Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow
Diffusion models have greatly improved visual generation but are hindered by
slow generation speed due to the computationally intensive nature of solving
generative ODEs. Rectified flow, a widely recognized solution, improves
generation speed by straightening the ODE path. Its key components include: 1)
using the diffusion form of flow-matching, 2) employing boldsymbol
v-prediction, and 3) performing rectification (a.k.a. reflow). In this paper,
we argue that the success of rectification primarily lies in using a pretrained
diffusion model to obtain matched pairs of noise and samples, followed by
retraining with these matched noise-sample pairs. Based on this, components 1)
and 2) are unnecessary. Furthermore, we highlight that straightness is not an
essential training target for rectification; rather, it is a specific case of
flow-matching models. The more critical training target is to achieve a
first-order approximate ODE path, which is inherently curved for models like
DDPM and Sub-VP. Building on this insight, we propose Rectified Diffusion,
which generalizes the design space and application scope of rectification to
encompass the broader category of diffusion models, rather than being
restricted to flow-matching models. We validate our method on Stable Diffusion
v1-5 and Stable Diffusion XL. Our method not only greatly simplifies the
training procedure of rectified flow-based previous works (e.g., InstaFlow) but
also achieves superior performance with even lower training cost. Our code is
available at https://github.com/G-U-N/Rectified-Diffusion.
6708940226920e3023fb2ed5
Fu-Yun Wang
claimed_verified
2024-10-11T07:43:36.040Z
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Fu-Yun Wang
user
wangfuyun
6708940226920e3023fb2ed6
Ling Yang
claimed_verified
2025-02-11T15:34:23.400Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64fde4e252e82dd432b74ce9/-CQZbBP7FsPPyawYrsi4z.jpeg
Ling Yang
user
Lingaaaaaaa
6708940226920e3023fb2ed7
Zhaoyang Huang
admin_assigned
2024-10-11T10:39:51.464Z
64c26e0e400274d129a10d5d
/avatars/c1debf3d7f61c2ea01d9a25d7e332712.svg
Zhaoyang Huang
user
DrinkingCoder
6708940226920e3023fb2ed8
Mengdi Wang
admin_assigned
2024-10-11T10:40:16.489Z
6599415e8c8ac79295e0b5e3
/avatars/85500bc8d2cd51444adcc19b1f8db313.svg
Mengdi Wang
user
Edify-Kd2024
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Hongsheng Li
admin_assigned
2024-10-11T10:40:23.050Z
65c04e9c27a5fdca81abcbd9
/avatars/12a155683c824fa23da4a9e2bed4f64e.svg
Hongsheng LI
user
hsli-cuhk
Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow
Diffusion models have greatly improved visual generation but are hindered by
slow generation speed due to the computationally intensive nature of solving
generative ODEs. Rectified flow, a widely recognized solution, improves
generation speed by straightening the ODE path. Its key components include: 1)
using the diffusion form of flow-matching, 2) employing boldsymbol
v-prediction, and 3) performing rectification (a.k.a. reflow). In this paper,
we argue that the success of rectification primarily lies in using a pretrained
diffusion model to obtain matched pairs of noise and samples, followed by
retraining with these matched noise-sample pairs. Based on this, components 1)
and 2) are unnecessary. Furthermore, we highlight that straightness is not an
essential training target for rectification; rather, it is a specific case of
flow-matching models. The more critical training target is to achieve a
first-order approximate ODE path, which is inherently curved for models like
DDPM and Sub-VP. Building on this insight, we propose Rectified Diffusion,
which generalizes the design space and application scope of rectification to
encompass the broader category of diffusion models, rather than being
restricted to flow-matching models. We validate our method on Stable Diffusion
v1-5 and Stable Diffusion XL. Our method not only greatly simplifies the
training procedure of rectified flow-based previous works (e.g., InstaFlow) but
also achieves superior performance with even lower training cost. Our code is
available at https://github.com/G-U-N/Rectified-Diffusion.
Rectified Diffusion improves generation speed and performance by retraining with matched noise-sample pairs, simplifying the training procedure compared to previous methods.
diffusion models generative ODEs rectified flow flow-matching v-prediction reflow Stable Diffusion v1-5 Stable Diffusion XL InstaFlow