https://github.com/penway/lfm
공식 코드Latent Space is Feature Space: Regularization Term for GANs Training on Limited Dataset
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/perceivelab/trf-sg2im
공식 코드https://github.com/petrmokrov/energy-guided-entropic-ot
공식 코드https://github.com/pfilo8/flow-plugin-network-for-conditional-generation
공식 코드https://github.com/pfnet-research/cg-transfer
공식 코드A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?
https://github.com/pfnet-research/multi-stage-blended-diffusion
공식 코드High-Resolution Image Editing via Multi-Stage Blended Diffusion
https://github.com/pfnet-research/sngan_projection
공식 코드https://github.com/pfnet-research/timesfm_fin
공식 코드https://github.com/pfriedri/cwdm
공식 코드cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis
https://github.com/pfriedri/wdm-3d
공식 코드WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis
https://github.com/philgzl/brever
공식 코드Investigating the Design Space of Diffusion Models for Speech Enhancement
https://github.com/phizaz/diffae
공식 코드Diffusion Autoencoders: Toward a Meaningful and Decodable Representation
https://github.com/phoenix104104/fast_blind_video_consistency
공식 코드https://github.com/phoenixtreesky7/cfen-vit-dehazing
공식 코드Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing
https://github.com/phymhan/p2gan
공식 코드Dual Projection Generative Adversarial Networks for Conditional Image Generation
https://github.com/phymhan/supervised-gan
공식 코드Learning Generative Models of Tissue Organization with Supervised GANs
https://github.com/phyrise/nnUNet_translation
공식 코드Anatomical feature-prioritized loss for enhanced MR to CT translation
https://github.com/picotrex/gpt-imgeval
공식 코드GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation
https://github.com/picsart-ai-research/hd-painter
공식 코드HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models
https://github.com/picsart-ai-research/ipl-zero-shot-generative-model-adaptation
공식 코드Zero-shot Generative Model Adaptation via Image-specific Prompt Learning
https://github.com/picsart-ai-research/social-reward
공식 코드https://github.com/picsart-ai-research/text2video-zero
공식 코드Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
https://github.com/picsart-ai-research/zero-painter
공식 코드Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis
https://github.com/pioneerAlpha/BanglaText2ImageGeneration
공식 코드https://github.com/pix2pixzero/pix2pix-zero
공식 코드https://github.com/pkorus/neural-imaging
공식 코드https://github.com/pku-dair/hetu
공식 코드Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference
https://github.com/pku-ml/terd
공식 코드TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors
https://github.com/pku-sec-lab/fpqvar
공식 코드FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-design
https://github.com/pku-yuangroup/imgedit
공식 코드https://github.com/pku-yuangroup/next-patch-prediction
공식 코드https://github.com/pku-yuangroup/wise
공식 코드WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
https://github.com/pkunliu/isotropic3d
공식 코드Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding
https://github.com/plai-group/ipa
공식 코드Conditional Image Generation by Conditioning Variational Auto-Encoders
https://github.com/plant-ai-biophysics-lab/CropGAN
공식 코드Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection
https://github.com/plstory/ds2d
공식 코드DStruct2Design: Data and Benchmarks for Data Structure Driven Generative Floor Plan Design
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.