lora guide

docs/source/tutorials/stable_diffusion.mdx

@@ -469,4 +469,51 @@ Inf2 instances contain one or more Neuron devices, and each Neuron device includ
 
 </Tip>
 
+
+## Load adapters
+
+### LoRA
+
+Low-Rank Adaptation is fast way to Stable Diffusion to adapt styles of generated images. In Optimum Neuron, we support using one or multiple LoRA adapters by fusing their parameters into the original parameters of the text encoder(s) and the unet during the compilation. Here below is an example of compiling stable diffusion models with LoRA adapters of your choice and using the compiled artifacts to generate styled images:
+
+```python
+
+from diffusers import LCMScheduler
+from optimum.neuron import NeuronStableDiffusionPipeline
+
+
+model_id = "Lykon/dreamshaper-7"
+adapter_id = "latent-consistency/lcm-lora-sdv1-5"
+input_shapes = {"batch_size": 1, "height": 512, "width": 512, "num_images_per_prompt": 1}
+compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
+
+# Compile
+pipe = NeuronStableDiffusionPipeline.from_pretrained(
+    model_id,
+    export=True,
+    disable_neuron_cache=True,
+    lora_model_ids=adapter_id,
+    lora_weight_names="pytorch_lora_weights.safetensors",
+    lora_adapter_names="lcm",
+    **input_shapes,
+    **compiler_args,
+)
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+# Save locally or upload to the HuggingFace Hub
+pipe.save_pretrained("dreamshaper_7_lcm_lora_neuron/")
+
+
+# Inference
+prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+image = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0]
+```
+
+<img
+  src="https://raw.githubusercontent.com/huggingface/optimum-neuron/main/docs/assets/guides/models/03-sd-lora.png"
+  width="256"
+  height="256"
+  alt="stable diffusion generated image with LoRA adapter."
+/>
+
 Are there any other stable diffusion features that you want us to support in 🤗`Optimum-neuron`? Please file an issue to [`Optimum-neuron` Github repo](https://github.com/huggingface/optimum-neuron) or discuss with us on [HuggingFace’s community forum](https://discuss.huggingface.co/c/optimum/), cheers 🤗 !