https://github.com/ZichengDuan/TheChosenOne
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Papers with Code 아카이브
Image Generation
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/a-imamshah/pixelcnn.jl
https://github.com/a-martyn/unet
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/a-maumau/psgan.pytorch
https://github.com/a-mcego/stylegannish
https://github.com/aayushmnit/diffusion_playground/blob/main/notebooks/4_DiffEdit_v4.ipynb
DiffEdit: Diffusion-based semantic image editing with mask guidance
https://github.com/abaietto/neural_ode_classification
https://github.com/abdallahyamie/python-master
https://github.com/abdulfatir/gan-metrics-pytorch
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/abhi8585/GeneratedART-NFT-VISION
https://github.com/abhishtagatya/paintgan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/abods/generative_painting
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/abuchin/resnet_sign
https://github.com/aby2s/sharpmask
https://github.com/acca3003/d2l-es
ControlVAE: Tuning, Analytical Properties, and Performance Analysis
https://github.com/acecreamu/angularGAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/acheketa/cwavegan
https://github.com/acht7111020/DSMAP
Domain-Specific Mappings for Generative Adversarial Style Transfer
https://github.com/acids-ircam/diffusion_models
https://github.com/acrarshin/RPNet
RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG
https://github.com/adam-mehdi/MuarAugment
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/adapt-python/adapt
https://github.com/adbobes/VideoSuperResolution
https://github.com/addtt/variational-diffusion-models
https://github.com/addy369/DCGANS
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/adelliax/StyleGAN2
https://github.com/adieyal/sd-dynamic-prompts
https://github.com/aditiasthana1004/StarGAN
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/adityabingi/DCGAN-TF2.0
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/adldotori/ResNet
https://github.com/adldotori/pSp
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/adler-j/bwgan
https://github.com/adnanalam53/cycleGAN
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/adobe-research/MakeItTalk
https://github.com/adriaciurana/DragGAN
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
https://github.com/adriaciurana/exploration-stylegan-nada
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.