Step-aware Preference Optimization: Aligning Preference with Denoising
  Performance at Each Step
Recently, Direct Preference Optimization (DPO) has extended its success from
aligning large language models (LLMs) to aligning text-to-image diffusion
models with human preferences. Unlike most existing DPO methods that assume all
diffusion steps share a consistent preference order with the final generated
images, we argue that this assumption neglects step-specific denoising
performance and that preference labels should be tailored to each step's
contribution. To address this limitation, we propose Step-aware Preference
Optimization (SPO), a novel post-training approach that independently evaluates
and adjusts the denoising performance at each step, using a step-aware
preference model and a step-wise resampler to ensure accurate step-aware
supervision. Specifically, at each denoising step, we sample a pool of images,
find a suitable win-lose pair, and, most importantly, randomly select a single
image from the pool to initialize the next denoising step. This step-wise
resampler process ensures the next win-lose image pair comes from the same
image, making the win-lose comparison independent of the previous step. To
assess the preferences at each step, we train a separate step-aware preference
model that can be applied to both noisy and clean images. Our experiments with
Stable Diffusion v1.5 and SDXL demonstrate that SPO significantly outperforms
the latest Diffusion-DPO in aligning generated images with complex, detailed
prompts and enhancing aesthetics, while also achieving more than 20x times
faster in training efficiency. Code and model:
https://rockeycoss.github.io/spo.github.io/
6662709b68009120affc7a1d
Zhanhao Liang
admin_assigned
2024-06-07T10:36:01.913Z
64218b9b8adc8881b9738059
/avatars/03b59d24e18a9aebd10925494304b388.svg
liangzhanhao
user
Chinho
6662709b68009120affc7a1e
Yuhui Yuan
admin_assigned
2024-06-07T10:36:15.204Z
631f108bb45367a05fe74260
/avatars/c20da6800b643728062712d9a2771648.svg
Researcher
user
YuanYuhui
6662709b68009120affc7a1f
Shuyang Gu
claimed_verified
2024-07-10T08:20:15.689Z
64c38fcf573c5a427e12cd37
/avatars/2b9de06f29147ed2c212e920afba0eaf.svg
cientgu
user
cientgu
6662709b68009120affc7a20
Bohan Chen
6662709b68009120affc7a21
Tiankai Hang
6662709b68009120affc7a22
Ji Li
claimed_verified
2024-06-13T18:37:01.644Z
6473768cbf9b32c6bbc9caa6
/avatars/7df22e0f33aac855a5d2b0e9286c7ad5.svg
aaa
user
jiangsuliji
6662709b68009120affc7a23
Liang Zheng
Step-aware Preference Optimization: Aligning Preference with Denoising
  Performance at Each Step
Recently, Direct Preference Optimization (DPO) has extended its success from
aligning large language models (LLMs) to aligning text-to-image diffusion
models with human preferences. Unlike most existing DPO methods that assume all
diffusion steps share a consistent preference order with the final generated
images, we argue that this assumption neglects step-specific denoising
performance and that preference labels should be tailored to each step's
contribution. To address this limitation, we propose Step-aware Preference
Optimization (SPO), a novel post-training approach that independently evaluates
and adjusts the denoising performance at each step, using a step-aware
preference model and a step-wise resampler to ensure accurate step-aware
supervision. Specifically, at each denoising step, we sample a pool of images,
find a suitable win-lose pair, and, most importantly, randomly select a single
image from the pool to initialize the next denoising step. This step-wise
resampler process ensures the next win-lose image pair comes from the same
image, making the win-lose comparison independent of the previous step. To
assess the preferences at each step, we train a separate step-aware preference
model that can be applied to both noisy and clean images. Our experiments with
Stable Diffusion v1.5 and SDXL demonstrate that SPO significantly outperforms
the latest Diffusion-DPO in aligning generated images with complex, detailed
prompts and enhancing aesthetics, while also achieving more than 20x times
faster in training efficiency. Code and model:
https://rockeycoss.github.io/spo.github.io/
Step-aware Preference Optimization improves alignment of text-to-image diffusion models with human preferences by independent evaluation and adjustment at each denoising step, outperforming existing methods in aesthetics and efficiency.
Direct Preference Optimization DPO text-to-image diffusion models step-specific denoising performance preference labels Step-aware Preference Optimization SPO post-training approach step-wise resampler step-aware preference model win-lose pair Stable Diffusion SDXL