https://github.com/hidream-ai/himar
공식 코드Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots
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/hidream-ai/spm-diff
공식 코드Incorporating Visual Correspondence into Diffusion Model for Virtual Try-On
https://github.com/hieuphan33/miccai2024-unest
공식 코드Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis
https://github.com/highwaywu/lasted
공식 코드Generalizable Synthetic Image Detection via Language-guided Contrastive Learning
https://github.com/hila-chefer/Conceptor
공식 코드https://github.com/hindawi91/dips
공식 코드https://github.com/hirochok/pregen
공식 코드Tackling Copyright Issues in AI Image Generation Through Originality Estimation and Genericization
https://github.com/hit-philiphan/remote_sensing_image_editing
공식 코드Exploring Text-Guided Single Image Editing for Remote Sensing Images
https://github.com/hitsz-tmg/agentic-cigeval
공식 코드A Unified Agentic Framework for Evaluating Conditional Image Generation
https://github.com/hitsz-tmg/anim-director
공식 코드Anim-Director: A Large Multimodal Model Powered Agent for Controllable Animation Video Generation
https://github.com/hiyaroy12/DFT_inpainting
공식 코드https://github.com/hj-harry/mcg_diffusion
공식 코드Improving Diffusion Models for Inverse Problems using Manifold Constraints
https://github.com/hku-medai/gem-3d
공식 코드https://github.com/hkust-vgd/architectural_style_transfer
공식 코드Time-of-Day Neural Style Transfer for Architectural Photographs
https://github.com/hkust-vgd/neural_scene_decoration
공식 코드https://github.com/hmrc-ael/tridentadapt
공식 코드https://github.com/hnu-vis/datacrafter
공식 코드Human-Guided Image Generation for Expanding Small-Scale Training Image Datasets
https://github.com/hoim/mobilefsgan
공식 코드Migrating Face Swap to Mobile Devices: A lightweight Framework and A Supervised Training Solution
https://github.com/hojonathanho/diffusion
공식 코드https://github.com/holmesshuan/fireflow
공식 코드FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
https://github.com/hologerry/AGIS-Net
공식 코드Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning
https://github.com/hongweilibran/DiamondGAN
공식 코드DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis
https://github.com/hongyehu/RG-Flow
공식 코드RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
https://github.com/honkamj/non-aligned-i2i
공식 코드Deformation equivariant cross-modality image synthesis with paired non-aligned training data
https://github.com/horizontel/aigibench
공식 코드Is Artificial Intelligence Generated Image Detection a Solved Problem?
https://github.com/horseee/deepcache
공식 코드https://github.com/hotfinda/Compensation-sampling
공식 코드Compensation Sampling for Improved Convergence in Diffusion Models
https://github.com/houliangict/adcgan
공식 코드https://github.com/houliangict/ssgan-la
공식 코드https://github.com/hp-l33/ARPG
공식 코드Autoregressive Image Generation with Randomized Parallel Decoding
https://github.com/hp-l33/aim
공식 코드https://github.com/hpcaitech/open-sora
공식 코드https://github.com/hqhqaq/mip-adapter
공식 코드Resolving Multi-Condition Confusion for Finetuning-Free Personalized Image Generation
https://github.com/hqhqaq/patchdpo
공식 코드PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation
https://github.com/hritikbansal/entigen_emnlp
공식 코드How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?
https://github.com/hse1032/Adversarial-Generation-of-Hierarchical-Gaussians-for-3D-Generative-Model
공식 코드GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.