Open-set Bias Detection in Text-to-Image Generative Models
OpenBias, a novel pipeline, identifies and quantifies biases in text-to-image generative models without relying on a predefined set of biases.
Contrastive Adapter Training for Preserving Model Knowledge in Personalized Image Generation
Contrastive Adapter Training (CAT) is a simple yet effective strategy to enhance adapter training in diffusion models, facilitating the preservation of the base model's original knowledge when initiating adapters for personalized image generation.
Object-Centric Attention Map Alignment for Improved Text-to-Image Diffusion Models
The core message of this paper is to introduce a novel object-conditioned Energy-Based Attention Map Alignment (EBAMA) method to address the issues of incorrect attribute binding and catastrophic object neglect in text-to-image diffusion models.
Efficient Multi-Concept Personalized Text-to-Image Generation with Resource-Friendly λ-ECLIPSE
λ-ECLIPSE is a resource-efficient prior-training strategy that enables fast and effective multi-subject-driven personalized text-to-image generation without relying on diffusion models.
Mitigating Unsafe Content Generation in Text-to-Image Models
SAFEGEN, a text-agnostic framework, can effectively mitigate the generation of sexually explicit content by text-to-image models, even under adversarial prompts, by removing unsafe visual representations from the model.
Rich Human Feedback Dataset and Model for Evaluating Text-to-Image Generation
This paper introduces a rich human feedback dataset (RichHF-18K) and a multimodal transformer model (RAHF) to provide detailed and interpretable evaluations of text-to-image generation models. The dataset contains fine-grained scores, implausibility/misalignment image regions, and misaligned keywords, which can be used to train the RAHF model to automatically predict such rich feedback on generated images.
Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following
Ranni introduces a semantic panel as a middleware between text and image, enabling accurate translation of natural language descriptions into visual concepts and allowing for intuitive image editing through panel manipulation.
Detecting Unauthorized Data Usage in Text-to-Image Diffusion Models
A method for detecting unauthorized data usage in text-to-image diffusion models by planting injected memorization into the models trained on the protected dataset.
SmartControl: Flexible and Adaptive Text-to-Image Generation under Rough Visual Conditions
SmartControl is a novel text-to-image generation method that can adaptively handle situations where there are disagreements between visual conditions and text prompts, by predicting a local control scale map to relax the constraints in conflicting regions.
NeuroPrompts: An Adaptive Framework for Optimizing Text-to-Image Prompts to Enhance Generation Quality
NeuroPrompts is a novel framework that automatically optimizes user-provided prompts to improve the quality of images generated by text-to-image diffusion models.
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