https://github.com/MiriUll/Image-Generation-for-Accessible-Communication
공식 코드Images Speak Volumes: User-Centric Assessment of Image Generation for Accessible Communication
Papers with Code 아카이브
Text to Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 1
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/NVlabs/Sana
공식 코드https://github.com/Narabzad/Set-Based-Text-to-ImageGeneration
공식 코드https://github.com/OPPO-Mente-Lab/GlyphDraw
공식 코드GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation
https://github.com/OPPO-Mente-Lab/PEA-Diffusion
공식 코드https://github.com/OPPO-Mente-Lab/Subject-Diffusion
공식 코드Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning
https://github.com/One-2-3-45/One-2-3-45
공식 코드One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
https://github.com/Owen-Oertell/rlcm
공식 코드RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
https://github.com/PixArt-alpha/PixArt-sigma
공식 코드PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
https://github.com/PlusLabNLP/Re-ReST
공식 코드Re-ReST: Reflection-Reinforced Self-Training for Language Agents
https://github.com/PrototypeNx/DETEX
공식 코드Decoupled Textual Embeddings for Customized Image Generation
https://github.com/Qrange-group/SUR-adapter
공식 코드SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
https://github.com/RAIVNLab/GenerateAnyScene
공식 코드Generate Any Scene: Evaluating and Improving Text-to-Vision Generation with Scene Graph Programming
https://github.com/RQ-Wu/LAMP
공식 코드LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation
https://github.com/SadilKhan/MARVEL-FX3D
공식 코드MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
https://github.com/SadilKhan/Text2CAD
공식 코드Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts
https://github.com/SalesforceAIResearch/bootpig
공식 코드https://github.com/SellLin/SketchFlex
공식 코드https://github.com/TAU-VAILab/Vox-E
공식 코드https://github.com/THU-LYJ-Lab/T3Bench
공식 코드T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation
https://github.com/THUDM/CogView
공식 코드CogView: Mastering Text-to-Image Generation via Transformers
https://github.com/TIGER-AI-Lab/ImagenHub
공식 코드ImagenHub: Standardizing the evaluation of conditional image generation models
https://github.com/Tan-H-C/DR-GAN-Distribution-Regularization-for-Text-to-Image-Generation
공식 코드DR-GAN: Distribution Regularization for Text-to-Image Generation
https://github.com/TencentARC/PhotoMaker
공식 코드PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
https://github.com/Texaser/MTN
공식 코드Progressive Text-to-3D Generation for Automatic 3D Prototyping
https://github.com/UT-Mao/Initial-Noise-Construction
공식 코드The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization
https://github.com/VITA-Group/NeuralLift-360
공식 코드NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360° Views
https://github.com/WU-CVGL/MVControl-threestudio
공식 코드Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting
https://github.com/Xiaojiu-z/Stable-Makeup
공식 코드Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model
https://github.com/XiaokunSun/StrandHead
공식 코드StrandHead: Text to Strand-Disentangled 3D Head Avatars Using Hair Geometric Priors
https://github.com/Yushi-Hu/tifa
공식 코드TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
https://github.com/Yux1angJi/DIFF
공식 코드Diffusion Features to Bridge Domain Gap for Semantic Segmentation
https://github.com/ZedFu/T23DAQA
공식 코드Multi-Dimensional Quality Assessment for Text-to-3D Assets: Dataset and Model
https://github.com/adlnlp/attention_vl
공식 코드https://github.com/afeng-x/pixwizard
공식 코드PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions
https://github.com/ai-forever/Kandinsky-2
공식 코드Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.