ProCreate, Dont Reproduce! Propulsive Energy Diffusion for Creative
  Generation
In this paper, we propose ProCreate, a simple and easy-to-implement method to
improve sample diversity and creativity of diffusion-based image generative
models and to prevent training data reproduction. ProCreate operates on a set
of reference images and actively propels the generated image embedding away
from the reference embeddings during the generation process. We propose FSCG-8
(Few-Shot Creative Generation 8), a few-shot creative generation dataset on
eight different categories -- encompassing different concepts, styles, and
settings -- in which ProCreate achieves the highest sample diversity and
fidelity. Furthermore, we show that ProCreate is effective at preventing
replicating training data in a large-scale evaluation using training text
prompts. Code and FSCG-8 are available at
https://github.com/Agentic-Learning-AI-Lab/procreate-diffusion-public. The
project page is available at https://procreate-diffusion.github.io.
66b1966f4cc2ff5816f36c9d
Jack Lu
claimed_verified
2024-08-08T07:41:53.156Z
66b2770092810adbb015d031
/avatars/ef08d3b8a12a288fb251164133a1f042.svg
Jack Lu
user
Jacklu0831
66b1966f4cc2ff5816f36c9e
Ryan Teehan
admin_assigned
2024-08-06T12:03:23.062Z
60899e6286bd6739b948fd9a
/avatars/1574cc8e748db1d028007cd792c4ce0f.svg
Ryan Teehan
user
rteehan
66b1966f4cc2ff5816f36c9f
Mengye Ren
admin_assigned
2024-08-06T12:03:17.424Z
632f67732a6ef6fb4ad3a16a
/avatars/6a0e09e9fdddcc6318ac0cdb925fd2bd.svg
Mengye Ren
user
renmengye
ProCreate, Dont Reproduce! Propulsive Energy Diffusion for Creative
  Generation
In this paper, we propose ProCreate, a simple and easy-to-implement method to
improve sample diversity and creativity of diffusion-based image generative
models and to prevent training data reproduction. ProCreate operates on a set
of reference images and actively propels the generated image embedding away
from the reference embeddings during the generation process. We propose FSCG-8
(Few-Shot Creative Generation 8), a few-shot creative generation dataset on
eight different categories -- encompassing different concepts, styles, and
settings -- in which ProCreate achieves the highest sample diversity and
fidelity. Furthermore, we show that ProCreate is effective at preventing
replicating training data in a large-scale evaluation using training text
prompts. Code and FSCG-8 are available at
https://github.com/Agentic-Learning-AI-Lab/procreate-diffusion-public. The
project page is available at https://procreate-diffusion.github.io.
ProCreate enhances sample diversity and creativity in diffusion-based image generation by repelling generated embeddings from reference images and prevents data reproduction.
diffusion-based image generative models sample diversity creativity image embedding reference embeddings FSCG-8 few-shot creative generation sample fidelity training data reproduction