Update README.md

README.md

@@ -124,4 +124,47 @@ Sampler: DPM++ 2M SDE
 Scheduler: Karras
 Resolution: 1024x1024
 
-The custom-trained CLIP is a significant point of differentiation, as very few models incorporate this feature. Enjoy creating with the fully released ProteusV0.5!
+The custom-trained CLIP is a significant point of differentiation, as very few models incorporate this feature. Enjoy creating with the fully released ProteusV0.5!
+
+
+Use it with 🧨 diffusers
+```python
+import torch
+from diffusers import (
+    StableDiffusionXLPipeline, 
+    KDPM2AncestralDiscreteScheduler,
+    AutoencoderKL
+)
+
+# Load VAE component
+vae = AutoencoderKL.from_pretrained(
+    "madebyollin/sdxl-vae-fp16-fix", 
+    torch_dtype=torch.float16
+)
+
+# Configure the pipeline
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "dataautogpt3/ProteusV0.5", 
+    vae=vae,
+    torch_dtype=torch.float16
+)
+pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.to('cuda')
+
+# Define prompts and generate image
+prompt = "a cat wearing sunglasses on the beach"
+negative_prompt = ""
+
+image = pipe(
+    prompt, 
+    negative_prompt=negative_prompt, 
+    width=1024,
+    height=1024,
+    guidance_scale=7,
+    num_inference_steps=50,
+    clip_skip=2
+).images[0]
+
+
+image.save("generated_image.png")
+```