Update README.md

README.md

@@ -20,6 +20,42 @@ widget:
 Downscale 8 times to get pixel perfect images (use Nearest Neighbors)
 Use a fixed VAE to avoid artifacts (0.9 or fp16 fix)
 
+### Need more performance?
+Use it with a LCM Lora!
+
+Use 8 steps and guidance scale of 1.5
+
+```python
+from diffusers import DiffusionPipeline, LCMScheduler
+import torch
+
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+lcm_lora_id = "latent-consistency/lcm-lora-sdxl"
+pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16")
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+pipe.load_lora_weights(lcm_lora_id, adapter_name="lora")
+pipe.load_lora_weights("./pixel-art-xl.safetensors", adapter_name="pixel")
+
+pipe.set_adapters(["lora", "pixel"], adapter_weights=[1.0, 1.2])
+pipe.to(device="cuda", dtype=torch.float16)
+
+prompt = "pixel, a cute corgi"
+negative_prompt = "3d render, realistic"
+
+num_images = 9
+
+for i in range(num_images):
+    img = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=8,
+        guidance_scale=1.5,
+    ).images[0]
+    
+    img.save(f"lcm_lora_{i}.png")
+```
+
 ### Tips:
 Don't use refiner