text-to-image diffusion models
Generative models that create images from text descriptions by gradually refining a noise-filled image through a series of steps. They leverage the diffusion process to achieve high-quality visual outputs based on textual input.
- Adjusting Initial Noise to Mitigate Memorization in Text-to-Image Diffusion Models
- Aligning Text to Image in Diffusion Models is Easier Than You Think
- Aligning Text-to-Image Diffusion Models to Human Preference by Classification
- AngleRoCL: Angle-Robust Concept Learning for Physically View-Invariant Adversarial Patches
- Comprehensive Assessment and Analysis for NSFW Content Erasure in Text-to-Image Diffusion models
- Continuous Concepts Removal in Text-to-image Diffusion Models
- DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks
- Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models
- FairImagen: Post-Processing for Bias Mitigation in Text-to-Image Models
- Free-Lunch Color-Texture Disentanglement for Stylized Image Generation
- From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging
- GOOD: Training-Free Guided Diffusion Sampling for Out-of-Distribution Detection
- LightFair: Towards an Efficient Alternative for Fair T2I Diffusion via Debiasing Pre-trained Text Encoders
- Ranking-based Preference Optimization for Diffusion Models from Implicit User Feedback
- Robustness in Both Domains: CLIP Needs a Robust Text Encoder
- ScaleDiff: Higher-Resolution Image Synthesis via Efficient and Model-Agnostic Diffusion
- Seg4Diff: Unveiling Open-Vocabulary Semantic Segmentation in Text-to-Image Diffusion Transformers
- Semantic Surgery: Zero-Shot Concept Erasure in Diffusion Models
- StyleGuard: Preventing Text-to-Image-Model-based Style Mimicry Attacks by Style Perturbations
- Towards a Golden Classifier-Free Guidance Path via Foresight Fixed Point Iterations