MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image
  Editing
Text-guided image editing is widely needed in daily life, ranging from
personal use to professional applications such as Photoshop. However, existing
methods are either zero-shot or trained on an automatically synthesized
dataset, which contains a high volume of noise. Thus, they still require lots
of manual tuning to produce desirable outcomes in practice. To address this
issue, we introduce MagicBrush (https://osu-nlp-group.github.io/MagicBrush/),
the first large-scale, manually annotated dataset for instruction-guided real
image editing that covers diverse scenarios: single-turn, multi-turn,
mask-provided, and mask-free editing. MagicBrush comprises over 10K manually
annotated triples (source image, instruction, target image), which supports
trainining large-scale text-guided image editing models. We fine-tune
InstructPix2Pix on MagicBrush and show that the new model can produce much
better images according to human evaluation. We further conduct extensive
experiments to evaluate current image editing baselines from multiple
dimensions including quantitative, qualitative, and human evaluations. The
results reveal the challenging nature of our dataset and the gap between
current baselines and real-world editing needs.
648faa9519234cd09c480d22
Kai Zhang
claimed_verified
2023-07-03T07:28:03.726Z
63e0a50242591dda0b9dca5c
https://cdn-avatars.huggingface.co/v1/production/uploads/63e0a50242591dda0b9dca5c/c7cBPEBWQDFYimfGnO_SI.png
Kai Zhang
user
drogozhang
648faa9519234cd09c480d23
Lingbo Mo
648faa9519234cd09c480d24
Wenhu Chen
648faa9519234cd09c480d25
Huan Sun
648faa9519234cd09c480d26
Yu Su
claimed_verified
2023-06-20T08:50:10.908Z
6477a323dbc2a416f8b852b3
https://cdn-avatars.huggingface.co/v1/production/uploads/6477a323dbc2a416f8b852b3/mRKW5kT9GASORT4YnaZz0.jpeg
Yu Su
user
ysu-nlp
MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image
  Editing
Text-guided image editing is widely needed in daily life, ranging from
personal use to professional applications such as Photoshop. However, existing
methods are either zero-shot or trained on an automatically synthesized
dataset, which contains a high volume of noise. Thus, they still require lots
of manual tuning to produce desirable outcomes in practice. To address this
issue, we introduce MagicBrush (https://osu-nlp-group.github.io/MagicBrush/),
the first large-scale, manually annotated dataset for instruction-guided real
image editing that covers diverse scenarios: single-turn, multi-turn,
mask-provided, and mask-free editing. MagicBrush comprises over 10K manually
annotated triples (source image, instruction, target image), which supports
trainining large-scale text-guided image editing models. We fine-tune
InstructPix2Pix on MagicBrush and show that the new model can produce much
better images according to human evaluation. We further conduct extensive
experiments to evaluate current image editing baselines from multiple
dimensions including quantitative, qualitative, and human evaluations. The
results reveal the challenging nature of our dataset and the gap between
current baselines and real-world editing needs.
MagicBrush, a large-scale manually annotated dataset for text-guided real image editing, enhances InstructPix2Pix model performance and highlights challenges in current baselines.
InstructPix2Pix text-guided image editing manually annotated source image instruction target image human evaluation