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/ai-forever/ruscode
공식 코드RusCode: Russian Cultural Code Benchmark for Text-to-Image Generation
https://github.com/ai-med/stablepose
공식 코드Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation
https://github.com/aidc-ai/awesome-unified-multimodal-models
공식 코드Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities
https://github.com/aidc-ai/ovis-u1
공식 코드https://github.com/aiiu-lab/DemonSampling
공식 코드Training-free Diffusion Model Alignment with Sampling Demons
https://github.com/ailab-cvc/seed
공식 코드https://github.com/ailab-cvc/vl-gpt
공식 코드VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation
https://github.com/aimagelab/mad
공식 코드Merging and Splitting Diffusion Paths for Semantically Coherent Panoramas
https://github.com/ajwad-abrar/religious-bias
공식 코드https://github.com/alexmaols/elucd
공식 코드Elucidating The Design Space of Classifier-Guided Diffusion Generation
https://github.com/alexrame/rewardedsoups
공식 코드https://github.com/alexzhou907/dreampropeller
공식 코드DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling
https://github.com/alibaba-damo-academy/DyDiT
공식 코드DyDiT++: Dynamic Diffusion Transformers for Efficient Visual Generation
https://github.com/alipay/style-tokenizer
공식 코드StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion Models
https://github.com/alpha-innovator/omnicaptioner
공식 코드https://github.com/alpha-vllm/lumina-image-2.0
공식 코드Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
https://github.com/alpha-vllm/lumina-mgpt
공식 코드https://github.com/alpha-vllm/lumina-t2x
공식 코드Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
https://github.com/amonroym99/iti-gen-reproducibility
공식 코드Reproducibility Study of "ITI-GEN: Inclusive Text-to-Image Generation"
https://github.com/andrew-miao/RPO
공식 코드Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning
https://github.com/anonymous5293/minorityprompt
공식 코드Minority-Focused Text-to-Image Generation via Prompt Optimization
https://github.com/anonymouspony/adap-edit
공식 코드https://github.com/aria-zhangjl/e3-facenet
공식 코드https://github.com/ashawkey/stable-dreamfusion
공식 코드https://github.com/autodeskailab/clip-forge
공식 코드https://github.com/avdpro/ai2apps
공식 코드AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents
https://github.com/baaivision/GeoDream
공식 코드GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation
https://github.com/baaivision/emu
공식 코드https://github.com/baaivision/nova
공식 코드https://github.com/baaivision/uni3d
공식 코드GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation
https://github.com/babahui/progressive-text-to-image
공식 코드Progressive Text-to-Image Diffusion with Soft Latent Direction
https://github.com/baofff/U-ViT
공식 코드https://github.com/bdvllrs/bimgw
공식 코드Semi-supervised Multimodal Representation Learning through a Global Workspace
https://github.com/bebebe666/optimalsteps
공식 코드https://github.com/beichenzbc/long-clip
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.