https://github.com/OpenGVLab/Diffree
공식 코드Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model
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/OpenGVLab/PonderV2
공식 코드PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
https://github.com/Owen-Oertell/rlcm
공식 코드RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
https://github.com/PITI-Synthesis/PITI
공식 코드https://github.com/PLUM-Lab/AR-RAG
공식 코드AR-RAG: Autoregressive Retrieval Augmentation for Image Generation
https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
공식 코드ContraGAN: Contrastive Learning for Conditional Image Generation
https://github.com/PRIS-CV/DemoFusion
공식 코드DemoFusion: Democratising High-Resolution Image Generation With No $$$
https://github.com/Panagiotou/Procedural3DTerrain
공식 코드Procedural 3D Terrain Generation using Generative Adversarial Networks
https://github.com/Pangyk/SinoSynth
공식 코드SinoSynth: A Physics-based Domain Randomization Approach for Generalizable CBCT Image Enhancement
https://github.com/Papple-F/csg
공식 코드Training-free Composite Scene Generation for Layout-to-Image Synthesis
https://github.com/PatrickTUM/SEN12MS-CR-TS
공식 코드Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
https://github.com/Paul92/cp_2020
공식 코드https://github.com/Penn000/SpA-GAN_for_cloud_removal
공식 코드Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network
https://github.com/Pepper-lll/LMforImageGeneration
공식 코드Elucidating the design space of language models for image generation
https://github.com/PerceptionComputingLab/LDSB
공식 코드Finding Local Diffusion Schrödinger Bridge using Kolmogorov-Arnold Network
https://github.com/PeterTor/TetraDiffusion
공식 코드TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape Generation
https://github.com/PeterWang512/CNNDetection
공식 코드CNN-generated images are surprisingly easy to spot... for now
https://github.com/PeterWang512/GANSketching
공식 코드https://github.com/PeterouZh/CIPS-3D
공식 코드CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis
https://github.com/PeterouZh/NAS_cGAN
공식 코드Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks
https://github.com/PeterouZh/Omni-GAN-PyTorch
공식 코드https://github.com/PierfrancescoArdino/SGINet
공식 코드Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation
https://github.com/PixArt-alpha/PixArt-alpha
공식 코드PixArt-$α$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
https://github.com/PixArt-alpha/PixArt-sigma
공식 코드PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
https://github.com/PlusLabNLP/Re-ReST
공식 코드Re-ReST: Reflection-Reinforced Self-Training for Language Agents
https://github.com/PrototypeNx/DETEX
공식 코드Decoupled Textual Embeddings for Customized Image Generation
https://github.com/PruneTruong/DenseMatching
공식 코드Learning Accurate Dense Correspondences and When to Trust Them
https://github.com/PruneTruong/PDCNet
공식 코드Learning Accurate Dense Correspondences and When to Trust Them
https://github.com/Purushothaman-natarajan/Synth-SONAR
공식 코드https://github.com/QiufanLin/ImageTranslation
공식 코드Galaxy Image Translation with Semi-supervised Noise-reconstructed Generative Adversarial Networks
https://github.com/QixuanJin99/generative_validation
공식 코드https://github.com/Qjizhi/GenStereo
공식 코드GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching
https://github.com/Qrange-group/SUR-adapter
공식 코드SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
https://github.com/RAIVNLab/GenerateAnyScene
공식 코드Generate Any Scene: Evaluating and Improving Text-to-Vision Generation with Scene Graph Programming
https://github.com/RQ-Wu/LAMP
공식 코드LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation
https://github.com/Raman1121/CheXGenBench
공식 코드CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.