https://github.com/donahowe/AutoStudio
공식 코드AutoStudio: Crafting Consistent Subjects in Multi-turn Interactive Image Generation
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/donahowe/theatergen
공식 코드TheaterGen: Character Management with LLM for Consistent Multi-turn Image Generation
https://github.com/donaldssh/LoRA.rar
공식 코드LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation
https://github.com/dong03/MVSS-Net
공식 코드Image Manipulation Detection by Multi-View Multi-Scale Supervision
https://github.com/dongdongdong666/CPGAN
공식 코드CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis
https://github.com/dongzhuoyao/self-guided-diffusion-models
공식 코드https://github.com/donydchen/sem2nerf
공식 코드Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields
https://github.com/dorarad/gansformer
공식 코드https://github.com/dotwang/dcn-t
공식 코드DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification
https://github.com/dptech-corp/uni-3dar
공식 코드Uni-3DAR: Unified 3D Generation and Understanding via Autoregression on Compressed Spatial Tokens
https://github.com/dqiaole/flowdiffusion_pytorch
공식 코드Open-DDVM: A Reproduction and Extension of Diffusion Model for Optical Flow Estimation
https://github.com/dqiaole/zits_inpainting
공식 코드Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding
https://github.com/dragonylee/facecom
공식 코드FaceCom: Towards High-fidelity 3D Facial Shape Completion via Optimization and Inpainting Guidance
https://github.com/drboog/Lafite
공식 코드LAFITE: Towards Language-Free Training for Text-to-Image Generation
https://github.com/drboog/Shifted_Diffusion
공식 코드https://github.com/drboog/profusion
공식 코드https://github.com/dreamgaussian/dreamgaussian
공식 코드DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
https://github.com/dreamsampler/dream-sampler
공식 코드DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
https://github.com/dreamtalecore/scs-uit
공식 코드Separating Content and Style for Unsupervised Image-to-Image Translation
https://github.com/dreamxfar/sp-ctrl
공식 코드Rethink Sparse Signals for Pose-guided Text-to-image Generation
https://github.com/drx-code/EquivariantModeling
공식 코드https://github.com/dsam99/pc_clip
공식 코드https://github.com/dsgiitr/flux-watermarking
공식 코드https://github.com/dsshim0125/s2p
공식 코드S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning
https://github.com/dufengfeng/FB-4D
공식 코드FB-4D: Spatial-Temporal Coherent Dynamic 3D Content Generation with Feature Banks
https://github.com/dunbar12138/pix2pix3d
공식 코드https://github.com/duxiaodan/fast_text2stylegan
공식 코드Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
https://github.com/duxingren14/BSD-GAN
공식 코드https://github.com/duxingren14/BranchGAN
공식 코드https://github.com/duxingren14/DualGAN
공식 코드DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
https://github.com/duxingren14/FaceEraser
공식 코드https://github.com/dvirsamuel/SeedSelect
공식 코드Generating images of rare concepts using pre-trained diffusion models
https://github.com/dvlab-research/Entity
공식 코드https://github.com/dvlab-research/LLMGA
공식 코드LLMGA: Multimodal Large Language Model based Generation Assistant
https://github.com/dvlab-research/rival
공식 코드Real-World Image Variation by Aligning Diffusion Inversion Chain
https://github.com/dylanauty/objcavit
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.