Papers with Code 아카이브
Image Inpainting
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lyndonzheng/TFill
공식 코드Bridging Global Context Interactions for High-Fidelity Image Completion
https://github.com/m3dv/lefusion
공식 코드LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models
https://github.com/ma-labo/promptcharm
공식 코드PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement
https://github.com/majedelhelou/BIGPrior
공식 코드BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration
https://github.com/mark-yu1/interactive-image-inpainting
공식 코드https://github.com/mcahny/Deep-Video-Inpainting
공식 코드https://github.com/mdhosen/mask-face-inpainting-using-residual-attention-unet
공식 코드https://github.com/minshu-kim/rewarp
공식 코드Learning Residual Elastic Warps for Image Stitching under Dirichlet Boundary Condition
https://github.com/mlvlab/davi
공식 코드Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
https://github.com/modelscope/DiffSynth-Studio
공식 코드EliGen: Entity-Level Controlled Image Generation with Regional Attention
https://github.com/mohammadrezanaderi4/sfi-swin
공식 코드https://github.com/mprzewie/dmfa_inpainting
공식 코드https://github.com/mycfhs/dreammix
공식 코드DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image Inpainting
https://github.com/nanjingxiaobawang/SieNet-Image-extrapolation
공식 코드https://github.com/naver-ai/zim
공식 코드https://github.com/ncherel/infusion
공식 코드Infusion: internal diffusion for inpainting of dynamic textures and complex motion
https://github.com/ncherel/inpaint-diff-mat
공식 코드https://github.com/ncherel/psal
공식 코드https://github.com/nhuang37/approx_equivariant_graph_nets
공식 코드https://github.com/nifangbaage/coordfill
공식 코드CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying
https://github.com/ohayonguy/PMRF
공식 코드Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration
https://github.com/omriav/blended-latent-diffusion
공식 코드https://github.com/open-mmlab/mmagic/tree/main/projects/powerpaint
공식 코드A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting
https://github.com/open-mmlab/mmediting
공식 코드https://github.com/openai/consistency_models
공식 코드https://github.com/openai/glide-text2im
공식 코드GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
https://github.com/owenzlz/pal4inpaint
공식 코드https://github.com/pajotarthur/unsupervised_image_inpaiting
공식 코드https://github.com/patrickwu2/Depth-Completion
공식 코드Indoor Depth Completion with Boundary Consistency and Self-Attention
https://github.com/peiman-m/ACQNP
공식 코드https://github.com/pfnet-research/multi-stage-blended-diffusion
공식 코드High-Resolution Image Editing via Multi-Stage Blended Diffusion
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/plai-group/ipa
공식 코드Conditional Image Generation by Conditioning Variational Auto-Encoders
https://github.com/potato-kitty/objectadd
공식 코드ObjectAdd: Adding Objects into Image via a Training-Free Diffusion Modification Fashion
https://github.com/pranavphoenix/WavePaint
공식 코드WavePaint: Resource-efficient Token-mixer for Self-supervised Inpainting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.