https://github.com/NVIDIA/pix2pixHD
공식 코드High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
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/NVlabs/FUNIT
공식 코드https://github.com/NVlabs/LACE
공식 코드Controllable and Compositional Generation with Latent-Space Energy-Based Models
https://github.com/NVlabs/LSGM
공식 코드https://github.com/NVlabs/NVAE
공식 코드https://github.com/NVlabs/SPADE
공식 코드Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/NVlabs/Sana
공식 코드https://github.com/NVlabs/VAEBM
공식 코드VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
https://github.com/NVlabs/denoising-diffusion-gan
공식 코드Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
https://github.com/NVlabs/ffhq-dataset
공식 코드A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/NVlabs/sds-complete
공식 코드Point-Cloud Completion with Pretrained Text-to-image Diffusion Models
https://github.com/NVlabs/stylegan
공식 코드A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/NVlabs/stylegan2
공식 코드https://github.com/NVlabs/stylegan2-ada
공식 코드https://github.com/NVlabs/stylegan3
공식 코드https://github.com/Narabzad/Set-Based-Text-to-ImageGeneration
공식 코드https://github.com/NetEase-GameAI/SPatchGAN
공식 코드SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation
https://github.com/NetEase-GameAI/clipvg
공식 코드CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphics
https://github.com/NetEase-Media/ControlTalk
공식 코드Controllable Talking Face Generation by Implicit Facial Keypoints Editing
https://github.com/NifTK/NiftyNet
공식 코드https://github.com/Nithin-GK/UniteandConquer
공식 코드Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
https://github.com/Noodles-321/Registration
공식 코드Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study
https://github.com/Nota-NetsPresso/BK-SDM
공식 코드BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion
https://github.com/NuayHL/TimeStepGenerating
공식 코드Time Step Generating: A Universal Synthesized Deepfake Image Detector
https://github.com/Null-0000/ViMAEdit
공식 코드Vision-guided and Mask-enhanced Adaptive Denoising for Prompt-based Image Editing
https://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/OPPO-Mente-Lab/attention-mask-control
공식 코드Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models
https://github.com/OUCVisionGroup/CLIP-UIE
공식 코드Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifier
https://github.com/OcraM17/eXIE
공식 코드Explaining Image Enhancement Black-Box Methods through a Path Planning Based Algorithm
https://github.com/OctThe16th/FIGR
공식 코드https://github.com/OliverRensu/xAR
공식 코드Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation
https://github.com/OmarSZamzam/PixelShuffler
공식 코드PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
https://github.com/OnomaAi101/CAT
공식 코드CAT: Contrastive Adapter Training for Personalized Image Generation
https://github.com/Onr/Council-GAN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.