SOTAVerified

Diffusion Personalization

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

Papers

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Showing 1–19 of 19 papers

TitleStatusHype
InstantID: Zero-shot Identity-Preserving Generation in SecondsCode11
PhotoMaker: Customizing Realistic Human Photos via Stacked ID EmbeddingCode6
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven GenerationCode5
Arc2Face: A Foundation Model for ID-Consistent Human FacesCode4
Multi-Concept Customization of Text-to-Image DiffusionCode3
TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text EncoderCode2
Face2Diffusion for Fast and Editable Face PersonalizationCode2
Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuningCode2
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized AttentionCode2
SVDiff: Compact Parameter Space for Diffusion Fine-TuningCode2
ClassDiffusion: More Aligned Personalization Tuning with Explicit Class GuidanceCode1
Image is All You Need to Empower Large-scale Diffusion Models for In-Domain GenerationCode1
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image ModelsCode1
ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization—0
IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual PromptingCode0
EmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation—0
Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All—0
A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization—0
InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning—0
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