arXiv AI
Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
Sep 2
DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models
arXiv:2511.21415v2 Announce Type: replace Abstract: We introduce DiverseVAR, a framework that enhances the diversity of text-conditioned visual autoregressive models (VAR) at test time without requir...
arXiv Machine Learning
Sep 1
TPSO: Training-Free Diverse Image Generation via Semantic Prompt Embedding Optimization
arXiv:2511.19811v2 Announce Type: replace-cross Abstract: Image diversity remains a fundamental challenge for text-to-image diffusion models. Low-diversity generation often leads to repetitive output...
arXiv AI
Jun 4
On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers
arXiv:2603. 28762v2 Announce Type: replace-cross Abstract: Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of variety, converging on a narrow set of visual solutions for any given prompt.
arXiv AI
Jun 2
Initialization is Half the Battle: Generating Diverse Images from a Guidance Potential Posterior
arXiv:2606. 02453v1 Announce Type: cross Abstract: Despite the remarkable fidelity of generative models, they frequently suffer from mode collapse.
Hugging Face Trending Papers
Aug 4
DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers
Recent advances in Diffusion Transformers (DiTs) have enabled remarkable progress in visual synthesis, benefiting from their superior scalability. To facilitate DiTs' capability of capturing meaningful internal representations, recent works such as REPA incorporate external pretrained encoders for representation alignment.
arXiv Computer Vision
3d ago
Beyond Pairwise Repulsion: Collective Diversity via Determinant Maximization in Diffusion Sampling
The paper introduces DetNO, a method that enhances diversity in text-to-image diffusion models by maximizing the log-determinant of an RBF Gram matrix over initial noise vectors. DetNO treats diversity as a collective property of the entire batch, using a volume maximization objective that self-attenuates as diversity improves, and includes an anchor-based fidelity term to keep samples realistic. Experiments on SD1.5, SDXL, and SD3 backbones show that DetNO outperforms existing pairwise repulsion methods in intra-batch diversity metrics and yields statistically significant accuracy gains in few-shot classification tasks when used for synthetic data augmentation.