Papers with Code 아카이브
Text to Image Generation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AMD-AIG-AIMA/ReNeg
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
https://github.com/CasualGANPapers/Make-A-Scene
Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors
https://github.com/ChihchengHsieh/AttnGAN_Implementation
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/Con6924/SPM
One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
https://github.com/DingDing33/BPS-v1-1
Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/Francis-Rings/MotionEditor
Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/FraunhoferChalmersCentre/mcmc_corr_score_diffusion
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
https://github.com/HFAiLab/clip-gen
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP
https://github.com/IIGROUP/Multi-Modal-CelebA-HQ-Dataset
Conditional Image Generation and Manipulation for User-Specified Content
https://github.com/IjustAi/smartbrush
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model
https://github.com/JoyPang123/Textmage
https://github.com/JunnYu/x-transformers-paddle
CogView: Mastering Text-to-Image Generation via Transformers
https://github.com/KU-CVLAB/Perturbed-Attention-Guidance
Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/Karine-Huang/T2I-CompBench
T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation
https://github.com/Kyfafyd/MirrorGAN
MirrorGAN: Learning Text-to-image Generation by Redescription
https://github.com/LemonTwoL/ReNeg
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
https://github.com/LiuHuijie6410/OmniDish
Omni-Dish: Photorealistic and Faithful Image Generation and Editing for Arbitrary Chinese Dishes
https://github.com/Maluuba/GeNeVA
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
https://github.com/Maluuba/GeNeVA_datasets
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
https://github.com/Mathux/TMR
TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesis
https://github.com/Maymaher/StackGANv2
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/MindSpore-scientific/code-10/tree/main/PR_Product
https://github.com/PKU-YuanGroup/UniWorld-V1
WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
https://github.com/PaddlePaddle/FleetX
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
https://github.com/PaddlePaddle/PaddleMIX
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/pipelines/examples/text_to_image
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
https://github.com/Qiyuan-Ge/PaintMind
Muse: Text-To-Image Generation via Masked Generative Transformers
https://github.com/SforAiDl/CountCLIP
https://github.com/Shuvrajit9904/PairedCycleGAN-tf
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/SusungHong/IF-DreamFusion
https://github.com/TFNTF/PostEdit
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
https://github.com/Vigneshthanga/stackGAN-v2
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/ZichengDuan/TheChosenOne
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
https://github.com/adieyal/sd-dynamic-prompts
https://github.com/aigtext/glyphcontrol-release
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.