https://github.com/andrew-miao/RPO
공식 코드Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning
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/andycao1125/sdm
공식 코드https://github.com/andyshih12/mac
공식 코드Training and Inference on Any-Order Autoregressive Models the Right Way
https://github.com/andysonys/selective-diffusion-distillation
공식 코드Not All Steps are Created Equal: Selective Diffusion Distillation for Image Manipulation
https://github.com/aniaggarwal/ecad
공식 코드Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model
https://github.com/anindyasdas/selfsupervisedimagetext
공식 코드https://github.com/ankanbhunia/Handwriting-Transformers
공식 코드https://github.com/ankanbhunia/PIDM
공식 코드https://github.com/annimukherjee/raise
공식 코드RAISE: Realness Assessment for Image Synthesis and Evaluation
https://github.com/annonnymmouss/vcgan
공식 코드https://github.com/anonym0u3/attentiveeraser
공식 코드https://github.com/anonymous-ai-for-earth/satellite-to-satellite-translation
공식 코드https://github.com/anonymous5293/minorityprompt
공식 코드Minority-Focused Text-to-Image Generation via Prompt Optimization
https://github.com/anonymouspony/adap-edit
공식 코드https://github.com/antaegun20/CycleGANAS
공식 코드CycleGANAS: Differentiable Neural Architecture Search for CycleGAN
https://github.com/antonibigata/Laughing-Matters
공식 코드Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models
https://github.com/anvoynov/RandomPathGAN
공식 코드https://github.com/aosokin/biogans
공식 코드https://github.com/apchenstu/Facial_Details_Synthesis
공식 코드https://github.com/apchenstu/sofgan
공식 코드https://github.com/apelykh/hand-to-diffusion
공식 코드https://github.com/apoll2000/stereofog
공식 코드STEREOFOG -- Computational DeFogging via Image-to-Image Translation on a real-world Dataset
https://github.com/appelmar/stpconv
공식 코드https://github.com/apple/ml-agm
공식 코드https://github.com/apple/ml-gaudi
공식 코드https://github.com/apple/ml-gie-bench
공식 코드GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing
https://github.com/apple/ml-mdm
공식 코드https://github.com/apple/ml-mgie
공식 코드Guiding Instruction-based Image Editing via Multimodal Large Language Models
https://github.com/apple/ml-no-token-left-behind
공식 코드No Token Left Behind: Explainability-Aided Image Classification and Generation
https://github.com/apple/ml-tarflow
공식 코드https://github.com/applied-ai-lab/genesis
공식 코드GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
https://github.com/arangesh/GPCycleGAN
공식 코드Gaze Preserving CycleGANs for Eyeglass Removal & Persistent Gaze Estimation
https://github.com/aravind0706/flowpp
공식 코드https://github.com/aravindsrinivas/flowpp
공식 코드https://github.com/arcadelab/counterfactual_diffusion
공식 코드https://github.com/archerfmy/sd-t2i-360panoimage
공식 코드Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.