InstructPix2Pix: Learning to Follow Image Editing Instructions
- URL: http://arxiv.org/abs/2211.09800v1
- Date: Thu, 17 Nov 2022 18:58:43 GMT
- Title: InstructPix2Pix: Learning to Follow Image Editing Instructions
- Authors: Tim Brooks, Aleksander Holynski, Alexei A. Efros
- Abstract summary: We propose a method for editing images from human instructions.
given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image.
We show compelling editing results for a diverse collection of input images and written instructions.
- Score: 103.77092910685764
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a method for editing images from human instructions: given an
input image and a written instruction that tells the model what to do, our
model follows these instructions to edit the image. To obtain training data for
this problem, we combine the knowledge of two large pretrained models -- a
language model (GPT-3) and a text-to-image model (Stable Diffusion) -- to
generate a large dataset of image editing examples. Our conditional diffusion
model, InstructPix2Pix, is trained on our generated data, and generalizes to
real images and user-written instructions at inference time. Since it performs
edits in the forward pass and does not require per example fine-tuning or
inversion, our model edits images quickly, in a matter of seconds. We show
compelling editing results for a diverse collection of input images and written
instructions.
Related papers
- Image Inpainting Models are Effective Tools for Instruction-guided Image Editing [42.63350374074953]
This technique report is for the winning solution of the CVPR2024 GenAI Media Generation Challenge Workshop's Instruction-guided Image Editing track.
We use a 4-step process IIIE (Inpainting-based Instruction-guided Image Editing): editing category classification, main editing object identification, editing mask acquisition, and image inpainting.
Results show that through proper combinations of language models and image inpainting models, our pipeline can reach a high success rate with satisfying visual quality.
arXiv Detail & Related papers (2024-07-18T03:55:33Z) - A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models [117.77807994397784]
Image editing aims to edit the given synthetic or real image to meet the specific requirements from users.
Recent significant advancement in this field is based on the development of text-to-image (T2I) diffusion models.
T2I-based image editing methods significantly enhance editing performance and offer a user-friendly interface for modifying content guided by multimodal inputs.
arXiv Detail & Related papers (2024-06-20T17:58:52Z) - InstructRL4Pix: Training Diffusion for Image Editing by Reinforcement Learning [31.799923647356458]
We propose Reinforcement Learning Guided Image Editing Method(InstructRL4Pix) to train a diffusion model to generate images that are guided by the attention maps of the target object.
Experimental results show that InstructRL4Pix breaks through the limitations of traditional datasets and uses unsupervised learning to optimize editing goals and achieve accurate image editing based on natural human commands.
arXiv Detail & Related papers (2024-06-14T12:31:48Z) - ReasonPix2Pix: Instruction Reasoning Dataset for Advanced Image Editing [77.12834553200632]
We introduce ReasonPix2Pix, a comprehensive reasoning-attentive instruction editing dataset.
The dataset is characterized by 1) reasoning instruction, 2) more realistic images from fine-grained categories, and 3) increased variances between input and edited images.
When fine-tuned with our dataset under supervised conditions, the model demonstrates superior performance in instructional editing tasks, independent of whether the tasks require reasoning or not.
arXiv Detail & Related papers (2024-05-18T06:03:42Z) - Real-time 3D-aware Portrait Editing from a Single Image [111.27169315556444]
3DPE can edit a face image following given prompts, like reference images or text descriptions.
A lightweight module is distilled from a 3D portrait generator and a text-to-image model.
arXiv Detail & Related papers (2024-02-21T18:36:26Z) - SmartEdit: Exploring Complex Instruction-based Image Editing with
Multimodal Large Language Models [91.22477798288003]
This paper introduces SmartEdit, a novel approach to instruction-based image editing.
It exploits Multimodal Large Language Models (MLLMs) to enhance their understanding and reasoning capabilities.
We show that a small amount of complex instruction editing data can effectively stimulate SmartEdit's editing capabilities for more complex instructions.
arXiv Detail & Related papers (2023-12-11T17:54:11Z) - Pix2Video: Video Editing using Image Diffusion [43.07444438561277]
We investigate how to use pre-trained image models for text-guided video editing.
Our method works in two simple steps: first, we use a pre-trained structure-guided (e.g., depth) image diffusion model to perform text-guided edits on an anchor frame.
We demonstrate that realistic text-guided video edits are possible, without any compute-intensive preprocessing or video-specific finetuning.
arXiv Detail & Related papers (2023-03-22T16:36:10Z) - Zero-shot Image-to-Image Translation [57.46189236379433]
We propose pix2pix-zero, an image-to-image translation method that can preserve the original image without manual prompting.
We propose cross-attention guidance, which aims to retain the cross-attention maps of the input image throughout the diffusion process.
Our method does not need additional training for these edits and can directly use the existing text-to-image diffusion model.
arXiv Detail & Related papers (2023-02-06T18:59:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.