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/lucidrains/muse-pytorch
Muse: Text-To-Image Generation via Masked Generative Transformers
https://github.com/lucidrains/nuwa-pytorch
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
https://github.com/lucidrains/parti-pytorch
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
https://github.com/lucidrains/soft-moe-pytorch
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
https://github.com/lucidrains/vector-quantize-pytorch
https://github.com/lucidrains/voicebox-pytorch
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces
https://github.com/lukemelas/realfusion
ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation
https://github.com/mahdip72/InstructPix2Pix
InstructPix2Pix: Learning to Follow Image Editing Instructions
https://github.com/matsagad/mres-project
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
https://github.com/mayuelala/followyourcanvas
Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos
https://github.com/microsoft/visual-chatgpt
Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/microsoft/vq-diffusion
Vector Quantized Diffusion Model for Text-to-Image Synthesis
https://github.com/mindspore-lab/mindone
PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
https://github.com/mingyuanzhou/sid
Long and Short Guidance in Score identity Distillation for One-Step Text-to-Image Generation
https://github.com/mkshing/prompt-plus-pytorch
P+: Extended Textual Conditioning in Text-to-Image Generation
https://github.com/mkshing/svdiff-pytorch
https://github.com/ml-research/i2p
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces
https://github.com/modelscope/modelscope
Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/muelea/buddi
https://github.com/mv-lab/InstructIR
InstructPix2Pix: Learning to Follow Image Editing Instructions
https://github.com/nemonameless/UniDiffuser_Paddle
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
https://github.com/neonbjb/tortoise-tts
https://github.com/nicolai256/stable-textual-inversion_win
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
https://github.com/omer11a/bounded-attention
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation
https://github.com/omerbt/tokenflow
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
https://github.com/openbmb/minicpm-v
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
https://github.com/orpatashnik/local-prompt-mixing
Localizing Object-level Shape Variations with Text-to-Image Diffusion Models
https://github.com/oxygenlu/ratlip
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/phymhan/prompt-to-prompt
MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing
https://github.com/priscillalui/StackGAN-Stories
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/quereste/implicit-deepfake
Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/rightlit/cycle-image-gan-rev
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/rishidarkdevil/daam-i2i
What the DAAM: Interpreting Stable Diffusion Using Cross Attention
https://github.com/roxanasoto/AttGanESRGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/royira/linguistic-binding-in-diffusion-models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.