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Found 29 papers, 14 papers with code
Tight Inversion: Image-Conditioned Inversion for Real Image Editing
This process has an inherent tradeoff between reconstruction and editability, limiting the editing of challenging images such as highly-detailed ones.
Dynamic Concepts Personalization from Single Videos
In this paper, we introduce Set-and-Sequence, a novel framework for personalizing Diffusion Transformers (DiTs)-based generative video models with dynamic concepts.
Nested Attention: Semantic-aware Attention Values for Concept Personalization
In this work, we introduce Nested Attention, a novel mechanism that injects a rich and expressive image representation into the model's existing cross-attention layers.
Object-level Visual Prompts for Compositional Image Generation
A key challenge in this task is to preserve the identity of the objects depicted in the input visual prompts, while also generating diverse compositions across different images.
Omni-ID: Holistic Identity Representation Designed for Generative Tasks
Our approach uses a few-to-many identity reconstruction training paradigm, where a limited set of input images is used to reconstruct multiple target images of the same individual in various poses and expressions.
Sharp-It: A Multi-view to Multi-view Diffusion Model for 3D Synthesis and Manipulation
In this work, we bridge the quality gap between methods that directly generate 3D representations and ones that reconstruct 3D objects from multi-view images.
Stable Flow: Vital Layers for Training-Free Image Editing
The main challenge is that, unlike the UNet-based models, DiT lacks a coarse-to-fine synthesis structure, making it unclear in which layers to perform the injection.
MaterialFusion: Enhancing Inverse Rendering with Material Diffusion Priors
This model is trained on albedo, material, and relit image data derived from a curated dataset of approximately ~12K artist-designed synthetic Blender objects called BlenderVault.
LCM-Lookahead for Encoder-based Text-to-Image Personalization
In this work, we explore the potential of using such shortcut-mechanisms to guide the personalization of text-to-image models to specific facial identities.
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation
Text-to-image diffusion models have an unprecedented ability to generate diverse and high-quality images.
ReNoise: Real Image Inversion Through Iterative Noising
However, applying these methods to real images necessitates the inversion of the images into the domain of the pretrained diffusion model.
Consolidating Attention Features for Multi-view Image Editing
In this work, we focus on spatial control-based geometric manipulations and introduce a method to consolidate the editing process across various views.
CLiC: Concept Learning in Context
This paper addresses the challenge of learning a local visual pattern of an object from one image, and generating images depicting objects with that pattern.
Cross-Image Attention for Zero-Shot Appearance Transfer
Recent advancements in text-to-image generative models have demonstrated a remarkable ability to capture a deep semantic understanding of images.
Noise-Free Score Distillation
Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains.
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
This unique combination of text and shape guidance allows for increased control over the generation process.
Ranked #3 on
Text to 3D
on T$^3$Bench
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes.
Ranked #7 on
Personalized Image Generation
on DreamBooth
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.
FEAT: Face Editing with Attention
In this paper, we build on the StyleGAN generator, and present a method that explicitly encourages face manipulation to focus on the intended regions by incorporating learned attention maps.
Third Time's the Charm? Image and Video Editing with StyleGAN3
In particular, we demonstrate that while StyleGAN3 can be trained on unaligned data, one can still use aligned data for training, without hindering the ability to generate unaligned imagery.
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image?
StyleFusion: A Generative Model for Disentangling Spatial Segments
Inserting the resulting style code into a pre-trained StyleGAN generator results in a single harmonized image in which each semantic region is controlled by one of the input latent codes.
ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement
Instead of directly predicting the latent code of a given real image using a single pass, the encoder is tasked with predicting a residual with respect to the current estimate of the inverted latent code in a self-correcting manner.
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images.
Designing an Encoder for StyleGAN Image Manipulation
We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on.
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
In this formulation, our method approaches the continuous aging process as a regression task between the input age and desired target age, providing fine-grained control over the generated image.
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
State-of-the-art image-to-image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity.
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).
