https://github.com/winstonhutiger/2d_vae_uda_for_3d_sythesis
공식 코드Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised Approach
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/witcherofresearch/forgedit
공식 코드Forgedit: Text Guided Image Editing via Learning and Forgetting
https://github.com/wittawatj/cadgan
공식 코드https://github.com/wl-zhao/unipc
공식 코드UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models
https://github.com/wlin-at/maxi
공식 코드https://github.com/wltjr1007/FDEN
공식 코드A Plug-in Method for Representation Factorization in Connectionist Models
https://github.com/wongzbb/diffma-diffusion-mamba
공식 코드https://github.com/wpp13749/block_flow
공식 코드https://github.com/wqlevi/disgan
공식 코드DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise Cleaning
https://github.com/wsx20003/cfg-rejection
공식 코드Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering
https://github.com/wtliao/layout2img
공식 코드Context-Aware Layout to Image Generation with Enhanced Object Appearance
https://github.com/wtliao/text2image
공식 코드https://github.com/wu-cvgl/mvcontrol
공식 코드MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation
https://github.com/wu-cvgl/scinerf
공식 코드SCINeRF: Neural Radiance Fields from a Snapshot Compressive Image
https://github.com/wu-zongyu/posi
공식 코드Universal Prompt Optimizer for Safe Text-to-Image Generation
https://github.com/wuhaozhe/style_avatar
공식 코드Imitating Arbitrary Talking Style for Realistic Audio-DrivenTalking Face Synthesis
https://github.com/wuhuikai/GP-GAN
공식 코드https://github.com/wujunde/medsegdiff
공식 코드MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
https://github.com/wuqiuche/micromotion-stylegan
공식 코드https://github.com/wusize/harmon
공식 코드Harmonizing Visual Representations for Unified Multimodal Understanding and Generation
https://github.com/wustl-cig/msm
공식 코드https://github.com/wustl-cig/score_pnp
공식 코드https://github.com/wutaiqiang/moslora
공식 코드https://github.com/wuyangluo/reffaceinpainting
공식 코드Reference-Guided Large-Scale Face Inpainting with Identity and Texture Control
https://github.com/wyf0912/aredit
공식 코드Training-Free Text-Guided Image Editing with Visual Autoregressive Model
https://github.com/wyhuai/ddnm
공식 코드Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
https://github.com/wyuan1001/adavd
공식 코드Precise, Fast, and Low-cost Concept Erasure in Value Space: Orthogonal Complement Matters
https://github.com/wywu/ReenactGAN
공식 코드https://github.com/wyysf-98/SweetDreamer
공식 코드SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3D
https://github.com/wyysf-98/craftsman
공식 코드https://github.com/wyysf-98/craftsman3d
공식 코드https://github.com/x1716/iqa-adapter
공식 코드https://github.com/xacheng1996/diff
공식 코드https://github.com/xaliceli/lemotif
공식 코드Lemotif: An Affective Visual Journal Using Deep Neural Networks
https://github.com/xavierjiezou/diffcr
공식 코드DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images
https://github.com/xavierjiezou/pmaa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.