https://github.com/DAMO-NLP-SG/DiGIT
공식 코드Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
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/DINGYANB/MUSES
공식 코드MUSES: 3D-Controllable Image Generation via Multi-Modal Agent Collaboration
https://github.com/DJ-LYH/EC-VAE
공식 코드https://github.com/DYEvaLab/EvalMuse
공식 코드https://github.com/DZY122/DiTAS
공식 코드DiTAS: Quantizing Diffusion Transformers via Enhanced Activation Smoothing
https://github.com/DanaCohen95/Set-the-Scene
공식 코드Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes
https://github.com/DartML/SteinGAN
공식 코드Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
https://github.com/DavidAbramian/refacing
공식 코드Refacing: reconstructing anonymized facial features using GANs
https://github.com/DeepCamera/HDR-chroma-compression
공식 코드https://github.com/DeepakSridhar/fgdm
공식 코드Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis
https://github.com/Delong-liu-bupt/Word4Per
공식 코드Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person Retrieval
https://github.com/DengPingFan/FS2K
공식 코드https://github.com/DengPingFan/FaceSketch-Awesome-List
공식 코드https://github.com/DetionDX/Attribute2Sketch2Face
공식 코드Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs
https://github.com/DetionDX/GP-GAN-Gender-Preserving-GAN-for-Synthesizing-Faces-from-Landmarks
공식 코드GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
https://github.com/DetionDX/GP-GAN-GenderPreserving-GAN-for-Synthesizing-Faces-from-Landmarks
공식 코드GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
https://github.com/DianaNerualNetwork/StainPromptInversion
공식 코드Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion
https://github.com/Dianezzy/ParaLip
공식 코드https://github.com/Diyago/GAN-for-tabular-data
공식 코드https://github.com/DmitryUlyanov/texture_nets
공식 코드Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/Dongqi-Fan/OnePoseTrans
공식 코드One-Shot Learning for Pose-Guided Person Image Synthesis in the Wild
https://github.com/ESanchezLozano/GANnotation
공식 코드Triple consistency loss for pairing distributions in GAN-based face synthesis
https://github.com/EkaterinaXie/LiteVAR
공식 코드LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and Quantization
https://github.com/Elvinky/IEGAN
공식 코드Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation
https://github.com/EnVision-Research/Decompose-and-Realign
공식 코드Text-Anchored Score Composition: Tackling Condition Misalignment in Text-to-Image Diffusion Models
https://github.com/EnVision-Research/Kiss3DGen
공식 코드Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
https://github.com/EndlessSora/TSIT
공식 코드TSIT: A Simple and Versatile Framework for Image-to-Image Translation
https://github.com/EndlessSora/focal-frequency-loss
공식 코드https://github.com/ErgastiAlex/U-Shape-Mamba
공식 코드https://github.com/EricGzq/AMTENnet
공식 코드Fake face detection via adaptive manipulation traces extraction network
https://github.com/EtreSerBe/PokeAE
공식 코드Generating Gameplay-Relevant Art Assets with Transfer Learning
https://github.com/EvaFlower/Pareto-guided-diffusion-model
공식 코드PROUD: PaRetO-gUided Diffusion Model for Multi-objective Generation
https://github.com/Evan1578/BIPSDA
공식 코드Can Diffusion Models Provide Rigorous Uncertainty Quantification for Bayesian Inverse Problems?
https://github.com/EvelynZhang-epiclab/DaTo
공식 코드Token Pruning for Caching Better: 9 Times Acceleration on Stable Diffusion for Free
https://github.com/EvgenyKashin/stylegan2-distillation
공식 코드https://github.com/ExplainableML/UncerGuidedI2I
공식 코드Uncertainty-Guided Progressive GANs for Medical Image Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.