https://github.com/hwang-cs-ime/hcma
공식 코드HCMA: Hierarchical Cross-model Alignment for Grounded Text-to-Image Generation
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/hwang1996/3D-Cartoon-Face-Generation
공식 코드3D Cartoon Face Generation with Controllable Expressions from a Single GAN Image
https://github.com/hwang1996/maniclip
공식 코드https://github.com/hxixixh/amo-release
공식 코드https://github.com/hyn2028/llm-cxr
공식 코드LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation
https://github.com/hyn2028/tpdm
공식 코드Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models
https://github.com/hyoo14/finetuned-sd3-correcting-anomalous-images
공식 코드Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous Images
https://github.com/hyperplane-lab/ACL-GAN
공식 코드Unpaired Image-to-Image Translation using Adversarial Consistency Loss
https://github.com/hytidel/unetreweighting
공식 코드Dynamic Importance in Diffusion U-Net for Enhanced Image Synthesis
https://github.com/hytruongson/mgvae
공식 코드https://github.com/hytseng0509/ArtEditing
공식 코드Modeling Artistic Workflows for Image Generation and Editing
https://github.com/hyudsl/FDM
공식 코드Improving Detail in Pluralistic Image Inpainting with Feature Dequantization
https://github.com/hzxie/GaussianCity
공식 코드GaussianCity: Generative Gaussian Splatting for Unbounded 3D City Generation
https://github.com/i2-multimedia-lab/styleprompter
공식 코드https://github.com/iDurugkar/GMAN
공식 코드https://github.com/iVMCL/LostGANs
공식 코드https://github.com/iamNCJ/DiLightNet
공식 코드DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation
https://github.com/iamalexkorotin/KernelNeuralOptimalTransport
공식 코드https://github.com/iamalexkorotin/Wasserstein2Benchmark
공식 코드Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
https://github.com/iamalexkorotin/Wasserstein2GenerativeNetworks
공식 코드Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
https://github.com/iamalexkorotin/neuraloptimaltransport
공식 코드https://github.com/iamcreateai/cyclevar
공식 코드CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
https://github.com/iamgmujtaba/ltc-gif
공식 코드LTC-GIF: Attracting More Clicks on Feature-length Sports Videos
https://github.com/ibalazevic/lp-ssl
공식 코드Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
https://github.com/ibrahimethemhamamci/generatect
공식 코드GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
https://github.com/iceli1007/3d-adapter
공식 코드https://github.com/iceli1007/DAT-GAN
공식 코드A New Perspective on Stabilizing GANs training: Direct Adversarial Training
https://github.com/icon-lab/I2I-Mamba
공식 코드I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling
https://github.com/icon-lab/ResViT
공식 코드ResViT: Residual vision transformers for multi-modal medical image synthesis
https://github.com/icon-lab/pGAN-cGAN
공식 코드Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks
https://github.com/icon-lab/syndiff
공식 코드Unsupervised Medical Image Translation with Adversarial Diffusion Models
https://github.com/icoz69/styleavatar3d
공식 코드StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation
https://github.com/ictnlp/sammt
공식 코드Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine Translation
https://github.com/idealwhite/tdanet
공식 코드https://github.com/idiap/hourglass_push
공식 코드https://github.com/idlabmedia/tgif-dataset
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.