+
+def build_pipeline():
+    model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
+    model_manager.load_lora(
+        download_customized_models(
+            model_id="DiffSynth-Studio/Eligen",
+            origin_file_path="model_bf16.safetensors",
+            local_dir="models/lora/entity_control"
+        ),
+        lora_alpha=1
+    )
+    model_manager.load_lora(
+        download_customized_models(
+            model_id="iic/In-Context-LoRA",
+            origin_file_path="visual-identity-design.safetensors",
+            local_dir="models/lora/In-Context-LoRA"
+        ),
+        lora_alpha=1
+    )
+    pipe = FluxImagePipeline.from_model_manager(model_manager)
+    return pipe
+
+
+def generate(pipe: FluxImagePipeline, logo_image, target_image, mask, height, width, prompt, logo_prompt, image_save_path, mask_save_path):
+    mask = Image.fromarray(np.concatenate([
+        np.ones((height, width, 3), dtype=np.uint8) * 0,
+        np.array(crop_and_resize(mask, height, width)),
+    ], axis=1))
+
+    input_image = Image.fromarray(np.concatenate([
+        np.array(crop_and_resize(logo_image, height, width)),
+        np.array(crop_and_resize(target_image, height, width)),
+    ], axis=1))
+
+    image = pipe(
+        prompt=prompt,
+        input_image=input_image,
+        cfg_scale=3.0,
+        negative_prompt="",
+        num_inference_steps=50,
+        embedded_guidance=3.5,
+        seed=0,
+        height=height,
+        width=width * 2,
+        eligen_entity_prompts=[logo_prompt],
+        eligen_entity_masks=[mask],
+        enable_eligen_on_negative=False,
+        enable_eligen_inpaint=True,
+    )
+    image.save(image_save_path)
+    visualize_masks(image, [mask], [logo_prompt], mask_save_path)
+
+
+pipe = build_pipeline()
+
+dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern="data/examples/eligen/logo_transfer*")
+logo_image = Image.open("data/examples/eligen/logo_transfer_logo.png")
+target_image = Image.open("data/examples/eligen/logo_transfer_target_image.png")
+
+prompt="The two-panel image showcases the joyful identity, with the left panel showing a rabbit graphic; [LEFT] while the right panel translates the design onto a shopping tote with the rabbit logo in black, held by a person in a market setting, emphasizing the brand's approachable and eco-friendly vibe."
+logo_prompt="a rabbit logo"
+
+mask = Image.open("data/examples/eligen/logo_transfer_mask_1.png")
+generate(
+    pipe, logo_image, target_image, mask, 
+    height=1024, width=736,
+    prompt=prompt, logo_prompt=logo_prompt,
+    image_save_path="entity_transfer_1.png",
+    mask_save_path="entity_transfer_with_mask_1.png"
+)
+
+mask = Image.open("data/examples/eligen/logo_transfer_mask_2.png")
+generate(
+    pipe, logo_image, target_image, mask, 
+    height=1024, width=736,
+    prompt=prompt, logo_prompt=logo_prompt,
+    image_save_path="entity_transfer_2.png",
+    mask_save_path="entity_transfer_with_mask_2.png"
+)