diff --git a/examples/model_load_store_lora/README.md b/examples/model_load_store_lora/README.md
new file mode 100644
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@@ -0,0 +1,82 @@
+# Model Load and Store with Multiple Models
+
+In this example, we will use LoRA and StableDiffusion to demonstrate how to load and store multiple models both for local models and remote models.
+
+## Install Dependencies
+```bash
+python -m venv model-load-store-lora
+source model-load-store-lora/bin/activate
+pip install -r requirement.txt
+```
+
+## Import Models
+
+Run the import_model.py script in this example to save models to the BentoML Model Store.
+```python
+import bentoml
+import shutil
+import os
+from diffusers import StableDiffusionPipeline
+
+model_id = 'CompVis/stable-diffusion-v1-4'
+pipeline = StableDiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
+
+local_lora = "./lora"
+with bentoml.models.create(
+    name='sd', # All models are created under ~/bentoml/models/{name = sd}
+) as model_ref:
+    local_diffusion_path = model_ref.path_of("diffusion")
+    local_lora_path = model_ref.path_of("lora")
+    pipeline.save_pretrained(local_diffusion_path)
+    shutil.copytree(local_lora, local_lora_path, dirs_exist_ok=True)
+    print(f"Model saved: {model_ref}")
+```
+
+When storing multiple models, you can simply create new directories corresponding to these models inside the `bentoml.models.create()` context manager. For local models, use `shutil.copytree()` to copy the local models and files. For remote models, use the respective library tools to save the models to the directory you just created.
+
+To view all the models in the Model Store, run bentoml models list:
+```bash
+$ bentoml models list
+ Tag                                                                             Module                              Size        Creation Time
+ sd:l42k7zxcmksxhyln                                                                                                 5.11 GiB    2024-03-15 00:24:22
+```
+
+## Load Models
+Now that the models are saved, we can get the directory of the saved models by using `model_ref = bentoml.models.get("sd:latest")`. You can get the base path by calling `model_ref.path`. This will return the sd directory you have just created. To
+get each individual model subpath, use `model_ref.path_of("{subpath}")`.
+
+```python
+class StableDiffusion:
+    model_ref = bentoml.models.get("sd:latest")
+
+    def __init__(self) -> None:
+        # Load model into pipeline
+        self.diffusion_ref = model_ref.path_of("diffusion")
+        self.lora_ref = model_ref.path_of("lora")
+        self.stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(self.diffusion_ref, use_safetensors=True)
+        self.stable_diffusion_txt2img.unet.load_attn_procs(self.lora_ref)
+        self.stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(
+            vae=self.stable_diffusion_txt2img.vae,
+            text_encoder=self.stable_diffusion_txt2img.text_encoder,
+            tokenizer=self.stable_diffusion_txt2img.tokenizer,
+            unet=self.stable_diffusion_txt2img.unet,
+            scheduler=self.stable_diffusion_txt2img.scheduler,
+            safety_checker=None,
+            feature_extractor=None,
+            requires_safety_checker=False,
+        )
+        self.stable_diffusion_txt2img.to('cuda')
+        self.stable_diffusion_img2img.to('cuda')
+```
+
+To serve this Service locally, run:
+```bash
+bentoml serve .
+```
+
+## Deploy to BentoCloud
+Run the following command to deploy this example to BentoCloud for better management and scalability. [Sign up](https://www.bentoml.com/) if you haven't got a BentoCloud account.
+```bash
+bentoml deploy .
+```
+For more information, see [Create Deployments](https://docs.bentoml.com/en/latest/bentocloud/how-tos/create-deployments.html).
diff --git a/examples/model_load_store_lora/bentofile.yaml b/examples/model_load_store_lora/bentofile.yaml
new file mode 100644
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+++ b/examples/model_load_store_lora/bentofile.yaml
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+service: "service.py:StableDiffusion"
+model:
+  tag: sd
+labels:
+  owner: bentoml-team
+  project: gallery
+include:
+  - "service.py"
+python:
+  requirements_txt: "./requirements.txt"
+  lock_packages: false
+docker:
+    distro: debian
diff --git a/examples/model_load_store_lora/import_model.py b/examples/model_load_store_lora/import_model.py
new file mode 100644
index 00000000000..36124642f8f
--- /dev/null
+++ b/examples/model_load_store_lora/import_model.py
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+import shutil
+
+from diffusers import StableDiffusionPipeline
+
+import bentoml
+
+model_id = "CompVis/stable-diffusion-v1-4"
+pipeline = StableDiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
+
+local_lora = "./lora"
+with bentoml.models.create(
+    name="sd",
+) as model_ref:
+    local_diffusion_path = model_ref.path_of("diffusion")
+    local_lora_path = model_ref.path_of("lora")
+    pipeline.save_pretrained(local_diffusion_path)
+    shutil.copytree(local_lora, local_lora_path, dirs_exist_ok=True)
+    print(f"Model saved: {model_ref}")
diff --git a/examples/model_load_store_lora/requirements.txt b/examples/model_load_store_lora/requirements.txt
new file mode 100644
index 00000000000..a5c279a38eb
--- /dev/null
+++ b/examples/model_load_store_lora/requirements.txt
@@ -0,0 +1,5 @@
+diffusers
+bentoml
+transformers
+torch
+accelerate
diff --git a/examples/model_load_store_lora/service.py b/examples/model_load_store_lora/service.py
new file mode 100644
index 00000000000..0f221b3cff9
--- /dev/null
+++ b/examples/model_load_store_lora/service.py
@@ -0,0 +1,51 @@
+import torch
+from PIL.Image import Image
+
+import bentoml
+
+
+@bentoml.service(
+    resources={
+        "memory": "500MiB",
+        "gpu": 1,
+        "gpu_type": "nvidia-tesla-t4",
+    },
+    traffic={"timeout": 600},
+)
+class StableDiffusion:
+    model_ref = bentoml.models.get("sd:latest")
+
+    def __init__(self) -> None:
+        from diffusers import StableDiffusionPipeline
+
+        # Load model into pipeline
+        self.diffusion_ref = self.model_ref.path_of("diffusion")
+        self.lora_ref = self.model_ref.path_of("lora")
+        self.stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(
+            self.diffusion_ref, use_safetensors=True
+        )
+        self.stable_diffusion_txt2img.unet.load_attn_procs(self.lora_ref)
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.stable_diffusion_txt2img.to(device)
+
+    @bentoml.api
+    def txt2img(
+        self,
+        prompt: str = "A Pokemon with blue eyes.",
+        height: int = 768,
+        width: int = 768,
+        num_inference_steps: int = 30,
+        guidance_scale: float = 7.5,
+        eta: int = 0,
+    ) -> Image:
+        input_data = dict(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            eta=eta,
+        )
+        res = self.stable_diffusion_txt2img(**input_data)
+        image = res[0][0]
+        return image