Update README.md

README.md

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 license: openrail++
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+
+[`latent-consistency/lcm-sdxl`](https://huggingface.co/latent-consistency/lcm-sdxl) compiled on an AWS Inf2 instance. ***INF2/TRN1 ONLY***
+
+***How to use***
+
+```python
+from optimum.neuron import NeuronStableDiffusionXLPipeline
+
+pipe = NeuronStableDiffusionXLPipeline.from_pretrained("Jingya/lcm-sdxl-neuronx")
+
+num_images_per_prompt = 2
+prompt = ["a close-up picture of an old man standing in the rain"] * num_images_per_prompt 
+
+images = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=8.0).images
+```
+
+If you are using a later neuron compiler version, you can compile the checkpoint yourself with the following lines via [`🤗 optimum-neuron`](https://huggingface.co/docs/optimum-neuron/index) (the compilation takes approximately an hour):
+
+```python
+from optimum.neuron import NeuronStableDiffusionXLPipeline
+
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+unet_id = "latent-consistency/lcm-sdxl"
+num_images_per_prompt = 1
+input_shapes = {"batch_size": 1, "height": 1024, "width": 1024, "num_images_per_prompt": num_images_per_prompt}
+compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
+
+stable_diffusion = NeuronStableDiffusionXLPipeline.from_pretrained(
+    model_id, unet_id=unet_id, export=True, **compiler_args, **input_shapes
+)
+save_directory = "lcm_sdxl_neuron/"
+stable_diffusion.save_pretrained(save_directory)
+
+# Push to hub
+stable_diffusion.push_to_hub(save_directory, repository_id="Jingya/lcm-sdxl-neuronx", use_auth_token=True)
+```
+
+And feel free to make a pull request and contribute to this repo, thx 🤗!