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/weichaozeng/textctrl
공식 코드TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control
https://github.com/weichenfan/hierarchyflow
공식 코드https://github.com/weifeng-chen/control-a-video
공식 코드https://github.com/weihaox/DCMIT
공식 코드https://github.com/weihaox/TediGAN
공식 코드TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
https://github.com/weihaox/awesome-3D-aware-synthesis
공식 코드https://github.com/weihaox/awesome-gan-inversion
공식 코드https://github.com/weijiawu/DiffusionMask
공식 코드https://github.com/weijiawu/paradiffusion
공식 코드Paragraph-to-Image Generation with Information-Enriched Diffusion Model
https://github.com/weijing-tao/MotionCom
공식 코드MotionCom: Automatic and Motion-Aware Image Composition with LLM and Video Diffusion Prior
https://github.com/weilunwang/semantic-diffusion-model
공식 코드https://github.com/weilunwang/sindiffusion
공식 코드SinDiffusion: Learning a Diffusion Model from a Single Natural Image
https://github.com/weimengting/magicportrait
공식 코드MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric Guidance
https://github.com/weitianxin/UniMP
공식 코드https://github.com/weixi-feng/layoutgpt
공식 코드LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
https://github.com/weixi-feng/structured-diffusion-guidance
공식 코드Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis
https://github.com/weixi-feng/tc-bench
공식 코드TC-Bench: Benchmarking Temporal Compositionality in Text-to-Video and Image-to-Video Generation
https://github.com/wellzline/trustworthy_t2i_dms
공식 코드Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey
https://github.com/wendongzh/spn
공식 코드Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid
https://github.com/wendybaiyunwei/fsl-rectifier-pub
공식 코드FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation
https://github.com/wenjuan7275/matir
공식 코드https://github.com/wenyi-li/fairdiff
공식 코드https://github.com/westlake-ai/sema
공식 코드https://github.com/wfanyue/dpg-t2i-personalization
공식 코드Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning
https://github.com/wgcban/ddpm-cd
공식 코드DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection
https://github.com/wghr123/mfgdiffusion
공식 코드MFGDiffusion: Mask-Guided Smoke Synthesis for Enhanced Forest Fire Detection
https://github.com/whlzy/fit
공식 코드https://github.com/whulizheng/anti-diffusion
공식 코드Anti-Diffusion: Preventing Abuse of Modifications of Diffusion-Based Models
https://github.com/wi-pi/limitations_of_face_generation
공식 코드https://github.com/willcagas/Synthetic-Medical-Image-Generation
공식 코드Medical Imaging Complexity and its Effects on GAN Performance
https://github.com/williamyang1991/gp-unit
공식 코드Unsupervised Image-to-Image Translation with Generative Prior
https://github.com/willisma/sit
공식 코드SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
https://github.com/windvchen/diff-harmonization
공식 코드https://github.com/windvchen/inr-harmonization
공식 코드Dense Pixel-to-Pixel Harmonization via Continuous Image Representation
https://github.com/winfredy/sadtalker
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.