README.md

metadata

license: cc
tags:
  - image-to-image

REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations

Project page: https://peter-sushko.github.io/RealEdit/
Data: https://huggingface.co/datasets/peter-sushko/RealEdit

This is the model introduce in realedit paper. There are 2 ways to run inference: either via Diffusers or original InstructPix2Pix pipeline.

Option 1: Diffusers library:

Install diffusers, transformers library:

pip install diffusers accelerate safetensors transformers

Download weights adapted for diffusers:

WEIGHTS

import PIL
import requests
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler

model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)

CODE TO RUN IT:


pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

url = "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/main/imgs/example.jpg"
def download_image(url):
    image = PIL.Image.open(requests.get(url, stream=True).raw)
    image = PIL.ImageOps.exif_transpose(image)
    image = image.convert("RGB")
    return image
image = download_image(url)

prompt = "turn him into cyborg"
images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images
images[0]

Option 2: via InstructPix2Pix pipeline:

Clone the repository and set up the directory structure:

git clone https://github.com/timothybrooks/instruct-pix2pix.git
cd instruct-pix2pix
mkdir checkpoints

Download the fine-tuned checkpoint into the checkpoints directory:

cd checkpoints
# wget https://huggingface.co/peter-sushko/RealEdit/resolve/main/realedit_model.ckpt

Return to the repo root and follow the InstructPix2Pix installation guide to set up the environment.

Edit a single image

python edit_cli.py \
  --input [YOUR_IMG_PATH] \
  --output imgs/output.jpg \
  --edit "YOUR EDIT INSTRUCTION" \
  --ckpt checkpoints/realedit_model.ckpt

Launch the Gradio interface

python edit_app.py --ckpt checkpoints/realedit_model.ckpt

Citation

If you find this checkpoint helpful, please cite:

@misc{sushko2025realeditredditeditslargescale,
      title={REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations}, 
      author={Peter Sushko and Ayana Bharadwaj and Zhi Yang Lim and Vasily Ilin and Ben Caffee and Dongping Chen and Mohammadreza Salehi and Cheng-Yu Hsieh and Ranjay Krishna},
      year={2025},
      eprint={2502.03629},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.03629}, 
}