Update README.md to include a diffusers example

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 out_ballerina.png filter=lfs diff=lfs merge=lfs -text
+darth_vader_grid.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -26,6 +26,58 @@ prompt: a ballerina, romantic sunset, 4k photo
 
 License: refers to the OpenPose's one.
 
+### Using in 🧨 diffusers
+
+First, install all the libraries:
+
+```bash
+pip install -q controlnet_aux transformers accelerate
+pip install -q git+https://github.com/huggingface/diffusers
+```
+
+Now, we're ready to make Darth Vader dance:
+
+```python
+from diffusers import AutoencoderKL, StableDiffusionXLControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+import torch
+from controlnet_aux import OpenposeDetector
+from diffusers.utils import load_image
+
+
+# Compute openpose conditioning image.
+openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+
+image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
+)
+openpose_image = openpose(image)
+
+# Initialize ControlNet pipeline.
+controlnet = ControlNetModel.from_pretrained("thibaud/controlnet-openpose-sdxl-1.0", torch_dtype=torch.float16)
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
+)
+pipe.enable_model_cpu_offload()
+
+
+# Infer.
+prompt = "Darth vader dancing in a desert, high quality"
+negative_prompt = "low quality, bad quality"
+images = pipe(
+    prompt, 
+    negative_prompt=negative_prompt,
+    num_inference_steps=25,
+    num_images_per_prompt=4,
+    image=openpose_image.resize((1024, 1024)),
+    generator=torch.manual_seed(97),
+).images
+images[0]
+```
+
+Here are some gemerated examples:
+
+![](./darth_vader_grid.png)
+
 
 ### Training