https://github.com/shi-labs/versatile-diffusion
공식 코드Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
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/shi-yupeng/resail-for-sis
공식 코드Retrieval-based Spatially Adaptive Normalization for Semantic Image Synthesis
https://github.com/shihaozhaozsh/lavi-bridge
공식 코드Bridging Different Language Models and Generative Vision Models for Text-to-Image Generation
https://github.com/shihongl1998/llm-as-a-blackbox-optimizer
공식 코드Language Models as Black-Box Optimizers for Vision-Language Models
https://github.com/shijianjian/dia
공식 코드Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection
https://github.com/shilin-lu/mace
공식 코드https://github.com/shim0114/ssm-meets-video-diffusion-models
공식 코드SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces
https://github.com/shiml20/flowturbo
공식 코드FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner
https://github.com/shinechen1024/magicclothing
공식 코드https://github.com/shinshoji01/Style-Restricted_GAN
공식 코드https://github.com/shivangi-aneja/TAFIM
공식 코드TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations
https://github.com/shiyujiao/sat2strpanoramasynthesis
공식 코드Geometry-Guided Street-View Panorama Synthesis from Satellite Imagery
https://github.com/shizenglin/Frequency-Supervised-MR-to-CT-Image-Synthesis
공식 코드https://github.com/shizhediao/davinci
공식 코드Write and Paint: Generative Vision-Language Models are Unified Modal Learners
https://github.com/shngjz/wildcamera
공식 코드Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
https://github.com/shoaibbinmasud/soft-rank-energy-and-applications
공식 코드Multivariate rank via entropic optimal transport: sample efficiency and generative modeling
https://github.com/shoufachen/pixelflow
공식 코드https://github.com/should-ai-lab/grid
공식 코드https://github.com/showlab/boxdiff
공식 코드BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion
https://github.com/showlab/datasetdm
공식 코드DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
https://github.com/showlab/evolvedirector
공식 코드EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models
https://github.com/showlab/idprotector
공식 코드IDProtector: An Adversarial Noise Encoder to Protect Against ID-Preserving Image Generation
https://github.com/shuangquanfeng/fergi
공식 코드https://github.com/shuyansy/survey-of-visual-text-processing
공식 코드Visual Text Processing: A Comprehensive Review and Unified Evaluation
https://github.com/shuyansy/visual-text-processing-survey
공식 코드Visual Text Processing: A Comprehensive Review and Unified Evaluation
https://github.com/sian-wusidi/stegogan
공식 코드StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image Translation
https://github.com/siatmmlab/awesome-diffusion-model-based-image-editing-methods
공식 코드https://github.com/sibozhang/Depth-Guided-Inpainting
공식 코드https://github.com/sierkinhane/boxdiff
공식 코드BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion
https://github.com/sierkinhane/visorgpt
공식 코드https://github.com/sihanxu/ace
공식 코드ACE: Zero-Shot Image to Image Translation via Pretrained Auto-Contrastive-Encoder
https://github.com/sihyun-yu/REPA
공식 코드Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
https://github.com/silence-tang/artnerf
공식 코드ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis
https://github.com/silence-tang/gaussianactor
공식 코드3D$^2$-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for Realistic Gaussian Avatar Modeling
https://github.com/simeonallmendinger/collafuse_imagen
공식 코드https://github.com/simeonallmendinger/syntheticimagegeneration
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.