FaceStudio: Put Your Face Everywhere in Seconds
This study investigates identity-preserving image synthesis, an intriguing
task in image generation that seeks to maintain a subject's identity while
adding a personalized, stylistic touch. Traditional methods, such as Textual
Inversion and DreamBooth, have made strides in custom image creation, but they
come with significant drawbacks. These include the need for extensive resources
and time for fine-tuning, as well as the requirement for multiple reference
images. To overcome these challenges, our research introduces a novel approach
to identity-preserving synthesis, with a particular focus on human images. Our
model leverages a direct feed-forward mechanism, circumventing the need for
intensive fine-tuning, thereby facilitating quick and efficient image
generation. Central to our innovation is a hybrid guidance framework, which
combines stylized images, facial images, and textual prompts to guide the image
generation process. This unique combination enables our model to produce a
variety of applications, such as artistic portraits and identity-blended
images. Our experimental results, including both qualitative and quantitative
evaluations, demonstrate the superiority of our method over existing baseline
models and previous works, particularly in its remarkable efficiency and
ability to preserve the subject's identity with high fidelity.
656fd910ec366e93ca2e26b1
Yuxuan Yan
656fd910ec366e93ca2e26b2
Chi Zhang
claimed_verified
2023-12-06T12:00:36.414Z
64196320ed725fef64419c2a
/avatars/96feb22fb5e8931d6c9e0ea06148266f.svg
Chi Zhang
user
DrChiZhang
656fd910ec366e93ca2e26b3
Rui Wang
claimed_verified
2023-12-12T08:09:20.353Z
630443483926de1f7ec6bda0
/avatars/e8e95ab0d03f58c61846ccc1318502fe.svg
Rui Wang
user
budui
656fd910ec366e93ca2e26b4
Pei Cheng
656fd910ec366e93ca2e26b5
Gang Yu
admin_assigned
2023-12-06T11:45:19.399Z
63417332c5565a4b8d43a0d8
https://cdn-avatars.huggingface.co/v1/production/uploads/63417332c5565a4b8d43a0d8/MmnYG7Wu2Z_lqCBvTfJmy.png
Gang Yu
user
skicy
656fd910ec366e93ca2e26b6
Bin Fu
FaceStudio: Put Your Face Everywhere in Seconds
This study investigates identity-preserving image synthesis, an intriguing
task in image generation that seeks to maintain a subject's identity while
adding a personalized, stylistic touch. Traditional methods, such as Textual
Inversion and DreamBooth, have made strides in custom image creation, but they
come with significant drawbacks. These include the need for extensive resources
and time for fine-tuning, as well as the requirement for multiple reference
images. To overcome these challenges, our research introduces a novel approach
to identity-preserving synthesis, with a particular focus on human images. Our
model leverages a direct feed-forward mechanism, circumventing the need for
intensive fine-tuning, thereby facilitating quick and efficient image
generation. Central to our innovation is a hybrid guidance framework, which
combines stylized images, facial images, and textual prompts to guide the image
generation process. This unique combination enables our model to produce a
variety of applications, such as artistic portraits and identity-blended
images. Our experimental results, including both qualitative and quantitative
evaluations, demonstrate the superiority of our method over existing baseline
models and previous works, particularly in its remarkable efficiency and
ability to preserve the subject's identity with high fidelity.
A hybrid guidance framework for identity-preserving image synthesis efficiently generates stylistic portraits by combining stylized images, facial images, and textual prompts.
identity-preserving synthesis Textual Inversion DreamBooth direct feed-forward mechanism hybrid guidance framework artistic portraits identity-blended images qualitative evaluations quantitative evaluations