GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis
Paper • 2412.06089 • Published • 4
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("aggr8/PixEdit-v1", 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]@misc{goswami2024grapegenerateplaneditframeworkcompositional,
title={GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis},
author={Ashish Goswami and Satyam Kumar Modi and Santhosh Rishi Deshineni and Harman Singh and Prathosh A. P and Parag Singla},
year={2024},
eprint={2412.06089},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.06089},
}
Project page: https://dair-iitd.github.io/GraPE/
Code: https://github.com/dair-iitd/PixEdit
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