diff --git a/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md b/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md
index cce21749979..7f1ee6df374 100644
--- a/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md
+++ b/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md
@@ -113,7 +113,7 @@ python -m pytorch_tokenizers.tools.llama2c.convert -t tokenizer.model -o tokeniz
 ### For LLaVA model
 * For the Llava 1.5 model, you can get it from Huggingface [here](https://huggingface.co/llava-hf/llava-1.5-7b-hf).
 * Run `examples/models/llava/install_requirements.sh` to install dependencies.
-* Run the following command to generate llava.pte, tokenizer.bin and an image tensor (serialized in TorchScript) image.pt.
+* Run the following command to generate llava.pte, tokenizer.bin and download an image basketball.jpg.
 
 ```
 python -m executorch.examples.models.llava.export_llava --pte-name llava.pte --with-artifacts
diff --git a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md
index dd6bb26fec1..fc47ce5938f 100644
--- a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md
+++ b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md
@@ -85,7 +85,7 @@ python -m examples.models.llama.export_llama --checkpoint <path-to-your-checkpoi
 ### For LLaVA model
 * For the Llava 1.5 model, you can get it from Huggingface [here](https://huggingface.co/llava-hf/llava-1.5-7b-hf).
 * Run `examples/models/llava/install_requirements.sh` to install dependencies.
-* Run the following command to generate llava.pte, tokenizer.bin and an image tensor (serialized in TorchScript) image.pt.
+* Run the following command to generate llava.pte, tokenizer.bin and download an image basketball.jpg.
 
 ```
 python -m executorch.examples.models.llava.export_llava --pte-name llava.pte --with-artifacts
diff --git a/examples/models/llava/README.md b/examples/models/llava/README.md
index 6ba9ef21555..86d522862f0 100644
--- a/examples/models/llava/README.md
+++ b/examples/models/llava/README.md
@@ -41,10 +41,8 @@ The model weights is 15GiB, and the other memory usage at export stage (`export_
 
 ### Generate ExecuTorch .PTE and other artifacts
 
-Run the following command to generate `llava.pte`, `tokenizer.bin` and an image
-tensor (serialized in TorchScript) `image.pt`.
+Run the following command to generate `llava.pte`, `tokenizer.bin` and an image `basketball.jpg`.
 
-> **Warning**: The C++ runner `llava_main` binary cannot process raw image inputs such as JPEG, PNG, or BMP files directly. You must convert these images to a `.pt` file format using the `examples/models/llava/image_util.py` script before using them with `llava_main`.
 
 Prerequisite: run `install_executorch.sh` to install ExecuTorch and run
 `examples/models/llava/install_requirements.sh` to install dependencies.
@@ -65,11 +63,6 @@ python -m executorch.examples.models.llava.test.test_pte llava.pte
 See or run `.ci/scripts/test_llava.sh` shell script to build a C++ runner. This
 script also has preliminary support to build the C++ runner for Android.
 
-This also has an image utility Python script to generate image in PyTorch
-loadable format. Alternatively, we are working on generating image format which
-doesn't need PyTorch to load an image. Motivation for this is to build the C++
-runner on Android.
-
 Then you should be able to find `llava_main` binary:
 
 ```bash
@@ -104,8 +97,8 @@ Run:
 cmake-out/examples/models/llava/llava_main \
     --model_path=llava.pte                 \
     --tokenizer_path=tokenizer.bin         \
-    --image_path=image.pt                  \
-    --prompt="ASSISTANT:" \
+    --image_path=basketball.jpg            \
+    --prompt="ASSISTANT:"                  \
     --seq_len=768                          \
     --temperature=0
 ```