Update README.md

README.md

@@ -58,20 +58,22 @@ In addition make sure to install `transformers`, `safetensors`, `accelerate` as
 pip install invisible_watermark transformers accelerate safetensors
 ```
 
-You can use the model then as follows
+Yon can then use the refiner to improve images.
+
 ```py
-from diffusers import DiffusionPipeline
 import torch
-
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
-pipe.to("cuda")
-
-# if using torch < 2.0
-# pipe.enable_xformers_memory_efficient_attention()
-
-prompt = "An astronaut riding a green horse"
-
-images = pipe(prompt=prompt).images[0]
+from diffusers import StableDiffusionXLImg2ImgPipeline
+from diffusers.utils import load_image
+
+pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+)
+pipe = pipe.to("cuda")
+url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
+
+init_image = load_image(url).convert("RGB")
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt, image=init_image).images
 ```
 
 When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
@@ -87,6 +89,7 @@ instead of `.to("cuda")`:
 + pipe.enable_model_cpu_offload()
 ```
 
+For more advanced use cases, please have a look at [the docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl).
 
 ## Uses