A technique for controllable diversity in text-to-image generation by inducing structured semantic variations at the prompt level via VLM and agentic workflow.
Particle guidance: non- 9 iid diverse sampling with diffusion models
6 Pith papers cite this work. Polarity classification is still indexing.
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STRIDE boosts diversity in one-step diffusion models by injecting PCA-aligned pink noise into transformer features while preserving text alignment and quality.
Noise optimization during sampling recovers diversity in mode-collapsed diffusion models while preserving output fidelity.
Feature self-guidance disperses internal features of flow models during batch generation and applies manifold regularization to increase output diversity while preserving condition alignment.
UAG is a universal avoidance generation method that increases multi-branch diversity in diffusion and transformer models by penalizing output similarity, delivering up to 1.9x higher diversity with 4.4x speed and 1/64th the FLOPs of prior methods.
Early DC component convergence in text-to-image Transformer features causes output homogeneity; selective early attenuation via DAVE improves diversity without retraining or extra cost.
citing papers explorer
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Semantic Browsing: Controllable Diversity for Image Generation
A technique for controllable diversity in text-to-image generation by inducing structured semantic variations at the prompt level via VLM and agentic workflow.
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STRIDE: Training-Free Diversity Guidance via PCA-Directed Feature Perturbation in Single-Step Diffusion Models
STRIDE boosts diversity in one-step diffusion models by injecting PCA-aligned pink noise into transformer features while preserving text alignment and quality.
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It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models
Noise optimization during sampling recovers diversity in mode-collapsed diffusion models while preserving output fidelity.
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Don't Settle at the Mode! Mitigating Diversity Collapse in Pretrained Flow Models via Feature Self-Guidance
Feature self-guidance disperses internal features of flow models during batch generation and applies manifold regularization to increase output diversity while preserving condition alignment.
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A Universal Avoidance Method for Diverse Multi-branch Generation
UAG is a universal avoidance generation method that increases multi-branch diversity in diffusion and transformer models by penalizing output similarity, delivering up to 1.9x higher diversity with 4.4x speed and 1/64th the FLOPs of prior methods.
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Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation
Early DC component convergence in text-to-image Transformer features causes output homogeneity; selective early attenuation via DAVE improves diversity without retraining or extra cost.