https://github.com/zhuhao-nju/describe3d
공식 코드High-Fidelity 3D Face Generation from Natural Language Descriptions
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/zhuhao-nju/mofanerf
공식 코드https://github.com/zhujiapeng/aurora
공식 코드Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis
https://github.com/zhuqiangLu/AOG-NET-360
공식 코드Autoregressive Omni-Aware Outpainting for Open-Vocabulary 360-Degree Image Generation
https://github.com/zhuxiangru/semvarbench
공식 코드Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective
https://github.com/zhuxiangru/winoground-t2i
공식 코드https://github.com/zhyzhouu/amed-solver
공식 코드Fast ODE-based Sampling for Diffusion Models in Around 5 Steps
https://github.com/ziangcao0312/DiffTF
공식 코드DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D Generation
https://github.com/ziangcao0312/PhysX
공식 코드https://github.com/zibojia/COCOCO
공식 코드https://github.com/zichuliu/submission
공식 코드Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method
https://github.com/zihangdai/cegan_iclr2017
공식 코드https://github.com/zijin-gu/NeuroGen
공식 코드NeuroGen: activation optimized image synthesis for discovery neuroscience
https://github.com/zipengxuc/ppe
공식 코드https://github.com/zipengxuc/spectralclip
공식 코드SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral Perspective
https://github.com/ziplab/ptqd
공식 코드PTQD: Accurate Post-Training Quantization for Diffusion Models
https://github.com/ziqihuangg/collaborative-diffusion
공식 코드Collaborative Diffusion for Multi-Modal Face Generation and Editing
https://github.com/ziqipang/addp
공식 코드https://github.com/zituitui/lml-diffusion-sampler
공식 코드https://github.com/ziyuguo99/image-generation-cot
공식 코드Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step
https://github.com/ziyuguo99/point-bind_point-llm
공식 코드https://github.com/zizhao-hu/l-mlp
공식 코드Lateralization MLP: A Simple Brain-inspired Architecture for Diffusion
https://github.com/zj-binxia/diffir
공식 코드https://github.com/zju-pi/awesome-conditional-diffusion-models
공식 코드https://github.com/zju-pi/diff-sampler
공식 코드Fast ODE-based Sampling for Diffusion Models in Around 5 Steps
https://github.com/zju-vipa/OSGAN
공식 코드https://github.com/zkx06111/rediffusion
공식 코드ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
https://github.com/znxlwm/UGATIT-pytorch
공식 코드https://github.com/zohrehazizi/torch_ssl
공식 코드PAGER: Progressive Attribute-Guided Extendable Robust Image Generation
https://github.com/zoie-ui/ai-generated-image-detection
공식 코드AI-Generated Image Detection using a Cross-Attention Enhanced Dual-Stream Network
https://github.com/zonglinl/consecutivebrownianbridge
공식 코드Frame Interpolation with Consecutive Brownian Bridge Diffusion
https://github.com/zoukaifeng/4dfm
공식 코드https://github.com/zrealli/TIGIC
공식 코드Tuning-Free Image Customization with Image and Text Guidance
https://github.com/zrrskywalker/personalize-sam
공식 코드https://github.com/zsyoaoa/resshift
공식 코드Efficient Diffusion Model for Image Restoration by Residual Shifting
https://github.com/zsyzzsoft/co-mod-gan
공식 코드Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.