mlsanityData to 7 Oct 2026

ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation

arXiv 2408.02226 ↗First posted 5 Aug 2024cs.CVcited by 12

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 45 of its 52 references that are in the map. First posted on arXiv 5 Aug 2024.

Builds on

45 of 52 references.

Closest ancestors · 17

Same direction, newest first

  1. 2023CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling26 Oct
  2. 2023Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models19 Oct
  3. 2023DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data25 Jun
  4. 2023Understanding and Mitigating Copying in Diffusion Models31 May
  5. 2023End-to-End Diffusion Latent Optimization Improves Classifier Guidance23 Mar
  6. 2023Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC22 Feb
  7. 2023Universal Guidance for Diffusion Models14 Feb
  8. 2022Classifier-Free Diffusion Guidance26 Jul
  9. 2022Generating High Fidelity Data from Low-density Regions using Diffusion Models31 Mar
  10. 2022Pseudo Numerical Methods for Diffusion Models on Manifolds20 Feb
  11. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  12. 2021Variational Diffusion Models1 Jul
5 more
  1. 2021Cascaded Diffusion Models for High Fidelity Image Generation30 May
  2. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  4. 2020Denoising Diffusion Implicit Models6 Oct
  5. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 28

  1. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  2. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  3. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  4. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  5. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  6. 2022Video Diffusion Modelscited by 2,280
  7. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  8. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  9. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  10. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  11. 2018Demystifying MMD GANscited by 1,268
  12. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,099
16 more
  1. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  2. 2023Extracting Training Data from Diffusion Modelscited by 723
  3. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  4. 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 418
  5. 2022The Vendi Score: A Diversity Evaluation Metric for Machine Learningcited by 299
  6. 2022Inversion-Based Style Transfer with Diffusion Modelscited by 290
  7. 2021How to Train Your Energy-Based Modelscited by 271
  8. 2023LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generationcited by 258
  9. 2022A Self-Supervised Descriptor for Image Copy Detectioncited by 175
  10. 2023StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Modelscited by 154
  11. 2022Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Trainingcited by 56
  12. 2023SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesiscited by 41
  13. 2023One-shot Unsupervised Domain Adaptation with Personalized Diffusion Modelscited by 35
  14. 2023A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsamplingcited by 28
  15. 2021On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competitioncited by 18
  16. 2022Reducing Training Sample Memorization in GANs by Training with Memorization Rejectioncited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 6.

12 papers.

  1. 2025SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Scorecited by 14
  2. 2025Enhancing Creative Generation on Stable Diffusion-based Modelscited by 13
  3. 2025Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generationcited by 5
  4. 2025Distribution-Conditional Generation: From Class Distribution to Creative Generationcited by 4
  5. 2025Context Tuning for In-Context Optimizationcited by 2
  6. 2026DAK-UCB: Diversity-Aware Prompt Routing for LLMs and Generative Modelscited by 2
  7. 2025GuessBench: Sensemaking Multimodal Creativity in the Wildcited by 1
  8. 2026A Universal Avoidance Method for Diverse Multi-branch Generationcited by 1
  9. 2026Wildfire Spread Scenarios: Increasing Sample Diversity of Segmentation Diffusion Models with Training-Free Methodscited by 0
  10. 2026Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidancecited by 0
  11. 2026Seeking the Unfamiliar but Memorable: Conceptual Creativity as Meta-Learningcited by 0
  12. 2026Self-Creative Text-to-Object Generation using Semantic-Aware Spatial Weightingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Creative Image Generation with Diffusion Models
  2. 2022Elucidating the Design Space of Diffusion-Based Generative Models
  3. 2023Measuring the Success of Diffusion Models at Imitating Human Artists
  4. 2024Replication in Visual Diffusion Models: A Survey and Outlook
  5. 2022A Survey on Generative Diffusion Model
  6. 2025ProxT2I: Efficient Reward-Guided Text-to-Image Generation via Proximal Diffusion
  7. 2022Towards Practical Plug-and-Play Diffusion Models
  8. 2023State of the Art on Diffusion Models for Visual Computing