DreamCache: Finetuning-Free Lightweight Personalized Image Generation
  via Feature Caching
Personalized image generation requires text-to-image generative models that
capture the core features of a reference subject to allow for controlled
generation across different contexts. Existing methods face challenges due to
complex training requirements, high inference costs, limited flexibility, or a
combination of these issues. In this paper, we introduce DreamCache, a scalable
approach for efficient and high-quality personalized image generation. By
caching a small number of reference image features from a subset of layers and
a single timestep of the pretrained diffusion denoiser, DreamCache enables
dynamic modulation of the generated image features through lightweight, trained
conditioning adapters. DreamCache achieves state-of-the-art image and text
alignment, utilizing an order of magnitude fewer extra parameters, and is both
more computationally effective and versatile than existing models.
6748216e80aebd7436f21d92
Emanuele Aiello
admin_assigned
2024-11-28T11:32:51.961Z
6515ac7f60757b8c8f6b8d31
/avatars/ed033f30f9f78ed68da976e6d01af0ea.svg
Emanuele Aiello
user
Ema97x
6748216e80aebd7436f21d93
Umberto Michieli
admin_assigned
2024-11-28T11:32:31.539Z
646f1879c261dc4133830dd6
/avatars/f4134a8fb171f93c098a43f1c7a94eb6.svg
Umberto Michieli
user
u-michieli
6748216e80aebd7436f21d94
Diego Valsesia
6748216e80aebd7436f21d95
Mete Ozay
admin_assigned
2024-11-28T11:32:12.229Z
65bced24c63d6a8d7f400b8b
/avatars/91212d7f39eba91158d7a0d0c823bc45.svg
Mete Ozay
user
mosams
6748216e80aebd7436f21d96
Enrico Magli
admin_assigned
2024-11-28T11:32:06.161Z
65e58cd65df8e81bed738067
/avatars/dd6eb8f9d2e0ae2c43727b9fe884f6e1.svg
Enrico Magli
user
emagli
DreamCache: Finetuning-Free Lightweight Personalized Image Generation
  via Feature Caching
Personalized image generation requires text-to-image generative models that
capture the core features of a reference subject to allow for controlled
generation across different contexts. Existing methods face challenges due to
complex training requirements, high inference costs, limited flexibility, or a
combination of these issues. In this paper, we introduce DreamCache, a scalable
approach for efficient and high-quality personalized image generation. By
caching a small number of reference image features from a subset of layers and
a single timestep of the pretrained diffusion denoiser, DreamCache enables
dynamic modulation of the generated image features through lightweight, trained
conditioning adapters. DreamCache achieves state-of-the-art image and text
alignment, utilizing an order of magnitude fewer extra parameters, and is both
more computationally effective and versatile than existing models.
DreamCache is a scalable and efficient personalized image generation method that leverages cached reference image features and conditioning adapters for high-quality and text-aligned image creation with reduced parameter overhead.
text-to-image generative models diffusion denoiser reference image features conditioning adapters image and text alignment parameter overhead