https://github.com/OPPO-Mente-Lab/Subject-Diffusion
공식 코드Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning
Papers with Code 아카이브
Personalized Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 7
Utilizes single or multiple images that contain the same subject or style, along with text prompt, to generate images that contain that subject as well as match the textual description. Includes finetuning-based methods (e.g. DreamBooth, Textual Inversion) as well as encoder-based methods (e.g. E4T, ELITE, and IP-Adapter, etc.).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/OnomaAi101/CAT
공식 코드CAT: Contrastive Adapter Training for Personalized Image Generation
https://github.com/SalesforceAIResearch/bootpig
공식 코드https://github.com/ZichengDuan/EZIGen
공식 코드https://github.com/baaivision/emu
공식 코드https://github.com/bytedance/MoMA
공식 코드MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
https://github.com/bytedance/uno
공식 코드Less-to-More Generalization: Unlocking More Controllability by In-Context Generation
https://github.com/controlgenai/persongensampler
공식 코드Beyond Fine-Tuning: A Systematic Study of Sampling Techniques in Personalized Image Generation
https://github.com/csxmli2016/w-plus-adapter
공식 코드When StyleGAN Meets Stable Diffusion: a $\mathscr{W}_+$ Adapter for Personalized Image Generation
https://github.com/csyxwei/awesome-personalized-image-generation
공식 코드Personalized Image Generation with Deep Generative Models: A Decade Survey
https://github.com/feifeiobama/RectifID
공식 코드RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance
https://github.com/hqhqaq/mip-adapter
공식 코드Resolving Multi-Condition Confusion for Finetuning-Free Personalized Image Generation
https://github.com/hqhqaq/patchdpo
공식 코드PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation
https://github.com/kaiyuesun98/t2i-personalization-with-ar
공식 코드Personalized Text-to-Image Generation with Auto-Regressive Models
https://github.com/louisYen/Gen4Gen
공식 코드Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition
https://github.com/mingxiao-li/towards-more-accurate-personalized-image-generation
공식 코드Towards More Accurate Personalized Image Generation: Addressing Overfitting and Evaluation Bias
https://github.com/mit-han-lab/fastcomposer
공식 코드FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention
https://github.com/modelscope/facechain
공식 코드FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content
https://github.com/nahyeonkaty/textboost
공식 코드TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text Encoder
https://github.com/qy-h00/conceptrol
공식 코드Conceptrol: Concept Control of Zero-shot Personalized Image Generation
https://github.com/redaigc/storymaker
공식 코드StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation
https://github.com/rinongal/textual_inversion
공식 코드An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
https://github.com/salesforce/lavis
공식 코드https://github.com/xzhe-vision/personamagic
공식 코드PersonaMagic: Stage-Regulated High-Fidelity Face Customization with Tandem Equilibrium
https://github.com/yiyanxu/pigeon
공식 코드https://github.com/yuangpeng/dreambench_plus
공식 코드DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers/examples/dreambooth
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
https://github.com/PrototypeNx/DETEX
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
https://github.com/SnailDev/github-hot-hub
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
https://github.com/XavierXiao/Dreambooth-Stable-Diffusion
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
https://github.com/brandontrabucco/da-fusion
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
https://github.com/cloneofsimo/lora
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
https://github.com/csguoh/intlora
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
https://github.com/fhshen2022/prunerepaint
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
https://github.com/giannisdaras/multires_textual_inversion
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
https://github.com/google/dreambooth
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.