IDAdapter: Learning Mixed Features for Tuning-Free Personalization of
  Text-to-Image Models
Leveraging Stable Diffusion for the generation of personalized portraits has
emerged as a powerful and noteworthy tool, enabling users to create
high-fidelity, custom character avatars based on their specific prompts.
However, existing personalization methods face challenges, including test-time
fine-tuning, the requirement of multiple input images, low preservation of
identity, and limited diversity in generated outcomes. To overcome these
challenges, we introduce IDAdapter, a tuning-free approach that enhances the
diversity and identity preservation in personalized image generation from a
single face image. IDAdapter integrates a personalized concept into the
generation process through a combination of textual and visual injections and a
face identity loss. During the training phase, we incorporate mixed features
from multiple reference images of a specific identity to enrich
identity-related content details, guiding the model to generate images with
more diverse styles, expressions, and angles compared to previous works.
Extensive evaluations demonstrate the effectiveness of our method, achieving
both diversity and identity fidelity in generated images.
65fb9d3d04769daf217987a2
Siying Cui
admin_assigned
2024-03-21T10:05:41.265Z
65b61966a0dc6ea5995b53e3
/avatars/c712b4ed8e4b5144f4be9b8b5e528ca7.svg
Siying Cui
user
hycsy2024
65fb9d3d04769daf217987a3
Jiankang Deng
extracted_pending
2024-03-21T02:36:47.176Z
62cc7a38376917c0223dd24b
https://cdn-avatars.huggingface.co/v1/production/uploads/1657566065867-noauth.png
JiankangDeng
user
JiankangDeng
65fb9d3d04769daf217987a4
Jia Guo
65fb9d3d04769daf217987a5
Xiang An
claimed_verified
2024-06-27T09:48:26.584Z
6478679d7b370854241b2ad8
https://cdn-avatars.huggingface.co/v1/production/uploads/6478679d7b370854241b2ad8/dBczWYYdfEt9tQcnVGhQk.jpeg
xiangan
user
xiangan
65fb9d3d04769daf217987a6
Yongle Zhao
admin_assigned
2024-03-21T10:06:28.451Z
6433e9e4ea46c00990460ab0
/avatars/3a4bec74eeec94713e8eb5e290d00765.svg
yonglezhao
user
yonglezhao
65fb9d3d04769daf217987a7
Xinyu Wei
65fb9d3d04769daf217987a8
Ziyong Feng
IDAdapter: Learning Mixed Features for Tuning-Free Personalization of
  Text-to-Image Models
Leveraging Stable Diffusion for the generation of personalized portraits has
emerged as a powerful and noteworthy tool, enabling users to create
high-fidelity, custom character avatars based on their specific prompts.
However, existing personalization methods face challenges, including test-time
fine-tuning, the requirement of multiple input images, low preservation of
identity, and limited diversity in generated outcomes. To overcome these
challenges, we introduce IDAdapter, a tuning-free approach that enhances the
diversity and identity preservation in personalized image generation from a
single face image. IDAdapter integrates a personalized concept into the
generation process through a combination of textual and visual injections and a
face identity loss. During the training phase, we incorporate mixed features
from multiple reference images of a specific identity to enrich
identity-related content details, guiding the model to generate images with
more diverse styles, expressions, and angles compared to previous works.
Extensive evaluations demonstrate the effectiveness of our method, achieving
both diversity and identity fidelity in generated images.
IDAdapter, a tuning-free method, enhances the diversity and identity preservation in personalized image generation using a single face image by integrating personalized textual and visual injections and a face identity loss.
Stable Diffusion personalized portraits high-fidelity character avatars test-time fine-tuning input images identity preservation diversity IDAdapter textual injections visual injections face identity loss mixed features reference images identity-related content details image generation diversity identity fidelity