Diffusion Personalization
9 papers with code • 0 benchmarks • 0 datasets
The goal of this task is to customize a generative diffusion model to user-specific datasets so that it can generate more user-specific dataset
Benchmarks
These leaderboards are used to track progress in Diffusion Personalization
Most implemented papers
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
PaddlePaddle/PaddleNLP
•
•
CVPR 2023
Once the subject is embedded in the output domain of the model, the unique identifier can be used to synthesize novel photorealistic images of the subject contextualized in different scenes.
Arc2Face: A Foundation Model of Human Faces
This paper presents Arc2Face, an identity-conditioned face foundation model, which, given the ArcFace embedding of a person, can generate diverse photo-realistic images with an unparalleled degree of face similarity than existing models.
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
By composing these weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth can generate a person's face in various contexts and styles, with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications.
InstantID: Zero-shot Identity-Preserving Generation in Seconds
There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA.
SVDiff: Compact Parameter Space for Diffusion Fine-Tuning
mkshing/svdiff-pytorch
•
•
ICCV 2023
Diffusion models have achieved remarkable success in text-to-image generation, enabling the creation of high-quality images from text prompts or other modalities.
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention
FastComposer proposes delayed subject conditioning in the denoising step to maintain both identity and editability in subject-driven image generation.
Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning
OPPO-Mente-Lab/Subject-Diffusion
•
•
In this paper, we propose Subject-Diffusion, a novel open-domain personalized image generation model that, in addition to not requiring test-time fine-tuning, also only requires a single reference image to support personalized generation of single- or multi-subject in any domain.
PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
Recent advances in text-to-image generation have made remarkable progress in synthesizing realistic human photos conditioned on given text prompts.
Concept-centric Personalization with Large-scale Diffusion Priors
priv-creation/concept-centric-personalization
•
•
In this work, we present the task of customizing large-scale diffusion priors for specific concepts as concept-centric personalization.
