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Diffusion Personalization
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting
TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text Encoder
EmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation
ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance
VisioBlend: Sketch and Stroke-Guided Denoising Diffusion Probabilistic Model for Realistic Image Generation
Arc2Face: A Foundation Model for ID-Consistent Human Faces
Face2Diffusion for Fast and Editable Face Personalization
Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
InstantID: Zero-shot Identity-Preserving Generation in Seconds
Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation
PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning
InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning
A Closer Look at Parameter-Efficient Tuning in Diffusion Models
SVDiff: Compact Parameter Space for Diffusion Fine-Tuning
Multi-Concept Customization of Text-to-Image Diffusion
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.