https://github.com/nannanli999/pt_square
공식 코드Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures
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/nannullna/safe-diffusion
공식 코드Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models
https://github.com/nasa/wind-generative-modeling
공식 코드Generative Modeling of Microweather Wind Velocities for Urban Air Mobility
https://github.com/nashrahhaque/sd-miae
공식 코드Boosting Imperceptibility of Stable Diffusion-based Adversarial Examples Generation with Momentum
https://github.com/naver-ai/StyleMapGAN
공식 코드Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing
https://github.com/naver-ai/Visual-Style-Prompting
공식 코드https://github.com/naver-ai/clip4dm
공식 코드https://github.com/naver-ai/densediffusion
공식 코드https://github.com/naver-ai/ggdr
공식 코드Generator Knows What Discriminator Should Learn in Unconditional GANs
https://github.com/naver-ai/mid.metric
공식 코드Mutual Information Divergence: A Unified Metric for Multimodal Generative Models
https://github.com/naver-ai/zim
공식 코드https://github.com/naylenv/uf-fgtg
공식 코드A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis
https://github.com/nbiclab/cem3dmg
공식 코드3D microstructural generation from 2D images of cement paste using generative adversarial networks
https://github.com/ncclab-sustech/cocog-2
공식 코드CoCoG-2: Controllable generation of visual stimuli for understanding human concept representation
https://github.com/ncherel/infusion
공식 코드Infusion: internal diffusion for inpainting of dynamic textures and complex motion
https://github.com/ncherel/inpaint-diff-mat
공식 코드https://github.com/ncherel/psal
공식 코드https://github.com/ncsoft/CaPa
공식 코드CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
https://github.com/nctu-eva-lab/antifakeprompt
공식 코드AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
https://github.com/ndrplz/semiparametric
공식 코드Warp and Learn: Novel Views Generation for Vehicles and Other Objects
https://github.com/neelmishra/gauss-newton-based-minimax-solver
공식 코드A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
https://github.com/nehamjain10/trudgcr
공식 코드Towards Realistic Underwater Dataset Generation and Color Restoration
https://github.com/neocxi/pixelsnail-public
공식 코드https://github.com/neonbjb/tortoise-tts
공식 코드https://github.com/nerminsamet/houghnet
공식 코드HoughNet: Integrating near and long-range evidence for bottom-up object detection
https://github.com/neuralchen/CooGAN
공식 코드CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing
https://github.com/nevsnev/fgdvi
공식 코드https://github.com/newbeeer/pfgmpp
공식 코드PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
https://github.com/newbeeer/poisson_flow
공식 코드https://github.com/newbeeer/stf
공식 코드Stable Target Field for Reduced Variance Score Estimation in Diffusion Models
https://github.com/nfsrules/seeds
공식 코드SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models
https://github.com/nguyenngocbaocmt02/boss
공식 코드Bellman Optimal Stepsize Straightening of Flow-Matching Models
https://github.com/nhuang37/approx_equivariant_graph_nets
공식 코드https://github.com/nicecv/diffharmony
공식 코드DiffHarmony: Latent Diffusion Model Meets Image Harmonization
https://github.com/nickhunt-smith/mcmc-diffusion
공식 코드Accelerating Markov Chain Monte Carlo sampling with diffusion models
https://github.com/nicolas-dufour/SCAM
공식 코드SCAM! Transferring humans between images with Semantic Cross Attention Modulation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.