https://github.com/xh-liu/Open-Edit
공식 코드Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions
Papers with Code 아카이브
Image Manipulation
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Image Manipulation is the process of altering or transforming an existing image to achieve a desired effect or to modify its content. This can involve various techniques and tools to enhance, modify, or create images based on specific requirements.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yanivnik/sinfusion-code
공식 코드SinFusion: Training Diffusion Models on a Single Image or Video
https://github.com/yhZhai/WSCL
공식 코드Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning
https://github.com/yikai-wang/res
공식 코드https://github.com/yikai-wang/semani
공식 코드https://github.com/yingchen001/cf-clip
공식 코드https://github.com/yueming6568/deltaedit
공식 코드DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation
https://github.com/yuval-alaluf/SAM
공식 코드Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
https://github.com/zhangzc21/petsgan
공식 코드https://github.com/zhenfeiz/cimd
공식 코드A New Benchmark and Model for Challenging Image Manipulation Detection
https://github.com/zhipeixu/fakeshield
공식 코드https://github.com/zhongping-zhang/sgc_net
공식 코드https://github.com/zipengxuc/ppe
공식 코드https://github.com/2023-MindSpore-1/ms-code-222/tree/main/SRFlow
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/771979972/paddle-SAM
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
https://github.com/Amrou7/Sin-GAN-master
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ByteDance-Seed/Bagel
https://github.com/CREVIOS/MediSinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/CompVis/iin
A Disentangling Invertible Interpretation Network for Explaining Latent Representations
https://github.com/Daniahblog/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/DanielaGall/AI-CHALLENGE2
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
https://github.com/Dou-Yu-xuan/FSRNet
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
https://github.com/FriedRonaldo/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Garfield35/Doodle
https://github.com/Gie-ok-Hie-ut/pytorch-stylegan2-distillation
https://github.com/HKUNLP/Dream
https://github.com/HideUnderBush/UI2I_via_StyleGAN2
https://github.com/HilaManor/Generative-deep-features
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/IIGROUP/Multi-Modal-CelebA-HQ-Dataset
Conditional Image Generation and Manipulation for User-Specified Content
https://github.com/IIGROUP/TediGAN
https://github.com/Iheb96/SinGAN_InPainting
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/JackonLiu/SinGAN-Easy
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/KongBOy/analyze_16_code_iin
A Disentangling Invertible Interpretation Network for Explaining Latent Representations
https://github.com/LAION-AI/phenaki
https://github.com/LONG-9621/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Labyz/SinGAN-MVA
SinGAN: Learning a Generative Model from a Single Natural Image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.