https://github.com/jingjiqinggong/specp2p
공식 코드SpecRef: A Fast Training-free Baseline of Specific Reference-Condition Real Image Editing
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 23
- 평가표
- 93
- 보존된 평가 기록
- 870
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/jingtaozhan/promptreformulate
공식 코드Capability-aware Prompt Reformulation Learning for Text-to-Image Generation
https://github.com/jingwu321/erasediff
공식 코드https://github.com/jingwu321/scissorhands
공식 코드Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks
https://github.com/jingyuanli001/RFR-Inpainting
공식 코드https://github.com/jinhaoduan/secmi
공식 코드Are Diffusion Models Vulnerable to Membership Inference Attacks?
https://github.com/jinhyunj/sanet
공식 코드https://github.com/jinnan-chen/3d-pose-transfer
공식 코드https://github.com/jiquan123/aigiqa4k
공식 코드https://github.com/jiquan123/i2iqa
공식 코드PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images
https://github.com/jiuhaichen/blip3o
공식 코드BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
https://github.com/jiuntian/interactdiffusion
공식 코드InteractDiffusion: Interaction Control in Text-to-Image Diffusion Models
https://github.com/jiwanhur/unlockmgm
공식 코드Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance
https://github.com/jiwoogit/dcp-gan
공식 코드https://github.com/jj-vice/bagm
공식 코드BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models
https://github.com/jjgo/hyperlight
공식 코드Magnitude Invariant Parametrizations Improve Hypernetwork Learning
https://github.com/jjuon/lfs-gan
공식 코드https://github.com/jlianglab/Fixed-Point-GAN
공식 코드https://github.com/jmhb0/view_neti
공식 코드https://github.com/jmyissb/SEAttnGAN
공식 코드A Simple and Effective Baseline for Attentional Generative Adversarial Networks
https://github.com/jnzju/coti
공식 코드Controllable Textual Inversion for Personalized Text-to-Image Generation
https://github.com/joanrod/ocr-vqgan
공식 코드https://github.com/joanrod/paper2figure-dataset
공식 코드https://github.com/joellliu/diffprotect
공식 코드DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection
https://github.com/jogo175/treediffusion
공식 코드Hierarchical Clustering for Conditional Diffusion in Image Generation
https://github.com/johannwyh/meat
공식 코드MEAT: Multiview Diffusion Model for Human Generation on Megapixels with Mesh Attention
https://github.com/johertrich/sliced_mmd_flows
공식 코드https://github.com/johndreamer/dualcyclediffsion
공식 코드Eliminating Contextual Prior Bias for Semantic Image Editing via Dual-Cycle Diffusion
https://github.com/johndreamer/twfa
공식 코드https://github.com/johnr0/promptpaint
공식 코드PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like Interactions
https://github.com/johnryh/Face_Embedding_GAN
공식 코드Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images
https://github.com/jonasricker/diffusion-model-deepfake-detection
공식 코드https://github.com/jongdory/aldm
공식 코드https://github.com/jonghyunharrylee/pyPCGA
공식 코드https://github.com/joonsungjeon/advpaint
공식 코드AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption
https://github.com/jordan-hs/diversity_is_definitely_needed
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.