https://github.com/Shilin-LU/TF-ICON
공식 코드TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition
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/ShinyCode/image-outpainting
공식 코드https://github.com/SijieSong/person_generation_spt
공식 코드Unsupervised Person Image Generation with Semantic Parsing Transformation
https://github.com/SilentView/GigaTok
공식 코드GigaTok: Scaling Visual Tokenizers to 3 Billion Parameters for Autoregressive Image Generation
https://github.com/SkyworkAI/Gamba
공식 코드Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction
https://github.com/Smith42/astroddpm
공식 코드Realistic galaxy image simulation via score-based generative models
https://github.com/Sonettoo/CRS-Diff
공식 코드CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model
https://github.com/SonyCSLParis/interactive-spectrogram-inpainting
공식 코드Spectrogram Inpainting for Interactive Generation of Instrument Sounds
https://github.com/SonyResearch/RAW-Diffusion
공식 코드RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image Generation
https://github.com/Soumyabrata/HDR-cloud-segmentation
공식 코드https://github.com/Spinozaaa/AdvI2I
공식 코드AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models
https://github.com/SteveImmanuel/OEM-Few-Shot-Learnable-Prompt
공식 코드Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain
https://github.com/SteveImmanuel/rs-paint
공식 코드Tackling Few-Shot Segmentation in Remote Sensing via Inpainting Diffusion Model
https://github.com/Stillrot/RGINP
공식 코드https://github.com/Streaming-Diffusion-Policy/streaming_diffusion_policy
공식 코드Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion Models
https://github.com/StrohmayerJ/wificam
공식 코드Through-Wall Imaging based on WiFi Channel State Information
https://github.com/Stry233/Visual-Story-Generation-Based-on-Emotional-and-Keyword-Scheme
공식 코드https://github.com/SuhoRyu/HATIE
공식 코드Towards Scalable Human-aligned Benchmark for Text-guided Image Editing
https://github.com/SyedA5688/HistopathologyDatasetGAN
공식 코드Histopathology DatasetGAN: Synthesizing Large-Resolution Histopathology Datasets
https://github.com/TAMU-VITA/AutoGAN
공식 코드AutoGAN: Neural Architecture Search for Generative Adversarial Networks
https://github.com/TAMU-VITA/GAN-Slimming
공식 코드GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
https://github.com/TCXX/ObjaversePlusPlus
공식 코드Objaverse++: Curated 3D Object Dataset with Quality Annotations
https://github.com/TEXTurePaper/TEXTurePaper
공식 코드https://github.com/THU-LYJ-Lab/T3Bench
공식 코드T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation
https://github.com/THUDM/CogView
공식 코드CogView: Mastering Text-to-Image Generation via Transformers
https://github.com/THUDM/RelayDiffusion
공식 코드Relay Diffusion: Unifying diffusion process across resolutions for image synthesis
https://github.com/TIGER-AI-Lab/ImagenHub
공식 코드ImagenHub: Standardizing the evaluation of conditional image generation models
https://github.com/TIGER-AI-Lab/VIEScore
공식 코드VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation
https://github.com/TLESORT/Generative_Continual_Learning
공식 코드Generative Models from the perspective of Continual Learning
https://github.com/TZW1998/Taming-Stable-Diffusion-with-Human-Ranking-Feedback
공식 코드Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles
https://github.com/TalnUPF/ConceptExtraction
공식 코드https://github.com/Tan-H-C/DR-GAN-Distribution-Regularization-for-Text-to-Image-Generation
공식 코드DR-GAN: Distribution Regularization for Text-to-Image Generation
https://github.com/Tangshitao/MVDiffusion
공식 코드MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion
https://github.com/TencentARC/PhotoMaker
공식 코드PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
https://github.com/TencentARC/ViT-Lens
공식 코드https://github.com/TencentARC/VideoPainter
공식 코드VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.