Instructions to use peter-sushko/RealEdit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use peter-sushko/RealEdit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("peter-sushko/RealEdit", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| license: cc | |
| tags: | |
| - image-to-image | |
| datasets: | |
| - peter-sushko/RealEdit | |
| pipeline_tag: 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 | |
| Paper: https://arxiv.org/pdf/2502.03629 | |
| <img src="https://peter-sushko.github.io/RealEdit/static/images/teaser.svg"/> | |
| **There are 2 ways to run inference: either via Diffusers or original InstructPix2Pix pipeline.** | |
| ## Option 1: With 🧨Diffusers: | |
| Install necessary libraries: | |
| ```bash | |
| pip install torch==2.7.0 diffusers==0.33.1 transformers==4.51.3 accelerate==1.6.0 pillow==11.2.1 requests==2.32.3 | |
| ``` | |
| Then run: | |
| ```python | |
| import torch | |
| import requests | |
| import PIL | |
| from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler | |
| model_id = "peter-sushko/RealEdit" | |
| pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16, | |
| safety_checker=None | |
| ) | |
| pipe.to("cuda") | |
| pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) | |
| url = "https://raw.githubusercontent.com/AyanaBharadwaj/RealEdit/refs/heads/main/example_imgs/simba.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 = "give him a crown" | |
| result = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=2).images[0] | |
| result.save("output.png") | |
| ``` | |
| ## Option 2: via InstructPix2Pix pipeline: | |
| Clone the repository and set up the directory structure: | |
| ```bash | |
| git clone https://github.com/timothybrooks/instruct-pix2pix.git | |
| cd instruct-pix2pix | |
| mkdir checkpoints | |
| ``` | |
| Download the fine-tuned checkpoint into the `checkpoints` directory: | |
| ```bash | |
| 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](https://github.com/timothybrooks/instruct-pix2pix) to set up the environment. | |
| Edit a single image | |
| ```bash | |
| python edit_cli.py \ | |
| --input [YOUR_IMG_PATH] \ | |
| --output imgs/output.jpg \ | |
| --edit "YOUR EDIT INSTRUCTION" \ | |
| --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}, | |
| } | |
| ``` |