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Found 13 papers, 6 papers with code
Implicit Style-Content Separation using B-LoRA
In this paper, we introduce B-LoRA, a method that leverages LoRA (Low-Rank Adaptation) to implicitly separate the style and content components of a single image, facilitating various image stylization tasks.
SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction
Sketching is a powerful tool for creating abstract images that are sparse but meaningful.
Breathing Life Into Sketches Using Text-to-Video Priors
A sketch is one of the most intuitive and versatile tools humans use to convey their ideas visually.
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Our quantitative analysis demonstrates that our method strikes a better balance between prompt alignment and identity consistency compared to the baseline methods, and these findings are reinforced by a user study.
Concept Decomposition for Visual Exploration and Inspiration
Each node in the tree represents a sub-concept using a learned vector embedding injected into the latent space of a pretrained text-to-image model.
Word-As-Image for Semantic Typography
A word-as-image is a semantic typography technique where a word illustration presents a visualization of the meaning of the word, while also preserving its readability.
Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt.
CLIPascene: Scene Sketching with Different Types and Levels of Abstraction
In this paper, we present a method for converting a given scene image into a sketch using different types and multiple levels of abstraction.
CLIPasso: Semantically-Aware Object Sketching
Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings.
Unpaired Learning for High Dynamic Range Image Tone Mapping
In this paper we describe a new tone-mapping approach guided by the distinct goal of producing low dynamic range (LDR) renditions that best reproduce the visual characteristics of native LDR images.
Image Shape Manipulation from a Single Augmented Training Sample
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image.
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Image Shape Manipulation from a Single Augmented Training Sample
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image.
Training End-to-end Single Image Generators without GANs
We present AugurOne, a novel approach for training single image generative models.
