NoHumansRequired: Autonomous High-Quality Image Editing Triplet Mining
Recent advances in generative modeling enable image editing assistants that
follow natural language instructions without additional user input. Their
supervised training requires millions of triplets: original image, instruction,
edited image. Yet mining pixel-accurate examples is hard. Each edit must affect
only prompt-specified regions, preserve stylistic coherence, respect physical
plausibility, and retain visual appeal. The lack of robust automated
edit-quality metrics hinders reliable automation at scale. We present an
automated, modular pipeline that mines high-fidelity triplets across domains,
resolutions, instruction complexities, and styles. Built on public generative
models and running without human intervention, our system uses a task-tuned
Gemini validator to score instruction adherence and aesthetics directly,
removing any need for segmentation or grounding models. Inversion and
compositional bootstrapping enlarge the mined set by approximately 2.2x,
enabling large-scale high-fidelity training data. By automating the most
repetitive annotation steps, the approach allows a new scale of training
without human labeling effort. To democratize research in this
resource-intensive area, we release NHR-Edit: an open dataset of 358k
high-quality triplets. In the largest cross-dataset evaluation, it surpasses
all public alternatives. We also release Bagel-NHR-Edit, an open-source
fine-tuned Bagel model, which achieves state-of-the-art metrics in our
experiments.
687dfaed2e8db0930be6f18d
Maksim Kuprashevich
admin_assigned
2025-07-22T14:10:51.469Z
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Maksim Kuprashevich
user
WildChlamydia
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Grigorii Alekseenko
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2025-07-22T14:01:03.228Z
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Grigorii Alekseenko
user
Riko0
687dfaed2e8db0930be6f18f
Irina Tolstykh
claimed_verified
2025-07-21T14:21:55.176Z
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Irina Tolstykh
user
iitolstykh
687dfaed2e8db0930be6f190
Georgii Fedorov
claimed_verified
2025-07-22T13:05:51.980Z
65c5f74e3cf0b5c5f3965354
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/tICFxe6taWxC6CrOSuDI5.jpeg
Georgii Fedorov
user
geobatch
687dfaed2e8db0930be6f191
Bulat Suleimanov
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Vladimir Dokholyan
687dfaed2e8db0930be6f193
Aleksandr Gordeev
claimed_verified
2025-07-22T13:05:47.852Z
65ae526111a0a3ff61d7d726
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Aleksandr
user
grac20101
NoHumansRequired: Autonomous High-Quality Image Editing Triplet Mining
Recent advances in generative modeling enable image editing assistants that
follow natural language instructions without additional user input. Their
supervised training requires millions of triplets: original image, instruction,
edited image. Yet mining pixel-accurate examples is hard. Each edit must affect
only prompt-specified regions, preserve stylistic coherence, respect physical
plausibility, and retain visual appeal. The lack of robust automated
edit-quality metrics hinders reliable automation at scale. We present an
automated, modular pipeline that mines high-fidelity triplets across domains,
resolutions, instruction complexities, and styles. Built on public generative
models and running without human intervention, our system uses a task-tuned
Gemini validator to score instruction adherence and aesthetics directly,
removing any need for segmentation or grounding models. Inversion and
compositional bootstrapping enlarge the mined set by approximately 2.2x,
enabling large-scale high-fidelity training data. By automating the most
repetitive annotation steps, the approach allows a new scale of training
without human labeling effort. To democratize research in this
resource-intensive area, we release NHR-Edit: an open dataset of 358k
high-quality triplets. In the largest cross-dataset evaluation, it surpasses
all public alternatives. We also release Bagel-NHR-Edit, an open-source
fine-tuned Bagel model, which achieves state-of-the-art metrics in our
experiments.
An automated pipeline mines high-fidelity image editing triplets using generative models and a task-tuned validator, enabling large-scale training without human labeling.
Gemini validator inversion compositional bootstrapping NHR-Edit Bagel-NHR-Edit Bagel model