# Enhancing Creative Generation on Stable Diffusion-based Models

arXiv: https://arxiv.org/abs/2503.23538 · HTML: https://mlsanity.com/paper/2503.23538/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 27 of its 31 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 27 of 27)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [Diffusion Models already have a Semantic Latent Space](https://mlsanity.com/paper/2210.10960/) (2022)
- [FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model](https://mlsanity.com/paper/2303.09833/) (2023)
- [SDXL-Lightning: Progressive Adversarial Diffusion Distillation](https://mlsanity.com/paper/2402.13929/) (2024)
- [P+: Extended Textual Conditioning in Text-to-Image Generation](https://mlsanity.com/paper/2303.09522/) (2023)
- [FreeU: Free Lunch in Diffusion U-Net](https://mlsanity.com/paper/2309.11497/) (2023)
- [Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion](https://mlsanity.com/paper/2310.03502/) (2023)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [Visual Style Prompting with Swapping Self-Attention](https://mlsanity.com/paper/2402.12974/) (2024)
- [Concept Decomposition for Visual Exploration and Inspiration](https://mlsanity.com/paper/2305.18203/) (2023)
- [ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints](https://mlsanity.com/paper/2308.02669/) (2023)
- [CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis](https://mlsanity.com/paper/2103.06242/) (2021)
- [DeSIGN: Design Inspiration from Generative Networks](https://mlsanity.com/paper/1804.00921/) (2018)
- [DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents](https://mlsanity.com/paper/2407.03300/) (2024)
- [Diffusion-Based Visual Art Creation: A Survey and New Perspectives](https://mlsanity.com/paper/2408.12128/) (2024)
- [ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation](https://mlsanity.com/paper/2408.02226/) (2024)

## Built on by (most cited first, 13 of 13)

- [Distribution-Conditional Generation: From Class Distribution to Creative Generation](https://mlsanity.com/paper/2505.03667/) (2025)
- [VLM-Guided Adaptive Negative Prompting for Creative Generation](https://mlsanity.com/paper/2510.10715/) (2025)
- [AGSwap: Overcoming Category Boundaries in Object Fusion via Adaptive Group Swapping](https://mlsanity.com/paper/2509.18699/) (2025)
- [GuessBench: Sensemaking Multimodal Creativity in the Wild](https://mlsanity.com/paper/2506.00814/) (2025)
- [What Do You Need for Compositional Generalization in Diffusion Planning?](https://mlsanity.com/paper/2505.18083/) (2025)
- [Diversity Has Always Been There in Your Visual Autoregressive Models](https://mlsanity.com/paper/2511.17074/) (2025)
- [CREward: A Type-Specific Creativity Reward Model](https://mlsanity.com/paper/2511.19995/) (2025)
- [Backdoor Sentinel: Detecting and Detoxifying Backdoors in Diffusion Models via Temporal Noise Consistency](https://mlsanity.com/paper/2602.01765/) (2026)
- [A Creative Agent is Worth a 64-Token Template](https://mlsanity.com/paper/2603.17895/) (2026)
- [Seeking the Unfamiliar but Memorable: Conceptual Creativity as Meta-Learning](https://mlsanity.com/paper/2605.16477/) (2026)
- [Self-Creative Text-to-Object Generation using Semantic-Aware Spatial Weighting](https://mlsanity.com/paper/2605.19554/) (2026)
- [Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation](https://mlsanity.com/paper/2606.06813/) (2026)
- [RankT2I: A Submodular Framework for Discovering Interpretable and Diverse Semantics in Text-to-Image Models](https://mlsanity.com/paper/2608.14226/) (2026)

## Abstract (arXiv)

Recent text-to-image generative models, particularly Stable Diffusion and its distilled variants, have achieved impressive fidelity and strong text-image alignment. However, their creative capability remains constrained, as including `creative' in prompts seldom yields the desired results. This paper introduces C3 (Creative Concept Catalyst), a training-free approach designed to enhance creativity in Stable Diffusion-based models. C3 selectively amplifies features during the denoising process to foster more creative outputs. We offer practical guidelines for choosing amplification factors based on two main aspects of creativity. C3 is the first study to enhance creativity in diffusion models without extensive computational costs. We demonstrate its effectiveness across various Stable Diffusion-based models.