https://github.com/bilican/sparse_peft
공식 코드Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets
Papers with Code 아카이브
Text to Image Generation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/blt2114/twisted_diffusion_sampler
공식 코드Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
https://github.com/boo-18/gr-gan
공식 코드https://github.com/boqian-li/GarmentDreamer
공식 코드GarmentDreamer: 3DGS Guided Garment Synthesis with Diverse Geometry and Texture Details
https://github.com/brambozz/medical-diffusion-on-a-budget
공식 코드Medical diffusion on a budget: Textual Inversion for medical image generation
https://github.com/buaacyw/it3d-text-to-3d
공식 코드IT3D: Improved Text-to-3D Generation with Explicit View Synthesis
https://github.com/byliutao/1prompt1story
공식 코드One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt
https://github.com/bytedance/xverse
공식 코드XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation
https://github.com/caraj7/comat
공식 코드CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching
https://github.com/caraj7/t2i-r1
공식 코드T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT
https://github.com/castorini/daam
공식 코드What the DAAM: Interpreting Stable Diffusion Using Cross Attention
https://github.com/changsn/FlexDiT
공식 코드FlexDiT: Dynamic Token Density Control for Diffusion Transformer
https://github.com/chenllliang/dreamengine
공식 코드https://github.com/chenqi008/leica
공식 코드Likelihood-Based Text-to-Image Evaluation with Patch-Level Perceptual and Semantic Credit Assignment
https://github.com/chromaica/chromaica.github.io
공식 코드ImagenHub: Standardizing the evaluation of conditional image generation models
https://github.com/chunsanHong/MemBench_code
공식 코드MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models
https://github.com/cientgu/vq-diffusion
공식 코드Vector Quantized Diffusion Model for Text-to-Image Synthesis
https://github.com/computer-vision-in-the-wild/cvinw_readings
공식 코드Multimodal Foundation Models: From Specialists to General-Purpose Assistants
https://github.com/controlgenai/persongensampler
공식 코드Beyond Fine-Tuning: A Systematic Study of Sampling Techniques in Personalized Image Generation
https://github.com/crockwell/cap3d
공식 코드https://github.com/croitorualin/curriculum-dpo
공식 코드Curriculum Direct Preference Optimization for Diffusion and Consistency Models
https://github.com/croitorualin/reverse-stable-diffusion
공식 코드Reverse Stable Diffusion: What prompt was used to generate this image?
https://github.com/csyxwei/elite
공식 코드ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation
https://github.com/csyxwei/masterweaver
공식 코드MasterWeaver: Taming Editability and Face Identity for Personalized Text-to-Image Generation
https://github.com/cuixing100876/instastyle
공식 코드InstaStyle: Inversion Noise of a Stylized Image is Secretly a Style Adviser
https://github.com/cvlab-stonybrook/pathldm
공식 코드PathLDM: Text conditioned Latent Diffusion Model for Histopathology
https://github.com/cvmi-lab/syntheticdata
공식 코드Is synthetic data from generative models ready for image recognition?
https://github.com/da-group-pku/magic-1-for-1
공식 코드Magic 1-For-1: Generating One Minute Video Clips within One Minute
https://github.com/damo-vilab/composer
공식 코드Composer: Creative and Controllable Image Synthesis with Composable Conditions
https://github.com/daooshee/hd-vg-130m
공식 코드Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation
https://github.com/david-friede/lddr
공식 코드https://github.com/dbsxodud-11/PAG
공식 코드Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation
https://github.com/ddw2aigroup2cqupt/face-makeup
공식 코드Face-MakeUp: Multimodal Facial Prompts for Text-to-Image Generation
https://github.com/deepseek-ai/janus
공식 코드Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
https://github.com/design-edit/DesignEdit
공식 코드DesignEdit: Multi-Layered Latent Decomposition and Fusion for Unified & Accurate Image Editing
https://github.com/dome272/paella
공식 코드A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.