https://github.com/yangling0818/consistency_flow_matching
공식 코드Consistency Flow Matching: Defining Straight Flows with Velocity Consistency
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/yangling0818/contextdiff
공식 코드Contextualized Diffusion Models for Text-Guided Image and Video Generation
https://github.com/yangling0818/itercomp
공식 코드https://github.com/yangling0818/realcompo
공식 코드RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models
https://github.com/yangling0818/rpg-diffusionmaster
공식 코드Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
https://github.com/yangling0818/sgdiff
공식 코드Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
https://github.com/yangxiaofeng/rectified_flow_prior
공식 코드https://github.com/yangxy/GPEN
공식 코드GAN Prior Embedded Network for Blind Face Restoration in the Wild
https://github.com/yangxy/pasd
공식 코드Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization
https://github.com/yangxy/sdl
공식 코드https://github.com/yangzhang-v5/attention_regulation
공식 코드Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models
https://github.com/yanivnik/sinfusion-code
공식 코드SinFusion: Training Diffusion Models on a Single Image or Video
https://github.com/yanjh97/transgrasp
공식 코드https://github.com/yanliang3612/diffrop
공식 코드Training Class-Imbalanced Diffusion Model Via Overlap Optimization
https://github.com/yanllan-ljb/contrastivegaussian
공식 코드ContrastiveGaussian: High-Fidelity 3D Generation with Contrastive Learning and Gaussian Splatting
https://github.com/yaofang-liu/fvdm
공식 코드Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach
https://github.com/yaolinli/capenrich
공식 코드CapEnrich: Enriching Caption Semantics for Web Images via Cross-modal Pre-trained Knowledge
https://github.com/yarqian/psfnet
공식 코드Progressive and Selective Fusion Network for High Dynamic Range Imaging
https://github.com/yasminzhang/ictm
공식 코드Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching
https://github.com/yaxingwang/DeepI2I
공식 코드DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs
https://github.com/yaxingwang/SDIT
공식 코드https://github.com/yaxingwang/SEMIT
공식 코드Semi-supervised Learning for Few-shot Image-to-Image Translation
https://github.com/yaziwel/multi-center-pet-image-synthesis
공식 코드DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image Synthesis
https://github.com/yaziwel/region-attention-transformer-for-medical-image-restoration
공식 코드https://github.com/yaziwel/restore-rwkv
공식 코드Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV
https://github.com/ybu-lxd/SRNDiff
공식 코드SRNDiff: Short-term Rainfall Nowcasting with Condition Diffusion Model
https://github.com/ybybzhang/controlvideo
공식 코드ControlVideo: Training-free Controllable Text-to-Video Generation
https://github.com/ybzh/lapt
공식 코드LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models
https://github.com/yc015/scene-representation-diffusion-model
공식 코드Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model
https://github.com/ychang686/scs-co
공식 코드SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization
https://github.com/yczhou001/longbench-t2i
공식 코드https://github.com/ydc123/mmp-attack
공식 코드https://github.com/yeates/MMT
공식 코드https://github.com/yeates/dmt
공식 코드https://github.com/yeates/promptfix
공식 코드https://github.com/yeez-lee/data-selection-and-reweighting-for-diffusion-models
공식 코드Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.