Search Results for author:
Found 15 papers, 8 papers with code
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.
PALP: Prompt Aligned Personalization of Text-to-Image Models
We term our approach prompt-aligned personalization.
Style Aligned Image Generation via Shared Attention
Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts.
AnyLens: A Generative Diffusion Model with Any Rendering Lens
State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth.
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.
P+: Extended Textual Conditioning in Text-to-Image Generation
Furthermore, we utilize the unique properties of this space to achieve previously unattainable results in object-style mixing using text-to-image models.
Sketch-Guided Text-to-Image Diffusion Models
In this work, we introduce a universal approach to guide a pretrained text-to-image diffusion model, with a spatial map from another domain (e. g., sketch) during inference time.
When, Why, and Which Pretrained GANs Are Useful?
The literature has proposed several methods to finetune pretrained GANs on new datasets, which typically results in higher performance compared to training from scratch, especially in the limited-data regime.
Label-Efficient Semantic Segmentation with Diffusion Models
Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-the-art generative performance.
On Self-Supervised Image Representations for GAN Evaluation
The embeddings from CNNs pretrained on Imagenet classification are de-facto standard image representations for assessing GANs via FID, Precision and Recall measures.
Navigating the GAN Parameter Space for Semantic Image Editing
In contrast to existing works, which mostly operate by latent codes, we discover interpretable directions in the space of the generator parameters.
Object Segmentation Without Labels with Large-Scale Generative Models
The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks.
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
The latent spaces of GAN models often have semantically meaningful directions.
RPGAN: GANs Interpretability via Random Routing
In this paper, we introduce Random Path Generative Adversarial Network (RPGAN) -- an alternative design of GANs that can serve as a tool for generative model analysis.
RPGAN: random paths as a latent space for GAN interpretability
In this paper, we introduce Random Path Generative Adversarial Network (RPGAN) --- an alternative scheme of GANs that can serve as a tool for generative model analysis.
