HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing
This study introduces HQ-Edit, a high-quality instruction-based image editing
dataset with around 200,000 edits. Unlike prior approaches relying on attribute
guidance or human feedback on building datasets, we devise a scalable data
collection pipeline leveraging advanced foundation models, namely GPT-4V and
DALL-E 3. To ensure its high quality, diverse examples are first collected
online, expanded, and then used to create high-quality diptychs featuring input
and output images with detailed text prompts, followed by precise alignment
ensured through post-processing. In addition, we propose two evaluation
metrics, Alignment and Coherence, to quantitatively assess the quality of image
edit pairs using GPT-4V. HQ-Edits high-resolution images, rich in detail and
accompanied by comprehensive editing prompts, substantially enhance the
capabilities of existing image editing models. For example, an HQ-Edit
finetuned InstructPix2Pix can attain state-of-the-art image editing
performance, even surpassing those models fine-tuned with human-annotated data.
The project page is https://thefllood.github.io/HQEdit_web.
661deabbc4be41f142731395
Mude Hui
admin_assigned
2024-04-18T10:17:30.131Z
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MudeHui
user
MudeHui
661deabbc4be41f142731396
Siwei Yang
claimed_verified
2025-04-22T10:17:54.366Z
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Siwei Yang
user
LAOS-Y
661deabbc4be41f142731397
Bingchen Zhao
claimed_verified
2024-06-19T07:39:45.398Z
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Bingchen Zhao
user
tennant
661deabbc4be41f142731398
Yichun Shi
admin_assigned
2024-04-18T10:58:40.160Z
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Shi
user
Yichun
661deabbc4be41f142731399
Heng Wang
661deabbc4be41f14273139a
Peng Wang
661deabbc4be41f14273139b
Yuyin Zhou
661deabbc4be41f14273139c
Cihang Xie
admin_assigned
2024-04-18T10:58:18.033Z
645eb61da3c5cd8a16efffff
/avatars/9112bfeed598dfabf9e077e69e09ecc9.svg
Cihang Xie
user
cihangxie
HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing
This study introduces HQ-Edit, a high-quality instruction-based image editing
dataset with around 200,000 edits. Unlike prior approaches relying on attribute
guidance or human feedback on building datasets, we devise a scalable data
collection pipeline leveraging advanced foundation models, namely GPT-4V and
DALL-E 3. To ensure its high quality, diverse examples are first collected
online, expanded, and then used to create high-quality diptychs featuring input
and output images with detailed text prompts, followed by precise alignment
ensured through post-processing. In addition, we propose two evaluation
metrics, Alignment and Coherence, to quantitatively assess the quality of image
edit pairs using GPT-4V. HQ-Edits high-resolution images, rich in detail and
accompanied by comprehensive editing prompts, substantially enhance the
capabilities of existing image editing models. For example, an HQ-Edit
finetuned InstructPix2Pix can attain state-of-the-art image editing
performance, even surpassing those models fine-tuned with human-annotated data.
The project page is https://thefllood.github.io/HQEdit_web.
HQ-Edit, a high-quality instruction-based image editing dataset using GPT-4V and DALL-E 3, enhances image editing models with precise text prompts and evaluation metrics.
GPT-4V DALL-E 3 diptychs Alignment Coherence InstructPix2Pix