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/ha0tang/ecgan
공식 코드Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis
https://github.com/haha-lisa/CreativeSynth
공식 코드CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis with Multimodal Diffusion
https://github.com/haha-lisa/RDM-Region-Aware-Diffusion-Model
공식 코드Region-Aware Diffusion for Zero-shot Text-driven Image Editing
https://github.com/haibo-qiu/SynFace
공식 코드https://github.com/hailsong/s3d
공식 코드https://github.com/hakansivuk/divsem
공식 코드https://github.com/hamedhaghighi/cls2r
공식 코드A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems
https://github.com/hamedhaghighi/coligen
공식 코드A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems
https://github.com/hammour-steak/amdm
공식 코드Improving Fine-Grained Control via Aggregation of Multiple Diffusion Models
https://github.com/hamrel-cxu/flowot
공식 코드Computing high-dimensional optimal transport by flow neural networks
https://github.com/hananshafi/llmblueprint
공식 코드LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts
https://github.com/hanbo-cheng/dawn-pytorch
공식 코드https://github.com/hancyran/lidar-diffusion
공식 코드Towards Realistic Scene Generation with LiDAR Diffusion Models
https://github.com/hanhuang22/VI-Diff
공식 코드https://github.com/hankpipi/diffusers-hetu
공식 코드Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference
https://github.com/hanlinwu/E2DiffSR
공식 코드https://github.com/hanmengya1996/pftgan
공식 코드https://github.com/hansam95/nmg
공식 코드Noise Map Guidance: Inversion with Spatial Context for Real Image Editing
https://github.com/hansam95/osasis
공식 코드https://github.com/hansenhuang0823/placiddreamer
공식 코드https://github.com/hanseuljo/alex-gda
공식 코드Fundamental Benefit of Alternating Updates in Minimax Optimization
https://github.com/hansolo9682/countercurate
공식 코드https://github.com/hanzhanggit/StackGAN
공식 코드StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/hanzhanggit/StackGAN-v2
공식 코드StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/haoli-zbdbc/hierafashdiff
공식 코드HieraFashDiff: Hierarchical Fashion Design with Multi-stage Diffusion Models
https://github.com/haolsun/dual-glow
공식 코드DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer
https://github.com/haoningwu3639/MRGen
공식 코드MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities
https://github.com/haoningwu3639/MegaFusion
공식 코드https://github.com/haoningwu3639/StoryGen
공식 코드Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Models
https://github.com/haoosz/BiGR
공식 코드https://github.com/haoosz/conceptexpress
공식 코드ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction
https://github.com/haoosz/vico
공식 코드ViCo: Plug-and-play Visual Condition for Personalized Text-to-image Generation
https://github.com/haoranhfut/ff-gan
공식 코드Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis
https://github.com/haoweiz23/distdiff
공식 코드https://github.com/haoygong/lpdgan
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.