title: HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing
summary: 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.
paper_authors._id: 661deabbc4be41f142731395
paper_authors.name: Mude Hui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T10:17:30.131Z
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paper_authors.fullname: MudeHui
paper_authors.type: user
paper_authors.user: MudeHui
paper_authors._id: 661deabbc4be41f142731396
paper_authors.name: Siwei Yang
paper_authors.status: claimed_verified
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paper_authors.fullname: Siwei Yang
paper_authors.type: user
paper_authors.user: LAOS-Y
paper_authors._id: 661deabbc4be41f142731397
paper_authors.name: Bingchen Zhao
paper_authors.status: claimed_verified
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paper_authors._id: 62dcd71075e9787ec5aa41ba
paper_authors.avatarUrl: /avatars/f37ce036b76180ed0fa004f9c8c09363.svg
paper_authors.fullname: Bingchen Zhao
paper_authors.type: user
paper_authors.user: tennant
paper_authors._id: 661deabbc4be41f142731398
paper_authors.name: Yichun Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T10:58:40.160Z
paper_authors._id: 6309ad81b105f8675bd5a796
paper_authors.avatarUrl: /avatars/01c2186924486da4606e128e83709164.svg
paper_authors.fullname: Shi
paper_authors.type: user
paper_authors.user: Yichun
paper_authors._id: 661deabbc4be41f142731399
paper_authors.name: Heng Wang
paper_authors._id: 661deabbc4be41f14273139a
paper_authors.name: Peng Wang
paper_authors._id: 661deabbc4be41f14273139b
paper_authors.name: Yuyin Zhou
paper_authors._id: 661deabbc4be41f14273139c
paper_authors.name: Cihang Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T10:58:18.033Z
paper_authors._id: 645eb61da3c5cd8a16efffff
paper_authors.avatarUrl: /avatars/9112bfeed598dfabf9e077e69e09ecc9.svg
paper_authors.fullname: Cihang Xie
paper_authors.type: user
paper_authors.user: cihangxie
paper_title: HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: GPT-4V DALL-E 3 diptychs Alignment Coherence InstructPix2Pix