LEdits++

Official Implementation of the Paper LEDITS++: Limitless Image Editing using Text-to-Image Models.

Interactive Demo

An interactive demonstration is available in Colab and on Huggingface

Installation

LEdits++ is fully integrated in the diffusers library as LEditsPPPipelineStableDiffusion and LEditsPPPipelineStableDiffusionXL, respectively. Just install diffusers to use it:

pip install diffusers

Notably, the diffusers implementation does NOT guarantee perfect inversion. If that is a required property for your use case or you are performing research based on LEdits++, we recommend using the implementation in this repository instead.

You can clone this repository and install it locally by running

git clone https://github.com/ml-research/ledits_pp.git
cd ./semantic-image-editing
pip install .

or install it directly from git

pip install git+https://github.com/ml-research/ledits_pp.git

Usage

This repository provides 3 new diffusion pipelines supporting image editing based on the diffusers library. The StableDiffusionPipeline_LEDITS, StableDiffusionPipelineXL_LEDITS and IFDiffusion_LEDITS extend the respective diffusers pipelines and can therefore be loaded from any corresponding pre-trained checkpoint like shown below.

import PIL
import requests
import torch
from io import BytesIO
from leditspp.scheduling_dpmsolver_multistep_inject import DPMSolverMultistepSchedulerInject
from leditspp import  StableDiffusionPipeline_LEDITS

model = 'runwayml/stable-diffusion-v1-5'
#model = '/workspace/StableDiff/models/stable-diffusion-v1-5'

device = 'cuda'

pipe = StableDiffusionPipeline_LEDITS.from_pretrained(model,safety_checker = None,)
pipe.scheduler = DPMSolverMultistepSchedulerInject.from_pretrained(model, subfolder="scheduler"
                                                             , algorithm_type="sde-dpmsolver++", solver_order=2)
pipe.to(device)


An exemplary usage of the pipeline could look like this:
```python
def download_image(url):
    response = requests.get(url)
    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
gen = torch.Generator(device=device)

gen.manual_seed(21)
img_url = "https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/cherry_blossom.png"
image = download_image(img_url)
_ = pipe.invert( image = image,
    num_inversion_steps=50,
    skip=0.1
    )
edited_image = pipe(
    editing_prompt=["cherry blossom"],
    edit_guidance_scale=10.0,
    edit_threshold=0.75,
        ).images[0]

Citation

If you like or use our work please cite us:

@article{brack2023Sega,
       title={LEDITS++: Limitless Image Editing using Text-to-Image Models}, 
      author={Manuel Brack and Felix Friedrich and Katharina Kornmeier and Linoy Tsaban and Patrick Schramowski and Kristian Kersting and Apolinário Passos},
      year={2023},
      eprint={2311.16711},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}