Controllable Generation with Text-to-Image Diffusion Models: A Comprehensive Survey
The author explores the controllable generation landscape with text-to-image diffusion models, emphasizing the importance of incorporating novel conditions beyond text prompts for personalized and diverse generative outputs.
Controlling Text-to-Image Diffusion with Orthogonal Finetuning: A Principled Approach
Orthogonal Finetuning (OFT) preserves hyperspherical energy, enhancing text-to-image model controllability and stability.
SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation
SwiftBrush introduces an image-free distillation scheme for one-step text-to-image generation, achieving high-quality results without reliance on training image data.
ECNet: Effective Controllable Text-to-Image Diffusion Models
Innovative solutions Spatial Guidance Injector (SGI) and Diffusion Consistency Loss (DCL) enhance controllability in text-to-image generation.
CTRLorALTer: A Novel Conditional LoRA Adapter for Efficiently Controlling Text-to-Image Diffusion Models with Zero-Shot Generalization
This paper introduces LoRAdapter, a novel and efficient method for controlling text-to-image diffusion models by leveraging conditional Low-Rank Adaptations (LoRAs) to enable zero-shot control over both image style and structure.
CTRLorALTer: 효율적인 제로샷 스타일 및 구조 제어를 위한 조건부 LoR 어댑터
이 논문은 텍스트-투-이미지 생성 모델에서 스타일과 구조를 모두 제어하기 위해 조건부 LoRA(Low-Rank Adaptation)를 사용하는 새로운 방법인 LoRAdapter를 제안합니다. LoRAdapter는 제로샷 일반화를 가능하게 하여 다양한 스타일과 구조를 갖춘 이미지를 효율적으로 생성할 수 있습니다.
Understanding the Two-Stage Working Mechanism of Text-to-Image Diffusion Models
Text-to-image diffusion models generate images in two distinct stages: an initial stage where the overall shape is constructed, primarily guided by the [EOS] token in the text prompt, and a subsequent stage where details are filled in, relying less on the text prompt and more on the image itself.
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