HelloMeme: Integrating Spatial Knitting Attentions to Embed High-Level
  and Fidelity-Rich Conditions in Diffusion Models
We propose an effective method for inserting adapters into text-to-image
foundation models, which enables the execution of complex downstream tasks
while preserving the generalization ability of the base model. The core idea of
this method is to optimize the attention mechanism related to 2D feature maps,
which enhances the performance of the adapter. This approach was validated on
the task of meme video generation and achieved significant results. We hope
this work can provide insights for post-training tasks of large text-to-image
models. Additionally, as this method demonstrates good compatibility with SD1.5
derivative models, it holds certain value for the open-source community.
Therefore, we will release the related code
(https://songkey.github.io/hellomeme).
6724b544b109aea3270856ea
Shengkai Zhang
admin_assigned
2024-11-04T17:42:50.830Z
63ff47b212c51862e5d9e193
/avatars/a3d7dee74a73bc56945ac19e8ddcaed9.svg
Shengkai Zhang
user
songkey
6724b544b109aea3270856eb
Nianhong Jiao
6724b544b109aea3270856ec
Tian Li
6724b544b109aea3270856ed
Chaojie Yang
6724b544b109aea3270856ee
Chenhui Xue
6724b544b109aea3270856ef
Boya Niu
6724b544b109aea3270856f0
Jun Gao
HelloMeme: Integrating Spatial Knitting Attentions to Embed High-Level
  and Fidelity-Rich Conditions in Diffusion Models
We propose an effective method for inserting adapters into text-to-image
foundation models, which enables the execution of complex downstream tasks
while preserving the generalization ability of the base model. The core idea of
this method is to optimize the attention mechanism related to 2D feature maps,
which enhances the performance of the adapter. This approach was validated on
the task of meme video generation and achieved significant results. We hope
this work can provide insights for post-training tasks of large text-to-image
models. Additionally, as this method demonstrates good compatibility with SD1.5
derivative models, it holds certain value for the open-source community.
Therefore, we will release the related code
(https://songkey.github.io/hellomeme).
Adapters inserted into text-to-image models enhance performance on complex downstream tasks like meme video generation while maintaining generalization.
adapters text-to-image foundation models attention mechanism 2D feature maps meme video generation SD1.5 derivative models