https://github.com/iyttor/GPNN
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models
Papers with Code 아카이브
Image Manipulation
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Image Manipulation is the process of altering or transforming an existing image to achieve a desired effect or to modify its content. This can involve various techniques and tools to enhance, modify, or create images based on specific requirements.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jiamings/d2c
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
https://github.com/jiamings/d2c_pre_release
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
https://github.com/kaist-cvml-lab/DreamCatalyst
DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
https://github.com/kaist-cvml/dreamcatalyst
DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
https://github.com/kingcong/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/kostagiolasn/Unsupervised-Discovery-of-Semantic-Concepts-in-Satellite-Imagery-with-Style-based-Wavelet-driven-Gen
https://github.com/kostagiolasn/satconcepts
https://github.com/kristerseiya/SinGAN_Implementation
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/leehomyc/Faster-High-Res-Neural-Inpainting
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
https://github.com/leongatys/PytorchNeuralStyleTransfer
https://github.com/liyuantsao/BFSR
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/liyuantsao/flowsr-lp
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/lucidrains/phenaki-pytorch
https://github.com/lucidrains/soundstorm-pytorch
https://github.com/macderru/hyperdomainnet
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
https://github.com/maminio/sample-generating
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/manyids2/kornia
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/mikelasz/fair-psp
https://github.com/mindspore-ai/models/tree/master/research/cv/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ml-research/ledits_pp
LEDITS++: Limitless Image Editing using Text-to-Image Models
https://github.com/mr-superonion/fpfs
https://github.com/mrlibw/Lightweight-Manipulation
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation
https://github.com/myscience/open-genie
https://github.com/neverbiasu/ComfyUI-BAGEL
https://github.com/nirajmahajan/Controllable-Gans-Through-Latent-Space-Navigation
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation
https://github.com/nitaifingerhut/SinGAN-for-Object-Removal
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/noyomer/few-shots-generation
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/open-mmlab/mmgeneration
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/opengvlab/internchat
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
https://github.com/opengvlab/interngpt
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
https://github.com/pacifinapacific/StyleGAN_LatentEditor
Interpreting the Latent Space of GANs for Semantic Face Editing
https://github.com/pengzhou1108/RGB-N
https://github.com/phymhan/stylegan2-pytorch
https://github.com/pksvision/Deep-WaveNet-Underwater-Image-Restoration
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/pku-yuangroup/imgedit
UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.