Step1X-Edit: A Practical Framework for General Image Editing
In recent years, image editing models have witnessed remarkable and rapid
development. The recent unveiling of cutting-edge multimodal models such as
GPT-4o and Gemini2 Flash has introduced highly promising image editing
capabilities. These models demonstrate an impressive aptitude for fulfilling a
vast majority of user-driven editing requirements, marking a significant
advancement in the field of image manipulation. However, there is still a large
gap between the open-source algorithm with these closed-source models. Thus, in
this paper, we aim to release a state-of-the-art image editing model, called
Step1X-Edit, which can provide comparable performance against the closed-source
models like GPT-4o and Gemini2 Flash. More specifically, we adopt the
Multimodal LLM to process the reference image and the user's editing
instruction. A latent embedding has been extracted and integrated with a
diffusion image decoder to obtain the target image. To train the model, we
build a data generation pipeline to produce a high-quality dataset. For
evaluation, we develop the GEdit-Bench, a novel benchmark rooted in real-world
user instructions. Experimental results on GEdit-Bench demonstrate that
Step1X-Edit outperforms existing open-source baselines by a substantial margin
and approaches the performance of leading proprietary models, thereby making
significant contributions to the field of image editing.
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Shiyu Liu
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Yucheng Han
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Peng Xing
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Fukun Yin
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Rui Wang
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Wei Cheng
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Wei Cheng
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Jiaqi Liao
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Yingming Wang
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Honghao Fu
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Chunrui Han
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Guopeng Li
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Yuang Peng
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Yuang Peng
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Quan Sun
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Jingwei Wu
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Yan Cai
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Zheng Ge
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Ranchen Ming
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Lei Xia
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Xianfang Zeng
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Yibo Zhu
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Binxing Jiao
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Xiangyu Zhang
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Gang Yu
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Gang Yu
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Daxin Jiang
Step1X-Edit: A Practical Framework for General Image Editing
In recent years, image editing models have witnessed remarkable and rapid
development. The recent unveiling of cutting-edge multimodal models such as
GPT-4o and Gemini2 Flash has introduced highly promising image editing
capabilities. These models demonstrate an impressive aptitude for fulfilling a
vast majority of user-driven editing requirements, marking a significant
advancement in the field of image manipulation. However, there is still a large
gap between the open-source algorithm with these closed-source models. Thus, in
this paper, we aim to release a state-of-the-art image editing model, called
Step1X-Edit, which can provide comparable performance against the closed-source
models like GPT-4o and Gemini2 Flash. More specifically, we adopt the
Multimodal LLM to process the reference image and the user's editing
instruction. A latent embedding has been extracted and integrated with a
diffusion image decoder to obtain the target image. To train the model, we
build a data generation pipeline to produce a high-quality dataset. For
evaluation, we develop the GEdit-Bench, a novel benchmark rooted in real-world
user instructions. Experimental results on GEdit-Bench demonstrate that
Step1X-Edit outperforms existing open-source baselines by a substantial margin
and approaches the performance of leading proprietary models, thereby making
significant contributions to the field of image editing.
Step1X-Edit, an image editing model that uses multimodal LLM and diffusion image decoder, outperforms open-source models and approaches proprietary models in quality.
Multimodal LLM diffusion image decoder latent embedding GEdit-Bench