https://github.com/OPPO-Mente-Lab/Subject-Diffusion
공식 코드Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
This is a sub-class of diffusion personalization methods where the model is not required to be tuned on few user-specific images. Rather, the diffusion models are additionally trained on some dataset to allow forward pass personalization during test time.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning
PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
InstantID: Zero-shot Identity-Preserving Generation in Seconds
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
InstantID: Zero-shot Identity-Preserving Generation in Seconds
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
InstantID: Zero-shot Identity-Preserving Generation in Seconds
InstantID: Zero-shot Identity-Preserving Generation in Seconds
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.