Update README.md

README.md

@@ -12,4 +12,77 @@ GitHub: https://github.com/iamlucaconti/InstructPix2Pix
 
 Given an input image and an accompanying edit instruction, our model generates the corresponding modification directly. Unlike approaches that rely on detailed textual descriptions of both the source and target images, our method requires only the instruction (`ediit_prompt`) and performs the edit in a single forward pass, without per-example inversion or additional fine-tuning. An example generated by our model is shown below:  
 
-![Home Alone](images/home_alone.png)  
+<img src='https://github.com/iamlucaconti/InstructPix2Pix/raw/main/images/home_alone.png'/>
+
+## How to use our model
+
+To edit an image using our model:
+
+```python
+import os
+import torch
+import requests
+import matplotlib.pyplot as plt
+from PIL import Image, ImageOps
+from io import BytesIO
+from diffusers import StableDiffusionInstructPix2PixPipeline
+
+
+def download_image(url: str, resize: bool = False, resolution: int = 512) -> Image.Image:
+    # Download and open the image
+    image = Image.open(BytesIO(requests.get(url, stream=True).content))
+    # Fix orientation issues from EXIF metadata
+    image = ImageOps.exif_transpose(image).convert("RGB")
+
+    if resize:
+        w, h = image.size
+        if w > h:
+            new_w = resolution
+            new_h = int(h * resolution / w)
+        else:
+            new_h = resolution
+            new_w = int(w * resolution / h)
+        image = image.resize((new_w, new_h))
+
+    return image
+
+
+# Parameters
+pretrained_model_name_or_path = "iamlucaconti/instruct-pix2pix-model"  # Custom Pix2Pix model
+image_url = "<URL OF IMAGE TO EDIT>"
+prompt = "<YOUR EDIT PROMPT>"  # Instructional edit prompt
+num_inference_steps = 50        # More steps = higher quality but slower
+image_guidance_scale = 2.0      # Strength of adherence to input image
+guidance_scale = 3.0            # Strength of adherence to text prompt
+seed = 0                        # Random seed (for reproducibility)
+output_path = "output.png"      # File to save result
+
+
+# Load pipeline
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+    pretrained_model_name_or_path,
+    torch_dtype=torch.float16,
+    safety_checker=None
+).to("cuda")
+
+# Load image
+image = download_image(image_url, resize=True)
+
+# Set seed
+generator = torch.Generator("cuda").manual_seed(seed)
+
+
+# Generate the edited image
+edited_image = pipe(
+    prompt=prompt,
+    image=image,
+    num_inference_steps=num_inference_steps,
+    image_guidance_scale=image_guidance_scale,
+    guidance_scale=guidance_scale,
+    generator=generator
+).images[0]
+
+
+# Save
+edited_image.save(output_path)
+```