https://github.com/sterzhang/image-textualization
공식 코드Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image Descriptions
Papers with Code 아카이브
Text to Image Generation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/studying910/BaTex
공식 코드Efficient Personalized Text-to-image Generation by Leveraging Textual Subspace
https://github.com/sunzey/x-prompt
공식 코드https://github.com/swizad/split-diffusion
공식 코드Accelerating Guided Diffusion Sampling with Splitting Numerical Methods
https://github.com/swsiwu/composition_and_deformance
공식 코드Composition and Deformance: Measuring Imageability with a Text-to-Image Model
https://github.com/syang-genai/mftf
공식 코드MFTF: Mask-free Training-free Object Level Layout Control Diffusion Model
https://github.com/takagi97/pmt2i
공식 코드Boosting Text-To-Image Generation via Multilingual Prompting in Large Multimodal Models
https://github.com/tang-bd/fuse-dit
공식 코드https://github.com/tencentarc/masactrl
공식 코드MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing
https://github.com/tgisaturday/L-Verse
공식 코드https://github.com/theericma/scaledreamer
공식 코드ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation
https://github.com/theericma/triplaneturbo
공식 코드https://github.com/thisisbillhe/nar
공식 코드Neighboring Autoregressive Modeling for Efficient Visual Generation
https://github.com/threestudio-project/threestudio
공식 코드Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation
https://github.com/thu-ml/unidiffuser
공식 코드One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
https://github.com/thudm/cogview2
공식 코드CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers
https://github.com/thudm/imagereward
공식 코드ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
https://github.com/tianyilt/textcengen_background_adapt
공식 코드TextCenGen: Attention-Guided Text-Centric Background Adaptation for Text-to-Image Generation
https://github.com/tianyu-z/strict-bench
공식 코드https://github.com/tiktok/fast_prompt_alignment
공식 코드https://github.com/timothybrooks/instruct-pix2pix
공식 코드InstructPix2Pix: Learning to Follow Image Editing Instructions
https://github.com/torchrwkv/flash-linear-attention
공식 코드https://github.com/tothebeginning/pulid
공식 코드PuLID: Pure and Lightning ID Customization via Contrastive Alignment
https://github.com/trapoom555/gradeadreamer
공식 코드GradeADreamer: Enhanced Text-to-3D Generation Using Gaussian Splatting and Multi-View Diffusion
https://github.com/tsunghan-wu/SLD
공식 코드https://github.com/ttchengab/continuous_3d_words_code
공식 코드https://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/uclanlp/diverse-factual
공식 코드https://github.com/ucsb-nlp-chang/diffusion_resampling
공식 코드https://github.com/ucsc-vlaa/mixcon3d
공식 코드Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training
https://github.com/univ-esuty/noisecollage
공식 코드NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging
https://github.com/valeoai/halton-maskgit
공식 코드https://github.com/vancyland/prompt-agnostic-adversarial-perturbation-for-customized-diffusion-models.github.io
공식 코드Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
https://github.com/vancyland/vancyland.github.io-project-PAP
공식 코드Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
https://github.com/vectorspacelab/omnigen
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.