Collaborative Score Distillation for Consistent Visual Synthesis
Generative priors of large-scale text-to-image diffusion models enable a wide
range of new generation and editing applications on diverse visual modalities.
However, when adapting these priors to complex visual modalities, often
represented as multiple images (e.g., video), achieving consistency across a
set of images is challenging. In this paper, we address this challenge with a
novel method, Collaborative Score Distillation (CSD). CSD is based on the Stein
Variational Gradient Descent (SVGD). Specifically, we propose to consider
multiple samples as "particles" in the SVGD update and combine their score
functions to distill generative priors over a set of images synchronously.
Thus, CSD facilitates seamless integration of information across 2D images,
leading to a consistent visual synthesis across multiple samples. We show the
effectiveness of CSD in a variety of tasks, encompassing the visual editing of
panorama images, videos, and 3D scenes. Our results underline the competency of
CSD as a versatile method for enhancing inter-sample consistency, thereby
broadening the applicability of text-to-image diffusion models.
64ae040b6864362a7e2311ce
Subin Kim
claimed_verified
2023-07-12T13:28:48.280Z
63eb83b74dcaaf08763766a9
/avatars/11e3e00c49a3b74947cce2f1ef624944.svg
Subin Kim
user
subin-kim
64ae040b6864362a7e2311cf
Kyungmin Lee
64ae040b6864362a7e2311d0
June Suk Choi
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2025-07-17T08:19:00.236Z
66c6edcc91dced946471bc13
/avatars/55cc8593da6540e1566e1de9d7133f9f.svg
June Suk Choi
user
wchoi403
64ae040b6864362a7e2311d1
Jongheon Jeong
claimed_verified
2023-07-13T08:32:47.469Z
630d92f54ca0a22768c2e76a
/avatars/92d613ddfc79cd44cc0c808bb3782282.svg
Jeong
user
Jongheon
64ae040b6864362a7e2311d2
Kihyuk Sohn
64ae040b6864362a7e2311d3
Jinwoo Shin
Collaborative Score Distillation for Consistent Visual Synthesis
Generative priors of large-scale text-to-image diffusion models enable a wide
range of new generation and editing applications on diverse visual modalities.
However, when adapting these priors to complex visual modalities, often
represented as multiple images (e.g., video), achieving consistency across a
set of images is challenging. In this paper, we address this challenge with a
novel method, Collaborative Score Distillation (CSD). CSD is based on the Stein
Variational Gradient Descent (SVGD). Specifically, we propose to consider
multiple samples as "particles" in the SVGD update and combine their score
functions to distill generative priors over a set of images synchronously.
Thus, CSD facilitates seamless integration of information across 2D images,
leading to a consistent visual synthesis across multiple samples. We show the
effectiveness of CSD in a variety of tasks, encompassing the visual editing of
panorama images, videos, and 3D scenes. Our results underline the competency of
CSD as a versatile method for enhancing inter-sample consistency, thereby
broadening the applicability of text-to-image diffusion models.
A novel method, Collaborative Score Distillation (CSD), based on Stein Variational Gradient Descent (SVGD), enhances consistency in text-to-image diffusion models across multiple images such as panoramas, videos, and 3D scenes.
text-to-image diffusion models Collaborative Score Distillation (CSD) Stein Variational Gradient Descent (SVGD) generative priors visual synthesis inter-sample consistency