Text-to-Image Generation with Diffusion Models: A Comprehensive Survey (Partial Content)
This survey paper reviews the evolution and advancements of text-to-image diffusion models, highlighting their superior performance in generating realistic and diverse images from text descriptions. The authors delve into the technical aspects of these models, including their architecture, training processes, and applications beyond image generation, while also addressing the ethical considerations and future challenges associated with this rapidly evolving field.
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.
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.
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.
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.
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.
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.
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.
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