Learning an Image Editing Model without Image Editing Pairs
Recent image editing models have achieved impressive results while following
natural language editing instructions, but they rely on supervised fine-tuning
with large datasets of input-target pairs. This is a critical bottleneck, as
such naturally occurring pairs are hard to curate at scale. Current workarounds
use synthetic training pairs that leverage the zero-shot capabilities of
existing models. However, this can propagate and magnify the artifacts of the
pretrained model into the final trained model. In this work, we present a new
training paradigm that eliminates the need for paired data entirely. Our
approach directly optimizes a few-step diffusion model by unrolling it during
training and leveraging feedback from vision-language models (VLMs). For each
input and editing instruction, the VLM evaluates if an edit follows the
instruction and preserves unchanged content, providing direct gradients for
end-to-end optimization. To ensure visual fidelity, we incorporate distribution
matching loss (DMD), which constrains generated images to remain within the
image manifold learned by pretrained models. We evaluate our method on standard
benchmarks and include an extensive ablation study. Without any paired data,
our method performs on par with various image editing diffusion models trained
on extensive supervised paired data, under the few-step setting. Given the same
VLM as the reward model, we also outperform RL-based techniques like Flow-GRPO.
68f1a20a6e0bef323a68fcc5
Nupur Kumari
68f1a20a6e0bef323a68fcc6
Sheng-Yu Wang
68f1a20a6e0bef323a68fcc7
Nanxuan Zhao
68f1a20a6e0bef323a68fcc8
Yotam Nitzan
68f1a20a6e0bef323a68fcc9
Yuheng Li
68f1a20a6e0bef323a68fcca
Krishna Kumar Singh
68f1a20a6e0bef323a68fccb
Richard Zhang
68f1a20a6e0bef323a68fccc
Eli Shechtman
68f1a20a6e0bef323a68fccd
Jun-Yan Zhu
68f1a20a6e0bef323a68fcce
Xun Huang
Learning an Image Editing Model without Image Editing Pairs
Recent image editing models have achieved impressive results while following
natural language editing instructions, but they rely on supervised fine-tuning
with large datasets of input-target pairs. This is a critical bottleneck, as
such naturally occurring pairs are hard to curate at scale. Current workarounds
use synthetic training pairs that leverage the zero-shot capabilities of
existing models. However, this can propagate and magnify the artifacts of the
pretrained model into the final trained model. In this work, we present a new
training paradigm that eliminates the need for paired data entirely. Our
approach directly optimizes a few-step diffusion model by unrolling it during
training and leveraging feedback from vision-language models (VLMs). For each
input and editing instruction, the VLM evaluates if an edit follows the
instruction and preserves unchanged content, providing direct gradients for
end-to-end optimization. To ensure visual fidelity, we incorporate distribution
matching loss (DMD), which constrains generated images to remain within the
image manifold learned by pretrained models. We evaluate our method on standard
benchmarks and include an extensive ablation study. Without any paired data,
our method performs on par with various image editing diffusion models trained
on extensive supervised paired data, under the few-step setting. Given the same
VLM as the reward model, we also outperform RL-based techniques like Flow-GRPO.
A new training paradigm for image editing models uses unrolled diffusion models and vision-language feedback to achieve performance comparable to supervised models without paired data.
diffusion model unrolling vision-language models distribution matching loss image manifold few-step setting RL-based techniques Flow-GRPO