https://github.com/0zgur0/Seq_Scene_Gen
공식 코드A Layer-Based Sequential Framework for Scene Generation with GANs
Papers with Code 아카이브
Conditional Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 11
- 보존된 평가 기록
- 75
Conditional image generation is the task of generating new images from a dataset conditional on their class.
<span style="color:grey; opacity: 0.6">( Image credit: PixelCNN++ )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Algolzw/FoD
공식 코드https://github.com/AlonzoLeeeooo/LCDG
공식 코드LaCon: Late-Constraint Diffusion for Steerable Guided Image Synthesis
https://github.com/BradSegal/CXR_PGGAN
공식 코드Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs
https://github.com/CIFASIS/splitting_gan
공식 코드https://github.com/ChatHouseDiffusion/chathousediffusion
공식 코드ChatHouseDiffusion: Prompt-Guided Generation and Editing of Floor Plans
https://github.com/CompVis/vunet
공식 코드A Variational U-Net for Conditional Appearance and Shape Generation
https://github.com/DAMO-NLP-SG/DiGIT
공식 코드Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
https://github.com/DartML/SteinGAN
공식 코드Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
https://github.com/FineDiffusion/FineDiffusion
공식 코드FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 Classes
https://github.com/Gsunshine/Enjoy-Hamburger
공식 코드https://github.com/Guim3/IcGAN
공식 코드https://github.com/Hosein47/IEA-GAN
공식 코드https://github.com/IdanAzuri/glico-learning-small-sample
공식 코드Generative Latent Implicit Conditional Optimization when Learning from Small Sample
https://github.com/IraKorshunova/bruno
공식 코드https://github.com/LTH14/rcg
공식 코드Return of Unconditional Generation: A Self-supervised Representation Generation Method
https://github.com/MKnoche/warp3d_reposing
공식 코드https://github.com/NVIDIA/pix2pixHD
공식 코드High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/NVlabs/stylegan2
공식 코드https://github.com/NVlabs/stylegan2-ada
공식 코드https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
공식 코드ContraGAN: Contrastive Learning for Conditional Image Generation
https://github.com/PeterouZh/Omni-GAN-PyTorch
공식 코드https://github.com/SSAW14/Image_Generation_with_Latent_Code
공식 코드Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes
https://github.com/Shilin-LU/TF-ICON
공식 코드TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition
https://github.com/TIGER-AI-Lab/ImagenHub
공식 코드ImagenHub: Standardizing the evaluation of conditional image generation models
https://github.com/TIGER-AI-Lab/VIEScore
공식 코드VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation
https://github.com/VITA-Group/Sandwich-Batch-Normalization
공식 코드Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity
https://github.com/VisualComputingInstitute/diffusion-e2e-ft
공식 코드Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
https://github.com/YangNaruto/FQ-GAN
공식 코드https://github.com/Yuanshi9815/OminiControl
공식 코드OminiControl2: Efficient Conditioning for Diffusion Transformers
https://github.com/Yuheng-Li/MixNMatch
공식 코드MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
https://github.com/ZGCTroy/ED-DPM
공식 코드Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generation
https://github.com/adobe/antialiased-cnns
공식 코드https://github.com/aim-uofa/Diception
공식 코드DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks
https://github.com/albertpumarola/GANimation
공식 코드GANimation: Anatomically-aware Facial Animation from a Single Image
https://github.com/alexmaols/elucd
공식 코드Elucidating The Design Space of Classifier-Guided Diffusion Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.