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Found 10 papers, 5 papers with code
TokenVerse: Versatile Multi-concept Personalization in Token Modulation Space
Building on this insight, we devise an optimization-based framework that takes as input an image and a text description, and finds for each word a distinct direction in the modulation space.
Still-Moving: Customized Video Generation without Customized Video Data
We assume access to a customized version of the T2I model, trained only on still image data (e. g., using DreamBooth or StyleDrop).
Lumiere: A Space-Time Diffusion Model for Video Generation
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis.
Ranked #5 on
Text-to-Video Generation
on UCF-101
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution
We propose an efficient diffusion-based text-to-video super-resolution (SR) tuning approach that leverages the readily learned capacity of pixel level image diffusion model to capture spatial information for video generation.
Teaching CLIP to Count to Ten
Our counting loss is deployed over automatically-created counterfactual examples, each consisting of an image and a caption containing an incorrect object count.
Imagic: Text-Based Real Image Editing with Diffusion Models
In this paper we demonstrate, for the very first time, the ability to apply complex (e. g., non-rigid) text-guided semantic edits to a single real image.
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.
Self-Distilled StyleGAN: Towards Generation from Internet Photos
To meet these challenges, we proposed a StyleGAN-based self-distillation approach, which consists of two main components: (i) A generative-based self-filtering of the dataset to eliminate outlier images, in order to generate an adequate training set, and (ii) Perceptual clustering of the generated images to detect the inherent data modalities, which are then employed to improve StyleGAN's "truncation trick" in the image synthesis process.
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
In this work, we introduce this approach into the realm of encoder-based inversion.
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.
