https://github.com/ryudrigo/my-gen-clip
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP
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/saiyan-world/goku
https://github.com/securade/hub
Semantic-Conditional Diffusion Networks for Image Captioning
https://github.com/showlab/show-o
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
https://github.com/sidward14/Style-AttnGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/sndnyang/Diffusion_ViT
https://github.com/stanifrolov/spotdiffusion
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
https://github.com/stanislavfort/Direct_Ascent_Synthesis
Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models
https://github.com/syang-lab/Pathway_Autoregressive_Text2Image_Model
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
https://github.com/taki0112/AttnGAN-Tensorflow
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/taki0112/ControlGAN-Tensorflow
https://github.com/taoxugit/AttnGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/tencent/hunyuandit
DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation
https://github.com/tencentqqgylab/ella
ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
https://github.com/teoaivalis/Search-Based_Data_Influence_Analysis
https://github.com/thu-cvml/texturediffusion
MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing
https://github.com/thudm/cogview4
CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion
https://github.com/thudm/visualglm-6b
CogView: Mastering Text-to-Image Generation via Transformers
https://github.com/ucsd-ml-arts/ml-art-final-jeffrey
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/urbanarchitect/urbanarchitect
Urban Architect: Steerable 3D Urban Scene Generation with Layout Prior
https://github.com/valeoai/maskgit-pytorch
https://github.com/visinf/diagen
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
https://github.com/vision-xl/codes
CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models
https://github.com/wdrink/simplear
Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding
https://github.com/weihaox/Multi-Modal-CelebA-HQ-Dataset
Conditional Image Generation and Manipulation for User-Specified Content
https://github.com/wuyou22s/diptych
Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator
https://github.com/xuanliu2000/vgcdm
Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/xuduo35/InstructPix2Pix
InstructPix2Pix: Learning to Follow Image Editing Instructions
https://github.com/xyzforever/bevt
https://github.com/yangyucheng000/University/tree/main/model-1/phi3
SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing
https://github.com/yangyucheng000/papercode-2/tree/main/MC-GRA_Mindscope-main
MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis
https://github.com/youweiliang/RichHF
https://github.com/yuanzhi-zhu/prolific_dreamer2d
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
https://github.com/yuhang-ma/llm4gen
LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation
https://github.com/zideliu/StyleDrop-PyTorch
https://github.com/zijieli-Jlee/Dual-Diffusion
Dual Diffusion for Unified Image Generation and Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.