https://github.com/fnzhan/generative-ai
공식 코드Multimodal Image Synthesis and Editing: The Generative AI Era
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/fnzhan/mise
공식 코드Multimodal Image Synthesis and Editing: The Generative AI Era
https://github.com/fnzhan/monce
공식 코드https://github.com/fodark/anonygan
공식 코드Graph-based Generative Face Anonymisation with Pose Preservation
https://github.com/forchchch/disenbooth
공식 코드DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation
https://github.com/fordevoted/uiess
공식 코드Domain Adaptation for Underwater Image Enhancement via Content and Style Separation
https://github.com/forever208/adm-es
공식 코드https://github.com/forever208/ddpm-ip
공식 코드Input Perturbation Reduces Exposure Bias in Diffusion Models
https://github.com/forever208/edm-es
공식 코드https://github.com/foundationvision/llamagen
공식 코드Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation
https://github.com/fpramunno/MAG2MAG
공식 코드Magnetogram-to-Magnetogram: Generative Forecasting of Solar Evolution
https://github.com/fpv-iplab/DA-RetinaNet
공식 코드An Unsupervised Domain Adaptation Scheme for Single-Stage Artwork Recognition in Cultural Sites
https://github.com/fqnchina/ImageSmoothing
공식 코드https://github.com/franciszzj/leffa
공식 코드Learning Flow Fields in Attention for Controllable Person Image Generation
https://github.com/frank-xwang/InstanceDiffusion
공식 코드InstanceDiffusion: Instance-level Control for Image Generation
https://github.com/frankfundel/sgcond
공식 코드https://github.com/freedomintelligence/sharegpt-4o-image
공식 코드ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation
https://github.com/fshp971/encoder-inference
공식 코드Pre-trained Encoder Inference: Revealing Upstream Encoders In Downstream Machine Learning Services
https://github.com/fudan-zvg/Brain3D
공식 코드https://github.com/fudan-zvg/diffusion-square
공식 코드https://github.com/fudan-zvg/efficient4d
공식 코드Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video
https://github.com/fudan-zvg/pds
공식 코드Accelerating Score-based Generative Models with Preconditioned Diffusion Sampling
https://github.com/fudan-zvg/pgc-3d
공식 코드Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping
https://github.com/fudan-zvg/tet-splatting
공식 코드https://github.com/furiosa-ai/eta-inversion
공식 코드Eta Inversion: Designing an Optimal Eta Function for Diffusion-based Real Image Editing
https://github.com/fusiming3/mars
공식 코드MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis
https://github.com/futurexiang/ddae
공식 코드Denoising Diffusion Autoencoders are Unified Self-supervised Learners
https://github.com/fuxivirtualhuman/styletalk
공식 코드StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles
https://github.com/fuyaluo/morngan
공식 코드Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization
https://github.com/fzj-inm1-bda/pli2cells
공식 코드https://github.com/g-milis/NEUTART
공식 코드https://github.com/g-u-n/animatelcm
공식 코드https://github.com/g-u-n/diffusion-npo
공식 코드https://github.com/g-u-n/gen-l-video
공식 코드Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising
https://github.com/gabriel-turinici/huber-energy-measure-quantization
공식 코드https://github.com/gal4way/tpd
공식 코드Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.