https://github.com/onkarsus13/d2styler
공식 코드D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/open-mmlab/StyleShot
공식 코드https://github.com/oppo-mente-lab/tlcm
공식 코드TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps
https://github.com/optml-group/unlearncanvas
공식 코드UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models
https://github.com/orpatashnik/BalaGAN
공식 코드BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
https://github.com/ostadabbas/SPAC-Net-Synthetic-Pose-aware-Animal-ControlNet
공식 코드SPAC-Net: Synthetic Pose-aware Animal ControlNet for Enhanced Pose Estimation
https://github.com/ostadabbas/prior-aware-synthetic-data-generation-pasyn-
공식 코드Prior-Aware Synthetic Data to the Rescue: Animal Pose Estimation with Very Limited Real Data
https://github.com/paloha/faket
공식 코드FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
https://github.com/paloha/redistributor
공식 코드https://github.com/panlingua/multilingual-tst-datasets
공식 코드Multilingual Text Style Transfer: Datasets & Models for Indian Languages
https://github.com/paper11667/clipstyler
공식 코드CLIPstyler: Image Style Transfer with a Single Text Condition
https://github.com/passeul/style-transfer-model-evaluation
공식 코드https://github.com/peiwang062/swag
공식 코드Rethinking and Improving the Robustness of Image Style Transfer
https://github.com/phflot/tfake
공식 코드https://github.com/phoenix104104/fast_blind_video_consistency
공식 코드https://github.com/pkuanjie/ArtFlow
공식 코드ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows
https://github.com/pl23k/icehrnet
공식 코드https://github.com/poonpura/DPAgg-TI
공식 코드Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings
https://github.com/postech-cvlab/style-agnostic-rl
공식 코드https://github.com/prerna135/prose-for-a-painting
공식 코드https://github.com/prgumd/0-MMS
공식 코드0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera
https://github.com/pschaldenbrand/styleclipdraw
공식 코드StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Synthesis
https://github.com/qiaott/AncientPainitng2NaturalImage
공식 코드Ancient Painting to Natural Image: A New Solution for Painting Processing
https://github.com/r-j96/stainfuser
공식 코드https://github.com/rahulbhalley/cyclegan-qp
공식 코드https://github.com/ranery/Bayesian-CycleGAN
공식 코드Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling
https://github.com/raosudha89/GYAFC-corpus
공식 코드https://github.com/reiinakano/neural-painters-pytorch
공식 코드Neural Painters: A learned differentiable constraint for generating brushstroke paintings
https://github.com/rem105-210/difffashion
공식 코드DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models
https://github.com/renanrojasg/adv_robust_autoencoder
공식 코드https://github.com/researchmm/sttr
공식 코드https://github.com/rezashkv/diffusion_pruning
공식 코드Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models
https://github.com/riccardomarin/InstantRecoveryFromSpectrum
공식 코드Instant recovery of shape from spectrum via latent space connections
https://github.com/rikiyay/style-transfer-for-digital-pathology
공식 코드https://github.com/ristea/cycle-transformer
공식 코드https://github.com/ritual-uh/da_ner
공식 코드Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.