https://github.com/toyotainfotech/stisr-tcdm
공식 코드Scene Text Image Super-resolution based on Text-conditional Diffusion Models
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/tpulkit/txt2vid
공식 코드Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text
https://github.com/trainlaboratory/multiscaletextureloss-mstlf
공식 코드https://github.com/tranluan/Nonlinear_Face_3DMM
공식 코드https://github.com/transdiff/transdiff
공식 코드Marrying Autoregressive Transformer and Diffusion with Multi-Reference Autoregression
https://github.com/transpchan/Live3D
공식 코드https://github.com/trapoom555/gradeadreamer
공식 코드GradeADreamer: Enhanced Text-to-3D Generation Using Gaussian Splatting and Multi-View Diffusion
https://github.com/trestad/cogattn
공식 코드https://github.com/trungpx/xmdpt
공식 코드Cross-view Masked Diffusion Transformers for Person Image Synthesis
https://github.com/trusthlt/private-synthetic-text-generation
공식 코드https://github.com/tsc2017/MIX-GAN
공식 코드Lessons Learned from the Training of GANs on Artificial Datasets
https://github.com/tsingqguo/inpaint4shadow
공식 코드https://github.com/tsingqguo/jpgnet
공식 코드JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting
https://github.com/tsingqguo/misf
공식 코드MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting
https://github.com/tson1997/SDL-Document-Image-Generation
공식 코드SDL: New data generation tools for full-level annotated document layout
https://github.com/tsun/bdmae
공식 코드Mask and Restore: Blind Backdoor Defense at Test Time with Masked Autoencoder
https://github.com/tsunghan-wu/SLD
공식 코드https://github.com/tsunghan-wu/depth-completion
공식 코드Indoor Depth Completion with Boundary Consistency and Self-Attention
https://github.com/ttchengab/continuous_3d_words_code
공식 코드https://github.com/tund/D2GAN
공식 코드https://github.com/turna1/genai-for-goods
공식 코드https://github.com/tvaranka/fineface
공식 코드Towards Localized Fine-Grained Control for Facial Expression Generation
https://github.com/tychovdo/RevGAN
공식 코드Reversible GANs for Memory-efficient Image-to-Image Translation
https://github.com/tyhuang0428/dreamcontrol
공식 코드DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior
https://github.com/tyshiwo1/Accelerating-T2I-AR-with-SJD
공식 코드Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding
https://github.com/tyshiwo1/dim-diffusionmamba
공식 코드DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis
https://github.com/tyxsspa/anytext
공식 코드https://github.com/tyxsspa/anytext2
공식 코드AnyText2: Visual Text Generation and Editing With Customizable Attributes
https://github.com/tzco/Diffusion-wo-CFG
공식 코드https://github.com/tzm-tora/Stroke-Based-Scene-Text-Erasing
공식 코드Stroke-Based Scene Text Erasing Using Synthetic Data for Training
https://github.com/tzt101/CLADE
공식 코드Efficient Semantic Image Synthesis via Class-Adaptive Normalization
https://github.com/tzt101/INADE
공식 코드Diverse Semantic Image Synthesis via Probability Distribution Modeling
https://github.com/tzt101/MichiGAN
공식 코드MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing
https://github.com/tzw1998/parataa-diffusion
공식 코드https://github.com/ualberta-nlp/v-wsd
공식 코드https://github.com/ubc-tea/sadm-longitudinal-medical-image-generation
공식 코드SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.