paint-brush
FaceStudio: Put Your Face Everywhere in Seconds: Method and Hybrid Guidance Strategyby@dilution

FaceStudio: Put Your Face Everywhere in Seconds: Method and Hybrid Guidance Strategy

by Dilution
Dilution HackerNoon profile picture

Dilution

@dilution

Transforming concentrations, unlocking new potential through careful calibration for a...

August 14th, 2024
Read on Terminal Reader
Read this story in a terminal
Print this story
Read this story w/o Javascript
Read this story w/o Javascript
tldt arrow

Too Long; Didn't Read

This paper is available on arxiv(https://arxiv.org/abs/2312.02663) under CC0 1.0 DEED license. In this section, we present the design and functionalities of our novel framework. Our method fundamentally builds on StableDiffusion 42, with several pivotal modifications, especially in the condition modules.
featured image - FaceStudio: Put Your Face Everywhere in Seconds: Method and Hybrid Guidance Strategy
1x
Read by Dr. One voice-avatar

Listen to this story

Dilution HackerNoon profile picture
Dilution

Dilution

@dilution

Transforming concentrations, unlocking new potential through careful calibration for a balanced and harmonious outcome.

About @dilution
LEARN MORE ABOUT @DILUTION'S
EXPERTISE AND PLACE ON THE INTERNET.
0-item

STORY’S CREDIBILITY

Academic Research Paper

Academic Research Paper

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Authors:

(1) Yuxuan Yan,Tencent with Equal contributions and yuxuanyan@tencent.com;

(2) Chi Zhang, Tencent with Equal contributions and Corresponding Author, johnczhang@tencent.com;

(3) Rui Wang, Tencent and raywwang@tencent.com;

(4) Yichao Zhou, Tencent and yichaozhou@tencent.com;

(5) Gege Zhang, Tencent and gretazhang@tencent.com;

(6) Pei Cheng, Tencent and peicheng@tencent.com;

(7) Bin Fu, Tencent and brianfu@tencent.com;

(8) Gang Yu, Tencentm and skicyyu@tencent.com.

Abstract and 1 Introduction

2. Related Work

3. Method and 3.1. Hybrid Guidance Strategy

3.2. Handling Multiple Identities

3.3. Training

4. Experiments

4.1. Implementation details.

4.2. Results

5. Conclusion and References

3. Method

In this section, we present the design and functionalities of our novel framework. Our method fundamentally builds on StableDiffusion [42], with several pivotal modifications, especially in the condition modules catering to hybridguidance image generation. We start by elaborating on our hybrid guidance design in the proposed condition module. Following that, we delve into the mechanism for managing multiple identities within images. Lastly, we discuss the training strategy of our models. The overview of our model structure is shown in Fig. 2.

3.1. Hybrid Guidance Strategy

image

image


This paper is available on arxiv under CC0 1.0 DEED license.


L O A D I N G
. . . comments & more!

About Author

Dilution HackerNoon profile picture
Dilution@dilution
Transforming concentrations, unlocking new potential through careful calibration for a balanced and harmonious outcome.

TOPICS

THIS ARTICLE WAS FEATURED IN...

Permanent on Arweave
Read on Terminal Reader
Read this story in a terminal
 Terminal
Read this story w/o Javascript
Read this story w/o Javascript
 Lite
Dilution
X REMOVE AD