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 1–10 of 19 papers

TitleStatusHype
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
TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text EncoderCode2
EmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation—0
ClassDiffusion: More Aligned Personalization Tuning with Explicit Class GuidanceCode1
Arc2Face: A Foundation Model for ID-Consistent Human FacesCode4
Face2Diffusion for Fast and Editable Face PersonalizationCode2
Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All—0
InstantID: Zero-shot Identity-Preserving Generation in SecondsCode11
Image is All You Need to Empower Large-scale Diffusion Models for In-Domain GenerationCode1
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