title: ProCreate, Dont Reproduce! Propulsive Energy Diffusion for Creative
    Generation
summary: 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.
paper_authors._id: 66b1966f4cc2ff5816f36c9d
paper_authors.name: Jack Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-08T07:41:53.156Z
paper_authors._id: 66b2770092810adbb015d031
paper_authors.avatarUrl: /avatars/ef08d3b8a12a288fb251164133a1f042.svg
paper_authors.fullname: Jack Lu
paper_authors.type: user
paper_authors.user: Jacklu0831
paper_authors._id: 66b1966f4cc2ff5816f36c9e
paper_authors.name: Ryan Teehan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T12:03:23.062Z
paper_authors._id: 60899e6286bd6739b948fd9a
paper_authors.avatarUrl: /avatars/1574cc8e748db1d028007cd792c4ce0f.svg
paper_authors.fullname: Ryan Teehan
paper_authors.type: user
paper_authors.user: rteehan
paper_authors._id: 66b1966f4cc2ff5816f36c9f
paper_authors.name: Mengye Ren
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T12:03:17.424Z
paper_authors._id: 632f67732a6ef6fb4ad3a16a
paper_authors.avatarUrl: /avatars/6a0e09e9fdddcc6318ac0cdb925fd2bd.svg
paper_authors.fullname: Mengye Ren
paper_authors.type: user
paper_authors.user: renmengye
paper_title: ProCreate, Dont Reproduce! Propulsive Energy Diffusion for Creative
    Generation
paper_summary: 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.
paper_ai_summary: ProCreate enhances sample diversity and creativity in diffusion-based image generation by repelling generated embeddings from reference images and prevents data reproduction.
paper_ai_keywords: diffusion-based image generative models sample diversity creativity image embedding reference embeddings FSCG-8 few-shot creative generation sample fidelity training data reproduction